POLITECNICO DI MILANO Scuola di Ingegneria Industriale e dell’Informazione Corso di Laurea Magistrale in Ingegneria Energetica
Energy Scenarios for Sustainable Development in the Mediterranean Region: the Case of Egypt
Relatore: Ch.ma Prof. Emanuela COLOMBO Correlatore: Dott. Gabriele CASSETTI
Tesi di Laurea di: Matteo AGOSTINELLI Matr. 804817
Anno Accademico 2014-2015
«Common sense in an uncommon degree is
what the world calls wisdom»
Samuel Taylor Coleridge
i
ii
Ringraziamenti
Giunto alla fine di questo percorso, dopo più di 5 anni di studi, sono davvero moltissime le persone che devo ringraziare. In primo luogo ringrazio i miei genitori: grazie per essere stati sempre presenti. Grazie per avermi sopportato e incoraggiato costantemente, senza stancarvi mai. Ringrazio mio fratello Alessio e la sua famiglia, per la serenità che sono sempre riusciti a trasmettermi e per avermi sempre ascoltato. Un ringraziamento particolare va anche a mio zio, per i preziosi consigli e la comprensione. Grazie a tutti i miei compagni di studi: con la vostra vicinanza vi siete rivelati amici più che semplici colleghi. In particolare ringrazio il gruppo storico, nonostante vi abbia spesso “paccato”, senza di voi questa esperienza sarebbe stata molto più monotona e decisamente meno divertente, quindi grazie Gian, Bri, Leo, Parlop, Edo, Cri e Mae. Grazie agli amici che in questi anni mi sono stati d’aiuto e supporto nelle difficoltà e con i quali ho condiviso avventure ed esperienze che mi hanno reso la persona che sono oggi. Siete troppi per essere citati singolarmente e rischierei di dimenticarne qualcuno, spero che non vi offendiate. Ho sempre cercato di dimostrarvi quanto la vostra amicizia sia fondamentale nella mia quotidianità e spero di essere riuscito a trasmettere ad almeno la metà di voi perlomeno metà dell’affetto che vi meritate. In caso contrario ci vedremo al Potus per una birra. Ringrazio Laura per la sua storica amicizia, per la sua allegria contagiosa e per avermi aspettato tutto questo tempo per la festa. Un grazie va anche a Simone, per la sua disponibilità e perché assieme a R&T mi hanno regalato innumerevoli ore di divertimento e di sfogo. Grazie a tutti voi, membri della GBS, che siete cresciuti insieme a me in questi anni. Grazie perché siete stati sempre presenti, soprattutto nei momenti difficili. Ho trovato in voi un vero tesoro. Grazie Presidente dimissionario. Infine grazie a te, Martina. Mi hai sempre incoraggiato e sostenuto, mi sei sempre stata accanto e so che non mi lascerai mai solo. Matteo
iii
iv
Index
RINGRAZIAMENTI ...... III
INDEX ...... V
ABSTRACT ...... IX
ESTRATTO IN LINGUA ITALIANA ...... XI
INTRODUCTION ...... 1
1.1 TRINEX PROJECT DESCRIPTION AND SUBSEQUENT DEVELOPMENT ...... 4
CHAPTER 2: NILE BASIN ANALYSIS ...... 7
2.1 THE RIVER NILE FRAMEWORK ...... 7
2.1.1 The Nile River...... 8
2.1.2 WEF Nexus in the Nile Basin ...... 10
2.1.3 Policy framework in Nile Basin ...... 16
2.1.4 Projects and strategies ...... 19
2.2 THE NILE QUESTION FROM AN EGYPTIAN PERSPECTIVE ...... 21
2.2.1 Water ...... 22
2.2.2 The impact of water shortage on food security ...... 25
2.2.3 Energy ...... 31
CHAPTER 3: METODOLOGICAL APPROACH ...... 41
3.1 GENERAL OVERVIEW ...... 41
3.1.1 Bottom-up Models ...... 42
3.1.2 LEAP: Long range Energy Alternatives Planning system ...... 44
3.2 FRAMEWORK OF ENERGY DEMAND MODEL ...... 49
3.2.1 Activity Analysis ...... 51
3.3 TRANSFORMATION CALCULATION ...... 53
3.3.1 Endogenous Capacity Expansion Calculations ...... 57
3.3.2. Process Dispatch Calculations ...... 58
3.3.3 Dispatching Processes on a Load Curve ...... 59
v
3.3.4 Shortfall and Surplus Calculations ...... 61
3.3.5 Calculation of Systems with Feedback Flows ...... 61
3.4 SCENARIO ANALYSIS ...... 62
3.5 OPTIMIZATION...... 65
3.5.1 OSeMOSYS Formulation ...... 67
CHAPTER 4: MODEL STRUCTURE ...... 75
4.1 INTRODUCTION ...... 75
4.2 INPUT DATA AND ASSUMPTIONS ...... 76
4.2.1 Socioeconomic Variables ...... 76
4.2.2 Boundaries and Assumptions ...... 79
4.2.3 Resources ...... 80
4.2.4 Fuel Prices ...... 82
4.3.5 Start up costs and fuel use of power plants ...... 83
4.2.6 Investments ...... 84
4.3 ENERGY DEMAND SETTING AND FORECAST ...... 85
4.3.1 Demand Setting ...... 86
4.3.2 Demand Analysis ...... 91
4.3.3 Demand Forecast Analysis ...... 94
4.4 TRANSFORMATION ...... 97
4.4.1 Transformation Processes and Losses ...... 98
4.4.2 Electricity Generation ...... 99
4.4.3 Oil Refining ...... 103
CHAPTER 5: SCENARIO SIMULATION AND RESULTS ...... 104
5.1 SCENARIO DESCRIPTION...... 104
5.1.1 REF: Reference Scenario ...... 105
5.1.2 GOV: Governmental Investment Scenario ...... 105
5.1.3 OPT: Optimized Investment Scenario ...... 107
vi
5.1.4 REN: Renewable Scenario ...... 107
5.2 REFERENCE SCENARIO ...... 109
5.2.1 Sensitivity Test...... 110
5.2.2 Generation Reaction to the demand forecast ...... 112
5.3 GOVERNMENTAL INVESTMENT SCENARIO ...... 114
5.3.1 Electricity Production ...... 114
5.3.2 Energy Mix ...... 116
5.3.3 Resources Requirement...... 118
5.4 RENEWABLE SCENARIO ...... 120
5.4.1 Energy Mix Changes ...... 120
5.4.2 Energy Savings ...... 123
5.5 COST COMPARISON ...... 124
5.6 ANALYSIS OF NILE WATER REDUCTION PERSPECTIVE ...... 126
5.6.1 Results of the analysis ...... 128
CONCLUSIONS ...... 131
LIST OF FIGURES ...... 135
LIST OF TABLES ...... 139
ACRONYMS, ABBREVIATIONS ...... 143
BIBLIOGRAPHY ...... 145
APPENDIX I - WATER BALANCE IN THE NILE BASIN ...... 151
APPENDIX II - SOCIOECONOMIC VARIABLES FORECAST RESULTS...... 153
APPENDIX III - GENERATION EXPANSION PLANS ...... 155
vii
viii
Abstract
In this thesis work I have addressed important aspects related to the role played by uncertainties in energy development strategies evaluation process at country level. In particular, I have proposed a combined approach, using statistical forecasting methods and WEF analysis which allowed the quantification of key parameters such as energy security in the analysis of development scenarios.
