UNDER WHAT CONDITIONS DO CLEAN FUEL BUSES PROSPER MASTER THESIS
By
Ovidio Hensley Savalie Djoe
1545000
in partial fulfilment of the requirements for the degree of
Master of Science
in System Engineering, Policy Analysis and Management
at the Delft University of Technology,
to be defended publicly on Wednesday February 15 th, 2017 at 15:00.
An electronic version of this thesis is available at http://repository.tudelft.nl/.
Thesis committee:
Dr. ir. Z. Lukszo, Delft University of Technology
Dr. ir. I. Nikolic, Delft University of Technology
Dr. W.W. Veenman, Delft University of Technology
A. Ashkenazy Delft University of Technology
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PREFACE
This thesis is the final requirement for me to finish my master System Engineering, Policy Analysis and Management. It is the result of years of personal development and dedication throughout my bachelor and master studies. Studying at the faculty of TPM has helped me learn how to get the best out of myself, reach my personal study goals and made me the person I am today. Finishing this study means that I have reached my academic goal, an achievement that I will always be proud of.
I would like to take this moment to express my deepest gratitude to the people that helped me throughout the past few months. I would like to thank my thesis committee for their valuable time and support.
I wish to thank Zofia Lukszo for offering to support my thesis already before I started. I enjoyed following your classes during the master which helped me refresh my mathematical skills at the very end of my studies. I appreciate the fact that you were always sharp during our feedback sessions which kept me consistent and forced me to critically reflect on my work.
I wish to thank Wijnand Veeneman for sharing his expertise in the field of public transport. I appreciate your efforts in improving my analysis of the public transport system and critically reflecting on the assumptions and modelling decisions I have made.
I wish the thank Igor Nikolic for supporting me throughout the thesis, and introducing me to the subject. I admire your passion for ABM and thank you for introducing me to the field of ABM. Your advice has been extremely valuable throughout my master thesis and helped me through my job search as well.
I wish to thank Amit Ashkenazy for his unconditional support throughout my thesis. Your knowledge on policy, the case study and your academic experience, have greatly improved the quality of my thesis. I enjoyed working with you for the past few months and I am very grateful for your guidance.
Finally I would like to thank my friends and family for their support during the last months, and the last few years. Your support has helped me through difficult and turbulent times, and inspired me finish my studies and graduate. Your efforts will forever be remembered and I will always be there for support in case you need it. After finishing my thesis I wish to spend much more time with you than I have done in recent years.
H. Djoe
Delft, January 2017
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And now for my final trick…………..
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EXECUTIVE SUMMARY
Public transport plays an important role in satisfying transportation demand, though its social and environmental benefits are much wider. Travelers switching from automobiles to public transport can save on costs of vehicle ownership, help cities reduce air pollution and public expenditure on health care, and reduce congestion in the city. Reduced costs and increased productivity can even affect international competitiveness. The public transport industry nevertheless faces serious challenges.
Urban transport is one of the main causes of air pollution. Reducing exhaust emissions from individual motorized vehicles has been a major policy goal, as motorized vehicles emit greenhouse gas emissions (GHG) and pollutants like CO, NOx, SO2 and particulate matter. Israel is currently facing serious issues with air pollution from public bus transport according to the Israeli Ministry of Transport. The World Health Organization has ranked air pollution levels in Israel among the highest in the West and 34th worldwide with air pollution as its biggest contributor.
Electric buses can contribute to a cleaner city environment, and indeed many big cities like London, Sao Paolo and New York have installed some form of low carbon buses. However, the full electrification of urban bus fleets so far has been limited. In order to better understand the potential of electrification and its possible impacts on the city environment and infrastructure, the Ministry of Energy and National Infrastructure in Israel has engaged an international research team to build several models that simulate and weigh the different scenarios for electrification over the coming years. This thesis is a part of that concerted effort.
This research will address the knowledge gaps concerning the benefits of electrification in relation to policy measures, and the impacts of electrification on operational performance in both the transport and energy systems. The main research question is:
How does transport policy and technology affect the market penetration rate of electric mobility in Tel Aviv, and it’s electricity consumption?
In order to answer the question, a literature review was conducted as well as interviews with experts in the public transport sector. The obtained knowledge on bus fleet replacement and the public transport system were used to create an agent-based simulation model capable of answering the main research question. A simulation period of 16 years was chosen and a second case study (Noord- Brabant) was included. Agent-based modelling (ABM) has proven to be a suitable method to model the market penetration of EVs in the consumer market and can be used to model market penetration of clean fuel buses in public transport as well. It is capable of modelling heterogeneous stakeholders and interactions between those stakeholders in a dynamic environment. The answer to the main research question, is that:
In general, the market penetration of electric buses is likely to increase steadily over time regardless of subsidies, as their total cost of ownership (TCO) steadily drops due to technological developments. Subsidies do increase the market penetration rate compared to no subsidies, in case the TCO of electric buses and diesel buses are a close match. A subsidy of 25% of the TCO was enough to stimulate the diffusion of electric buses. The modelling experiments indicated that the diffusion of electric buses in bus fleets can result considerable amounts of additional electricity demand due to charging of
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batteries. Additional energy demands beyond 10MW were measured in peak times in bus fleets containing around 1200 electric buses.
Ebuses were generally found more attractive in both case studies under different diesel price scenarios. The market penetration rates of electric buses in the Noord Brabant model were on average considerably lower than they were in the Tel Aviv case. This was likely caused by differences in daily kilometre range between diesel buses and electric buses, and an absence of subsidies.
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TABLE OF CONTENT
Executive summary ...... v
Table of content ...... vii list of figures ...... xii list of tables ...... xv list of abbreviations ...... xvi
1 Introduction ...... 1
1.1 Israel case ...... 1 1.2 Research problem ...... 2 1.3 Problem statement ...... 3 1.3.1 Expected outcome of the model ...... 3 1.4 Research question and sub questions ...... 4 1.5 Research method ...... 4 1.5.1 Agent-based modelling method ...... 4 1.5.2 Modelling and analysis tools ...... 6 1.6 Thesis outline...... 7 2 What are the considerations that affect governments’ decisions to subsidize electric mobility and other clean fuel buses in public transport fleets? ...... 8
2.1 The importance of public goods ...... 8 2.2 The relationship between government, private market and civil society in public transport 8 2.2.1 The advocacy process: Raising awereness for public values in public transport ...... 9 2.2.2 The political process: Debating the allocation of public resources ...... 10 2.2.3 The bureaucratic proces: Translating public values into transport policy ...... 10 2.2.4 Competitive tendering in the Netherlands, France and London ...... 11 2.3 How electric mobility is affected by competitive tendering ...... 12 2.4 conclusions ...... 17 3 What are the considerations that affect bus operators’ decisions on bus fleet replacement, and in particular adopting new technologies...... 18
3.1 Public image of bus operators ...... 18 3.2 The need for cost effiency ...... 20
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3.2.1 Increasing income ...... 21 3.2.2 Reducing costs ...... 24 3.3 Conclusions ...... 27 4 System identification and decomposition ...... 28
4.1 Definition of a System ...... 28 4.2 Demarcation of bus transportation system ...... 29 4.2.1 Public transport ...... 29 4.2.2 Policy ...... 30 4.2.3 Environmental challenges ...... 31 4.2.4 Bus transit ...... 32 4.2.5 Transport planning ...... 33 4.3 Concept formalisation ...... 34 4.3.1 Most relevant agents ...... 35 4.3.2 Most relevant agent properties ...... 38 4.4 Model formalisation ...... 41 4.4.1 Model narrative ...... 41 4.4.2 Most relevant agent actions ...... 43 4.5 Conclusions ...... 46 5 Model implementation: Bus fleet replacement with agent-based modelling...... 47
5.1 Software implementation ...... 47 5.2 Model verification ...... 49 5.2.1 Recording and tracking agent behavior ...... 49 5.2.2 Single agent testing ...... 50 5.2.3 Interaction testing in a minimal model ...... 50 5.2.4 Multi agent testing ...... 50 5.3 Conclusions ...... 54 6 Experiments and Results ...... 55
6.1 Experiments ...... 55 6.1.1 Experimental design ...... 55 6.1.2 Possible Impact of modelling assumptions on experimental results based on theory ..... 55 6.1.3 Parameters and parameter settings ...... 56 6.1.4 How to measure relevant emergent behaviour ...... 60
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6.2 Results ...... 60 6.2.1 Descriptive statistics ...... 60 6.2.2 Diffusion of electric buses ...... 61
6.2.3 Impact of Electric buses on CO2 emissions ...... 67 6.2.4 Impact of electric buses on energy grid ...... 68 6.3 Model validation...... 69 6.4 Conclusions ...... 70 7 Discussion of modelling results ...... 71
7.1 Descriptive statistics analysis ...... 71 7.2 How do different amounts of subsidies impact the diffusion of electric buses? ...... 72 7.3 How does electrification affect CO2 emissions? ...... 75 7.4 Impact of ebuses on the electricity grid ...... 76 7.5 Conclusions ...... 77 8 Conclusions & Recommendations ...... 79
8.1 Answers to system level sub questions ...... 79 8.1.1 What are the considerations that affect goverments’ decisions to subsidize electric mobility and other clean fuel buses in public transport fleets? ...... 79 8.1.2 What are the considerations that affect bus operators’ decisions on bus fleet replacement, and in particular adopting new technologies? ...... 79 8.1.3 How do different amounts of subsidies impact the diffusion of electric buses? ...... 80 8.1.4 What will be the impact of different levels of electric bus diffusion on the electricity grid? 80 8.1.5 How does electrification affect urban GHG emissions? ...... 80 8.1.6 How does the diffusion of electric buses in Tel Aviv compare to the diffusion of electric buses in a Dutch city? ...... 80 8.2 Answers to methodological sub questions ...... 81 8.2.1 How can we use agent-based modelling to simulate the socio-technical system revolving bus fleet electrification? ...... 81 8.2.2 How can we model bus electrification diffusion and policy at both micro scale (minutes) and macro scale (years)? ...... 82 8.3 Answer to the main research question ...... 83 8.3.1 How does transport policy and technology affect the market penetration rate of electric mobility in Tel Aviv, and its electricity consumption? ...... 83 8.4 Under what condititions do Clean fuel buses prosper? ...... 83
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8.5 Strenghts of the research ...... 85 8.6 Main limitations of the research ...... 85 8.7 Impact of uncertainties & assumptions ...... 85 8.7.1 Social ...... 85 8.7.2 Institutional ...... 86 8.7.3 Technical ...... 86 8.8 Reflection on social relevance and scientific relevance ...... 87 8.8.1 scientific relevance ...... 87 8.8.2 Social relevance ...... 87 8.9 Contribution to SEPAM...... 87 8.10 Recommendations for future research ...... 88 8.11 Personal Reflection ...... 89 8.11.1 Decision to stay at tpm ...... 89 8.11.2 Gathering information ...... 89 8.11.3 Scoping ...... 89 8.11.4 Planning ...... 89 8.11.5 Modelling ...... 89 8.11.6 Experimentation ...... 90 8.11.7 Guidance ...... 90 9 Bibliography ...... 91
calculations and scenarios ...... 96
Assumptions ...... 101
Expert interviews ...... 103
Tel aviv Model data and concept formalisation ...... 111
Noord brabant model data and formalisation ...... 119
Model formalisation ...... 124
Pseudo code Tel aviv policy model ...... 128
Pseudo code Dutch policy model ...... 142
Model code ...... 154
Model verification and validation ...... 181
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Experimental design ...... 192
Tel aviv experimentation results ...... 197
Noord Brabant experimentation ...... 216
Bus data ...... 219
Actor analysis ...... 221
Israel Data ...... 222
Noord brabant data ...... 236
Scientific article ...... Error! Bookmark not defined.
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LIST OF FIGURES
Figure 1; Agent-based model structure (Nikolic & Kasmire, 2013, p.58)...... 5 Figure 2; Visual representation of decision making process (de Bruijne et al., 2009) ...... 9 Figure 3; Type of contracts and involved production and revenue risk (Van de Velde et al., 2008). ... 13 Figure 4; Expected risk of the contracts (Van de velde et al., 2008) ...... 14 Figure 5; Main goals of public transport operators...... 19 Figure 6; Bus operators' environmental considerations ...... 19 Figure 7; Bus operators' financial considerations ...... 20 Figure 8; Bus operator' service goals...... 22 Figure 9; Comparison of bus technologies on costs based on (TNO, 2013) ...... 25 Figure 10; Average unit price electric vehicles market prediction (IDTechEx, 2015) ...... 25 Figure 11; Demarcation of transport system. Blue = to be included, white = to be excluded ...... 30 Figure 12; System boundaries of public transport system. Blue = to be included, white = to be excluded...... 31 Figure 13; Bus technologies. Blue = to be included, white = to be excluded ...... 33 Figure 14; Conceptual system design. Blue arrows = technical relations, Green arrows = social relations. Dashed line represents system boundary ...... 34 Figure 15; Global variables in the model...... 39 Figure 16; Bus operator properties ...... 40 Figure 17; Bus properties ...... 40 Figure 18; Public transport authority properties ...... 41 Figure 19; High level model sequence...... 42 Figure 20; Decision making of buses in the mode ...... 44 Figure 21; Visualization of model in Netlogo ...... 48 Figure 22; Modelling parameters ...... 48 Figure 23; Monitoring agent behaviour ...... 49 Figure 24; Debug switches in the model ...... 50 Figure 25; Scatterplot of the number of ebuses and dbuses over time ...... 51 Figure 26; Boxplots of total buses over time per year ...... 52 Figure 27; Boxplots of total buses of operator Dan's bus fleet per year ...... 53 Figure 28; Model output diagrams ...... 53 Figure 29; The amount of buses per operator under different innovation rates in the Tel Aviv model. Top left (Dan 0.05, Egged 0.05) Top right (Dan 0.10, Egged 0.05). Bottom left (Dan 0.05, Egged 0.1). Bottom right (Dan 0.1, Egged0.1)...... 62
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Figure 30; Development of bus operators in Noord Brabant model ...... 62 Figure 31; Scatterplots of the impact of diesel price scenario’s on TCO of dbuses ...... 63 Figure 32; Boxplots of the impact of different diesel price scenarios and Ebus bonuses on the amount of ebuses. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv Model...... 64 Figure 33; Ebuses vs dbuses under different ebus bonuses and diesel price scenarios. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv Model...... 65 Figure 34; Ebuses vs dbuses under different diesel price scenarios and starting ebus purchase prices. The ebus purchase prices are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Noord Brabant model...... 65 Figure 35; Scatterplots of the impact of diesel prices and ebus bonuses on tendering penalties. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv model...... 66 Figure 36; CO2 from buses under different diesel price scenarios and starting ebus bonuses. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv model...... 67 Figure 37; Impact of diesel price scenarios and starting ebus purchase prices on total CO2 emissions in gram/year. The ebus purchase prices are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Noord Brabant model...... 68 Figure 38; Impact of electric buses on the energy grid under different ebus bonuses and diesel price scenarios. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv model...... 69 Figure 39; Conceptual system design. Blue arrows = technical relations, green arrows = social relations. Dashed line represents system boundary ...... 81 Figure 40; Basic modelling sequence in the agent based model on bus fleet replacement...... 82 Figure 41 Age distribution of Dan bus fleet, real (left) vs model (right), (Dan, 2015) ...... 117 Figure 42; Decision making of buses in the model ...... 124 Figure 43; Visual representation of operators decision making ...... 125 Figure 44; Visual representation of governments decision making ...... 126 Figure 45; Debugging code example ...... 181 Figure 46; Example ouput from debugging procedure ...... 182 Figure 47; single agent test example ...... 182 Figure 48; Number of buses over time at 100 runs ...... 189 Figure 49; Number of buses over time at 10 runs...... 190 Figure 50; Development of bus operator Dan's bus fleet over time in a boxplot...... 190 Figure 51; Development of operator Dan’s bus fleet over time. Left 100 runs. Right 10 runs ...... 190
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Figure 52; Scatterplot of number of buses over time at default parameter settings...... 197 Figure 53; Boxplot of number of buses over time at default parameter settings...... 197 Figure 54; Boxplots of the impact of different diesel price scenarios and Ebus bonuses on the amount of ebuses...... 214 Figure 55; Effect of maximum allowed age ebuses on total ebuses ...... 214 Figure 56; Boxplot of impact of electric buses on the energy grid under different ebus bonuses and diesel price scenarios...... 215 Figure 57; total ebuses under different diesel price scenarios and ebus purchase prices ...... 216 Figure 58; Impact of diesel price scenarios and starting ebus purchase prices on penalty scores during tendering ...... 217 Figure 59; Demarcation of the Dutch case. Provincie Noord Brabant ...... 217 Figure 60; Agent-based model structure (Nikolic & Kasmire, 2013, p.58)...... Error! Bookmark not defined. Figure 61; Conceptual system design. Blue arrows = technical relations, Green arrows = social relations. Dashed line represents system boundary ...... Error! Bookmark not defined. Figure 62; High level model sequence ...... Error! Bookmark not defined. Figure 63; Scatterplots of the impact of diesel prices and ebus bonuses on tendering penalties. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv model...... Error! Bookmark not defined. Figure 64; Ebuses vs dbuses under different ebus bonuses and diesel price scenarios. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv Model...... Error! Bookmark not defined. Figure 65; Ebuses vs dbuses under different diesel price scenarios and starting ebus purchase prices. The purchase prices are displayed at the top of the figure. The different fuel price scenarios are displayed at the right of the figure. Noord Brabant model...... Error! Bookmark not defined. Figure 66; CO2 from buses under different diesel price scenarios and starting ebus bonuses. The purchase prices are displayed at the top of the figure. The different fuel price scenarios are displayed at the right of the figure. Tel Aviv model...... Error! Bookmark not defined. Figure 67; Impact of electric buses on the energy grid under different ebus bonuses and diesel price scenarios. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv model. .Error! Bookmark not defined.
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LIST OF TABLES
Table 1; Performance indicators in public transport (based on De Borger et al. 2002) ...... 22 Table 2; Parameter settings Tel Aviv experiments ...... 56 Table 3; Parameter settings Noord-Brabant experiments ...... 57 Table 4; Measured outputs from models ...... 60 Table 5; Important differences in parameters between the two case studies ...... 72 Table 6; Results from exploring the effect of different diesel price scenarios and ebus bonuses, on the diffusion of ebuses...... 73
Table 7; Results from exploring the impact of electrification on CO2 emissions...... 75 Table 8; Impact of electric buses on the local energy grid ...... 76 Table 9; Language primitives in Netlogo (Nikolic & Kasmire, 2013; Netlogo Programming Guide, 2016) ...... 118 Table 10; Parameter settings Tel Aviv experiments ...... Error! Bookmark not defined. Table 11; Parameter settings Noord Brabant experiments ...... Error! Bookmark not defined. Table 12; Measured outputs from models ...... Error! Bookmark not defined.
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LIST OF ABBREVIATIONS
ABM – Agent based modelling
EMA – Exploratory Modelling Analysis
PTA – Public transport authorities
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1 INTRODUCTION
Public transport plays an important role in satisfying transportation demand, though its social and environmental benefits are much wider. Travelers switching from automobiles to public transport can save on costs of vehicle ownership, help cities reduce air pollution and public expenditure on health care, and reduce congestion in the city. Reduced costs and increased productivity can even affect international competitiveness (Weisbrod & Reno, 2009). The public transport industry nevertheless faces serious challenges.
Urban transport is one of the main causes of air pollution in Europe. Reducing exhaust emissions from individual motorized vehicles has been a major policy goal, as motorized vehicles emit greenhouse gas emissions (GHG) and pollutants like CO, NOx, SO2 and particulate matter (Ercan, Zhao, Tatari, &
Pazour, 2015). Although traditional fossil fuel based vehicles are consistently becoming cleaner, CO2 emissions in the sector have grown 40% since 1990 due to increasing demand for transport and traveling (Samaras & Meisterling, 2008). As concerns about air pollution and GHG emissions are growing, the development of alternative fuel vehicles is accelerated (Qian, Zhou, Allan, & Yuan, 2011). One of the solutions being developed are electric buses. These electric vehicles (EVs) have zero hazardous street level tailpipe emissions.
Electric buses can contribute to a cleaner city environment, and indeed many big cities like London, Sao Paolo and New York have installed some form of low carbon buses (Hogg, 2014). However, the full electrification of urban bus fleets so far has been limited. This is expected to change as fuel costs are likely to become an important driver for changes in bus fleets (Shafiei et al., 2012). In China per example, government funding and support has already helped increase the share of clean fuel buses, compensating operators for higher upfront costs compared with traditional fuel buses. Even so, while policy makers can promote clean fuel bus adoption, there are still barriers to overcome such as uncertainty about operational performance, lack of charging infrastructure and the need to adjust the electricity grid. Furthermore, while clean fuel buses create public benefits, they are not necessarily translated to economic benefits for the public transport operators themselves (Hogg, 2014).
1.1 ISRAEL CASE
The city of Tel Aviv in Israel is currently facing similar challenges with its bus fleet. Buses help to reduce congestion in the busy streets of Tel Aviv, but are noisy and pollute the local environment (Mill, 2013). Dan, the biggest bus company in the city, started testing electric buses in 2013 in an effort to reduce its environmental impact. It initiated a pilot program with 5 electric buses produced by the Chinese company ‘Build Your Dreams’ (BYD). The buses were capable of driving 250km before requiring recharging. Dan planned to replace 25% of its total bus fleet consisting out of 1300 buses with electric buses. The total purchasing costs associated with this transition were 111 million dollars (Itrade, 2013). However, so far the company has not purchased the vehicles as it had pledged. An interview with planners from the Ministry of Transportation in Israel, concluded that there is still much doubt about whether electric buses should be deployed on a national scale.
In order to better consider the potential of electrification and its possible impacts on the city environment and infrastructure, the Ministry of Energy and National Infrastructure in Israel has
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engaged an international research team to build several models that simulate and weigh the different scenarios for electrification over the coming years. This thesis is a part of that concentrated effort. This research proposal will therefore propose a thesis that will contribute to a better understanding of the socio-technical system of public transport electrification. It will do so by bridging the gap between existing research on bus fleet replacement, and simulation methods which currently mostly focus on private electric vehicle diffusion.
1.2 RESEARCH PROBLEM
Electric buses have become a growingly popular technology in urban public transport fleets around the world. While their diffusion is still in its early stages, pilot programs have been performed in many cities, among them Tel Aviv. Despite higher purchasing costs, the BYD buses tested in Tel Aviv reportedly require a third of the energy of its fossil fuel competitors and lower operational costs by 25% (Itrade, 2013). Additionally, they have been receiving political support from the Israeli Minister of Transportation, Israel Katz (Mill, 2013). According to the minister, the new BYD buses should contribute to reducing fuel consumption in public transport by 60% (Itrade, 2013).
While the BYD buses have an expected range of 250km, their actual range will likely decrease due to high congestion in Tel Aviv (Itrade, 2013). Charging the buses would take 4-5 hours but there is uncertainty on how the operational costs and charging times may vary after many charging cycles. As the investment costs are high, it is important for the Israeli government to know how electrification will impact the urban environment, what is required to maintain the current level of operational performance, and whether existing infrastructure is sufficient in providing the new technology’s energy needs.
Furthermore, when considering bus fleet replacement, purchasing costs and fuel costs play an important role in models. In a research by Schneck (2007), subsidizing schemes were of big influence to higher levels of EV market penetration, compensating for higher purchasing costs of clean fuel bus technologies. Other previous studies concluded that capital funding in combination with high fossil fuel prices, is expected to lead to higher levels of hybrid and electric bus purchase in future bus fleets.
This eventually results in environmental benefits like reductions in CO2, emissions and reductions in fuel consumption (Novosel et al., 2015). Moreover, EVs require charging. As the amount of EVs grows so does the electricity demand (Novosel et al., 2015) & (Qian et al., 2011). Not being able to meet this demand efficiently, will have a negative impact on the operational performance of the public transport sector as well as on the electricity system.
On the methodological side, Integer programming has proved to be a suitable method for modelling decision making in bus fleet replacement (Feng & Figliozzi, 2014; Keles & Hartman, 2004; Stasko & Oliver Gao, 2010). However it is limited by the fact that the addition of integers in order to model yes/no decisions, make it far more difficult to find an optimal solution does and in some cases even impossible (Rockafellar, 1997). Using agent-based modelling (ABM) and consumer choice modelling, the market penetration of EVs has been modelled to a large extent (Eppstein, Grover, Marshall, & Rizzo, 2011; Querini & Benetto, 2014; Shafiei et al., 2012). While the outcomes of such models are not perfectly accurate, they are good enough to assess interactions between policy measures, environmental impact, the effects of purchasing decisions, and vehicle technologies. Purchasing costs
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and fuel prices are important factors to consider. When fossil fuel prices are high, the percentage of EV market penetration tends to grow. However, while agent based modelling was useful in simulation electric technologies diffusion in the automobile market, it has not been widely used to explore electrification of public transport fleets.
Thus, the following research gaps have been identified:
How would implementing clean fuel bus technologies affect the rate of clean fuel bus diffusion? How big are the environmental benefits of clean fuel bus fleets under different rate of clean fuel bus diffusion? ABM has proven to be a suitable method for modelling EV diffusion in the private market, but has not yet been used to model clean fuel bus diffusion in public transport
1.3 PROBLEM STATEMENT
While electric buses and other clean fuel technologies can potentially mitigate carbon emissions and improve air quality in urban areas, the circumstances (technical and institutional) in which these technologies are likely to diffuse in public transport are unclear. Additionally, the impact of the diffusion of electric buses on the existing electricity infrastructure is unknown. The reason being that it is a complex task to assess the amount of environmental benefits due to it depending on various characteristics of the city’s electricity grid and existing assets in the public transport fleet. These uncertainties impede policy makers’ preparedness for electrification and their willingness to support the new technology.
1.3.1 EXPECTED OUTCOME OF THE MODEL This research will address the knowledge gaps concerning the benefits of electrification in relation to policy measures, and the impacts of electrification on the transport and energy systems. As a result, electrification policy can be adjusted to ensure the systems’ resilience. Additionally, a second comparative case study of a Dutch city considering bus fleet replacement has been done. The Netherlands was chosen as a suitable location as there are already pilot projects experimenting with EVs in public transport. The addition a second case study helped validating the model and led to new insights.
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1.4 RESEARCH QUESTION AND SUB QUESTIONS
The main research question to be answered in the study is:
How does transport policy and technology affect the market penetration rate of electric mobility in Tel Aviv, and its electricity consumption?
The following sub question will contribute to answering the main research question:
System level questions 1. What are the considerations that affect bus operators’ decisions on bus fleet replacement, and in particular adopting new technologies? 2. What are the considerations that affect governments’ decisions to subsidize electric mobility and other clean fuel buses in public transport fleets? 3. How do different amounts of subsidies impact the diffusion of electric buses? 4. What will be the impact of different levels of electric bus diffusion on the electricity grid?
5. How does electrification affect urban CO2 emissions? 6. How does the diffusion of electric buses in Tel Aviv compare to the diffusion of electric buses in a Dutch City?
Methodological questions
1. How can we use agent-based modelling to simulate the socio-technical system revolving bus fleet electrification? 2. How can we model bus electrification diffusion and policy at both micro scale (minutes) and macro scale (years)?
1.5 RESEARCH METHOD
The type of research will be a theory development research. Theory on bus fleet replacement will be used to model bus fleet transitions. ABM will be applied as a modelling method. The model will include the interests and interactions between public transport authorities, and public transport operators.
1.5.1 AGENT-BASED MODELLING METHOD ABM has proven to be a suitable method to model the market penetration of EVs in the consumer market and can be used to model market penetration of clean fuel buses in the public transport market as well. It is capable of modelling heterogeneous actors and interactions between those actors in a dynamic environment. As stated by (Nikolic & Kasmire, 2013), ABM is best suited to model complex systems when the following conditions apply:
1. “Each actor is to some extent autonomous” 2. “The agents operate in a dynamic environment 3. “The interaction within subsystems is characterized by flexibility”
All three attributes are apparent in the bus fleet system as we will discuss in section 4.2. It contains heterogeneous actors in both the social and technical subsystems (operators, policy makers) which can be modelled separately. The actors included in the system are interdependent and have both social
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and technical relations. The system is dynamic as relations change, policy changes and both financial and technical specifications of vehicles change over time.
AGENT-BASED MODEL
Agent-based modelling is a method that examines the relations between ‘things’ or ‘agents’ rather than a particular phenomenon. Agent-based models are constructed to discover potential emergent properties from a bottom-up perspective. The models attempt to replicate certain actions, relations or mechanisms which are believed to exists in the real-world. In general, agent-based modelling has no intention to reach a certain state, instead the focus is just on describing entities and observing their interaction in order to explore a systems’ possible states. (Nikolic & Kasmire, 2013).
WHAT ARE AGENTS
Agents can be described in several ways. One of them is that agents are proactive, reactive, autonomous and social software entities (Nikolic & Kasmire, 2013). Agents are able to perform actions on themselves and other agents, receive inputs from the environment and other agents, and behave autonomously (Figure 1). This autonomous behaviour is a result of an agents’ state and rules.
Figure 1; Agent-based model structure (Nikolic & Kasmire, 2013, p.58).
An agents’ state consists of all the relevant information on what the agent is at a particular moment (Nikolic & Kasmire, 2013).
Rules describe how agent states are converted into actions or new states (Nikolic & Kasmire, 2013). Actions of agents are the concrete tasks that agents execute based on the application of decision rules and their states. Those actions can also be aimed at other agents, thus changing their states. Important to note is that agents can also choose to not perform an action, because the rules allow them to do so, or if a certain condition to perform an action is not met (Nikolic & Kasmire, 2013).
An agents behaviour is the total observable sum of the agent’s state changes and actions. Agent behaviour is an emergent property caused by the interaction of its internal, local and environmental
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states and decision rules. The overall behaviour of the system is an emergent property of the interactions between all of the agents and the environment (Nikolic & Kasmire, 2013).
ENVIRONMENT
Agents must be in an environment from which they receive input through their senses, and which they can influence through their own actions. The environment contains all of the input external to the agent that is used in its decision making, and provides structure or space for agent interaction. Agents can influence the environment or be influenced by their environment, based on the specific rules they use for their actions (Nikolic & Kasmire, 2013). In case an aspect of the environment is provided by the modeller, it can be deemed a model parameter. These parameters can be static or dynamic, and sets of these parameters can often be seen as scenario’s a modeller wishes to explore (Nikolic & Kasmire, 2013). Environmental parameters can also be an emergent property of the model. Emergent properties are the result of interactions between agents.
TIME
Time is another parameter which can be considered part of the modelling environment, but it requires special attention. In real complex adaptive systems, time is a constant factor and many activities take place in parallel. In agent-based models however, time is forced to take place in discrete steps called time steps. The term ‘tick’ in agent-based modelling refers to the smallest time step in the model. Simulation can play with discrete time and define how much time a tick is supposed to represent (Nikolic & Kasmire, 2013).
PREVIOUS MODEL
Together with A. Ashkenazy, a PhD student from the TUDelft faculty of Technology, Policy and Management, I have already created a basic ABM of bus fleet replacement and electrification. This model has served as a basis for this research, but has been extended and tailored to reflect our case studies. This process will be discussed in more detail in chapter 4.
1.5.2 MODELLING AND ANALYSIS TOOLS MODELLING SOFTWARE
The modelling environment, also often referred to as the modelling suite, provides the software infrastructure for programming agents. There are numerous agent-based modelling environments and platforms, each with their own strengths and weaknesses. Nikolai & Madey (2009) provided an overview of the available tools. The three most used agent-based models of sociotechnical systems are Netlogo, Repast and Custom code.
Netlogo is a free program which offers easy implementation of agent-based models. The entry barrier is low, and it is often used for teaching due to its reasonably simple programming syntax and numerous examples. The simplicity of the programming language however can be a limit to the complexity of the models created with it. Repast is another agent-based modelling tool much more advanced than Netlogo (Nikolai and Madey, 2009). The increased complexity however makes the tool less accessible to non-programmers. For complete flexibility it also possible to build agents from scratch in
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programming languages like Java. A disadvantage of this method is that it required comprehensive understanding of advanced programming languages. Because the complexity of the model in this research is limited, Netlogo will be used to as programming tool (Nikolic & Kasmire, 2013).
DATA ANALYSIS TOOLS
There are several software tools that can be used to analyse data generated from agent-based models. Four of these are Excel, Matlab, R and SPSS (Nikolic & Kasmire, 2013). While all four tools are useful there are differences in capabilities of handling large amounts of data, availability of the software and the amount of personal experience we have with each tools. Based on previous experience and compatibility with Netlogo, R will be used as the main data analysis tool in this model.
1.6 THESIS OUTLINE
Chapter 2 will explore the considerations that governments make when deciding to subsidize electric mobility and clean fuel buses. Chapter 3 explores the considerations that bus operators make in their decisions on bus fleet replacement, and adopting new vehicles. Chapter 4 presents the system and the conceptual model. Chapter 5 presents the agent based model and discusses the model verification steps taken to verify the model. The performed experiments and results are presented in chapter 6. Chapter 7 discussed the results of the experiments. Chapter 8 presents the conclusions and recommendations, and reflects on the research.
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2 WHAT ARE THE CONSIDERATIONS THAT AFFECT GOVERNMENTS’ DECISIONS TO SUBSIDIZE ELECTRIC MOBILITY AND OTHER CLEAN FUEL BUSES IN PUBLIC TRANSPORT FLEETS?
This chapter will explore the considerations that affect governments’ decisions to subsidize electric mobility and other clean fuel buses. It does so by firstly acknowledging the importance of public values in section 2.1 . Secondly by explaining how public values, through governmental intervention, are implemented into public services (section 2.2). Thirdly by explaining how public procurement affects clean fuel bus penetration in section 2.3, and finally with the conclusions in section 2.4. The findings from this chapter in terms of the government’ decision making concerning new technologies, were required in order to simulate the role of the government in our models.
2.1 THE IMPORTANCE OF PUBLIC GOODS
The reason that many infrastructures exist today, is public values. Urban sewerage systems and drinking water for example, promote public health and hygiene in urban environments. Levees and dams protect society from the dangers of rivers and seas. But beyond protection against nature, as modern societies became more sophisticated, their dependence on infrastructure services increased. A society without proper mobility, energy and telecommunication infrastructure is destined to crumble (Veeneman, de Bruijne, & Dicke, 2009). It is because of their critical function, that societies have successfully made use of public institutions to interfere in infrastructure markets in the last two centuries (Veeneman et al., 2009).
In numerous regions and states, public institutions took a commanding role in the provision of infrastructure and public service. That until recently, when many western countries have broken with this historical trend, and started reverting to liberalization, contracting, and privatization. As a result, commercial parties have become an important part of public services.
Dealing with public services thus has become both increasingly complex and an explicit public task (Veeneman et al., 2009). Where in the past it was up to states and regions to define public values, the current policy focuses on decentralization which is leading to new challenges. Which public services should be delegated to commercial parties? What would be the correct trade-off between innovation and affordability? How can all these values be achieved after privatization and liberalization? What would be the role of the state and political decision making in the daily operation of infrastructure organizations? The next section describes a framework of four stages in a decision making process, that copes with answering such questions.
2.2 THE RELATIONSHIP BETWEEN GOVERNMENT, PRIVATE MARKET AND CIVIL SOCIETY IN PUBLIC TRANSPORT
Veeneman et al. (2009) distinguished four crucial stages in the decision making process of public values in infrastructures: the advocacy process, political process, bureaucratic process and provision process (Figure, 4).
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1. In the advocacy process, public values are articulated at a highly abstracted level. Typical values at this level could be for example: public transport should be safe, reliable and efficient. 2. The political process describes a process of deciding which values are seen as public, and the prioritization of public values. Due to limited resources, governments and parliament have to make trade-offs between public values through debating. 3. The bureaucratic process, is of great importance in reaching public values. In this process, abstract public values are translated into concrete norms. The focus of the decision making shifts to the relation between the government and the provider of public transport services. The process often involves multiple stakeholders including regulators, different public bodies and private providers. It is common for the bureaucratic process not the run smoothly. Instead, the interests of the involved parties are negotiated until more detailed norms are reached. 4. The provision process describes how after implementation of the public service, it is evaluated if the desired public values have eventually been reached. After concretizing public values in the bureaucratic process, public values tend to contradict each other, or the private values set by the service providers. The stakeholders involved in all the previous stages are involved in this process.
Figure 2; Visual representation of decision making process (de Bruijne et al., 2009)
In this thesis the focus is mainly on the bureaucratic process due to its prominent role in reaching public values. However, since the advocacy and provisional process form the basis for the bureaucratic process, they will be touched upon as well. The next sections (2.2.1 & 2.2.2) describe how these processes take place in public transport.
2.2.1 THE ADVOCACY PROCESS: RAISING AWERENESS FOR PUBLIC VALUES IN PUBLIC TRANSPORT In the present day, civil society has been defined as a social sphere distinct from both the state and the market (Smismans, 2006). ‘Voluntary organisations, such as neighbourhood associations, choral societies, bird watching clubs, sport clubs or bowling leagues, are supposed to generate social capital by supporting norms of reciprocity and civic engagement, building social trust and providing networks of social relations that can be mobilised for civic action’ (Smismans, 2006, p.3).
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In public transport, civil society is able to identify those needs that the government and public transport market are not fulfilling. The most important of these needs can be further divided into economical needs, service needs and environmental needs. Examples are issues with ticket fare prices, quality and comfort of transport, and noise and air pollution. As stated by Ida & Talit (2015), an effective public transport service has the benefits of improving social welfare, stimulating the economy, expanding labour markets and many more.
Civil society can raise awareness to public values in public transport. If we were to apply the theory from the previous section to public transport, it could look like this: In a certain area, civilians might experience increased levels of smog caused by the buses in their neighbourhood. As a form of action they would then lobby individually or as a group for cleaner buses, or even consult a public institution and raise their concerns. Or they could revert to another important tool for raising attention, the media. The media is powerful because political issues that are broadcasted in the media, get much more attention in subsequent stages of the political process (Veenaman et al., 2009). As the attention grows, the government might pick up the issue and note that it is in the public’s interest to have cleaner transport.
Apart from civil society, cities can also influence public transport policy. Cities from around the world have set ambitious environmental targets (Brown, Patil, & Herder, 2010). The challenge that all these cities face, is how to protect their citizens from harmful forms of pollution without hampering the movement of people. If cities are to reach their environmental targets, it will be essential to reduce pollution from public transport services as it involves high usage vehicles that operate in heavily congested areas (Brown et al., 2010). In an attempt to raise attention, cities may also lobby the government for subsidizing cleaner public transport in their city, starting their own advocacy efforts, sometimes even as a result of complaints from citizens.
2.2.2 THE POLITICAL PROCESS: DEBATING THE ALLOCATION OF PUBLIC RESOURCES Public values are obviously important, but it is unlikely that all public values are seen as equally important to all involved stakeholders. Different parts and levels of the government frequently have diverging policy preferences on issues like regulation, taxation and the provision of public goods, leading to vertical competition (Fossati, 2016). Additionally, it might not be possible to achieve all of them at once (Bovaird, 2005). This raises the challenge of multi-level governance. If governmental resources are scarce this might lead to competition between cities and governmental organisations at the sub-national level for scarce governmental resources. The government has to weigh clean public transport against other public interests, which is done in a political process. Debates will take place to decide if clean public transport is indeed of public interest, and thus deserves more resources than other public values.
2.2.3 THE BUREAUCRATIC PROCES: TRANSLATING PUBLIC VALUES INTO TRANSPORT POLICY Once a decision has been made on which public value to pursue, the next step is turning the public value into concrete norms. In recent years many countries in Europe have opted for a decentralized form of governance in public transport. Most of them have appointed public transport authorities
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(PTA’s) to regulate public transport services. These are either public or publicly-owned organizations with a legal obligation to govern and provide public transport services, in a clearly defined geographical region (Hidson & Müller, 2003).
While some countries have one central PTA, others have opted for several regional PTA’s. In Israel for example, the Ministry of Transport still serves as the PTA for the entire country (Ida & Talit, 2015), while the Netherlands, Norway and Germany have appointed regional transport authorities (Beck, 2011; Longva & Osland, 2010; Veeneman & van de Velde, 2014).
Alongside decentralization, competitive tendering became a popular way of awarding contracts to public transport operators. Competitive tendering stands for awarding public transport operators the exclusive right to operate a certain transport route or network of routes, after a competitive procedure (Hidson & Müller, 2003). Competitive tendering will be discussed comprehensively in the next section.
In public transport, the competitive tendering procedure sets the criteria that ultimately influence clean fuel bus penetration. Values like improved efficiency and decreased environmental impact, are translated into concrete norms like lower total cost of transport and higher emission standards for buses. Furthermore, remuneration schemes are implemented, contract duration times are set, and rules on asset ownership are implemented. There are endless possibilities in setting up tender procedures. As a result, it is highly unlikely that you would ever find two identical procedures.
There have been many examples in literature on how public transport policy has changed in order to achieve public values in transport. Cases studies from cities in Germany, England, France, South Africa, Sweden, Israel and The Netherlands have showed how public transport has implemented competitive tendering in order to increase efficiency, increase quality of service, increase competition, reduce cost and reduce environmental impact (Amaral, Saussier, & Yvrande-Billon, 2009; Hidson & Müller, 2003; Ida & Talit, 2015; Veeneman & van de Velde, 2014; Walters, 2010)
We discuss three tendering models from the Netherlands, London and France to showcase the differences in application.
2.2.4 COMPETITIVE TENDERING IN THE NETHERLANDS, FRANCE AND LONDON THE NETHERLANDS
In the Netherlands, the current public transport regime aims at increased cost recovery and patronage through competitive tendering (Veeneman & Van de Velde, 2014). It is required by law that public transport authorities establish public transport policy goals, define concessions areas, and gradually organize competitive tendering procedures to award exclusive concessions for a duration of between 8 and 15 years (D. van de Velde, Veeneman, & Lutje Schipholt, 2008). Separate PTA’s operate each of the 12 Provinces and 7 urban areas. Except for the four biggest cities, all other concessions are tendered out. The current tendering procedures are developed to be somewhere in the middle between extreme and lenient. Contracts are awarded based on costs and service incentives, but with minimum price limits and punishment for extreme bids (Veeneman & Van de Velde, 2014).
The tendering procedures starts with preparing the concession, determining the requirements and creating the tender documents. Afterwards the formal tendering directive begins and potential 11 | P a g e
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operators are invited to bid. After the potential operators have submitted their bids, the PTA picks the winner based on the selection criteria described in the tender document. Before the concession starts, the final details are discussed between the winner and the PTA. During the concession period, the PTA will monitor and evaluate the concession according to the agreements in the tender document (Ongkittikul, 2006). The concession ends after a maximum length of 6 years, extensions aside.
LONDON
In London, around 20% of the London bus network is tendered out yearly by the local transport operator (Transport for London). After the invitation, the regulator makes a selection of pre-qualified operators who are then requested to submit sealed bids for the tender. The bids are evaluated on, but not limited to, price and the ability to deliver the required quality of services. The basic principle throughout the tender process is that all parties are treated fairly. The Senior Bus Contracts Evaluation Manager takes the lead in the tender evaluations, which are carried out by a small team of experienced technical and commercial staff (Transport for London, 2008). Contract evaluation managers are allowed to contact bidders to clarify uncertainties in their bids if necessary.
