Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid
Ahmad Karnama
Master of Science Thesis Stockholm, Sweden 2009
Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid
Ahmad Karnama
Thesis for rewarding Master of Science Degree in Electric Power Engineering from School of Electrical Engineering at Royal Institute of Technology (KTH), Stockholm, Sweden.
Involved Academic Divisions: Electric Power Systems Electrical Machines and Power Electronics Academic Supervisors: Professor Lennart Söder Professor Stefan Östlund Industrial Partner: Fortum Distribution AB Industrial Supervisors: Mr. Olle Hansson Mr. Jonas Tosting Examiner: Professor Lennart Söder
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در ر وزه ری رم دوش ددم دو ھزار وزه و و وش وزه رآورد روش و وزه ر و وزه ر و وزه روش
Once, in a potter's shop, a company Of cups in converse did I chance to see, And lo! One lifted up his voice, and cried, "Who made, who sells, who buys this crockery? " 1
,born 1048 AD, Neyshapur, Iran—1123 AD, Neyshapur) ,( ر م :From Omar Khayyam (Persian 1 Iran), a Persian polymath, mathematician, philosopher, astronomer and poet. [III]
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Abstract
The new generation of cars are so-called Plug-in Hybrid Electric Vehicles (PHEVs) which has the grid connection capability. By the introduction of these vehicles, the grid issues will be connected to the private car transportation sector for the first time. The cars from the gird perspective can be considered as a regular load with certain power factor. The effects of this type of new load in distribution grid are studied in this thesis.
By modelling the cars as regular load, the effects of the cars in three distinct areas in Stockholm are investigated. The car number in each area is estimated based on the population and commercial density of electricity consumption in the three areas. Afterward, the average electricity consumption by the cars in one day is distributed among 24 hours of the day with peak load in the studied year. This distribution is done by two regulated and unregulated methods.
The regulated method is based on the desired pattern of electricity consumption of PHEVs by vehicle owners. On the other hand, the regulated pattern is designed based on encouragement of the car owners to consume electricity for charging their car batteries at low-power hours of day (usually midnight hours).
The power system from high voltage lines in Sweden down to 11 kV substations in Stockholm simulated in PSS/E software has been used in this study. The automation program (written in Python) is run in order to get the output report (voltage variation and losses) of the load flow calculations for different hours of day by adding the required power for PHEVs both by regulated and unregulated patterns.
The results show the possibility of introducing growing number of cars till year 2050 in each area with existing grid infrastructures. Moreover, the number of cars, yearly and daily electric consumption for PHEVs in pure electric mode are shown in this project and the effects of regulated electricity consumption are investigated.
It is concluded that since the car number is estimated based on the population, the areas with higher residential characteristics are more problematic for integration of PHEVs from capacity point of view. Moreover, by regulating the charging pattern of PHEVs, the higher number of PHEVs can be integrated to the grid with the existing infrastructures. In addition, the losses have been decreased in regulated pattern in comparison with unregulated pattern with the same power consumption. The voltage in different substations is within the standard boundaries by adding 100 percent of PHEVs load for both regulated and unregulated patterns in all three areas.
Keywords
Plug-in Hybrid Electric Vehicles (PHEVs), grid integration, voltage variation, PSS/E, program automation in PSS/E by Python, valley filling, demand side management
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Acknowledgements
This thesis was performed in cooperation with Fortum Distribution AB and divisions of Electric Power Systems and Electrical Machines and Power Electronics in School of Electrical Engineering at Royal Institute of Technology (KTH). My special thanks go to my academic supervisors; Professor Stefan Östlund and Professor Lennart Söder. The idea of the thesis was initiated during the summer internship which I did with supervision of Professor Östlund. Moreover, his valuable comments and positive energy despite of his busy schedule was always the greatest assistance for me. From the power system side, I have to confess that I was fortunate that I had Professor Söder’s supervision. He was always welcome to answer my questions and his comments formed my way of thinking to do the thesis. I want to kindly appreciate Dr. Valerijs Knazkins my former supervisor who is now in ABB in Switzerland and Dr. Mehrdad Ghandhari for his comments and assistants.
I really enjoyed the friendly environment in Fortum Distribution AB together with nice place and enough facilities to do my thesis. My special thank go to my industrial supervisors in Fortum; Mr. Olle Hansson and Mr. Jonas Tosting. Thank you so much Mr. Hansson for trusting me, for productive discussions and meetings and for invaluable comments. Thanks you Mr. Tosting for practical comments and discussions on business side of my thesis. I learned a lot with exceptional assistance from my industrial supervisors in Fortum’s nice work environment.
I would like to kindly appreciate Fortum employees for their kind behavior and their undeniable help during my thesis. I want to specially thank Mr. Anders Ekberg, Ms. Marie Fossum, Mr. Daniel Terranova, Mr. Oskar Engblom, Ms. Emilia Käck, Mr. Christer Bergerland, Mr. Stefan Råstrom, Mr. Jan-Rune Thun, Mr. Thomas Josefsson and Mr. Åke Norman for productive discussions and invaluable assistance during my thesis.
At last but not the least I want to thank my great family and specially my beloved parents in Iran for all their supports and understandings. Although I am physically far from them but their positive energy is continuously passing the 4535 km of distance between Kerman and Stockholm.
Ahmad Karnama
ا ر
August 2009
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Table of Contents
Abstract ...... V Acknowledgements ...... VII Table of Contents ...... IX List of Figures ...... XII List of Tables ...... XIV CHAPTER 1 ...... 1 Introduction ...... 1 1.1 Background ...... 1 1.2 How did the thesis get started? ...... 1 1.3 Thesis Description ...... 3 1.4 Report Outline ...... 4 CHAPTER 2 ...... 6 Transition from conventional vehicles to PHEVs ...... 6 2.1 History ...... 6 2.2 Introduction ...... 7 2.3 Different types of vehicles ...... 8 2.3.1 Conventional vehicles ...... 8 2.3.2 Hybrid electric vehicles ...... 9 2.3.3 Plug-in Hybrid Electric Vehicles ...... 9 CHAPTER 3 ...... 11 Plug-in Hybrid Electric Vehicles ...... 11 3.1 Why PHEV? ...... 11 3.2 Comparison between a HEV and a PHEV ...... 12 3.3.2 Available EV and PHEVs in the market ...... 14 CHAPTER 4 ...... 17 PSS/E and Simulation Automation in PSS/E ...... 17 4.1 PSS/E introduction ...... 17 4.2 Static analysis of the power system in PSS/E ...... 19 4.3 Automation tools in PSS/E ...... 21 4.4 Automation program for the case study ...... 22 CHAPTER 5 ...... 25 Defining Scenarios for Penetration of PHEVs ...... 25 5.1 Introduction ...... 25 5.1.1 Scenarios ...... 25 5.1.2 Scenario Planning ...... 26 5.2 Scenario Planning Method ...... 27 Step 1 - Decide assumptions/drivers for change ...... 28 Step 2 - Bring drivers together into a viable framework ...... 28 Step 3 - Produce initial mini-scenarios ...... 29 Step 4 - Reduce number of scenarios ...... 29 Testing ...... 30 Step 5 - Write the scenarios ...... 30 Step 6 - Identify issues arising ...... 30 5.3 Scenario planning for penetration of PHEVs in Stockholm ...... 30 5.3.1 Geographical Area Selection ...... 32 5.3.1.1 Nockeby ...... 36 5.3.1.2 Brunkeberg ...... 38
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5.3.1.3 Gärdet ...... 40 5.3.2 Selection of a Day in Year ...... 41 5.3.3 Estimation of total number of cars in each area ...... 42 5.3.4 Penetration growth of PHEVs in the city ...... 43 5.3.5 Average Electricity Consumption of a PHEV ...... 44 5.3.6 Charging Infrastructure ...... 45 5.4 Output Results from Scenario Planning ...... 47 5.4.1 Stockholm ...... 48 5.4.1.1 Nockeby ...... 49 5.4.1.2 Brunkeberg ...... 50 5.4.1.3 Gärdet ...... 51 5.4.2 Comparison ...... 52 Chapter 6 ...... 54 Implementation of Scenarios in PSS/E Automation; Results and Conclusions ...... 54 6.1 Hourly Charging Pattern for a Day ...... 54 6.1.1 Unregulated charging ...... 54 6.1.2 Regulated charging ...... 55 6.2 Nockeby Area ...... 57 6.2.1 Load increment and Voltage variation...... 58 6.2.2 Possible expansion in the capacity ...... 61 6.3 Brunkeberg Area ...... 61 6.3.1 Load increment and Voltage variation...... 62 6.3.2 Possible expansion in the capacity ...... 64 6.4 Gärdet ...... 65 6.4.1 Load increment and Voltage variation...... 65 6.4.2 Possible expansion in the capacity ...... 68 6.5 Losses ...... 68 6.6 Conclusions ...... 69 Future Works ...... 71 Power system technical issues ...... 71 Electricity market issues ...... 71 References...... 72 Appendix 1 ...... 75 Python code for bus voltages report...... 75 Python code for losses report ...... 78 Appendix 2 ...... 83 A2.1 Nockeby ...... 83 A2.1.1 Penetration of 20 Percent of the PHEVs ...... 83 A2.1.1.1 Load curve ...... 83 A2.1.1.2 Voltage curve with unregulated distribution of the PHEVs load ...... 83 A2.1.1.3 Voltage curve with regulated distribution of the PHEVs load ...... 84 A2.1.2 Penetration of 40 Percent of the PHEVs ...... 84 A2.1.2.1 Load curve ...... 84 A2.1.2.2 Voltage curve with unregulated distribution of the PHEVs load ...... 85 A2.1.2.3 Voltage curve with unregulated distribution of the PHEVs load ...... 85 A2.2 Brunkeberg ...... 86 A2.2.1 Penetration of 20 Percent of the PHEVs ...... 86 A2.2.1.1 Load curve ...... 86 A2.2.1.2 Voltage curve with unregulated distribution of the PHEVs load ...... 86 A2.2.1.3 Voltage curve with regulated distribution of the PHEVs load ...... 87 A2.2.2 Penetration of 40 Percent of the PHEVs ...... 87 A2.2.2.1 Load curve ...... 87
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A2.2.2.2 Voltage curve with unregulated distribution of the PHEVs load ...... 88 A2.2.2.3 Voltage curve with regulated distribution of the PHEVs load ...... 88 A2.3 Gärdet ...... 89 A2.3.1 Penetration of 20 Percent of the PHEVs ...... 89 A2.3.1.1 Load curve ...... 89 A2.3.1.2 Voltage curve with unregulated distribution of the PHEVs load ...... 89 A2.3.1.3 Voltage curve with regulated distribution of the PHEVs load ...... 90 A2.3.2 Penetration of 40 Percent of the PHEVs ...... 90 A2.3.2.1 Load curve ...... 90 A2.3.2.2 Voltage curve with unregulated distribution of the PHEVs load ...... 91 A2.3.2.3 Voltage curve with regulated distribution of the PHEVs load ...... 91 A2.4 PSS/E .sld files ...... 92 A2.4.1 Nockeby ...... 92 A2.4.1 Gärdet and Brunkeberg ...... 92
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List of Figures
Figure 1 Defining process of the thesis ...... 2 Figure 2 Thesis milestones ...... 3 Figure 3 Schematic on development in car industry ...... 7 Figure 4 Energy losses in a typical ICE (3) ...... 8 Figure 5 Series and Parallel Hybrid configurations (Left: Series- Right: Parallel) (3) ..... 9 Figure 6 Incentives to introduce PHEV ...... 11 Figure 7 Global Green house gasses emissions (5) ...... 12 Figure 10 PHEV and EV pictures ...... 16 Figure 11 PSS/E interface with key elements (7) ...... 18 Figure 12 Automation procedure in Python ...... 23 Figure 13 Sample load variation in substation 38274 and ID number 1 ...... 24 Figure 14 Focuses and purposes of scenario planning (reproduced from (8)) ...... 27 Figure 15 Six steps to make scenarios ...... 28 Figure 16 Outline of the scenario planning ...... 31 Figure 17 Stockholm’s city districts ...... 32 Figure 18 Electricity consumption density in Stockholm area (11) ...... 32 Figure 19 Geographical locations of selected areas in Stockholm ...... 33 Figure 20 Substation is Stockholm area ...... 36 Figure 21 Active power consumption in Nockeby substation for peak days ...... 37 Figure 22 Percent of electricity consumption in 2 different sectors in Nockeby ...... 38 Figure 23 Active power consumption in Tegner substation for peak days in ...... 39 Figure 24 Percent of electricity consumption in 2 different sectors in Brunkeberg ... 39 Figure 25 Active power consumption in Gärdet substation for peak days ...... 40 Figure 26 Percent of electricity consumption in 2 different sectors in Gärdet ...... 41 Figure 27 Penetration growth of PHEVs in Stockholm ...... 43 Figure 28 Driving distance of cars ...... 44 Figure 29 Required energy to run electric cars in Stockholm ...... 48 Figure 30 Percentage of required electricity for PHEVs in Stockholm ...... 48 Figure 31 Car number in Nockeby Number of cars in Nockeby till 2050 ...... 49 Figure 32 Percentage of required electricity for PHEVs in Nockeby ...... 50 Figure 33 Number of cars in Brunkeberg ...... 50 Figure 34 Percentage of required electricity for PHEVs in Brunkeberg ...... 51 Figure 35 Car number in Gärdet ...... 51 Figure 36 Percentage of required electricity for PHEVs in Gärdet ...... 52 Figure 37 Percentage of charging per day in unregulated charging...... 55 Figure 38 Demand side management in a typical daily load curve ...... 56 Figure 39 Percentage of charging per day in regulated charging ...... 57 Figure 40 Load curve with 100 percent of PHEVs in Nockeby ...... 58
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Figure 41 Voltage variation without of PHEV Nockeby ...... 59 Figure 42 Voltage variation with unregulated charging pattern in Nockeby ...... 60 Figure 43 Voltage variation with regulated charging pattern in Nockeby ...... 60 Figure 44 Load curve with 100 percent of PHEVs in Brunkeberg ...... 62 Figure 45 Voltage variation without PHEV in Brunkeberg ...... 63 Figure 46 Voltage variation with unregulated charging pattern in Brunkeberg ...... 63 Figure 47 Voltage variation with regulated charging pattern in Brunkeberg ...... 64 Figure 48 Load curve with 100 percent of PHEVs in Gärdet ...... 66 Figure 49 Active and reactive power and voltage in Gärdet without cars ...... 66 Figure 50 Active and reactive power distributed unregulated and voltage in Gärdet 67 Figure 54 Active and reactive power distributed regulated and voltage in Gärdet .... 67
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List of Tables
Table 1 Different pure electric vehicles and PHEVs (data from different company websites) ...... 15 Table 2 Data for studied power system ...... 20 Table 3 Python and IPLAN differences ...... 22 Table 4 Comparison between Scenario, forecast and Vision (8) ...... 26 Table 5 Selected areas in Stockholm (10) ...... 34 Table 6 Private cars and population in Stockholm and Sweden ...... 34 Table 7 Electricity consumption in areas per year ...... 35 Table 8 Selected substations in the case study ...... 35 Table 9 Selection of a sample day ...... 41 Table 10 Estimated number of cars in each area in year 2007 ...... 42 Table 11 Average distance and electricity consumption for a typical PHEV ...... 45 Table 12 charging type and time (15) (16) ...... 45 Table 13 Battery chargers ...... 46 Table 14 Required power and energy for PHEVs in Sweden ...... 47 Table 15 Yearly required electricity for PHEVs ...... 52 Table 16 Average daily needed energy for PHEVs ...... 53 Table 17 Peak load in Nockeby ...... 61 Table 18 Peak load in Brunkeberg ...... 64 Table 19 Peak load ...... 68 Table 20 losses in Fortum distribution area and whole network ...... 69
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid
CHAPTER 1
Introduction
1.1 Background Plug-in Hybrid Electric Vehicles (PHEV) can be considered as new generation of vehicles. Apart from their high battery capacity, the new feature of PHEV in comparison with existing hybrid vehicles is their grid connection capability. Nowadays increasing amount of electricity is produced by renewable sources like wind and solar. This clean electricity can be consumed by vehicles which now has a great share of the total emissions. Moreover, due to the increasing fossil fuel cost, driving PHEV annually can save a lot from fuel cost.
The recharging capability of PHEVs from power grid needs to be investigated from Power System point of view. Although the size of the energy storage on each vehicle is not considerable for the power system but due to increasing number of private and public vehicles converting to PHEVs, the total stored energy can be even considered as a Distributed Generation resource for power system. This thesis has mainly aimed to investigate the effects of the PHEVs as new type of load on distribution grid with different penetration scenarios of the vehicles.