In this context a model of Egyptian energy system, combining statistical forecasting in the operative and environmental conditions, WEF Nexus analysis and advanced simulation techniques is developed. The computational framework, which allowed to simulate and evaluate development scenarios, is shown able to reproduce a realistic response. Interesting results are shown about Governmental development plans.
The first part of this work focuses on the realization of an analysis of the country through an integrated approach (Water Energy and Food Nexus approach) to identify the key variables in the development problem. Then the focus shifts to the realization of a simulation model, considering the previously highlighted variables, able to forecast the energy system growth and reaction to external variations. The novelty of the approach lies in the realization of a model utilizing both statistical forecasting and WEF analysis as drivers.
The second part of this work focuses on the implementation on the Egyptian context and the analysis of different scenarios. The novelty of the approach is the quantification of the consequences of energy system development policies with a relation to WEF issues, in particular related to the future management of the Nile River at basin level in the next decades. The Governmental strategy has been evaluated and changes to the development plan have been proposed in order to achieve the objectives declared by Egypt Government.
ix
x
Estratto in Lingua Italiana
In questo lavoro di tesi ho affrontato importanti aspetti legati al ruolo svolto dalle incertezze nel processo di valutazione di strategie di sviluppo energetico a livello Paese.
In particolare, ho proposto un approccio combinato, utilizzando sia metodi di previsione statistica sia un’analisi integrata relativa ad acqua, energia e cibo (WEF Nexus) che hanno permesso la quantificazione dei parametri chiave quali la sicurezza energetica per l'analisi di scenari di sviluppo.
In questo contesto è stato realizzato un modello del sistema energetico egiziano congiungendo: metodi di previsione statistica in condizioni operative e soggetti a variabili ambientali, un’analisi delle interconnessioni WEF e tecniche avanzate di simulazione. Il modello, che ha permesso di simulare e valutare scenari di sviluppo, è stato in grado di riprodurre una risposta realistica. Inoltre sono stati ottenuti risultati importanti dalla simulazione del piano di sviluppo governativo Egiziano.
La prima parte di questo lavoro si concentra sulla realizzazione di un'analisi del paese attraverso un approccio integrato (WEF Nexus) per identificare le variabili chiave da tenere in considerazione in un piano di sviluppo energetico. Successivamente l’attenzione si è spostata sulla realizzazione di un modello di simulazione in grado di prevedere la crescita del sistema energetico e le reazioni di questo alle variabili esterne precedentemente identificate. La novità dell'approccio consiste nella realizzazione di un modello utilizzando sia previsioni statistiche sia un’analisi WEF.
La seconda parte di questo lavoro si concentra sulla realizzazione e l'analisi di diversi scenari energetici ottenuti attraverso l’utilizzo del modello precedentemente elaborato. La novità di questo approccio è la quantificazione delle conseguenze di piani di sviluppo energetico e la valutazione dell’incidenza su di essi di variabili esterne (come ad esempio la riduzione della portata del fiume Nilo). È stata valutata la strategia governativa e sono state proposte delle integrazioni che permettessero il raggiungimento degli obiettivi governativi in termini di mix energetico.
xi
xii
Introduction
Egypt is currently experiencing both economic growth and a steep rise in population, resulting in a high growth in terms of water, energy and food needs. In order to meet this growing demand, Egypt is exploring paths of development and strategies to improve its water management, its energy system and the food subsidy policies.
In this context the TriNex project (Knowledge-Triangle Platform for the Water-Energy-Food Nexus), a project that received founds by European Union and in which European and Egyptian universities collaborate, aims to develop research and innovation in a multidisciplinary way to provide sustainable development strategies. The key issue of TriNex strategies is the WEF (Water Energy and Food) Nexus approach, a kind of analysis that evaluates water energy and food problems in an integrated way.
This thesis is deeply linked to TriNex project with whom shares the case study, Egypt, and the approach. In detail I am proposing a combined approach, which unites statistical forecasting methods and WEF nexus analysis in order to quantify the key parameters for sustainable development scenarios.
Talking about Water, Energy and Food nexus I mean that the three aspects are inextricably linked and that actions in one area more often than not have impacts in one or both of the others. The security of the nexus means:
• Water security that is defined in the Millennium Development Goals (MDGs) as “access to safe drinking water and sanitation”, both of which have become a human right.
• Energy security that has been defined by the United Nations as “access to clean, reliable and affordable energy services for cooking and heating, lighting, communications and productive uses” and as “uninterrupted physical availability of energy at a price which is affordable, while respecting environment concerns”.
• Food security that is defined by the Food and Agricultural Organization (FAO) as “availability and access to sufficient, safe and nutritious food to meet the dietary needs and
1 Introduction
food preferences for an active and healthy life”. Adequate food has also been defined as a human right.
The emphasis on access in these definitions also implies that security is not so much about average availability of resources; it has to encompass variability and extreme situations such as droughts or price shocks, and the resilience of the poor.
The WEF Nexus approach integrates management and governance across sectors and scales. A nexus approach aims, among other things, at resource use efficiency and greater policy coherence. Given the increasing interconnectedness across sectors and in space and time, a reduction of negative economic, social and environmental externalities can increase overall resource use efficiency, provide additional benefits and secure the human rights to water and food. In a nexus-based approach in policy and decision-making would give way to an approach that reduces trade-offs and builds synergies across sectors.
Referring to statistical forecasting I mean the process of making predictions of the future based on past and present data and analysis of trends. This approach requires a deep knowledge, so an accurate analysis, of the available, present and historical, data to choose among the forecasting methods the best suited one and an evaluation of the accuracy of the forecast.