FRANCE
The tendering procedure in France goes through a couple of steps. In the first step, the PTA publishes a rough description of the required service and calls up candidates for their application. A selection is then made of qualified bidders which may place a bid. The qualified bidders are operators that can provide both financial and professional guarantees. In the second step, the PTA provides the bidders with a detailed consultative document explaining the technical characteristics, service conditions, financial information and contracting information. The operators place their bids based on the document. In the third step a selection is made of one or multiple winning bids. The PTA then enters into separate negotiations with the selected operators. The final outcome of these negotiations is the winning bid. An important note is that the PTA according to the ‘Sapin’ act is not obligated to select the winning bid based on the criteria and the objectives published in the detailed document (Amaral et al., 2009). Neither are they required to publish selection criteria in their detailed document.
The local transport authorities are able to choose between different modes of organization for the procurement of services. They can either operate the service directly, delegate the services to a mixed public private company, or delegate the services to a fully private company. In 69% of the cases, the services are delegated to a private company, in 21% of the cases to a semi private company, and in the remaining 10% to direct public administration.
2.3 HOW ELECTRIC MOBILITY IS AFFECTED BY COMPETITIVE TENDERING
It is clear that the tendering procedures adapted by various countries are quite similar. Overall the base of the tendering procedure contains an invitation phase, a bidding phase and a selection phase. However, the differences within the procedure could have substantial impacts on the outcome of the tendering procedure. This section will further discuss those differences and their possible effects.
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THE EFFECT OF CONTRACT TYPES
The contracts in public transport are typical risk contracts which allocate financial risks among the involved stakeholders. Additionally, incentive mechanisms can be applied such as quality incentives (D. M. Van de Velde, Beck, Van Elburg, & Ter-Schüren, 2008). The most common risks involved in public transport contracting, are costs risks and revenue risks.
1. Costs risks can be further divided into operational costs risks and investment costs risks. a. Operational costs risks: In terms of operational costs, it is possible that costs end up higher or lower than mentioned in the contract. The question then becomes which party has to deal with the additional benefits or additional costs. Operational costs risks can be either internal (directly influenced by the operator) or external (not directly influenced by the operator). b. Investment costs risks define who carries the risks involved in owning the assets like vehicles, and transport infrastructure. Essentially it is about the residual value of the assets after the contract period. 2. Revenue risks decide who carries the risk involved with the ticket fare earnings. Essentially, it determines who will have to deal with income ending up higher or lower than expected in the contract.
There are typically three different types of contracts in public bus transport (Papaioannou & Adamantidou, 2014). These are management contracts, gross cost contracts and net cost contracts. Combining the contracts with specific incentives and risk divisions, can lead to several variations of contracts as seen in Figure 3.
Figure 3; Type of contracts and involved production and revenue risk (Van de Velde et al., 2008).
1. Management contracts a. In this contract form, the responsible PTA owns and controls the infrastructure and assets. It pays all the expenses and collects all the ticket revenues (Papaioannou & Adamantidou, 2014). The operator is only responsible for the daily operation of services. In this scheme the PTA bares all the risks.
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2. Gross cost contracts a. In this contract form it is the operator who controls the infrastructure and assets, though the PTA might remain the owner. The PTA covers the costs of service and collects the fair box revenues. The operator is paid a lump sum (Veeneman et al., 2014). The operators covers the cost risks. 3. Net cost contracts a. Similar to the gross cost contract, the PTA does not control the infrastructure and resources but typically remains the owner. The fleet is managed by the operator while fulfilling a set of quality standard set by the PTA (Papaioannou & Adamantidou, 2014). The operator receives the income for fares and a fixed subsidy. Because the operator has all the responsibilities chances are that it would aim at maximizing its income only focusing on profitable lines and neglecting others. The operator covers both the cost and revenue risks. i. A special form of the net cost contract is the super-incentive contract. Similar to the net cost contract, but with the difference that subsidy is related to the performance of the operator instead of a fixed subsidy (Veeneman et al., 2014).
Research has compared gross cost and management contracts (also called full-cost contracts) on one end, and net cost contracts on the other. The conclusions were that the main benefits from full-cost contracts, are reduced costs for the PTA, greater coordination between operators and better control for the PTA. The downside however is that the income for the operator does not depend on the number of passengers, which might result in a lower level of service (Ida & Talit, 2008). The amount of risk can shift either towards the operator or the PTA depending on the type of contact. A management contract presents the least amount of risk for the operator while the net cost contract presents the most. For the PTA the opposite is true: it assumes the most risk in a management contract and the least risk in a net cost contract (Figure 4). As the risk shifts towards the operator, the amount of subsidy from the government decreases.
Figure 4; Expected risk of the contracts (Van de velde et al., 2008)
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In terms of electric mobility how operators will respond to electric mobility will be considerably impacted by the type of contract. In case of a management contract, operators are likely to accept new technologies without problems. The PTA would simply grant the amount of subsidies required for new technologies, taking all the risk involved with the new technologies away from the operators, hence decreasing the barrier to adopt them. The same applies to a gross net contract, with the difference that the operators themselves will own the assets and will carry certain investment risks. This will mean a couple of things. As new technologies come with higher investment costs, and costs are likely to be included in the tendering criteria, the operators’ are likely to pick the cheapest technologies and be reluctant towards new technologies. However, this will also depend on the value of other selection criteria besides cost and the availability of subsidies. We will further discuss this later in this chapter. Additionally, higher investment costs require additional budget, which should fall within capable budget of operators if they are to pick them (M. Post, personal communication, June 3, 2016). Net cost contracts on the other hand, could discourage operators to try new technologies as they would not only have to deal with the uncertainties in operational costs and investment costs, but also with uncertain revenues. If revenues depend on the operational performance of buses, it would be logical for operators to pick a bus technology that has proven to be reliable and capable of running many kilometres service.
Based on the contract types that are used in our case studies, gross net contracts were used in our simulation models. This will be further discussed in chapter 4.
THE EFFECT OF DIFFERENT CONTRACT LENGTHS
Besides the type of contract, it is important to carefully consider the duration of the contract. Contract lengths vary throughout Europe (Ongittikul, 2006). The shortest contracts are found in Germany ranging from 2 to 5 years for publicly tendered contracts. The most common range is between 5 and 10 years with the exception of France where contracts last up to 30 years. The Netherlands is at a maximum of 6 years. There are many reasons for such differences in contract duration of which one important reason is the investment risk. The higher the investment risk, the higher the duration of the contract. Electric buses can have write-off times of up to around 15 years, which would make a 5 year contract very risky as operators have to earn back investments in those 5 years. (M. Post, personal communication, June 3, 2016). It is up to the auctioneer to make an estimation of these risks for its bidders and decide upon a fitting contract length for their service demands.
THE EFFECT OF AWARDING PROCEDURES
Picking a winner is an essential step in the tendering procedure. As seen in section 2.2.4, there are differences in selection methods as an authority can either directly award a contract or award a contract to the best offer. Direct selection of an operator can lead to long and trusting relationship. The downside however is that this might lead to inefficient services with room for improvement. Awarding a contract based on specific criteria and especially costs, has therefore been the prime awarding method in many cases. Conversely, the problem with specifically setting awarding criteria is the risk of ignoring of other important criteria. In the Netherlands for example, awarding contracts to the lowest bidders led to reduced cost but also to a decrease in patronage in public transport, while the original goals were to reduce cost but also increase patronage (Veeneman & Van de Velde, 2014).
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In an effort to improve the level of service, including incentives for increased service frequency or number of passengers can motivate operators to not only focus on cost reduction. A recent Swedish study concluded that combining a net cost contract with a subsidy for passenger patronage would encourage operators to provide better service in an effort to attract passengers (Ida & Talit, 2014). A comparable study in France & Britain showed that a full-cost contract combined with incentives for high level service led to improved services for passengers (Amaral at al., 2009). This indicates that the best solution is not simply selecting one of the contract types, but combining a contract with fitting incentivizing criteria.
The awarding criteria hence greatly influences the potential for the diffusion of new technologies. Selection based on cost criteria, considering the higher cost associated with new technologies, would certainly slow down the diffusion rate. As will criteria based on performance considering the uncertainties around performance with new technologies. The only case where new technologies are likely to benefit and diffuse faster, would be if certain environmental benefits such as hazardous emissions would be part of the selection criteria. The awarding criteria that were used in our simulation models were cost, and innovation, giving us the possibility to explore the value of costs against innovation.
THE EFFECT OF DIFFERENT FORMS OF OWNERSHIP
The rolling stock, materials or infrastructure can either be owned by PTA or the operators. While in France the PTA owns all the assets, in London the operators own all the assets. Whereas in the Netherlands the ownership of assets in can be divided between the PTA and the operators (Amaral et al., 2009; Transport for London, 2008; Veeneman & van de Velde, 2014).
Since electric mobility is rather new, purchasing assets required to operate and maintain electric vehicles, could involve much more risk compared to diesel buses. Both in terms costs and performance. As a result, the PTAs might opt to purchase some of the electric buses, infrastructure, or maintenance materials, in order to reduce some of the risk for the operators with this new technology. In case technology matures, or operators become able to carry the risks themselves, the PTA can then opt to assign the ownership of the assets back to the operators.
An example of such a switch in ownership was given in the Netherlands. Historically the regional authorities left all of the assets in the hands of the operators. Recently however, several authorities have chosen to break with this tradition and purchased facilities. The PTA in Noord-Brabant purchased charging infrastructure in earlier concessions, in order to support operators with implementing novel electric bus technology. In recent concessions however, as technology matured, this responsibility returned to the operators (M. Post, personal communication, June 3, 2016).
The models that were created in this thesis assumed that the operators themselves own the rolling stock, materials and infrastructure. This due to the fact that in both case studies such ownership structures are currently in use. This means that the operators in the models will have to make the consideration if new technologies are worth investing in, considering their costs and performance compared to traditional technologies.
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2.4 CONCLUSIONS
The goal of this chapter was to explore what considerations affect governments’ decisions to subsidize electric mobility and other clean fuel buses in public transport fleets. It was discussed that public values are the reason that many important infrastructures today exist, and how public institutions took a commanding role in the provision of public services.
The theory of (Veeneman et al., 2009) was used to describe how through a process of advocacy, attention for public values is raised and translated into goals for public transport. Next, in a political process, the public values in transport are weighed against other public values and a decision is made on which public value is granted public resources. Once a decision is made, a bureaucratic process is responsible for translating public transport values into concrete norms. In public transport, this role belongs to the PTA which translates goals like reducing cost, increasing efficiency, and reducing environmental impact, into concrete values with a competitive tendering procedure. In the procedure, contracts are awarded giving operator the exclusive right to operate a route or a network of routes.
In general, the PTAs introduced competitive tendering to stimulate competition in public transport hoping it would result in the reduction of costs, increased quality and more patronage while reducing environmental impact. There are multiple tendering designs with diverging methods of awarding procedures, remuneration schemes, and division of ownership of assets. Buses, materials and infrastructure can either be publicly owned, privately owned, or even mixed between the two.
Different contract designs trigger different results and therefore require careful considerations by the PTAs. The type of contract influences the behaviour of operators. Where a gross-cost contract stimulates cost efficiency, and can greatly reduce costs for PTA, it doesn’t necessarily stimulate increased patronage which could result in a lack of service. Net-cost contracts do stimulate increased passenger travel, but could result in operators opting for the most reliable buses and most profitable lines. This can lead to operators allocating resources to only the most profitable routes, and reluctance to adopt new vehicles. The contract length can either stimulate new technologies with long term contracts, or stimulate risk aversion with short term contracts. The awarding criteria in competitive tendering can lead to cost reduction, but too much emphasis on costs results in a lack of quality and polluting buses. As new technologies like electric buses come with higher purchasing costs and uncertain operational costs, diesel buses remain the more secure solution.
As the goals in public transport or not only about increasing patronage, quality and reducing costs, governments often decide to subsidize new technologies to stimulate clean buses’ market penetration and decrease the environmental impact of public transport. Clever contract designs can reduce the risk for operators to adopts new technologies. As an extra incentive those contracts can be combined with subsidies. PTAs can also take ownership over clean fuel assets to reduce the risk for operators. The amount of influence or subsidy granted depends on the ability of the operator to take the risk, or the amount of emphasis the PTA puts on reaching the goals.
Having discussed the consideration that governments make in designing the tender, the next chapter will focus on the considerations that bus operators make in bus fleet replacement and clean fuel vehicles in particular.
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3 WHAT ARE THE CONSIDERATIONS THAT AFFECT BUS OPERATORS’ DECISIONS ON BUS FLEET REPLACEMENT, AND IN PARTICULAR ADOPTING NEW TECHNOLOGIES.
This chapter explores the considerations that affect bus operators’ decisions on bus fleet replacement, and in particular adopting new technologies. It will do so by firstly discussing the importance of public image for bus operators in section 3.1. Next it discusses the significance of income which factors are related to generating income in section 3.2.1. After that the need to reduce costs is discussed in section 3.2.2. The chapter ends with a concluding section (3.3). The insights gained in this chapter were required and used to simulate the behaviour of transport operators in our simulation models.
3.1 PUBLIC IMAGE OF BUS OPERATORS
The mobility demand of urban and metropolitan citizens continues to grow, caused by a desire to participate in increasingly varied activities. In order to satisfy this transport demand, people tend to make greater use of individual motorized transportation modes (Eboli, Mazzulla, & Ttrv, 2010). This can be seen in the share of cars in the modal split, and the rate of car ownership, which have risen over the recent past, and are likely to keep growing. In the EU15 member states, car ownership has increased 34% between 1985 and 1995, and is likely to keep increasing towards 50% in 2020 (Banister & Berechman, 2003). Since road capacity has not increased by a similar amount (10% between 1985 and 1995), congestion has increased, especially in cities where not a lot of new infrastructure has been built.
Now more than ever, public transport operators are challenged to provide adequate services in order to address the issues with vehicle ownership, worsening traffic conditions, and a growing urban mobility demand (Tyrinopoulos & Antoniou, 2008). Public transport provides commuters that do not have access to motorized vehicles, a way to fulfil their traveling demand. Additionally, public transport comes with the ability to transfer more travellers while requiring less road space.
In order to provide adequate services, and maintain high scores on quality in tenders, evaluating and improving quality of service holds a high priority for transport operators (Tyrinopoulos & Antoniou, 2008). Above all considerations that operators make, the main focus is on winning contracts. An operator without any contracts, but still having to pay for its assets, will basically run out of business. Because the tendering process awards contracts to the highest scoring bid, or at least the bid closest to the criteria, operators will always want to match the criteria as close as possible. This will give them the highest chance of winning a contract and securing their existence.
As the quality of service is a possible criterion in the tendering procedure, keeping the quality as high as possible is an important endeavour for operators. Quality of service represents the perception passengers hold of transit performance, and can be measured in several quantitative and qualitative performance indicators.
Another quality indicator that is often mentioned by operators and very applicable to this research, is the environmental impact of bus services. This indicator can be further divided into improving cost efficiency and reducing bus emissions (Arriva, 2016; Dan, 2016; Egged, 2016; RET, 2016; Synthus, 2016). A graphical representation is given in a goal tree in Figure 5. A comprehensive version of the tree can be found in Appendix O.
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Reduce environmental impact
Reduce bus Improve cost emissions efficiency
Figure 5; Main goals of public transport operators
Public operators are more prone to preserve their public image and therefore emphasize reducing environmental impact (Figure 5). Since environmental quality is an important public value, polluting vehicles could reduce an operators’ credibility as a public service. Opting for clean fuel vehicles, like electric buses, is expected to improve their environmental image.
Reduce bus emissions
More clean fuel Reduce fuel buses consumption
More hydrogen More electric More CNG More euro 6 Less aggresive buses buses buses engines driving
Figure 6; Bus operators' environmental considerations
In a survey by (Mattson, 2012), reaching out to 110 operators, fleet managers stated that reducing emissions, energy dependency concerns, political directives, and overall improving public perception and fuel costs were common motivating factors for the implementation of alternative fuel vehicles. In terms of energy dependency, important factors were the yearly amount of energy that can be supplied, the reliability and storage of energy. The fact that some operators mentioned energy dependency suggests that they are becoming increasingly sensitive to their role as energy consumers and are starting to seek ways to reduce national dependence on fossil fuels (Mattson, 2012).
Besides the intrinsic motivation to provide cleaner services, there are important governmental regulations in urban transport that push operators towards clean fuel vehicles. These are usually direct environmental policies and indirect environmental policies. An indirect environmental policy can be in the form of regulations that limit the amount of particles and pollutants released by diesel engines, which usually become more stringent with time (Keles & Hartman, 2004).
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The European emission standards are a form of such an indirect environmental policy. Euro 6 is a European emission standards for new heavy duty diesel vehicles, which are generally defined in a series of EU directives (Brown et al., 2010). Its previous version, the Euro I standard, was initiated in 1992 and has developed all the way to the Euro VI standard in 2013. In that period the allowed Particular Matter limits, a health risking air pollution emitted from diesel engines, have been reduced by over 97%, CO limits by 67%, NOx emissions by over 94% and HC limits by over 88%. As the emission standards have become much more stringent in recent years, bus manufacturers have made significant steps in increasing thermal efficiency and reducing emissions.
3.2 THE NEED FOR COST EFFIENCY
Many studies share a common thread of defining public transport operators as private companies trying to maximize their profit (Berechman, 1993; Cole, 2005). This however is not entirely correct as we have seen in chapter 2 that operators are either partially or fully limited in their behaviour by PTAs. There are even cases where the PTA sets the output level of service and the ticket fares, leaving it up to the operators to decide how to minimize their costs. As this section aims at giving a general overview of all operators, we will assume that operators act as private companies trying to maximize profit and compete more efficiently for the transport market (Cole, 2005). This way it is possible to discuss all the financial considerations that operators make.
Improve cost efficiency
Increase income Decrease cost
Decrease Increase Decrease Increase subsidies operational costs patronage investment cost
Decrease Attractive ticket Better operational Decrease vehicle Decrease material Less fuel maintenance prices services costs costs consumption costs
Figure 7; Bus operators' financial considerations
Improving cost efficiency can be done by increasing income and decreasing costs (Figure 7). The next section discusses how operators can increase their income, assuming they have the possibility to do so.
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3.2.1 INCREASING INCOME This section describes how operators can increase their income, and how this is influences their decisions to adopt new technologies. Increasing income in public transport can be reached by increasing patronage or receiving more subsidies. An operator however cannot simply increase patronage, because patronage is dependent on transport demand. Transport demand is given and it is up to the operator to establish a demand pattern for its services. Operators however can try to influence demand in two ways which are either by making ticket prices more attractive, or improving the quality of service (Cole, 2005).
3.2.1.1 MAKING TICKET PRICES MORE ATTRACTIVE Price changes can encourage new travellers, or steal travellers away from other operators. The lower the price, the more people are likely to make use of the transport service offered. This is generally applicable to transport and most other products, with the exception of some exclusive goods and services. Travel demand will also largely be determined by the fares of competing transport modes like rail, coach, underground and air services. Furthermore, the perceived costs of car travel including petrol prices and parking charges have an influence on modal choice. In a large urban area like London, ticket fares and petrol prices will determine the size of the passenger transport market. When fares and petrol prices are low, more trips will be made by public transport and vice versa (Cole, 2005).
If new technologies are to be adopted, fare prices should be able to compete with the fare prices of traditional transport costs. The implication is that for electric, hydrogen or biofuel technology, the prices of electricity, hydrogen and biofuel should be able to compete with diesel prices. Likewise, the operational costs and investment costs of the new technology to be embedded in the ticket fares, should be competitive. If new technologies can lead to competitive ticket prices and lower subsidy needs, the adoption of new technologies will be more easily considered. If new technologies come with higher fares and subsidies, operators are likely to stay away from them. Ultimately however this will also depend on the type of contract as discussed in section 2.3.
3.2.1.2 IMPROVING OPERATIONAL PERFORMANCE OF BUS SERVICES The quality of service in terms of frequency, regularity, convenience, comfort, reliability and safety, will act as a stimulus to transport demand if service quality is perceived by the customer to provide value for money (Figure 8). Transport demand depends on performance in each of these factors, and therefore requires operators (as well as transport authorities) to continually consider what effect a change in price, income or quality will have on the demand for its services. Customers will make the choice between price and quality in transport as they will in purchasing other consumer goods (Cole, 2005).
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Better operational services
Higher Improving area Better traveling Improve Improved frequency of of service comfort reliability safety rides
Improve Decrease punctuality failures
Figure 8; Bus operator' service goals
Quality of service can be translated into performance indicators. De Borger, Kerstens, & Costa( 2002) performed a study on public transport performance. It discussed a wide variety of performance indicators, which give valuable insight into how operators measure their performance. Table 2 gives an overview the indicators found in the study. With this knowledge it is possible to describe how operators respond to new technologies in transport, using the factors from Figure 8. Improving the area of service is closely related to winning tenders in certain countries, and will be further discussed in the next section.
Table 1; Performance indicators in public transport (based on De Borger et al. 2002)
Performance indicators Units
Percentage fleet utilization (buses in use / total fleet / year) Number of breakdowns (vehicle breakdown)
Vehicle load factor (passenger / bus capacity / year) Network length (km’s)
Vehicle miles (miles / year) Number of stops (bus stop)
Percentage of urban public transport (bus transport / total Average speed (km/h) transport)
Vehicle hours (hour / month) Number of vehicles (vehicle)
Number of bus trips (trips / year) Average bus age (year)
IMPROVING FREQUENCY OF RIDES
A higher frequency of rides can be reached by either using more buses, or improving the percentage of fleet utilization. When operators consider new vehicle technologies they take into account how they perform compared to regular diesel buses. One of the key weaknesses of electric vehicles for example, is their relatively small cruising range of around 200km’s. Then there is the notion that in general in EV
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battery systems, the battery usage drops to 70% capacity after 10 years and needs recycling (Thein & Chang, 2014). In addition, there are disadvantages to the extra time required to recharge batteries, and the need to install new charging infrastructure (Tzeng, Lin, & Opricovic, 2005). The time required to charge the batteries means that buses need to be taken out of operation for a considerable amount of time (Ding, Hu, & Song, 2014), while diesel buses could have continued their operation. This would negatively impact the percentage of fleet utilization. In Mattson’s survey (Mattson, 2012), 45% of the operators had concerns about vehicle availability when considering hybrid vehicles.
A lack of charging infrastructure reduces the convenience of electric buses (Morita, 2003). Alongside other concerns mentioned, reaching the desire to improve a higher frequency of rides might be difficult with electric vehicles, hence keeping operators from adopting the technology.
IMPROVING TRAVELING COMFORT
The desire to improve traveling comfort however, might lure operators towards electric buses. Compared to diesel buses, the electric ride is quiet both on the inside and the outside of the vehicles (VDL, 2015). This was confirmed in an expert analysis study by (Tzeng et al., 2005), scoring electric and other clean fuel technologies higher in terms of comfort compared to diesel buses.
IMPROVING RELIABILITY
Improving reliability can be further dissected into improving punctuality and decreasing the number of vehicle breakdowns. The rate of punctuality can be affected by many variables including, planning, traffic and other external circumstances. A decrease in technical failures however, is related to the reliability of the vehicles. The problem with electric buses and other new technologies is the lack of experience with the technology, which makes it difficult to predict the reliability of these new vehicles which could hold operators back from applying them.
In a Mattson’s survey (Mattson, 2012), maintenance issues were considered a major deterrent to adopting hybrid and other clean fuel technologies. It was found however, that there was a difference between the expectations of non-users, and the actual experiences of agencies that did adopt alternative fuel vehicles. Overall, hybrid electric users did not experience greater issues with their new buses than their regular diesel buses, and many operators plan to purchase new hybrid vehicles in the coming years.
IMPROVING SAFETY
Improving safety is a general focus for many transport operators (Dan bus, 2016; Egged, 2016; RET, 2016, Arriva, 2016; Synthus, 2016). Concerning new technologies, it seems that electric vehicles overall do not pose a reason for safety concerns. Hydrogen as a fuel has also not raised significant concerns (Ricci, Bellaby, & Flynn, 2008), just as biofuel. In the survey performed by Mattson (2012) however, propane (38% of respondents) and natural gas (37%) did raise some safety concerns under operators considering those technologies.
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3.2.1.3 IMPROVING AREA OF SERVICE Improving the quality of service can be done by simply offering more services. In bus transport this would mean offering services in more regions and running additional bus lines. As we have discussed in chapter 2, the tendering process is responsible for awarding the exclusive right to operate specific lines or networks of lines after a competitive process (Hidson & Müller, 2003). If operators want to increase their area of service, this simply means that they have to win more tenders.
Because the tendering process awards contracts to the highest scoring bid, or at least the bid closest to the criteria, operators want to match the criteria as close as possible. If those criteria explicitly mention the usage of clean fuel technologies, operators have no choice other than implementing these technologies in their bids if they are to win. Chapter 2 explained that PTAs have conflicting goals, and how new technologies come with higher cost overall. As a way of compensation, PTAs add subsidies to bids in a way of stimulating new technologies. If operators want to win more subsidies, they must include more new technologies in their bids. The next section will take a closer look at the impact of costs on new technologies.
3.2.2 REDUCING COSTS Having discussed how operators can increase their income, this section discusses how operators can reduce costs. Reducing costs can be achieved by decreasing investment costs and decreasing operational costs.
3.2.2.1 REDUCING INVESTMENT COSTS The operational performances of traditional buses are generally quite similar, but purchase prices can diverge. A moderate increase in purchase prices can often lead to an operator switching supplier. Operators have the luxury of being able to select a supplier from a market in which suppliers are under extreme pricing pressures. For that reason some vehicle suppliers might even decide to offer lower initial prices in return for long term maintenance contracts (Keles & Hartman, 2004).
Capital cost are also a criterion in the tendering procedure where low capital costs are awarded with a higher score. In general, the purchasing prices of conventional diesel buses are still lower than its competitors. Fossil fuel buses are the cheapest around €220.000, followed by CNG buses as €250.000, Biofuel buses cost between €220.000 and €250.000, hybrid at €270.000, And the most expensive type of buses are electric at €300.000 to €500.000 and hydrogen at €800.000 (Figure 9). As the prices of new technologies are higher than those of fossil fuel buses, operators will only consider them if the costs are compensated by lower operational costs, governmental subsidies or implemented as a criterion in a tendering procedure. In a survey by (Mattson, 2012), operators stated vehicle costs as the greatest deterrent from hybrids, and one of the most significant deterrents for the use of natural gas. The higher costs however are expected to drop in the future as technology matures (Figure 10), and fossil fuel prices increase.
Important to note is that some technologies require additional infrastructure investment costs. As can be seen in Figure 9, all other technologies with the exception of hybrid require additional fuel station assets costing up to €100.000,- per bus per station. These additional costs also need to be taken into
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consideration. Together with the purchase cost and operational cost a total cost of ownership (TCO) calculation can be made. While the 2012 TCOs in Figure 9 are outdated, it is interesting to see that the TCOs of traditional technologies are expected to rise over time, while the TCOs of the new technologies are generally expected to drop. The simulation models in this thesis will calculate TCOs of bus technologies using recent numbers.
Bus technology / Fossil fuel CNG Biofuel Electric Hydrogen Hybrid Energy source
Indication +/- 220 +/- 250 +/- 220 - 250 +/- 300 - 500 +/- 800 +/- 270 purchase price, 1000 euro
TCO 2012, 2.1 2.1 2.2 – 2.5 3.1 – 5.5 4.6 2.4 euro/km
TCO 2030, 2.5 2.6 2.9-3.8 2.72 2.7 euro/km
Additional No 500-1000 per 50 – 200 per 10 – 100 per 100 per bus per No infrastructure fueling station fueling station bus per station station investment, x1000 euro
Figure 9; Comparison of bus technologies on costs based on (TNO, 2013)
After the contract duration buses can either be reused, or sold with a residual value. It is financially attractive to delay the purchasing of electric vehicles, as the investment costs drop every year (Figure 10). Operators therefore not only consider the current prices of clean technologies but also their expected price developments in the coming years. In some cases, operators have to get a bank loan to purchase assets, as the contractual payment is done gradually throughout the concession. In case operators have to replace their entire fleet at once with clean fuel buses, operators might not be able to secure a high enough banking loan. In that case operators will simply not be able to construct a bid (M. Van der Steen, personal communication, 28-06-2016).
2014 2015 2016 2017 2018 2019 2020 2021 2022 20223 2024 2025
Pure 510 315 311.5 308 304.5 301 297.5 294 290.5 287 283.5 280 electric
HEV/ 400 390 381 372 363 354 345 336 327 318 309 300 PHEV
Figure 10; Average unit price electric vehicles market prediction (IDTechEx, 2015)
3.2.2.2 REDUCING OPERATIONAL COSTS After discussing how operators can reduce their investment cost we discuss the operational costs. As seen in Figure 7, less operational cost can be further divided into less fuel consumption, less empty buses and less maintenance costs.
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FUEL CONSUMPTION
Of all the expenses that bus operators have, fuel costs account for as much as 30%. There is a strong need to improve fuel efficiency on buses not only for financial reasons but also to reduce the environmental impact of buses. In order to reduce fuel consumption, smart planning of lines can have an impact as well as the driving speed of buses (Gota, 2014). In case the routes and schedules however are fixed, operators are left with the option of either purchasing more fuel efficient buses, or replacing engines in old buses with cleaner engines in order to reduce fuel consumption.
(Lajunen, 2015) compared the energy consumption of diesel buses versus electric buses and concluded that a diesel bus consumed 3.64 kWh/km while an electric bus consumed 1.02 kWh/km. It has to be noted however that the benefits of electric driving are limited to urban driving where repeated acceleration and braking is combined with modest speeds. With hybrid buses, fuel consumption savings are small in rural areas and almost non-existent on highways where the electric motor is hardly able to assist the combustion engine (Mattson, 2012).
As buses require fuel and a driver to run, running them near empty is expensive as costs are not compensated by passengers paying ticket fees. As a result, operators prefer running busy routes compared to empty routes. This is especially the case in net costs contracts where operators carry the revenue risk (Veeneman et al., 2014). While this is a challenge for all bus types and technologies, buses with the highest operational costs will be the most expensive to run empty and therefore are the least favoured by public transport operators. Buses with the lowest operational cost will be favoured the most.
MAINTENANCE COST
Operation and maintenance costs are significant expenses for transit agencies. As buses age, their per mile operating and maintenance costs (O&M) tend to increase. Replacing buses for newer technologies and models can significantly reduce O&M costs, making it a challenge to find the optimal time to replace each bus. In addition, the maintenance costs tend to vary across different bus types. As with the operational costs, transit agencies are likely to prefer fuel technologies with the lowest maintenance costs (Feng & Figliozzi, 2014).
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3.3 CONCLUSIONS
This chapter explored the considerations that affect bus operators’ decisions in bus fleet replacement, and the decision to purchase clean fuel buses in particular. Now more than ever public transport operators are challenged to provide adequate services in an effort to reduce traffic congestion, and help fulfil the rising demand in urban transport. To provide those adequate services, operators put a lot of emphasis in monitoring and increasing the quality of their services.
As public service providers, operators are prone to preserve their image and therefore frequently state their goal to reduce their environmental impact. Taking into consideration political directives, energy dependency concerns and rising fuel costs, the implementation of alternative fuel vehicles is higher on the agenda.
However, as operators are generally perceived as private companies trying to maximize their profit, clean fuel vehicles are not automatically preferred over traditional technologies. Operators can increase patronage with attractive ticket prices, improved operational capacity and reliability, bigger service coverage and higher frequency of rides. New technologies like electric buses, however, are still disadvantaged in competing with diesel buses in terms of operational performance. Diesel buses are a safe bet, while new technologies come with the risk of not performing as expected, a risk that can be overcome only with growing operational experience.
Additionally, operators are constantly under pressure to reduce costs in order to increase their chances of winning tenders and thus secure their income flow. Reducing overall costs can be done by either reducing investment or operational costs. While new technologies like electric buses require less overall energy than diesel buses, they also require additional capital investments for charging or refuelling infrastructure. Furthermore, as the technologies are still new, purchasing costs are also higher than those of diesel buses.
The disadvantages of the new technologies however can be reduced in several ways: First, research has shown that concerns about operational performance of clean fuel buses are usually unjustified, as operators using them have overall not experienced more issues than with regular diesel buses. Second, as explained in chapter 2, different tendering schemes have the possibility to reduce additional costs that currently keep operators from adopting clean fuel technologies in order to promote the technology’s diffusion.
Having created an understanding of the decisions that both the PTAs and bus operators make concerning new technologies, it was possible to implement those into our simulation models. Before this could be done however, a conceptual model on bus fleet replacement was required. A conceptual model describes how we see the transport system, its inputs and outputs, which parts are included, and how these parts are related to each other. The next chapter discusses the conceptual model that was created in this thesis.
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4 SYSTEM IDENTIFICATION AND DECOMPOSITION
This chapter presents the conceptual model for urban transport with bus fleet replacement. It is a systematic representation of the ‘real world’ within the boundaries of this research. The chapter will answer the two research questions: 1. How can we use agent-based modelling to simulate the socio- technical system revolving bus fleet electrification? 2. How can we model bus electrification diffusion and policy at both micro scale (minutes) and macro scale (years)?
Section 4.1 explains the methodology that is used to describe the system. Section 4.2 gives a clear description of the system boundaries. Section 4.3 introduces the system, its environment, the actors and their most relevant properties. Section 4.4 discusses the most relevant tasks the agents perform, and the general modelling sequence. The chapter ends with a concluding section 4.5.
4.1 DEFINITION OF A SYSTEM
Due to the bus transportation systems’ dynamics and interactions between the involved actors, the complex adaptive system theory is proposed as a suitable way to describe the system. Such systems are defined by John H. Holland as’ a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what other agents are doing’ (Nikolic & Kasmire, 2013, .P13).
Systems consist of interacting and interrelated components that act as whole, in which some pattern or order can be differentiated. To further specify systems, we use the definition from Ryan (Nikolic & Kasmire, 2013). It explains that systems are not actual entities but only an abstraction of a part of the world. They consist of multiple components often guided by the structure of the system. The components within the system are interrelated and depend on each other. Those relations do not happen loosely but follow a certain pattern of interaction, hence systems are deemed as structured. System also include feedback loops which simply means that component A can influence component B, which in turn can influence component A again.
To understand the behaviour of the system, it is not enough to simply observe individual parts of the system, as system behaviour is the outcome of the structured interactions of its components. A system can be considered a system if it’s behaviour can be observed for a prolonged period of time. A system is not complete without an explicit definition of its boundaries, which explains what is part of the system and what is not. What is not a part of the system is considered the system’s environment.
Besides a complex adaptive system, the transportation system can also be considered a socio-technical system. A system in which components have both physical (technical) relations and social relations (Nikolic & Kasmire, 2013). In such systems, stakeholders might be connected physically but will not interact socially. The opposite might be the case as well. An example of such a system is the Dutch energy system where households are connected physically to the local energy distribution grid. Socially however, households sign contracts with an energy supplier from the supply of energy. The energy supplier however does not have any physical connection with the households nor the distribution grid operators. It sole role is to provide the distribution gird operators with the expected energy demand for the day, and to introduce competition in the supply of energy (de Vries, Correlje, & Knops, 2016).
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4.2 DEMARCATION OF BUS TRANSPORTATION SYSTEM
In this section the system boundaries are described. It is important to note that the system in this chapter will form the base for two case studies for which separate agent based models will be built. The main case study will be about the Tel Aviv metropolitan area, the second case study will be about Noord-Brabant in the Netherlands. As a reminder systems are only fictional, therefore the system described in this section must not be seen as reality, but as a useful way of structuring this research and its limitations. First a description will be given of the system demarcation. The next section (4.3) will describe the system including relevant actors and objects, interactions and the system environment.
In Israel, the ministry of transportation is the regulator of public transport provision, with the ministry of finance allocating the funds for transport services. Before the year 2000, two companies provided the transport services for the entire country (Ida & Talit, 2015). The first being Dan who operated in the Tel Aviv metropolitan area, and Egged who operated in the rest of the country outside of the Tel Aviv metropolitan area. The industry therefore suffered from a lack of competition with two companies that were so dominant, that the public and state became very dependent on them (Ida & Talit, 2015). Meanwhile, the lack of efficiency resulted in high costs, a large dependency on subsidies, and a decrease in the consumption of public transport services.
After the year 2000, a reform was introduced in the public bus services sector requiring clusters of bus routes to be awarded using competitive tendering. The reform aimed at increasing competition while providing a suitable level of mobility at a reasonable price, reducing traffic and congestion, and reaching a higher level of efficiency in the operation of public transportation (Ida & Talit, 2015). As mentioned in the introduction, the city of Tel Aviv is testing electric buses as they might be able to reduce the environmental impact of bus fleets.
The Dutch case will be applied to the province of Noord-Brabant in the Netherlands. The province of Noord-Brabant is the PTA in the region and applied similar transport policy as the City of Tel Aviv. As the province aims for a zero emission bus fleet in 2025 (Provincie Noord Brabant, 2016), several pilot projects using electric buses have been tested. It will be interesting to see if the model is able to reach such market penetration levels of electric buses.
4.2.1 PUBLIC TRANSPORT The focus in this research is on the transport system, which can be further divided into public transport, and private transport. Private transport is excluded as the focus is on public transport. Figure 11 is a graphical representation of the transport system. The blue blocks will be included in research while the white parts will be excluded from the research.
Every transport mode apart from bus transport is out of the scope of this research. Transport policy, civil society, environmental challenges and transport planning are important aspects in our socio technical system and will be included. Civil society will not be included in the system. While we have reasons to assume that civil society influences transport policy, considering the public values at stake in public transport, this research has not found empirical evidence on how civil society impacts
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transport policy. In case such information becomes available, civil society could be added in future models.
Policy
Civil society
Environmental challenges
Bus Transit
Transit planning
Trolleybus Transit
Airline
Rail Transit Public transport
Transport Light Rail Transit Private transport
Water transportation
Metro Transit
Private motorized transport
Private bicycle transport
Figure 11; Demarcation of transport system. Blue = to be included, white = to be excluded
4.2.2 POLICY Figure 12 is a graphical representation of the transport system in this research, indicating which parts are included in the research. As discussed in chapter 2, public bus transport is regulated by the PTA by means of competitive tendering of contracts. In those contracts, rules are made on vehicle ownership,
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the ownership of assets like charging infrastructure, subsidies and payment schemes and selection criteria. This research will include all these factors.
Vehicle ownership
Infrastructure ownership
Subsidies
Competitive tendering Tendering criteria
Energy demand from Local environment households
Energy demand from Regional environment other transport industries
Policy
Global environment Noise pollution
Environmental challenges Energy demand from bus Rural bus transport Bus vehicles transport Bus Transit Air pollution Urban bus transport Infrastructure
Transit planning Charging/fueling infrastructure Bus operators
Bus stops Future road transport demand
Bus routes Future demand for other transit services Future demand for bus services
Future demand for other transport modes
Figure 12; System boundaries of public transport system. Blue = to be included, white = to be excluded.
4.2.3 ENVIRONMENTAL CHALLENGES While there are multiple environmental challenges that are associated with buses in local public transport systems such as air pollution, noise pollution and greenhouse gas emissions, this model will only focus on the potential climate change impacts due to electrification. The reasoning behind that is that the model’s original problem owner, the Ministry of Energy in Israel, is primarily concerned with the energy system per se, and the impact that changes in the transport system may have on energy infrastructure. Furthermore, noise pollution in particular has not yet been associated with
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improvement to the bus service in Israel. However, noise pollution and to a larger degree urban air pollution, are expected to gain in prominence and may be represented in the PhD thesis discussed at the start of this chapter.
4.2.4 BUS TRANSIT Bus transit can mainly be divided into urban and rural bus transport. Because the main modelling case study is on the Tel Aviv metropolitan area, which is an urban area, the bus system will only be considered on an urban level. Urban bus transport consists of bus vehicles, infrastructure, bus operators, and future road transport demand. Bus operators are part of the research as they are the main decisions makers in bus fleet replacement. The infrastructure will be considered in terms of bus stops, charging/refuelling stations, and additional costs of charging and refuelling systems. For the sake of simplicity, the locations of the charging/refuelling stations and bus stops, do not represent reality. The only charging method that electric buses will use will be depot charging as this has already been tested in pilot projects in Tel Aviv. It is assumed that buses only refuel/recharge at their operators’ station.
The bus vehicles will be included alongside their energy demands. Figure 13 shows the most popular bus types that are currently on the market (TNO, 2013). Each bus technology on the market differs in costs, maturity, emission profiles, refuelling and infrastructure requirements. The focus in this research will be on diesel buses and electric buses. Diesel buses currently dominate the market and are thus the reference scenario. Electric buses have zero hazardous street level tailpipe emissions, and have the potential to reduce CO2 as the power generation system reduces its carbon intensity (Wu et al., 2012). Furthermore, their implementation does not require systemic changes to infrastructure, and they have already proven their maturity in pilot programs in our main case study of Tel Aviv.
It would be ideal to have examples of how bus manufacturers calculate lease prices of their vehicles. Because we did not have access to such data, we used an example on how to calculate the lease price for a road car (Reed, 2014) to calculate the lease prices for bus vehicles in our model. The lease prices we calculated were compared to lease bus prices found in a preliminary financial agreement between Anaheim Resort Transportation and BYD regarding the purchase of four BYD K9 electric buses (Transportation, 2015). The lease prices were a match only diverging 10% at max. Therefore, the calculation method was deemed valid enough to determine the lease prices for buses. We determined the monthly TCO by calculating the monthly lease prices, plus the monthly fuel and maintenance costs (Guo, 2016). The fuel costs were determined by multiplying the energy or fuel prices with the monthly kilometres travelled. Salaries of bus drivers were not included, as they were considered equal for both electric buses and diesel buses. TCO calculation examples can be found in Appendix A.
Due to the limited time and scope of the model, future electricity prices are projected from 2016 until 2032, based on the extrapolation of the electricity prices of the last 10 years. For the Dutch case data was gathered from the Environmental Data Compendium (CLO, 2016), while for Israel data was gathered from the Israel Electric Company (IEC, 2012). In the model, the impact of the energy prices is approximately 20% of the TCO per month for electric buses. The purchase price, which accounts to approximately 60% of the TCO per month (Appendix A). In the Dutch case, Bus operators are assumed to buy energy from an energy company through a bilateral contract. In the Israel case, the electricity
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price is determined by the electricity authority, which acts as an independent agency that regulates the electricity market in Israel. Due to uncertainty in national electricity rate fluctuations, it is assumed that operators consider the current electricity rate as reflective of electricity prices for the duration of the contract. All of the bus operators are assumed to pay the same fee for energy.
Diesel buses
Electric buses
CNG buses
Bus vehicles
Hybrid buses
Hydrogen buses
Biofuel
Figure 13; Bus technologies. Blue = to be included, white = to be excluded
Because the diesel prices determine 50% of the TCO per month, the impacts of diesel prices were explored comprehensively. Just like the energy prices, the diesel prices were estimated out of historic diesel prices. However, instead of just trend extrapolation, three different diesel price scenarios were created and explored. The three scenarios are a ‘’business as usual’ scenario in which the diesel price follows trend of the past five years, a growth scenario in which the diesel price keeps increasing, and a scenario in which the diesel price decreases over time. As diesel buses are considered mature in terms of technology, their purchase price is assumed not to change significantly in the model. Data on diesel prices were gathered from The World Bank (The World Bank, 2016).