1.2 How did the thesis get started? The first incentives to define the thesis were the writer’s familiarization with hybrid vehicles in summer 2008 while he did a summer internship with supervision of Professor Stefan Östlund at KTH. This directs to a proposal on the investigation of the effects of the Plug-in Hybrid Electric Vehicles from the power grid perspective. In the following figure, the steps to start the thesis are shown.
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid
Figure 1 Defining process of the thesis
In November 2008, a proposal about the integration of Plug-in Hybrid Electric Vehicles to the power system was written. The proposal was divided to the three main areas which a power system engineer can deal within the integration of PHEVs. The main areas are as following:
• Modeling of PHEVs for investigation of their effects in power system. • Dynamic and static effects of integration of the PHEVs in the power system. • Effects of the integration of PHEVS on the electricity market.
The scope of the thesis was extremely wide in the beginning and it was quite clear that the whole scope could not be covered within a five-month master thesis. Therefore, with the Dr. Valerijs Knazkins guidelines, the scope of the thesis shortened to a reasonable amount of tasks in order to be done during 5 months. The other tasks regarding the integration of PHEVs to the grid are left as future works or possible continuation of the thesis. Finally, thanks to Fortum Distribution AB, the thesis got started officially there in January 2009.
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1.3 Thesis Description The penetration of PHEVs is still under assessment from both technical and business perspective. Technically, the battery and charging infrastructure are the key challenges in the penetration of these type vehicles. Moreover, from power system perspective, the vehicles are interesting to be studied as they are considered as new type of load with different behavior as other types of loads. Their power injection capability (V2G, Vehicle to Grid concept) makes them more interesting and challenging.
In this thesis, the power system effects without implementation of Vehicle-to-Grid concept are investigated. The question that needs to be answered is the number of PHEVs that can be integrated to the power system (Stockholm area) with existing power system facilities. This is done by considering different penetration scenarios of PHEVs, different areas of the power system with different electricity consumption pattern and different consumption pattern of PHEVs. The effects of the integration of PHEVs in losses in distribution grid and voltage profile at 11 kV substations are investigated in this thesis.
The thesis is defined in the following milestones as shown in Figure 2.
Figure 2 Thesis milestones
After thesis approval by Fortum and literature study stages, the thesis goes to its main stages. For the purpose of power system simulation, Fortum is using the software called PSS/E 2. The whole Sweden’s power grid down to 11 kV substations is
2 Power System Simulation for Engineering
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid simulated in PSS/E. As the primary stages of the work, the writer got familiar with the software and necessary data were collected.
In the next stage, in order to see the effects of the PHEVs with high load variation, the simulations in PSS/E are automated. The purpose of automation is to run the power flow with different load values in a day. On the other word, the simulations are to run for 24 times with different loads values. This is done by extra tool in the PSS/E called Python. This will be described more chapter 4.
The different scenarios for the penetration of PHEVs are defined in the Stockholm area. Three areas in Stockholm are investigated as the sample areas. These areas are decided wisely in order to be enough distinctive from the ratio of commercial to residential buildings point of view and show extreme cases. The important issue in this stage is the estimation of the total number of cars in each area in each hour of day. This estimation is done based on some assumptions and some measurements which are used from other resources.
The output of the scenario planning which is the power needed in each area per day is distributed among the 24-hour of a day with peak yearly load. These loads are imported to the Python simulation to get output report about voltage and losses in different buses and areas.
In this study, the number of cars in each area and the penetration percentage of the cars until 2050 have to be estimated. The penetration percentage of the cars in the city is assumed to get to 100 % till 2050. This rate is based on the Fortum’s uptake model till 2030 and linearly distributed from 2030 till 2050. In addition, the number of cars is estimated based on the population in each area.
By introducing two regulated (based on valley filling techniques) and unregulated (the desired pattern by consumers) charging habits for the cars in 24-hour, the effects of the penetration of the cars in the three different areas with focus on voltage variation and losses are investigated and then feasibility, advantages and disadvantages of introducing of the electric cars are discussed in further details.
1.4 Thesis Report Outline The report is written in 6 chapters with the following descriptions:
Chapter 1: This chapter has been specified to describe the thesis. This includes how the thesis has been started and a description of different thesis steps.
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid
Chapter 2: In this chapter, developments on car industry from the ICE based cars to PHEVs are described. Conventional vehicles and HEVs are explained in more details in this chapter.
Chapter 3: In the next chapter, the necessity for introduction of PHEVs is explained in more details. Moreover, available PHEVs in the market by their detailed specifications are shown.
Chapter 4: The software PSS/E and automation tool Python that are used in this thesis for the simulation of the power system are introduced in this chapter. Moreover, the automation program which is planned for the analysis of the effects of PHEVs on power system is illustrated in this chapter.
Chapter 5: In this chapter, scenario and scenario planning are defined. Moreover, different scenarios for penetration of PHEVs for the purpose of investigation of effects of PHEVs in the grid are formulated.
Chapter 6: The results from the implementation of the scenarios in PSS/E simulation and output from automation program are shown in this chapter. Discussions around the result and suggestions for the penetration of PHEVs and possible substation expansion are shown in this chapter.
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CHAPTER 2
Transition from conventional vehicles to PHEVs
2.1 History Making transportation easier and more efficient is one of the common human beings efforts from the creation-day. This effort comes to new stages of development by the invention of wheel when pushing or pulling carriage on wheels makes it much easier for people to carry their goods. Therefore, the invention of wheel can be considered as the first revolution in transportation industry. The second revolution occurs by the invention of the first self-propelled vehicle. There are two different stories about the inventors which none has been proved. Nicolas-Joseph Cugnot , French inventor, is often recognized as the first self-propelled mechanical vehicle (adapted horse-drawn vehicle) designer in about 1769. This claim is uncertain while the other groups believe that Ferdinand Verbiest built the first steam-powered vehicle around 1672 which was originally designed as a toy for the Chinese Emperor (1).
The steam-powered vehicles gradually converted to vehicles with ICE (Internal Combustion Engines) which are still the most common type of vehicle. The main purpose of the vehicle designer at that time was to convert chemical potential energy in fuel to mechanical propulsion energy. This was the most interesting expectation from a vehicle while efficiency and environmental aspects were not actually the highlighted design factors. This type of design strategy leads to serious fossil fuel shortage (due to high consumption) and environmental problems (due to high emissions production) from transportation system.
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After the serious oil crises in 1973 and 1979, the industrial, oil-dependent countries change to invest on other energy alternatives not only for their power plants but also in transportation sector. Moreover, severe environmental impacts of inefficient ICEs such as high emissions, was the other incentive to invest on environmentally-friendly energy convertors.
In fact, electricity is the most interesting and flexible energy carrier from efficiency and capability perspectives. This makes the scientists to look for solutions for using electricity in transportation sector.
2.2 Introduction For the first time it was German inventor, Nikolaus Otto , who made it possible to use combustions engines in cars for the first time by the invention of the first four-stroke internal combustion engine in 1862. These types of engines are continuously being used in so-called conventional vehicles. The low-efficiency of ICE (Internal Combustion Engines) and high emission production are the most negative points about these types of vehicles. In the following figure, the recent development in car industry is been shown.
Figure 3 Schematic on development in car industry
As it can be seen from the figure, the first important breakthrough in car industry after the implementation of ICE in vehicles is the transfer from conventional vehicles to hybrid electric vehicles. These types of vehicles are first commenced in 1997 in Japan by the introduction of Toyota Prius (2). The main specification of this type of vehicle is the operation of the ICE on its efficient interval by means of a regenerative
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid braking system. The internal systems of this type of vehicles will be discussed more in the next sections in this chapter.
The latest generation of the vehicle is not introduced in the market yet. They are mostly called PHEVs (Plug-in Hybrid Electric Vehicles) with additional capability to be charged from the grid. More detailed specifications of this type of vehicle will come on the next chapter.
2.3 Different types of vehicles In this section three types of vehicles described above generally, are extensively explained.
2.3.1 Conventional vehicles They are primary type of vehicles which their efficiency is around 30 percent which means that 70 percent of the energy is being wasted in the process of energy conversion in an ICE. Figure 1 shows the major losses in a typical ICE.
Figure 4 Energy losses in a typical ICE (3)
As can be seen from the figure, the total energy that is actually being used by an ICE is approximately 13 percent of the total input energy. There are several technologies existed to improve the efficiency; such as variable valve timing and lift, turbo charging, direct fuel injection, and cylinder deactivation (4). But by introduction of hybrid vehicles, these losses have been decreased significantly.
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2.3.2 Hybrid electric vehicles Saving braking energy in battery by means of regenerative braking system, the ICE can be adjusted to work on its efficient velocity and torque. This can be done by extracting additional required energy from the battery when higher torque or velocity than the ICE production is needed. In contrast, the excess of energy while lower torque or velocity that the ICE production is needed can be saved in the battery. This will lead to more efficient operation of the ICE and consequently less emissions production.
The ICE and the electric machine can be connected to each other in different configurations. The basic configurations are series and parallel hybrid as shown in Figure 5.
Figure 5 Series and Parallel Hybrid configurations (Left: Series- Right: Parallel) (3)
As a matter of fact, series and parallel refer to the orientation of the two power sources in the propulsion system (3). The presented configurations in Figure 5 are actually the basic ones in which more advanced configurations with the combination of the series and parallel designs are used for different types of recent vehicles. For example, in Toyota Prius, by using the planetary gear, a configuration has been implemented which has both advantages of parallel and series hybrid. For more information regarding the different hybrid configuration please refer to (3).
2.3.3 Plug-in Hybrid Electric Vehicles The new generation of the vehicle which are not still in the market are Plug-in Hybrid Electric Vehicles (PHEVs). A PHEV is basically has the same structure as a HEV but the grid charging capability is additional feature which consequently result in the necessity of higher battery capacity.
Grid connection capability in PHEVs will make it possible to coordinate energy resources for domestic consumption and also will lead to lower emission production from private cars in the business and residential areas.
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Larger battery capacity is always a challenge from weight, cost and viability perspectives. Battery industry has grown fast during the last years and the price and weight had dropped off significantly while the efficiency and capacity have improved a lot. In the next chapter a study about PHEVs has been presented.
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CHAPTER 3
Plug-in Hybrid Electric Vehicles
Higher battery capacity in comparison with HEVs is the most important specification of PHEVs. However, the grid charging is additional feature of PHEVs. In this chapter the necessity of introduction of PHEVs is described and existing brand of PHEVs are shown and explained.
3.1 Why PHEV? The large percentage of the total emissions production is from the low-duty cars which are private and company cars. Reducing emission production is a big challenge for both developed and developing countries. On the other hand, the other major challenge in today’s world in the high consumption of fossil fuels with increasing price and diminishing number of resources.
Figure 6 Incentives to introduce PHEV
Low-duty cars are one of the major sources of fossil fuel consumption. Therefore, high fuel consumption and emission production are the major incentives to make
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid changes in the low-duty car sector. Moreover, the new ways of electricity generation can be considered as an incentive for introduction of PHEVs. These problems and the incentives are shown graphically in Figure 6.
Global green house gas emissions from the different sectors are show graphically in Figure 7. These gases are included Carbon Dioxide (72% in total), Methane (72% in total) and Nitrous oxide (26% in total) (5).
Transportation fuel
Idustrial process 13% 14% Power station 11% 17% Waste disposal and treatment 10% Land use and biomass burning 10% 22% Residential, commercial and othe sources Fossil fuel retrieval, processing and distribution 3% Agricultural byproducts
Figure 7 Global Green house gasses emissions (5)
As shown in the above figure, 14 percent of the emissions are produced by transportation sector which is close to the industrial sector. This means that by removing the emissions from the transportation sector, the to tal emissions can be reduced approximately as much as industrial sector. The introduction of PHVs can be even more interesting when the emissions from power station are low and the electricity is generated from clean resources (like Sweden).
Conversion of the cars from the ones with fossil fuel consumption to the ones with electricity consumption is not just interesting from the car sector but also it is interesting from the grid point of view. The high intermittency of the electricity from renewable resou rces can be synchronized with the intermittency of consumption of electric cars. However, new generation is needed in order to charge the electric cars.
3.2 Comparison between a HEV and a PHEV In Figure 8 the percentage of the battery running of a vehicle with its type is shown.
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Figure 8 Percentage of battery running and type of vehicle (27)
As it can be seen from the figure, the vehicle is called micro hybrid with less than 10 percent of the electric driving time. The electric motor of micro hybrid vehicles doesn't add thrust to the combustion engine, but in contrast acts as a starter/generator in order to allow the combustion engine to stop and restart instantly to avoid idling, and to enable regenerative braking. It is common to call micro hybrid a "stop/start hybrid" or a "hollow hybrid". The last name is used due to the fact that a micro hybrid drivetrain doesn't improve fuel economy as much as a mild or full hybrid drivetrain. The ICE has to provide almost 90 percent of required power for driving the car.
Mild hybrid is a type of hybrid whose electric motor cannot turn the drive wheels. Therefore the electric motor only assists the combustion engine, in contrast to a full hybrid. In mild hybrid, near to 70 percent of the power has to be taken from the ICE. The difference between a mild and micro hybrid is the smaller ICE in a mild hybrid with the same performance.
Full hybrid vehicles are mostly used for the cars which their electric motor is directly assisting the ICE and also the electric motor and the battery have enough power to turn the drive wheels independently. Therefore, the ICE has nearly 50 percent of the required power of the vehicle.
Plug-in Hybrid Electric Vehicles (PHEVs) with more 50 percent of the battery driving power have additional capability to get their charging energy from the power grid. HEVs have also larger battery size which literally called range extending. On the other word, the distance that the car can drive on electric mode has been extended by larger battery size.
Increasing battery capacity will make it possible to decrease the ICE size. This can be also done in a HEV by extending the range (implementing larger battery). Generally, usage of larger batteries has resulted in introduction of full electric vehicles with ICE independent driving cycle. In Figure 9 a comparison between different vehicles described above is shown.
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Figure 9 Comparison between different vehicle types (27)
As described above, the smaller ICE is the only difference between Mild and Micro Hybrid. On the other hand, a PHEV is a full hybrid vehicle with larger battery size and capability of grid charging.
Implementing new capability in order to charge the batteries from the grid, new generation of vehicles called PHEVs are come to the existence. Although the ICE has been implemented in these types of vehicles but the energy usage preference is electricity which can be generated from environmental-friendly energy resources. Even if it is not the case, the transition from HEVs to PHEVs will make it possible to manage the emission production from numerous car exhausts to fewer power plants chimneys.
3.3.2 Available EV and PHEVs in the market The automobile companies especially those companies which were active in HEV market are vigorously increasing their investment on a full electric or plug-in hybrid electric vehicle. For example both Toyota (with Prius hybrid car) and GM in NAIAS 2009 (North American International Auto Show) has announced for soon market release of the PHEVs. Other company like Think in Norway has specifically focused on Electric Vehicles. In Table 1 some available PHEVs with their characteristics are shown.
Toyota is the company which introduced the first HEV in 1997. By having a lot of experience in HEV industry, the expectations are higher than the other companies for the introduction of PHEVs. But Toyota is waiting for progress in battery industry in order to introduce its PHEV model. But unofficially the battery size for the Prius PHEV is estimated to be 10 kWh with new generation of lithium-ion batteries.
General Motors and Chevrolet have close cooperation for the development of new series PHEV called Volt. Volt with 16 kWh lithium-ion batteries has the electric range of 64 kilometers. The price of the Volt is expected to be around US$ 40K. The price is subject to decrease with government approved subsidies to around US$ 32.5K.
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Table 1 Different pure electric vehicles and PHEVs (data from different company’s websites) Full Electr ic Battery Electric Charging Motor Car model Manufacturer Capacity Battery Type Range time Power (kWh) (km) (hour) (kW) Prius (PHEV) Toyota 10 lithium -ion 48 8 N/A Volt (EV 3) Chevrolet/GM 16 lithium -ion 64 N/A 111 Tesla Tesla Motors 53 (6) Lithium ion 362 3.5 185 Roadster(EV) Think City 28.3 (MES MES DEA-Zebra Think 170-203 9.5-13 30 (EV) DEA-Zebra) Enerdel, Li-ion ReCharge C30 Lithium - Volvo 12 100 3 N/A (PHEV) polymer iMiev (EV) Mitsubishi 16 Lithium ion 160 7-14 47 BYD Auto lithium iron F3DM 16 100 7 50 (PHEV) phosphate
Tesla Motors has delivered more than 250 full Electric cars till March 2009. They have the capability to be charged in 3.5 hours with 70 Amps home charging station (3-phase). A 375 volt AC induction air-cooled electric motor with variable frequency drive has been implemented in this car to cover all required power to run at maximum speed of 200 km/h.