In order to relate these approaches and synthetize them I decided to use a software tool, LEAP (Long Energy Alternatives Planning System), to help me in the organization of the modules of the energy system, to take into account and better analyse the flows of fuels through the processes, and to better confront the different scenarios.
In order to better organize the issues of the analysis and to evaluate correctly the march of the work I divided it in three steps trying to achieve different goals. The different objectives I wanted to obtain are:
• An accurate analysis of Egyptian situation from a WEF nexus approach, with a deep focus of the collected data. This is done in order to discover the critical variables and to choose the correct forecasting method
• The realization of a model of Egyptian energy system utilizing both the forecasting approach and the data from WEF nexus approach analysis. After the modelling part, this section includes also the evaluation of the model itself.
2 Chapter 1
• The implementation in the model of developing strategies to evaluate the future scenarios and the creation of alternatives related to non-energy variables from the WEF nexus. In doing this I implemented the Governmental capacity addition strategy and I tried to evaluate possible addition or changes to it both in a normal condition and in a Nile flow reduction perspective.
The framework of this thesis is divided in four chapters reporting analyses, methodology, model structure and results.
The first part, Chapter 2, consists of the analysis of the Nile basin from a WEF Nexus approach, where all the challenges and the critical issues in the path of development are highlighted. Than the analysis focuses on Egyptian water, energy and food issues to achieve a better understanding of the relations among these key resources. This chapter is a turning point to evaluate variables that must be taken into account in doing development strategies.
The second part focuses on the methodological issues of the model, here are reported the mathematical formulas that drive the model through a description of LEAP functioning. As stated before, LEAP has been used as a tool to elaborate the model, so, focusing on its calculation flow, a better understanding of the model is achieved. To model an energy system, demand, transformation and resources are taken into account, and here are reported the logical features of their analysis and how they have been modelled from a literature point of view.
In Chapter 4, the third part, the assumptions and the boundaries of the model are described and discussed. This is a key issue in the realization of a model because through these hypotheses the shape of the forecast is outlined. Than the main parts of the framework are analysed, energy demand and transformation modules. Beginning year’s demand is described and, through the forecasting process, his evolution is evaluated. Transformation processes are described with particular attention to the flow of fuels among them.
The last chapter presents the scenarios and reports their evaluation. The first scenario evaluated is the Reference scenario, which is built to make an esteem of the accuracy of the model. Than other scenarios are reported and confronted in terms of energy mix and costs, to highlight if they can succeed in reaching the governmental objectives of 20% of electricity
3 Introduction
from renewable sources in 2030 and 30% by solar in 2030. In the end an evaluation of the development strategies reaction to a critical issue highlighted by the WEF nexus analysis is set up. In detail the development strategies face a reduction of the Nile river flow elaborated from a literature review.
1.1 TriNex project description and subsequent development
The Knowledge-Triangle Platform for the Water-Energy-Food Nexus (TriNex) is a multidisciplinary project aimed at addressing the deep-rooted research and innovation vacuum in Egypt. With its emphasis on the importance of the Water-Energy-Food (WEF) Nexus, as the launching pad for Egypt’s development, will provide a unique and vital boost to Egypt’s ailing research establishment with particular focus on Egypt’s looming security difficulties.
The project aims at improving the role of universities in the Egyptian society by developing a national strategy and a university platform to promote the WEF-Nexus and the related synergy between Research, Education and Innovation (Knowledge-Triangle).
Five are the specific objectives of the project:
• Developing a National Strategy & Platform for the WEF-Nexus. • Establishing WEF-Nexus Coordination Bodies in the partner universities • Creating a qualified generation of WEF academicians. • Developing and pilot delivering of an EU-EG PhD Summer School to strengthen cooperation between junior researchers. • Developing a Web Based Knowledge-Sharing System to facilitate academic cooperation.
The kick-off meeting of the project was held from 4 to 5 March 2014 at the American University in Cairo. After a presentation of the general concept and its particular relevance
4 Chapter 1
for sustainable development in Egypt, all consortium members (Main Partners: American University in Cairo, University of Cairo, Alexandria University, Heliopolis University, TuGraz, Montpellier Supagro, RWTH Aachen, Politecnico di Milano) presented the profile of their institutions and the members of the project team were introduced. This helped uncovering the different areas of research expertise of the participants in the project and also the experiences in other international large scale projects, which raises the expectation of a promising implementation and successfully reaching the objectives set.
The TriNex project will be implemented in stages over a period of 36 months and split into nine overlapping work packages. Five of these work packages will be development oriented, while the remaining four will cover dissemination, exploitation, the quality plan, and management of the project.
The development oriented work packages will lay the foundation by creating the project infrastructure necessary for continuation such as developing the coordination bodies and PhD programme that are so central to the project’s activity. Once these are in place, subsequent work packages will involve the dissemination of the project in order that the results be readily accessible for addressing the WEF Nexus in the real world, as well as dealing with the management and quality control of the project.
With the implementation of these work packages the infrastructure, then researchers, and finally the means to disseminate the results will come about in a systematic and orderly fashion. Beyond the lifetime of the project, the bodies it creates will be permanently in place to serve the needs of Egyptian society.
The project is ongoing and has already achieved, through collaboration between the consortium members, some of its main objectives.
5 Introduction
6
Chapter 2: Nile Basin Analysis
2.1 The River Nile Framework
The Nile Basin drains a total of approximately three million square kilometers of territory in eleven riparian countries: Ethiopia, Sudan, South Sudan, Egypt, Rwanda, Tanzania, Uganda, Burundi, Democratic Republic of Congo, Eritrea, and Kenya [1]. The Basin's climate ranges from tropical in the equatorial region of the Great Lakes area and the Ethiopian highlands, to arid in Sudan and Egypt [2]. Much of this population relies almost exclusively on the Nile as its source of freshwater and the agricultural sector, which constitutes a significant portion of the economy of the basin, is heavily dependent on crops that require extensive irrigation (Table 1
Based on measurements taken at Aswan at Lake Nasser in Egypt, the Nile's flow has diminished over the last century. The Nile's flow is also highly seasonal, approximately 80% of its flow occurs from August to October.
The Nile Basin is also thought to be particularly sensitive to the impact of climate change and prone to climate-induced water scarcity [4]. These factors are especially notable in a river basin where many riparians are already considered “water scarce” by 2050 [3].
The Nile waters play a vital role in the socio-economic development of the Nile Basin Countries. Agriculture is the dominant economic sector in most Nile riparian countries, and reliable access to water remains key to increasing agricultural productivity, providing employment, and raising the standards of living of the people residing in the basin.