4.2.5 TRANSPORT PLANNING Transit planning is considered part of the system, as the current and future transport demand needs to match the transport capacity. A distinction has been made between road transport planning and other forms of transport. In this research, the focus is on road transport which bus transport will satisfy. Satisfying demand requires bus routes that provide the services needed in the areas needed. As specific bus routes do not contribute to answering our research question, these are not included in the model. Instead it is assumed that bus demand only depends on the amount kilometres service that is required per month, with each contract specifying its own transport demand.
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4.3 CONCEPT FORMALISATION
Having discussed the boundaries of our system in section 4.2, this section shows what the system looks like. It will identify the most relevant agents and objects, the most important behaviours, the interactions between the agents and the agent states and properties. It also discusses defines the external world the agents live in.
The social technical system in this research is illustrated in Figure 14. It shows the relevant actors, objects and their interactions. The identified actors will be the main agents in the system. These are the bus operators and PTAs. Next to these actors, the buses are considered agents as well. The blue arrow are technical (physical) relations while the social interactions are indicated with green arrows. The dashed blue line forms the separation between the agents and their environment.
Bus stations Own
Pollute Complain about air quality / GHG emissions to local environment Diesel buses Own Offers bids to
Require fuel from
Consume fuel Public Bus from local Fuel stations Own Tenders contracts to Transport operators environment Authorities
Subject to Electric Own technological innovation buses Adjusts policy to meet transport demand from local citizens
Require electricity from r
Sensitive to fuel prices and capitical costs of buses e
d r
Own o b
Depot
chargers m
e
t
s
y S
Consume electricity from local environment System environment
Figure 14; Conceptual system design. Blue arrows = technical relations, Green arrows = social relations. Dashed line represents system boundary
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4.3.1 MOST RELEVANT AGENTS THE PUBLIC TRANSPORT AUTHORITY
The models include one PTA as the regulator of public transport services within the system. The PTA is responsible for adhering to the transport demand from consumers. The unit for transport demand is km’s service per month. This unit was chosen because there can be important differences between diesel buses and electric buses in terms of range, which would not be taken into account in case the demand was given in unit such as number of buses. Another unit considered, was the number of service hours. However, this would have left the opportunity open for operators to register buses as currently on duty, while having them remaining idle.
The transport demand is dynamic and as the demand changes, the PTA adjusts the transport demand in the tendered contracts. The total monthly transport demand of the region is determined at the start of the model, and increases by 1% at the end of each year.
Every year a tender will take place, for which the transport demand will be set at the difference between the total monthly transport demand, and the total monthly capacity of all the buses in the region. Because the transport demand increases each year, and old contracts run out each year, there will always be a transport gap to be filled at the start of each new year.
BUS OPERATORS
Bus operators are the companies that have been awarded the concession to operate a particular cluster of bus lines. They own a bus fleet and receive subsidies from the government for operating the lines. Operators are privately owned companies that aim at maximizing their profits by winning bids for public transport tenders, and minimizing their expenditure. Their bus fleets consist of a mixture between diesel buses and electric buses.
As seen in section 2.3, there are different ways to divide ownership of assets in public transport. Because the model in this research focusses on Tel Aviv Israel, we have chosen the ownership structure applied in Tel Aviv. This means gross –net contracts, in which the operators own the assets, and the government pays a lump-sum but collects the ticket fare revenues (Ida & Talit, 2015). This has some important implications for the decision making of the bus operators. Meaning they do not have to consider changes in risks caused by different ownership structures.
However, operators do have different views on renewable bus technologies. Not only on how the technologies fit into the company culture, but also on the speed of price developments and technological developments (D. Eerdmans, personal communication, Augustus 10, 2016). Operators can also have different strategies for electric mobility. Some operators want to take a first movers advantage and implement electric buses in their fleets early to gain valuable experience with the technology (M. van der Steen, personal communication, June 28, 2016). While other are more hesitant to adopt new technologies, due to the operational risks and a lack of experience. Those operators would rather wait until the technology is forced upon them.
Ideally, we would apply real world information from the bus operators in our case studies on how they approach renewables bus technologies. Because we do not have access to such information, the bus operators in our case studies are given innovation profiles which limit the amount of electric buses
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they can offer in a bid. Operators are given a property called ‘ability to innovate’. The higher this property, the more confident they are in electric mobility and the more electric buses they are able to offer in their bids. The lower this property, the less confident they are with electric mobility meaning they will bid less electric vehicles in tenders. The property increases over time until there is no limit on the amount of electric buses that operators can bid. Experiments will be done with different innovation limits applied to the operators.
As for bidding and winning tenders, operators want to score as much points as possible in order to win the bid. If it is allowed to reuse buses, operators will count the amount of buses that can be reused, and determine how much buses will be needed additionally. Operators must make sure that they bid enough buses to be able to provide the required services for the entire tender period. This means taking into account battery degradation of electric buses, and the maximum age limit that buses have. Operators prepare for buses that reach their maximum age, by ordering additional replacement buses in advance and having them delivered when they are needed.
Operators have some important trade-offs to make when placing their bid. Diesel buses have a higher range then electric buses they are able to travel more kilometres per month. On the other hand, electric buses are awarded with extra points in the tendering procedure, and have lower operational costs. However, the lease prices of the electric buses are higher than diesel buses. In order to place the best possible bid, operators run an optimization procedure aimed at scoring the highest amount of points. In case operators win the bid, they will update their bus fleets based on their bid.
Buses operators determine the maximum amount of points using an integer optimization procedure. We will discuss the optimization method using the steps from Ragsdale (2008).
1. Understanding the integer optimization problem
The goal is to score as many points as possible in the tendering procedure, while providing the required numbers of kilometres service per month, without using buses that are over the age limit.
2. What are the decision variables?
There are two decision variables. The number of diesel buses an operator will bid, represented by X1. The number of electric buses an operator will bid, represented by X2.
3. What is the objective function?
A is the weight of the cost criteria. B is the weight of the innovation criteria. Together A and B will always be 1 and cannot be 0. TCOD Represents the monthly total costs of ownership for 1 diesel bus
(dbus). TCOE Represents the monthly total costs of ownership for 1 electric bus (ebus). Every electric bus will be granted BONUSE points for innovativeness. The objective function will be:
MAX: - A( X1 (dbus) * TCOD (euro/month) + X2(ebus)* TCOE (euro/month) ) +
B ( X2(ebus)* BONUSE )
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4. What are the constraints?
The transport demand per month needs to be satisfied. Each diesel bus X1 can drive KPMD amount of kilometres per month. Each electric bus X2 can drive KPME amount of kilometres per month. The transport demand however should be satisfied for the entire duration of the tender. If operators can reuse their bus fleets, they only need to buy a fraction of the total demand, dependant on how much kilometres their old buses are able to run. This is called the personal transport demand which is equal to:
푝푒푟푠표푛푎푙 푡푟푎푛푠푝표푟푡 푑푒푚푎푛푑 = (푀표푛푡ℎ푙푦 푡푟푎푛푠푝표푟푡 푑푒푚푎푛푑 − ∑ 푚표푛푡ℎ푙푦 푘푚 푟푎푛푔푒 표푓 표푙푑 푟푒푢푠푎푏푙푒 푏푢푠푒푠)
The second constraint is the max amount of electric buses an operator can buy. Every operator can only bid a maximum amount of electric buses which is based on the operators’ capability to innovate. To determine the personal electric bus limit, the maximum amount of ebuses in a bid is determined first which is:
푚표푛푡ℎ푙푦 푡푟푎푛푠푝표푟푡 푑푒푚푎푛푑 푚푎푥𝑖푚푢푚 푎푚표푢푛푡 표푓 푒푏푢푠푒푠 𝑖푛 푎 푏𝑖푑 = 푚표푛푡ℎ푙푦 푘푚 푟푎푛푔푒 표푓 1 푒푏푢푠
The personal electric bus limit is then determined by multiplying the maximum amount of ebuses in a bid with the operators’ personal capability to innovate. This means that the constraints are:
Constraint 1: X1 * KPMD + X2 * KPME = personal transport demand Constraint 2: X2 < Personal electric bus limit
5. Upper and lower bounds on the decision variables
Negative bids are impossible which means that operators cannot bid less than 0 dbuses or less than 0 ebuses:
X1 ≥ 0 X2 ≥ 0 X1, X2 = Integer
6. Summary of optimization
MAX: - A( X1* TCOD + X2* TCOE ) + B ( X2* BONUSE )
Subject to: X1 * KPMD + X2 * KPME = personal transport demand X2 < Personal electric bus limit X1 ≥ 0 X2 ≥ 0
. X1 is amount of dbuses to bid . X2 is amount of ebuses to bid . A is a ratio for the impact of costs in the score . B is a ratio for the impact of innovation in the score
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. TCOD is the amount of costs per month per dbus
. TCOE is the amount of costs per month per ebus
. KPMD is the amount of km’s a dbus can run per month
. KPME is the amount of km’s an ebus can run per month
. BONUSE is the amount of bonus points per ebus for innovativeness
BUSES
Diesel buses pollute the local environment and refuel at bus depots in order to run. Bus depots contain fuel stations that consume fuel. In reality, the fuel is supplied by a fuel supplier. In the model the fuel is assumed to come from the modelling environment. The electric buses are considered clean and do not pollute the local environment. Electric buses require energy from charging stations to recharge their batteries. The charging stations also reside in the bus depots. In reality, the energy from the charging stations is supplied by an energy supplier. In the model energy is supplied by the modelling environment. Because the charging stations require energy from the local grid, electric buses are responsible for a certain amount of carbon emissions based on the carbon intensity of the local energy grid.
The buses have a certain range they can drive on a full tank, or full battery charge. When diesel buses are out of fuel they can simply refuel in a matter of minutes and continue their service. This indicates that the daily range of diesel buses are limited only by their shift times. For electric buses there are several recharging methods available on the market (Appendix N). For this research, the chosen recharging method will be depot charging, as this method has already tested and used in pilot electric bus projects in Tel Aviv. This does mean that electric buses require hours of charging after their batteries are depleted. This puts a limit on the daily range that electric buses can operate. The range of new buses however will increase through technological developments.
As time advances, the batteries of electric buses degrade reducing the daily range of electric buses. This happens at a certain rate per year. As guideline, data is used from (Thein & Chang, 2014) who in their research on battery degradation found that batteries degrade to 80% of their maximum capacity after 10 years. As technology advanced the degradation rate of batteries will become less for new batteries. For the sake of simplicity, the technological developments in terms of battery degradation will not be taken into account.
4.3.2 MOST RELEVANT AGENT PROPERTIES Now that we have identified the most important agents in the model, this section describes the most important attributes that the agents have. As we are building a model, this requires us to translate the attributes into a very specific format that is understandable for computers. Two well-known ways of doing this are language primitives and formal ontologies.
Formal ontologies can be defined as a formalisation of a conceptualisation (Nikolic & Kasmire, 2013). It is a collection of ’is a’ and ‘has a’ relationships between concepts which are relevant to the system. Designing an ontology however can be a difficult task, and it will likely require major changes to previous system designs due to new insights on how to formalise concepts in a system (Nikolic & Kasmire, 2013). Language primitives are easier to use, but are limited to a small set of basic concepts.
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Moreover, language primitives are dependent on computer language used. The language primitives used by the Netlogo agent-based software used in this research are more than capable to describe our agents. Therefore, Netlogo language primitives were used to formalise the agents in this research. A full description of the agent properties and their selected language primitives can be found in Appendix D.
GLOBAL PROPORTIES
Before discussing the agent properties, it is important to discuss the global variables that are used in the model. The global variables in Netlogo represent those variables that are in the agents’ environment, and might influence their behaviour. Figure 15 shows the most important global variables in the model. One of the most important global variables is the time. The smallest time step in the model is one tick, which represents an hour in real time. The simulation stops after a total of 16 years, as data from Dan’s bus fleet shows that it takes around 16 years before a bus fleet is fully depreciated (Dan, 2015).
The time until next tender determines when the next tender will take place. The bus transport demand is a dynamic value that indicates the consumer demand for bus transport. The maximum allowed bus age sets a limit on the age buses can have during their operational lifetime. The carbon intensity of the local grid is used to indicate how much the local energy grid makes use of renewable energy sources. The technical specifications of the buses determine the energy usage maximum energy capacity of new buses. The specifications change as time advances.
Carbon intensity of the Operational costs of buses local energy grid
Maximum allowed bus Lease prices of buses age
Technical specification of Global Bus transport demand buses variables
Time Fuel costs
Time till next tender Energy costs
Figure 15; Global variables in the model.
BUS OPERATOR PROPERTIES
Figure 16 displays the properties that belong to the bus operator agents. As discussed before bus operators have a bus fleet consisting of a set of electric and diesel buses. Operators also have charging depots and stations. Bus operators have contracts with the PTA which determines the amount of transport is needed, and for how long the bus operators have the concession to provide their services.
At the start of the model, operators are given a certain amount of contracts and buses based on their real world market share. Because the contracts have different starting dates, the time left before the starting contracts expire, is based on bus age data and contract data from the existing real world bus
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fleet. In case no data on existing contracts was available, bus ages of the existing bus fleets were taken as a guideline Appendix D).
Another property is the share of new technologies that operators have. Related to this property is their ability to innovate which sets a limit on the amount of new technology buses they can offer in their bids.
Electric buses Contracts
Diesel buses Bus operators Share of new technologies
Stations Ability to innovate
Charging depots
Figure 16; Bus operator properties
BUSES
The bus properties are displayed in Figure 17. Buses can either be electric or diesel, and accordingly consume fuel or require energy in order to run. The rate in which they consume energy is determined by their fuel consumption rate or battery usage rate. Buses have a shift time which determines when they are out on shift. Specific to diesel buses is the amount of air pollutions they emit during their driving. As time advances buses age.
Fuel level Shift time
Type diesel or electric Air pollution rate Buses
Battery level Fuel consumption rate
Age Battery usage rate
Figure 17; Bus properties
PTA
The public transport authority properties are given in Figure 18. The global variable for transport demand is translated into a required number of kilometres for the tender. It has a price criteria and an EV criteria which indicates how much emphasis is put on the two factors when selecting a bid.
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Kilometers required for tender
Price criteria PTA
EV criteria
Figure 18; Public transport authority properties
4.4 MODEL FORMALISATION
Section 4.3 discussed the agents in the model and their most important properties. This section explains the most important agent behaviour and what decision making processes drive their behaviour. This is done in two steps. Firstly by means of a model narrative, which is a detailed textual description of the modelling sequence. Secondly using a pseudo code, which is basically a textual description of how the algorithm flows (Nikolic, 2015).
4.4.1 MODEL NARRATIVE The model narrative is a story which explains who does what in the model, when it is done and with whom. More specifically the narrative explains how the agents and the environment do what they do, when they do it and with whom (Nikolic, 2015). Typically, the narrative is as precise and accurate as possible. Achieving this accuracy is an iterative process in which many missing elements are found, such as missing actions, states and decision algorithms. The narrative for the model in this research is as follows.
THE STARTING CONDITION OF THE MODEL
At the start of the model, the modelling time is set to 0:00 hours in the 0th year and all other counters are reset as well. Every tick, which is the smallest time step in the model, corresponds to an hour in real time. After 24 hours the model advances one day. Each day the daily operational activities of the bus operators and buses are monitored. After five days have passed the model advances one year (Figure 19; High level model sequence.). The model is ends after 16 years have passed.
BUS GO ON SHIFT
Before the start of the day, buses check if they are still part of a concession. If not, buses remain at their station. Buses that have passed the maximum allowed operational age can no longer be used, and will be removed from the model. Starting at 5 am, buses start leaving their stations according to their starting shift time. During their shift buses will travel from station to station consuming fuel or energy at each tick. The amount of fuel a diesel bus consumes each tick, is calculated using the traveling speed and fuel consumption specifications of current diesel technology. Similarly the energy consumption is calculated. Each bus runs at an average speed of 20 km/h continuously without stopping, and needs to reserve enough fuel or energy to return to a station. We assume that this is calculated in advance and that stations are also recharging/refuelling stations.
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Vehicle Is it time for a technology is Vehicles are Buses perform After 5 days have Year starts tendering No updated. Costs updated daily activities passed, year ends procedure? are updated
Yes
PTA requests bids. Operators place bids
PTA selects winning bid
Operators update fleets
Every year
Figure 19; High level model sequence.
BUSES GO BACK TO THE DEPOT AND RECHARGE
As buses reach the end of their shift time, they return to their starting station. For simplicity it is assumed that stations are also depots where buses stay overnight. As buses arrive at the depot, they will refuel or in case of electric buses recharge until their maximum fuel capacity / battery capacity is reached. The buses will not leave unless their maximum fuel capacity / battery capacity is reached. For diesel buses refuelling is completed within minutes. Because each tick takes ten minutes, it is assumed that the fuel tanks of diesel buses are filled instantaneously. Electric buses recharge at a charging rate of 60 kWh / tick.
As time advances and batteries go through many charging cycles, battery degradation takes place reducing the maximum battery capacity of electric buses. See Appendix A for the rate of battery degradation. The amount of battery degradation is fixed for the entire lifetime of a bus.
PTA SETS CONTRACT CRITERIA AND REQUESTS BIDS
Every year, the PTA requests operators to place their bids for a new round of tenders. Before the tendering begins, the bus related costs and technological specifications are updated to the most recent values. See Appendix A for the price and technology developments. PTA then creates a contract in which it specifies the monthly km demand to be fulfilled.
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The bus operators are requested to place their bids while satisfying the required demands. Every bid is given a total score based on how well it matches the required demands. After the bids are in, the PTA compares them and selects the bid that scores the highest total amount of points.
Every bid is given a total score based on the monthly costs of the bus fleet and the innovativeness of the buses that are implemented. The lower the monthly costs, the higher the points scored on the pricing criteria. The higher the amount of electric buses, the higher the amount of points scored on the innovation criteria. How much emphasis the PTA puts on costs or innovation is determined by a weighting criteria.
The winning operator is the operator with the least amount of penalty points. The operator will then randomly assign a set of old buses and a set of new buses to run in the new contract. The reason that old will be assigned to run, is because the operator wants to use its old buses until they are depreciated. The operator will assign new buses because it feels the need use its new buses to improve the quality of its bus fleet.
4.4.2 MOST RELEVANT AGENT ACTIONS The model narrative explained which decisions are made when, and to whom. All of our agents make their decisions based on a decision making algorithm. This section gives an overview of all the actions that agents can perform in the model in bullet points.
Every agent follows a detailed decision making algorithm. The algorithms were created in such a way that they match the decision making of real world agents. While our conceptual model might be clear to our stakeholders, and understandable to humans, they are certainly not clear enough for computers to understand. Computers aren’t very capable of understanding context dependency or ambiguity. A computer does not know what a bus is, or what refuelling means. It is therefore required to make our model of the world so explicit and formal, that it becomes understandable for a computer in an agent- based model. An example an algorithm is given in Figure 20. The algorithm describes the behaviour of buses in the model. See Appendix F for the decision making algorithms of all the agents.
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Charge Charge Refuel Do nothing
yes Yes yes Yes
Is fulltime Is now off Am I a Diesel no charging No No Am I full yes peak? bus allowed
no
Go to nearest Am I at the Do not charge No station station?
Stop
Keep driving. Reduce fuel/ Am I on No Do nothing battery level. yes shift? Pollute the environment No yes
Is my age Check if Start of the time Die Yes below my bus Stop No company still step for buses lifetime? has tender
Stop Start
Figure 20; Decision making of buses in the mode
A summary of all the actions that the agents in the model perform:
BUSES
Check if their bus operator still has a contract Update age O Age = age + 1 [every year] Check if their age is below the maximum allowed lifetime o My Age < allowed age Check if they are still on shift o Current time = within shift time Drive from station to station o Average speed of 20 [km/h] Drive to a depot o If company is out of tender o If out of energy or fuel Consume energy o Electric buses consume energy [KWh per tick] o Diesel buses consume diesel [ liters per tick]
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Degrade their battery o Maximum battery capacity * (1 – degradation rate) Pollute the environment o Environmental air quality counter = environmental air quality counter + x [gram CO/kWh of grid] Refuel at stations o Diesel buses refuel: fuel level = max fuel level o Electric buses recharge: battery level = battery level + battery level [ kWh per tick ]
OPERATORS
Evaluate the awarding criteria O What is the bonus rating on the price criteria O What is the bonus rating on the EV criteria O Check the transport demand of contract [kilometres / month] Calculate their additional capacity needed in order to fulfill the transport demand O (monthly transport demand of contract) – ∑ (monthly km range of buses that can be reused for a full contract period) Calculate the maximum amount of electric buses they can bid O (monthly transport demand of contract / monthly km range of 1 ebus) * ability to innovate Determine the optimal bid through an optimization procedure. The goal is to optimize the score.
o MAX: - A( X1* TCOD + X2* TCOE ) + B ( X2* BONUSE ) . A is a ratio for the impact of costs in the score . B is a ratio for the impact of innovation in the score . X1 is amount of dbuses to bid . X2 is amount of ebuses to bid
. TCOD are the costs per month per dbus
. TCOE are the costs per month per ebus
. BONUSE is the amount of bonus points per ebus for innovativeness Place bids o When it is time to place bid Update their bus fleet o Remove old buses from fleet o Add new buses to fleet
PTAS
Determine if it’s time for a tender o Has a year past since the last tender? Start a tender procedure o Every year Update tender criteria o Determine transport demand 45 | P a g e
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Request bids from operators Select the best bid o Highest scoring bid out of all bids
4.5 CONCLUSIONS
This chapter presented the conceptual model of the diffusion of new technologies in urban bus fleets, that was used in this thesis. It’s goal was to answer the questions: 1. How can we use agent-based modelling to simulate the socio-technical system revolving bus fleet electrification? 2. How can we model bus electrification diffusion and policy at both micro scale (minutes) and macro scale (years)?
The system was described as a complex socio technical system. A clear system demarcation explained what belongs to the system and which parts belong to the system environment. Relevant actors, their interrelations, and their interaction with the environment were given. The relevant actors were translated into agents and given specific properties and actions. The overall behaviour of actors were translated into detailed algorithms, making it possible for computers to understand and simulate actor behaviour. The next step will be to translate the conceptual model into an agent-based model. This will be discussed in chapter 5.
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5 MODEL IMPLEMENTATION: BUS FLEET REPLACEMENT WITH AGENT-BASED MODELLING
This chapter discusses the model that was designed to analyse the diffusion of new technologies in urban bus fleets. It translates the conceptual model from chapter 4 into an agent-based model. This chapter additionally explains which methods were used to verify if the model was built correctly (model verification). Section 5.1 will introduce the model and explains how it can be operated. The model is verified in section 5.2.
The model in this thesis is not a standalone model. Preceding this model, a general bus fleet replacement model was created during an agent-based modelling course in 2015. This particular model can be easily adjusted so that it reflects different cities and explore several things. Firstly the diffusion of electric buses in a city over time, under different initial market penetration rates. Secondly it is able to measure the impacts of electric buses on a fictive local energy grid. Thirdly, how the local air quality affects, and is affected by the diffusion of electric buses. And fourth, how the carbon intensity of the local energy grid impacts the emergence of electrification.
Based on this generic model, the model in this thesis was created and applied to two real life case studies. Experts were consulted for feedback on the general model, which was implemented in the modelling design. The model was further enhanced with specific transport policy currently in use in the case studies, such as tendering procedures and awarding criteria. Demographic data on bus fleets were gathered as well as updated technical specifications and financial aspects of bus vehicles.
The model will be used in a PhD thesis on policy emergence by Amit Ashkenazy. In his thesis several policy process theories will be modelled using agent based modelling. The policy theories will be applied to several case studies including bus fleet replacement. Adding this complexity creates the possibility to not only test several policy measures as input variables, but also explore how transport policy emerges and influences bus fleet replacement.
5.1 SOFTWARE IMPLEMENTATION
The model was build using Netlogo 5.3. Before creating the model in Netlogo, the model narrative from section 4.4.1 was translated into a pseudo code. The pseudo code is basically a textual description of how the algorithm flows (Nikolic, 2015). What makes a pseudo code useful is that it forces a modeller to precisely describe the algorithms to be used in the model, before actually writing the algorithms. Fixing unforeseen errors can quickly be done in the pseudo code, whereas fixing errors in the actual model can be time consuming. The full pseudo code used in the models can be found in Appendix G and Appendix H. Using the pseudo code as a basis, the model was then written into Netlogo programming code. The full code can be found in Appendix L.
The model contains several parts. Figure 21 displays the agents in the model and their movements. It shows a grid of white roads on which the buses drive. The blue buses represent the diesel buses, and the yellow buses electric buses. The coloured factories represent the bus operators in the model and the small coloured houses are their bus depots. The large grey building near the centre is the PTA.
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Figure 21; Visualization of model in Netlogo
The roads and the positons of the stations do not actually represent the case studies but are purely fictive and only used as a visual enhancement.
Figure 22 displays the rest of the model interface. It contains monitors and plots of the most relevant model outputs, so that the user can track the modelling behaviour. Next there are sliders with which the user can change the most relevant parameters in the model. Finally, there are switches and buttons so that the user can give the model instructions, or toggle between different options.
Figure 22; Modelling parameters
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Pressing the setup button creates the roads and the agents, and resets all the model outputs such as the time. After pressing the go button, the model is initiated. The buses start moving, and the outputs can be followed through the plots and the monitors. After 16 years have passed the model stops and the setup button has to be pressed to reset the model making it ready for another run.
5.2 MODEL VERIFICATION
After a model has been built, it is important to test if the conceptual model has been properly translated into a model that is working as designed. Model verification checks if the relevant agents and relations between agents in the conceptual model, have been translated into a computational model correctly (Nikolic & Kasmire, 2013). In order to verify our model, four verification techniques were used.
1. Recording and tracking agent behaviour, where relevant output variables are identified and observed. 2. Single agent testing where the behaviour of a single agent is analysed. 3. Interaction testing in a minimal model, in which the model is tested keeping the number of agents as low as possible. 4. Multi agent testing, in which the emergent behaviour caused by agents interacting is observed.
5.2.1 RECORDING AND TRACKING AGENT BEHAVIOR
In the model we used several methods to record and track agent behaviour. These methods proved to be very useful in detecting unexpected behaviour. In order to track and record agent behaviour, the outputs of important global variables such as the total amount of buses and the TCO per month, were plotted and monitored (Figure 23). This gave us a good indication of the overall model behaviour. Labels were attached to agents to monitor relevant agent properties, such as remaining years on bus contracts or fuel levels of buses.
Figure 23; Monitoring agent behaviour
Throughout the modelling code, additional lines of debugging codes were added in order to keep track of relevant model behaviour. These included agent states, interactions between agents and internal processes. This has been especially important in keeping track of technical specification updates, the tendering procedure, and relations between bus operators and buses. In order to select which debug code to run, a list of switches was made for different debugging procedures. This made it possible to filter out specific modelling behaviour and debug the code step 49 | P a g e
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by step. Figure 24 shows three switches used in the model to monitor bus ages, contract durations and the monthly capacity of new buses.
Figure 24; Debug switches in the model
If a debug method is switched on, the outputs are printed in the Netlogo command centre. The outputs were designed in such a way, that they read as parts of the model narrative. An example of the debugging code and the application of this debugging method can be found in Error! Reference source n ot found..
5.2.2 SINGLE AGENT TESTING The second model verification method used, was single agent testing in order to explore the behaviour of the model if only type of agent existed in the model. The tests were performed on each of our three main agents which were bus operators, buses and the PTA. The single agents test consists out of two steps (Nikolic & Kasmire, 2013).
1. Testing theoretical predictions and sanity, in which it was monitored if agents behave as expected under normal conditions. 2. Break the agent tests, in which agents were given extreme values in order to find the edges of normal behaviour.
The single agent test exploited multiple errors which were corrected afterwards. The list of errors and corrections can be found in Error! Reference source not found..
5.2.3 INTERACTION TESTING IN A MINIMAL MODEL In the third model verification method that was used, the interactions between agents were tested in a minimal model. This meant running the model with only one of each agent. Hence the model was tested with 1 operator, 1 bus and the PTA. It was explored if the agents interacted as expected, if the results of those interactions were in tune with the model narrative, and if there were any unexpected interactions. Just like with the single agent test, we tried breaking the agents and tested theoretical predictions. Again, errors were found and fixed appropriately (Error! Reference source not found.).
5.2.4 MULTI AGENT TESTING The fourth model verification model used, was multi agent testing in which the model was tested with all the agents included. The same theoretical prediction tests, and breaking the agent test as explained
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in the previous sections were performed. The results from these tests can be found in Error! Reference s ource not found.. Additionally, two extra tests were performed. Firstly, a variability test in which the variability of several important model outputs was evaluated. Secondly, a timeline sanity tests in which we monitored several outputs of the model at the default parameter setting, and tried to identify unexpected modelling behaviour.
VARIABILITY TESTING In the variability test the model was run for many repetitions under the default parameter settings (Error! Reference source not found.). A single run in agent-based model is not sufficient enough for s table conclusions. The reason being the use of stochastics during the initialization and the execution of the model, causing the parameters to vary between different runs (Nikolic et al., 2013). Therefore multiple runs are needed, and one needs to consider the mean and the spread of the data over many runs. Between 100 and 1000 repetitions are usually performed variability testing. Firstly the model was run at 100 repetitions and the spread of the data was explored in order decide if less runs would suffice, or additional runs would be needed to capture interesting modelling behaviour.
Figure 25 displays a scatterplot of the development of the number of buses over time in the model, within the time span of 16 years. The dots show that the number of buses grows linear with very little variance within the total number of buses. This is to be expected as the number of buses depends on the transport demand which steadily grows each year. The increase in dbuses at the start is caused by the limitations on the amount of ebuses that operators can bid, due to their limited experience with ebus technology and focus on cost reduction. As time increases and the TCO of ebuses decreases, the number of ebuses increases and eventually becomes greater than the number of dbuses. Around year 10 there is a big shift in the fleet composition, caused by a large amount of contracts expiring. The reason this occurs in year 10 is due to the distribution of the bus contract durations at the start of the model.
Figure 25; Scatterplot of the number of ebuses and dbuses over time
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Although scatterplots are handy, a separate tool is required in order to inspect the spread of the data more closely. An example of such a tool is a boxplot, which is a useful for graphically comparing different datasets. It contains information on the centre, skewness and range of datasets. A boxplot consists of a box, and a line across the box which usually indicates the median of the data (Figure 27). The upper part of the box is the value of the 75th percentile (also called upper quartile) of the dataset, indicating that 75% of the data is equal or lower than that value. The same accounts to the lower part of the box except than for the 25th percentile of the data (or lower quartile). Therefore the box indicates the area in which 50% of the data lies. The interquartile range is called the distance between the upper quartile and the lower quartile (upper quartile – lower quartile). From each side of the box, lines extend up to the outermost observations that lie within 1.5 time the interquartile range. Any values beyond the lines are considered extreme outliers and are drawn as dots (Frigge, Hoaglin, & Iglewicz, 1989).
Figure 26; Boxplots of total buses over time per year
Figure 26 shows boxplots of the total number of buses for each year. As expected, there is very little spread in the data as the boxes are so flat, they are invisible to see. While the spread increases over time, the largest interquartile range does not exceed 5 and the extreme outliners remains close to the boxes. This is much less the case for the number of buses per bus operator. Figure 27 is an example of the development of bus operator Dan’s bus fleet over time. The figure shows an increasing spread in the number of buses as time advances, with the IQR the size of up to around 750 buses. This is to be expected at the default parameter settings, where the bus operators apply identical strategies in the tendering procedure therefore submitting similar bids in the tendering procedures.
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Figure 27; Boxplots of total buses of operator Dan's bus fleet per year
TIMELINE SANITY TESTS
Figure 28 shows some important outputs of the models generated with the default parameter setting. The outputs show various changes in behaviour. The TCO per month for dbuses is closely related the diesel price. As the diesel price is historically cyclic, the TCO for dbuses is cyclic as well. The TCO for ebuses however shows a small decline, while the energy prices are cyclic. The reason behind this, is that the energy price is only a small fraction of the TCO for ebuses. The purchasing price however has a big influence on the TCO for ebuses, which slowly decreases as time advances.
Figure 28; Model output diagrams
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As explained in Figure 25, there is the big change in the composition of the bus fleet around year 10. Therefore, the operator that wins the contract in year 10 gets a huge increase in market share. In Figure 28 it turned out to be the bus operator Egged that won the tender in year 10. As the fleet composition changes so does the amount of air emissions caused by ebuses and dbuses. Where at the start of the model dbuses are solely responsible for all of the CO2 emissions, ebuses eventually become responsible for most of the CO2 emissions. This due to the fact that the amount of ebuses grows over time and ebuses require increasingly more energy from the electricity grid.
5.3 CONCLUSIONS
This chapter presented the agent-based model in this research. The conceptual model from chapter 4 was firstly translated into a pseudo code. The pseudo code was then used to create and write the model in Netlogo modelling code. In order to verify if the model was created correctly, several model verification tests were performed. The tests included recording and tracking agent behaviour, single agent testing, interaction testing in a minimal model, and multi agent testing. Based on the outcomes of the tests it can be concluded that the model was built correctly and is suited for experimentation. The next chapter introduces the experiments and modelling results.
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6 EXPERIMENTS AND RESULTS
This chapter discusses the results gained from experimentation with the models. As a reminder, not one but two models were created and consequently two sets of experiments were performed. The main model was based on the city of Tel Aviv in Israel (Appendix P), while the Dutch Province of Noord- Brabant was the region modelled in the second case study (Appendix Q). The two sets of experiments however are complementary and contribute to answering the same research questions. The chapter starts with an explanation of chosen experiments, research questions, and explains for which purpose they were designed. It continues with results plus an explanation of the findings. The chapter ends with the model validation.
6.1 EXPERIMENTS
6.1.1 EXPERIMENTAL DESIGN In chapters 2 and 3 we explored the considerations of governments in subsidizing electric mobility, and the considerations that bus operators make when adopting new technologies. With the insights gained from those chapters and the designed model from chapter 5, we created a modelling capable of answering the following research questions:
1. How do different amounts of subsidies impact the diffusion of electric buses?
2. How does electrification affect urban CO2 emissions? 3. What will be the impact of different levels of electric bus diffusion on the electricity grid? 4. How does the diffusion of electric buses in Tel Aviv compare to the diffusion of electric buses in a Dutch City?
In order to answer those questions a lot of time was spend on designing effective experiments. This was needed because of the many assumptions made when designing the model, which had an influence on the outcomes that the model generated. Moreover, thoughtless experimenting could have resulted vast amounts of data, taking much time to analyse, and not capable of answering the questions. Therefore, we considered the following when designing experiments:
The impact of modelling assumptions on experimental results based on theory. Parameters and parameter settings How to measure relevant emergent behaviour
6.1.2 POSSIBLE IMPACT OF MODELLING ASSUMPTIONS ON EXPERIMENTAL RESULTS BASED ON THEORY In the model we have chosen gross-net contracts as the contract between the operators and the PTA, as it is the type of contract used in both case studies. We learned from chapter 2 that gross-net contracts stimulate cost efficiency which means that cost could become of great impact on the modelling results. Additionally, we learned that PTA’s can make use of subsidies to try and stimulate the market penetration of clean fuel buses, thus decreasing the environmental impact of public transport. Chapter 3 explained that bus operators value a clean image, but are also under constant pressure of increasing their cost efficiency. Resulting in diesel buses mostly remaining the preferred
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option in terms of operational performance and costs. This means that the outcome of the model is likely to be cost driven. Therefore, to explore the diffusion of electric buses, we experimented with the effects of changing the costs and subsidies of bus technologies.
Because operators can have different strategies which influence their decisions on adopting new technologies, the models are equipped with a parameter that’s puts a limit on the amount of electric buses an operator can bid in a tender (chapter 4). This limit increases yearly hence increasing the maximum number of electric buses per bid. This could mean that we might observe different speeds of electric bus diffusion, and big differences between bus fleet developments of operators due to diverging strategies.
6.1.3 PARAMETERS AND PARAMETER SETTINGS Table 2 shows the selected parameters and parameter settings for the Tel Aviv experiments. The selected parameters and setting for the Noord-Brabant model are displayed in Table 3, which only shows the parameters that were different form the Tel Aviv model.
Table 2; Parameter settings Tel Aviv experiments
Parameters Experimented values Unit
["current-ebus-bonus"] 0 6000,12000 -
["contract-duration"] 10 year
["chosen-diesel-price-scenario"] 1 2 3 -
["Purchase price dbus"] 220,000 Euro
["Purchase price ebus"] 290,000 Euro
["Purchase price development ebus"] 3500 Euro/year
["daily-km-range-ebus"] 200 Km / day
["daily-km-range-dbus"] 200 Km / day
["dbus-air-quality-emission-factor"] 1000 Gram CO2 / km
["development-speed-egged"] 5%, 10% Percentage increase / year
["development-speed-kavim"] 5%, 10% Percentage increase / year
["development-speed-dan"] 5%, 10% Percentage increase / year
["full-time-charging-allowed"] true false -
["initial-maintenance-costs-per-km-dbus"] 0.5 Euro / km
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["initial-maintenance-costs-per-km-ebus"] 0.38; 0.25 Euro / km
["initial-monthly-transport-demand"] 6,500,000 Km / month
["maximum-allowed-age-ebuses"] 8 12 Year
["maximum-allowed-age-dbuses"] 8 Year
["Carbon intensity of the grid"] 630 Gram CO2 / kWh
["innovation-limit-start"] 10% -
["starting-market-share-dan"] 70% -
["starting-market-share-egged"] 25% -
["starting-market-share-kavim"] 5% -
["transport-demand-growth-rate"] 1% Percentage increase / year
Table 3; Parameter settings Noord-Brabant experiments
Parameters Experimented values Unit
["current-ebus-bonus"] 0 -
["chosen-diesel-price-scenario"] 1 2 3 -
["daily km range dbus"] 380 Km / day
["daily km range ebus"] 360 Km / day
["Purchase price ebus"] 450,000; 350,000 euro
["initial-monthly-transport-demand"] 4,600,000 Km / month
["maximum-allowed-age-ebuses"] 12, 15 year
["maximum-allowed-age-dbuses"] 12 year
["Carbon intensity of the grid"] 530 Gram CO2 / kWh
["innovation-limit-start"] 20% -
["starting-market-share-Hermes"] 35% -
["starting-market-share-Arriva"] 65% -
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["starting-market-share-Veolia"] 0% -
PARAMETERS FOR MODEL ENVIRONMENT
The parameters settings for the environment make it possible to explore the effect of electrification on the CO2 emissions, as electric buses are responsible for a certain amount of CO2 emissions per driven km. For diesel buses in both the Tel Aviv and Noord Brabant the pollution rate was set at 1000 gram CO2 per km (TNO, 2013). As for electric buses, every consumed kWh from the energy grid comes with a certain amount of CO2 emissions based on the carbon intensity of the grid. For the Tel Aviv model the carbon intensity was set at 630 gram CO2 / kWh (OECD, 2016), while in the Noord Brabant case this was set at 530 gram CO2 / KWh (Noothout, Eggink, & van der Leun, 2015).
For the Tel Aviv case, the bus operators Dan, Egged and Kavim were chosen as providers based on empirical data of the Tel Aviv bus fleet (Appendix P). Based on the same data containing a detailed description of the bus fleet composition, Dan was given a starting market share of 70%, Egged 25% and Kavim 5%. The same data was used to determine the transport demand of 6,500,000 km/month.
For the Noord Brabant case the selected operators were based on the operators that had contracts in the area in the last 5 years. An assumption had to be made on which operators to include as interviews with experts concluded that it is difficult to determine beforehand exactly which operators will participate in a bid (M. Post, personal communication, November 30, 2016). The selected operators, Hermes, Arriva and Veolia were given starting market shares of respectively 35%, 65% and 0%. The transport demand, based on the bus fleet composition, was set at 4,600,000 km/month (Provincie Noord Brabant, 2016).
PARAMETERS FOR BUS OPERATORS
In the Tel Aviv model, all the bus operators were assumed to start with an innovation limit of 10%. This meant that at the start of the model, all the operators could only bid a maximum 10% electric buses in their total bids. This was based on the fact that only ebuses in Israel have only been applied in pilot projects before. Hence bus operators are assumed to only have bus fleets consisting out of diesel buses at the start of the model. In the Noord Brabant model the operators were given a starting innovation limit of 20% based on the fact that one of the operators contains a bus fleet of which 20% are electric buses.
As for the development speed, the rate at which the innovation limit increases each year, it was decided to experiment with different values for the operators. The chosen development speeds were 5% per year, or 10% per year. At 10% per year it takes around 10 years before operators can submit bids containing only electric buses. At a development speed of 5% per year, this takes double the time. This means that we might see different rates of electric bus diffusion due to innovation limit differences.
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PARAMETERS FOR BUS PERFORMANCE
The daily kilometre range in the Tel Aviv case was set at 200km per day for both diesel buses and electric buses, based on the average daily km’s that buses travel in Tel Aviv (Source). As mentioned in chapter 4 electric buses are well capable of driving 200km’s for an entire contract period of 10 years, even when incorporating 2% battery degradation per year, assuming they only charge after their shift. The buses in the Noord Brabant case however have different daily ranges. The diesel buses were tested at 380km per day while the electric buses were tested at 360km per day. The electric buses were given lower daily ranges because they charge in between shifts (M. Post, personal communication, November 30, 2016), costing them an estimated hour a day of running time (Appendix A). This might lead to differences in modelling outcomes between the Tel Aviv and Noord Brabant case, as it is more difficult for electric buses to compete with diesel buses in the Noord Brabant case. This could result in a lower diffusion of electric buses in Noord Brabant.
PARAMETERS FOR COSTS
To test the effect of subsidies on electric bus diffusion, we experimented using three different amounts of subsidies for electric buses. These were 0, 6000 and 12000. The number 0 indicates no subsidies, 6000 an estimated 25% of the TCO / month, and 12000 represented an extreme amount of 50% of the TCO / month. We would have preferred testing more values, but due to the amount of experiments rapidly growing only three values were tested. Because there are no subsidies in the Noord Brabant case, we instead experimented with different purchase prices of electric buses, as purchase prices largely determine the TCO of electric buses (chapter 4). Purchase prices of 350.000 euro and 450.000 euro were tested in the Noord Brabant case based on various data sources (Appendix D). The purchase price of the electric buses in Tel Aviv were lower at 290.000 euro (Harrop & Gonzalez, 2015), because they only support depot charging and not opportunity charging like the buses in Noord Brabant. In chapter 3 it was mentioned that the prices of electric buses were expected to drop as technology matured. Therefore electric buses become 3500 euro cheaper each year (Harrop & Gonzalez, 2015).
As discussed in chapter 4, fuel prices largely determine the TCO of diesel buses. Because diesel prices historically fluctuate, three different types of diesel price scenarios were created and experimented with in both case studies. The three scenarios are a ‘’business as usual’ scenario in which the diesel price follows trend of the past five years, a growth scenario in which the diesel price keeps increasing, and a scenario in which the diesel price keeps decreasing. As diesel buses are considered mature in terms of technology, their purchase price was assumed to stay fixed. The prices of the diesel buses were set at 220.000 in both models (TNO, 2013).
PARAMETERS FOR TENDERING CRITERIA
The tendering criteria influence the bids of the bus operators and therefore impact the diffusion of electric buses. Experiments were performed with the maximum allowed age of both electric buses and diesel buses. In both model the allowed age of diesel buses remained fixed, as this is set by law based on extensive experience with the technology. Because the lifetime of electric buses is relatively unkown we experimented with it. The allowed age of diesel buses was set at 8 years, while the allowed age of electric buses was set at 8 or 12 years in Tel Aviv. In Noord Brabant the allowed age of diesel
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buses was set at 12 and the allowed age of electric buses either 12 or 15 (M. Post, personal communication, November 30, 2016).