Norwegian company, Think has manufactured the modern urban car TH!NK city since July 2008. A rack of sodium or lithium batteries with capacity of near to 30 kWh have made it possible to travel up to 180 kilometers in one charge, with a top speed of 100km/h. With US$ 5.69 million load the company is now recovering from a financial distress in December 2008 which had been led to all vehicle production and laid off 50% of its staff pending.
ReCharge is called to the Plug-in Hybrid Concept which has been introduced by Volvo. The car is more fuel efficient and is producing fewer emissions in comparison with the other hybrid electric models. ReCharge Concept has used series hybrid technology where there is no mechanical connection between the engine and the wheels (refer to section 2.3.2). Moreover, four electric motors, one at each wheel, provide independent traction power for the vehicle, acceleration from 0-100 km/h takes 9 seconds and top speed is 160 km/h.
iMiev is Electric Vehicle produced by Japanese company called Mitsubishi. The highly efficient permanent magnet synchronous motor has been implemented on this vehicle which has led to quiet car with higher efficiency. The car can be charged in
3 Electric Vehicle
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid half an hour with fast charging stations (e-phase 200v 50 kW) while both 100 V with 15 A power supply in 14 hours and 200 V with 15 A power supply in 7 hours can charge the battery.
Figure 10 shows the pictures of different PHEV and EV discussed in the preceding paragraphs.
Toyota Prius PHEV Volt GM/Chevrolet Tesla Roadster
Think City ReCharge Volvo iMiev Mitsubishi
BYD F3DM
Figure 10 PHEV and EV pictures
BYD Auto is Chinese company which basically produces 65% of the world’s nickel- cadmium batteries and 30% of the world's lithium-ion mobile phone batteries has focused on PHEV and EV manufacturing. F3DM model which is a dual mode car is mostly called as first mass produced plug-in hybrid which has entered the market in December 2008. The gasoline engine of this vehicle is a 2.4 liter engine and its battery can be recharged by normal household power outlet.
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CHAPTER 4
PSS/E and Simulation Automation in PSS/E
Power System Simulation for Engineering (PSS/E) is a powerful tool both for steady state and dynamic simulation of power system. In this chapter after an introduction to PSS/E, different ways to automate the simulations in PSS/E are analyzed. Afterward a comparison between different automation tools in PSS/E has been done and it has been clarified why Python has been chosen for the automation of the simulation in PSS/E for the study case.
4.1 PSS/E introduction PSS/E is recognized software for the power system analysis from transmission and distribution perspectives. The software was first introduced in 1976 and it has been used in 115 countries (7). The software is developed by Power Technologies Inc (PTI), Siemens. The probabilistic analyses and dynamic modeling capabilities included in PSS/E provide transmission planning and operations more reliable.
PSS/E is an integrated, interactive program for simulating, analyzing, and optimizing power system performance. It provides methods to do the following applications in the power system, including:
• Power Flow • Optimal Power Flow • Balanced or Unbalanced Fault Analysis • Dynamic Simulation
In this thesis, the power flow capability of the PSS/E is mostly used. The main interface of the software is shown in Figure 11. The different components of the PSS/E Interface include the following:
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• Tree View: All network items are represented as selectable elements in a hierarchical list. Items in the list are organized by data type and reside in expandable/collapsible folders. • Spreadsheet View: All network data is presented in the Spreadsheet View. Tabs along the bottom of the Spreadsheet View allow selection and editing of the various data categories. The Spreadsheet View only appears when a case is opened. • Output Bar: All progress and report output is directed toward this expandable window. Tabs along the bottom allow selection of reports and progress output. • Diagram View: Facilitates the creation and display of one-line diagrams in the new Slider format. In addition to the display of power flow results, the Diagram View facilitates the building of new diagrams and networks bus by bus. Further, for existing power flow cases, this view enables the "growing" of one-line diagrams by automatically drawing selected buses and all their equipment and connected buses. The Diagram View appears only when a diagram is opened or created.
Figure 11 PSS/E interface with key elements (7)
• Toolbars: Allows convenient selection of analytical tools, creation of one-line diagrams, generation of reports, selection of subsystems, and view management.
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• Main Menu: Provides access to file handling, interface views, analytical functions, automation tools, I/O formatting, toolbar organization and online help. • Status Bar: Provides information related to the diagram status and operating mode (7).
In order to simulate power system in PSS/E for static analysis, it is needed to define number of files. The raw data files which contain power flow system specification data for the establishment of an initial working case. This file is saved by .raw extension and its presence is essential for the power system simulation in PSS/E.
The other essential file is saved case file with .sav extension which is the binary image of the existing load flow. The file is created to optimize the disk space usage in the sense that unoccupied part of the data structure are not stored when the capacity limits of the program are larger than system model.
The slider format files are to show the graphical view of the simulated case. The file is saved with .sld extension and can be run with different saved cases ( .sav files). The files which described before are made from Sweden’s high voltage level (400 kV) down to Stockholm regional network with 11 to 33 kV bus bars. The raw data file and part of slider format files are made by Fortum’s experts.
4.2 Static analysis of the power system in PSS/E Running power flow is one of the applications of PSS/E. The software has the following capabilities within the area of Power Flow applications.
• Load flow study and short circuit calculations. • Modeling of different transformer types. • Fault analysis applications. • Modeling of FACTS devices. • Voltage analysis like PV and QV analyses. • Optimal Power flow. • Contingency analysis. • Program automation in PSS/E.
In this study by using the load flow capability and by taking the advantages of automation program, the effects of PHEVs on different hours of a day with different load values are investigated. Since load flow analysis shows the state of the power system with certain load values while in real power system the load is changing continuously. Therefore, the power system simulation is automated in Python in
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid such a way that the loads are changed automatically with predefined values and the power flow is run for each series of different load values.
The load flow (power flow) analyzes the power system in normal steady state operation. Planning the future expansion of the power system as well as determining the best operation of existing systems is the most important issue related to the load flow studies. The principal information obtained from the power flow study is the magnitude and phase angle of the voltage at each bus and the real and reactive power flowing in each line.
In the power flow study, the buses can be distinguished by connected or not connected generators to them. The PQ buses which are so-called load buses are the ones with at least one connected load and no generator. The load values (reactive and active power) are known while voltage value and angle are unknown values in a PQ buses. On the other hand, those buses which are connected to at least one generator are so-called PU or slack buses. The PU buses are carrying the unknown values of reactive power and voltage angle whereas the active power and voltage value are known in these types of buses. The slack bus with known voltage value and phase has been selected arbitrarily. The generated active and reactive power in this bus is found based on the power flow calculations. The excess or lack of power is compensated by this bus.
The load flow in the PSS/E can be run with three solution methods as below:
• Fixed slope decoupled Newton-Raphson • Full Newton-Raphson • Decoupled Newton-Raphson
In this thesis the first method (Fixed slope decoupled Newton-Raphson) is used for the power flow simulation in PSS/E due to rapid convergence and small bus mismatches.
The power system used in this study contains Sweden’s high voltage power components (400 kV) down to regional network substations (11 kV). The residential loads are connected to 11 kV substations in this study. Table 2 shows the number of load and buses for studied case.
Table 2 Data for studied power system Number of in service buses 1912 Number of in service loads 852 Number of in service load buses 828 Number of areas 50
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The slack bus in the system is the 400 kV bus which is connected to a hydro power plant called Kilforsens in Sollefteå in Västernorrland County.
4.3 Automation tools in PSS/E There are two different types of programs used for automation in PSS/E which are based on the Application Program Interface (API) processor (BAT_commands). They are as below:
• The Python interpreter (Python programs) • The IPLAN simulator (IPLAN programs)
Application Program Interface (API) is a set of routines, data structures and object classes provided by libraries services in order to support the building of applications in the PSS/E program. Both IPLAN and Python are based on the API.
Although IPLAN and Python have great level of similarities but there are some differences as well. In Python, the routines are directly accessible while a push statement is needed in IPLAN in order to run programs. This can be considered as the basic difference between Python and IPLAN.
IPLAN can control the PSS/E progress or the variable from a separated file written in Fortran programming language. The IPLAN file needs to be compiled before being used as an input in PSS/E. Moreover, IPLAN program tests the load flow solution and extracts summary results into text files. The compilation necessity and push statement are the negative points about IPLAN
Python is an interpreter, interactive, object-oriented programming language which is extensively used for software development. The programming language is first appeared in 1991 by the Dutch computer programmer Guido van Rossum . Python programming language has vast area of application from web and internet development to database access and Game and 3D Graphics applications. This is due to its very clear, readable syntax, intuitive object orientation and very high level dynamic data types.
In PSS/E the API batch files for Python are available and they are used by the PSS/E users extensively. Unlike IPLAN, Python is more users friendly and the compilation is done in Python environment. Python and IPLAN are compared briefly in the following table.
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Table 3 Python and IPLAN differences Python IPLAN Has its own environment The code has to be written in a word possessor. Interpreted, interactive, object-oriented Fortran base program (old one). PSS/E APIs are direct access ’PUSH’ statements are required No compilation needed Compiled program will be the input to PSS/E
As shown in the table, Python has its own programming environment which the color text has made it easier to write the programs. Moreover, the Python program will be compiled itself in the PSS/E environment and unlike IPLAN no compilation is needed.
4.4 Automation program for the case study By the introduction of PHEVs, the cars will get their required energy from the power grid instead of the gas station. The car batteries are charged with different charging patterns which causes the load in the different substations to change more than before by the introduction of PHEVs. The purpose of automation program is to investigate the effects of these hourly changes of the load on the power system. Therefore, it is needed to run the power flow for different load values at each hour in order to see the voltage variation and losses caused by different charging patterns.
By selecting Python as automation tool for the study case, the following flowchart shown in Figure 12 is designed as the framework of the automation procedure.
In the first step, as shown in the flow chart, of the automation procedure the power flow is run automatically from the Python environment. This is done by API commands saved in the PSSPY library. This library has all API commands related to PSS/E application and has to be imported in the beginning of the code.
In the second step, the load flow results are saved in a library variable in Python. Voltage angle, voltage value in different buses and losses in different areas are the outputs of the load flow calculations. The buses voltages are kept within the permitted tolerance values by voltage regulation devices (VAR compensations).
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Figure 12 Automation process in Python
The output results from the load flow calculations in Python are sent to a .csv (Comma Separated Values) file in excel. This has been done by existing functions in Python written particularly for this purpose. There is a special library with all APIs related to .csv interactions with Python that has to be imported in the beginning of coding procedure which is called .csv library.
The reason that the library variable values in Python are sent to .csv file is due to the fact that dealing with data for plotting purposes is easier in Excel. Moreover, the capability of converting the data from the .csv file to MATLAB 4 for more flexibility purposes is higher.
On the same step, the .csv file values are sent to a variable in MATLAB. Once more, this has been done for the purpose of easier and more flexible interaction with the data for plotting purposes. Moreover, the writers MATLAB skills can be considered as the other reason in this step.
Each load in PSS/E simulations is known by its bus number and an ID number. In fact, there is just one load in the system with unique bus number and ID. Therefore, in the load data Excel sheet ( .csv file), each column shows a load in the system which is labeled with the bus number and ID number of the load in the first two rows. The
4 Matrix Library, The language of Technical Computing
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid remaining rows for each load are the hourly load of the system for one day. The reason for using ID number in addition to bus number is the fact that some buses have more than one load so the bus number cannot show a unique load in the system.
On the third step, the new load data, saved in .csv file, are read by Python and kept in a library variable. This library will be used to change the load for each hour in the next steps in the automation procedure.
Forth step is assigned to change the load in PSS/E simulation. The bus number and ID from hourly load data, read from the .csv file, are compared with existing data in PSS/E saved case. The load which its value has to be changed in the simulation is found. At that time, the load is changed in the .sav file (described above) and the load flow is run for the second time. Like before, the results from the load flow are then saved in a .csv file and sent to MATLAB. By continuing this procedure, the load value can be changed repeatedly and the voltage with each load is found and saved in a .csv file. With the same procedure, the losses in each area with different load values is calculated and saved in a .csv file in Excel. The corresponded .csv files are shown in Appendix 1.
In the following figures, as a sample for the automation program, the load variation and corresponded voltage variation is shown. The load variation is as in the Figure 13.
60 10.938
55 10.936
50 10.934
45 10.932 40
Load MW 10.93 35 Volatge (kV)
10.928 30
25 10.926
20 10.924 0 5 10 15 20 25 0 5 10 15 20 25 Time (Hours of a day) Time (Hours of a day)
Figure 13 Sample load variation in substation 38274 and ID number 1 The corresponded voltage value variation for the bus number 38274 and ID number 1 is shown in Figure 13. As shown the figures above, the voltage is decreased when the load is increased and the voltage is grown by reduced load values. In the top figure, the reactive power is kept constant. More results will come in Chapter 6 of this report.
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CHAPTER 5
Defining Scenarios for Penetration of PHEVs
In this chapter different scenarios for investigation of the effects of PHEVs in the power system are defined. These scenarios are characterized by considering all different areas of the power system and various penetration rates of the PHEVs in the city of Stockholm.
5.1 Introduction Before starting with the business and technical related issues of scenario planning for penetration of PHEVs, a description about the principles of scenario and scenario planning can be useful. Therefore, in this section scenario is defined and the methods that are being used for scenario planning are illustrated.
5.1.1 Scenarios There is no unique definition for scenario but different theorists have made different definitions as following (8):
• A disciplined method for imaging possible futures in which organizational decisions may be played out (Paul Shoemaker, 1995). • The part of strategic planning which relates the tool and technologies for managing uncertainties for the future (Gill Ringland, 1998). • A tool [for] ordering one’s perception about alternative future environment in which ones decision might be played out right (Peter Schwartz, 1991). • An internally consistent view of what future might run out to be (Michael Porter, 1985).
Focusing more on the definitions, more distinct differences between forecast and scenario can be vividly seen. A scenario is more like to have its base in present and
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid can be imagined as the future possible cases of a technology, company or even a thought based on present conditions and assumptions. Unlike a forecast or a vision, scenario is a group of anticipations of the future behaviour of an ongoing activity. In fact, a forecast is a single prediction of probable case while scenario shows wider area of possible futures while vision is the desired future. Thus, the scenario has to be based on present conditions and with high degree of uncertainty will show the possible situation if some of the assumptions may come true. Table 4 shows a comparison between scenario, vision and forecast.
Table 4 Comparison between Scenario, forecast and Vision (8) Scenario Forecast Vision Possible future Probable future Desired future Uncertainty based Based on certain relations Value based Illustrates risk Hide s risk Hide s risk Needed to know what we decide Need to dare to decide Energizing Rarely used Daily used Relatively often used Long term perspective Long term perspective For voluntary change
As shown in the table above, by planning scenario, the risk for long term planning with high level of uncertainty can be investigated. The scope of a scenario is generally wider than a forecast while it gets its inspiration from the defined visions. Scenarios are mostly generated for long term strategic planning. By knowing about scenarios, in the next section, scenario planning is described in more details.
5.1.2 Scenario Planning Traditionally the strategic planning methods have been used for the future planning purposes while now scenario planning methods are being used. The outputs of the scenario planning which are the generated scenarios are being used as an input for the strategic planning purposes. On the other hand, the scenario planning is continuously being used for the other purposes with different focuses as shown in Figure 14.
As shown in the figure, scenario planning can be used for four distinct purposes as below:
• Innovation. • Strategy and planning. • Scenario learning. • Evaluation.
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The scenarios can be generated in order to inspire new ideas which are called innovation purposes. By the innovation purpose, the focus will be on new business in which new ideas can be generated and out of them new acceptable ideas can be passed by scenario planning techniques.
Focus: New business
Business Development / Innovation New thinking / Paradigm shift Concept development
Focus: Strategy/ Scenario Scenario Focus: old Action Planning Planning learning Prerequisite for change
Strategy development / Evaluation Risk consciousness/ Organizational development Need for renewal
Focus: old business
Figure 14 Focuses and purposes of scenario planning (reproduced from (8) ) Explicit planning and strategic purposes are the most common purpose of scenario planning. The result has to be decisions on concrete action which are within the visions of the organization.
The action of scenario planning can be also be used for learning and drive changes. The outputs which are scenarios are powerful for the purpose of testing the existing paradigm and assumptions.
Evaluation purposes are the key factor for scenario planning. This happens when testing of an existing business concept, strategy or technology is of the interest.
5.2 Scenario Planning Method The actual production of the scenarios is radically different from most other forms of long-term planning. Even this, though, is relatively simple, at its most basic level. As derived from the approach most commonly used by Shell , it follows six steps:
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1. Decide drivers for change/assumptions. 2. Bring drivers together into a viable framework. 3. Produce initial mini-scenarios. 4. Reduce the number of scenarios. 5. Write the scenarios. 6. Identify the issues arising.