7 Nile Basin Analysis
Table 1 - Per capita GDP, HDI index and% of area covered by the Nile for each Nile Basin Country
GDP per HDI As% of total As% of total capita area of the basin area of the country
Burundi 267,47 0,355 0,4 47,6
Rwanda 632,75 0,506 0,6 75,5
Tanzania 694,77 0,488 2,7 8,9
Kenya 994,3 0,535 1,5 8
Democratic Republic 453,67 0,338 0,7 0,9 of the Congo
Uganda 571,67 0,484 7,4 98,1
Ethiopia 498,07 0,435 11,7 33,2
Eritrea 543,82 0,381 0,8 20,4
Sudan 1752,9 0,473 63,6 79
South Sudan 1221,35 0,379
Egypt 3314,46 0,682 10,5 32,6
2.1.1 The Nile River
At 6825 km, the Nile is the longest river in the world. It has two main tributaries (Figure 1): the White Nile, originating from the Equatorial Plateau of East Africa, and the Blue Nile, with its source in the Ethiopian highlands. Other significant tributaries are the Atbara and the Sobat, both originating in the Ethiopian highlands. All tributaries begin their journeys in
8 Chapter 2
relatively humid areas, with an annual rainfall of 1200 to 1500 mm [5]. The downstream stretch of the river, by contrast, flows northwards to the Mediterranean through the Sahara Desert. While the Blue Nile flows are highly seasonal, the White Nile waters have a steady flow but contribute only 10 to 20% of the total Nile runoff. Lake Nasser, a major reservoir on the Sudan-Egypt border, provides inter-annual regulation for Egypt [6].
Figure 1 - Nile Basin scheme - State of the Nile River Basin 2012 Report NBI, chapter 2, The Water Resources of the Nile Basin
The Nile flow is small in relation to its area. From a hydrologic point of view, this is among the most characteristic features of the Nile. In spite of the size of its basin, which measures more than 3 million 푘푚2, the mean annual flow of 80 • 109 m3 equals that of the Rhine[6]. If this total yearly volume of runoff were spread over the entire watershed, it would represent a layer of not more than 30 mm.
The Nile is the only significant source of water for the downstream riparians [23]. Egypt and northern Sudan are situated in a hot and arid region with only sparse and insignificant rainfall (see
9 Nile Basin Analysis
Table 2 and Appendix I). Close to 80 million people in the downstream stretch of the river depend exclusively on the Nile for their water supply [6].
Table 2 - Nile basin countries water balances - Virtual water ‘flows’ of the Nile Basin, 1998-2004: A first approximation and implications for water security, M. Zeitouan, J.A. Allan, Y. Mohieldeen, Global Environmental Change 20 ( 2010)
2.1.2 WEF Nexus in the Nile Basin
Agriculture Large-scale traditional irrigated agriculture is practiced throughout the Nile Basin, excepting Burundi, Rwanda and the Democratic Republic of the Congo. The major large scale traditional systems are located in Egypt and the Sudan [7]. Together with the small scale traditional irrigation categories, it covers 7.3 million ha (2% of the basin), an area inhabited by over 56 million people (34% of the total). The average population density is 770/ha, within over 4.2 million ha of cropland, thus giving 0.08 ha cropland/person. This farming system is highly dependent on the availability of stored water and irrigation services.
The system is complex and, like any large scale irrigation system, it is centrally managed and generally mechanized [16]. It is designed to use high levels of agrochemicals to maximize production, with full or partial water control. Crop failure is generally not a problem; consequently the incidence of poverty is lower than in other farming systems and
10 Chapter 2
the absolute numbers of poor are small. However, livelihoods are vulnerable to water shortages, breakdowns and higher input prices [7]. In many cases, the commercial crops grown on irrigated areas are now supplemented by rainfed cropping and animal husbandry, added in recognition of the need to ensure local food security.
Water When a water scarce country ‘imports’ crops and livestock produced in another state, it links its food security to the international market and its own diverse economy, freeing it partly from its local climate and growing conditions [8]. A country ‘export’ of virtual water in the form of crops may lead to improved relations and thus greater national security [9] [10]. A country may further draw upon virtual water ‘trade’ to weather economic uncertainty by significantly increasing levels of economic development.
Table 3 - Water requirement by crops in Nile Basin countries balances - Virtual water ‘flows’ of the Nile Basin, 1998-2004: A first approximation and implications for water security, M. Zeitouan, J.A. Allan, Y. Mohieldeen, Global Environmental Change 20 ( 2010)
The virtual water content of is listed in terms of weight of production, i.e. ‘x’ cubic meters per ton for each type of crop or livestock, in keeping with the method established by Chapagain and Hoekstra [11].
The method enables the comparison of the volumes of virtual water flows, and contributes to the concept of water security, though its direct use to food security or economic policy is limited [12] [24]:
11 Nile Basin Analysis
Crop-derived virtual water ‘trade’ within the Nile Basin is small relative to crop-derived virtual water ‘trade’ from outside and into the basin. 900 million m3 of water in the form of crops is ‘traded’ annually between Nile Basin countries (three-quarters of which derives from rainfed agriculture). This figure compares with roughly 39 • 103 million m3/y ‘imported’ in the form of crops each year by Nile Basin countries from the rest of the world. In other words, intra-basin virtual water crop ‘trade’ is about 2.3% of virtual water ‘imported’ by all countries. Roughly 11 • 103 million m3/y is ‘exported’ in the form of crops by Nile Basin counties to countries outside the basin (over 90% of which derives from rainfed agriculture) [25]. There is a strong net virtual water ‘trade’ ‘deficit’ between Nile Basin countries and the rest of the world of about 28 • 103 million m3/y. The results shows that intra-basin crop trade alone is not in the order of magnitude necessary to secure the water and food of the Nile Basin countries [12]. There is furthermore a very high degree of dependence on the part of some Nile Basin countries on virtual water from outside the basin. There is a strong discrepancy in levels of ‘trade’ and dependence between Southern and Eastern Nile countries. Despite the relatively much greater river flows in the Eastern Nile, Southern Nile countries produce nearly four times (722 million m3/y compared with 183 million m3/y) more than Eastern Nile countries of the virtual water ‘traded’ within the basin [12]. Similarly, Southern Nile countries produce more than three times more than their Eastern co-riparians (8582 million m3/y compared with 2739 million m3/y) of the virtual water traded with countries outside of the basin. There is furthermore a pronounced net virtual water ‘trade’ surplus, Eastern Nile countries ‘import’ about 500 million m3/y of virtual water in the form of crops from Southern Nile countries. The bulk of these ‘flows’ are from tea imported by Sudan and Egypt from Kenya, as well as coffee imported by Sudan from Uganda. Indeed, essentially all of this trade surplus derives from rainfed agriculture [11]. Southern Nile countries ‘import’ in return only about 90 million m3/y, essentially all of which is from irrigated agriculture (mainly sugar imported by Kenya from Egypt and the Sudan).