6.1.4 How to measure relevant emergent behaviour In order to answer the research questions, several emergent regularities needed to be observed. These were the growing amount of electric buses in the bus fleet, and its effect on GHG emissions and the energy grid. Furthermore, we were interested in the effects of different subsidies and diesel price scenario’s. The outputs we monitored are displayed in Table 4.
Table 4; Measured outputs from models
Output Unit
Number of buses over time 0
Number of buses per operator 1 2 3
Diesel price Euro / liter
Electricity price Euro / kWh
CO2 emissions Gram CO2 / year
Energy usage KW
Ebus bonus -
6.2 RESULTS
The model was initially run for 100 repetitions on a default parameter setting (Error! Reference source n ot found.). Then, the model was run for 10 repetitions at the default parameters settings, and the results from the two sets of data were compared. The emergence of ebuses in the bus fleet showed no significant difference between 100 runs and 10 runs. Therefore 10 repetitions was considered accurate enough for the experiments.
Because the development speeds lead to such variances in the modelling results, which made it difficult to measure the influence of other parameters, separate experiments were run in which the effects of different development speeds were tested. In those experiments we compared the bus fleet developments of our operators at diverging development speeds. For all of the other experiments, the developments speeds of all the operators were fixed at 10% increase per year.
6.2.1 DESCRIPTIVE STATISTICS Descriptive statistics of the results were gathered to get an overall for the numbers generated by the model. In the Tel Aviv model the total number of buses was on average 1603 throughout the 16 years. The minimum number of total buses measured in all of the experiments was 1538. The maximum 60 | P a g e
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number of buses measured was 1734. There were on average 782 ebuses in the model, which was lower than the amount of dbuses on average which was 821. There were large variances in both the amounts of ebuses and dbuses. The amount of ebuses in the model varied between 0 and 1708, while the amount of dbuses varied between 0 and 1486. As for the TCO, the TCO of ebuses was on average 4613 euro/month, which lower was than the TCO of dbuses which averaged 6208 euro/ month. The
CO2 emissions per year were on average 68 ktCO2/year throughout the model.
In the Noord Brabant model the total number of buses was on average 567 throughout the 16 years. The minimum number of total buses measured in all of the experiments was 468. The maximum number of buses measured was 589. The amount of ebuses was on average 120, lower than the average number of dbuses at 447. There were significant variances in the number of ebuses and dbuses in the model. Ebuses varied between 0 and 313, while the number of dbuses varied between 311 and 554. On average, ebuses were cheaper to operate with a TCO of 8013 euro/month while dbuses were on average 11290 euro/month. The CO2 emissions were on average 49ktCO2 / year.
6.2.2 DIFFUSION OF ELECTRIC BUSES THE IMPACT OF DIFFERENT DEVELOPMENT SPEEDS
As mentioned in section 6.2 separate experiments were performed to measure the effects of different development speeds (innovation strategies) between the operators. In the Tel Aviv case we tested bus fleets development of operators at development speeds of either 5% per year or 10% per year. Figure 29 shows the results of comparing bus operators Dan and Egged. The top of the figure describes in which quadrants Dan has a development speed of 5% and in which it has a development speed of 10%. The right side of the figure displays in which quadrants Egged has a development speed of 5% and in which it is 10%. It was found that in case there was a difference in developments speeds, the operator with the highest development speed ended up with the biggest market share. For Dan, who starts the model with the biggest market share, a higher development speed than egged resulted in Dan gradually expanding its market share and Egged ending with 0 market share. For Egged, a higher development speed than Dan resulted in Egged rapidly increasing its market share and Dan losing most of its market share. In case the operators had the same development speeds, the three operators ended up equally dividing market shares. Interestingly the first 9 years were far more dynamic in terms of changes in market shares than the other years.
In the Noord Brabant model operators were also compared, however at equal development speeds of 10% per year each. The results showed that the different operators ended up with an equal market share at the end of year 16 (Figure 30). For Arriva, who starts the model with the biggest market share, this meant a gradual decrease in market share. Hermes throughout the model kept around a third of the market. Arriva, the third operator, gradually increased its market share from 0 to around 30%.
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Figure 29; The amount of buses per operator under different innovation rates in the Tel Aviv model. Top left (Dan 0.05, Egged 0.05) Top right (Dan 0.10, Egged 0.05). Bottom left (Dan 0.05, Egged 0.1). Bottom right (Dan 0.1, Egged0.1).
Figure 30; Development of bus operators in Noord Brabant model
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THE IMPACT OF DIFFERENT DIESEL AND OIL PRICE SCENARIOS ON ELECTRIC BUS DIFFUSION
Figure 31 shows the impact of the three diesel price scenario’s on the TCO of diesel buses per month. The top of the figure indicates the three different scenario’s. Scenario 1 represents a scenario in which the diesel price gradually increases. Scenario 2 represents a scenario in which the diesel prices follows the historical trend. Scenario 3 represents a scenario in which the diesel price gradually increases. The diesel price scenarios had a big influence on the TCO of dbuses. The different scenario’s shaped the TCO development of dbuses.
Figure 31; Scatterplots of the impact of diesel price scenario’s on TCO of dbuses
To explore the effects of the different diesel price scenarios and different ebus penalties, facet grid diagrams were created. In these diagrams the different ebus penalties are indicated at the top of the diagram, while the different oil prices scenarios are displayed on the right side of the diagram. An example of such a diagram is displayed in Figure 35, which explores the impact of diesel prices and ebus bonuses on the number of ebuses in the Tel Aviv model. It shows that there is a gradual increase in the number of ebuses in each of the scenarios. The amount of ebuses in the model started at 0 which was likely to end up at around 1200 at the end of the time period. Only in case of the low diesel price scenario combined with no subsidies, the amount of dbuses ended up at around 800.
Because Figure 32 is also a boxplot, the spread of the data could also be explored. The flatness of the boxes showed that spread of the data was very limited. Even the outliers remained relatively close to the bulk of the data. A similar analysis was done for the Noord Brabant model. The results were very comparable, also displaying limited spread of the data (Appendix M).
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Figure 32; Boxplots of the impact of different diesel price scenarios and Ebus bonuses on the amount of ebuses. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv Model.
The impacts of diesel prices and ebus bonuses on the development of ebuses and buses in the Tel Aviv model was explored (Figure 33). In general, the number of ebuses steadily increased over time. The rate at which the ebuses increased were similar in all scenarios, apart from the scenario with a low diesel price and no ebus bonus. In that scenario ebus diffusion was considerably slower. While the number of ebuses steadily increased over time, the number of dbuses steadily decreased over time. In all of the scenarios with the exception of 1, diesel buses decreased from 1500 in year 0, to approximately 500 in year 16. In case of no ebus bonus and low diesel prices, the number of dbuses decreased from 1500 in year 0 to around 900 in year 16. Overall the number of ebuses in the bus fleets increased and became greater than the number of dbuses around year 13.
For the Noord Brabant model we explored the development of ebuses and dbuses under different diesel price scenarios and different initial ebus purchase prices (Figure 34). Compared to the Tel Aviv model, the development was less gradual and more discrete. In general, the number of ebuses steadily increased until year 10. In year 10 itself the number of ebuses dramatically increased even more. After year 10 the number of ebuses gradually increased again apart from 1 scenario in which the number of ebuses gradually decreased. This occurred in a scenario with high purchase prices of ebuses combined with low diesel prices. Overall the number of ebuses was likely to increase from 0 in year 0, to around 300 in year 16. The number of dbuses overall decreased from around 600 in year 0 to 300 in year 16. The developments in number of dbuses over time were very similar to those of the ebuses, however in the opposite direction.
Comparing the two models, it was observed that in general the developments in ebuses and dbuses were similar, with the number of ebuses increasing over time and the number of dbuses decreasing. However, in the Noord Brabant model the number of ebuses never became significantly higher than the number of dbuses, while this did occur in the Tel Aviv model.
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Figure 33; Ebuses vs dbuses under different ebus bonuses and diesel price scenarios. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv Model.
Figure 34; Ebuses vs dbuses under different diesel price scenarios and starting ebus purchase prices. The ebus purchase prices are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Noord Brabant model.
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Figure 35 shows the penalty points for ebuses and dbuses given in a tender, under different oil prices and amount of ebus bonus points in the Tel Aviv model. In general, the penalty points for ebuses decreased steadily as time advanced. The different amount of bonus points had a big influence on the penalty points per ebus. Implementing the ebus bonus led to at least 1000 penalty points less per ebus.
The developments in dbus bonus points showed large variances under different diesel price scenarios. The dbus bonus seemed to exactly follow the developments in diesel prices. Comparing the penalty points of dbuses and ebuses, showed that in case of no ebus bonuses and a low diesel price scenario, the penalty points given for ebuses and dbuses were very close to each other. In all of the other scenario’s, the dbus penalty and ebus penalty remained separated. The gap between the dbus and ebus penalty widened as the ebus bonus points increased.
Figure 35; Scatterplots of the impact of diesel prices and ebus bonuses on tendering penalties. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv model.
Similarly for the Noord Brabant model we explored the impact of different purchase prices of ebuses, and different diesel prices on the amount of penalty points for ebuses and dbuses (Appendix M). In general, the number of ebus penalty points remained considerably lower than the penalty points per dbus. The diesel price largely determined the difference between penalties per ebus and dbus. The higher the diesel prices the higher the difference in penalty points, in favour of the ebuses. Different initial purchase prices of ebuses only had a minor effect on the ebus penalty points.
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6.2.3 IMPACT OF ELECTRIC BUSES ON CO 2 EMISSIONS
Figure 36; CO2 from buses under different diesel price scenarios and starting ebus bonuses. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv model.
Figure 36 displays the result from exploring the CO2 emissions by dbuses and ebuses under different scenarios in Tel Aviv. As a reminder ebuses do not emit CO2 directly but through charging of batteries which requires an energy production source that does emit CO2. Overall the amount of CO2 emissions caused by ebuses gradually increased from 0 to approximately 70 ktCO2/year as time advanced from year 0 to 16. On the other hand, the CO2 emissions from dbuses gradually decreased from approximately 80 ktCO2/year to almost 0 as time advanced. From year 9 onwards the ebuses became the primary source of CO2 emissions in the bus fleets. The total CO2 emissions overall decreased from 80 ktCO2/year in year 0 to 70ktCO2/year in year 16.
Similar results were found for the Noord Brabant model. The CO2 emissions from ebuses increased over time, while the CO2 emissions from dbuses decreased over time (Figure 37). Also, the total CO2 emissions ended up lower in year 16 than they were initially in year 0. What was different from the Tel
Aviv model, was that the dbuses remained responsible for most of the CO2 emission in all of the scenarios.
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Figure 37; Impact of diesel price scenarios and starting ebus purchase prices on total CO2 emissions in gram/year. The ebus purchase prices are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Noord Brabant model.
6.2.4 IMPACT OF ELECTRIC BUSES ON ENERGY GRID The energy usage per hour was measured in order to assess the impact of electric buses on the energy grid in Figure 38. In general, as the number of electric buses increased the energy demand increased. The energy consumption gradually increased from around 4:00 pm. Peak energy demands were reached at around 0:00am, and the energy consumption gradually decrease until 5:00am when there was no more energy demand. The peaks show that energy demand can go beyond 10MW. Exploring boxplots of the energy consumption showed that such energy peaks occur regular and that there are almost no cases in which energy consumption reaches extreme values beyond 10MW. The boxplots can be found in Appendix L.
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Figure 38; Impact of electric buses on the energy grid under different ebus bonuses and diesel price scenarios. The values of the ebus bonuses are displayed at the top of the figure. The different fuel price scenarios are displayed on the right of the figure. Tel Aviv model.
6.3 MODEL VALIDATION
The model validation determines if the correct model has been build. Does the model create convincing outcomes and is the model suitable for answering the research question? There are several ways of model validation such as: historic replay, face validation, literature validation and model replication. This section explains which methods were used to validate the model, and which methods could be used to further validate the current model (Nikolic & Kasmire, 2013).
As mentioned in chapter 5, the model used in this research is the second iteration of a previous agent- based model on bus fleet replacement. The general model was validated for the purpose of this this thesis through several expert validation round. The consulted experts included bus operators, PTAs, public transport NGO’s and public transport consultants. The validation sessions consisted out of structured interviews with individual experts. The interviews can be found in Appendix C.
The PTAs assisted in improving the tendering mechanisms and the behaviour of the PTA agent. This was done step by step through inspection and correction of the actions performed by the PTA agent. Similarly, the bus operators assisted in step by step validating the actions performed by the transport operator agents. The consultants and public transport NGO’s assisted in face validating the overall modelling components, tendering procedure, modelling dynamics and the interactions between the agents.
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That being said, the model in this thesis is already partially validated. In particular the conceptual model and the Netlogo model. However, the modelling result are yet to be validated through experts. The same sets of experts that assisted in building the model, could be consulted for validating the modelling outcome. However, it might be a challenge for operators to validate what might happen in the future (Nikolic & Kasmire, 2013).
Additionally, the modelling outcome could be validated through model replication by creating additional iterations of the model or even validating the results through other research methods. An example could be a serious game about bus fleet replacement which includes tendering and day to day operators. Players could include PTAs and bus operators. The game could simulate different scenarios and the evaluation session could discuss interactions between players, the results of tender outcomes, economical performance and operational performance.
6.4 CONCLUSIONS
This chapter discussed the experimental design and the results from the experiments. It explained how and for which purpose the experiments were created, and how to measure the results. Additionally, it was mentioned how the modelling assumptions might have influenced the outcomes of the experiments.
It was found that in of diverging developments speeds between operators, the operator with the highest development speed ended up with the biggest market share. In case the operators had the same development speeds, the three operators ended up equally dividing market shares.
In general, the number of ebuses steadily increased over time. The rate at which the ebuses increased were similar in all scenarios, apart from the scenario with a low diesel price and no ebus bonus. Overall the number of ebuses in the bus fleets increased and became greater than the number of dbuses around year 13 in the Tel Aviv model.
As time advanced in the Tel Aviv model, ebuses became responsible for the most of the CO2 emissions in the bus fleets. However, the total CO2 emissions overall decreased from 80 ktCO2/year in year 0 to 70ktCO2/year in year 16.
As time advanced in the Noord Brabant model, dbuses remained responsible for most of the CO2 emissions in the bus fleet. The energy consumption gradually increases from around 16:00. The peak is reached at around 0:00, and the energy consumption gradually decreased until 05:00 when there was no energy consumption. The peaks showed that energy consumption went beyond 10MW in the Tel Aviv model.
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7 DISCUSSION OF MODELLING RESULTS
This chapter discusses the modelling results from chapter 6. First, the results from the descriptive statistical analysis are discussed. Next, for each of the sub questions listed below, a quick recap is made of the main results before discussing them. During the chapter, we refer back to the theory from chapters 2 and 3, the assumptions made in creating the model in chapter 4, and the experimental design in chapter 6. The following research questions are answered in this chapter:
1. How do different amounts of subsidies impact the diffusion of electric buses? 2. How does electrification affect CO2 emissions? 3. What will be the impact of different levels of electric bus diffusion on the electricity grid? 4. How does the diffusion of electric buses in Tel Aviv compare to the diffusion of electric buses in a Dutch City?
Before answering the research questions, lets first discuss the descriptive statistics to get an overall feel for developments in the bus fleets.
7.1 DESCRIPTIVE STATISTICS ANALYSIS
The descriptive statistics indicated that the bus fleet in the Tel Aviv model slightly increased over time. The biggest increase measured was 12%. This was a logical result of the transport demand increasing over time. Adding to that, it was assessed in chapter 5 that the model has very little variance in terms of total buses. In the Tel Aviv case however, the daily range of ebuses were similar to dbuses. Even after incorporating battery degradation, no additional ebuses were needed in order to reach the same level of performance of dbuses. Because in the Tel Aviv model, ebuses in general ended up dominating the market, it seems that the diffusion of ebuses in Tel Aviv would not lead to extreme increase in the number of required buses.
In the Noord Brabant model the dbuses were able to run 20 kilometres per day more than ebuses. Similar to the Tel Aviv model, the bus fleet increased over time. The biggest increase measured however was 25%. While ebuses did not end up dominating the market in Noord Brabant, they did manage to get significant shares of 50% of the bus fleet. This seems to indicate that the lower range of ebuses has a noticeable impact on the total number of buses required in the fleet.
What has to be taken into account, is that transport demand was measured in kilometres per month and not in passengers per month. Additionally the model did not include the passenger capacity per bus. Therefore the findings could have been different in case the transport demand was in passengers per month, considering different buses are able to carry different amounts of passengers. As mentioned by (Veeneman & Van de Velde, 2014), awarding contracts to the lowest bidders in the Netherlands, led to reduced cost but also to a decrease in patronage in public transport, while the original goals were to reduce cost but also increase patronage. It would be interesting to explore additional impacts of different bus size, combined with another unit for passenger demand than kilometres per month.
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7.2 HOW DO DIFFERENT AMOUNTS OF SUBSIDIES IMPACT THE DIFFUSION OF ELECTRIC BUSES?
This section addresses the following questions:
How do different amounts of subsidies impact the diffusion of electric buses? How does the diffusion of electric buses in Tel Aviv compare to the diffusion of electric buses in a Dutch City?
As a reminder, there were some important differences in the experimental designs for the Tel Aviv model and the Noord Brabant (Table 5).
Table 5; Important differences in parameters between the two case studies
Tel Aviv Noord-Brabant Units
Ebus bonus 0 6000 12000 0 -
Initial ebus purchase price 280,000 350,000; 450,000 euro
Initial ebus limit 10% 20% Ebuses / total buses in bid
Development speed of operators 5% ; 10% 10% Increase per year
Allowed age ebuses 8 12 12 15 year
Allowed age dbuses 8 12 year
Daily km range ebus/dbus 200 360 for ebus; 380 for dbus Km / day
Carbon intensity 600 530 Gram CO2 / kWh
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The main results are presented in Table 6. We will discuss them one by one.
Table 6; Results from exploring the effect of different diesel price scenarios and ebus bonuses, on the diffusion of ebuses.
Observations:
1. In general the number of ebuses steadily increased over time. The rate at which the ebuses increased were similar in all scenarios, apart from the scenario with a low diesel price and no ebus bonus in the Tel Aviv model.
2. In the Noord Brabant model, ebuses increased in market share over time reaching a market share of 50% at the end of the modelling time. Compared to the Tel Aviv model, the development was less gradual and more discrete.
3. In a scenario where diesel prices are low and no ebus bonuses are granted, the amount of dbuses and ebuses both end up at around 800 buses. In all of the other scenario’s ebuses end up with a higher market share than dbuses (Tel Aviv model).
4. There was no difference between a subsidy of 25% or 50% of the TCO per ebus in terms of ebus diffusion in the Tel Aviv model. However, there was a noticeable difference between no subsidy and 25% subsidy.
1. In general, ebuses gradually increased in market share as time advanced up to at least 50% of the total market at the end of the modelling time in the Tel Aviv model. Regardless of different ebus bonuses or diesel prices.
The reason for the gradual increase and not a sudden increase of ebuses, can be explained by the bus operators starting the model with a large number of contracts containing diesel buses, that are yet to expire. Because the remaining contract durations were exponentially distributed, it took years for dbuses to be replaced by ebuses. Additionally, bus operators were limited in the amount of ebuses they could bid in the first years of the model because of their innovation limit.
The constant increase in ebuses was likely caused by two things factors. The TCO of ebuses on average being lower than the TCO of dbuses, and the use of gross net contracts in the tendering procedures. In chapter 6 we mentioned that gross net contracts stimulate cost efficiency, which meant that cost could become of great impact on the modelling results. In the tendering procedure, the bid that scored the least amount of penalty points won. The model results concluded that in each of the tested diesel price and ebus bonus scenarios, there was a close link between the TCO of the buses and the penalty points. Adding to that, the amount of penalty points per dbus were either just as high or much higher than the penalty points per ebus in all of the scenarios. The results indicate that operators were likely to opt for ebuses as they were the most cost effective technology, and scored the least amount of penalty points in the tender.
However, we do need to take into account that some deterrents of ebuses were not included in the model. A lack of charging infrastructure reduces the convenience of electric buses (Morita, 2003). Alongside other concerns, this could keep operators from adopting the technology. In the model however, this inconvenience was not included. Instead the additional costs of infrastructure were incorporated in the lease prices, and infrastructure was assumed to be built effortlessly. Thus possibly lowering the barrier for adopting ebuses.
Considering daily range of the buses, both dbuses and ebuses travelled 200 km’s per day. The 200 km’s is close to the maximum of 250 kilometres that ebuses can drive on a full charge, before requiring time
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to recharge. Dbuses however can drive more than double the distance on a full tank. In other case studies where the daily travelled distance is much higher than 200 kilometres per day, ebuses might have a harder time competing with dbuses, hence reducing the rate of market penetration.
Adding different contract types could have also affected the results. As discussed in chapter 2 there are typically three different types of contracts in public bus transport. These are management contracts, gross cost contracts and net cost contracts. With a management or a gross net contract, the PTA guarantees a given amount of subsidy which would help operators in their decision to pick electric mobility. While a net cots contract on the other hand could discourage operators to try electric mobility as they would not only have to deal with the uncertainties in operational costs and investment costs, but also with uncertain revenues. Including net cost contracts therefore might have resulted into less ebus diffusion than the model generated.
2. In the Noord Brabant model, ebuses increased in market share over time reaching a market share of 50% at the end of the modelling time. Compared to the Tel Aviv model, the development were less gradual and more discrete.
In general, ebuses were more favourable over the different scenarios as they had the least amount of penalty points per bus. Compared to the Tel Aviv model, the developments in the Noord Brabant model were less gradual and more discrete. The reason for this difference was that in the Tel Aviv model, every year a tender occurred, while in the Noord Brabant there was detailed data available which indicated that tenders would occur year 6, 8, 10 and 16.
In terms of ebus diffusion, the Noord Brabant case did not reach the same levels as the Tel Aviv case. In almost all of the scenarios the total number of dbuses and the total number of ebuses both ended up at 300. This meant dbuses and ebuses ended up with an equal market share of 50%. There was one exception, which was the scenario where the initial purchases prices of ebuses were 450.000 and the diesel prices were low. In that specific scenario, the amount of dbuses ended up higher than the number of ebuses. Additionally, the market penetration of dbuses in that scenario even started increasing again from year 16 onwards. This indicates that the diffusion of ebuses in the Noord-Brabant model is not guaranteed and is highly dependent on different diesel prices scenario’s and purchase prices of ebuses.
The reason that ebuses in the Noord Brabant model did not reach the same levels of market penetration as in Tel Aviv, could be explained by the difference in daily range. While in the Tel Aviv model both ebuses and dbuses drove 200 km’s/day, in the Noord Brabant model diesel buses were able to drive 380 km’s/day, while ebuses were limited to 360 km’s/day. As stated by Ding, Hu & Song (2014), the time required to charge the batteries means that buses need to be taken out of operation for a considerable amount of time, while in the meantime dbuses can continue their operation. The lower the daily range of a bus, the higher the amount of buses needed to fulfil the transport demand. More buses means a higher total monthly TCO. A higher total monthly TCO leads to more penalty points in a tender, which reduces the chance of winning a tender. Therefore, the lower the daily range of a certain technology, the less likely it will diffuse in large amounts.
The results seem to indicate that the difference in daily range was an additional barrier for operators to adopt ebuses, resulting into lower market penetration rates in Noord-Brabant than in Tel Aviv. Additional experiments on the daily ranges of buses could further strengthen this claim.
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3. In a scenario where diesel prices are low and no ebus bonuses are granted, the amount of dbuses and ebuses both end up at around 800 buses. In all of the other scenario’s ebuses end up with a bigger market share than dbuses (Tel Aviv model).
In case diesel prices were low and no ebus bonuses were granted, the amount of penalty points given for ebuses and for dbuses were closely matched. This meant there was less of a clear advantage for ebuses as tested in other scenario’s. It is safe to assume that that was the main reason for a lower ebus market penetration rate compared to other scenarios.
The results however were limited by the fact that all buses, both ebus and dbus, were assumed drive at a speed of 20kph. As stated by Gata (2014) however, driving speeds of buses can have an impact of fuel consumptions and therefore fuel costs. For cases where there is little difference between technologies, such influences might be enough to shift the results in one way or the other. Hence, the addition of driving speed dynamics could have led to different results.
4. There was no difference between a subsidy of 25% or 50% of the TCO per ebus in terms of ebus diffusion in the Tel Aviv model.
When exploring the results, there were no differences found in terms of market penetration of ebuses when either 25% or 50% ebus bonus was granted. A noticeable difference was however found between 0 subsidies and 25% subsidies. This would implicate a subsidy of 25% would be enough to stimulate the diffusion of ebuses. What has to be noted is that the subsidies were granted for the modelling duration of 16 years. In reality however, subsidies might get granted only temporarily. What can also be insightful in further research, is the addition of more subsidy categories such as 5%, 10%, or 15%, in order to create a better understanding of the impact of subsidies.
7.3 HOW DOES ELECTRIFICATION AFFECT CO2 EMISSIONS?
This section addresses the following question:
How does electrification affect CO2 emissions?
The main results from the research are presented in Table 7. We will discuss them one by one.
Table 7; Results from exploring the impact of electrification on CO2 emissions.
Observations:
1. As time advanced in the Tel Aviv model, ebuses became responsible for most of the CO2 emissions in bus fleet.
2. As time advanced in the Noord Brabant model, dbuses remained responsible for most of the CO2 emissions in the bus fleet.
3. The diffusion of ebuses led to an overall decrease in CO2 emissions in both the Tel Aviv and Noord Brabant model.
In the Tel Aviv model, dbuses emitted 1000g CO2/km while ebuses emitted 630g CO2/kWh in the Israel case. The 630g CO2/kWh represented the carbon intensity of the Israel grid. The modelling results concluded that in all of the scenarios, ebuses eventually become responsible for almost all of the CO2 emissions as the market share of ebuses grow. However, the total CO2 emissions in the area did
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decrease as time advanced, as a result of the growing market share of ebuses. This indicated that ebuses were beneficial to the environment.
In the Noord-Brabant case, comparing CO2 emissions from ebus and dbuses indicated that in general, the total CO2 emissions decreased over time. This due the fact that the number of ebuses increased over time, and ebuses are responsible for less CO2 emissions per km. In the Noord Brabant model, the ebuses were responsible for 530g of CO2 emissions per kWh.
It might be interesting to see what happens in case the CO2 emissions per km per dbus were to rise in the future. However, with the expectations of the carbon intensity steadily decreasing in the future, as more and more renewable energy sources enter the market, that seems an unlikely scenario.
The findings were in line with Liu (2012) who stated that electric buses have zero hazardous street level tailpipe emissions, and have the potential to reduce CO2 as the power generation system reduces its carbon intensity. The findings were important because the challenge that big cities around the world face in general, is how to protect their citizens from harmful forms of pollution without hampering the movement of people. Reducing pollution from public transport services will be essential, as it involves the usage vehicles that operate in heavily congested areas (Brown, 2010).
7.4 IMPACT OF EBUSES ON THE ELECTRICITY GRID
This section addresses the following question:
What will be the impact of different levels of electric bus diffusion on the electricity grid?
The main results from the research are presented in Table 8. We will discuss them one by one.
Table 8; Impact of electric buses on the local energy grid
Observations:
1. Ebus diffusion leads to an increased electricity demand between 16:00 and 05:00 as a result of buses recharging.
2. Peak energy demands beyond 10MW are reached around 0:00am when the bus fleet contains 1200 ebuses
1. Ebus diffusion leads to an increased electricity demand between 4:00 pm and 5:00 am as a result of buses recharging.
As the number of electric buses increased in the model, a charging pattern emerged. This indicated that charging mostly occurred between 16:00 and 5:00. This can be explained by the fact that buses started their shifts between 5:00 and 13:00, and ended their shifts between 15:00 pm and 23:00. The charging usually started around 16:00 as the first buses ended their shifts and drove back to their depots. From that moment on charging gradually increases until the peak energy demand is reached around 0:00. The peak was likely observed around 0:00, because at that time none of the ebuses were out on shift, but were charging at their depot. This included the buses that ended their shift early in the afternoon.
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The charging pattern could have been very different if buses had more variance in their shift times or even split shift times. For example, if buses were to go out on shift for four hours and have a break for several hours before going out on shift again. Implementing such split shifts in the model might have resulted in a more evenly spread charging pattern, as batteries would not have been fully depleted before charging. Furthermore, buses were assumed only to charge at their depots. If buses were given the possibility to charge during shifts, for example at their final destination, the charging pattern could have resulted in a lower energy peak a less volatile energy demand.
2. Peak energy demands beyond 10MW are reached around 0:00am when the bus fleet contains 1200 ebuses
The observed peak charging demand reached values of 10MW. However, it has to been taken into consideration that all the ebuses in the model charged at 60kW. Adding more bus manufacturers with diverging charging rates and charging times, could further improve the accuracy of the energy demand. The observed peak demand of 10MW, would in reality represent approximately 0.5% of the total electricity grid capacity of the region around Tel Aviv based on data from The Israel electric corporation (Israel Electric, 2010) and a power outage in 2015 (Bar-Eli, 2015). Further research will be needed to assess the impact of an additional 0.5% of energy on the generation capacity, and the electricity distribution grid of the Tel Aviv region.
7.5 CONCLUSIONS
This chapter discussed the outcomes of the modelling results. It discussed the results from the descriptive statistics analysis from the Tel Aviv case, and the results of box plot analysis and scatterplot analysis for both the Tel Aviv case and Noord-Brabant case. The focus was on answering the sub research questions posed at the start of the chapter. A summary of the results:
How do different amounts of subsidies and diesel price scenarios impact the diffusion of electric buses?
1. Ebuses gradually increase in market share up to at least 50% of the total market regardless of diesel price scenario or ebus bonus in the Tel Aviv case study. 2. Ebuses are likely to end up dominating the market in the Tel Aviv cases study, in most of the diesel price and subsidy scenarios in the Tel Aviv case study. 3. Ebuses are generally more attractive in tendering procedures as they score less penalty points, except for scenarios where diesel prices gradually decrease and no ebus subsidies are granted.
The impact of electrification on GHG emissions
1. As time advances and the number of ebuses increases, the overall CO2 emissions decreases.
The impact of ebuses on the electricity grid
1. Ebus diffusion leads to an increased electricity demand between 16:00 and 5:00 as a result of buses recharging. 2. Peak energy demands beyond 10MW are reached around 0:00 when the bus fleet contains 1200 ebuses
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How does the Tel Aviv case study compare to the Noord-Brabant case study?
1. Ebuses were in general the more attractive option in both case studies under different diesel price scenarios. Unless diesel prices gradually decrease, and no ebus subsidies are granted in the Tel Aviv case, or diesel prices gradually decrease and ebus purchase prices initially start at 450.000 in the Noord-Brabant case. ‘ 2. In the Tel Aviv case study, ebuses were likely to end up replacing all but 10%of the diesel buses, while in the Noord-Brabant case study, ebuses did not expand past 50% of the total bus fleet.
3. Electrification of the bus fleets in both case studies, leads to an overall decrease in CO2 emissions emitted from the bus fleets.
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8 CONCLUSIONS & RECOMMENDATIONS
This chapter answers the main research question of this thesis. It starts with a summary of all the system level sub questions, and methodological sub questions together with answers to the sub questions. Afterwards the main research question is answered. Followed by recommendations for future research and a personal reflection on the process of writing a master thesis.
8.1 ANSWERS TO SYSTEM LEVEL SUB QUESTIONS
8.1.1 WHAT ARE THE CONSIDERATIONS THAT AFFECT GOVERMENTS’ DECISIONS TO SUBSIDIZE ELECTRIC MOBILITY AND OTHER CLEAN FUEL BUSES IN PUBLIC TRANSPORT FLEETS? The government has the important task of dealing with a multitude of diverse public services. This includes assessing how attention is raised on different public services and how to allocate public resources. For public transport this comprises assessing how much attention is raised for attending issues around public transport, and does it suffice granting additional public resources to public transport instead of other public values? In case it is decided to address the issues in public transport, next step becomes translating the public values at stake into concrete norms, and determining how they can be reached.
For public transport, the consensus is that competitive tendering is the go to method of reaching concrete norms in public transport. Considerations to be made by the governments when applying competitive tendering schemes consists out of the following:
Determining which contracts out of gross cost, net cost and management contracts to apply to tendering procedures, considering the behaviour that each contract type triggers from bus operators? How much cost risks and revenue risk to take upon itself, and how much risk to delegate to bus operators?
8.1.2 WHAT ARE THE CONSIDERATIONS THAT AFFECT BUS OPERATORS’ DECISIONS ON BUS FLEET REPLACEMENT, AND IN PARTICULAR ADOPTING NEW TECHNOLOGIES? Bus operators are prone to preserve their public image and therefore emphasize reducing environmental impact. However, because operators are also generally perceived as private companies trying to maximize their profit, clean fuel vehicles are not automatically preferred over traditional technologies. While clean fuel buses come with the benefits of less environmental impact, they are also at a disadvantage in terms of operational performance compared to diesel buses. Additionally, bus operators are constantly under pressure to reduce costs in order to increase their chances of winning tenders, and securing their income flow. Operators therefore constantly consider how adopting new technologies will contribute to reaching their goals and if it’s worth pursuing them.
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8.1.3 HOW DO DIFFERENT AMOUNTS OF SUBSIDIES IMPACT THE DIFFUSION OF ELECTRIC BUSES? In general the number of ebuses steadily increased over time. This was likely caused by the TCO of electric buses which in general was lower than the TCO of diesel buses in both models. The rate at which the ebuses increased were similar in all scenarios, apart from the scenario with a low diesel price and no ebus bonus in the Tel Aviv model. In the Noord Brabant model, ebuses increased in market share over time reaching a market share of 50% at the end of the modelling time. Compared to the Tel Aviv model, the development of the bus fleet was less gradual and more discrete. There was no noticeable difference between subsidizing 25% of the monthly TCO of ebuses, or 50% when looking at the diffusion of electric buses in Tel Aviv. This implicated that a subsidy of 25% was enough to have a maximum effect on the diffusion of electric buses.
8.1.4 WHAT WILL BE THE IMPACT OF DIFFERENT LEVELS OF ELECTRIC BUS DIFFUSION ON THE ELECTRICITY GRID? The results from experimenting with the Tel Aviv model, have indicated that the diffusion of electric buses in bus fleets will likely result in a noticeable increase in electricity demand due to charging of electric buses. The model found increased electricity demand between 16:00 and 05:00. Peak energy demands beyond 10MW were reached around 0:00am in bus fleets containing around 1200 electric buses.
8.1.5 HOW DOES ELECTRIFICATION AFFECT URBAN GHG EMISSIONS? The results from experimenting with both the Tel Aviv model and the Noord-Brabant model indicated that electric buses are likely to become responsible for significant amounts of CO2 emissions as their numbers increase. In case of the Tel Aviv model, electric buses became the main source of CO2 emissions after 16 years of simulation. However, while the number of buses increases, the total CO2 emissions of the bus fleets decreased. This indicates that electric buses on average are responsible for less CO2 emissions per travelled kilometre compared to diesel buses.
8.1.6 HOW DOES THE DIFFUSION OF ELECTRIC BUSES IN TEL AVIV COMPARE TO THE DIFFUSION OF ELECTRIC BUSES IN A DUTCH CITY? Ebuses were generally found more attractive in both case studies under different diesel price scenarios. The absence of subsidies in the Noord Brabant overall did not shift the preference from electric buses into diesel buses in terms of TCO. The market penetration rates of electric buses in the Noord Brabant model were on average considerably lower than they were in the Tel Aviv case. This was likely caused by differences in daily kilometre range between diesel buses and electric buses in the Noord Brabant model. In the Tel Aviv model both bus technologies were able to drive the same amount of kilometres per day.
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8.2 ANSWERS TO METHODOLOGICAL SUB QUESTIONS
8.2.1 HOW CAN WE USE AGENT-BASED MODELLING TO SIMULATE THE SOCIO- TECHNICAL SYSTEM REVOLVING BUS FLEET ELECTRIFICATION? This was done by firstly creating a conceptual model on bus fleet replacement using system theory (Figure 39). The bus fleet replacement system was seen as a dynamic socio technical system, demarcated by a clear system boundary. The involved actors in the socio technical system were translated into agents. Their goals, actions, attributes and possible interactions were clearly specified using literature and expert interviews. After empirical data was gathered, the conceptual model was translated into a pseudo code, and eventually into an agent based model using Netlogo. The created agent based model became the tool in this research to simulate the socio technical system revolving bus fleet replacement.
Bus stations Own
Pollute Complain about air quality / GHG emissions to local environment Diesel buses Own Offers bids to
Require fuel from
Consume fuel Public Bus from local Fuel stations Own Tenders contracts to Transport operators environment Authorities
Subject to Electric Own technological innovation buses Adjusts policy to meet transport demand from local citizens
Require electricity from r Sensitive to fuel prices and capitical costs of buses e d r
Own o b
Depot
chargers m e t s y S
Consume electricity from local environment System environment
Figure 39; Conceptual system design. Blue arrows = technical relations, green arrows = social relations. Dashed line represents system boundary
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8.2.2 HOW CAN WE MODEL BUS ELECTRIFICATION DIFFUSION AND POLICY AT BOTH MICRO SCALE (MINUTES) AND MACRO SCALE (YEARS)? Figure 40 shows the basic modelling sequence that was used in the agent based model created for this research. The basic modelling sequence consisted out of two parts. Firstly, a daily sequence in which day to day activities of the bus transport system were simulated such as driving of buses, fuel consumption, local CO2 emissions and electricity usage due to charging. Secondly, a yearly sequence in which vehicle technologies and costs were updated, tenders occurred and bus fleets were updated. The combination of the two sequences in one model made it possible to simulate policy at both a micro scale and macro scale. Combining the two sequences into one model did result into a large amount of residual data when performing experiments with the model. The data was carefully filtered extracting only the useful data and disregarding the residual data.
Vehicle Is it time for a technology is Vehicles are Buses perform After 5 days have Year starts tendering No updated. Costs updated daily activities passed, year ends procedure? are updated
Yes
PTA requests bids. Operators place bids
PTA selects winning bid
Operators update fleets
Every year
Figure 40; Basic modelling sequence in the agent based model on bus fleet replacement.
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8.3 ANSWER TO THE MAIN RESEARCH QUESTION
8.3.1 HOW DOES TRANSPORT POLICY AND TECHNOLOGY AFFECT THE MARKET PENETRATION RATE OF ELECTRIC MOBILITY IN TEL AVIV, AND ITS ELECTRICITY CONSUMPTION? The answer to the main research question was mainly based on the outcome of the experiments with the Tel Aviv model. By answering the methodological sub questions, and system level questions, it became possible to create an agent-based model capable of answering the main research question. Summarizing the outcomes of the sub questions gives us the following answer to the main research question:
In general, the market penetration of electric buses is likely to increase steadily over time regardless of subsidies, as their TCO steadily drops due to technological developments. Subsidies do increase the market penetration rate compared to no subsidies in case the TCO of electric buses and diesel buses are a close match. Subsidizing more than 25% of the TCO of electric buses will probably have no additional benefit on market penetration rates of electric buses. The diffusion of electric buses in bus fleets can result in a considerable amount of additional electricity demand due to charging of electric buses. Peak energy demands beyond 10MW were observed in bus fleets containing around 1200 electric buses.
8.4 UNDER WHAT CONDITITIONS DO CLEAN FUEL BUSES PROSPER?
In the introduction it was mentioned that public transport policy makers are aware of the possible environmental benefits of electric vehicles. However, they have been facing difficulties assessing the degree of environmental impact, the impact of large-scale electric bus diffusion on the local energy grid, and the influence of governance. So under what conditions do clean fuel buses prosper and what are the implications for future policy?
1. Electric buses can reach large-scale diffusion rates in case public transport policy acknowledges the benefits of new technologies, and specifically implements criteria for clean fuel buses in tendering procedures. The TCO of electric buses is able to compete with the TCO of diesel buses, and match operational performance. Those conditions can be met in urban areas at limited speeds and limited daily ranges of buses.
In the model, transport policy in the form of subsidies did have an impact on the diffusion of electric buses leading to higher rates of electric bus diffusion. In the introduction it was pointed out that bus operator Dan planned to replace a quarter of its bus fleet with electric buses, which accounts to 300 buses. In the model, such numbers were well within reach even without subsidies. As mentioned in chapter 4 the PTA in Noord Brabant strived for zero emission in 2025. Such numbers were not reached in the model. Based on the tendering criteria in the model, which were mainly costs and ability to meet transport demand, it seems that electric buses will have a difficult time to compete with diesel buses in the Dutch case. Hence, additional policy could be required to reach the zero emission goal.
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In terms of governance, subsidies seemed to be an effective policy instrument for electric mobility. A subsidy of 25% was enough, indicating that lobbyist advocating for electric mobility only need to advocate up to a certain degree. Additionally, whereas in the model subsidies were granted for 16 years, it seemed that subsidizing for such a long period was not necessary. This due to the fact that subsidies were not necessarily required in cases where the TCO of ebuses was lower than the TCO of dbuses. Meaning that subsidies are only required until the TCO of ebuses becomes lower than the TCO of dbuses, and the amount of subsidies only need to account for 25% of the purchasing cost. Of course, the 25% found in the model depends on the data used in the model. It will therefore be Interesting to research policy in more detail to gain further insights on governance. Possibly by adding more subsidy levels such as 5%, 10%, 25%, or experimenting with different durations of subsidizing. Other than subsidies, it could be interesting to assess the impact of other policy measures such as more emphasis on innovation in tenders in the shape of granting points for innovative buses.
Multi-level governance was mentioned in chapter 2 and indeed the stakes of multiple private and public institutions should be aligned if large-scale electric bus diffusion is to be reached. This includes PTAs rewarding electric buses in tenders, financial public institutions granting funds, NGO’s raising awareness for clean transport methods, bus manufacturers reducing costs of electric buses through technical developments, and bus operators pursuing electric buses in their strategies. If the interests of involved stakeholders would not be properly aligned, it could lead to several barriers for electric bus diffusions. It could lead to PTAs receiving limited funds from financial institutions, limiting their influence on transport policy. Inadequate performance of electric buses due to limited technological developments from manufacturers, making it difficult to compete with diesel buses. Alternatively, it could lead to bus operators and PTAs neglecting clean fuel buses, with manufacturers well capable of producing innovative and well performing buses. Adding policy emergence to the model will give the model the tools to assess such aspects in more detail. Amit Ashkenazy will investigate this further in his PhD thesis.
Gross net contracts combined with subsidies turned out to be an incentive for operators to pursue electric buses. The same would likely occur in case the PTA implemented a management contract. However, a net cost contract might have discouraged operators to select electric buses due to their limited range. While range may not be an issue cases such as Tel Aviv where the daily ranges are limited, it can be an issue in cases similar to Noord Brabant where the daily kilometres range of a bus is much higher.
2. Implementing electric buses can lead to a reduced environmental impact of bus fleets in urban areas, even at current carbon intensity levels of energy grids.
Based on this research it is safe to say that electric buses are a worthy investment in urban areas in terms of environmental benefits. This however only applies to cases in which traveling speeds are around 20 km/h and without considering elevation changes.
Assessing if electrification is financially beneficial remains a challenge. It will depend on how much environmental benefits are valued, and if the environmental benefits of a clean fuel bus fleet are high enough to justify the additional costs.