These six steps are shown graphically in Figure 15.
Find Make a Initial Reduce Write Identify
Drivers Work Scenarios Scenarios Scenarios Effects frame
Figure 15 Six steps to make scenarios Step 1 - Decide assumptions/drivers for change Observing the results of environmental analysis is the first stage to determine the most important factors in the nature of the future environment within the operation area of the organization. These factors are sometimes called variables or drivers . The main issue is that these variables (drivers or factors) will form the assumptions. These variables may be derived partly from the previous studies in the same area. However, in the ideal approach, the assumptions on which the scenarios will be based have to be carefully decided in the first stage. Only then, as a second stage, should the various drivers be specifically defined.
In any case, the brainstorming which should then take place, to guarantee that the list is complete, may unearth more variables - and, in particular, the combination of factors may suggest yet others. Moreover, the important and uncertain factors have to be differentiated in the first stage of scenario planning. The drivers for effects of penetration of PHEVs in a city are found and are written in section 5.3 and also shown graphically in Figure 16.
Step 2 - Bring drivers together into a viable framework In the next step the drivers will be put together in order to make a viable framework to make the scenarios. This has to be made easily since the relations of the different factors are by some means obvious for the last stage. For instance, a technological parameter may lead to market changes, but may be constrained by legislative parameter.
On the other hand, some of the factors may need to keep artificial at this stage. This is due to the fact that at a later stage more meaningful links may be found, or the
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid factors may then be rejected from the scenarios. In the most theoretical approaches to the subject, probabilities are considered in the event strings. This is extremely difficult to achieve, however, and generally adds little - except complexity - to the outcomes. Conceptually, this stage is probably the most difficult one. It is where intuition will be helpful tool in order to make sense of complex patterns for analyzing important effects. This viable framework for the case study is shown in Figure 16.
Step 3 - Produce initial mini-scenarios The results of the previous step usually make logical groupings of drivers. This is usually easy to achieve. Having placed the factors in these groups, the next action is to make some initial scenarios.
The scenarios are produced based on the initiated drivers in section 5.3. Some of the scenarios are basically redundant due to similarities of some scenarios which will be discussed more in next section.
Step 4 - Reduce number of scenarios Knowing about the initial scenarios and by having a fairly apparent picture of the scenarios, the next action is to reduce the number of the scenarios which are bigger in size but less in number. The challenge in practice is to come down to as few as possible containers into which all the topics can be sensibly fitted. This usually requires a considerable amount of discussions.
There is no theoretical reason for reducing to as few as possible scenarios, only a practical one. It has been found that the managers who will be asked to use the final scenarios can only cope efficiently with a maximum of three versions! Shell started, more than three decades ago, by building half a dozen or more scenarios - but found that the result was that the managers selected just one of these to concentrate on. Moreover, there are few distinct differences between the planned scenarios when the number of them is more than five. Therefore, the planners decreased the number to three, which managers could handle easily and more distinct. In most of the literature, this is the number now recommended most frequently.
In this thesis, the number of initial scenarios has been reduced. For example for the selection of a typical day for investigation of penetration of PHEVs, in the beginning, four days with peak load as typical days for each season were selected. But due to the fact that main purpose of the study was to investigate the effect of the PHEVs in the extreme case, the peak winter day (peak day of year) was selected. Therefore the number of scenarios was reduced by removing these redundant scenarios.
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Testing Having grouped the factors into these two scenarios, the next step is to test them, again, for viability if they make sense. This can be done by logical analysis, but it may also be in terms of intuitive gut-feel. Once more, intuition often may offer a useful - if academically less respectable – feeling of the complex matters. If the scenarios do not intuitively hang together, the usual problem is that one or more of the assumptions turns out to be unrealistic. If this is the case then you need to return to the first step - the whole scenario planning process has to be repeated in order to return outcome which makes the best sense.
Step 5 - Write the scenarios This step is really easy in terms of describing but in practice can be even considered as the most difficult part. Simply in this part, by considering the aim group, the scenarios have to be written up. The scenarios have to be written in the simplest way in order to be easy to follow up the text. They may also include numeric data and/or diagrams. The scenarios for the case of penetration of PHEVs are written in section 5.4.
Step 6 - Identify issues arising The final stage of the scenario planning process is to scrutinize these scenarios to determine the most critical outcomes. The subsequent approach will have to address these effects. The effects of the scenarios are investigated in Chapter 6 of this report.
5.3 Scenario planning for penetration of PHEVs in Stockholm The scenarios have to be defined by considering most possible effective factors in such a way that they cover all possible cases of integration of PHEVs to the power grid. Moreover, as the cars are not yet introduced in large scale in the city, it is needed to make some assumptions.
In this section, in order to get the final daily electric energy consumption by the PHEVs, numbers of assumptions are made. Then the estimated energy is divided equally among 365 days of a year. The results of this chapter are the daily estimated required energy for the private car transportation sector (in pure electric mode).The different stages of the of scenario planning for the estimation of required energy per day for the private car sector in Stockholm are shown graphically in Figure 16.
As shown in the Figure, for the purpose of scenario planning for the penetration of PHEVs in the Stockholm, the following steps have been done:
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1. Area Selection: In this study, three distinct areas are selected in Stockholm as the baseline of the study. 2. Selection of a Day in Year: In order to investigate the most extreme case, the day in the year with highest load level is selected. 3. Estimation of Number of Cars in each Area: Base on the population in each area and estimated persons per car coefficient, number of cars in each area is estimated. 4. Estimation of Penetration Growth Rate of PHEVs in the city till 2050: This estimation is based on the Fortum’s uptake rate of the PHEVs till 2030 and estimated rate by writer from 2030 to 2050. 5. Estimation of Average Electricity Consumption by a PHEV: This estimation is based on the energy consumption per kilometer and average travelling distance of the cars in one day. 6. Selection of Valid Charging Infrastructure: Normal sockets are decided to be the only available infrastructure for charging of the PHEVs. In addition, the power factor of the consumed power is decided in this section.
Figure 16 Outline of the scenario planning
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5.3.1 Geographical Area Selection Stockholm is divided to 12 districts as shown in Figure 17. The population of the city is 814,418 persons in year 2008 and the total area of 21,592 hectares (10).
Figure 17 Stockholm’s city districts
The downtown area (Kungsholm, Norrmalm and Södermalm) has high penetration of company offices, shopping and commercial centers. The farther from the city center, the more residential areas are located. For example in Bromma, there are few offices and shopping centers while mainly lots of housings are located.
Basically, the electricity consumption in downtown area is higher as shown in Figure 18. In this type of area, although the density of local residents is lower, due to high penetration of shopping centres and company offices, more people are travelling for shopping and working. This makes the total electricity consumption higher in comparison with residential areas. These are shown in the following figure.
Figure 18 Electricity consumption density in Stockholm area (11)
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Considering different geographical areas will make it possible to investigate the effects of integration of plug-in vehicles with different electricity consumption profile. For example power system in business areas with the high penetration of shops, markets and offices and low number of private houses and apartments has certainly different behaviour than a residential area with large number of private houses and apartments. Therefore, three distinct areas with different load profile are considered as below:
• Nockeby • Brunkeberg • Gärdet
These three areas are shown geographically in the following map and will be described in more details in the following sections.
Figure 19 Geographical locations of selected areas in Stockholm
As the 11 kV substations in these three areas are planned to be investigated, the wider areas need to be considered to cover all electricity injection from the 11 kV substation. Therefore, the three areas are shown in more details in Table 5.
As shown in the table and as it was expected, the population density in area with characteristics of residential areas (Gärdet and Nockeby) is lower than the business area (Brunkeberg). Nockeby is a pure residential area with the least population density and Gärdet is semi-residential area which leads to population density in the region of while Nockeby. On the other hand, Brunkeberg, as commercial area, has double population density as Nockeby and Gärdet.
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Table 5 Selected areas in Stockholm (10) Area type Residential Commercial Residential -Commercial Area given Nockeby Brunkeberg Gärdet name City’s main Bromma Norrmalm Östermalm district Nockeby, Gärdet, Stureplan - Nockebyhov, Lärkstaden, Djurgården, Included Klara/Jakob/Johannes Höglandet, Olovslund, Universitet, Oscars districts(s) (Södra and Norra) Smedslätten, Ålsten, Kyrka, Hjorthagen Äppelviken Värtahamnen Total land area 917 214 1586 (ha) Population 15436 12852 47677 Population 1 0.2 0.5 growth (%) Population density 31 60 30 (persons per ha) Assumed number of 2 3 2.5 person per registered car
The last row in Table 5 shows the assumed number of persons per registered car in these three areas. These numbers are attained based on the number of cars and population in Stockholm and Sweden. Table 6 shows these numbers for year 2007 (10).
Table 6 Private cars and population in Stockholm and Sweden Sweden Stockholm Personal car number 4,258,463 296,207 Population 9,113,257 795,163 Number of person 2.14 2.68 per registered car
As can be seen from the table, the average number of person per registered car is 2.68 in Stockholm and 2.14 in total Sweden. It is assumed that this ratio is even less in Nockeby area with more wealthy residents and more cars. Similarly, it is presumed this ratio is more in Gärdet and Brunkeberg with apartments and more shopping centres. It can be generally said that the lower the population density, the higher density of residential buildings and the lower persons per car ratio.
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The total electricity consumption per year in each area is calculated based on the available data in Fortum in each district. The results are as in Table 7.
Table 7 Electricity consumption in areas per year Nockeby Brunkeberg Gärdet Area (substation) (Nockeby) (Tegner) (Gärdet) Estimated y early electricity consumption per person (MWh) 6 30 9 Calculated t otal consumption in substation (GWh) 93.60 402.53 440.14 Calculated el ectricity consumption in area (GWh) 92.616 385.56 429.093
As shown in the table, the average electricity consumption per person is much lower in Nockeby than in other areas. This is due to less penetration of commercial centres. The extra power of the substation is used in the other neighbour areas and vice versa lack of power in the area is fed by neighbour substations. The electricity consumption in the substations is considered for the purpose of this study.
For each area one substation is selected for the investigation of the penetration of PHEVs. The following substations are selected for the case study for the investigation of the effects of PHEVs.
Table 8 Selected substations in the case study Area Name Nockeby Brunkeberg Gärdet Substation name Nockeby Tegner Gärdet Voltage level 11 kV 11 kV 11 kV Each t ransformer capacity 20 80 (each 3-winding) 80 (each 3-winding) (MVA) 5 Number of transformer in 2 two 3-winding two 3-winding substation Transformer voltages 33/11 kV 220/11 kV 220/11 kV 38136, 38711, 38712, 38273, 38274, PSS/E bus numbers 6 38137 38714, 38715 38276, 38277
The substations are shown in Figure 20 with their geographical situations and connected higher voltage substations.
The substation in Nockeby is connected to 33 kV substation while Tegner and Gärdet are connected to 110 kV. As the result, the capacity of transformer in Tegner and Gärdet is higher. This is due to Tegner and Gärdet’s higher load. Nockeby substation has a transformer with approximately half capacity as the other substation in this study.
5 The nominal capacity of transformer is considered in this case which the capacity for 25 °C of temperature without overloading possibility. 6 This is specified for each substation in PSS/E program.
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Figure 20 Substation is Stockholm area
The available load data for the case study is from 25 October 2006 till 25 October 2007. Due to low energy consumption growth and technical problems for load data collection after this time, these data are considered for the case study.
5.3.1.1 Nockeby Area The first area is selected as a residential area with high density of villa houses in which mainly people with higher financial ability are living. Nockeby has the required characteristics from a residential area. It is located in North West of Stockholm with long sea border.
The electricity consumption is high soon in the morning when people mostly have their breakfast and get ready to go to work. But in the remaining hours of the day low electricity consumption is expected while in the afternoon when people come back home from the work, more electricity consumption is expected. The active power consumption profile for the Nockeby 11 kV substation is shown in Figure 21.
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In this study, January, February and March are considered as winter; April, May and June as spring; July, August and September are summer and October, November and December are fall Months.
20 20
15 15
10 10
5 5 0 5 10 15 20 25 0 5 10 15 20 25 Power (MW) For For Nockeby (MW) in Power Winter Time of a day (Hour) For Nockeby (MW) in Power Spring Time of a day (Hour)
20 20
15 15
10 10
5 5
0 5 10 15 20 25 NockebyFor (MW) in Power Fall 0 5 10 15 20 25
Power (MW) NockebyFor (MW) Power in Summer Time of a day (Hour) Time of a day (Hour)
Figure 21 Active power consumption in Nockeby substation for peak days in 4 seasons
It is assumed that the demand of electricity for the PHEVs is high during the night while people are mostly at home and want to charge their cars for the following day. On the other hand, low demand for charging of PHEVs is expected during the day when people are mostly at work.
The same as the last area, the electricity consumption is much higher in cold weather than in warm spring and summer days. As shown the above figure, the peak electricity is about double in winter as in summer. The reason, as mentioned before, is electric heating instruments.
Due to the lower price of land in comparison with downtown area, deployment of fast charging stations can be an option in this and similar areas among the city.
The area in mostly surrounded with residential buildings which as the result the electricity consumption from the residential sector is far more than the commercial consumption. Figure 22 shows the percentage of electricity consumption in each sector.
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80.00% 75.24%
60.00%
40.00% 24.76% 20.00%
0.00% Residential Commercial
Figure 22 Percent of electricity consumption in 2 different sectors in Nockeby
The electricity consumption in commercial sector is almost one third of consumption from residential sector. This shows the high density of residential consumer and consequently building in this area.
5.3.1.2 Brunkeberg Area Brunkeberg is located in downtown area in Stockholm. Although it is not known as the main district or parish in Stockholm area but in this study, it is considered as integration of three city parishes. Klara, Jakob, Johannes Södra and Johannes Norra are the districts which are considered as Brunkeberg in this study.
Electricity Demand in this business area is high during the day when people are mostly at work. The high demand for electricity can be continued till evening when people usually go shopping. Due to low penetration of private houses and apartments in the downtown area, the electricity demand drops significantly during the night till soon in the morning. Figure 23 shows the electricity consumption profile for the Tegner substation located in the Brunkeberg area. The hourly active load profile is shown for four days with the highest consumption hour in four seasons.
A shown in the figure and as it was expected, the load is low in the morning but increase during the day. The peak of the load is in the afternoon when large group of people are at work or go out for shopping.
There is a distinct difference between the load in cold days (autumn) and summer days. The peak load in a cold day in autumn is approximately 2 times more than peak load in summer. This is due to electric heating devices in cold Stockholm.
The demand of electricity for PHEVs is apparently high during the day when people like to charge their cars for the way back to home in the afternoon while this demand plunges dramatically during the night when people have left their work place to home.
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80 80
70 70
60 60
50 50
40 40
30 30
Power (MW) For Tegner in Winter For Tegner (MW) Power 0 5 10 15 20 25 in Spring For Tegner (MW) Power 0 5 10 15 20 25 Time of a day (Hour) Time of a day (Hour)
80 80
70 70
60 60
50 50
40 40
30 30 0 5 10 15 20 25 in Fall Tegner For (MW) Power 0 5 10 15 20 25 Power (MW) For Tegner in Summer For Tegner (MW) Power Time of a day (Hour) Time of a day (Hour)
Figure 23 Active power consumption in Tegner substation for peak days in 4 seasons
The area in mostly surrounded with commercial buildings. Therefore, the electricity consumption from the commercial sector is far more than the residential consumption. Figure 24 shows the percentage of electricity consumption in each sector (residential and commercial).
100.00% 94.79%
80.00%
60.00%
40.00%
20.00% 5.21% 0.00% Residential Commercial
Figure 24 Percent of electricity consumption in 2 different sectors in Brunkeberg
As shown in the figure above, the total electricity consumption from commercial sector is about 19 times more than residential sector. This area has probably low
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid capability of installation of fast charging poles. This can be due to dense congestion of buildings, high price of land, low possibility for cars to stay and traffic issues.
5.3.1.3 Gärdet Area The third area is Gärdet in Stockholm with the combination characteristic of residential and commercial areas. This area is surrounded by high density of apartments and fair amount of business offices. The area has a lot of open-air and on-street parking lots.
The electricity consumption in this area is shown in Figure 25. As can be seen from the figure, the electricity consumption is high near to the evening when people start to use their illumination and heating instruments both at work and home. Generally the electricity consumption is higher during the cold days (winter and fall) and less in warmer days (spring and summer). The active power consumption in Gärdet substation for peak days in 4 seasons is shown in the following figure.