12 Chapter 2
Energy Electricity consumption per capita in the Nile region is very low compared to the rest of the world. All the Nile countries with the exception of Egypt had a per capita electricity consumption of less than 200 kWh/year in 2011, compared to the 10,000 kWh/year or higher that is common in the industrialized world [6]. Egypt's per capita consumption (1,769 KWh/year), although higher than that of the other basin countries, is still below the world average of 2,803 KWh/year [43] (Figure 2).
Figure 2 - Electrification rates in Nile Basin countries scheme - State of the Nile River Basin 2012 Report NBI, chapter 6, Hydropower Potential and the Region’s Rising Energy Demand
The low electrification rates constitute a formidable bottleneck to economic development, as it is widely recognized that access to cheap electricity plays a significant role in poverty alleviation and in promoting economic productivity [6]. A further implication of the low electrification rates is that large segments of the population, in particular in rural areas, remain dependent on biomass energy sources that include wood and charcoal, with serious consequences to the environment in terms of deforestation and land degradation. Lands left bare of vegetation are prone to soil erosion and become less productive as fertile soils are washed away during rainstorms. Other impacts of using traditional biomass energy include increased respiratory diseases due to indoor air pollution, lower productivity because of time spent in the search for wood fuel, and negative impacts on girl children, who spend insufficient time in education because of gathering firewood [6].
13 Nile Basin Analysis
Throughout 2011, the power supply situation in most of the Nile Basin countries was extremely challenging. Most were unable to meet the projected national demand. They also fell short of the constrained demand resulting in substantial load curtailment in a number of countries [6].
Figure 3 - Hydropower potential in Nile Basin countries scheme - State of the Nile River Basin 2012 Report NBI, chapter 6, Hydropower Potential and the Region’s Rising Energy Demand
The Nile also represents a vast resource for hydropower generation [13]. The extent of dependence on hydropower as an energy source varies from country to country. Hydropower is the single most important source of electricity in Democratic Republic of Congo, Ethiopia, Burundi, and Uganda, and it provides a substantial share of total power production in Kenya, The Sudan, Tanzania, and Rwanda (Figure 3). However, its contribution is relatively insignificant in Egypt, South Sudan, and Eritrea. The latter two countries rely almost entirely on thermal plants and operate no hydropower facilities, although South Sudan has vast untapped hydropower potential [6]. Existing hydropower generation facilities only represent a small portion (26%) of the potential capacity.
14 Chapter 2
Despite the limited amount of available water, many riparians, particularly Egypt and Ethiopia, have ambitious plans to use more water and develop hydroelectricity projects along the Nile [14]. One example is given in Figure 4 [13]. Egypt has also embarked on the New Valley Project (also known as the Toshka Project), designed to redirect 10% of its allotment from the Nile (approximately 4.94 billion m3) to essentially establish and maintain a second Nile Valley and increase habitable land in Western Desert [15].
Figure 4 - Nile Decision Support Tool (DST) for River Simulation and Reservoir Operation (RS-RO) module for water resources management. It includes 14 existing or planned reservoirs, 20 existing or planned hydropower plants, 13 inflow nodes, 16 river nodes, and 15 demand nodes - FAO Synthesis Report, Information Products for Nile Basin Water Resources Management, Rome 2011
15 Nile Basin Analysis
2.1.3 Policy framework in Nile Basin
The 1929 Nile Waters Agreement between Sudan and Egypt further prioritized Egyptian water needs and purported to give Egypt the right to veto future hydroelectric projects in British colonies (which then included Kenya, Sudan, Tanganyika, and Uganda) along the Nile [14].
Sudan and Egypt subsequently replaced the 1929 treaty in 1959 with the Agreement for the Full Utilization of the Nile Waters, which essentially allocated the entire flow of the Nile at the Aswan Dam to Sudan and Egypt. This caused regional tension amongst the other riparians, who invoked the Nyerere Doctrine, and general principles of international water law to contest the 1959 Agreement and claim a share of Nile waters [16].
In1992, following a series of consultations with Nile Basin countries, the ministries responsible for water affairs met in Kampala to approve the establishment of the Technical Cooperation Committee for the Promotion of the Development and Environmental Protection of the Nile Basin (TECCONILE) [14]. TECCONILE aimed to contribute in the development of the Nile Basin in an integrated and sustainable manner through basin-wide cooperation and the determination of equitable sharing of its waters. The objectives were to develop infrastructure, techniques and build capacity for the management of water resources and to formulate national master plans and integrate them into a Nile Basin Action Plan [14]. TECCONILE became operational in 1993 with the signing of the TECCONILE Agreement by Ministers from Egypt, Sudan, Rwanda, Tanzania, Uganda and Zaire. Ethiopia and Kenya refused to join as full members. These two Nile riparians objected to TECCONILE because its framework did not address the fundamental issue of equitable water apportionment. In addition, Egypt was again perceived to dominate [3].
Nevertheless, as part of the meetings of TECCONILE, the Nile River Basin Action Plan was created. All of the Nile Basin countries were involved in the creation of this plan, which was formerly adopted by the Council of Ministers for Water Affairs of all the Nile Basin (“Nile- COM”) countries in 1995 [13].
16 Chapter 2
The Nile Basin Initiative In February 1999, the water ministers of nine of the then ten Nile riparians (Eritrea participated only as an observer) agreed to replace TECCONILE with the Nile Basin Initiative (NBI).The primary purpose of the NBI was to seek “to develop the river in a cooperative manner, share substantial socioeconomic benefits, and promote regional peace and security” [17]. One of its primary goals of the NBI was to negotiate a “cooperative framework agreement” that would supersede past bilateral treaties and provide for sharing of the Nile's resources. The NBI agreement was supposed to lead to the adoption of a comprehensive permanent Nile River Basin legal and institutional framework. Negotiations toward such an agreement have yet to be successfully completed. However, a CFA has now been signed by a number of the Nile riparian countries with the notable exceptions of Egypt, Sudan and South Sudan [14] (see Appendix I - Water balance in the Nile Basin).
Third party involvement, particularly the World Bank and the UNDP was critical in the development of the NBI [18]. The Nile countries did not have the resources to implement the Nile River Basin Action Plan, and thus looked to international organizations for support. Nile-COM first requested the World Bank's assistance to coordinate donor involvement and establish a Consultative Group to raise financing for cooperative projects in the Nile Basin. UNDP and the Canada International Development Agency (CIDA) also agreed to play lead roles in developing Nile Basin cooperation [14].