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3. Electrification does require that the local energy grid is capable of coping with several MW’s of additional electricity demand.
The model has given an initial indication of the impact of large-scale electrification on the local energy grid in terms of additional required energy demand. A more detailed analysis is needed to assess the impact of the additional required energy. This comprises checking if there is enough energy production capacity, and determining if the power lines and substations are able to cope with the additional load.
8.5 STRENGHTS OF THE RESEARCH
The research offers useful insights in the considerations that both bus operators and governments make concerning electric buses. It contains the most important decisions that bus operators and PTAs make when considering electric mobility, using literature and expert interviews as sources of information. The research incorporates both the technical and social system, making a distinction between technical relations between stakeholders, and social relations between stakeholders.
Additionally, the model created in the research is capably of quickly making a rough estimation of the development of bus fleets, in very specific cases. The model does not require additional knowledge of agent-based modelling and can easily be operated. Additionally, if needed the model can be expanded incorporating more complexity and increasing its validity.
8.6 MAIN LIMITATIONS OF THE RESEARCH
There are some important limitations to agent-based modelling studies. Because a model is not capable of fully grasping the complexities of the real world, numerous simplifications were made. The imposed simplifications logically limited the validity of modelling results.
Another limitation of this research was a lack of data in certain areas. The lack of data was compensated for with assumptions and simplifications. Due to those simplifications, the model is several steps removed from reality.
The outcomes of the research were based on a literature review, experts’ interviews and a computer based simulation model. As the literature was selected based on perceived purpose, the research might be limited on this front. Similarly, the model included opinions from experts. Potential biases from experts, or bounded rationalities have likely been translated into the model.
8.7 IMPACT OF UNCERTAINTIES & ASSUMPTIONS
Throughout this research assumptions have been made in order to compensate for uncertainties in data, and to reduce the complexity of the bus transport system. This section summarizes the main assumptions in this research, and how they have shaped the results.
8.7.1 SOCIAL The research focused mainly on two stakeholders, PTAs and bus operators. Which means that the results are shaped by the interests of operators and PTAs, and their capabilities. For the PTA this meant improving quality of transport, while reducing costs and environmental impacts of transport using
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competitive tendering as a policy tool. The bus operators on the other hand aimed to increase their market share by bidding for contracts, and preserve their image as public service providers. The addition of other stakeholders and their interest could have several impacts on the results. Per example, a restriction on the PTAs budget and capability to subsidize, due the competition for governmental funds with other governmental institutions. On the other hand, the addition of environmental NGO’s or international environmental agencies could lead to more strict policy on clean fuel buses.
8.7.2 INSTITUTIONAL Gross net contracts where used in the tendering procedures in this research, which created stability in income and reduced the financial risks for bus operators. This resulted in possibly a friendly climate for electric buses to diffuse. While gross net type contracts are widely used in many cities including our own case studies, adding other contract types might lead to different outcomes. If net costs contracts were applied instead, operators might have had more trouble to adopt electric buses. Having to deal with additional uncertainties in revenues due to net costs contracts, operators might have been incentivized to stick with traditional bus technologies.
Additionally no permits were required to place charging infrastructure. While this is not expected to be an issue in cases where buses charge at bus depots only, it might become troublesome for buses that require opportunity charging at bus stations. This however depends on the difficulty of acquiring such permits.
8.7.3 TECHNICAL The model included a limited amount of vehicles resulting in less diversity in cost and performance between buses than likely to be observed in reality. Additionally charging of electric buses was limited to depot charging, where in reality opportunity charging is also a possibility. The addition of more bus types could lead to smaller differences between the buses, possibly reducing the diffusion rate of electric buses.
Scientific literature and different fuel/energy scenarios were used in order to estimate uncertainties in costs and performance of buses in the next years. As the numbers are only estimations it would be wise to critically reflect upon them, and keep the estimations up to date as they greatly influence results. Electric bus diffusion could be even higher if purchase prices were to drop faster than predicted or driving ranges were to increase faster than predicted. Conversely, the rate of electric bus diffusion could slow down if purchase prices and operational performance do not develop. While the expectations are that electric buses will become cheaper than diesel buses over time, it would be beneficial to add a proven TCO calculation tool to the model. Such a TCO tool does insist in the Dutch case created by a Dutch QUANGO and is accessible.
The model also assumed fixed carbon intensities of the local energy grids, while it is expected that the carbon intensity of grids will decrease in the future due to renewable energy sources. Therefore, a dynamic carbon intensity could lead to even higher benefits of electrification.
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The buses in the model ran at speeds of 20kph in an urban environment. At such speeds and dynamics in driving, the benefits of electric buses are greater than in cases of higher and more constant speeds.
The daily ranges of buses were limited to 200km in the Israel case, which was well driving range of an electric bus. However, if much higher daily ranges are required, it can be become very difficult for electric buses to compete with diesel buses which are capable of running more kilometres per day.
8.8 REFLECTION ON SOCIAL RELEVANCE AND SCIENTIFIC RELEVANCE
8.8.1 SCIENTIFIC RELEVANCE As research on bus fleet replacement using agent-based modelling was missing, this research made a clear scientific contribution. It combined two unconnected strand of literature namely bus fleet replacement, and agent-based modelling of EV diffusion. We added to the methodologies used to analyse bus fleet replacement, and enriched agent-based modelling literature with technologies that are grounded in public sector policies. Furthermore, as we incorporated the impact of electric buses on the energy grid, this research strengthened the combined research of transport and energy systems.
8.8.2 SOCIAL RELEVANCE In the introduction, it was mentioned that PTAs were aware of the potential of electric buses and other clean fuel technologies to reduce environmental impact and air pollution, but unaware under which circumstances they were likely to diffuse and the extent of their benefits. Additionally it was unknown how electric buses would affect electric infrastructure. This research has provided PTAs with insights on how electric buses might diffuse in public bus fleets over time, and how diffusion might be impacted by transport policy. In addition, the impact of electric buses on electric infrastructure was assessed, by giving an estimate of the additional required energy demand. Finally, an initial indication was given on the amount of carbon emissions that can be reduced with electric buses.
8.9 CONTRIBUTION TO SEPAM
Reflecting on the subject, this thesis was a typical example of a SEPAM project as it contained the ingredients of SEPAM project. The system has the characteristics of a complex socio technical system with clear interactions between a social and a technical system. It contains the technical subsystem consisting out of vehicles, charging infrastructure and the energy system, and combines it with the social interactions between multiple actors. Actors include the public transport authority, bus operators, bus manufactures, passengers, NGO’s, energy producers, distribution system operators and the national government. It includes the institutions involved in transport policy, and the role of the PTA in creating policy. As the model in the thesis simulates the coming 16 years, it involves the dynamics and uncertainties involved in typical SEPAM projects.
Was the agent-based model a SEPAM model? Maybe to a lesser extent because of the limited amount of actor complexity. However, the model does include the interaction between a social and technical system in a dynamic environment. Additionally, forms a great base for a more complex model, as it was designed in such a way that could very easily be expanded with more agents and more aspects.
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8.10 RECOMMENDATIONS FOR FUTURE RESEARCH
This section recommends future additions to this research and the model. The following improvements are suggested:
Adding climate and air quality NGO’s to further explore the impact of public values on transport policy. o Enable them to measure the extent of pollution and lobby for clean fuel vehicles. o Enable them to identify the amount of noise pollution and lobby for less noisy vehicles if needed. The addition of bus manufacturers to increase the robustness of the model. o Explore the addition of more vehicle types. o Explore different scenarios of technological development. The addition of more operators to improve the robustness of the model. o Further explore the impact of diverging innovation strategies on market share development. Explore the effect of more energy price and fuel price projections to improve the accuracy and robustness of the results. The addition of specific bus routes. o Explore the impact of differences in profitability of lines. o Explore the impact of different operating speeds. Adding specific charging points locations and more charging methods to further explore the operational possibilities of electric buses. Adding case specific environmental effects. o Incorporate the impact of road grade on the fuel/energy consumption of buses. The addition driving behaviour on fuel consumption. Incorporating the effects of agents learning. O Enable agents to learn from winning bids. The addition of more contract schemes. o The addition of net cost contracts and management contracts. Exploring the effect of subsidies in greater detail. o Experimenting with different subsidizing durations. o Experimenting with additional amounts of subsidies. Adding a proven TCO calculation method to the model. Further exploring the impact of the additional energy demand on the energy grid. o Incorporate the capacity of local power lines. o Incorporate the capacity of power stations. Adding the dynamics of renewable energy development to the carbon intensity of local energy grid. o Explore the impact of a dynamic carbon intensity of the grid, on the environmental impact of electric buses.
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8.11 PERSONAL REFLECTION
The past few months have been an amazing learning experience. It has opened my eyes and really helped me understand what it means to be a research scientist. I went from not knowing how to start, all the way to not knowing how to finish, to a master thesis report that I am proud of.
8.11.1 DECISION TO STAY AT TPM Initially I wanted to perform my master thesis at an external company, because of the fear that my lack of experience with big corporations would hamper my chances on the labour market. But when the opportunity came the work on a project with both ABM and diffusion of electric mobility I changed my mind. Having found a job before finishing my master thesis, I can say that the advice I got from friends, family and members of my committee turned out to be excellent.
8.11.2 GATHERING INFORMATION Gathering information from reports was an important step in my research. With the help of Mendeley, and the gained insights from the thesis definition course, I created a structured approach which helped me get the most of my literature review. I thoroughly enjoyed expanding the knowledge I gained from my literature review through interview with experts from public transport. The gained insights really helped in improving my research in terms of scope and results. Gather detailed technical information turned out to be a small disaster. Not because of a lack of information but rather the amount of information and inconsistency in the information that was gathered.
8.11.3 SCOPING Scoping my research has been a challenge to say the least. During my literature review and the interviews with experts, the scope kept expanding. In combination with my urge to try and incorporate all the new information I received, and insecurity about what would be enough for a master thesis, I sometimes lost valuable time during my research.
8.11.4 PLANNING Planning my research path and sticking to a plan remains a future challenge for me. I did not take into account any unexpected delays due to waiting for interview and getting stuck in scoping. I don’t have any doubts on the effort put into the research, and still doubt if additional planning could have made a big difference to my ability to anticipate on unforeseen things. I do believe that creating a detailed starting research path and sticking to that would have greatly improving my research efficiency.
8.11.5 MODELLING I initially expected to modelling to be a breeze due to my previous experience with ABM and the luxury of expanding upon an existing model. While the modelling itself did not pose any significant challenges, creating a conceptual model and thinking about how to model that conceptual model did turn out to
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be a time consuming process. Especially as I turned out adjusting the conceptual model many times. I do believe however that that is part of the process of building a model.
8.11.6 EXPERIMENTATION Experimentation turned out to be quite a hassle, because I had to work with big datasets for the first time. Additionally, the results from my experimentation did not generate similar results to my ABM. Dividing the experiments turned out to be the solution.
8.11.7 GUIDANCE Overall I received great guidance from my research committee. I greatly appreciate the feedback gained from all the sessions and the amount of time spend in helping me improve my research. I do apologize for some of the confusing meetings that might have occurred at the start of my research. I can only say I have always had the best intentions but am still learning how to be less of a perfectionist, and be confident about presenting unfinished work. I had the luxury working with a committee consisting out of experts from various field that could help me in different aspects of my research.
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CALCULATIONS AND SCENARIOS
DATA AND CALCULATIONS
Modelling time xxxxx
How much electricity does a bus consume in a tick? power required for driving bus with air conditioning:
1.5 kWh/km
(http://www.greencarcongress.com/2014/01/20140125-byd.html ) buses drive at an average speed of: 20 km/h km per shift of 8 hours: 160 kwh required for 8 hours: 240 every tick requires how many kwh? 5
Full capacity is 324 kWh
Recharge rate is 60 kW
Recharge time is 5,4 hours
Recharge rate used in model is 60 kWh / h
Per tick that is 10 kWh
http://www.byd.com/la/auto/ebus.html https://www.ovmagazine.nl/wp- content/uploads/2015/11/ThomasWard_Jun173113Adams4.15pm_000.pdf
Driving an ebus for a day
Range on full battery: 200km (Vdl, 2015 http://www.vdlbuscoach.com/News/News- Library/2015/Levering-van-eerste-VDL-Citea-SLFA-180-Electric-aa.aspx)
Average energy consumption: 1.3 kWh/km
Full capacity = 260kwh
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Service hours a day in the dutch case: 19 hour a full day (expert interview m.post 2016)
At 20 km/h the battery is empty after 10 hours. 9 Hours left in the day
Fast charging at 250kw for 30 minutes. 8.5 hours left in the day.
450000 kw charged after 30 minutes = 125 kWh = 96 km( +/- 100km)
5 Hour drive then battery empty again. 3.5 Hours left in the day.
Charge again 30 minutes. Drive 3 hours (60km) and reach end of the day.
Total daily range = 200 + 100 + 60 = 360km
LEASE PRICE CALCULATION
Requires:
Bus price o IDtechX, Civitas policy note Residual price o (assumed always to be 0) Interest rate o 1.60 (http://www.boi.org.il/en/DataAndStatistics/Pages/MainPage.aspx?Level=3&Sid=22 &SubjectType=2) Money factor = Interest rate / 2400 Lease term o Lease term: 8 – 12 years
Example calculation monthly lease price for a 12 year contract
The total amount per bus is 497,500
For a residual price of 0
Interest rate is 6% -> Money factor = 6/2400 = 0.0025
Lease term = 12 x 12 = 144 months
497,500 / 144 = 3454.86 per month
Interest = 0.0025 x 497500 = 1243.75 per month
Total = 3453.86 + 1243.75 = 4697.6 per month ( before taxes )
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Lease price calculation in the model: Total bus price / (contract duration * 12) + interest rate / money factor * total bus price.
TCO CALCULATION EXAMPLES
DIESEL PRICE PROJECTIONS
Projected diesel price developments Israel 3,5 3 2,5 2 1,5
1 Price Price $ in Liter / 0,5
0
1995 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 Year
Senario 1 growth scenario 2 trend Scenario 3 decay
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Projected diesel price developments Netherlands 3 2,5 2 1,5 1
Price Price $ in Liter / 0,5
0
1995 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 Year
Senario 1 growth scenario 2 trend Scenario 3 decay
1995 - 2014 = worldbank data http://data.worldbank.org/indicator/EP.PMP.DESL.CD 2016 = globalpetrolprices data http://www.globalpetrolprices.com/Netherlands/diesel_prices/ 2017 - 2040 = own future price projections http://www.eia.gov/analysis/
ENERGY PRICE PROJECTIONS
Energy price development dollar Israel 0,160 0,140 0,120 0,100 0,080 0,060 0,040 0,020 0,000 20022004200620082010201220142016201820202022202420262028203020322034
Energy price dollar
2002-2013 IEC 2013-2034 = Price projections
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Average energy price projections 0,080 0,070 0,060 0,050 0,040
0,030 euro/kWh 0,020 0,010 0,000 2005 2010 2015 2020 2025 2030 2035 year
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ASSUMPTIONS
This section gives a general overview of the assumptions. See chapter 4 for a more in depth version of the assumptions.
ELECTRIC BUSES
When calculating the residual amount of km’s service for ebuses, it is assumed that buses use 20% of their battery capacity compared to their newly stated. Therefore, their residual km’s is calculated by taking 20% off their initial range, and multiplying that with the amount of months they have left before reaching their maximum age. It is assumed that electric buses do not run out of battery. Once the battery level reaches zero, buses are still allowed to continue to run. However, with buses recharging way before their batteries are empty, a battery level of 0 should not occur when driving. The total daily range of an ebus per day is assumed to be the range of an ebus with a full battery before it requires recharging.
DIESEL BUSES
Off peak times are 20:00-06:00, representing a night-time average across seasons. Buses’ energy usage includes both drive time + return to the station. (only for operational model) It is assumed that diesel buses do not run out of fuel. Once the fuel tank level reaches zero, diesel buses are still able to drive. However, with buses refueling way before their fuel tanks are empty, a fuel level of 0 should not occur when driving. The total daily range of a dbus is based on the number of km’s it can run on a full tank. Bus operators are rational and bid to score the least amount of penalty points. Charging can occur simultaneously with no limits on charging points When charging, buses do not leave the depot until they are fully refueled or recharged (operational model).
ROUTES AND INFRASTRUCTURE
Bus stops are not considered in the system Bus routes are not considered in the systems. Fueling only takes part at central depots
GENERAL
Buses can drive the entire shift without recharging or refueling. Battery replacement is seamless, and does not affect lifetime or costs over the bus’ allowed lifetime Electric buses have fewer mechanical parts requiring maintenance and thus their allowed age can be greater Companies that won the tender will replace the buses they cannot use according to the terms of the tender. But companies that didn’t win will keep the buses a stock in order to reduce purchasing costs for their next bid.
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Bus operators will buy pre additional buses at the start of their contract, so they can replace buses in operation that only have a few years of service left. The electricity grid can supply the required electricity for recharging with no interruptions Innovation is not represented in this model due to limitations in implementation, but will be incorporated in future versions of the model Ridership is also not represented in the model, and is assumed to be constant relative to population growth (which is the reason operators are required to add 3% to their fleet each new tender). Public transport will be included, private transport will be excluded The environmental effects are limited to a local or city scale. Noise pollution will not be considered in the research Every year there will be a tender in the Israel model. The starting bus fleet is adjustable by the user. It is assumed that when determining the maximum amount of ebuses that operators can bid, that maximum amount of calculated as a fraction of the total km service demand of the tender. This is done to simulate the differences in strategy that operators have when bidding for electric vehicles. Some have a strategy in favor of innovation, while others prefer a strategy in favor of cost reduction. The operators that opt for a strategy in favor of cost reduction have a high limit on the amount of ebuses they can offer, while companies that are in favor of innovation have a low limit on the amount of ebuses they can offer.
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EXPERT INTERVIEWS INTERVIEW MAARTEN POST PROVINCIE NOORD BRABANT PROVINCIE BRABANT 03-06-2016
1. Hoe zit het budgetproces in elkaar?. Heeft u voldoende budget. Zijn er extra inkomstenbronnen. Is er sprake van concurrentie voor budget?
Geld is afkomstig uit het rijk in een brede doel uitreiking mobiliteit. Lumpsum bedrag mogen ze zelf kiezen hoeveel geld ze in ov stoppen en hoeveel in andere OV maatregelen. Provinciale staten wordt bepaalt welk deel naar ov gaat.
Het budget is het budget. Of dat voldoende is? De kunst is met dat budget zoveel mogelijk mensen te vervoeren. Dan zijn de kosten per reiskilometer te laag. Soms is zo efficiënt mogelijk niet geëist. Het platteland moet ook worden bereikt en dan rijd je soms met lege bussen.
2. Kwam het idee van elektrische bussen voort uit milieuorganisaties? Hadden zij klachten. Op een schaal van 1 tot 10 hoe belangrijk was de invloed van milieuorganisaties. Wat voor nationaal milieubeleid heeft invloed om uw beslissingen? Wat betreft luchtkwaliteit en klimaat?
Gezien vanuit Brabant, het beleid vanuit het OV ging in de richting van vergroening. Beleid uit 2000. Daarachter zit de doelstelling om binnen stedelijk milieu te optimaliseren. De bussen vervuilen natuurlijk ook. Ten tweede, vanuit het oogpunt van energie willen we toe naar duurzame energie en die willen we ook gebruiken voor elektrisch vervoer. Ten derde, in Brabant zit een sterke automotive sector en een van die niches dat is heavy duty power trains (zware aandrijvingen). Om die markt een push te geven konden ze elektrisch vervoer aansturen. Daar zit wel een maatschappelijk drive achter, maar er zat geen milieuorganisatie achter die bij de gemeente heeft aangeklopt. Degene die er weleens een push geven zijn de steden zelf. In de aanloop naar staatsbesluiten kunnen ze lobbyen. Hij invloed is beperkt.
Het idee kwam vanuit de economie. Die zijn toen met ov gaan bedrijven. Er was geen beperking doordat men niet onbekend. Dat komt omdat ze enkel vragen om uit te voeren. Kun je van A naar B rijden. Hoe je het doet bepaalt U zelf. Niet verteld hoe ze elektrisch moeten gaan rijden. Hoe ze moeten laden enz enz. Een van de incentives was dat vervoerders ook met oudere bussen mochten rijden zodat ze die kosten konden besparen om vervolgens naar elektrisch konden gaan. Er zijn verschillen in afschrijfperiodes van elektrische bussen +- 15 jaar. De concessie +- 8 jaar, de oplaadinfrastructuur +-30 jaar. Dat risico hebben ze aan de partijen overgelaten.
4. Hoe werkt de aanbestedingsprocedure en hoe gaan ze om met concurrentie?
Een jaar voor de concessie start worden de concessie aangeboden. Dat bod representeert natuurlijk bedrag van de dienst op dat moment.
Extra incentives: Als u aangeeft dat u binnenkort overgaat naar elektrisch Als u begint met elektrisch dan krijgt u zoveel punten
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U mag rijden met uw oude bussen (zodat je meer geld krijgt om elektrisch te rijden)
Niemand wordt uitgesloten van deelname. Wat je wel ziet gebeuren is dat er grote spelers op de markt ontstaan. Dus daardoor ontstaan er steeds minder biedingen. Grote internationale spelers die erachter zitten. Die bepalen dus wel of niet of zij deelnemen. Bij de concessie van hermes met elektrische bussen maar 1 bod gekregen. Terwijl er meerdere hebben meegesproken in de concept besprekingen.
5. Volgens onderzoek lag de focus van de aanbestedingen de afgelopen jaren op het reduceren van de kosten waardoor er geen budget over was voor innovatieve bus. Dat is langzamerhand aan het veranderen, waardoor komt dit?
6. Hoe hoog ligt de nadruk op kosten in het bepalen van de score.
Ongeveer 40% Kosten 40% kwaliteit en 20%. Optiestructuur aan de markt voorgesteld. Aangedragen aan de markt in verschillende opties: 1 rijd volledig elektrisch, u rijd alleen elektrisch.
7. Hoe hoog ligt de nadruk op kwaliteit van de dienstverlening, milieu en innovatie?
Ten tijde van de aanbestedingsprocedure met de gedeputeerden mobiliteit. De prioriteit lag op kwaliteit van de dienstverlening. Zoveel mogelijk mensen vervoeren met een zo hoog mogelijke mate van comfort. Als dat schoon kan dan is dat meegenomen. Omvang en kwaliteit op 1
8. Hoe gaat u om met het gebrek aan flexibiliteit in strenge aanbestedingsprocedures. I.v.m. innovatie.
De definitie is: er vallen geen dienstregeluren uit. In maar 2% van de gevallen wordt er niet gereden. Als de vervoerder dan beseft dat zijn elektrische bussen dat niet gaan halen dan moet de vervoerder zorgen voor vervangende dieselbussen bijvoorbeeld. Als blijkt dat een ov bedrijf dat niet gaat halen, dan kan de ov autoriteit een boete geven. Het ligt eraan hoe goed de samenwerking verloopt. Sommige partijen stellen van tevoren een contract op wat daarna niet duidelijk genoeg is dan komen de juridische ‘problemen’. Als blijkt bijvoorbeeld dat na twee jaar die elektrische bussen helemaal niet rendabel rijden, kan aan de hand van besprekingen nog. Er is in beginsel geen maatregelen ingenomen waarin afwijking van dienstregeling ingenomen is door het rijden van elektrische.
9. Wanneer zou u kiezen voor elektrische bussen. Wat zou u meer in de richting van elektrische bussen duwen en wanneer zou u er juist vanaf zien. Als het probleem in de kosten ligt, wat doet u daar dan aan?
De elektrische bussen staan in het begin van de ontwikkeling. Als je nu zou kiezen dan krijg je vooral een grote floot van dure bussen. Het is beter om die ontwikkeling geleiding te laten verlopen. Als het probleem in de kosten ligt, dan krijgen de bussen de flexibiliteit om die snelheid naar elektrisch te bepalen. Zelf bepalen in welke volume en welke snelheid. Het enige wat dan afgesproken wordt is aan het begin zijn er zoveel elektrische bussen en aan het eind zoveel. OV bedrijven die bespreken dat met de bus producenten en op basis van die kennis doen ze hun eindbod.
10. Hoe heeft u zich voorbereid op de benodigde infrastructuur voor elektrisch vervoer. Waren er grote obstakels?
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Daar heeft de provincie zich op voorbereid maar de vervoerder. Er is wel mee gediscussieerd. Er was een verplichting om mee te doen aan pilots en de meerkosten waren op kosten van de provincie. De OV autoriteit heeft ook geen invloed op hoe dieselbussen tanken enzovoort. Het bleek dat er problemen waren en vervolgens heeft de provincie gezorgd voor de infrastructuur. In het begin problemen vanuit de provincie van hoe gaan we dat doen. In zuidoost Brabant heeft hermes helemaal zelf gezorgd voor de oplaadinfrastructuur. Hoe de vervoerder dat heeft gedaan. Eerst bedacht waar de pantograaf komt, op de bus of langs de route.
INTERVIEW MAARTEN POST PROVINCIE NOORD BRABANT PROVINCIE BRABANT 30-11-2017
1. Rijden er momenteel nog bussen op aardgas rond in een van de concessies in Noord Brabant? Nee en komen er ook niet. 2. Ik heb startcondities nodig voor het model. Ik ben op zoek naar de leeftijdsverdeling van de bussen in Noord Brabant Alle euro 6 bussen zijn vanaf 2014. (Zuidoost Brabant wordt nieuw euro 6 waarvan 43 ebussen). 3. Welke bus vervoerders verwacht je dat deel zullen nemen in de volgende aanbestedingen. Ik dacht zelf aan hermes Veolia en Arriva. Kunnen van alles zijn, over het algemeen stellen 5 6 partijen vragen maar doet maar een deel daarvan uiteindelijk ook echt mee. 4. Hoeveel dienstregeluren kan een diesel bus max rijden per dag? Eerste bus rijdt van 5 uur sochtends tot 12 uur savonds. 19 uur. 5. Hoeveel dienstregeluren kan een elektrische bus max rijden per dag? Zou ook gewoon 19 uur per dag moeten rijden. Maar wel tussen laden. In de nacht worden ze volgeladen. Tussendoor laden ze hem bij zodat hij inzetbaar blijft. Half uur. 6. Wat is de maximale leeftijd van een bus. Maximale leeftijd bus 12 jaar. Elektrische bussen 15 jaar? Akkoord maar dat moet nog blijken in het geval van elektrische bussen.
How influential is the zero emission to 2025 guideline? Verschilt per concessie. In 1 van de concessies afhankelijk van of het TCO neutraal kan.
Which bus operators are expected to be involved in future tenders?
INTERVIEW THEO KONIJNENDIJK 11-07-2016
1. Kunt u de denkwijze van bus vervoerders omschrijven bij het plaatsen van een bod?
In eerste instantie de gunningscriteria bekijken en het bod zo maken dat je de meeste punten scoort in de aanbesteding. Is de aanbesteding op prijs wil je de prijs het laagst. Ligt de nadruk op kwaliteit, dan probeer je zo hoog mogelijk te scoren op kwaliteit, terwijl je de prijs zo laag mogelijk houdt. Je
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kan altijd zijn de passagier die de minste uitstoot wil, of de meeste vervoerders maar uiteindelijk gaat t om de meeste punten.
2. Welke factoren bepalen voor vervoersbedrijven of zij wel of niet een bod plaatsen?
Het lijstje erbij pakken met wat de overheid precies wil. En dat kan het best zijn dat je dat als vervoerder bepaalt om dat niet te doen. Het gaat erom dat je tegen de laagste prijs het beste product.
Wat je ziet in de afgelopen aanbestedingen op de markt, waar de nadruk vooral op elektrische bussen lag, hebben heel weinig vervoerders gereageerd.
3. Hoeveel invloed heeft het ontwerp van een aanbestedingsprocedure op het plaatsen van een bod voor vervoersbedrijven?
Het heeft zeker een 8. Je vergeet de rest van de wereld. Dan is het wellicht meer dan 80%, stel je bent eigenwijs dan sla je de plank mis en dan win je niks. Het heeft ook met de vrijheid te maken. Soms vraagt de regio precies de dienstregeling die het wil tegen de laagste kosten.
4. Zijn er behalve het winnen van de concessie nog andere factoren die een rol spelen bij het plaatsen van een bod
Wat zou het aandeel milieu zijn in het model. In de verdeling van de punten zitten ook weer trappen. Bijvoorbeeld lagere vloer, etc.
5. Hoe reageren bus vervoerders op het herhaaldelijk mislopen van concessies?
Na een verlies is het weer de vraag op welke prijs je probeert te scoren. En anders verlies je en dan moet je je wel aanpassen. Omdat het vaak om grote hoeveel heen gaat. Kan je scoren op de indeling van je personeel en de voertuigen.
Kosten in de aanbesteding.
De laatste variant, de vervoerder is verantwoordelijk voor een del van de kosten.
Verschillende cases
In Engeland mag je als je business case goed is best een lijn rijden. In Engeland In Nederland mag je alleen als je een concessie hebt rijden. Daar ligt de selectie in het aanbestedingsproces. De bussen in Nederland, wie kan bieden mag bieden, maar de partijen worden wel getoetst op kunde. Vervolgens de normale procedure.
Biedingen van anderen
Het kan zijn dat je tijdens je bieding al weet dat je het bod krijgt, dan kan je al met busbedrijven met in gesprek gaan. Kan soms zijn dat busbedrijven offertes doen aan 3 verschillende vervoerders voor dezelfde aanbesteding. In het algemeen zijn er weinig nieuwe bus vervoerders die op de markt komen. Gedurende de concessie gaat de overheid altijd controleren of je wel voldoet aan de eisen.
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INTERVIEW MARC VAN DER STEEN RG 28-06-2016
1. Kunt u de denkwijze van bus vervoerders omschrijven bij het plaatsen van een bod?
Wat levert mij de meeste punten op. Er is een puntenteling en er wordt niet verder gekeken dan dat.
2. Welke factoren bepalen voor vervoersbedrijven of zij wel of niet een bod plaatsen?
Investeringsrisico, haalbaarheid, bekendheid met het gebied.
3. Hoeveel invloed heeft het ontwerp van een aanbestedingsprocedure op het plaatsen van een bod voor vervoersbedrijven?
Gigantisch veel. Het is toegestaan om een concessie 15 jaar te later duren in plaats van 8 jaar dat maakt veel uit voor security of demand. Stel de ov autoriteit zegt, ik wil vanaf de start van de concessie volledig elektrisch, dan gaat dat niet lukken voor sommige vervoerders. De up front kosten worden dan te hoog, kopen van infra lenen bij de bank kopen van nieuwe bussen. De inkomsten uit de aanbesteding zijn niet binnen bij de start van de concessie. Dat vraagt veel extra investering van OV bedrijven en dan lukt het niet. Vergeleken met wanneer de transitie in stappen toegepast zou worden.
4. Zijn er behalve het winnen van de concessie nog andere factoren die een rol spelen bij het plaatsen van een bod
Wat levert mij de meeste punten op.
5. Wat voor invloed heeft het bestaande wagenpark bij het doen van een bod?
De vraag is altijd, mogen bestaande bussen worden meegenomen in de concessie. Wat is het transitie pad? Stel je hebt nog 200 euro 6 bussen, die nog 3 jaar nodig hebben om afgeschreven te worden. Stel je mag zo meenemen, dan kan je over 3 jaar 200 nieuwe bussen kopen en ook nog eens elektrische bussen. Dan heb je een voordeel ten opzichte van bedrijven die de concessie niet hebben. Het gebeurt ook dat bedrijven bussen van de ene concessie omzetten naar andere concessies. Ook Alleen is dat je dan moet omstickeren en nieuwe OV chip apparatuur moet kopen. De kosten kunnen wel 25,000 per bus worden.
6. Zijn er behalve de OV autoriteit nog andere bedrijven die van invloed zijn op de beslissingen van bus vervoerders?
De bus producenten grijpen een deel van de macht weer sinds de komst van elektrische bussen. Wij leveren de bussen wel, wij onderhouden de bussen wel, regelen de laadinfrastructuur.
7. Hoeveel invloed heeft competitie bij het plaatsen van een bod?
Competitie is er altijd in een marktwerking. Het is een oligopolie omdat er niet zoveel aanvoerders zijn. Dus ja het spelt, niks meer niks minder.
8. Hou zou u de scores aanpassen als u elektrisch vervoer zou willen stimuleren?
Niet behandeld
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9. Wat motiveert busbedrijven om te kiezen voor elektrische bussen, of juist niet.
Ervaring, first movers advantage pakken, een onomkoombaar iets. Elektrisch komt echter steeds vaker in aanbestedingen voor, het is daarom geen keuze meer maar eerder noodgewonden zo. Het OV bedrijf dat het beste begrijpt hoe het KWH spel in combinatie met busroutes in elkaar zit, wint de concessie. Je hebt constructies waarin OV overheden geld kunnen lenen met lage rente, aan bus vervoerders. De overheden hebben als doel zoveel mogelijk kilometers rijden voor zo weinig mogelijk geld. De bus producenten grijpen een deel van de macht weer sinds de komst van elektrische bussen. Wij leveren de bussen wel, wij onderhouden de bussen wel, regelen de laadinfrastructuur.
10. Hoeveel invloed heeft ervaring met elektrische bussen door het bedrijf zelf, of door concurrenten, op de keuze voor elektrische bussen?
Veel, 8 van een 10. Het heeft te maken met KWh berekeningen.
11. Hoe reageren bus vervoerders op het herhaaldelijk mislopen van concessies?
Ze gaan failliet, worden overgenomen door een ander bedrijf of vinden wel een andere concessie.
INTERVIEW FREDERIK DE VRIES, DELFT 28-07-2016
1. How to implement the effect of experience with electric mobility into the model A: There are three main factors at place: 1. Is it technologically feasible: The answer to this is yes, the vehicles exists, batteries work, charging works. 2. Is it cost-efficient?: No clear answer but with subsidies it can be made cost-efficient. 3. The spreading of technology: If they have it than so can we. This applies between provinces but also between companies. It is the old chicken and egg story. In the Noord Brabant case, the barrier was broken by the PTA with a tendering procedure requiring fully electric. This was fueled by the province having a strong automotive sector. If one province starts to drive electric, it’s neighboring provinces are likely to introduce electric mobility as well. As a province you cannot stay behind in developments, and I you do, you will be resented staying behind in developments where your neighboring provinces have succeeded in providing its citizens clean fuel transport. This spread of technology can be expected in Israel as well. If you are able to replace diesel buses for electric without extra costs, the decision is a no brainer.
2. In the model there is a problem that operators only go fully electric or fully diesel A: Has to do with bus routes. The focus with electric mobility is in the urban area. However electric mobility is only beneficial in urban areas and not so much in rural area’s due to its range restriction. As the range of electric mobility grows, the percentage of electric vehicles in bids will get bigger.
3. Production capacity Currently there is a problem with production capacity. This also limits electric mobility.
4. Eindhoven concession (Noord Brabant) only 1 offer A: There is the idea that this was more case specific than caused by electric buses. The expectation now is that in case of another tendering procedure requiring electric mobility, all operators would offer bids.
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The development is rapid, four years ago all the PTAs expected that electric mobility would be to expensive too dangerous too unreliable. But now electric mobility is mentioned in all new concessions.
5. The rate of diffusion A: Once every 8 years there is a concession so that’s what you have to deal with. The expectation in the Netherlands is fully electric starting 2025.
6. The intrinsic motivation to Innovate by companies Q: Could you say that the innovation capacity that a company has depends on the percentage of electric buses it is currently using?
A: That is difficult to say. There might be companies that are willing to innovate but don’t have the concession to do so. There are even companies that could go all out in the next concession on electric just in order to win the concession. The real innovation in terms of buses does not come from the operators but from the producers. Operators can innovate in their schedules, but it are the producers that innovate with the buses.
7. Modelling competition How many companies are currently in business and how many are out of business at the moment. How often does a tender occur. For how many buses each time?
A: Currently all of the operators have a concession. It could however be that in the future there will be only three operators. All the operators have contracts but there are only five of them and in the future maybe only three. So it could happen that some operators end up without a concession.
8. Division of costs Costs that are not considered in the lease prices are the:
Maintenance costs Infrastructure costs Energy costs
INTERVIEW DAVID EERDMANS, AMSTERDAM 10-08-2016
In the model there is a problem that operators only go fully electric or fully diesel
A: Has to do with bus routes. The focus with electric mobility is in the urban area. However electric mobility is only beneficial in urban areas and not so much in rural area’s due to its range restriction. As the range of electric mobility grows, the percentage of electric vehicles will get bigger.
Ontwikkeling van dienstregeling.
De vervoerder bepaalt de dienstverlening. Gunningsaspecten worden daar ook op bepaald. Ook met expert ga je bepalen hoe goed zijn de lijnen, hoe goed is de aanbesteding. Ten eerste is het belangrijk om te weten welke vrijheidsgraden de overheid heeft.
Criteria
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In eerste instantie gaat het om zoveel mogelijk ov, dan goed openbaar vervoer en daarna pas hoeveel elektrisch vervoer. Er zullen niet zoveel verschillen ontstaan in het aantal punten op sommige categorieën. In de categorie het aantal ritten bijvoorbeeld zullen de verschillen klein zijn. Je kan niet ipv 2x per uur ineens 8x per uur gaan rijden. Echter in andere onderdelen zoals elektrisch vervoer, kunnen wel grote verschillen ontstaan. Dat kan dan van doorslaggevende waarde zijn.
Formules
De scoreverdeling zal je zelf moeten inschatten. Wat erbij kan helpen is om het naar euros te vertalen. Wat kost het extra om meer ritten te bieden. Hoeveel kost dat en hoeveel punten kan je daarmee verdienen. Een vervoerder zal binnen zn budget de beste proberen te scoren.
Onderscheid tussen partijen.
De marges zitten in de 10, 20%. De een is echt een prijsvechter. De ander is meer innovatiegericht.
Onderscheid in ontwikkeling. Het verschil zit hem niet in de kosten. De kosten zullen voor iedereen ongeveer gelijk zijn. Maar het verschil zit hem in de verwachting van de ontwikkeling van de prijzen. De 1 kan verwachten dat de ontwikkeling snel zal gaan. Maar de ander verwacht dat die langzamer zal gaan.
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TEL AVIV MODEL DATA AND CONCEPT FORMALISATION
MODEL VARIABLES
The following variables are present in the model.
GLOBAL VARIABLES
Environment related globals
Variable Unit Range
Air-quality-counter [integer] 0 – emergent value
Dbuses-total-distance-traveled km 0 – emergent value [integer]
Ebuses-total-distance-traveled km 0 – emergent value [integer]
Time related globals
Variable Unit Range
Clock [integer] Hour Between 0 - 23
Year [integer] Year Between 0 and infinity
Time-till-next-tender [integer] Year Between 0 and 8
Time-for-tender [Boolean] - Yes / No
Starting-off-peak-time Hour 20:00
Ending-off-peak-time Hour 06:00
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Bus related globals
Variable Unit Range
Electricity-used-this-year kWh / year emergent [integer]
current-operational-costs- Euro / month See list Appendix A ebuses [integer]
current-operational-costs- Euro / month See list Appendix A dbuses [integer]
current-fuel-costs-dbuses Euro / liter See list Appendix A [integer]
current-electricity-costs- Euro / kWh See list appendix A ebuses [integer]
current-lease-prices-dbuses Euro / month emergent [integer]
current-lease-prices-ebuses Euro / month emergent [integer]
Co2-emissions-per-km-dbus g/km 1000 (TNO, 2013)
Co2-emissions-per-km-gbus g/km 1000 (TNO, 2013)
Infrastructure costs ebus Euro/bus 0 (assumed to part of purchase price ebus)
current-purchasing-costs- Euro 290.000 (IDtechx, 2015) ebuses [integer]
current-purchasing-costs- Euro 220.000 (TNO, 2013) dbuses [integer]
Current-purchasing-costs- Euro 250.000 (TNO, 2013) gbuses
purchasing-costs- Euro/year - 3500 (IDtechx,, 2015) development-ebuses
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Purchasing-costs- Euro assumed to remain fixed development-dbuses
Operational-costs- Euro assumed to remain fixed development-ebuses
Maintenance-costs-per-km- Euro/km 0.50 (Guo, 2016) dbus
Maintenance-costs-per-km- Euro/km 50% / 75% of dbus maintenance costs. (BYD, 2013) ebus (Aber, 2016)
0.38 or 0.30
http://www.byd.com/news/news-171.html
Maintenance-costs-per-km- Euro/km 0.48 (Guo, 2016)) gbus
Interest rate - http://www.tradingeconomics.com/israel/interest- rate/forecast 1.25 (
Money factor - 2400 http://www.edmunds.com/car- leasing/calculate-your-own-lease-payment.html
Total daily km’s dbus Km / day 200
Total daily km’s ebus Km / day 200
Total monthly km’s dbus Km / month Daily km’s * 22
Total monthly km’s ebus Km / month Daily km’s * 22
Energy consumption ebus kWh / km 1.3 kWH/km (Byd, 2016)
Fuel consumption dbus Liter / km 2
TCO per month ebus Euro / month emergent
TCO per month dbus Euro / month emergent
Operational-costs- Euro Assumed to remain fixed development-dbuses
current-total-costs-ebuses Euro / month emergent [integer]
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current-total-costs-dbuses Euro / month emergent [integer]
Buses-required-for-tender Bus emergent [integer]
Kilometers-service-required- Km/month emergent for-tender
Battery degradation rate per - 20% per 10 years (value of 1 in the model) year 30% per 10 years (value of 0.99 in the model)
(Thein & Chang, 2014)
Battery degradation - Assumed to remain fixed technological development
Government related globals
Variable Unit Range
Kilometers-required-for-tender Km / month emergent [integer]
Government-price-preference Km / month emergent [integer]
Government-ev-preference - emergent [integer]
Maximum-air-quality-preference - emergent [integer]
Maximum-technology-preference - emergent [integer]
Maximum-GHG-preference - emergent [integer]
Yearly-transport-demand-increase - 1%
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Sliders and switches for globals
Value Unit Range
Initial number of electric buses [ bus emergent integer ] (for each company)
initial number of diesel buses [ integer bus emergent ] (for each company)
Maximum allowed bus age dbuses[ Year 10 years integer ]
Maximum allowed bus age ebuses[ Year 10 years integer ]
Grid grams CO2 per kWh [integer] Gram CO2 / kWh 700 (in 2012) (end of this document)
Diesel buses emission factor [integer] Gram co2/km 1000
Full time charging allowed indicator [ Yes / No - boolean ]
OPERATORS
Bus operators have:
Value Unit Range
Name [ string ] - emergent
Bus fleet [ agentset of buses ] - emergent
Required-number-of-kilometers-for-bid Km / month emergent
Tender-won? variable [ boolean ] - -
ebuses-stock [agentset of buses ] ebuses emergent
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dbuses-stock [agentset of buses ] dbuses emergent
Total-price-of-bid [ integer ] Penalty points emergent
Central stations [ agentset of patches ] - -
Time-left-on-tender [ integer ] year 0 - 10
Number-of-good-ebuses [ integer ] Bus emergent
Number-of-good-dbuses [integer ] Bus emergent
Ability to innovate at the start [ integer - 0 - 1 ]
Innovation development rate per year - 0 – 0.10
Starting market share[ integer ] - 0 - 1
Monthly-demand-of-this-contract km emergent
BUSES
Buses Have:
Value Unit Range
Operator name [ string ]
Type [ string ]
Fuel level [ integer ] liter 0-280
Gas fuel level [ integer ] Kg 0-200
Battery level [ integer ] kWh 0 - emergent
Battery usage [ integer ] kWh/km 1,3 (BYD, 2016) http://www.byd.com/la/auto/ebus.html
Fuel usage (diesel) [ integer ] l/100km 40 (Busmagazin, 2016)
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Fuel usage (gas) [integer ] Kg/100km 40 (Kyto, Rantanen & Nylund, 2012)
Maximum fuel level Liter 280 (Busmagazin, 2016)
Maximum fuel level (gas) Kg 200. Based on volume capacity in liters 1,176 – 1,712 (Man, 2015).