80 80
70 70
60 60
50 50
40 40
30 30
Power (MW) For Gardet in Gardet Winter For (MW) Power 0 5 10 15 20 25 in Gardet Spring For (MW) Power 0 5 10 15 20 25 Time of a day (Hour) Time of a day (Hour)
80 80
70 70
60 60
50 50
40 40
30 in Gardet FallFor (MW) Power 30 0 5 10 15 20 25 0 5 10 15 20 25 Power (MW) For Gardet in Gardet SummerFor (MW) Power Time of a day (Hour) Time of a day (Hour)
Figure 25 Active power consumption in Gärdet substation for peak days in 4 seasons
Due to fair the penetration of company offices, almost 72 percent of electricity in consumed in commercial sector as shown in Figure 26.
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80.00% 72.01% 70.00% 60.00% 50.00% 40.00% 27.99% 30.00% 20.00% 10.00% 0.00% Residential Commercial
Figure 26 Percent of electricity consumption in 2 different sectors in Gärdet
This area can be considered as the area with highest possibility of installation of fast charging facilities. The reason is the higher penetration of existing gas stations which can be enhanced to electric charging stations.
5.3.2 Selection of a Day in Year It is favorable to study the most extreme case for the grid impact of PHEVs. Therefore, the day with the peak load of the year (25 October 2006 till 25 October 2007) has been considered as the base study. As the peak load is different in different areas, therefore, different days are selected. Table 9 shows the selected days in each area.
Table 9 Selection of a sample day Area Nockeby Brunkeberg Gärdet Supply Substation name (11 kV) Nockeby Tegner Gärdet Peak load (MW) 23.5 73.95 81.97 Season with peak load Winter Winter Winter Date with peak load 21 -Feb -2007 23 -Feb -2007 22 -Feb -2007 Hour with peak load 21:00 & 22:00 13:00 13:00
As shown in Table 9 and Figures 21, 23 and 25 the peak load power is occurred in fall for Gärdet and Brunkeberg and in winter for Nockeby. The peak load is in cold winter night in Nockeby when all people were using their heating devices and at noon for Gärdet and Brunkeberg when people are at work or at shopping centres.
The total electricity consumption in each area is not the same as the consumption in each substation since the substation is feeding the other areas as well.
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5.3.3 Estimation of total number of cars in each area As the next step, the total number of cars in each area has to be estimated. For this purpose, the cars situated in each area are assumed to be in any of the following groups:
• Registered cars in certain area • Passing cars in a certain area
The assumed number of registered cars located in each area is calculated based on the population and number of persons per car ratio. The calculations are based the assumptions and statistics in Table 6 in section 5.3.1.
The passing cars are considered in order to cover the electricity consumption by cars which are outside of the investigated area but travelling to that area. The numbers of passing cars is directly correlated with the commercial coefficient (density of electricity consumption in commercial sector) and number of registered cars in the area. This relation is shown in equation 1.
Equation 1 = × Therefore, it is assumed that the number of passing car is a percentage of registered cars. This percentage is derived directly from the portion of electricity consumption in commercial sector in the area. The total number of cars is calculated as following:
Equation 2 = + By estimating the number of cars in this way, the number of registered, passing and total cars in each area are summarised as in Table 10. The yearly population growth as shown in Table 10 is also considered as the growth rate of cars in each area till 2050.
Table 10 Estimated number of cars in each area in year 2007 Area Nockeby Brunkeberg Gärdet Population 15436 12852 47677 Persons per car coefficient 2 3 2.5 Calculated n umber of registered cars 7718 4284 19071 Assumed coefficient for passing cars 0. 25 0.95 0.72 Number of passing cars 1930 4070 13731 Total number of cars 9648 8354 32802 Population growth (%) 1 0.2 0.5
In order to find the total electricity consumption by PHEVs, the total travelling distance per day and electricity consumption per kilometer is estimated in section 5.3.5. Then by finding total electricity consumption per day by a PHEV, the total
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid electricity consumption by PHEVs in each area is found by multiplying the total consumption per day by number of PHEVs.
5.3.4 Penetration growth of PHEVs in the city The introduction of PHEVs as described before has double advantage for Sweden due to their high electricity generation from clean resources and the high dependency on foreign oil. Therefore, the major energy companies in Sweden have planned to prepare enough infrastructures for the introduction of PHEVs. The planning for new infrastructures is following the estimated growth rate of PHEVs in each city.
Fortum Distribution AB as the distribution system owner and operator in Stockholm is playing the key role in providing facilities for the introduction of PHEVs. In order to keep track of introduction of PHEVs, Fortum has its own growth rate for the PHEVs in Stockholm. Figure 27 shows the assumed penetration growth of PHEVs in Stockholm till 2050.
120.00%
100.00%
80.00%
60.00%
40.00%
PHEVs PHEVs in Stockholm 20.00%
Percentageof penetrationof 0.00% 2000 2010 2020 2030 2040 2050 2060 Year
Figure 27 Penetration growth of PHEVs in Stockholm
From 2007 till 2050, the growth rate has been considered to be base on the Fortum’s model and assumptions but from 2030 till 2050, the growth rate is assumed to be linear worth the same slope as the last years of the previous period.
As can be seen from the figure, the PHEVs penetration percentage reaches to 1 percent in Stockholm for the first time in 2012. The growth rate is slower in the primary stages of the introduction of PHEVs. But by the development of technology, reduction of the cost (especially battery) and attraction of people interest, the growth rate will increase significantly. In this study, it is assumed that 40 percent of the cars will change to PHEVs till 2030 and optimistically has been assumed that 100 percent of the cars will be converted to PHEVs by the end of 2050.
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As the grid development till 2050 in this study has been disregarded, the growth rate does not affect the final results. This means that the final results of the study are not affected by slower or faster growth rate. In fact, this corresponds the purpose of the study which is investigation of effects of each percentage of PHEVs in the existing power grid.
5.3.5 Average Electricity Consumption of a PHEV With the today’s technology, typical PHEVs are consuming 0.2-0.25 kWh electricity per kilometer (12) (13) in its electric mode. This extensively depends on the environment temperature, type of the electric motor inside the car and car brand. In this study, it is assumed the PHEVs are consuming in average 0.25 kWh per kilometer.
According to the study done by KAIROS FUTURE in 2009 (14) the distance that cars in Stockholm are run in average during a day is estimated. Their estimation is based on research at Swedish Institute for Transport and Communications Analysis (SIKA) . The result is shown in Figure 28.
18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0
Percentage thecars of 2.0 0.0 0 1 to 5 6 to 10 11 to 21 to 31 t0 41 to 50 to 61 to 71 to 81 to 91 to More 20 30 40 50 60 70 80 90 100 than 100 Driving Distance per day, work travels are included (km)
Figure 28 Driving distance of cars
The figure shows that 15 percent of the cars travel more than 70 kilometer in total per day which means that 85 percent of the cars travel less than 70 kilometers per day. Nevertheless, more that 30 percent of the cars travel between 6-20 kilometers per day.
There is little number of cars which are traveling more that 100 km per day (around 5 percent). By assuming the last division in distance axis (more than 100 km) as 170 km and considering the average distance for the other section, it can be concluded that each car in average is run 32.7 km per day. By considering the average 0.25 kWh
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid per kilometer of electricity consumption, each car needs 8.2 kWh of electricity per day to drive for the distance of 32.7 km. These numbers are summarized in Table 11.
Table 11 Average distance and electricity consumption for a typical PHEV Required electricity Average running Average required per kilometer (kWh) distance per day (km) electricity per day (kWh) 0.25 32.7 8.2
In this study the average electricity consumption for the PHEVs is considered as the base case for the study of PHEVs in the grid. In fact, in order to avoid complexity, it is assumed that battery size for the cars in average is the same as their consumption per day. On the other word, each car in average consumes 8.2 kWh electricity from the power grid. It is worth mentioning that it is assumed that the cars are driving the same distance in all three areas and the cars are consuming the same amount of electricity per kilometre.
5.3.6 Charging Infrastructure The car batteries will mainly charge at stationary position. Therefore, the charging infrastructures have to be implemented in the places where cars will stay for long enough time. The cars are staying in garages, beside the street, the company parking lots, parking lots for commercial buildings or at deployed fast charging stations. Table 12 shows different charging infrastructures.
Table 12 charging type and time (15) (16) Power Charging Name Voltage Current (A) (KW) time 1 Normal socket 240V AC 16 3 2.7 h 3-phase connection 400V 3 -phase AC 16 10 0.82 h High DC off -board charging 600 V dc 110 -165 50 -75 7-10 min Ultra high DC off -board 600V dc 550 250 2 min charging 1charging time is calculated for 8.2 kWh
As shown in the above table, the cars can be charged from a normal 1-phase plug. As discussed before, in average each car needs 8.2 kWh electricity for daily driving. Therefore, battery charging will take 2.7 h in average per day with this type of charging infrastructure. Nevertheless, due to slow expected growth rate of the cars, it is considered as the primary solution for charging of the PHEVs batteries. Although this charging method is the easiest way due to it high availability but it has its own disadvantages.
The major problem with this method is that the regulation of car charging can be implemented with more difficulty. Since the sockets are available everywhere, the
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid car itself has to be known by the socket in order to regulate the charging. Furthermore, long waiting time is not desirable for drivers and planning for car owners to charge their cars is extremely difficult when they are on trip private car.
Three phase sockets are rarely available in the residential and commercial areas. The changing time is one third of the 1-phase method and charging power is three times more. Due to complexity and high cost of providing three phase sockets and the shorter achieved time of fast charging facilities, this method has low priority among the other charging methods.
By increasing the supply voltage, more power can be injected to the battery and consequently the car battery can be charged quicker. This is done by high DC off- board charging where the converter is located in the charging pole (15). In Europe, International Electrotechnical Commission (IEC) 61851 is working in application of equipment for charging electric road vehicles. They try to standardize AC supply voltages (IEC 60038) up to 690 V and at DC voltages up to 1,000 V. Moreover, providing electrical power for any additional services on the vehicle in case of necessity from the supply network is the other main filed of effort (15).
In this study, it is assumed that the required charging is provided by 1-phase normal socket which can be used from house and parking lots. The capacity of installing fast charging infrastructures in each area is investigated but no fast charging is assumed to be available in the defined areas. This also corresponds the fact that at primary stages of introduction of PHEVs, fast charging stations due to their high cost, low demand and not commercially available battery racks adaptable with fast charging stations, are not feasible to be introduced.
The power factor of the battery load is specified by the connected battery charger which can be installed in the car or alternatively in the charging pole. In the following table, some available battery chargers are introduced.
Table 13 Battery chargers Manufacturer AC Input Output power AC Input AC Power Efficiency (Model) Voltage range Frequency Factor delta -q 85-265VAC 0.84-1.3kW 45-65 Hz >0.99 90 % (QuiQ HF/PFC) HZTC 90 - 260VAC 1.5-3 kW 45 - 65 Hz > 0.98 92% (TCCH) Deltran 120 or 240 0.3-0.7 kW 50/60 Hz 0.95 - (HF-SmartCharge) VAC
The purpose of the preceding table is to clarify that the power factor which has been assumed in this study is 0.95.
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Therefore, the average active power consumption by the PHEVs per day is calculated by the following equation:
Equation 3 , = × ×
Equation 4 , = , × Where P, Q and PF are active power, reactive power and power factor respectively and d refers to daily required energy.
5.4 Output Results from Scenario Planning In this part, output results from scenario planning are shown separately for Stockholm for each previously defined areas. At the end of this chapter, the results are compared.
5.4.1 PHEVs in Sweden As described before, for driving distance of 32.7 km per day and by assuming 0.25 kWh per kilometre of electricity consumption, each car will need 8.2 kWh of electricity per day to drive on pure electric mode.
Sweden has around 4.3 million personal cars in 2007 (22). In Table 14, it is considered that all the cars in Sweden are converted to PHEVs. As the result, the required energy and power for PHEVs are shown.
As shown in the table, in total, 8.9 percent of the generated electricity is needed to run all the cars in electric mode in Sweden. Moreover, it 1.47 GW installed capacity with capacity factor of 100% in needed for running all the cars in pure electric mode. This capacity is approximately equal to average installed capacity of two nuclear power plants with capacity factor of 85 percent (average capacity of a nuclear power plant is 0.8 GW in (23))
Table 14 Required power and energy for PHEVs in Sweden Number of cars in Sweden 4.3 million Energy needed for each car per day (refer to section 5.3.5) 8.2 kWh Required electricity per year for 100% of PHEVs 12.87 Twh Total electricity consumption in Sweden 145 Twh The required energy for PHEV on total energy consumption 8.9 % Total installed capacity in Sweden 34.1 Required power for cars 1.47 GW The required power for PHEV on installed capacity 4.31 %
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5.4.1 Stockholm Stockholm has population of about 795,163 persons in 2007. The number of private cars on that year was about 296,207. By considering the growth rate of PHEVs shown in Figure 27 and average 8.2 kWh of electricity consumption per day, the required electricity for the cars is shown in Figure 29.
1.4 1.2 1 0.8 0.6
per year) per 0.4 0.2 0 Requiredenergy for(TWh PHEVs 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 Year
Figure 29 Required energy to run electric cars in Stockholm
At most 1.145 TWh of electricity per year is needed in case of 100 percent of the PHEVs running on pure electric mode in Stockholm. The total electricity consumption in Stockholm area is 6.944 GWh in 2007 (data from Fortum AB). By considering growth rate of 2 percent for electricity consumption in Stockholm, the percentage of required electricity for the PHEVs from the total consumption is shown in Figure 30.
2047 2043 2039 2035 2031 2027 Year 2023 2019 2015 2011 2007 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% Percentage of required electricty for PHEVs in Stockholm
Figure 30 Percentage of required electricity for PHEVs of total consumption in Stockholm
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As shown in the figure by converting 100 percent of the cars to PHEVs in 2050 and in order to run in pure electric mode, at most around 7 percent of the total consumption in Stockholm needs to be added for running the cars. In the next section the case for three areas in Stockholm are investigated.
5.4.1.1 Nockeby Area In this residential area, the number of passing cars is extremely lower than registered cars due to low penetration of commercial centres. Therefore, it is assumed that 25 percent (share of electricity consumption in commercial sector) of registered cars are added to registered cars to make the final number of cars in Nockeby. Figure 31 shows the passing, registered and total number of cars and also number of PHEVs in Nockeby till 2050.
Passing Cars Registered Cars Total Cars Number of PHEV
16000 14000 12000 10000 8000 6000 4000 2000 0 Number of PHEVs ofNumber PHEVs in Nockeby 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 Year
Figure 31 Car number in Nockeby Number of cars in Nockeby till 2050
The growth rate of the cars is based on population growth (1%) (10) (refer to section 5.3.3). Based on the assumption of the PHEVs electricity consumption (8.2 kWh per day) and the the growth rate of PHEVs in the city based on figure 27, the percatage of required eletricty to feed PHEVs from the total consumption is shown in Figure 32.
At most 13.28 percent of electricity consumption is needed to feed PHEVs in Nockeby. This percentage is for the time that 100 percent of the cars are converted to PHEVs which will require 44.3 GWh of additional electricity.
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2047 2043 2039 2035 2031
Year 2027 2023 2019 2015 2011 2007 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% 45.00% Percentage of required electricty for PHEVs in Nockeby
Figure 32 Percentage of required electricity for PHEVs of total consumption in Nockeby 5.4.1.2 Brunkeberg Area The number of cars in this area is estimated with same method as in section 5.3.3. In this area the number of passing cars is considered to be 95 percent of registered cars. Therefore, the number of passing cars and registered cars are near to each other and there are as double cars as registered car in Brunkeberg. The car number in different sectors in Brunkeberg is shown in Figure 33.
Passing Cars Registered Cars Total Cars Number of PHEV
10000 8000 6000 4000 2000 0 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 Number of PHEVs of PHEVs Number in Brunkeberg Year
Figure 33 Number of cars in Brunkeberg
With the same method as in previous section, required electricity for PHEVs to run in pure electric mode in the future years in Brunkeberg is shown in Figure 34.
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2047 2042 2037 2032 2027 Year 2022 2017 2012 2007 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% Percentage of required electricty for PHEVs in Brunkeberg
Figure 34 Percentage of required electricity for PHEVs of total consumption in Brunkeberg
As shown in the figure, at most (for 100 percent of PHEVs in pure electric mode) 4.41 percent of electric consumption in Brunkeberg has to be increased in order to supply PHEVs. This is equals to 27.2 GWh of electricity.
As shown in the above figure, even by considering 95 percent of passing cars in Brunkeberg (25 percent in Nockeby), the ratio required electricity for PHEVs to total electricity consumption is more than double in Nockeby. This shows that the required energy for PHEVs in Nockeby is far more than in Brunkeberg in comparison with existing electricity consumption.