The NBI is financed by the Nile riparians through the collection of annual dues from each country. Substantial financing is also provided by various international donors, primarily the World Bank, UNDP, and CIDA [14].The NBI does not have a specific dispute resolution mechanism. The Nile-COM is the highest decision-making body of the NBI. The Technical Advisory Committee (“Nile-TAC”) renders technical advice and provides assistance to Nile- COM. The Nile Basin Initiative Secretariat (“Nile-SEC”) renders administrative services and advice to Nile-TAC and Nile-COM. The NBI, however, has formal legal status as an institution only in Uganda where it is headquartered, Rwanda, where the Nile Equatorial Lakes Subsidiary Action Program (NELSAP) [16] is headquartered, and in Ethiopia, where the Eastern Nile Lakes Subsidiary Action Program (ENSAP) [16] is headquartered.
To support the vision of NBI, the Shared Vision Program (SVP) of basin-wide projects, and the Subsidiary Action Programs (SAPs), consisting of investment programs at a sub-basin
17 Nile Basin Analysis
level, were developed. The main objective of these programs is to build capacity, trust, and confidence among the riparian Countries, to develop the river in a cooperative manner, share socio-economic benefits, and promote regional peace and security [6].
Eight basin-wide projects have been implemented under SVP:
Nile Transboundary Environmental Action: to provide a strategic framework for environmentally sustainable development of the Nile River Basin; Nile Basin Regional Power Trade: to establish the institutional means to coordinate the development of regional power markets among the Nile Basin countries; Efficient Water Use for Agricultural Production: to provide a conceptual and practical basis for increasing water availability and efficient water use for agricultural production; Water Resources Planning and Management: to enhance the analytical capacity for a basin-wide perspective that supports the development, management and protection of Nile Basin waters; Confidence-Building and Stakeholder Involvement: to develop confidence in regional cooperation under the NBI and ensure full stakeholder involvement in the NBI and its projects; Applied Training: to strengthen institutional capacity in selected subject areas of water resources planning and management in public and private sectors and community groups; Socio-Economic Development and Benefit Sharing: to strengthen Nile River Basin-wide cooperation; SVP Coordination: to coordinate the above projects and capture synergies.
The SVP projects build on each other to form a coordinated program [6]. They aim to apply an integrated and comprehensive approach to water resources development and management, and to ensure that this serves as a catalyst for broader socio-economic development and regional cooperation. The SVP projects have been designed to pave the way for investments on the ground through the SAPs. Through two groups of Nile countries, one in the Eastern Nile and the other in the Nile Equatorial Lakes Region, joint and mutually beneficial investment opportunities have been identified [6].
18 Chapter 2
The NBI and Nile-SEC Strategic Planning Processes NBI has adopted a clear and logical path in developing this basin-wide Strategic Plan [19]. The short-term (2012-2016) planning has been carried out by Nile-TAC through a logical series of steps. In brief, these were processes of review, followed by a resetting of short-term priorities, culminating in formulation of the broad, over-arching set of NBI Objectives (Appendix I - Water balance in the Nile Basin).
All international agencies have strategies and programs that focus on improving the cooperation through the Nile Basin countries to make each develop his own WEF strategy cooperating with the others [19].
2.1.4 Projects and strategies
Realizing the full potential of agricultural production and trade will require generating surplus production in one or more Nile Basin countries, and creating conditions conducive to cross-border trade in agricultural products. Both of these conditions are currently absent from the basin. Many of the approximately 172 million people who reside within rural areas in the Nile Basin (the combined rural population for the Nile countries is 317 million) depend on agriculture for their nutrition and livelihoods. Therefore, for most of the Nile countries, strengthening the agricultural sector holds the key to national food security and poverty eradication [38]. To generate surplus production, it will be necessary to increase investments in the agricultural sector. Under the African Union’s New Partnership for Africa’s Development (NEPAD) a Comprehensive African Agricultural Development Program (CAADP) has been developed as a blueprint for increasing investments to the agriculture sector [6]. One of the key goals of CAADP is to increase allocations to agriculture to 10% of national budgets [26] so as to raise agricultural production by at least 6% per year, thereby contributing to improvement in food security, enhancement of nutrition, and increase in rural incomes. Countries in the Nile Basin are in the process of aligning their medium-term plans [27][28] to the CAADP goals. Change, however, has been slow in coming, and by 2011 only Ethiopia had achieved the 10% allocation to the agricultural sector [6].
19 Nile Basin Analysis
The private sector looks set to play an important role in agricultural development in the basin. Seeing an opportunity to profit from recent world food-price hikes and strong demand for food, biofuels, and essential cash crops, an increasing number of foreign firms are showing interest in acquiring agricultural land in the basin [43]. Most riparian governments have welcomed this initiative, viewing it as an opportunity to make productive use of idle land while at the same time increasing foreign direct investments to the agricultural sector, creating employment in rural areas, enhancing national food security and catalyzing economic growth. Land allocations to foreign investors have been sanctioned in all Nile countries except for Egypt, Burundi, and Eritrea [6].
Cooperation further paves the way for integration of national power generation and transmission infrastructure, thereby increasing the economic viability of investments in the power sector and making the region more attractive for investment. The Nile Basin is the only region on the African continent without a functional regional power grid. The level of interconnection amongst Nile countries is generally unsatisfactory, with power being generated almost exclusively at national level. Bilateral power exchange agreements exist between some countries, but the volume of power exchanged is not significant, and exporting parties have frequently failed to meet their contractual obligations because of deficits in their own systems. This status quo is a major weakness, considering that the region is characterized by large asymmetries in endowment of energy resources and of demand, and has the lowest levels of electricity access and per-capita electricity consumption in the world [6][7].
The region has substantial hydropower potential, both within the Nile Basin and in adjacent river basins which, if developed, could help quench the region’s power thirst.
The Nile Basin contains untapped potential for hydropower generation, irrigation, and navigation. A number of possible multi- lateral projects have been identified where, if sufficient inter-basin cooperation can be achieved, the total benefits to the Nile system will amply exceed losses to individual stakeholders. Examples of these projects include [6]:
Construction of dams to increase over-year water storage for hydropower production, irrigation, and flood protection. Location of the dams in high-altitude upstream areas could result in substantial reductions in evaporation losses. Cooperation will be needed to minimize downstream impacts during construction and operation.
20 Chapter 2
Construction of a dam on the Baro could reduce overbank spillage into the Machar marshes and wetlands around the Baro-Akobo confluence. Regulation of outflows from Lake Victoria or Lake Albert, possibly in conjunction with increased abstractions upstream of the Sudd to minimize water losses from the Nile system.