Maximum battery level [ integer ] kWh 326 – emergent value (BYD, 2016)
Am I full indicator[ boolean ] Yes/ No -
Age [ integer ] year Based on distribution see section 4.3.2
Starting shift time [ integer ] Hour 05:00 – 13:00
Ending shift time [ integer ] Hour emergent
Air Pollution rate [ integer ] Gram co2/km 1000
next-station [ string ] - -
monthly-km-demand-of-my- Km/month emergent contract
R² = 0,5998 buses vs ages 250 200 150 100 50 0
Numberbuses of 0 5 10 15 20 25 Bus age
Figure 41 Age distribution of Dan bus fleet, real (left) vs model (right), (Dan, 2015)
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Language primitive Description
Numbers Integer, floating point
Strings Essentially text
Booleans To represent truth values in logic. Can have the value TRUE or FALSE
Turtles, patches, nodes, edges and breeds of them Agents and agent types
Agent sets or patch sets Collection of agents and/or patches
Lists Can contain any of the above mentioned
Matrices and tables Can contain any of the above mentioned
Table 9; Language primitives in Netlogo (Nikolic & Kasmire, 2013; Netlogo Programming Guide, 2016)
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NOORD BRABANT MODEL DATA AND FORMALISATION
(Only values that differ from the Tel Aviv model)
Time related globals
Variable Unit Range
Time-till-next-tender [integer] Year Specific tender times. Year 22, 24, 26. (Provincie noord Brabant, 2016)
Time-for-tender [Boolean] - Yes / No
Bus related globals
Variable Unit Range
Maximum-allowed- year 12 age-dbuses
Maximum-allowed- year 12 - 15 age-ebuses
current-fuel-costs- Euro / liter See Appendix A dbuses [integer]
current-electricity-costs- Euro / kWh See appendix A ebuses [integer]
current-purchasing- Euro 350.000, 450.000 (TNO, 2015) for opportunity charging costs-ebuses [integer] buses
current-purchasing- Euro 220.000 (IDtechex, 2015) costs-dbuses [integer]
purchasing-costs- Euro/year -3500 (IDtechex, 2015) development-ebuses
Interest rate - http://www.tradingeconomics.com/netherlands/interest- rate 2.2 (netherlands)
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Money factor - 2400 http://www.edmunds.com/car-leasing/calculate-your- own-lease-payment.html
Total daily km’s dbus Km / day 380
Total daily km’s ebus Km / day 360
Total monthly km’s dbus Km / month Daily km’s * 22
Total monthly km’s ebus Km / month Daily km’s * 22
Energy consumption kWh / km 1.3 kWH/km (Byd, 2016) http://www.byd.com/news/news- ebus 171.html
Fuel consumption dbus Liter / km 2
TCO per month ebus Euro / month emergent
TCO per month dbus Euro / month emergent
Operational-costs- Euro Assumed to remain fixed development-dbuses
current-total-costs- Euro / month emergent ebuses [integer]
current-total-costs- Euro / month emergent dbuses [integer]
Buses-required-for- Bus emergent tender [integer]
Kilometers-service- Km/month emergent required-for-tender
Battery degradation - 20% per 10 years (value of 1 in the model) rate per year 30% per 10 years (value of 0.99 in the model)
(Thein & Chang, 2014)
Battery degradation - Assumed to remain fixed technological development
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Sliders and switches for globals
Value Unit Range
Initial number of electric buses [ bus 40 (Provincie Noord Brabant, integer ] 2016)
initial number of diesel buses [ integer bus 510 (Provincie Noord Brabant, ] 2016)
Grid grams CO2 per kWh [integer] Gram CO2 / kWh 530 (Noothout, P., Eggink, E., & van der Leun, K.,2015)
OPERATORS
Bus operators have:
Value Unit Range
Name [ string ] - emergent
Bus fleet [ agentset of buses ] - emergent
Required-number-of-kilometers-for-bid Km / month emergent
Tender-won? variable [ boolean ] - -
ebuses-stock [agentset of buses ] ebuses emergent
dbuses-stock [agentset of buses ] dbuses emergent
Total-price-of-bid [ integer ] Penalty points emergent
Central stations [ agentset of patches ] - -
Time-left-on-tender [ integer ] year 0 - 10
Number-of-good-ebuses [ integer ] Bus emergent
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Number-of-good-dbuses [integer ] Bus emergent
Ability to innovate at the start [ integer - 0.20 ]
Innovation development rate per year - 0 – 0.10
Starting market share Hermes[ integer ] - 35 (Provincie Noord Brabant, 2016)
Starting market share Arriva [ integer ] - 65 (Provincie Noord Brabant, 2016)
BUSES
Buses Have:
Value Unit Range
Fuel level [ integer ] liter 0-280
Gas fuel level [ integer ] Kg 0-200
Battery level [ integer ] kWh 0 - emergent
Daily km range dbus Km/day 380
Daily km range ebus Km/day 360
Maximum battery level [ integer kWh 260 kWh (Vdl, 2015) ] http://www.vdlbuscoach.com/News/News- Library/2015/Levering-van-eerste-VDL- Citea-SLFA-180-Electric-aa.aspx
Starting bus ages [ integer ] year 0-2
Starting shift time [ integer ] Hour 05:00
Ending shift time [ integer ] Hour 0:00
Air Pollution rate [ integer ] Gram co2/km 1000
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MODEL FORMALISATION
Charge Charge Refuel Do nothing
yes Yes yes Yes
Is fulltime Is now off Am I a Diesel no charging No No Am I full yes peak? bus allowed
no
Go to nearest Am I at the Do not charge No station station?
Stop
Keep driving. Reduce fuel/ Am I on No Do nothing battery level. yes shift? Pollute the environment No yes
Is my age Check if Start of the time Die Yes below my bus Stop No company still step for buses lifetime? has tender
Stop Start
Figure 42; Decision making of buses in the model
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Update bus fleet Calculate the with the amount of maximum score and Yes Bid won? dbuses and ebuses send score to determined. government
Evaluate the current prices of ebuses/ dbuses.
Evaluate the current operational performance of buses/ebuses
No
Determine the Evaluate the amount of buses I awarding criteria need to buy
Yes
Check if monthly Bus companies Am i requested capacity is No wake up and check to place bid? sufficient. time
Start If needed, activate buses from reserve capacity until capacity sufficient
Stop
Figure 43; Visual representation of operators decision making
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Stop
Select the best bid based on Penalty points
Ask for bids from bus operators
Start small Have 8 years past Start big tender tender No since the last big Yes procedure procedure tender?
Yes
Have 2 years past No Do nothing since te last tender?
PTA Tel Aviv at the start of day
Start
Figure 44; Visual representation of governments decision making
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GLOBAL VARIABLES The following global variables are present in the model. They have been categorized for convenience.
Environment related globals air-quality-counter [integer} Government related globals buses-required-for-tender [integer} dbuses-total-distance-traveled [integer} government-price-preference [integer}
government-ev-preference [integer} Time related globals maximum-air-quality-preference [integer} clock [integer} maximum-technology-preference [integer} year [integer} maximum-ghg-preference [integer} time-till-next-tender [integer}
time-for-tender [boolean} Ngos related globals
climate-ngo-support-of-ebus Bus related globals air-quality-complaints Electricity-used-this-year [integer} current-operational-costs-ebuses [integer} Sliders and switches for globals current-purchasing-costs-ebuses [integer} Initial number of electric buses [ integer ] (for each company) current-electricity-costs-ebuses [integer} Initial number of diesel buses [ integer ] (for each current-operational-costs-dbuses [integer} company) current-purchasing-costs-dbuses [integer} Maximum allowed bus age dbuses[ integer ] current-fuel-costs-dbuses [integer} Maximum allowed bus age ebuses[ integer ] current-total-costs-ebuses [integer} Grid grams CO2 per kWh [integer] current-total-costs-dbuses [integer} Diesel buses emission factor [integer] starting-off-peak-time [integer} Full time charging allowed indicator [ boolean ] ending-off-peak-time [integer} Profit percentage for operators
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PSEUDO CODE TEL AVIV POLICY MODEL
TEL AVIV POLICY MODEL
MODEL START
At the start of the model the bus transport system is created. It includes roads, stations, buses, bus operators and the transport authority. This happens after the user presses the setup button.
Create roads: Color all the patches grey
Color all patches with coordinates (x1 to x2 and y1 to y2) white
Create intersections:
All patches with coordinates [x,y] are intersections
Color the intersections
Intersection 1 is blue
Intersection 2 is green
Intersection 3 is red
Create stations:
Hatch stations at intersections 1, 2 and 3
Station 1 set owner Dan
Station 2 set owner Egged
Station 3 set owner Kavim
Create buses:
At each station hatch diesel buses, hatch electric buses
Set color of diesel buses blue
Set color of electric buses yellow
Set used-before? true
Apply bus specifications for all buses
Setup starting buses:
Let’s say the starting transport demand is. 100.000.0000 Each operator gets a percentage of that demand. The transport demand that each operator gets, needs to be divided into contracts. Let’s say each contract can be a maximum of 100 buses (approx 500.000 per month). Then each bus operator needs a certain amount of contracts. A bus operator comes in and gets 2 million km’s/month in total. Then it needs 2mil / 500.000 = 4 contracts Those 4 contracts get picked from the contract list. (in case 4.4 contracts needed, round 4.4 to 4 first, and each contract gets a demand of 2million / 4 ) So somehow pick 1 contract from the contract list (item 0 of the list), remove that item from the contracts list (set the list but-first of the list), add that contract to personal contract list and buy the required buses. Set contract duration round random 8 Repeat 4 times. Contract100 contract101 102 103 4 5…
Globals
Dbus-score
Ebus-score
Weighting-ebuses (slider so maybe not here)
Weighting-dbuses (slider so maybe not here)
Contracts-list
Contracts-table
(under setup) set contract-list [“contract0” “contract1” “contract2” etc… ]
;;tables are created, every contract is paired with the monthly km demand of that contract set contracts-table table:make table:put contracts "contract0" 35000 table:put contracts "contract1" 40353 table:put contracts "contract2" 53344 etc..
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Operators own
[ name bus-fleet bidding-moment? required-number-of-buses-for-bid tender-won? ebuses-stock dbuses-stock total-price-of-bid central-stations time-left-on-tender number-of-good-ebuses number-of-good-dbuses income-from-ebuses income-from-dbuses ebuses-to-be-purchased dbuses-to-be-purchased innovation-limit personal-ebus-limit total-bid my-contracts monthly-km-demand-of-this-contract ]
Update data ebuses ;; needs to happen before buses are created Updata data dbuses ;; needs to happen before buses are created
Buses own
operator-name type? liters-refueled battery-level maximum-fuel-level ;;[ 21L ] maximum-battery-level ;;[326 kWh] electricity-used
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am-i-full-indicator am-i-active used-before? age age-counter maximum age starting-shift-time ending-shift-time pollution-rate contract monthly-demand-of-contract co2 footprint per km residual service km’s ]
SETUP to apply-dbus-specs
face one-of neighbors4 set type? "dbus" set color blue set next-station one-of stations-here set am-i-active? “false” set reserved? “true” set used-before? “false” set age 0 set maximum age (allowed-age-dbuses) set pollution-rate 0 set size 2 set-shift-time set contract “contract0”
set monthly km demand of my contract ;;TBD set daily km range ( current range dbus ) set yearly km range (daily km range * 365) set fuel consumption per km ( current fuel consumption dbus ) set yearly fuel consumption ( fuel consumption per km * yearly km range) set yearly co2 emissions = ( CO2 emissions per km * yearly km range )
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end
Set ebus specs to apply-ebus-specs face one-of neighbors4 set type? "ebus" set color yellow set am-i-active? “false” set reserved? “true” set used-before? “false” set age 0 set maximum age (allowed-age-ebuses) set battery degradation rate ;; 0,98 every year the battery capacity reduces by 2% set reduced monthly km’s service ( monthly km’s service * battery degradation rate ^ maximum allowed age ebuses) set pollution-rate 0 set size 2 set-shift-time set contract “contract0” set monthly km demand of this contract ;; emergent factor set co2 footprint per km (current co2 footprint per km)
set daily km range ( current range ebus) set yearly km range (daily km range * 365) set energy consumption per km ( current energy consumption ebus) set yearly energy consumption ( energy consumption per km * yearly km range) set yearly co2 footprint (yearly km range * co2 footprint per km ) end to activate bus set am-i-active? “true” set reserved? “false” set used-before? “true”
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activate bus
GO PROCEDURE The go button starts the model and brings the agents into actions. The following procedures are started once go button has been pressed.
update-time
To update-time
Change years
Update-time-till-next-tender
to change-years set year-counter (year-counter + 1)
to update-time-till-next-tender
set time-till-next-small-tender (time-till-next-tender - 1)
ifelse time-till-next-small-tender = 0
[set time-for-small-tender true]
[set time-for-small-tender false]
Set time-till-next-big-tender (time till next big tender -1)
Ifelse time till next big tender = 0
[set time-for-big-tender true]
[set time-for-big-tender false]
to check-my-age ;; buses check their age to see if they can still drive or need to stop
let ebuses buses with [type? = "electric"]
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let dbuses buses with [type? = "diesel" ] ask ebuses [ if age >= allowed-age-of-ebuses [ DIE ] ask dbuses [ if age >= allowed-age-of-ebuses [ DIE ] end
Update data dbuses
Set purchase price dbus ( list item year purchase price development dbus)
Set interest rate dbus ( list item year interest rate development )
Set lease price dbus (purchase price of dbus / lease term + ( (interest rate / money factor ) * purchase price)
Set total daily km’s dbus (list item year dbus daily km development )
Set monthly km’s dbus ( dbus total daily km’s * 30,42 ) ;; every month is 30,42 days on average
Set current fuel consumption dbus ( list item year fuel consumption of dbus development)
Set diesel price (list item year diesel price development)
Set maintenance cost per km dbus ( list item year maintenance cost development dbus)
Set monthly maintenance costs dbus ( maintenance costs per km dbus * monthly km’s dbus)
Set fuel costs of dbus ( dbus fuel consumption / 100 * monthly km’s dbus * diesel price)
Set TCO per month dbus ( lease price dbus + fuel costs dbus + maintenance costs dbus)
Update data ebuses
Set purchase price of ebus ( list item year purchase price development )
Set infrastructure costs ebus ( list item year infrastructure costs development )
Set interest rate (list item x interest rate development )
Set lease price of ebus ( (purchase price ebus + infrastructure costs of ebus ) / lease term + ( (interest rate / money factor ) * purchase price of ebus + infrastructure costs of ebus)
Set current daily range of ebus (list item year daily range development ebus)
Set monthly km’s ebus ( total daily km’s ebus* 30,42 ) ;; every month is 30,42 days on average
Set energy consumption ebus ( list item year fuel consumption development ebus)
Set energy price (list item year energy price development)
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Set maintenance cost per km ebus ( list item year maintenance cost development ebus)
Set monthly maintenance costs ebus ( maintenance costs per km * ebus monthly km’s)
Set monthly energy costs ebus ( fuel consumption ebus / 100 * monthly km’s ebus * energy price)
Set tco per month ebus ( lease price of ebus + monthly energy costs of ebus + monthly maintenance costs ebus )
Set current-degradation-rate-new-ebus-batteries ( current-degradation-rate-new-ebus-batteries + degradation-rate- development-ebus )
Set current co2 footprint per km (list item year co2 footprint per km development )
To update-bus-specifications-of-active-buses
;;only buses that have been used before get older. This to simulate that when operators win a tender, and decide to ;;reuse their old buses, they can replace the old buses with fresh new buses. Otherwise old buses would ;;be replaced new buses that have been waiting in a depot for a few years and already started aging a bit.
Ask buses [if used-before? = true [ set age counter (age counter + 1)
If type? = “ebus” set monthly km’s service (monthly km’s service * battery degradation rate )]
Set residual km’s (maximum age – age * 12 * monthly km’s service ) ;;calculates the total km’s left
If type? = “ebus” set residual km’s (maximum age – age * 12 * battery reduced monthly km’s service)
;; Ebuses are special and are subject to yearly range reduction due to battery degradation. For that
;; reason it becomes tricky when projecting the total km’s service an ebus has left because its range
;; decreases every year. Therefore an ebus’ total range is calculated using the range it has in its last
;; service year which is called its reduced monthly km’s service.
If type? = “ebus” set yearly-co2-footprint ( current-co2-footprint-per-km * monthly km’s service * 12 )
] to remove-old-buses ;; buses check their age to see if they can still drive or need to stop
let ebuses buses with [type? = "electric"]
let dbuses buses with [type? = "diesel" ] ask ebuses [ if age >= allowed-age-of-ebuses [ DIE ] ask dbuses [ if age >= allowed-age-of-ebuses [ DIE ] end
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check-if-time-for-tender to check-if-time-for-tender if time-for-small-tender = true or if time-for-big-tender = true [ tender-procedure ]
to tender-procedure
;; the amount of buses required for tender depends on if either small tender is taking place or a big tender is taking ;;place. In case a small tender is taking place, the required number of buses is 10% of the combined fleet size of all ;;the operators in the city. In case a big tender is taking place, the required number of buses is 20% of the combined ;;fleet size of all the operators in the city.
if time-for-small tender = true [set monthly-km’s-required-for-tender (sum [range of buses ] * 0.10 * contract duration * 12 if time-for-big tender = true [ set monthly-km’s-required-for-tender (sum [range of buses ] * 0.20 * contract duration * 12
;; the contract is added to the contract table ;; this results in a new pair in the contract table [ “contract x” “x (km’s)” ] depending on which year it is now
Table:put contracts-table item year contract list monthly-km’s-required-for-tender
;; the penalty on the score for ebuses and dbuses is calculated here, see section 4.4.2 for more info. set dbus-score -1 * current-weighting-costs * TCO per month dbus set ebus-score -1 * (current-weighting-costs * TCO per month ebus - current-weighting-innovation* current-ebus- bonus)
;; the following gives the total amount of km’s that every dbus or ebus can run for the entire tender ;; for ebuses the battery reduced monthly km service (monthly km’s service * degradation rate ^ maximum age ) is ;; used. Ideally we would calculate the expected km range for every year of service left and sum that until the max age is reached. ;; But simplicity reasons and as an extra guarantee that ebuses can provide the required services, the reduced monthly km service is used. set total-number-of-dbus-kms-for-bid ( monthly-km-range-dbus * contract duration * 12 ) set total-number-of-ebus-kms-for-bid ( battery-reduced-monthly-km-service * contract duration * 12 )
update-government-preferences
bid-for-tender
determine-tender-outcome
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if time-for-small-tender = true [ set time-for-small-tender false set time-till-next-small-tender 2 ] if time-for-big-tender = true [ set time-for-big-tender false set time-till-next-big-tender 8 ]
;; maybe easy to just use a list tender-status [small no-tender small big small small big ] ;; if tender-status = small do this, if tender-status = big do this, if tender-status = no-tender do this ;; but then it would not be possible to try different tender times easily
End
to update government preferences ask government
[ set weighting on costs (list item year weighting development costs ) set weighting on electric buses (list item year weighting development costs ) set subsidy on electric buses (list item year subsidy on electric buses )
;; old code maybe reintroduce ;; set air-quality-preference (round (maximum-air-quality-preference * (air-quality-complaints * 0.0001))) ;; Set technology-preference round (maximum-technology-preference * ((count buses with [type? = "ebus"]) / (count buses)))
to bid-for-tender
ask bus-operators [place-your-bids ] to place-your-bids
;; Bus operators place their bids. First they count the number of buses they are able to reuse.
Let my-residual-buses buses with [operator-name = [name] of myself] with [ am-i-active? = false ] with [reserved? = false ]
;; Every bus operator calculates how many additional km’s service is needed to meet the monthly transport demand. ;;Because every operator has a different fleet composition, this number is different for each operator. set personal-transport-demand-for-this-contract ( (monthly-km’s-required-for-tender * current-contract duration * 12 ) – sum [total operational km’s left] of my-residual-buses
;; the personal ebus limit is set as a fraction of the total ebuses that would be required of only ebuses would fulfil the ;; transport demand. set personal ebus limit (innovation rate * ( monthly km’s required for tender / current monthly km’s ebus ) )
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MASTER THESIS HENSLEY DJOE
determine optimal bid
to determine optimal bid
;; The objective function is put into R
;; explain what the terms mean, add the variable names
;; the objective is to max: - A( X1* TCOD + X2* TCOE ) + B ( X2* BONUSE )
;; Splitting the terms results into: - X1 * A * TCOD - X2 ( A * TCOE - B * BONUSE )
(r:put "objective.in" list dbus-score ebus-score)
;; show (r:get "objective.in")
;; The RHS of the constraints are put into R
;; a constraint matrix is made for the amount of dbuses and ebuses to be purchased.
(r:put "flatconstmat" [total-number-of-kms-per-dbus total-number-of-kms-per-ebus 0 1])
(r:eval "f.con <- matrix(flatconstmat, nrow=2,ncol=2,byrow=TRUE)")
;; The direction of the constraints is put into R
(r:putlist "f.dir" "=", "<=")
*/(r:put "f.rhs" personal-transport-demand-for-this-contract personal-ebus-limit )
r:eval" bidresult <- lp(direction = 'max', objective.in, f.con, f.dir, f.rhs)"
;; the total bid is stored in the variable total-bid
set total-bid r:get "bidresult"
;; the value of the bid is stored in the total-price-of-bid
set total-score-of-bid round item 10 total-bid
set ebuses-to-be-purchased round item 0 item 11 total-bid
set dbuses-to-be-purchased round item 1 item 11 total-bid
;; Governments will select the best bid from the participating operators
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MASTER THESIS HENSLEY DJOE
to determine-tender-outcome
ask governments [
let winner max-one-of operators [total-score-of-bid]
[ ask winner [buy-required-buses]
if debug-tender
[ type "these are the results of the tender for year " type year + 1 type ": " type [name] of winner
type " won the bid. " type "its winning bid price was " type [total-score-of-bid ] of winner
type " dollars. " type [ebuses-to-be-purchased] of winner type " ebuses will be purchased and "
type [dbuses-to-be-purchased] of winner type " dbuses will be purchased" type "\n" type "\n"
]
;; old pseudo code still to determine what to do with it
;; set air-quality-complaints 0
;; set ghg-preference 0
;; set technology-preference 0
]] end
To buy-required-buses
;; the winning contract is added to the list of contracts the winner has which is his my-contracts list
;; the contract-list is list which contains the contract for each year
Set my-contracts lput list item year contract-list my-contracts let my-station stations with [station-name = [name] of myself]
;; the old buses to be reused are given the new contract name
Let my-residual-buses buses with [operator-name = [name] of myself] with [ am-i-active? = false ] with [reserved? = false ]
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Ask my-residual-buses [set contract list item year contract-list
Set monthly-range-of-contract monthly-km’s-required-for-tender ]
;; the new buses are created here
Ask my-station [ hatch-buses [ebuses-to-be-purchased] of myself
[ apply-ebus-specs
set operator-name [station-name] of myself
set contract list item year contract-list
]
]
ask my-station
[hatch-buses [dbuses-to-be-purchased] of myself
[ apply-dbus-specs
set operator-name [station-name] of myself
set contract list item year contract-list
]
]
To update-monthly-range-buses
;;What each operators needs to do for each of it’s contracts is
;;count the total number of kilometer service that the buses on that contracts can provide
;;compare that number with the number of kilometers service required for that contract
;;compensate the difference with buses purchased for that contract that have the status reserve bus. ;; and are waiting at the depot waiting to be activated
;; foreach is used because the code below needs to be run for each of the contracts that the buses have ;; the active buses are the buses that run daily an decide the monthly range corresponding contract
Ask operators foreach my-contracts [
Let active-contract-buses (buses with name = name myself and contract-name = ? and active? = true ]
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Let reserve-contract-buses (buses with name = name myself and contract-name = ? and active? = false ] set monthly-range sum [monthly-range of one-of active-contract-buses ]
;; the monthly km range that belongs to the contract is grabbed from the contracts table here ;; the ? refers to the specific contract the foreach command is running the procedure for.
Set monthly-km-range-of-this-contract [table:get contracts-table ?]
Set residual-monthly-range (monthly-km-range-of-this-contract – monthly range )
;;Your new buses should become active (maybe one by one) until the monthly capacity is reached. So the new buses ;;will become active, and their reserved status will become false.
While [ residual-monthly-range > 0 ] [ ask one of reserve-contract-buses [ activate-bus ] set residual-monthly-range [monthly-km-range-of-this-contract - sum [monthly-range of active-contract-buses ] ]
]]] ;;; needs verification might get stuck in a loop. If the allowed age of buses is changed during the model, an operator might have reserved buses which he does not have.
to continue-bus-service ;; calculation are made for an entire your which include, km’s traveled energy used etc.. let active-buses buses with type? = “ebus”] with [ active? = true ] let active-ebuses buses with [type? = "ebus"] with [ active? = true ] let active-dbuses buses with [type? = "dbus"] with [active? = true ]
ask active-ebuses [ set carbon emission this year = ( carbon intensity of the grid * yearly energy consumption) ) ] set total km traveled this year sum [ yearly km range ] of active-buses set total energy consumption ebuses sum [ yearly energy consumption] of active-ebuses set total co2 footprint ebuses sum [ yearly-co2-footprint] of active-ebuses set total fuel consumption dbuses sum [ yearly fuel consumption] of active-dbuses set total co2 emissions dbuses sum [ yearly co2 emissions] of active-dbuses
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PSEUDO CODE DUTCH POLICY MODEL
POLICY MODEL DUTCH PROVINCE OF NOORD BRABANT
Create stations:
Hatch stations at intersections 1, 2 and 3
Station 1 set owner Veolia
Station 2 set owner Connexxion
Station 3 set owner Kavim…
Create buses:
At each station hatch diesel buses, hatch electric buses
Set color of diesel buses blue
Set color of electric buses yellow
Set color of gas buses green
Set used-before? true
Apply bus specifications for all buses
Globals
Dbus-score
Ebus-score
Gbus-score
Weighting-ebuses (slider so maybe not here)
Weighting-dbuses (slider so maybe not here)
Weighting-gbuses (slider so maybe not here)
Contracts-list
Contracts-table
(under setup) set contract-list [“contract0” “contract1” “contract2” etc… ]
;;tables are created, every contract is paired with the monthly km demand of that contract set contracts-table table:make table:put contracts "contract0" 35000 table:put contracts "contract1" 40353 table:put contracts "contract2" 53344 etc..
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Operators own
[ name bus-fleet bidding-moment? required-number-of-buses-for-bid tender-won? ebuses-stock dbuses-stock gbuses-stock total-price-of-bid central-stations time-left-on-tender number-of-good-ebuses number-of-good-dbuses number-of-good-gbuses ebuses-to-be-purchased dbuses-to-be-purchased gbuses-to-be-purchased innovation-limit personal-ebus-limit total-bid my-contracts monthly-km-demand-of-this-contract ]
Update data ebuses ;; needs to happen before buses are created Updata data dbuses ;; needs to happen before buses are created
Buses own
operator-name type? liters-refueled electricity-used gas-used battery-level fuel-level gas-level maximum-fuel-level ;;[ 21L ] maximum-gas-level maximum-battery-level ;;[326 kWh]
am-i-full-indicator am-i-active used-before? age 143 | P a g e
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age-counter maximum age starting-shift-time ending-shift-time pollution-rate next-station contract monthly-demand-of-contract co2 footprint per km residual service km’s ]
SETUP to apply-dbus-specs
face one-of neighbors4 set type? "dbus" set color blue set next-station one-of stations-here set fuel-level 220 set maximum-fuel-level 220 ;;[220 liters is max fuel level for dbuses] set am-i-full-indicator "true" set am-i-active? “false” set reserved? “true” set used-before? “false” set age 0 set maximum age (allowed-age-dbuses) set pollution-rate 0 set size 2 set-shift-time set contract “contract0”
set monthly km demand of my contract TBD set daily km range ( current range dbus ) set yearly km range (daily km range * 365) set fuel consumption per km ( current fuel consumption dbus ) set yearly fuel consumption ( fuel consumption per km * yearly km range) set yearly co2 emissions = ( CO2 emissions per km * yearly km range ) end
Set ebus specs to apply-ebus-specs face one-of neighbors4 set type? "ebus" set color yellow set next-station one-of stations-here
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set am-i-full-indicator "true" set am-i-active? “false” set reserved? “true” set used-before? “false” set age 0 set maximum age (allowed-age-ebuses) set battery degradation rate ;; 0,98 set reduced monthly km’s service ( monthly km’s service * battery degradation rate ^ maximum allowed age) set pollution-rate 0 set size 2 set-shift-time set contract “contract0” set monthly km demand of this contract TBD set co2 footprint per km (current co2 footprint per km)
set daily km range ( current range ebus) set yearly km range (daily km range * 365) set energy consumption per km ( current energy consumption ebus) set yearly energy consumption ( energy consumption per km * yearly km range) set yearly co2 footprint (yearly km range * co2 footprint per km ) end to apply-gbus-specs
face one-of neighbors4 set type? "gbus" set color green set next-station one-of stations-here set fuel-level 220 set maximum-fuel-level 220 ;;[220 liters is max fuel level for dbuses] set am-i-full-indicator "true" set am-i-active? “false” set reserved? “true” set used-before? “false” set age 0 set maximum age (allowed-age-dbuses) set pollution-rate 0 set size 2 set-shift-time set contract “contract0”
set monthly km demand of my contract TBD set daily km range ( current range gbus ) set yearly km range (daily km range * 365) set fuel consumption per km ( current-gas-consumption-gbus ) set yearly gas consumption ( gas consumption per km * yearly km range)
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set yearly co2 emissions = ( CO2 emissions per km * yearly km range ) end
to activate bus set am-i-active? “true” set reserved? “false” set used-before? “true” activate bus
GO PROCEDURE The go button starts the model and brings the agents into actions. The following procedures are started once go button has been pressed.
update-time
To update-time
Change years
Update-time-till-next-tender to change-years set year-counter (year-counter + 1) to update-time-till-next-tender
set time-till-next-small-tender (time-till-next-tender - 1)
ifelse time-till-next-small-tender = 0
[set time-for-small-tender true]
[set time-for-small-tender false]
Set time-till-next-big-tender (time till next big tender -1)
Ifelse time till next big tender = 0
[set time-for-big-tender true]
[set time-for-big-tender false]
to check-my-age ;; buses check their age to see if they can still drive or need to stop
let ebuses buses with [type? = "electric"]
let dbuses buses with [type? = "diesel" ]
let gbuses buses with [type? = “gas” ] ask ebuses [ if age >= allowed-age-of-ebuses [ DIE ] ask dbuses [ if age >= allowed-age-of-dbuses [ DIE ]
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ask gbuses [ if age >= allowed-age-of-gbuses [ DIE ] end
Update data dbuses
Set purchase price dbus ( list item year dbus purchase price development)
Set interest rate dbus ( list item year interest rate development )
Set lease price dbus (purchase price of dbus / lease term + ( (interest rate / money factor ) * purchase price)
Set total daily km’s dbus (list item year dbus daily km development )
Set monthly km’s dbus ( dbus total daily km’s * 30,42 ) ;; every month is 30,42 days on average
Set current fuel consumption dbus ( list item year fuel consumption of dbus development)
Set diesel price (list item year diesel price development)
Set maintenance cost per km dbus ( list item year maintenance cost development dbus)
Set monthly maintenance costs dbus ( maintenance costs per km dbus * monthly km’s dbus)
Set fuel costs of dbus ( dbus fuel consumption / 100 * monthly km’s dbus * diesel price)
Set TCO per month dbus ( lease price dbus + fuel costs dbus + maintenance costs dbus)
Update data ebuses
Set purchase price of ebus ( list item year purchase price development )
Set infrastructure costs ebus ( list item year infrastructure costs development )
Set interest rate (list item x interest rate development )
Set lease price of ebus ( (purchase price ebus + infrastructure costs of ebus ) / lease term + ( (interest rate / money factor ) * purchase price of ebus + infrastructure costs of ebus)
Set current daily range of ebus (list item year daily range development ebus)
Set monthly km’s ebus ( total daily km’s ebus* 30,42 ) ;; every month is 30,42 days on average
Set energy consumption ebus ( list item year fuel consumption development ebus)
Set energy price (list item year energy price development)
Set maintenance cost per km ebus ( list item year maintenance cost development ebus)
Set monthly maintenance costs ebus ( maintenance costs per km * ebus monthly km’s)
Set monthly energy costs ebus ( fuel consumption ebus / 100 * monthly km’s ebus * energy price)
Set tco per month ebus ( lease price of ebus + monthly energy costs of ebus + monthly maintenance costs ebus )
Set battery degradation rate new ebuses (list item year degradation rate development ebus )
Set current co2 footprint per km (list item year co2 footprint per km development )
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Update data gbuses
Set purchase price gbus ( list item year gbus purchase price development)
Set interest rate gbus ( list item year interest rate development )
Set lease price gbus (purchase price of gbus / lease term + ( (interest rate / money factor ) * purchase price)
Set total daily km’s gbus (list item year gbus daily km development )
Set monthly km’s gbus ( gbus total daily km’s * 30,42 ) ;; every month is 30,42 days on average
Set current fuel consumption gbus ( list item year fuel consumption of gbus development)
Set gas price (list item year gas price development)
Set maintenance cost per km gbus ( list item year maintenance cost development gbus)
Set monthly maintenance costs gbus ( maintenance costs per km gbus * monthly km’s gbus)
Set fuel costs of gbus ( gbus fuel consumption / 100 * monthly km’s gbus * gas price)
Set TCO per month gbus ( lease price gbus + fuel costs gbus + maintenance costs gbus)
To update-bus-specifications-of-active-buses
;;only buses that have been used before get older. This to simulate that when you win a tender and first decide to ;;reuse your old buses, you can replace the old buses with fresh new buses. Without this, you would replace your old ;;with new buses that have been waiting in a depot for a few years and already started aging a bit.
Ask buses [if used-before? = true [ set age counter (age counter + 1)
Set residual km’s (maximum age – age * 12 * monthly km’s service ) ;;calculates the total km’s left
;; Ebuses are special and are subject to yearly range reduction due to battery degradation. For that
;; reason it becomes tricky when projecting the total km’s service an ebus has left because its range
;; decreases every year. Therefore an ebus’ total range is calculated using the range it has in its last
;; service year which is called its reduced monthly km’s service.
If type? = “ebus” set monthly km’s service (monthly km’s service * battery degradation rate )]
If type? = “ebus” set residual km’s (maximum age – age * 12 * battery reduced monthly km’s service)
If type? = “ebus” set yearly-co2-footprint ( current-co2-footprint-per-km * monthly km’s service * 12 )
] to remove-old-buses ;; buses check their age to see if they can still drive or need to stop
let ebuses buses with [type? = "electric"]
let dbuses buses with [type? = "diesel" ]
let gbuses buses with [type? = "gas" ] ask ebuses [ if age >= allowed-age-of-ebuses [ DIE ] ask dbuses [ if age >= allowed-age-of-ebuses [ DIE ] 148 | P a g e
MASTER THESIS HENSLEY DJOE
ask gbuses [ if age >= allowed-age-of-gbuses [ DIE ]
end
to check-if-time-for-tender if time-for-small-tender = true or if time-for-big-tender = true [ tender-procedure ]
to tender-procedure
;; the amount of buses required for tender depends on if either small tender is taking place or a big tender is taking ;;place. In case a small tender is taking place, the required number of buses is 10% of the combined fleet size of all ;;the operators in the city. In case a big tender is taking place, the required number of buses is 20% of the combined ;;fleet size of all the operators in the city.
if time-for-small tender = true [set monthly-km’s-required-for-tender (sum [range of buses ] * 0.10 * contract duration * 12 if time-for-big tender = true [ set monthly-km’s-required-for-tender (sum [range of buses ] * 0.20 * contract duration * 12
;; the contract is added to the contract table ;; this results in a new pair in the contract table [ “contract x” “x (km’s)” ] depending on which year it is now
Table:put contracts-table item year contract list monthly-km’s-required-for-tender
;; the penalty on the score for ebuses and dbuses is calculated here, see section 4.4.2 for more info. set dbus-score -1 * current-weighting-costs * TCO per month dbus set gbus-score -1 * current-weighting-costs * TCO per month gbus set ebus-score -1 * (current-weighting-costs * TCO per month ebus - current-weighting-innovation* current-ebus-bonus)
;; gives the total amount of km’s that every dbus or ebus can run for the entire tender ;; for ebuses the battery reduced monthly km service (monthly km’s service * degradation rate ^ maximum age ) is ;; used. Ideally we would calculate the expected km range for every year and sum that until the max age is reached. ;; But simplicity reasons and as an extra guarantee the reduced monthly km service is used. set total-number-of-dbus-kms-for-bid ( monthly-km-range-dbus * contract duration * 12 ) set total-number-of-gbus-kms-for-bid ( monthly-km-range-gbus * contract duration * 12 ) set total-number-of-ebus-kms-for-bid ( battery-reduced-monthly-km-service * contract duration * 12 )
update-government-preferences
bid-for-tender
determine-tender-outcome
if time-for-small-tender = true [ set time-for-small-tender false set time-till-next-small-tender 2 ] if time-for-big-tender = true [ set time-for-big-tender false set time-till-next-big-tender 8 ]
;; maybe easy to just use a list tender-status [small no-tender small big small small big ] ;; if tender-status = small do this, if tender-status = big do this, if tender-status = no-tender do this ;; but then it would not be possible to try different tender times easily end
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MASTER THESIS HENSLEY DJOE
to update government preferences ask government
[ set weighting on costs (list item year weighting development costs ) set weighting on electric buses (list item year weighting development costs ) set subsidy on electric buses (list item year subsidy on electric buses )
;; old code maybe reintroduce ;; set air-quality-preference (round (maximum-air-quality-preference * (air-quality-complaints * 0.0001))) ;; Set technology-preference round (maximum-technology-preference * ((count buses with [type? = "ebus"]) / (count buses)))
to bid-for-tender
ask bus-operators [place-your-bids ] to place-your-bids
;; Bus operators place their bids. First they count the number of buses they are able to reuse.
Let my-residual-buses buses with [operator-name = [name] of myself] with [ am-i-active? = false ] with [reserved? = false ]
;; Every bus operator calculates how many additional km’s service is needed to meet the monthly transport demand. ;;Because every operator has a different fleet composition, this number is different for each operator. set personal-transport-demand-for-this-contract ( (monthly-km’s-required-for-tender * current-contract duration * 12 ) – sum [total operational km’s left] of my- residual-buses
;; the personal ebus limit is set as a fraction of the total ebuses that would be required of only ebuses would fulfil the ;; transport demand. set personal ebus limit (innovation rate * ( monthly km’s required for tender / current monthly km’s ebus ) )
determine optimal bid
to determine optimal bid
;; The objective function is put into R
;; the objective is to max: - A( X1* TCOD + X2* TCOE + X3* TCOG) + B ( X2* BONUSE )
;; Multiplying the terms results into: - X1 * A * TCOD - X2 ( A * TCOE - B * BONUSE ) – X3 * A * TCOG
(r:put "objective.in" list score-dbuses score-ebuses)
;; show (r:get "objective.in")
;; The RHS of the constraints are put into R
;; a constraint matrix is made for the amount of dbuses and ebuses and gbuses to be purchased.
(r:put "flatconstmat" [total-number-of-kms-per-dbus total-number-of-kms-per-ebus total-number-of-kms-per-ebus 0 1 0])
(r:eval "f.con <- matrix(flatconstmat, nrow=2,ncol=2,byrow=TRUE)") 150 | P a g e
MASTER THESIS HENSLEY DJOE
;; The direction of the constraints is put into R
(r:putlist "f.dir" "=", "<=")
*/(r:put "f.rhs" personal-transport-demand-for-this-contract personal-ebus-limit )
r:eval" bidresult <- lp(direction = 'max', objective.in, f.con, f.dir, f.rhs)"
;; the total bid is stored in the variable total-bid
set total-bid r:get "bidresult"
;; the value of the bid is stored in the total-price-of-bid
set total-score-of-bid round item 10 total-bid
set ebuses-to-be-purchased round item 0 item 11 total-bid
set dbuses-to-be-purchased round item 1 item 11 total-bid
set gbuses-to-be-purchased round item ? item 11 total-bid
;; Governments will select the best bid from the participating operators
to determine-tender-outcome
ask governments [
let winner max-one-of operators [total-score-of-bid]
[ ask winner [buy-required-buses]
if debug-tender
[ type "these are the results of the tender for year " type year + 1 type ": " type [name] of winner
type " won the bid. " type "its winning bid price was " type [total-score-of-bid ] of winner
type " dollars. " type [ebuses-to-be-purchased] of winner type " ebuses will be purchased and "
type [gbuses-to-be-purchased] of winner type " gbuses will be purchased and "
type [dbuses-to-be-purchased] of winner type " dbuses will be purchased" type "\n" type "\n"
]
;; old pseudo code still to determine what to do with it
;; set air-quality-complaints 0
;; set ghg-preference 0
;; set technology-preference 0
]] end
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MASTER THESIS HENSLEY DJOE
To buy-required-buses
;; the winning contract is added to the list of contracts the winner has which is his my-contracts list
;; the contract-list is list which contains the contract for each year
Set my-contracts lput list item year contract-list my-contracts let my-station stations with [station-name = [name] of myself]
;; the old buses to be reused are given the new contract name
Let my-residual-buses buses with [operator-name = [name] of myself] with [ am-i-active? = false ] with [reserved? = false ]
Ask my-residual-buses [set contract list item year contract-list
Set monthly-range-of-contract monthly-km’s-required-for-tender ]
;; the new buses are created here
Ask my-station [ hatch-buses [ebuses-to-be-purchased] of myself
[ apply-ebus-specs
set operator-name [station-name] of myself
set contract list item year contract-list
]
]
ask my-station
[hatch-buses [dbuses-to-be-purchased] of myself
[ apply-dbus-specs
set operator-name [station-name] of myself
set contract list item year contract-list
]
]
ask my-station
[hatch-buses [gbuses-to-be-purchased] of myself
[ apply-gbus-specs
set operator-name [station-name] of myself
set contract list item year contract-list
]
]
To update-monthly-range-buses
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MASTER THESIS HENSLEY DJOE
;;What each operators needs to do for each of it’s contracts is
;;count the total number of kilometer service that the buses on that contracts can provide
;;compare that number with the number of kilometers service required for that contract
;;compensate the difference with buses purchased for that contract that have the status reserve bus. ;; and are waiting at the depot waiting to be activated
;; foreach is used because the code below needs to be run for each of the contracts that the buses have ;; the active buses are the buses that run daily an decide the monthly range corresponding contract
Ask operators foreach my-contracts [
Let active-contract-buses (buses with name = name myself and contract-name = ? and active? = true ]
Let reserve-contract-buses (buses with name = name myself and contract-name = ? and active? = false ] set monthly-range sum [monthly-range of one-of active-contract-buses ]
;; the monthly km range that belongs to the contract is grabbed from the contracts table here ;; the ? refers to the specific contract the foreach command is running the procedure for.