5.4.1.3 Gärdet Area The same as the past two sections, the number of cars in three sectors is shown in Figure 35.
Passing cars Registered Cars Total Cars Number of PHEV
45000 40000 35000 30000 25000 20000 15000 10000 5000 0 Number of PHEVs PHEVs of Number in Gärdet 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 Year
Figure 35 Car number in Gärdet
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By considering 72 percent of passing cars, the ratio of required electricity for PHEVs on the total area electricity consumption is as blow.
2047 2043 2039 2035 2031 2027 Year 2023 2019 2015 2011 2007 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% Percentage of required electricty for PHEVs in Gärdet
Figure 36 Percentage of required electricity for PHEVs of total consumption in Gärdet
As shown in the figure, at most (for 100 percent of PHEVs in pure electric mode) 11.8 percent of electric consumption in Gärdet has to be increased in order to supply PHEVs. This is equals to 121 GWh of electricity.
5.4.2 Comparison As it was considered that the only charging facility is the normal sockets and the car number is calculated base on the population, the areas with the higher population will need more capacity than the areas with lower population. Table 15 shows the yearly required energy for the different percentage of the PHEVs in three areas in Stockholm.
Table 15 Yearly required electricity for PHEVs Area Nockeby Brunkeberg Gärdet Electricity needed for 20 % of PHEVs (GWh) 6.4 4.9 20.2 Electricity needed for 40 % of PHEVs (GWh) 21 14.5 62.1 Electricity needed for 100 % of PHEVs (GWh) 44.3 27.2 121.7
As shown in the table, although substations in Gärdet and Brunkeberg have the same nominal capacity but the required energy for PHEVs in Brunkeberg is a quarter of required energy in Gärdet. This is due to the fact the area covered by Gärdet substation is much larger than Brunkeberg because of lower population density in Gärdet and consequently the total assumed population is higher in Gärdet. Therefore, since the model for the prediction of the car number in each area is
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directly correlated with population, the number cars will be higher in Gärdet and consequently the higher amount of energy is needed.
On the other word, the total population who getting their electricity from Gärdet substation is much higher due to lower consumption per person in Gärdet. Therefore, the total car number and consequently the PHEVs number and required electricity for them will be higher in Gärdet.
The total electricity consumption is increased with higher rate in Brunkeberg than in Gärdet due to the penetration of commercial and business centers. Nonetheless, the average daily required energy in three areas for PHEVs is summarized in Table 16.
Table 16 Average daily needed energy for PHEVs Area Nockeby Brunkeberg Gärdet Required energy per day with 20 % of PHEVs (kWh) 17624.80 13438.01 55350.53 Required energy per day with 40 % of PHEVs (kWh) 57489.66 39843.43 170107.56 Required ene rgy per day with 100 % of PHEVs (kWh) 121352.21 74646.58 333312.70
As shown in the above table, since the model is based on the population, the more residential areas are more problematic for the integration of PHEVs by this model. These numbers in table 16 are as the input to the next stage of thesis to see the effects of PHEVs with different charging distribution pattern.
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Chapter 6
Implementation of Scenarios in PSS/E Automation; Results and Conclusions
In this chapter the daily required energy for PHEVs is distributed by two different charging patterns among 24 hours of a day. The peak hour load in the year (25 October 2006 till 25 October) is included in the selected day. The results which are voltage and losses in the system are analysed and possible required developments are discussed.
As described in chapter 4, in order to get the voltage variation in different buses and losses in different areas, the load flow calculations are automated to run for 24 times for different value of the load in 24 hours. The other loads than the loads in investigated substations (Nockeby, Brunkeberg and Gärdet) are kept constant. This means that the other loads have their average value (the average value of the load on that substation) while loads in three investigated substations are changing hourly.
6.1 Hourly Charging Pattern of PHEVs for a Day In this study the effects of PHEVs on the grid is investigated based on two charging habit of the car drivers. They are called as following:
• Unregulated charging habit • Regulated charging habit
6.1.1 Unregulated charging The unregulated charging habit is based on the people’s interest to connect their cars to electricity when they don’t have enough information about electricity price at each hour and limited number of charging infrastructures are available. This has been measured by putting measurement devices on nine Toyota Prius cars (17).
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The effect of limited charging infrastructure can be seen in the charging trends for PHEVs operated in five different states in USA during the months of January and February 2008, which was very early in the adoption cycle of PHEVs and prior to the development of a rich charging infrastructure to support PHEV charging (17). These vehicles are designed with a charge depleting range of approximately 30 miles. The result is shown in Figure 37.
As can be seen from the figure, it is assumed that people like to charge their cars as soon as they get home when they don’t know about electricity price and no incentives are defined to make them consume on the other times. According to the above figure near to 60 percent of the charging is happened between 14:00 -20:00. Moreover, it is worth to mention that the same unregulated pattern has been considered for three different areas.
12 10 8 6 4 2 Percent of charging of Percent 0 0 5 10 15 20 25 Hour of day
Figure 37 Percentage of charging per day in unregulated charging (for Nockeby, Brunkeberg and Gärdet) 6.1.2 Regulated charging On the other hand, regulated charging is defined based on the incentives and/or information which make the people charging their cars in the hours when the electricity consumption from the other sectors is minimum (demand-side management methods).
The method which is used in this thesis is called valley filling which is different from load shifting. In valley filling, the new loads (in this study, PHEVs) are managed by demand-side management programs in order to fill the valleys (hours with low energy consumption) in load curve. On the other hand, load shifting is to manage the existing load in the system by shifting the peak load to the low consumption hours. The described DSM (Demand Side Management) methods are shown graphically in Figure 38.
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Valley Filling Load Shifting
Figure 38 Demand side management in a typical daily load curve
In this thesis since new load has been introduced in the power system (PHEVs), the valley filling method has been implemented to define the regulated charging pattern.
As can be seen in Figures 21, 23 and 25, the electricity consumption is minimum during night hours for all three areas. Therefore, in order to fill the valleys of the daily load curve, the maximum consumption for PHEVs should be managed to be on night hours.
In this study for each area, as described before, average daily required electricity for PHEVs is estimated. Then different percentage (limited between 1 to 12 percent) of daily required charging is added to different hourly load in each area as following:
Equation 5 , = , + , = , ,…,
Where h in the above equation shows the hourly power and area refers to the power in three defined areas (Nockeby, Brunkeberg and Gärdet). Ph,PHEV is defined as below( Pd,PHEV is daily electricity consumption charging PHEVs, refer to section 5.4.3):
Equation 6 , = × , = , ,…, % , = , ,…, Where
Equation 7 = % In that case, the variance of the daily load curve with PHEVs is calculated as below:
Equation 8 = , −
Where Ph,total is the power with adding a percentage of the required daily power of PHEVs and is the average power. The optimum distribution of daily load from system operator point of view is when the variance is minimum. Moreover, it is assumed that in each hour, the specified load is not more than 12 percent and less than 1 percent of the daily load. This is due
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid to existence of the least charging at each hour even though the electricity price is high. In addition, the upper boundary is defined based on maximum demand by knowing all about the electricity price and defining the incentives for regulated charging. The calculated percentage of daily required power of PHEVs in each hour for regulated electricity consumption for PHEVs in each area is shown in Figure 39.
The figure shows that the near to 50 percent of charging is done between 12-5 AM in all three areas when the electricity consumption from the other sectors is the lowest. The regulated method is beneficial for grid operators since the peak load is managed and consequently the voltage variations in the substations are less and the necessity for expansion in the substation has been reduced.
Nockeby Brunkeberg Gärdet
14 12 10 8 6 4 PHEVs PHEVs (%) 2 0 0 5 10 15 20 25 Specified charging percentagefor Time of day
Figure 39 Percentage of charging per day in regulated charging
6.2 Nockeby Area The substation in Nockeby has two 20 MVA transformers which both can be operated in maximum load of 28 MVA (40 percent more than nominal capacity). But one of the transformers is always out of service due to reliability purposes in case there is no fault in the system. In fact, the capacity of one transformer shows the capacity shows the capacity of the substation. In order to satisfy the reliability (N-1 criteria) of the power system operation, the same capacity as the operating capacity in substation is needed as reserve capacity. This means that in Nockeby, although the installed capacity is 40 MVA but 20 MVA has to keep unused for the case of fault in the power system.
As shown in Figure 23, the maximum load in Nockeby substation is around 23 MW in winter which is more than 20 MW. This is due to the fact that the transformer can be operated with higher capacity than nominal capacity for some short hours in the
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid peak load (extra 40 percent of the nominal capacity which is up to 28 MVA). Nevertheless, the load is much less during the remaining hours of the year.
6.2.1 Load increment and Voltage variation As described before, in this area the day with peak load is selected in order to investigate the extreme case. On the other hand, the average daily required energy to run 20, 40 and 100 percent of electric cars in pure electric mode is calculated in the last chapter and shown in Table 16. The daily required energy is distributed among 24 hours with two different patterns. The first pattern is desired by drivers and shown in the figure 37 and the second one is base on minimum variance of the load curve in 24 hours shown in figure 39. Figure 40 shows the load curve for the peak day without PHEVs and with PHEVs by regulated and unregulated patterns with 100 percent of PHEVs.
As the reactive power is extremely smaller than reactive power, it can be neglected in comparison with active power. Therefore, only active power curve is shown in the following load curves.
As shown in the figure, in the regulated pattern, the load from PHEVs is distributed in 24 hours in order to make the load curve as flat as possible. Therefore, the peak load has reduced noticeably in comparison with unregulated mode. Instead the demanded energy from PHEVs is consumed in low power hours like night time and soon in the morning. This is beneficial for grid operator since the peak load has been managed and the operation of the grid is less costly.
Regulated Unregulated Without PHEVs
40000.00 35000.00 30000.00 25000.00 20000.00 15000.00
KWh per hour KWh per 10000.00 5000.00 0.00 0 5 10 15 20 25 Time of day
Figure 40 Load curve with 100 percent of PHEVs in Nockeby
In the unregulated pattern maximum 64.84 percent of the average load (without PHEVs) is added to base load for PHEVs at 12 PM while in the regulated pattern
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid maximum 53.05 percent of the average load in added to the base load for charging the PHEVs at 3 AM.
According to Standard SS-EN50160 (Voltage Characteristics in Public Distribution Systems) (26), the supply voltage magnitude variation has to be within ±10% for 95% of week in mean 10 minutes rms values for low and high voltage buses.
The load in Nockeby substation is divided equally between two available transformers. The voltage in one of the buses (connected to one of the transformers) in Nockeby substation is shown in Figure 41, 42 and 43. First, the voltage variation, active power and reactive power in the primary power system without PHEVs is shown in Figure 41.
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Voltage (kV) in Nockeby in (kV) Voltage 11.24 0 5 10 15 20 25 Time (Hours of day)
Figure 41 Voltage variation without of PHEV Nockeby
As shown in the above figure, the voltage is within the allowed tolerance (±10%) in Standard SS-EN50160. This is due to reasonable reactive power consumption and load level in Nockeby substation. The voltage variation included with active and reactive power consumption in the substation by distribution the load from 100 percent of PHEVs in unregulated pattern described in section 6.1.1 is shown in Figure 42.
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Voltage (kV) in Nockeby in (kV) Voltage 10.8 0 5 10 15 20 25 Time (Hours of day)
Figure 42 Voltage variation with 100 percent of PHEV with unregulated charging pattern in Nockeby
As shown in the above figure, the voltage variation is within the standard values according to SS-EN50160 voltage standard. The minimum voltage in the 11 kV substation is 10.84 kV and the maximum voltage is 11.31 kV which are both within the limits. However, the maximum load is 34.61 MW which is high enough to make the substation development necessary in case of 100 percent of PHEVs in pure electric mode. In the next figure, the voltage variation in 24 hour with regulated charging pattern (refer to section 6.1.2) is shown.
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23 0 5 10 15 20 25 Time (Hours of day)
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Figure 43 Voltage variation with 100 percent of PHEV with regulated charging pattern in Nockeby
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The minimum voltage in this case is 11.251kV and maximum voltage of 11.38 kV. The voltage variation within 24-hour has been decreased. Moreover, the peak load has decreased from 17.3 MW to 14.89 MW by regulating the electricity demand from the PHEVs. The same figures with 20 and 40 percent of PHEVs together with Slider Diagram files of the PSS/E simulation are shown in the Appendix 2.
6.2.2 Possible expansion in the substation capacity The 20 MVA transformer in Nockeby can be operated for some hours by the capacity of 28 MVA. The peak load in one transformer in Nockeby by adding PHEVs load with different percentages and without adding the vehicles are shown in Table 17.
Table 17 Peak load in Nockeby Maximum hourly load Penetration case of PHEVs per transformer (MW) Without PHEVs 23.05 Unregulated with 20 % of PHEVs 24.23 Regulated with 20 % of PHEVs 23.4 Unregulated with 40 % of PHEVs 28.22 Regulated with 40 % of PHEVs 24.24 Unregulated with 100 % of PHEVs 34.61 Regulated with 100 % of PHEVs 28.56 If regulated charging pattern is used, the capacity of the substation is enough even for integration of near to 100 percent of PHEVs. This is shown in table 17 where the peak load with unregulated charging pattern is about 34.61 MW in Nockeby while by regulating the charging of PHEVs the peak load has decreased to 28.56 MW. Moreover, in unregulated charging pattern, the capacity of the substation in violated for the first time in 2023 when about 40 percent of the cars are converted to PHEVs whereas in regulated pattern the substation can handle around 100 percent of PHEVs in Nockeby.
6.3 Brunkeberg Area The selected substations in Brunkeberg is called Tegner which has two 3-winding transformers. Both transformers have the capacity of 80 MVA. This means that total installed capacity of the substation is 160 MVA (two 40 MVA transformers in each there winding transformer). But the substation can be operated with maximum 112 MVA of capacity since the N-1 criteria has to be satisfied with one unused transformer. In fact, in case of fault in one transformer, there should be one unused transformer to handle the load.
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6.3.1 Load increment and Voltage variation The same as the last part, the average daily required electricity for PHEVs is distributed among 24 hours of the day with peak hour load in Brunkeberg with two regulated and unregulated charging patterns. The load curve with PHEVs load in regulated and unregulated pattern and without load from PHEVs is as in Figure 44.
Unregulated Regulated Without PHEVs
90000.00 80000.00 70000.00 60000.00 50000.00 40000.00 30000.00
KWh KWh perhour 20000.00 10000.00 0.00 0 5 10 15 20 25 Time of day
Figure 44 Load curve with 100 percent of PHEVs in Brunkeberg
In this area, the lower percentage of required electricity from the total load is required to feed PHEVs that in Nockeby.
In the unregulated pattern maximum 13.90 percent of the peak load is added to base load for PHEVs at 12 PM while in the regulated pattern maximum 16.67 percent of the peak load in added to the base load for charging the PHEVs at 3 AM.
The voltage variation in one of the 40 MVA transformers in Tegner substation is shown in Figure 45, 46 and 47; figure 45 shows the voltage variation without electric cars, figure 49 shows the voltage variation with unregulated charging pattern of PHEVs and figure 47 shows the regulated charging pattern.
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Voltage (kV) in Brunkeberg in (kV) Voltage 10.9 0 5 10 15 20 25 Time (Hours of day)
Figure 45 Voltage variation without PHEV in Brunkeberg
In Brunkeberg, the voltage is kept within the 10 percent of permitted tolerance with installed 6 MVAr capacitor bank in the substation.
As shown in figure 46, the minimum voltage in this substation is 10.93 kV. In fact the voltage is kept within permitted tolerance with installed capacitor banks in Tegner substation. By implementing the regulated electricity consumption by PHEVs, the voltage, active and reactive power will be as in Figure 47.
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Figure 46 Voltage variation with 100 percent of PHEV with unregulated charging pattern in Brunkeberg
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And for case of regulated charging patter, the active, reactive and voltage variation is as in Figure 47.
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Figure 47 Voltage variation with 100 percent of PHEV with regulated charging pattern in Brunkeberg
The minimum voltage in case of regulated charging pattern is 11.95 and maximum voltage of 11.07 kV. The voltage variation within 24-hour has been decreased in comparison with unregulated form. Moreover, the peak load has from 76.58 MW to 72.42 MW by regulating the electricity demand from the PHEVs. The same figures with 20 and 40 percent of PHEVs together with Slider Diagram files of PSS/E simulation are shown in the Appendix 2.
6.3.2 Possible expansion in the substation capacity The necessity for possible expansions in the substation directly depends on the peak load in the system. The peak loads by adding and without adding PHEVs for three different percentage of the PHEVs penetration by two different charging patterns is shown in Table 18.