To develop the hydropower potential without harmful impacts on the environment and riparian communities, and to work towards regional energy security, the countries should [6]:
Continue with joint implementation and exploitation of hydropower options on shared water resources. This presents opportunities for significant reduction in project financing risks, and enhances regional cooperation and trust. Mainstream environmental and social management in the project life-cycle of power generation and transmission projects. Mainstream climate-change adaptation in the project life-cycle of power generation and transmission projects. Develop an integrated basin-wide water resources management and development plan that incorporates various uses of water across the basin. Diversify financing sources for power projects, targeting, among others, private sector and local capital markets. Increase interconnection between national power grids, and power trade between the countries.
2.2 The Nile Question from an Egyptian perspective
One of the most important issues for societal stability is the allocation of resources among the riparian countries. based on data of key countries in the Nile River Basin regarding various economic, environmental, developmental, and political dimensions, an analysis of possible regional security implications and conflicts that may arise, if the influence of
21 Nile Basin Analysis
climate change alters the current status quo, is conducted. In the past, Egypt has established itself as a water hegemon, controlling a majority of the water resources of the region. This situation led to a non-cooperative water, energy and food management. This status has been recently challenged by developments and alliances among the upstream countries, increasing the tension between Egypt and some of its neighbours. Unfavourable shifts in precipitation patterns can augment the pressure on the downstream countries, causing them to consider shifting towards strategies that are based on threats rather than on cooperation [16]. This suggest to analyse from an Egyptian point of view the resources allocation.
2.2.1 Water
The Nile is essentially the Egypt's only source of water. The vast majority of Egypt's population lives in the Nile Valley. This population of about 300 million people depends almost exclusively on the waters of the Nile as its source of freshwater. Demands for water and land are rapidly increasing in response to a growing population, industrialization, food production, and employment generation. Available land and water resources are limited, so a concerted effort to better manage the country’s limited water resources has become a
Figure 5 - Population Growth and Water Availability, data from World Bank and FAO report
22 Chapter 2
national priority. The unrestricted use of water, to which users have been accustomed for many centuries, is no longer possible. There are some prospects for increasing the supply, but only at an increased cost and with a demand that is destined to rise. The per capita availability of water is currently just under 1,000 m3/year, a figure that, according to international standards, is equal to chronic water scarcity [29]. With Egypt’s projected population growth, the per capita water availability might drop to 500 m3/year (absolute scarcity) by the year 2025 [5].
Water quality and water quantity are closely interrelated. If water supplies remain constant or decrease, the water quality will deteriorate. Devising mitigating actions requires reliable knowledge of the current situation, of potential improvements and readily available technologies combined with enforcement of extant laws. M. Hefny and S. El-Din Amer assessment indicates that the Nile water quality is generally acceptable, except at some industrial effluents and disposal sites [30]. Drainage water quality is less acceptable due to the excessive use of agricultural chemicals. Sewage and industrial waste are disposed via drains, thereby threatening the possibility of reuse and present a health hazard to farmers. The state of Egypt’s water and its vulnerability requires the following issues to be investigated.
Water availability and variability The main water resources in Egypt originate outside its borders. The upper Nile catchments that contribute to the Nile and discharge at Aswan include the White Nile, the Blue Nile and the Atbara River. For the period 1871/1872 to 1998/1999, the average natural volume flowing at Aswan was estimated to be 88.350 푘푚3 per year [43]. The maximum volume is about 142.378 푘푚3/year, as measured in 1878-1879; the minimum volume is about 45.879 푘푚3/year, measured in the year 1913-1914.
The variability in water availability at Aswan is represented by the standard deviation for the same period as above and is estimated to be 15.705푘푚3/year [43][30]
Consumptive water use The crop consumptive use is the amount of water that is consumed by crops through evapotranspiration. This amount varies from one crop to another and is also affected by meteorological conditions. The total amount of annual crop consumptive use is estimated to be about 48.820 푘푚3/year. The municipal water consumption is the amount of water that
23 Nile Basin Analysis
does not return to the system. It is lost through human activities like transpiration and is estimated to be about 0.920 푘푚3/year. For industrial activities, a small part of the water used is lost through evaporation and is estimated to be about 0.450 푘푚3/year [43].
Annual water withdrawal The total amount of water diverted for agricultural use includes the amount of water required for evapotranspiration, conveyance, and application losses in both the irrigation network and at the farm level. The total amount of water diverted annually for agriculture in Egypt is estimated to be about 54 푘푚3. This amount does not come solely from the Nile, however, but also from groundwater extraction and drainage reuse. Municipal water withdrawal includes water supplies for both urban areas and rural villages. Some of that water comes from the Nile system, either through canals or direct intake from the river; the balance originates from groundwater sources [31]. The total annual municipal water use is estimated to be about 4.54 푘푚3. Water withdrawal for the industrial sector is estimated to be about 7.5 푘푚3/year. About 6% of this amount is consumed; the balance returns to the system, creating major environmental problems [42]. The Nile’s main stem and part of the irrigation network are used both for tourist cruises between Aswan and Luxor and for the transportation of commodities between upper and lower Egypt. During the winter closure period (about 3 weeks), a minimum release from the Aswan High Dam must be made in order to maintain sufficient water levels for navigation. This amount of water is estimated to be about 0.26 푘푚3/year [43].
Annual water balance Within Egypt the Nile basin is a closed system in which water volume inputs and outputs must be balanced. If one excludes the small amounts of rainfall on the northern coast and deep groundwater reservoirs, release of water from the High Aswan Dam is the only input to the system. According to the Nile Water Agreement with Sudan [6], Egypt’s share of water is relatively high because of the recycling of water along the system.
From the total amount of water used through the system, about 12.97 푘푚3/year comes from reused agriculture drainage, 4.8 푘푚3/year from groundwater extracted from renewable aquifers in the valley and delta, and 0.7 푘푚3/year from the reuse of treated selvage water. The key aspect of the 1959 Agreement with Sudan is that the principle of acquired rights
24 Chapter 2
was accepted and that water was shared on the basis of a ratio (depending on the average)[42].
Water resources in Egypt are a prime security concern because Egypt depends on the Nile for its water, but its source lies outside of Egypt’s borders. The Egyptian concerns with regard to the Nile are, therefore, both a matter of National security and a life or death issue [39]. The recently published “Water Policy Paper” highlights the challenges ahead: “Water resources in Egypt are deemed to be limited when compared with the rapid growth of population that is increasing continuously. The Egyptian share of the Nile waters is 55.5 푘푚3/year, as agreed upon between Egypt and Sudan in their 1959 Agreement. The share per person for all water uses in Egypt is estimated at 2.5 m3/day; according to international standards this is considered close to the poverty line.” [43].