Set monthly-km-range-of-this-contract [table:get contracts-table ?]
Set residual-monthly-range (monthly-km-range-of-this-contract – monthly range )
;;Your new buses should become active (maybe one by one) until the monthly capacity is reached. So the new buses ;;will become active, and their reserved status will become false.
While [ residual-monthly-range > 0 ] [ ask one of reserve-contract-buses [ activate-bus ] set residual-monthly-range [monthly-km-range-of-this-contract - sum [monthly-range of active-contract-buses ] ]
]]] ;;; needs verification might get stuck in a loop. If the allowed age of buses is changed during the model, an operator might have reserved buses which he does not have. to continue-bus-service ;; calculations are made for an entire your which include, km’s traveled energy used etc.. let active-buses buses with type? = “ebus”] with [ active? = true ] let active-ebuses buses with [type? = "ebus"] with [ active? = true ] let active-dbuses buses with [type? = "dbus"] with [active? = true ] let active-gbuses buses with [type? = "gbus"] with [active? = true ] ask active-ebuses [ set carbon emission this year = ( carbon intensity of the grid * yearly energy consumption) ) ] set total km traveled this year sum [ yearly km range ] of active-buses set total energy consumption ebuses sum [ yearly energy consumption] of active-ebuses set total co2 footprint ebuses sum [ yearly-co2-footprint] of active-ebuses set total fuel consumption dbuses sum [ yearly fuel consumption] of active-dbuses set total co2 emissions dbuses sum [ yearly co2 emissions] of active-dbuses set total gas consumption gbuses sum [ yearly fuel consumption] of active-gbuses set total co2 emissions gbuses sum [ yearly co2 emissions] of active-gbuses
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MODEL CODE
extensions [r table ] globals [ grid-x-inc grid-y-inc intersections roads
;;environment related air-quality-counter dbuses-total-distance-traveled
;;time related hour hour-counter day day-counter time-till-next-small-tender time-for-small-tender time-for-big-tender year year-clock year-counter ticks-in-a-day days-in-a-year ticks-in-a-year
;; bus related globals allowed-age-gbuses battery-degradation-rate-development-new-ebuses battery-reduced-monthly-km-service initial-battery-reduced-monthly-km-range-ebus initial-co2-footprint-per-km-ebus co2-emissions-per-km-dbus co2-emissions-per-km-gbus co2-footprint-per-km-development current-co2-footprint-per-km-ebus initial-degredation-rate-new-ebus-batteries current-degradation-rate-new-ebus-batteries current-fuel-costs-dbuses current-fuel-costs-gbuses current-energy-costs-ebuses
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current-purchasing-costs-ebuses current-electricity-costs-ebuses current-operational-costs-dbuses current-purchasing-costs-dbuses current-total-costs-ebuses current-total-costs-dbuses charging-speed buses-required-for-tender initial-daily-km-range-dbus initial-daily-km-range-ebus initial-daily-km-range-gbus daily-km-range-development-dbus daily-km-range-development-ebus daily-km-range-development-gbus current-daily-km-range-dbus current-daily-km-range-ebus current-daily-km-range-gbus current-maximum-battery-capacity-ebus current-maximum-fuel-level-dbus ;;liters current-maximum-fuel-level-gbus ;; chosen-diesel-price-scenario applied-diesel-price-scenario diesel-price-development-low diesel-price-development-medium diesel-price-development-high diesel-price electricity-used-this-year initial-energy-consumption-ebus infrastructure-costs-ebus infrastructure-costs-development energy-consumption-development-ebus energy-consumption-ebus energy-price-development-low energy-price-development-medium energy-price-development-high energy-price fuel-consumption-development-dbus fuel-consumption-dbus fuel-consumption-development-gbus fuel-consumption-gbus gas-price-development-low gas-price-development-medium gas-price-development-high gas-price interest-rate initial-infrastructure-costs-ebus
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kilometers-service-required-for-tender lease-price-dbus lease-price-ebus lease-price-gbus initial-maximum-battery-capacity-ebus initial-maintenance-costs-per-km-dbus initial-maintenance-costs-per-km-ebus initial-maintenance-costs-per-km-gbus maintenance-costs-per-km-development-dbus maintenance-costs-per-km-dbus maintenance-costs-per-km-development-ebus maintenance-costs-per-km-ebus maintenance-costs-per-km-development-gbus maintenance-costs-per-km-gbus maximum-battery-capacity-development money-factor monthly-maintenance-costs-dbus monthly-maintenance-costs-ebus monthly-maintenance-costs-gbus monthly-km-range-dbus monthly-km-range-ebus monthly-km-range-gbus monthly-energy-costs-ebus monthly-fuel-costs-dbus monthly-fuel-costs-gbus initial-purchase-price-dbus initial-purchase-price-ebus initial-purchase-price-gbus purchase-price-development-dbus purchase-price-development-ebus purchase-price-development-gbus purchase-price-dbus purchase-price-ebus purchase-price-gbus starting-off-peak-time ending-off-peak-time TCO-per-month-dbus TCO-per-month-ebus TCO-per-month-gbus total-km-traveled total-energy-consumption-ebuses total-co2-footprint-ebuses total-fuel-consumption-dbuses total-fuel-consumption-gbuses total-co2-emissions-dbuses total-co2-emissions-gbuses
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MASTER THESIS HENSLEY DJOE
total-co2-output
;;PTA related globals PTA-price-preference PTA-ev-preference maximum-air-quality-preference maximum-technology-preference maximum-ghg-preference
;;Bus operator related globals innovation-rate-dan innovation-rate-egged innovation-rate-kavim
;;tender related globals dbus-penalty ebus-penalty current-weighting-costs current-weighting-innovation current-monthly-demand-of-all-contracts contracts-list contracts-table starting-contracts-list this-year's-contract-name this-year's-contract-monthly-demand this-year's-contract-total-demand this-year's-contract-duration monthly-transport-demand-of-the-city monthly-km's-service-required-for-tender last-year's-tender-requirement total-required-km's-for-tender total-number-of-dbus-km's-for-bid total-number-of-ebus-km's-for-bid
] undirected-link-breed [contracts contract] breed [PTAs PTA] breed [buses bus] breed [stations station] breed [bus-operators bus-operator] patches-own [ intersection? my-row my-column
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MASTER THESIS HENSLEY DJOE
] buses-own [ age age-counter am-i-active? am-i-full-indicator battery-degradation-rate battery-level currently-charging? current-energy-usage co2-footprint-per-km daily-km-range electricity-used energy-consumption-per-km fuel-level liters-refueled maximum-age maximum-fuel-level ;;TBD [ 220? ] maximum-battery-level monthly-km-demand-of-contract monthly-km-range my-operator's-contract next-station operator-name personal-contract-duration pollution-rate reduced-monthly-km's-service reserved? residual-service-kms starting-shift-time ending-shift-time type? used-before? yearly-co2-emissions yearly-co2-footprint yearly-energy-consumption yearly-fuel-consumption yearly-km-range ] bus-operators-own [ bidding-moment? bus-fleet
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central-stations dbuses-stock dbuses-to-be-purchased dbuses-to-bid ;;points per dbus in tendering procedure ebuses-stock ebuses-to-be-purchased ebuses-to-bid ;;points per ebus in tendering procedure income-from-dbuses income-from-ebuses innovation-limit my-contracts-list name number-of-good-dbuses number-of-good-ebuses personal-ebus-limit required-number-of-buses-for-bid starting-market-share tender-won? time-left-on-tender total-bid total-score-of-bid
ebuses-to-be-activated dbuses-to-be-activated buses-to-be-activated my-buses my-reserve-ebuses my-reserve-dbuses buses-in-this-contract active-contract-buses reserve-contract-buses range-of-ebus-in-this-contract range-of-dbus-in-this-contract buses-to-order-at-start buses-to-order-at-start-per-contract starting-contracts-needed my-monthly-range-per-starting-contract my-total-monthly-range monthly-km-demand-of-this-contract this-contract's-km-demand this-contract's-unsatisfied-demand this-contract's-range personal-transport-demand-for-this-contract ;;this value is only used in the Tendering procedure ]
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stations-own [ station-name ] PTAs-own [ air-quality-preference technology-preference ghg-preference name ] links-own [this-contract's-duration contract-name monthly-km-service-of-contract total-km-service-of-contract] to setup ;;The world and the participants in the model are created here clear-all set-default-shape buses "bus" create-grid setup-patches setup-intersections setup-stations setup-PTA setup-globals setup-bus-operators ;; setup-buses setup-buses setup-bid-optimizer reset-ticks end to create-grid set grid-x-inc world-width / 4 set grid-y-inc world-width / 4 end to setup-globals ;; All the global starting variables are set here ;;clock setup set hour-counter 0 set starting-off-peak-time 20 set ending-off-peak-time 6 set year 0 set year-counter 0 set days-in-a-year 5 ;;determines howmany days a year will take set ticks-in-a-day 24 ;;determines howmany ticks go in a day set ticks-in-a-year (ticks-in-a-day * days-in-a-year) set time-for-small-tender false set time-for-big-tender false
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set time-till-next-small-tender (ticks-in-a-day * days-in-a-year * 1 + 5) ;;the next tender will be in year 2 at 05:00AM around this time all buses are at the depot and done charging
;;bus related globals ;; Due to innovation, every year battery technology increases leading to lower levels of batter degradation of new buses. set initial-degredation-rate-new-ebus-batteries 1 ;; TBD? this is the initial degradation rate of new ebus batteries set current-degradation-rate-new-ebus-batteries (initial-degredation-rate-new-ebus-batteries) set battery-degradation-rate-development-new-ebuses 0 set initial-co2-footprint-per-km-ebus 0 ;;source needed this needs to be kwh per km using the carbon intensity of the grid which is in g/kwh set current-co2-footprint-per-km-ebus (initial-co2-footprint-per-km-ebus) set co2-emissions-per-km-dbus 1000 set co2-emissions-per-km-gbus 1000 set charging-speed 60 ;;buses charge at 60kW
set diesel-price-development-low [1.5 1.2 0.9 0.7 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.3 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.3] set diesel-price-development-medium [1.5 1.2 0.9 0.7 0.5 0.6 0.6 0.8 0.9 1.1 1.2 1.3 1.4 1.6 1.7 1.9 2.0 2.0 2.0 2.0] set diesel-price-development-high [1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.1 3.1 3.1] set energy-price-development-low [1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.0] set energy-price-development-medium [0.11 0.10 0.11 0.12 0.11 0.10 0.11 0.12 0.13 0.12 0.12 0.10 0.12 0.12 0.13 0.12 0.10 0.10 0.10] set energy-price-development-high [1.5 1.2 0.9 0.7 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.3 0.3 0.4 0.4 0.3 9.3 0.3] set gas-price-development-low [1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.0] set gas-price-development-medium [1.9 1.2 0.9 0.7 0.5 0.6 0.6 0.8 0.9 1.1 1.2 1.3 1.4 1.6 1.7 1.9 2.0] set gas-price-development-high [1.5 1.2 0.9 0.7 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.3 0.3 0.4 0.4 0.3 0.3] set chosen-diesel-price-scenario 2 if chosen-diesel-price-scenario = 1 [set applied-diesel-price-scenario diesel-price-development-low] if chosen-diesel-price-scenario = 2 [set applied-diesel-price-scenario diesel-price-development-medium] if chosen-diesel-price-scenario = 3 [set applied-diesel-price-scenario diesel-price-development-high] set initial-energy-consumption-ebus 26 ;;26 kWh / 20 km set energy-consumption-ebus (initial-energy-consumption-ebus) ;;number needed set energy-consumption-development-ebus 0 ;;source needed or experiment with set fuel-consumption-dbus 8 ;; 40 liters per 100km -> 20km/tick means 8 liters per tick set fuel-consumption-gbus 8 ;; ;; 40 kg / 100km. 20km / tick means 8 kg / tick set fuel-consumption-development-gbus 0 set initial-infrastructure-costs-ebus 0 ;; assumed to included in ebus purchase price set infrastructure-costs-ebus (initial-infrastructure-costs-ebus) set infrastructure-costs-development 0 ;;100 source needed every year the infrastructure costs for ebuses become cheaper by this amount source? set initial-purchase-price-dbus 220000 ;;these are the initial purchase prices of dbuses set initial-purchase-price-ebus 290000;;these are the initial purchase prices of ebuses set initial-purchase-price-gbus 250000 ;;these are the iniital purchase prices of gbuses set purchase-price-dbus initial-purchase-price-dbus set purchase-price-ebus initial-purchase-price-ebus set purchase-price-gbus initial-purchase-price-gbus set interest-rate 1.25 ;; set interest-rate 2.2 for dutch model set initial-daily-km-range-dbus 200 ;; Unlimited in the model.700km on a full tank set initial-daily-km-range-ebus 200 set initial-daily-km-range-gbus 200 ;;unlimited in the model. 500km on a full tank
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set current-daily-km-range-dbus initial-daily-km-range-dbus set current-daily-km-range-ebus initial-daily-km-range-ebus set current-daily-km-range-gbus initial-daily-km-range-gbus set initial-maximum-battery-capacity-ebus 324 ;;kWh set current-maximum-battery-capacity-ebus (initial-maximum-battery-capacity-ebus) set current-maximum-fuel-level-dbus 280 ;;liters set current-maximum-fuel-level-gbus 200 ;;kg set daily-km-range-development-dbus 0 ;;every year the range increases by this amount of km's source?? set daily-km-range-development-ebus 1 ;;every year the range increases by this rate. set daily-km-range-development-gbus 0 ;;every year the range increases by this amount of km's source?? set maximum-battery-capacity-development (daily-km-range-development-ebus) ;;should increase together with the daily km range development set monthly-km-range-dbus ( current-daily-km-range-dbus * 22 ) ;;in the model there are on average 22 working days a month set monthly-km-range-ebus ( current-daily-km-range-ebus * 22 ) set monthly-km-range-gbus ( current-daily-km-range-gbus * 22 ) set initial-battery-reduced-monthly-km-range-ebus round (monthly-km-range-ebus * current-degradation-rate-new-ebus-batteries ^ (maximum-allowed-age- ebuses) ) set money-factor 2400 set initial-maintenance-costs-per-km-dbus 0.50 set initial-maintenance-costs-per-km-ebus 0.38 set initial-maintenance-costs-per-km-gbus 0.48 set maintenance-costs-per-km-development-ebus 0 set maintenance-costs-per-km-development-gbus 0 set maintenance-costs-per-km-dbus initial-maintenance-costs-per-km-dbus set maintenance-costs-per-km-ebus initial-maintenance-costs-per-km-ebus set maintenance-costs-per-km-gbus initial-maintenance-costs-per-km-gbus set purchase-price-development-ebus 3500 set purchase-price-development-dbus 0 set purchase-price-development-gbus 0
;;Operators get a contract from this list if they win a tender. Each year has a different contract. set contracts-list ["contract0" "contract1" "contract2" "contract3" "contract4" "contract5" "contract6" "contract7" "contract8" "contract9" "contract10" "contract11" "contract12" "contract13" "contract14" "contract15" "contract16" "contract17" "contract18" ]
;;The starting contracts are distribution from this list. set starting-contracts-list ["contract100" "contract101" "contract102" "contract103" "contract104" "contract105" "contract106" "contract107" "contract108" "contract109" "contract110" "contract111" "contract112" "contract113" "contract114" "contract115" "contract116" "contract117" "contract118" "contract119" "contract120" "contract121" "contract122" "contract123" "contract124" "contract125" "contract126" "contract127" "contract128" "contract129" "contract130" "contract131" "contract132" "contract133" "contract134" "contract135" "contract136" "contract137""contract138" "contract139" "contract140" ]
;;The contracts table is created here. Every contract is paired with the monthly km demand of that contract. set contracts-table table:make table:put contracts-table "contract0" 0 table:put contracts-table "contract1" 0
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table:put contracts-table "contract2" 0 table:put contracts-table "contract3" 0 table:put contracts-table "contract4" 0 table:put contracts-table "contract5" 0 table:put contracts-table "contract6" 0 table:put contracts-table "contract7" 0 table:put contracts-table "contract8" 0 table:put contracts-table "contract9" 0 table:put contracts-table "contract10" 0 table:put contracts-table "contract11" 0 table:put contracts-table "contract12" 0 table:put contracts-table "contract13" 0 table:put contracts-table "contract14" 0 table:put contracts-table "contract15" 0 table:put contracts-table "contract16" 0 table:put contracts-table "contract17" 0
;; tender related setup set current-weighting-costs 0.8 set current-weighting-innovation 0.2 set monthly-transport-demand-of-the-city (initial-monthly-transport-demand) update-data-dbuses ;;needs to happen before buses are created update-data-ebuses ;;needs to happen before buses are created ;;update-data-gbuses End to update-data-dbuses set purchase-price-dbus (purchase-price-dbus - purchase-price-development-dbus) set lease-price-dbus (purchase-price-dbus / lease-term + ( (interest-rate / money-factor ) * purchase-price-dbus)) set diesel-price (item year applied-diesel-price-scenario) set monthly-maintenance-costs-dbus ( maintenance-costs-per-km-dbus * monthly-km-range-dbus) set monthly-fuel-costs-dbus ( (fuel-consumption-dbus / 20) * monthly-km-range-dbus * diesel-price) ;;fuel consumption per 20km's, therefore needs division by 20 set TCO-per-month-dbus round ( lease-price-dbus + monthly-fuel-costs-dbus + monthly-maintenance-costs-dbus) end to update-data-ebuses set purchase-price-ebus ( purchase-price-ebus - purchase-price-development-ebus ) set infrastructure-costs-ebus ( infrastructure-costs-ebus - infrastructure-costs-development ) set lease-price-ebus ( (purchase-price-ebus + infrastructure-costs-ebus ) / lease-term + ( (interest-rate / money-factor ) * (purchase-price-ebus + infrastructure- costs-ebus))) set current-daily-km-range-ebus (current-daily-km-range-ebus * daily-km-range-development-ebus) set current-maximum-battery-capacity-ebus (current-maximum-battery-capacity-ebus * maximum-battery-capacity-development) set monthly-km-range-ebus round ( current-daily-km-range-ebus * 22 ) ;; every month is 30,42 days on average set battery-reduced-monthly-km-service round (monthly-km-range-ebus * current-degradation-rate-new-ebus-batteries ^ (maximum-allowed-age-ebuses) ) ;;lease-term / 12 gives the contract duration in years
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set energy-consumption-ebus (energy-consumption-ebus - energy-consumption-development-ebus) ;;energy consumption ebus is per 20km, therefore needs division by 20 set energy-price (item year energy-price-development-medium) set maintenance-costs-per-km-ebus (maintenance-costs-per-km-ebus - maintenance-costs-per-km-development-ebus) set monthly-maintenance-costs-ebus ( maintenance-costs-per-km-ebus * monthly-km-range-ebus) set monthly-energy-costs-ebus ( (energy-consumption-ebus / 20) * monthly-km-range-ebus * energy-price) set TCO-per-month-ebus round ( lease-price-ebus + monthly-energy-costs-ebus + monthly-maintenance-costs-ebus ) Set current-degradation-rate-new-ebus-batteries ( current-degradation-rate-new-ebus-batteries + battery-degradation-rate-development-new-ebuses ) ;;cannot become higher than 0! set current-co2-footprint-per-km-ebus ( (energy-consumption-ebus / 20 ) * grid-grams-co2-per-kwh) end to update-data-gbuses set purchase-price-gbus (purchase-price-gbus - purchase-price-development-gbus) set lease-price-gbus (purchase-price-gbus / lease-term + ( (interest-rate / money-factor ) * purchase-price-gbus)) set gas-price (item year gas-price-development-medium) set monthly-maintenance-costs-gbus ( maintenance-costs-per-km-gbus * monthly-km-range-gbus) set monthly-fuel-costs-gbus ( fuel-consumption-gbus * monthly-km-range-gbus * gas-price) set TCO-per-month-gbus round ( lease-price-gbus + monthly-fuel-costs-gbus + monthly-maintenance-costs-gbus) end to setup-patches ask patches [ set intersection? false set my-row -1 set my-column -1 set pcolor grey ] set roads patches with [(floor((pxcor + max-pxcor - floor(grid-x-inc - 1)) mod grid-x-inc) = 0) or (floor((pycor + max-pycor) mod grid-y-inc) = 0)]
set intersections roads with [(pxcor = -9 and pycor = 9) or (pxcor = 8 and pycor = -7) or (pxcor = -9 and pycor = -7) or (pxcor = 8 and pycor = 9) ] ask roads [ set pcolor white ] end to setup-intersections ask intersections ;;bus stations will be build within a rectangle [ set intersection? true set my-row floor((pycor + max-pycor) / grid-y-inc)
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set my-column floor((pxcor + max-pxcor) / grid-x-inc) ] ask intersections with [pxcor = -9 and pycor = 9] [ set pcolor 104 ] ask intersections with [pxcor = 8 and pycor = 9] [ set pcolor 75 ] ask intersections with [pxcor = -9 and pycor = -7] [ set pcolor yellow] ask intersections with [pxcor = 8 and pycor = -7] [ set pcolor grey ] end
;; Stations are build here and given the name of their company to setup-stations ask intersections [ if pcolor = 104 [ sprout-stations 1 [ set color 104 set shape "house" set station-name "Dan" ] ] if pcolor = 75 [ sprout-stations 1 [ set color 75 set shape "house" set station-name "Egged" ] ] if pcolor = yellow [ sprout-stations 1 [ set color yellow set shape "house" set station-name "Kavim" ] ] if pcolor = grey [ sprout-stations 1 [ set color grey set shape "house" set station-name "Extra-station"] ] ] end to setup-bus-operators ;; create bus operators create-bus-operators 1 [set name "Dan" setxy -12 12 set shape "factory" set size 3 set color 104 set innovation-limit (dan-innovation-limit-start) set starting-market-share (starting-market-share-dan)
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] create-bus-operators 1 [set name "Egged" setxy 11 12 set shape "factory" set size 3 set color 75 set innovation-limit (egged-innovation-limit-start) set starting-market-share (starting-market-share-egged) ] create-bus-operators 1 [set name "Kavim" setxy -12 -9 set shape "factory" set size 3 set color yellow set innovation-limit (kavim-innovation-limit-start) set starting-market-share (starting-market-share-kavim) ] ask bus-operators [ set bidding-moment? false set required-number-of-buses-for-bid 0 set my-contracts-list [] if (show-contracts-of-bus-operators?) [type "\n" type "I am " type name type " my contracts for year " type year type " are :" type my-contracts-list ] set tender-won? true set total-score-of-bid 0 set ebuses-stock count buses with [operator-name = [name] of myself and type? = "ebus"] set dbuses-stock count buses with [operator-name = [name] of myself and type? = "dbus"] set bus-fleet (ebuses-stock + dbuses-stock) ] end to setup-PTA ;; The PTA is created here create-PTAs 1 [ setxy 4 5 set shape "building institution" set size 2 set color 9 set air-quality-preference 0 set technology-preference 1 set ghg-preference 2 set pta-ev-preference 3 set name "PTA" ] end to activate-bus ;;get buses out of their reserved status and gives them an active status set used-before? true set am-i-active? true set reserved? false
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end to apply-ebus-specs ;;sets parameter settings of ebuses face one-of neighbors4 set age 0 set am-i-active? false set battery-degradation-rate ( current-degradation-rate-new-ebus-batteries ) ;; 0,98 every year the battery capacity reduces by 2% set co2-footprint-per-km (current-co2-footprint-per-km-ebus) set currently-charging? false set current-energy-usage 0 set color yellow set daily-km-range (current-daily-km-range-ebus) set energy-consumption-per-km (energy-consumption-ebus / 20) ;;Divide by 20 because a bus travels 20km's per tick set maximum-age (maximum-allowed-age-ebuses) set maximum-battery-level round (current-maximum-battery-capacity-ebus) set battery-level (maximum-battery-level) set monthly-km-range round (daily-km-range * 22) set my-operator's-contract ( item year contracts-list ) set next-station one-of stations-here set personal-contract-duration 0 set pollution-rate 0 set reduced-monthly-km's-service (battery-reduced-monthly-km-service) ;; set reduced-monthly-km's-service round ( monthly-km-range-ebus * (current-degradation-rate-new-ebus-batteries ^ maximum-allowed-age-ebuses)) set residual-service-kms round (reduced-monthly-km's-service * 12 * maximum-age) ;;A 12 because a year takes 12 months. set reserved? true set size 2 set type? "ebus" set used-before? false set yearly-km-range (reduced-monthly-km's-service * 12) set yearly-co2-footprint (yearly-km-range * co2-footprint-per-km ) set yearly-energy-consumption round (yearly-km-range * energy-consumption-per-km) set-shift-time create-contract-with-your-operator end to apply-dbus-specs ;;sets parameter settings of dbuses face one-of neighbors4 set age 0 set color blue set am-i-active? false set currently-charging? false set current-energy-usage 0 set daily-km-range (current-daily-km-range-dbus) set maximum-age (maximum-allowed-age-dbuses) set monthly-km-range round (daily-km-range * 22) set maximum-fuel-level (current-maximum-fuel-level-dbus)
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set fuel-level (maximum-fuel-level) set residual-service-kms round (monthly-km-range * 12 * maximum-age) ;;A 12 because a year takes 12 months. set my-operator's-contract ( item year contracts-list ) set next-station one-of stations-here set personal-contract-duration 0 set reserved? true set used-before? false set size 2 set type? "dbus" set yearly-km-range (monthly-km-range * 12) set yearly-co2-emissions (yearly-km-range * co2-emissions-per-km-dbus ) set yearly-fuel-consumption round (yearly-km-range * (fuel-consumption-dbus / 20)) ;;divided by 20 because fuel consumption dbus is per 20 km's set-shift-time create-contract-with-your-operator end to apply-gbus-specs ;;sets parameter settings of gbuses face one-of neighbors4 set age 0 set color green set currently-charging? false set current-energy-usage 0 set daily-km-range (current-daily-km-range-gbus) set maximum-age (allowed-age-gbuses) set monthly-km-range round (daily-km-range * 22 ) set maximum-fuel-level (current-maximum-fuel-level-gbus) set fuel-level (maximum-fuel-level) set residual-service-kms round (monthly-km-range * 12 * maximum-age) ;;A 12 because a year takes 12 months. set my-operator's-contract ( item year contracts-list ) set next-station one-of stations-here set personal-contract-duration 0 set pollution-rate 0 set size 2 set type? "gbus" set reserved? true set used-before? false set yearly-km-range (monthly-km-range * 12) set yearly-co2-emissions (yearly-km-range * co2-emissions-per-km-gbus) set yearly-fuel-consumption (yearly-km-range * (fuel-consumption-gbus / 20)) set-shift-time create-contract-with-your-operator end to create-contract-with-your-operator ;;buses create a contract with their operator let my-operators bus-operators with [name = [operator-name] of myself] create-contract-with one-of my-operators
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[set contract-name (item year contracts-list) set this-contract's-duration (0) ;;lease-term / 12 is the contract duration in years set monthly-km-service-of-contract ([personal-transport-demand-for-this-contract] of one-of my-operators) ;;personal transport demand chosen instead of previously required km's service of tender. 13-11-2016 21.26 set total-km-service-of-contract (total-required-km's-for-tender) ] end to setup-buses
ask bus-operators [ let my-ebuses buses with [type? = "ebus"] with [operator-name = [name] of myself] let my-dbuses buses with [type? = "dbus"] with [operator-name = [name] of myself]
let total-monthly-range (initial-monthly-transport-demand * starting-market-share) ;;bus operators determine their total monthly demand at the start of the model, based on their market share let total-demand-of-contract (total-monthly-range * lease-term) ;;bus operators determine demand for entire contract
set starting-contracts-needed (round (total-monthly-range / starting-contract-size)) ;;determines howmany starting contracts are needed. if total-monthly-range <= starting-contract-size [ set starting-contracts-needed 1 ]
determine-howmany-buses-to-order-at-start
let total-monthly-range-per-contract ( buses-to-order-at-start-per-contract * monthly-km-range-dbus)
repeat starting-contracts-needed [ ;;this needs to be done for every contract the operator needs. ask stations with [station-name = [name] of myself ] [ hatch-buses [buses-to-order-at-start-per-contract] of myself [ set operator-name [station-name] of myself apply-dbus-specs set my-operator's-contract item 0 starting-contracts-list set monthly-km-demand-of-contract total-monthly-range-per-contract activate-bus ] ] set my-contracts-list lput item 0 starting-contracts-list my-contracts-list ;;operators add the first contract from the starting contract list to their own contract list set starting-contracts-list but-first starting-contracts-list ]
ask my-contracts [ set contract-name [my-operator's-contract] of end2 set monthly-km-service-of-contract [monthly-km-demand-of-contract] of end2 ;;the starting contracts are given the monthly demand set total-km-service-of-contract [monthly-km-demand-of-contract] of end2 * lease-term ;;the same but then for the entire contract ]
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if show-how-operators-update-bus-fleets? [ type name type " starts the model with a bus fleet that has a total monthly range of " type total-monthly-range type " km's per month. " type "\n" type "This will be divided between " type starting-contracts-needed type " contracts at the start. " type "\n" type "Those contracts will have a monthly demand of " type total-monthly-range-per-contract type " km per month each, and will have " type buses-to-order-at-start-per-contract type " buses each. " type "\n" type "Its total bus fleet will consist out of " type buses-to-order-at-start-per-contract * starting-contracts-needed type " buses. " type "\n" type "The starting contracts will be " type my-contracts-list type "\n" ;; type "Its starting monthly km demand will be " type total-monthly-range type " km per month. " type "\n" type "In order to fulfill this demand for the entire starting contract duration, it needs to order " type buses-to-order-at-start type " additional buses " type "\n" type "---" type "\n" ] ] ask contracts [ ;; Because not all buses and contracts have the same age in a bus fleet, therefore bus ages and contract ages are based on emperical data from Hermes's bus fleet. let r round random-exponential 4 ;; The most fitting distrubtion was an exponential distribution, capped at 10 years. ifelse r < 11 [set this-contract's-duration r ask end2 [set age r set personal-contract-duration r]] [set this-contract's-duration 10 ask end2 [set age 10 set personal-contract-duration 10 ]] ] end to determine-howmany-buses-to-order-at-start ;;determines howmany buses the operators need to buy at the start of the model. let total-monthly-range (initial-monthly-transport-demand * starting-market-share) ;;determines howmany buses are needed for each contract set buses-to-order-at-start-per-contract ceiling ( ;;ceiling is used, meaning that operators have a slight overcapacity per contract (total-monthly-range / starting-contracts-needed) / monthly-km-range-dbus) End to set-shift-time ;;Determines shift times for buses. All buses have shift times between 5 and 23 let r random 9 if r = 0 [set starting-shift-time 5 ] if r = 1 [set starting-shift-time 6 ] if r = 2 [set starting-shift-time 7 ] if r = 3 [set starting-shift-time 8 ] if r = 4 [set starting-shift-time 9 ] if r = 5 [set starting-shift-time 10] if r = 6 [set starting-shift-time 11] if r = 7 [set starting-shift-time 12] if r = 8 [set starting-shift-time 13] if ( type? = "dbus") or ( type? = "gbus") [set ending-shift-time (starting-shift-time + 10)] ;;buses run 200 km/day, 20km/hour so 10 hours if type? = "ebus" [set ending-shift-time (starting-shift-time + 10)] end
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to setup-bid-optimizer ;; The R extension is used to run the lpSolve optimization algorithm from R. With the lpSolve algorithm , r:eval "library(lpSolve)" ;; bus operators make bids by maximizing an objective function. Netlogo must import the lpSolve library from R. r:clear ;; All variables are cleared from R r:clearLocal end to go update-time update-bus-data update-operators check-if-time-for-tender update-monthly-range-bus-operators continue-bus-service tick ;; if year > 16 [user-message "The simulation has ended, press setup then go for another run " STOP] End to update-time change-clock update-time-till-next-tender end to update-bus-data update-specifications-of-active-ebuses ;;buses with their age above the maximum allowed age are removed if (hour = 5) AND (day = 0 AND year > 0) [ ;;only once a year are the buses specifications updated update-data-dbuses ;;all the bus specifications of new buses are updated update-data-ebuses update-data-gbuses update-specifications-of-active-buses ;;all the bus specifications of the already active buses are updated remove-old-buses ] if (hour = 6) AND (day = 0 AND year > 0) [ ;;to remove big drops in monitoring CO2 output update-yearly-service-report ] end to change-clock set hour (hour + 1) if hour = ticks-in-a-day [ set hour 0 set day (day + 1) ] change-years end
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to change-years ;;5 days are simulated in each year. 5 days represent 5 working days in a week. if day = days-in-a-year ;;after days, time advances a year and the days are reset to day 0 [ set year (year + 1) set day 0 ] End to update-time-till-next-tender ;;Here we count down till the next tenders. Tenders should happen at 5:00AM in the morning when all buses are idle. set time-till-next-small-tender (time-till-next-small-tender - 1) ifelse time-till-next-small-tender = 0 [set time-for-small-tender true if show-tender-procedure? [type "\n" type "It's now " type hour type ":00 AM of day " type day type " in year " type year type " It is time for a small tender " type "\n" ]] [set time-for-small-tender false] end to update-operators ask bus-operators [ if (hour = 5) AND (day = 0 ) [ set ebuses-stock count buses with [operator-name = [name] of myself and type? = "ebus"] set dbuses-stock count buses with [operator-name = [name] of myself and type? = "dbus"] set bus-fleet (ebuses-stock + dbuses-stock) update-contracts] ]
if show-monthly-capacity-of-new-buses = true [ ifelse count buses < 100 [if any? buses with [age = 0] [ask buses with [age = 0] [ type "I am an " type type? type " with a monthly range of " type round monthly-km-range type "\n"]]] [user-message "too many buses (100+) in the model to run monthly km range debugging procedure"] ] end to update-contracts ask my-contracts [set this-contract's-duration (this-contract's-duration + 1 )] ask my-contracts [if this-contract's-duration = contract-duration [ ask end1 [set my-contracts-list remove [contract-name] of myself my-contracts-list] DIE] ;;When a contract expires it ceases to exists, and the contract is removed from the operator's contract list. ifelse show-contract-durations = true [set label round this-contract's-duration] [set label "" ] ] End
To update-specifications-of-active-buses ;;only buses that have been used before get older. This to simulate that when operators win a tender, and decide to
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;;reuse their old buses, they can replace the old buses with fresh new buses. Otherwise old buses would ;;be replaced new buses that have been waiting in a depot for a few years and already started aging a bit. Ask buses [if used-before? = true [ set age (age + 1) set residual-service-kms round ( (maximum-allowed-age-dbuses - age) * 12 * monthly-km-range ) ] ;;calculates the total km’s left set personal-contract-duration (personal-contract-duration + 1) if personal-contract-duration = 11 [set personal-contract-duration contract-duration] ;;contract-duration is the maximum contract duration if personal-contract-duration = contract-duration [set am-i-active? false set reserved? false ] ] end to update-specifications-of-active-ebuses ;; Ebuses are special and are subject to yearly range reduction due to battery degradation. For that ;; reason it becomes tricky when projecting the total km’s service an ebus has left because its range ;; decreases every year. Therefore an ebus’ total residual range is calculated using the range it has in its last ;; service year which is called its reduced monthly km’s service. ask buses[ If type? = "ebus" AND ((hour = 5) AND (day = 0 AND year > 0))[ ;;only once a year at 5:am wil buses update their specifications let old-range round monthly-km-range let old-battery-capacity round maximum-battery-level set maximum-battery-level round (maximum-battery-level * battery-degradation-rate) ;;the battery degradation rate is indicates how much the battery decreases in capacity per year set monthly-km-range round (monthly-km-range * battery-degradation-rate) ;;the decrease is given in percentages set daily-km-range round (monthly-km-range / 22) ;;a month takes 22 days let new-range round monthly-km-range let new-battery-capacity round maximum-battery-level set ending-shift-time (starting-shift-time + (precision (daily-km-range / 20) 1 )) ;;as the daily km range changes, so does the hours a bus can run per day, and the ending shift time set yearly-km-range (reduced-monthly-km's-service * 12) ;;20 represents the driving speeds of 20 kph. set residual-service-kms round ( (maximum-allowed-age-ebuses - age) * 12 * reduced-monthly-km's-service) ;;per year in percenatages set yearly-co2-footprint round ( current-co2-footprint-per-km-ebus * yearly-km-range ) set yearly-energy-consumption round (yearly-km-range * (energy-consumption-per-km / 20)) if show-monthly-range-update-buses?[ ifelse count buses < 100 [ type "I am an " type type? type ". I have updated my range. My old range was: " type old-range type " .My new range is: " type new-range type "\n" type "I have also updated my max battery capacity. My old max battery capacity was: " type old-battery-capacity type " .My new max battery capacity is: " type new-battery-capacity type "\n"] [user-message "too many buses (100+) in the model to run monthly km update debugging procedure"] ] ] ] end to update-yearly-service-report ;;collects the yearly energy consumption and co2 output of the running buses set total-energy-consumption-ebuses (sum [yearly-energy-consumption] of buses with [type? = "ebus"] with [am-i-active? = true])
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set total-fuel-consumption-dbuses (sum [yearly-fuel-consumption] of buses with [type? = "dbus"] with [am-i-active? = true]) set total-fuel-consumption-gbuses (sum [yearly-fuel-consumption] of buses with [type? = "gbus"] with [am-i-active? = true])
set total-co2-footprint-ebuses (sum [yearly-co2-footprint] of buses with [type? = "ebus"] with [am-i-active? = true]) set total-co2-emissions-dbuses (sum [yearly-co2-emissions] of buses with [type? = "dbus"] with [am-i-active? = true]) set total-co2-emissions-gbuses (sum [yearly-co2-emissions] of buses with [type? = "gbus"] with [am-i-active? = true])
set total-co2-output (total-co2-footprint-ebuses + total-co2-emissions-dbuses + total-co2-emissions-gbuses) end to remove-old-buses ;; buses check their age to see if they can still drive or need to stop ask buses [ if age > maximum-age [ DIE ]] end to check-if-time-for-tender if (time-for-small-tender = true) or (time-for-big-tender = true) [ tender-procedure ] end to tender-procedure set last-year's-tender-requirement (monthly-km's-service-required-for-tender) ;;last years tender requirement is recalled. set monthly-transport-demand-of-the-city (monthly-transport-demand-of-the-city * transport-demand-growth-rate) ;; at the start of the tender procedure a new monthly km demand is needed let ebuses buses with [type? = "ebus"] with [am-i-active? = true] let dbuses buses with [type? = "dbus"] with [am-i-active? = true] set monthly-km's-service-required-for-tender (monthly-transport-demand-of-the-city - sum [monthly-km-range] of dbuses - sum [reduced-monthly-km's- service] of ebuses ) if monthly-km's-service-required-for-tender < 0 [ set monthly-km's-service-required-for-tender (last-year's-tender-requirement) ] ;;to make sure the monthly km demand of the tender cannot go negative. set total-required-km's-for-tender round (monthly-km's-service-required-for-tender * lease-term ) set this-year's-contract-name item year contracts-list ;; the contract is added to the contract table ;; this results in a new pair in the contract table [ “contract x” “x (km’s required per month)” ] depending on which year it is now Table:put contracts-table item year contracts-list monthly-km's-service-required-for-tender if show-tender-procedure? [type "----" type "\n" if time-for-small-tender = true [ type "The tender procedure will determine the winner of " type item year contracts-list type "\n" type "The required monthly km demand for this year's (" type year type ") small tender is: " type monthly-km's-service-required-for-tender type " km's per month" type "\n" ] if time-for-big-tender = true [ type "The tender procedure will determine the winner of " type item year contracts-list type "\n" type "The required monthly km demand for this year's (" type year type ") BIG tender is: " type monthly-km's-service-required-for-tender type " km's per month" type "\n" ] type "The total transport demand required for the entire contract duration is " type total-required-km's-for-tender type " km's" type "\n" ]
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set dbus-penalty round ( -1 * (current-weighting-costs * TCO-per-month-dbus )) set ebus-penalty round ( -1 * (current-weighting-costs * TCO-per-month-ebus - current-weighting-innovation * current-ebus-bonus)) if show-tender-procedure? [ type "----" type "\n" type "For this year's tender each dbus gets a penalty of : " type dbus-penalty type " points. " type "\n" type "Each ebus gets a penalty of : " type ebus-penalty type " points. " type "\n" type "----" type "\n" ] set total-number-of-dbus-km's-for-bid ( monthly-km-range-dbus * lease-term ) set total-number-of-ebus-km's-for-bid ( battery-reduced-monthly-km-service * lease-term ) update-PTA-preferences bid-for-tender determine-tender-outcome reset-tender-times end
to update-PTA-preferences ask bus-operators with [name = "Dan" ] [set innovation-limit (innovation-limit + development-speed-dan)] ask bus-operators with [name = "Egged" ][set innovation-limit (innovation-limit + development-speed-egged)] ask bus-operators with [name = "Kavim" ][set innovation-limit (innovation-limit + development-speed-kavim)] end to bid-for-tender ;; Operators are requested to place their bids ask bus-operators [place-your-bids ] end to place-your-bids ;; Bus operators place their bids. let my-residual-buses buses with [operator-name = [name] of myself] ;; First they count the number of buses they are able to reuse. with [am-i-active? = false ] with [reserved? = false ]
let my-fully-reusable-buses my-residual-buses with [(maximum-age - age) >= contract-duration] ;; Residual buses are buses that are able to be reused for a full contract duration
let my-fully-reusable-ebuses my-fully-reusable-buses with [type? = "ebus"] let my-fully-reusable-dbuses my-fully-reusable-buses with [type? = "dbus"]
set personal-transport-demand-for-this-contract ((monthly-km's-service-required-for-tender) - (sum [monthly-km-range] of my-fully-reusable-dbuses) - (sum [reduced-monthly-km's-service] of my-fully-reusable-ebuses))
;; the personal ebus limit is set as a fraction of the total ebuses that would be required of only ebuses would fulfill the transport demand. set personal-ebus-limit (innovation-limit * ( monthly-km's-service-required-for-tender / battery-reduced-monthly-km-service ) )
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determine-optimal-bid if show-tender-procedure? = true [ ;; if debugging is true operators will describe their bids type "Bus operator " type name type " is participating in the tender. " type "It has a fleet of " type count my-residual-buses type " buses that can be reused, " type "\n" type "and " type count my-fully-reusable-ebuses + count my-fully-reusable-dbuses type " buses that can be used for a full second term. " type name type "'s bid scored of total of " type total-score-of-bid type " penalty points. "type "\n" type "Its personal transport demand, taking into account the buses it can reuse, is " type round personal-transport-demand-for-this-contract type " km's per month. " type "\n" type "If " type name type " wins the tender, it will buy " type dbuses-to-be-purchased type " dbuses and " type ebuses-to-be-purchased type " ebuses. " type "\n" type "---" type "\n" ] end to reset-tender-times if time-till-next-small-tender = 0 [ set time-till-next-small-tender (1 * ticks-in-a-year) ;;The next small tender will be 240 ticks later. 240 Ticks is 10 days. 5 days is one year, 10 days is 2 years. set time-for-small-tender false ] end to determine-optimal-bid (r:put "objective.in" list dbus-penalty ebus-penalty) (r:putnamedlist "constraints" "v1" monthly-km-range-dbus "v2" battery-reduced-monthly-km-service "v3" 0 "v4" 1) (r:eval "f.con <- matrix(constraints, nrow=2,ncol=2,byrow=TRUE)") (r:putlist "f.dir" "=" "<=") (r:put "f.rhs" list personal-transport-demand-for-this-contract personal-ebus-limit ) r:eval" bidresult <- lp(direction = 'max', objective.in, f.con, f.dir, f.rhs)" set total-bid r:get "bidresult" set total-score-of-bid round item 10 total-bid set ebuses-to-be-purchased ceiling item 1 item 11 total-bid ;;switched the outcome of the tender procedure around! set dbuses-to-be-purchased ceiling item 0 item 11 total-bid end to determine-tender-outcome ask PTAs [ let winner max-one-of bus-operators [total-score-of-bid] if any? bus-operators [ ask winner [set tender-won? true set time-left-on-tender 1152] ask winner [buy-required-buses] if show-tender-procedure? [ type "The " type name type " has evaluated the bids, and selected the winning bid with the lowest penalty points for this year's(" type year type ") tender." type "\n" type "The winning operator is " type [name] of winner type " with a penalty score of " type [total-score-of-bid ] of winner type " points. " type "\n" type [name] of winner type " now owns " type item year contracts-list type " and will buy " type [dbuses-to-be-purchased] of winner type " dbuses and " type [ebuses-to-be-purchased] of winner type " ebuses. " type "\n" type [name] of winner type " now owns the following contracts " type [my-contracts-list] of winner type "\n" type "---" type "\n" ] ]
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] end to buy-required-buses ;; companies buy the amount of buses that they require after winning the tender ;; the winning contract is added to the list of contracts the winner has which is his my-contracts list set my-contracts-list lput item year contracts-list my-contracts-list let my-station stations with [station-name = [name] of myself] let my-residual-buses buses with [operator-name = [name] of myself] with [ am-i-active? = false ] with [reserved? = false ] let my-residual-ebuses my-residual-buses with [type? = "ebus"] let my-residual-dbuses my-residual-buses with [type? = "dbus"] ask my-residual-buses [set my-operator's-contract [name] of myself] ;; the old buses to be reused are given the new contract name ask my-residual-buses [create-contract-with-your-operator] ;; a new contract is created between the old buses and the winning operator set ebuses-to-be-purchased (ebuses-to-be-purchased ) set dbuses-to-be-purchased (dbuses-to-be-purchased ) ask my-station [ hatch-buses [ebuses-to-be-purchased] of myself [ set operator-name [station-name] of myself apply-ebus-specs create-contract-with-your-operator] ] ask my-station [hatch-buses [dbuses-to-be-purchased] of myself [ set operator-name [station-name] of myself apply-dbus-specs create-contract-with-your-operator ] ] end
To update-monthly-range-bus-operators if (hour = 5) AND (day = 0 ) [ ;;only once a year are the bus fleets updated, and not in year 0. ask bus-operators [ if (show-how-operators-update-bus-fleets?) [ type "\n" type "Bus operator " type name type "'s contracts for year " type year type " are :" type my-contracts-list type "\n" ] foreach my-contracts-list [ ;;update-total-range-of-contract. calculations are made so that the monthly range of the contract is updated set my-buses buses with [ operator-name = [name] of myself ] if show-how-operators-update-bus-fleets? [ type name type " has a total bus fleet of " type count my-buses type " buses in total. " type "\n"] set buses-in-this-contract my-buses with [my-operator's-contract = ? ] ;;the ? refers to the contract that is currenlty going to the procedure. if show-how-operators-update-bus-fleets? [ type "For " type ? type ", " type name type " has " type count buses-in-this-contract type " buses." type "\n"] set active-contract-buses buses-in-this-contract with [am-i-active? = true] if show-how-operators-update-bus-fleets? [ type "From those buses in " type ? type ", " type count active-contract-buses type " buses are active buses. " type "\n"]
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set reserve-contract-buses buses-in-this-contract with [reserved? = true] if show-how-operators-update-bus-fleets? [ type count reserve-contract-buses type " Of those buses that belong to " type ? type " are stored in reserve. " type "\n"] set my-total-monthly-range (sum [monthly-km-range] of active-contract-buses with [type? = "dbus"] + sum [reduced-monthly-km's-service] of active-contract-buses with [type? = "ebus"]) if show-how-operators-update-bus-fleets? [ type "The total monthly km range of the active buses in " type ? type " is " type round my-total-monthly-range type "km's per month" type "\n"] ;;this task can only be done if an operator has atleast one contract if any? my-contracts with [ contract-name = ? ] [set this-contract's-range [monthly-km-service-of-contract] of one-of my-contracts with [ contract-name = ? ]] if show-how-operators-update-bus-fleets? [ type "The transport demand that " type name type " has to fulfill with these buses for " type ? type " is, "type round this-contract's-range type "km's per month." type "\n" type "--" type "\n"] while [this-contract's-range - my-total-monthly-range > 0 AND count reserve-contract-buses > 0] [ ifelse count reserve-contract-buses > 100 ;;Reserve buses are activated. The more reserve buses there are, the faster the rate of activation. [ask n-of 100 reserve-contract-buses [activate-bus]] [ ifelse count reserve-contract-buses > 10 [ ask n-of 10 reserve-contract-buses [activate-bus]] [ ask one-of reserve-contract-buses [activate-bus]] ]
set my-buses buses with [ operator-name = [name] of myself ] ;;the monthly range of the contracts needs to be recalculated after the buses have been activated if show-how-operators-update-bus-fleets? [ type name type " needs to update its bus fleet in order to fulfill the transport demand. " type "\n" type name type " has a total bus fleet of " type count my-buses type " buses in total. " type "\n"]
set buses-in-this-contract my-buses with [my-operator's-contract = ? ] ;;the ? refers to the contract that is currenlty going to the procedure. if show-how-operators-update-bus-fleets? [ type "For " type ? type ", " type name type " has " type count buses-in-this-contract type " buses." type "\n" ]
set active-contract-buses buses-in-this-contract with [am-i-active? = true] if show-how-operators-update-bus-fleets? [ type "After updating its bus fleet for " type ? type ", " type name type " now has " type count active-contract-buses type " active buses in this contract. " type "\n"]
set reserve-contract-buses buses-in-this-contract with [reserved? = true] if show-how-operators-update-bus-fleets? [ type count reserve-contract-buses type " Buses in " type ? type " are now left in reserve. " type "\n"]
;;set my-total-monthly-range sum [monthly-km-range] of active-contract-buses set my-total-monthly-range (sum [monthly-km-range] of active-contract-buses with [type? = "dbus"] + sum [reduced-monthly-km's-service] of active-contract-buses with [type? = "ebus"])
if show-how-operators-update-bus-fleets? [ type "The total monthly km range of the active buses in " type ? type " is " type round my-total-monthly-range type "km's per month" type "\n"]
;;this task can only be done if an operator has atleast one contract if any? my-contracts with [ contract-name = ? ] [set this-contract's-range [monthly-km-service-of-contract] of one-of my-contracts with [ contract-name = ? ]]
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if show-how-operators-update-bus-fleets? [ type "The transport demand that " type name type " now has left to fulfill with these buses for " type ? type " is, "type round this-contract's-range type "km's per month." type "\n" type "---" type "\n" ] ] if show-how-operators-update-bus-fleets? [ if my-total-monthly-range - this-contract's-range >= 0 [ type name type " is well capable of fulfilling the transport demand for " type ? type ". " type "\n" type "It has " type round (my-total-monthly-range - this-contract's-range) type " km's overcapacity per month for this contract, and " type count reserve-contract-buses type " buses left in reserve. " type "\n" type "---" type "\n" ] if this-contract's-range - my-total-monthly-range > 0 AND count reserve-contract-buses <= 0 [ type name type " is NOT able to fulfill the transport demand for " type ? type ". " type "\n" type name type " still has " type round (this-contract's-range - my-total-monthly-range) type " km's per month left to supply, but has " type count reserve-contract-buses type " buses left in reserve. " type "\n" type "---" type "\n" ] ] ] if (show-contracts-of-bus-operators?) [ask bus-operators [ type "\n" type "Bus operator " type name type "'s contracts for year " type year type " are : " type my-contracts-list type "\n" type "---" type "\n"] ] ] ] end to continue-bus-service ;; Buses go out on their service. ask buses with [am-i-active? = true ] ;;only active buses go out on service [if any? my-links [am-I-still-on-shift] ] end to am-I-still-on-shift ;; Buses check their shift time and decide of they either keep driving or go back to the station. ifelse hour >= starting-shift-time AND hour <= ending-shift-time ;; If buses see that the time falls within their shift time, they keep driving. Otherwise they [ keep-driving] ;; go back to the station. [ go-back-to-station] end to keep-driving ;; When buses drive they reduce their fuel level. Diesel buses lose 1.6 liters per tick and electric buses 5MW per tick. if type? = "dbus" ;; to keep track of the total amount of km traveled by dbuses and the total amount pollution [ set dbuses-total-distance-traveled ( dbuses-total-distance-traveled + 20 ) set fuel-level round (fuel-level - fuel-consumption-dbus) if fuel-level < 0 [ set fuel-level 0 ] ] ;; fuel/battery levels cannot go negative
if type? = "ebus" [set battery-level round (battery-level - energy-consumption-ebus) if battery-level < 0 [ set battery-level 0 ] ] if show-animations? [fd 2] ;;buses only move of the animations are switched on
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ifelse show-bus-ages? [set label age if age > 8 [set label-color red]] [set label "" ] End to go-back-to-station let my-station one-of stations with [station-name = [operator-name] of myself] ifelse distance my-station = 0 ;;if buses are not on shift they drive back to the station, stop at the station and then refuel [ refuel-to-max] ;;for simplicity it is assumed that buses have enough fuel/battery beforehand to return to the station [ move-to my-station] end to refuel-to-max ;; Buses are refueled to max. A distinction is made between the refueling of ebuses and dbuses if type? = "dbus" [ refuel-dbus ] ;; Ebuses can charge whenever needed when fulltime charging is allowed. However, when fulltime charging if type? = "ebus" [ ;; is not allowed, charging is only allowed during off peak times ifelse full-time-charging-allowed [ refuel-ebus ] [ if hour >= starting-off-peak-time or hour <= ending-off-peak-time [refuel-ebus]] ] end to refuel-dbus ;; Diesel buses refuel procedure if fuel-level < maximum-fuel-level [ ;; Diesel buses are refueled to max capacity in an instance set liters-refueled round (liters-refueled + (220 - fuel-level)) set fuel-level (maximum-fuel-level)] if fuel-level > maximum-fuel-level [ set fuel-level (maximum-fuel-level)] end to refuel-ebus ifelse battery-level < maximum-battery-level [ set currently-charging? true set current-energy-usage 60 set electricity-used (electricity-used + charging-speed) set battery-level (battery-level + charging-speed) set electricity-used-this-year (electricity-used-this-year + charging-speed)] [set currently-charging? false set current-energy-usage 0] ;; if battery-level > maximum-battery-level [ set battery-level (maximum-battery-level) ] end
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MODEL VERIFICATION AND VALIDATION
VERIFICATION OF TCO CALCULATIONS Using an excel model I did a pre calculation of the lease prices and TCO values that our model needs to generate. Using monitors in Netlogo I compared the values from the model with the values from the excel file as a validation method. The following errors were detected and corrected.