Table 18 Peak load in Brunkeberg Penetration case of PHEVs Maximum hourly load (MW) Without PHEVs 71.68 Unregulated with 20 % of PHEVs 72.36 Regulated with 20 % of PHEVs 71.82 Unregulated with 40 % of PHEVs 73.68 Regulated with 40 % of PHEVs 72.08 Unregulated with 100 % of PHEVs 76.58 Regulated with 100 % of PHEVs 72.42
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The capacity of the substation is enough even for integration of to 100 percent of PHEVs if regulated charging pattern is used. This is shown in table 20 where the peak load with unregulated charging pattern is about 76.58 MW in Brunkeberg while by regulating the charging of PHEVs the peak load has decreased to 72.42 MW. Moreover, in both regulated and unregulated charging pattern, the capacity of the substation has never violated. This is due to low required energy for the cars in Brunkeberg area. This is because of the fact that only normal sockets are considered as the only charging infrastructure. Therefore, no additional power is added from other charging infrastructure. On the other hand, the number of cars in the area is estimated based on the population. Therefore, due to commercial characteristics of Brunkeberg area and even though reasonable number of passing cars are added to the registered cars, but the required energy for PHEVs is low in comparison with total energy consumption.
6.4 Gärdet The selected substations in Gärdet area is also called Gärdet which has two 3- winding transformers. Both transformers have the capacity of 80 MVA. This means that total installed capacity of the substation is 160 MVA (two 40 MVA transformers in each there winding transformer). But the substation can be operated with maximum 112 MVA (40 percent more that nominal capacity) of capacity since the N- 1 criteria has to be satisfied with one unused transformer. In fact, in case of fault in one transformer, there should be one unused transformer to handle the load.
6.4.1 Load increment and Voltage variation The same as the last areas, the day with the peak load is selected in Gärdet for the investigation of effects of PHEVs. The calculated daily required energy to drive the 100 of PHEVs in pure electric mode in Gärdet is distributed among 24 hours of the selected day with two charging pattern. The load curves with regulated and unregulated charging pattern and without electric cars are shown in Figure 48.
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Unregulated Regulated Without PHEVs
140000.00 120000.00 100000.00 80000.00 60000.00
KWh KWh perhour 40000.00 20000.00 0.00 0 5 10 15 20 25 Time of day
Figure 48 Load curve with 100 percent of PHEVs in Gärdet
The valleys in the primary load curve without PHEVs are filled with the PHEVs load in regulated mode. This is due to the fact that in regulated mode, the regulation method is to minimize the variance of the load curve by distribution of PHEVs load among 24 hours. Moreover, in the unregulated mode, the desired charging demand is considered.
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Figure 49 Active and reactive power and voltage in Gärdet without cars
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Thanks to the reactive power injection in the peak load, the voltage is kept within the allowed values. By distributing the daily required power for PHEVs among 24 hours by unregulated pattern, the voltage is as shown in Figure 50.
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Figure 50 Active and reactive power distributed unregulated and voltage in Gärdet with 100 percent PHEVs
The voltage variation, thank to enough reactive power compensation is within the permitted values. In the next figure, the voltage with regulated charging pattern together with active and reactive power in Gärdet substation is shown.
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Figure 51 Active and reactive power distributed regulated and voltage in Gärdet with 100 percent PHEVs
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The minimum voltage in this case is 11.93 and maximum voltage of 11.07 kV. The voltage variation within 24-hour has been decreased in comparison with unregulated pattern. Moreover, the peak load has from 114.7 MW to 82.62 MW by regulating the electricity demand from the PHEVs. The same figures with 20 and 40 percent of PHEVs together with Slider Diagram files of PSS/E simulation are shown in the Appendix 2.
6.4.2 Possible expansion in the substation capacity The necessity for possible expansions in the substation directly depends on the peak load in the system. The peak loads by adding and without adding PHEVs for three different percentage of the PHEVs penetration by two different charging patterns is shown in Table 19.
Table 19 Peak load Penetration case of PHEVs Maximum hourly load (MW) Without PHEVs 79.3 Unregulated with 20 % of PHEVs 84.16 Regulated with 20 % of PHEVs 79.84 Unregulated with 40 % of PHEVs 96.78 Regulated with 40 % of PHEVs 81 Unregulated with 100 % of PHEVs 114.7 Regulated with 100 % of PHEVs 82.62
The capacity of the substation is enough even for integration of to 100 percent of PHEVs if regulated charging pattern is used. This is shown in table 19 where the peak load with unregulated charging pattern is about 114.7 MW in Gärdet while by regulating the charging of PHEVs the peak load has decreased to 82.62 MW. Moreover, in both regulated and unregulated charging pattern, the capacity of the substation has never violated.
6.5 Losses The whole transmission system in Sweden included the distribution system down to 11 kV substation is simulated in this thesis. In this study, the losses are calculated for the day that the whole study has been performed (the day with peak hourly load). The losses in the Fortum distribution area and whole simulated area are shown in Table 20.
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Table 20 losses in Fortum distribution area and whole network Sweden Penetration rate and charging pattern of PHEVs FD 1 area area Without PHEVs (MWh per day) 141.87 21538 Regulated charging pattern of 20 % of PHEVs (MWh per day) 142.38 21549 Unregulated charging pattern of 20 % of PHEVs (MWh per day) 142.5 21550 Regulated charging pattern 40 % of PHEVs (MWh per day) 143.55 21574 Unregulated charging pattern 40 % of PHEVs (MWh per day) 143.96 21576 Regulated charging pattern of 100 % of PHEVs (MWh per day) 145.65 21610 Unregulated charging pattern of 100 % of PHEVs (MWh per day) 146.35 21614 1Area owned by Fortum Distribution
As shown in the table, the losses are less with regulated charging pattern than unregulated pattern with the same daily load. This is due to the direct relation of losses with the second power of current which make the losses higher with the higher load. The lower losses size is the other advantage of implementing regulated charging pattern for the PHEVs and demand side management.
By introduction of regulated charging pattern, apart from cost reduction in capacity expansion, the losses reduction will cause some cost savings. By considering the electricity price of 1.5 SEK per kWh, the total saving in Fortum distribution area by regulation of electricity consumption of PHEVs will be 180- 1050 SEK in the peak power day. In addition, in whole Sweden, the savings will be from 3000 SEK to 6000 SEK on the peak day depending on the percentage of PHEVs.
6.6 Conclusions In this study the number of cars is estimated based on the population in each area. The total number of cars is overestimated by the introduction of passing cars. On the other hand, by assuming the same average travel distance for all cars in all three area, the travel distance per car has been underestimated.
As the result, the only substation in which development is needed by introduction of PHEVs is Nockeby. Tegner and Gärdet due to the commercial characteristic of the area and large difference between nightly and daily consumption, the load can be distributed by valley filling method more efficiently. Therefore, less development is needed on those areas.
Moreover, it can be concluded that regulating energy consumption of the PHEVs by valley filling methods can reduce the necessity for substation expansion for the case of integration of PHEVs to the grid. Moreover, regulation of electricity consumption can also moderately decrease the losses in the power system. This
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The area with more residential characteristics will have more problems from capacity point of view for integration of PHEVs if the normal sockets are used as charging infrastructure. This is due to the fact that the number of cars in each area was estimated with the population in that area. Therefore, the areas with more residential density of electricity consumption like Nockeby and Gärdet need sooner and larger expansions than commercial area like Brunkeberg.
Moreover, between 6 to 38 percent of total energy consumption is needed to charge the PHEVs in different areas of Stockholm and with 100 percent of PHEVs in pure electric mode. This percentage directly depends on the population density in each area as the number of cars is estimated by population.
The voltage in 11 kV substations due to reasonable amount of reactive power compensation and voltage regulation facilities is within its standard permitted tolerance even with 100 percent of the cars in pure electric mode. But on the lower voltage level (less than 11 kV), the high amount of injected power from 11 kV substations (with high percentage of PHEVs) may cause problem from both capacity and voltage point of view. This remains as future work for this study.
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Future Works
The integration of the PHEVs to the grid have connected the grid issues and transportation sector. Therefore, the common issues are highlighted and new problems are defined. Moreover, as the cars are not still in the market, the possible problems needs to be predicted.
In this study, the model for estimation of the car number in each area was based on the population. As the future work, the model can be enhanced based on the traffic information in each area and at each hour. The other suggested future works are introduced in two categories of Electricity market related issues and power system technical issues.
Power system technical issues • Investigation of the effects of integration of PHEVs on the 11 kV network stations. • Investigation of the effects of PHEVs in the power grid as a distributed generation in case of application of Vehicle-to-Grid concept. • Investigation of the effects of PHEVs on the power quality. • Stability analysis of the power system in case of large disconnection or connection of the cars to the grid.
Electricity market issues • Analysis of market regulations and pricing impacts on the development of PHEVs. • Investigation of consumer’s savings due to integration of PHEV by considering the smart grid concept. • Investigation of emissions status by removing emissions production from cars exhausts and increasing the power generation in power plants in different countries.
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References
References
1. Automobile. Wikipedia. [Online] 01 17, 2009. [Cited: 01 27, 2009.] http://en.wikipedia.org/wiki/Automobile#cite_note-4.
2. Hybrid Synergy View September 2007. [Online] Toyota, 09 2007. [Cited: 02 06, 2009.] http://www.toyota.com/html/dyncon/2007/september/birthday.html.
3. Karnama, Ahmad. Hybrid Vehicle Drives and their Application in Electric Railway Traction. Stockholm : Royal Institute of Technology (KTH), 2008.
4. Advanced technologies and energy efficiency. [Online] http://www.fueleconomy.gov/feg/atv.shtm.
5. Rosmarino, Tanyalynnette. A Self-Funding Enterprise Solution to Reduce Power Consumption and Carbon Emissions. [Online] [Cited: May 2009, 30.] http://www.nysfirm.org/documents/html/2007/execcommittee/may/enterprisepow erconsumptionreduction_files/800x600/index.html.
6. Simpson, Andrew. Response to the CARB ZEV Expert Panel Position on Lithium-Ion Full-Performance Battery Electric Vehicles. San Carlos : Tesla Motors Inc., 2008.
7. Power Transmission & Distribution PSS/E™ 30.2 Guide. s.l. : Siemens, November 2005.
8. Lindgren, Mats and Bandhold, Hans. Scenario Planning, The Link Between Future and Strategy. Norwich : Palgrave McMillan, 2003.
9. Michael Kintner-Meyer, Kevin Schneider, Robert Pratt. Impact assessment if Plug-in Hybrid Electric Vehicles on electric utility and regional U.S. power grid. s.l. : Pacific Northwest National Laboratory, 2007.
10. AB, Stockholms Stads Utrednings och Statistikkontor. Statistisk Årsbok för Stockholm 2009. Stockholm : Stockholm Office of Research and Statistics, 2009.
11. Robin Bjäråker, Stefan Råström. LÅNGTIDSPLAN FÖR REGIONNÄT. Stockholm : Fortum Distribution, 2007.
12. Gremban, Ronald. PHEVs: the Technical Side. Brussels, Belgium : 1California Cars Initiative, 2007.
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13. Fritz, Peter. Plug-in elhybrider. 2007.
14. HANS BANDHOLD, JESSICA CARRAGHER WAL LNER,MATS LINDGREN. PLUG IN ROAD 2020 RAPPORT BASERAD PÅ KONSUMENTUNDERSÖKNING, INTERVJUER OCH SEMINARIUM. Stockholm : KAIROS FUTURE, 2009.
15. Fast Charging vs. Slow Charging: Pros and cons for the New Age of Electric Vehicles. Charles Botsford, Adam Szczepanek. Stavanger, Norway, : EVS24, 2009.
16. Fortum Internal Presentations. Tosting, Jonas. Stockholm : s.n., 2009.
17. Kevin Morrow, Donald Karner, James Francfort. Plug-in Hybrid Electric Vehicle Charging Infrastructure Review. s.l. : U.S. Department of Energy , 2008.
18. The electricity year 2007. s.l. : Svensk Energi-Sweden Energy AB, 2008.
19. Greenhouse gas emission trends and projections in Europe 2007 – Sweden. Copenhagen : EEA (European Environment Agency), OPOCE (Office for Official Publications of the European Communities) , 2007. EEA Report No 5/2007.
20. Sweden Crude Oil Production by Year. [Online] Index Mundi. [Cited: May 30, 2009.] http://indexmundi.com/energy.aspx?country=se&product=oil&graph=production.
21. United States Energy Information Administration. Sweden Crude Oil Production by Year. [Online] Index Mundi. [Cited: May 30, 2009.] http://indexmundi.com/energy.aspx?country=se&product=oil&graph=production.
22. Maria Melkersson. FORDON 2007, TEMA MILJÖ. Östersund : STATENS INSTITUT FÖR KOMMUNIKATIONSANALYS, SIKA, 2007.
23. International Atomic Energy Agency (IAEA). [Online] [Cited: 06 08, 2009.] http://www.iaea.org/programmes/a2/.
24. Wind Energy Basics. AWEA. [Online] [Cited: January 28, 2009.] http://www.awea.org/faq/wwt_basics.html#top..
25. Update on US Geothermal Power Production and Development. s.l. : Geothermal Energy Association, 2007.
26. Wind Power:Capacity Factor, Intermittency,and what happens when the wind doesn’t blow? Community Wind Power Fact Sheet # 2a. s.l. : Renewable Energy Research Laboratory, University of Massachusetts at Amherst.
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27. Holger Jené, Ernst Scheid and Hans Kemper , Hybrid Electric Vehicle (HEV) Concepts - Fuel Savings and Costs
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Appendix 1
Python code for bus voltages report ################################################## ## Importing library ############################# ################################################## import os import psspy import csv # ------################################################## ## Defining Functions ############################ ################################################## def array2dict(dict_keys, dict_values): '''Convert array to dictionary of arrays. Returns dictionary as {dict_keys:dict_values} ''' tmpdict = {} for i in range(len(dict_keys)): tmpdict[dict_keys[i].lower()] = dict_values[i] return tmpdict # ------def busindexes(busnum, busnumlist): '''Find indexes of a bus in list of buses. Returns list with indexes of 'busnum' in 'busnumlist'. ''' busidxes = [] startidx = 0 buscounts = busnumlist.count(busnum) if buscounts: for i in range(buscounts): tmpidx = busnumlist.index(busnum,startidx) busidxes.append(tmpidx) startidx = tmpidx+1 return busidxes # ------def splitstring_commaspace(tmpstr): '''Split string first at comma and then by space. Example: Input tmpstr = a1 a2, ,a4 a5 ,,,a8,a9 Output strlst = ['a1', 'a2', ' ', 'a4', 'a5', ' ', ' ', 'a8', 'a9'] ''' strlst = [] commalst = tmpstr.split(',') for each in commalst: eachlst = each.split() if eachlst: strlst.extend(eachlst) else: strlst.extend(' ')
return strlst #------def readloaddata(loadcsvfile): j=0
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loaddict = {} readcsv = csv.reader(open(loadcsvfile)) for row in readcsv: i=0 for string in row: if j <= 1: row[i] = int(float(row[i])) else: row[i] = float(row[i]) if EOFError: i = i + 1 loaddict[j] = row j = j+1 return loaddict ################################################## ## Get the integer and real data################## ################################################## ierr1, number_of_loads = psspy.aloadcount(-1, 1) # only in- service loads including those at type 4 buses ierr2, number_of_loadbuses = psspy.alodbuscount(-1, 2) # only in- service load buses including those with only out-of-service loads ierr3, load_id = psspy.aloadchar ( -1, 2, ['ID'] ) ierr4, NAZOS = psspy.aloadint (-1, 2, ['NUMBER', 'AREA' , 'ZONE' , 'OWNER' , 'STATUS' ] ) ierr6, lbv = psspy.alodbusreal (-1,4 , ['KV']) NAZOS = array2dict(['NUMBER' , 'AREA' , 'ZONE' , 'OWNER' , 'STATUS' ], NAZOS) load_data = NAZOS load_data['ID'] = load_id [0] ################################################## ## Remove repeated load bus data ################# ################################################## p=range(len(load_data['number'])) m=[] for h in p: for d in p: if load_data['number'][h] == load_data['number'][d] and d > h: n = 0 for s in range(len(m)): if m[s] == d: n = n + 1 if n == 0: m.append(d) m.sort() g=0 for r in range(len(m)): del load_data['number'][m[r]-g] del load_data['area'][m[r]-g] del load_data['zone'][m[r]-g] del load_data['owner'][m[r]-g] del load_data['status'][m[r]-g] del load_data['ID'][m[r]-g] g = g + 1 ################################################## ## Write Output Prompt ########################### ################################################## psspy.prompt(' Please enter a CVS file wit hthe following format: \n\ \n\
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- The CSV file can be saved in the same directory as Python file in advance or just type a new file name') psspy.prompt("\n\ - The file name has to be typed in the following space in this format: \n\ \n\ *********************'FILENAME.csv'**************************") # ------################################################## ## Get load data from a CSV file ################# ################################################## hourload = readloaddata('load.csv') # ------################################################## ## Changing the load in the system ############### ################################################## busvoltage={} for k in range(24): k = k + 2 for i in range(len(hourload[0])): busnumber = hourload[0][i] busid = str(hourload [1][i]) for j in range(len(load_data ['ID'])): busid_PSSE = load_data ['ID'][j] if busid_PSSE[1] == ' ' : busid_PSSE = busid_PSSE[0] if busid == busid_PSSE and busnumber == load_data ['number'][j]: ierr5 = psspy.load_data(load_data['number'][j],load_data['ID'][j],[load_data[ 'status'][j] ,load_data['area'][j] ,load_data['zone'][j], load_data['owner'][j]],[hourload[k][i],0 , 0 ,0 ,0,0]) if i <= 19: reactive = hourload[k][i+1] ierr5 = psspy.load_data(load_data['number'][j],load_data['ID'][j],[load_data[ 'status'][j] ,load_data['area'][j] ,load_data['zone'][j], load_data['owner'][j]],[hourload[k][i],reactive,0,0,0]) # ------################################################## ## Run the power flow agai for new load ########## ################################################## ## if k == 80: ## ErrLF = psspy.fnsl([1,0,0,1,0,1,0,0]) ## else: ErrLF = psspy.fdns([0,0,0,0,0,0,0,0]) # 1.Tap:disable///2.AreaExchange:disable///2.PhaseShif:disable///4.dcTa p:disable///5.ShuntAdj:disable///# 6.FlatStart:enable///7.ApplyVarL:on interations///8.non- divergent:disable # ------################################################## ## Get the new voltage magnitude for new load #### ################################################## ierr, rval = psspy.alodbusreal(-1 ,2 ,['KV']) busvoltage [k-2] = rval [0] ##------
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################################################## ## Send the voltage to the provided csv file ##### ################################################## rvaldimension = len(rval[0]) csvfile = input('Please one .csv file') if csvfile: # open CSV file to write csvfile_h = open(csvfile,'w') report = csvfile_h.write else: # send results to PSS/E report window psspy.beginreport() report = psspy.report for i in range(len(busvoltage[1])): busn = load_data['number'][i] report("%(busn)6d," %vars()) for j in range(len(busvoltage)): busvoltag = busvoltage[j][i] report("%(busvoltag)3.4F," %vars()) report("\n ") # ------################################################## ## Close The CSV file ############################ ################################################## if csvfile: csvfile_h.close() print '\n Done ..... Power Flow Results Report saved to file %s' % csvfile else: print '\n Done ..... Power Flow Results Report created in Report window.' Python code for losses report
################################################## ## Importing library ############################# ################################################## import os import psspy import csv ################################################## ## Defining Functions ############################ ################################################## def array2dict(dict_keys, dict_values): '''Convert array to dictionary of arrays. Returns dictionary as {dict_keys:dict_values} ''' tmpdict = {} for i in range(len(dict_keys)): tmpdict[dict_keys[i].lower()] = dict_values[i] return tmpdict def busindexes(busnum, busnumlist): '''Find indexes of a bus in list of buses. Returns list with indexes of 'busnum' in 'busnumlist'. ''' busidxes = [] startidx = 0 buscounts = busnumlist.count(busnum) if buscounts: for i in range(buscounts):
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tmpidx = busnumlist.index(busnum,startidx) busidxes.append(tmpidx) startidx = tmpidx+1 return busidxes # ------def splitstring_commaspace(tmpstr): '''Split string first at comma and then by space. Example: Input tmpstr = a1 a2, ,a4 a5 ,,,a8,a9 Output strlst = ['a1', 'a2', ' ', 'a4', 'a5', ' ', ' ', 'a8', 'a9'] ''' strlst = [] commalst = tmpstr.split(',') for each in commalst: eachlst = each.split() if eachlst: strlst.extend(eachlst) else: strlst.extend(' ')
return strlst #------def readloaddata(loadcsvfile): j=0 loaddict = {} readcsv = csv.reader(open(loadcsvfile)) for row in readcsv: i=0 for string in row: if j <= 1: row[i] = int(float(row[i])) else: row[i] = float(row[i]) if EOFError: i = i + 1 loaddict[j] = row j = j+1 return loaddict #------################################################## ## Get the integer and real data################## ################################################## ierr1, number_of_loads = psspy.aloadcount(-1, 1) # only in- service loads including those at type 4 buses ierr2, number_of_loadbuses = psspy.alodbuscount(-1, 2) # only in- service load buses including those with only out-of-service loads ierr3, load_id = psspy.aloadchar ( -1, 2, ['ID'] ) ierr4, NAZOS = psspy.aloadint (-1, 2, ['NUMBER', 'AREA' , 'ZONE' , 'OWNER' , 'STATUS' ] ) ierr6, lbv = psspy.alodbusreal (-1,4 , ['KV']) ierr9, area = psspy.aloadint (-1, 2, ['AREA' ] ) NAZOS = array2dict(['NUMBER' , 'AREA' , 'ZONE' , 'OWNER' , 'STATUS' ], NAZOS)
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid load_data = NAZOS load_data['ID'] = load_id [0] # ------################################################## ## Remove repeated laod bus data ################# ################################################## p=range(len(load_data['number'])) m=[] for h in p: for d in p: if load_data['number'][h] == load_data['number'][d] and d > h: n = 0 for s in range(len(m)): if m[s] == d: n = n + 1 if n == 0: m.append(d) m.sort() g=0 for r in range(len(m)): del load_data['number'][m[r]-g] del load_data['area'][m[r]-g] del load_data['zone'][m[r]-g] del load_data['owner'][m[r]-g] del load_data['status'][m[r]-g] del load_data['ID'][m[r]-g] g = g + 1 ##------################################################## ## Write Output Prompt ########################### ################################################## psspy.prompt(' Please enter a CSV file wit hthe following format: \n\ \n\ - The CSV file can be saved in the same directory as Python file in advance or just type a new file name') psspy.prompt("\n\ - The file name has to be typed in the following space in this format: \n\ \n\
*********************'FILENAME.csv'**************************") ################################################## ## Find area numbers ############################# ################################################## area = area[0] p=range(len(area)) m=[] for h in p: for d in p: if area[h] == area[d] and d > h: n = 0 for s in range(len(m)): if m[s] == d:
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n = n + 1 if n == 0: m.append(d) m.sort() g=0 for r in range(len(m)): del area[m[r]-g] g = g + 1 ##Area = load_data['area'] ##for q in range(len(load_data['area'])): ## for w in range(len(Area)): ## if load_data['area'][q] == Area[w]: ## Area['area'][w] = 0 # ------################################################## ## Get load data from a CSV file ################# ################################################## hourload = readloaddata('load.csv') # ------################################################## ## Changing the load in the system ############### ################################################## Lossa=[] Loss = {} dict = 0 for k in range(24): k = k + 2 for i in range(len(hourload[0])): busnumber = hourload[0][i] busid = str(hourload [1][i]) for j in range(len(load_data ['ID'])): busid_PSSE = load_data ['ID'][j] if busid_PSSE[1] == ' ' : busid_PSSE = busid_PSSE[0] if busid == busid_PSSE and busnumber == load_data ['number'][j]: ierr5 = psspy.load_data(load_data['number'][j],load_data['ID'][j],[load_data[ 'status'][j] ,load_data['area'][j] ,load_data['zone'][j], load_data['owner'][j]],[hourload[k][i],0 , 0 ,0 ,0,0]) if i <= 19: reactive = hourload[k][i+1] ierr5 = psspy.load_data(load_data['number'][j],load_data['ID'][j],[load_data[ 'status'][j] ,load_data['area'][j] ,load_data['zone'][j], load_data['owner'][j]],[hourload[k][i],reactive,0,0,0]) # ------################################################## ## Run the power flow again for new load ######### ##################################################
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ErrLF = psspy.fdns([0,0,0,0,0,1,0,0]) # 1.Tap:disable///2.AreaExchange:disable///2.PhaseShif:disable///4.dcTa p:disable///5.ShuntAdj:disable///# 6.FlatStart:enable///7.ApplyVarL:on interations///8.non- divergent:disable # ------################################################## ## Get the new loss in each area with new load ### ################################################## Lossa=[] for q in range(len(area)): ierr12, cmpval = psspy.ardat(area[q], 'LOSS') Lossa.append(cmpval.real) Loss [k-2] = Lossa ##------################################################## ## Send the voltage to the provided csv file ##### ################################################## ##rvaldimension = len(cmpval[0]) csvfile = input('Please one .csv file') if csvfile: # open CSV file to write csvfile_h = open(csvfile,'w') report = csvfile_h.write else: # send results to PSS/E report window psspy.beginreport() report = psspy.report for i in range(len(area)): busn = area[i] report("%(busn)6d," %vars()) for j in range(len(Loss)): busvoltag = Loss[j][i] report("%(busvoltag)3.4F," %vars()) report("\n ") # ------################################################## ## Close The CSV file ############################ ################################################## if csvfile: csvfile_h.close() print '\n Done ..... Power Flow Results Report saved to file %s' % csvfile else: print '\n Done ..... Power Flow Results Report created in Report window.' # ------
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Appendix 2
A2.1 Nockeby
A2.1.1 Penetration of 20 Percent of the PHEVs
A2.1.1.1 Load curve
Regulated Unregulated Without PHEVs
30000.00 25000.00 20000.00 15000.00 10000.00
KWh KWh perhour 5000.00 0.00 0 5 10 15 20 25 30 Time of day
A2.1.1.2 Voltage curve with unregulated distribution of the PHEVs load
26
24
22
20 Load MW in Nockeby Load 18 0 5 10 15 20 25 Time (Hours of day)
6
5.5
5
4.5 0 5 10 15 20 25 Reactive power MVArReactive in Nockeby power Time (Hours of day)
11.4
11.35
11.3
11.25
Voltage Voltage (kV) in Nockeby 11.2 0 5 10 15 20 25 Time (Hours of day)
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A2.1.1.3 Voltage curve with regulated distribution of the PHEVs load
24
23
22
21
20 Load MW in Nockeby Load 19 0 5 10 15 20 25 Time (Hours of day)
5.4 5.3 5.2
5.1 5 4.9
0 5 10 15 20 25 Reactive power ReactiveMVAr in power Nockeby Time (Hours of day)
11.32
11.3
11.28
11.26
11.24
Voltage Voltage (kV) in Nockeby 11.22 0 5 10 15 20 25 Time (Hours of day)
A2.1.2 Penetration of 40 Percent of the PHEVs
A2.1.2.1 Load curve
Regulated Unregulated Without PHEVs
30000.00
25000.00
20000.00
15000.00
10000.00 KWh perhour KWh 5000.00
0.00 0 5 10 15 20 25 30 Time of day
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A2.1.2.2 Voltage curve with unregulated distribution of the PHEVs load
30
25
20 Load MW in Nockeby Load 15 0 5 10 15 20 25 Time (Hours of day)
8
7
6
5
4 0 5 10 15 20 25 Reactive power MVAr Reactivein Nockeby power Time (Hours of day)
11.35
11.3
11.25
11.2
11.15 Voltage Voltage (kV) in Nockeby 0 5 10 15 20 25 Time (Hours of day)
A2.1.2.3 Voltage curve with unregulated distribution of the PHEVs load
12.5
12
11.5
11 Load MW in Load Nockeby 10.5 0 5 10 15 20 25 Time (Hours of day)
3.1
3
2.9
2.8
2.7
2.6 0 5 10 15 20 25 Reactive power ReactiveMVAr power in Nockeby Time (Hours of day)
11.44
11.42
11.4
Voltage Voltage (kV) in Nockeby 11.38 0 5 10 15 20 25 Time (Hours of day)
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A2.2 Brunkeberg
A2.2.1 Penetration of 20 Percent of the PHEVs
A2.2.1.1 Load curve
Unregulated Regulated Without PHEVs
80000.00 70000.00 60000.00 50000.00 40000.00 30000.00
KWh KWh perhour 20000.00 10000.00 0.00 0 5 10 15 20 Time of day
A2.2.1.2 Voltage curve with unregulated distribution of the PHEVs load
80
70
60
50
40
30
Active MW in Brunkeberg power 0 5 10 15 20 25 Time (Hours of day)
2
0
-2
-4
-6 0 5 10 15 20 25 Reactive power ReactiveMVAr in power Brunkeberg Time (Hours of a day)
11.15
11.1
11.05
11
Voltage (kV) in Brunkeberg 10.95 0 5 10 15 20 25 Time (Hours of day)
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A2.2.1.3 Voltage curve with regulated distribution of the PHEVs load
80
70
60
50
40
30
Active MW in Brunkeberg power 0 5 10 15 20 25 Time (Hours of day)
2
0
-2
-4
-6 0 5 10 15 20 25
Reactive power MVArReactive in power Brunkeberg Time (Hours of a day)
11.15
11.1
11.05
11
Voltage (kV) in Brunkeberg 10.95 0 5 10 15 20 25 Time (Hours of day)
A2.2.2 Penetration of 40 Percent of the PHEVs
A2.2.2.1 Load curve
Unregulated Regulated Without PHEVs
80000.00 70000.00 60000.00 50000.00 40000.00 30000.00
KWh KWh perhour 20000.00 10000.00 0.00 0 5 10 15 20 Time of day
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A2.2.2.2 Voltage curve with unregulated distribution of the PHEVs load
80
70
60
50
40
30 0 5 10 15 20 25 Active MW in Brunkeberg power Time (Hours of day)
2
0
-2
-4
-6 0 5 10 15 20 25
Reactive power ReactiveMVAr power in Brunkeberg Time (Hours of a day)
11.1
11.05
11
10.95
Voltage (kV) in Brunkeberg 10.9 0 5 10 15 20 25 Time (Hours of day)
A2.2.2.3 Voltage curve with regulated distribution of the PHEVs load
80
70
60
50
40
30 0 5 10 15 20 25 Active MW in Brunkeberg power Time (Hours of day)
2
0
-2
-4
-6 0 5 10 15 20 25
Reactive power MVArReactive power in Brunkeberg Time (Hours of a day)
11.15
11.1
11.05
11
Voltage Voltage (kV) in Brunkeberg 10.95 0 5 10 15 20 25 Time (Hours of day)
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A2.3 Gärdet
A2.3.1 Penetration of 20 Percent of the PHEVs
A2.3.1.1 Load curve
Unregulated Regulated Without PHEVs
90000.00 80000.00 70000.00 60000.00 50000.00 40000.00 30000.00 KWhperhour 20000.00 10000.00 0.00 0 5 10 15 20 25 30 Time of day
A2.3.1.2 Voltage curve with unregulated distribution of the PHEVs load
100
80
60 Load MW Load in Gärdet 40 0 5 10 15 20 25 Time (Hours of day)
15
10
5
0
-5 0 5 10 15 20 25 Reactive power MVArReactive in power Gärdet Time (Hours of a day)
11.1
11.05
11
10.95
Voltage Voltage (kV) in Gärdet 10.9 0 5 10 15 20 25 Time (Hours of day)
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A2.3.1.3 Voltage curve with regulated distribution of the PHEVs load
80
70
60
50 Load MW Load in Gärdet 40 0 5 10 15 20 25 Time (Hours of day)
15
10
5
0
-5 0 5 10 15 20 25 Reactive power ReactiveMVAr in power Gärdet Time (Hours of day)
11.1
11.05
11
10.95
Voltage (kV) in Gärdet 10.9 0 5 10 15 20 25 Time (Hours of day)
A2.3.2 Penetration of 40 Percent of the PHEVs
A2.3.2.1 Load curve
Unregulated Regulated Without PHEVs
120000.00
100000.00
80000.00
60000.00
40000.00 KWh KWh perhour 20000.00
0.00 0 5 10 15 20 25 30 Time of day
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A2.3.2.2 Voltage curve with unregulated distribution of the PHEVs load
100
80
60 Load MW Load in Gärdet 40 0 5 10 15 20 25 Time (Hours of day)
15
10
5
0
-5 0 5 10 15 20 25 Reactive power ReactiveMVAr power in Gärdet Time (Hours of a day)
11.1
11.05
11
10.95
Voltage (kV) in Gärdet 10.9 0 5 10 15 20 25 Time (Hours of day)
A2.3.2.3 Voltage curve with regulated distribution of the PHEVs load
90
80
70 Load MW in Gärdet Load 60 0 5 10 15 20 25 Time (Hours of day)
15
10
5
0
-5 0 5 10 15 20 25 Reactive power MVArReactive in Gärdet power Time (Hours of a day)
11.1
11.05
11
10.95
Voltage (kV) in Gärdet 10.9 0 5 10 15 20 25 Time (Hours of day)
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Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid
A2.4 PSS/E .sld files
A2.4.1 Nockeby
A2.4.1 Gärdet and Brunkeberg
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