Calculation TCO per month for dbuses. Error found. Validated using excel model. Fixed, reassessed, confirmed
Error in calculation of fuel costs per month of dbuses. corrected with excel model. Fixed, reassessed, confirmed
Error in calculation of lease price per month for dbuses corrected. Fixed, reassessed, confirmed
Error in calculation of lease price per month for ebuses corrected with excel model. Fixed, reassessed, confirmed
Error in calculation of maintenance costs per month for ebuses corrected with excel model. Fixed, reassessed, confirmed
Error in calculation of TCO per month for ebuses corrected with excel model. Fixed, reassessed, confirmed
Error in calculation of the monthly km’s for gbuses corrected with the excel model . Fixed, reassessed, confirmed
Error in calculation of the TCO per month for gbuses corrected with the excel model. Fixed, reassessed, confirmed
RECORDING AND TRACKING AGENT BEHAVIOUR
Figure 45; Debugging code example
Figure 46 the ouput generated in the command center caused by the debugging code. This example shows that one of the bus operators calculates its monthly capacity and check if it has enough buses to meet the transport demand.
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Figure 46; Example ouput from debugging procedure
SINGLE AGENT TESTING
There were three single agent tests in the model as seen in the figure below. The tests can be activated from the model using the test buttons.
Figure 47; single agent test example
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SINGLE PTA TEST
THEORETICAL PREDICTION AND SANITY CHECK o Even with no bus operators in the model, the PTA will request tenders every year. Every eight years there will be big tender. Error found. The model crashed after a procedure requested an action from a bus-operators. Added an extra condition to check if any bus operators exist in the model before running the procedure. Fixed, reassessed, confirmed. o The km demand of the tenders is a percentage of the total transport capacity of the current bus fleet. If there are no bus operators and no buses, all the tenders will have a km demand of 0. Confirmed. o When the PTA starts a tender procedure, it requests operators to place bids. Without bus operators in the model, no bids will be handed in. Confirmed.
BREAKING THE AGENT TEST o The PTA does not own any numerical values, hence a breaking the agent test is not applicable in case of a single PTA.
SINGLE BUS OPERATOR TEST
THEORETICAL PREDICTION AND SANITY CHECK o Operators only offer bids if the PTA starts a tender procedure. Confirmed. o If the bus operator is given a contract at the start of the model, eventually the operator will run out of contracts because no tenders are taking place. Error found. The contract never ran out. This is true because whenever a contract is made a link is supposed to connect an operator with a bus. Whenever the contract runs out the link is deleted and the contract removed from the operators’ contract list. Without buses however this does not work. Fixed, reassessed, confirmed.
BREAKING THE AGENT TEST o Operators have lists of contracts. In the single agent test the bus operators gets contract0, in which the 0 indicates the year the contract was given. If the value contract0 is given the value -1, the model could possibly crash. But testing this showed that the model did not crash with the -1 value instead of the contract name contract0.
SINGLE BUS TEST
THEORETICAL PREDICTION AND SANITY CHECK o If buses were given a shift time between 08:00AM and 17:00PM, buses will start driving from station to station during their shift time and return to the depot after their shift time. Error found. The model crashed when a bus tried to create a contract with a bus operator, but no operators were in the model. The single bus test was adjusted so that buses do not create contracts with operators. However not that the buses did not have a contract, they did not start moving. Fixed, reassessed, confirmed. o Buses in the model given a maximum age of 12 years will die after they have reached year 12. Error found. Even after year 12 the buses did not die. In fact, buses were not updating their age at all. There was an error in the age counter, the error was corrected. Fixed, reassessed, confirmed.
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o If a single ebus in the model starts with a daily km range of 200 km, it should have a monthly km range of 200 * 30,42 = 6084 km’s per month. Confirmed. o If an ebus starts with a range of 6084 km’s per month, and the battery degradation rate is set at 0.99 per year, and the bus runs for 12 years, the reduced service km’s should be 4774km’s per month after 12 years. Confirmed. o If the same ebus from the previous test was used and runs for 12 years, at the start of the model it should have a total residual km’s left of 4774 * 12 * 12 = 687456. Confirmed. Error found. The residual service km’s started at 0. Fixed, reassessed, confirmed. o If an ebus starts at a total residual km’s left of 687456, and has a maximum age of 12 years, then after 12 years it should have a total residual km’s left of 0. Confirmed. o If a single dbus in the model starts with a daily km range of 250km, it should have a monthly km range of 250 * 30,42 = 7605 km’s per month. Confirmed. o If a single dbus in the model starts with a daily km range of 250km, it should have a monthly km range of 250 * 30,42 = 7605 km’s per month. If the bus has a maximum age of 12 years, it should have a total residual km’s of be 7605 * 12 *12 = 1095120 km’s in year 0. In year 12 it should be 0. Confirmed. o If a bus in the model starts with a personal contract duration of 10 in year 0, then in year 10 it should have a personal contract duration of 0. Confirmed. o If a bus has a contract, and is active, it cannot have the reserved status. Confirmed. o If an active bus has a contract and runs out of contract, it should become inactive. Confirmed. o If a bus has the status reserved, and has a contract, it should lose its reserved status when its contract runs out. Confirmed. o At the start of the model, the batteries of all the ebuses should be filled. Error found. The battery levels of all the ebuses was 0. Fixed, reassessed, confirmed. o At the start of the model, all ebuses should have a maximum battery level equal to the maximum battery capacity of current battery technology. Error found. The maximum battery capacity of all the buses was 0. Fixed, reassessed, confirmed. o At the start of the model, all the dbuses should start with full tanks. Error found. The fuel levels of all the dbuses started at 0. Fixed, reassessed, confirmed. o At the start of the model, all the buses should have a maximum fuel capacity equal to the fuel capacity of current fuel technology. Error found. All the dbuses had a maximum fuel capacity of 0. Fixed, reassessed, confirmed.
BREAKING THE AGENT TEST o In the single bus test, bus ages were set to negative. However, even with negative ages the model was able to run. Because in the model bus ages are always set to 0 when buses are created and can only improve, negative ages can never occur. Set bus ages at a million, caused buses to instantly die because they were way past their maximum allowed age. o Setting the fuel and battery levels negative did not matter in the model because buses didn’t run. Similarly, setting the maximum fuel level at a million litres, or the maximum battery capacity at a million kWh did not have any impact.
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THEORETICAL PREDICTION TEST o If a dbus has a maximum age of 10 and an ebus a maximum age of 12, the dbus should be removed from the model after 10 years of service and the ebus after 12 years of service. Error found. All buses kept on getting removed at the same time even with different maximum ages between dbuses and ebuses. Fixed, reassessed, confirmed o Buses should have a monthly km range they can drive. Error found. When monitoring this value, many buses seemed to have a monthly km range of 0. Fixed, reassessed, confirmed o Every year a tendering procedure starts where the PTA requests operators to place their bids. As a results every year the bus operator should place a bid. Confirmed. o Buses can only keep driving if they are connected with their bus operator through a contract. If no tenders were to take place, all the contracts would run out and buses should start moving after the contracts run out. Confirmed. o If there is only one bus operator in the model, then it should win all the tenders it participates in. Confirmed. o When the ebus in the model starts driving it should lose 26 kWh of energy every tick. When its battery is empty it should go back to a station and recharge. Confirmed. o Every year the ebus in the model should decrease its maximum battery capacity due to battery degradation. At the same time, its ending shift should end earlier every year. Confirmed. o When a dbus in the model starts driving it should lose 8 litres of fuel on every tick. When the ending shift time is reached the bus should be back to a station and refuel. Confirmed. o At the end of their shift, ebuses should go back to their depot and start recharging. They should recharge at a rate of 60kWh per tick and reach their full battery capacity within 6 ticks. Confirmed. o As ebuses age, their battery capacity decreases every year. As a result, their shift times should become smaller. Confirmed. o If an ebus starts with a daily km range of 260 km’s, and has a battery degradation rate of 0.98, then after a year the ebus should have a daily km range left of 255 km’s. Error found. The daily km range left was 250km’s. The reason being that battery degradation was applied in year 0 as well, while battery degradation should start from year 1. Fixed, reassessed, confirmed. o If an ebus has a reduced monthly km range of 4489km/month, it should have a yearly km range of 53,868 km. Confirmed. o If an ebus has an energy consumption per km of 1.3 kWh and a yearly km range of 53868, it should have a yearly energy consumption of 70028. Confirmed. BREAK THE AGENT TEST o If an ebus in the model gets a battery level of 0 during the shift, it should immediately return back to the station and recharge. Error found. The bus did not return back to the station. In hindsight this is logical, because the ending shift time of the bus has not been reached yet. Because the movement of buses in the model is only for visualization purposes, it doesn’t matter. Fixed, reassessed, confirmed. o If an ebus in the model gets a fuel level of -10, the fuel level should return back to 0. Confirmed. o If a dbus in the model gets a negative fuel level, the fuel level should return back to 0. Confirmed. o If an ebus gets a battery level of a 100.000, it should not impact the running of the bus. Error, found. While the ebus kept running, unexpectedly the battery level stayed above the maximum battery level. This should not happen. Fixed, reassessed, confirmed.
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o If a dbus gets a fuel level of a 100.000, it should not impact the running of the bus. Error, found. While the dbus kept running, unexpectedly the fuel level stayed above the maximum fuel level. This should not happen. Fixed, reassessed, confirmed. o If an ebus suddenly gets a monthly km range of 0, the operator that owns the bus, will not be able to fulfil its transport demand anymore. The bus should keep running however. Error, found. The bus kept running, but the bus operator was also still able to fulfil the transport demand. In hindsight, this is logical as it is not the monthly km range, but the reduced monthly km range that decides if an operator is able to fulfil the transport demand or not. Fixed, reassessed, confirmed.
Errors: Verification Error setting the battery degradation rate. set battery-degradation-rate ( item year current-degradation-rate-new-ebus-batteries)
Error: buses breed does not have own value Verification set monthly-km-demand-of-this-contract (table:get contracts-table contract) ;; emergent factor Fixed, reassessed, confirmed Error: cannot add contracts to list of my-contracts that bus operators have. Verification Corrected error caused by not pre defining the my-contracts list. show contracts-list set my-contracts [] let this-contract item 0 contracts-list show this-contract
Error: Model crashes after 14 years because one of the bus operators loses all of his contracts. The errors was caused by a command which required bus operators to have at least one contract. Fixed, reassessed, confirmed
Error: Model crashes after 12 years because one of the bus operators loses all of his contracts. The errors was caused by a command which required bus operators to have at least one contract. The previous solution “if count contracts > 0 then procedure” did not work. New solution “if any? Contracts with [name = ?]” did the trick. Fixed, reassessed, confirmed
Error: Buses stopped moving after a few hours. Error was caused by the personal contract duration of the buses reaching the number 0 triggering the buses to become inactive and stop moving. Error corrected. Fixed, reassessed, confirmed
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MULTI AGENT TESTING
THEORETICAL PREDICTION TEST o If all the costs related to the price of dbuses are fixed apart from the oil prices, the TCO for dbuses should follow the same trend as the oil price developments. Error found. While the TCO for dbuses did follow the oil price trend, the TCO for dbuses became very high, at over 100k per month. The error was caused by the fuel consumption rate for dbuses being to high. Fixed, reassessed, confirmed. o If the TCO of ebuses is decreasing every year through technological developments, there should be a point at which the TCO of ebuses becomes lower than the TCO of dbuses. Error found. The TCO of ebuses kept rising. The error was caused by the monthly energy costs being too high. Fixed, reassessed, confirmed. o The total bus fleet should slighty increase as time advances. Error found. The bus fleet rapidly decreases as time advances, this was caused by a big number of starting buses, that all started with the same specifications, exiting the model at the same time. In order to fix this problem, the starting contracts were made smaller, and random starting conditions were used. Fixed, reassessed, confirmed. o The total bus fleet should slightly increase as time advances. Error found. While the total bus fleet does slightly increase as time advances, the total bus fleet is subject to large drops in bus fleet size from time to time. o If the starting transport demand in the city is initially 2.000.000, and the growth rate per year is 1.06 then at in year 16 the transport demand should around 5.000.000. Confirmed. o If the transport demand at the start is, then the total demand should be, if the growth rate is, then in the next year the total demand should be …. o When A bus starts in the model, and belongs to contract100, its link should also belong to contract 100. Error found. The link did not belong to contract 100, instead its contract name was the name of the operator. Fixed, ressassed, confirmed. o At the start of the model the bus ages and contract durations are exponentially distributed according to Dan’s bus fleet data. It is expected that most buses and contracts have an age of 0. Therefore a big drop in contracts, active buses can be expected in years 8-10. Confirmed. o If dbuses have a lower TCO than ebuses, they are more likely to be picked in the tender procedure. If ebuses have a lower TCO than ebuses, ebuses are more likely to be picked. Confirmed. o If the current ebus bonus is 1000 the incentive to buy ebuses will be low. There will be no ebus adoption untill the TCO of ebuses drops below that of dbuses. Confirmed. o If the three bus operators start with an innovation limit of 0.1 (which means a maximum of 10% of the bids can be ebuses for each operator) and the ebus bonus is set to 0, the diffusion of electric mobility will be very slow. Confirmed. o If the ebus bonus is set at its max value of 5000, and the bus operators start with an innovation limit of 0.1, the diffusion of ebuses will be moderately quick, but still rather slow due to the innovation limit. Confirmed. o If the ebus bonus is set at 5000 and the bus operator start with an innivation limit of 1, rapid diffusion of ebuses occurs. Confirmed.
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o If the ebus bonus is set at 8000, which is equavalent , 2 of the operators start with an innovation limit of 0.1, and one of the operators starts with an innovation limit of 1, that last operator will dominate the market with ebuses. Confirmed. o If the initial total monthly transport demand of the city is 5.000.000 km’s, and operator Dan has a market share 70%, the starting contract sizes are set at 300.000, then Dan should get a transport demand of 3.500.000 and 12 starting contracts. Confirmed. o The ending shift time of bus operators should end before deep into the night. Confirmed.
BREAK THE AGENT TEST O The initial monthly demand is determined by the user and increases yearly by the growth rate of the demand. If the initial value is set at 0, the bus demand will be 0 and no buses are purchased. Therefore the slider that determines the initial monthly demand cannot be set to 0. Confirmed. o If the initial monthly demand is set at a rate of 10.000, the bus demand will be so low that each operator will only have one initial contract. Because the ages of the initial contracts are based on an exponential distribution, there is a big risk that all contracts run out early and the model end up with 0 buses. Error found. Additionally the initial starting demand of 10000 km’s/month resulted into negative km demands in tenders. The reason being that the km demand in the tender is calculated by: (current demand of the city – total monthly range of city fleet). If the total monthly range is too high, a negative demand occurs. Therefore the initial monthly km demand start lower than 300.000. Fixed, ressassed, confirmed. o If the initial monthly demand is given a very big number of 100.0000.0000, and the maximum size of the starting contracts is put at 300.000 each, the model crashes because there are not enough starting contracts to be distributed among the operators. Therefore the initial monthly demand is limited to 10.000.000. Confirmed. o If the initial monthly demand is given set at 10.000.000 and the growth rate is set at 10% per year, the bus fleet ends with 13.000 buses which is almost 6 times as much as the starting fleet size of 2300. Therefore the growth rate is maxed at 5%. Confirmed.
The emergence of electric buses (ebuses) in the bus fleet showed no significant difference between 100 runs and 10 runs (Figure 49). Therefore the experiments were performed only 10 times instead of 100 times to save time required to performed the experiments. There was a bigger difference when comparing the development of the bus fleet of the bus operators over time. Figure 50 shows is a boxplot of the development of bus operator Dan’s bus fleet over time at 100 runs. The box plot in Figure 51 is similar to the boxplot from Figure 50 but then at 10 runs. Comparing the two figure shows that the overall pattern is similar as well as the increase in the spread of data as time advances. However, it does show that there are differences in the IQR between the two figures, as well as differences in the median values in each year. Because the overall differences were minimal, 10 repetitions were considered accurate enough for the experiments.
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Figure 48; Number of buses over time at 100 runs
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Figure 49; Number of buses over time at 10 runs
Figure 50; Development of bus operator Dan's bus fleet over time in a boxplot.
Figure 51; Development of operator Dan’s bus fleet over time. Left 100 runs. Right 10 runs
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Default parameter settings
["development-speed-egged" 0.05 ["contract-duration" 10 ["starting-contract-size" 600000 ["full-time-charging allowed?" true false ["development-speed-kavim" 0.05 ["show-how-operators-update-bus-fleets?" false ["maximum-allowed-age-ebuses" 12 ["dan-innovation-limit-start" 0.1 ["lease-term" 120 ["dbus-air-quality-emission-factor" 1000 ["show-monthly-range-update-buses?" false ["show-contract-durations" false ["maximum-allowed-age-dbuses" 12 ["show-monthly-capacity-of-new-buses" false ["grid-grams-co2-per-kwh" 700 ["show-operators-reserve-contract-buses" false ["kavim-innovation-limit-start" 0.1 ["starting-market-share-egged" 0.25 ["egged-innovation-limit-start" 0.1 ["show-bus-ages?" false ["show-animations?" false ["show-tender-procedure?" false ["starting-market-share-dan" 0.7 ["starting-market-share-kavim" 0.05 ["development-speed-dan" 0.05 ["transport-demand-growth-rate" 1.01 ["show-contracts-of-bus-operators?" false ["current-ebus-bonus" 3000 ["initial-monthly-transport-demand" 6500000
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EXPERIMENTAL DESIGN
NETLOGO PROFILER
The netlogo profiler was used to identify the most time consuming procedures. Improvements were made to those procedures which resulted in a 20 second time reduction per run with 2000 agents.
Profiling dump of netlogo profiler
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Modeling the transition from diesel to electric buses
Parameters Experimented values
["current-ebus-bonus"] 0 3000 6000 12000
["contract-duration"] 10
["chosen-diesel-price-scenario"] 1 2 3
["dbus-air-quality-emission-factor"] 1000
["development-speed-egged"] 0.05, 0.1
["development-speed-kavim"] 0.05 0.1
["development-speed-dan"] 0.05 0.1
["full-time-charging-allowed"] true false
["initial-maintenance-costs-per-km-dbus"] 0.5
["initial-maintenance-costs-per-km-ebus"] 0.38 0.25
["initial-monthly-transport-demand"] 6500000
["maximum-allowed-age-ebuses"] 8 12
["maximum-allowed-age-dbuses"] 8
["Carbon intensity of the grid"] 630
["innovation-limit-start"] 0.1
["starting-market-share-dan"] 70%
["starting-market-share-egged"] 25%
["starting-market-share-kavim"] 5%
["transport-demand-growth-rate"] 1%
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Modeling the transition from diesel to electric buses JUSTIFICATION OF PARAMETER SETTINGS (TEL AVIV CASE)
Ebus bonus The current ebus bonus was experimented with variables between 0 and 12.000 with 12.000 representing a value of 50% subsidy per bus. The values were based upon the amount of subsidies granted by the Israeli ministry of public transport in pilot electric bus projects.
Contract duration
The contract duration is set at 10 years based on the contract durations of current bus concessions in Tel Aviv.
Diesel price scenarios
Diesel price of known to fluctuate. For that reason, different scenarios were created in order the model outcome under different circumstances. The three diesel price scenarios represent a scenario where the diesel price gradually decreases (scenario 1), a scenario where the diesel price follows the historical trend of the last 10 years (scenario 2), and a scenario where the diesel price gradually increases.
Diesel bus air quality emission factor
The diesel bus air quality emission factor indicates the amount of co2 emissions per km per diesel bus. The number represent empirical data (Appendix D). Because of the assumption that diesel bus technology has reached its technological peak, the emissions per km are not expected to change significantly in the next 16 years. The number therefore remains fixed.
Development speeds
The development speeds indicate how fast reach a state where they can submit bids containing only electric buses. It is assumed that in the fastest scenario, it would take around 10 years before such a state is reached. In the slowest scenario it would take around 20 years.
Fulltime charging
Two scenarios were explored, a scenario in which fulltime charging is allowed, and a scenario in which fulltime charging is not allowed.
Maintenance costs dbus
Gathered from research. Appendix D
Maintenance costs ebus
Gathered from research. Appendix D
Initial monthly transport demand
Gathered from empirical data. Appendix D
Maximum allowed age of ebuses
194
Modeling the transition from diesel to electric buses Sources from the PTA show that the maximum allowed age of buses is 8 which is based on the performance of diesel buses over time. Because ebuses are expected to last longer, a maximum allowed age of 12 was also tested.
Maximum allowed age of dbuses
Gathered from empirical data. Appendix D
Carbon intensity of the grid
Gathered from empirical data. Appendix D
Innovation limit start
All buses are assumed to start at 10% based on the fact that only ebuses in Israel have only been applied in pilot projects before.
Starting market shares, Dan, Egged, Kavim
Gathered from empirical data. Appendix D
Transport demand growth rate
Gathered from empirical data. Appendix D
JUSTIFICATION OF PARAMETER SETTINGS (NOORD BRABANT)
Ebus bonus There are no subsidies on the buses in the Noord Brabant case.
Contract duration
The contract duration is set at 10 years based on the contract durations of current bus concessions in the Netherlands.
Diesel price scenarios
Similar to Tel Aviv case.
Diesel bus air quality emission factor
Similar to Tel aviv case
Development speeds
All assumed to be 0.1 per year, as the PTA is expecting full ebus bids in 10 years’ time.
Maintenance costs dbus
Gathered from research. Appendix D
Maintenance costs ebus 195
Modeling the transition from diesel to electric buses Gathered from research. Appendix D
Initial monthly transport demand
Gathered from empirical data. Appendix D
Maximum allowed age of ebuses
Expert interviews indicated that the maximum allowed age of buses is 12 which is based on the performance of diesel buses over time. Because ebuses are expected to last longer and around 15 years, a maximum allowed age of 15 was also tested.
Maximum allowed age of dbuses
Gathered from empirical data. Appendix D
Carbon intensity of the grid
Gathered from empirical data. Appendix D
Innovation limit start
All buses are assumed to start at 10% based on the fact that only ebuses in Israel have only been applied in pilot projects before.
Starting market shares
Gathered from empirical data.
Transport demand growth rate
Gathered from empirical data.
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Modeling the transition from diesel to electric buses TEL AVIV EXPERIMENTATION RESULTS
100 RUNS AT DEFAULT SETTING
Figure 52; Scatterplot of number of buses over time at default parameter settings.
Figure 53; Boxplot of number of buses over time at default parameter settings.
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Modeling the transition from diesel to electric buses
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Modeling the transition from diesel to electric buses
200
Modeling the transition from diesel to electric buses
201
Modeling the transition from diesel to electric buses
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Modeling the transition from diesel to electric buses
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Modeling the transition from diesel to electric buses 10 RUNS AT DEFAULT SETTING
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Modeling the transition from diesel to electric buses Figure 54 shows the effects of the different scenarios on the amount of ebuses in the model in the form of boxplots. It shows that there is a gradual increase in the number of ebuses in each of the scenarios. This also indicates that the spread of the data is very limited as the boxes are as good as flat. The amount of ebuses in the model are likely to end up at around 1200.
Figure 54; Boxplots of the impact of different diesel price scenarios and Ebus bonuses on the amount of ebuses.
Figure below shows the effect of increasing the maximum allowed age of ebuses in the model. As the figure shows, allowing ebuses to reach a higher age has little effect. The bifurcation indicates the difference between a low innovation development speed of bus operators or a high innovation development speed of bus operators.
Figure 55; Effect of maximum allowed age ebuses on total ebuses
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Figure 56; Boxplot of impact of electric buses on the energy grid under different ebus bonuses and diesel price scenarios.
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Modeling the transition from diesel to electric buses NOORD BRABANT EXPERIMENTATION
Figure 57; total ebuses under different diesel price scenarios and ebus purchase prices
Figure 64 shows the development of the number of ebuses in the model under different diesel price scenarios and ebus purchase price scenarios. All scenarios indicate that the number of ebuses increases each at similar moments. The different diesel price scenarios have no impact on the development of ebuses. The higher purchase price only has a slight impact on the development of ebus in the low diesel price scenario.
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Modeling the transition from diesel to electric buses In Figure 52 the penalty points given per ebus and per dbus in a tender are explored. It shows that in all cases, the penalty for ebuses is lower than the penalty for dbuses. In scenario 1 when the diesel prices are the lowest, the dbus and does come close the ebus penalty. In the scenarios where the diesel prices are higher, the margin between the ebus penalty and the dbus penalty increases. (Patterns, ranges, bigger images, increases decreases)
Figure 58; Impact of diesel price scenarios and starting ebus purchase prices on penalty scores during tendering
Figure 59; Demarcation of the Dutch case. Provincie Noord Brabant
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Demarcation: Noord – Brabant
INVOLVED ACTORS
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Modeling the transition from diesel to electric buses BUS DATA
Bus data
Daily km range of at least 250km http://www.byd.com/la/auto/ebus.html Buses in the model run at 20km/h so buses initially can run for 12.5 hours. If the range decreases at a rate of 0.99 each year, then after 10 years the range is 226 km per day.
The maximum battery capacity is 324 kWh. If buses run for 12.5 hours per day. the battery usage is 25.92 kWh per hour.
If the battery degradation is 0.99 rate is 0.99 each year, then after 10 years the battery capacity is 293 kWh The bus can then run for 11.27 hours. The range will then be 225.4 km per day.
Different charging methods used by Heliox.
1. Opportunity charging o Ultrafast charging at turn-around locations of the bus routes. With this method buses are able to run all day without the need for a scheduled charging sessions. Bus operation can be optimized with 2 to 5 minutes of charging at turn around locations at the end of bus stops. The charging systems are able
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Modeling the transition from diesel to electric buses to connect directly to the 400V or 10/20kV electricity grid. The systems charges at rates of 150/200/300/400/500 kW. Charging can commence in under 3 seconds. 2. Depot charging o Offers a fast charging solution for a single vehicle or small fleets. Buses compatible with Combined Charging Systems (CCS) can charge at speeds of 30/50/150kW. The charger systems are compatible with vehicle to grid and smart grid technology. 3. Smart fleet charging o A charging systems that distributes power between vehicles based on charging priority. Intelligent software monitors the demand and status of all charge points and the required level of charge by each of the electric buses. The systems makes sure that all buses are charged and able to commence their service in the morning.
VDL Bus & coach has delivered their Citea Electric bus combined with Heliox CCS-compliant DC Fast charger. It can charge vehicles at a rates up to 150kW at 97% energy efficiency. There are three power solutions available: 30kW, 50kW and 150kW.
BATTERY DEGRADATION
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Modeling the transition from diesel to electric buses ACTOR ANALYSIS
Decrease environmental impact
Reduce bus Improve cost emissions efficiency
More clean fuel Reduce fuel Increase Decrease cost buses consumption patronage
Decrease Decrease More hydrogen More CNG More electric More euro 6 Less aggressive Attractive ticket Better services investment operational buses buses buses buses driving prices costs costs
Higher Decrease Improve Better traveling Improve Improved Decrease Decrease Less fuel frequency of maintenance services area comfort reliability safety vehicle costs material costs consumption rides costs
Win more Improve Decrease tenders punctuality failures
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Modeling the transition from diesel to electric buses ISRAEL DATA
Dan drives 5 million km’s per month. http://www.dan.co.il/pages/1105.aspx
Dan’s bus fleet contains 1299 buses http://www.sviva.gov.il/subjectsEnv/SvivaAir/CarPollution/HeavyVehicleFleets-AP/Documents/reports2015- HeavyVehicles/Dan-2015.xlsx
As Friday afternoons until Saturday nights are off, the overall number of km’s is divided by 22 working days. This indicates that buses drive 174 km’s per bus per day. Taking into account the extra distance travelled from and to the depot, 25 km’s were added resulting into 200km’s per day on average.
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Carbon intensity of the energy grid in gram CO2 / kWh
1990 1995 2000 2003 2004 2005 2006 2007 2008 2009 Iceland 1 1 0 0 0 0 0 1 1 0 Mozambique 241 64 5 3 3 1 1 1 0 1
Netherlands 607 546 477 484 467 454 452 455 442 420 Côte d'Ivoire 205 275 379 384 356 457 385 409 449 389 Pakistan 408 405 479 371 397 380 413 433 451 458 Mexico 549 539 559 571 495 509 482 479 430 455 Egypt 521 443 343 397 489 474 473 450 460 466 Kazakhstan 611 560 692 634 584 570 839 658 541 433 Ireland 740 727 642 600 575 584 537 510 471 452 Belarus 548 500 472 443 463 459 461 452 465 466 Romania 855 741 579 643 528 493 521 542 512 472 Germany 607 581 522 512 503 486 483 504 476 467 UK 672 529 472 489 491 491 515 506 499 453 Tunisia 651 588 574 489 477 469 492 506 494 472 Other Africa 374 322 366 438 442 451 496 475 484 477 Philippines 341 463 493 449 448 491 429 443 483 475 OECD Americas 533 533 544 534 528 523 505 510 494 471 North Korea 566 481 584 542 528 522 533 469 481 499 Nicaragua 345 473 591 543 536 481 522 533 480 506 Turkey 568 512 529 451 426 438 452 494 511 496
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Modeling the transition from diesel to electric buses Azerbaijan 1147 822 833 653 677 650 671 570 534 499 Singapore 908 933 762 592 561 539 528 524 515 485 OECD Asia Oceania 492 481 487 520 512 502 497 514 507 503 Qatar 1077 1131 771 779 649 618 617 565 534 494 Korea 520 554 529 476 503 487 491 481 487 525 Moldova 723 748 829 753 526 529 506 530 510 526 Thailand 626 605 567 536 543 535 511 546 529 513 Haiti 408 327 346 320 301 307 305 513 480 547 USA 582 590 593 579 577 574 552 560 545 517 Bulgaria 761 582 478 532 537 502 490 592 565 537 Uzbekistan 624 572 629 607 588 588 583 609 543 566 Iran 603 606 574 529 542 541 549 546 582 578 Jordan 815 834 708 680 682 660 626 587 589 581 Jamaica 757 888 824 822 618 572 400 400 491 544 Bangladesh 554 601 556 574 546 553 574 567 574 585 Algeria 631 633 620 632 632 606 621 597 596 643 Czech Republic 744 794 728 618 617 614 606 636 621 588 Dominican Republic 845 876 759 700 704 649 668 675 634 591 Syria 553 586 567 620 571 607 612 623 627 629 Senegal 889 881 940 626 674 741 751 635 590 645 Taiwan 463 533 625 649 644 649 657 653 648 635 Non-OECD total 577 604 606 623 635 641 652 644 643 642 Yemen 746 946 930 884 874 841 781 679 636 630 Africa 670 690 649 628 649 634 627 616 667 641 Bahrain 1061 815 868 883 881 873 824 837 651 665 UAE 743 737 728 805 913 844 820 720 729 631 Zimbabwe 714 920 740 515 572 572 658 660 660 660 Malaysia 677 543 495 539 561 618 598 611 653 600 Eritrea 0 1463 698 694 711 666 679 655 669 672 Middle East 737 809 701 692 679 676 668 650 673 688 Israel 827 820 765 805 809 776 774 770 712 694
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Modeling the transition from diesel to electric buses NOORD BRABANT DATA
The Three concession areas of the province Noord Brabant. (Provincie, Noord Brabant, 2016)
(Provincie Noord Brabant, 2016)
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Modeling the transition from diesel to electric buses The percentage of each bus type in each of the three concessions in Noord Brabant.
Zuid oost Brabant has 183 buses in total. Hermes o 171 EEV buses all diesel o 12 euro III buses o 33% of total fleet Oost Brabant has 207 buses in total Arriva o 78 EEV buses (14 CNG buses) o 11 euro V buses o 118 euro VI buses o 38% of total fleet West Brabant has 161 buses in total Arriva o 161 euro VI buses o 29% of total fleet
The Netherlands has a total of 3787 EEV buses
666 of those are cng buses which is 17,5%
Therefore, it is assumed that 17,5% of the EEV buses are CNG buses.
Guideline to zero emission
The Province Noord-Brabant is striving for zero emission in 2025. This means that in 2025, the only buses in the area should be buses with zero hazardous emissions. The current bus operators have been challenged to reach zero emission by the year 2025. This would mean that all the buses during the concession should be replaced with zero emission buses by the year 2025. In the research however, it was assumed that no zero emission guideline existed. Instead it was explored what how the bus fleet would develop without a guideline towards zero emission in 2025.
(1 - starting-market-share-Hermes)
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