2.2.2 The impact of water shortage on food security
Egypt faces severe threats to its water security. The UN predicts that Egypt could be water scarce by 2025. Water security is at risk for a number of reasons, increasing water demand from the growing population, inefficient irrigation, pollution and degradation. The greatest though, is the possibility of Egypt losing control of the Nile. Because the country has no viable alternative water supplies possession of the Nile is central to Egypt’s sustainability. Demands for water and land are rapidly increasing in response to a growing population, industrialization, food production, and employment generation. Available land and water resources are limited, so a concerted effort to better manage the country’s limited water resources has become a national priority. Water shortages will have a severe impact on Egypt’s food security. Of Egypt’s total water supply, 80% is used in agriculture. As water flow shrinks and available agricultural land is used up, Egypt’s food production capacity will fall. The impacts of resource scarcity will be exacerbated by the changing global climate.
The United Nations Environment Program lists Egypt as highly vulnerable to the impending impacts of climate change. Studies of a range of potential climate scenarios overwhelmingly
25 Nile Basin Analysis
predict long-term reductions in the Nile’s flow. Estimates range between 10 and 90% loss by 2095 [21]. Egypt could also experience coastal damage from rising sea levels, together with land deterioration, soil salinity and an increased incidence of drought. Further temperature rises would also increase agricultural water requirements, while decreasing crop water efficiency. Food production in Southern Egypt is expected to decline by 30% by 2050 as a result of climate change. Egypt needs to build resilience in its agricultural sector now to minimise severe consequences for food security [42].
As mentioned, the Nile is Egypt’s only renewable water source, but the shifting geostrategic balance between States in the Nile Basin means Egypt is likely to lose part of its share of the Nile’s flow. Consequently, Egypt faces rising food insecurity and is on track to become a country of close to 100 million people struggling to meet its basic food and water needs. Egypt’s food security issues are largely rising poverty, wasteful food production and distribution systems, and exposure to price volatility. The effects of these challenges have been accumulating for over a decade and will be difficult to fix, given the country’s ongoing political instability and stagnant economy [21].
Egypt’s rapidly growing population will demand more and more food over the coming decades. Meanwhile, resources for food production will become increasingly scarce as Egypt’s water access shrinks, available arable land is used up and climate change disrupts agricultural systems. Egypt faces an expanding gap between its long-term food production capacity and the food and water needs of its population. This could further undermine its food security, as close to one in five people are already experiencing food insecurity and many more are vulnerable [31].
Table 4 – Production of food crops in the Nile Basin, data from FAO report 2010, unit tons.
26 Chapter 2
Hunger in Egypt Egypt’s developing food security issues are structural and cumulative. They have their roots in years of instability. The worrying upward trend in food insecurity has been caused by an accumulation of crises: the avian influenza epidemic in 2006; the food, fuel and financial crises of 2007-2009; the 2010 global food price spike; the economic deterioration caused by political instability since the 2011 revolution.
As a result of these crises, a growing number of Egyptian people are experiencing hunger.
The most recent figures from the World Food Program show that 17% of the Egyptian population, 13.7 million people, were food insecure in 2011, up from 14% two years earlier. These figures are likely to be even higher in 2015 after three more years of economic stagnation. Food insecurity in Egypt is an issue of economic access; a growing number of people can’t afford to purchase enough nutritious food. The average Egyptian household spends more than 40% of its income on food (for the poorest families it’s more than half), making them vulnerable to sudden increases in food prices. Poverty has risen by 40% in the past decade to affect over a quarter of the population, some 21 million people. Almost another 20 million are hovering just above this level. Increased poverty results in overreliance on cheap, calorie-dense foods with limited nutritional content. As a result, nutritional outcomes are worsening; chronic malnutrition among children has reached high levels and Egypt now has the world’s highest rate of double nutritional burden (consumption of low nutrition calorie-dense foods can cause people to be overweight, while also suffering from nutritional deficiencies) [21].
The food subsidy system The Egyptian Government’s food subsidy system masks the full extent of food insecurity. The government supplies ration cards to 80% of the population, allowing quotas of specific foods to be purchased at subsidised prices. It also provides an unrestricted supply of baladi bread, sold at 5 piastres (around US $0.01) per loaf.
The subsidy system has played an important role in protecting the poor from high food prices in the recent crises. Without immediate substitution of another form of safety net, removing food subsidies would push those below the poverty level from 25.2 to 34% of the population.
27 Nile Basin Analysis
Food subsidies fail to reach 19% of Egypt’s most vulnerable households, however, and don’t provide good nutrition. Most critically, the subsidy system is a major drain on the country’s fiscal position; the government’s ability to finance its spending. Food subsidies account for at least 14% of the government budget, at a cost of over US$2 billion a year. Furthermore, wastage of subsidised baladi bread is over 30%. The wheat purchased by the government to support the subsidy system is predominantly imported (placing pressure on the trade balance) or purchased at inflated, subsidised rates from domestic producers. This high-cost system creates an unsustainable fiscal burden and the wastage it creates undermines long term food security.
Resource Scarcity Poverty and the structural problems in Egypt’s food system will be difficult to fix in the current political and economic environment. The more serious threat to Egypt’s food security is long-term, however. There is a growing gap between its long-term agricultural production potential and population growth estimates.
Egypt has one of the world’s highest fertility rates and its population growth has surged in the past three years. The population is expected to grow by over 20 million in the next decade, to almost 97 million people by 2025. Rapid population growth will esacerbate pressure on Egypt’s, already overburdened, resource base, increasing food and water insecurity [40].
While the agricultural plains of the Nile have long been famed for their fertility, only 3.5% of Egypt’s landmass is potentially arable. The remaining land is arid desert. Agricultural production is concentrated along narrow strips of fertile land adjacent to the Nile.
Population growth and urbanisation are now encroaching on this land. The consequent environmental degradation is leading to the contamination and desertification of Egypt’s already limited fertile areas. Efforts to reclaim land from the desert to counteract this trend are restricted by the need to secure sufficient water supplies [44].
Food security can be based on two possible sources of supply: production from domestic agriculture or imported food commodities from food surplus nations. Egypt already relies on the global market for up to 60% of its food needs. Egypt is self-sufficient in the production of most fruit, vegetables and livestock, but is unable to produce enough grains, sugar or
28 Chapter 2
vegetable oil; foods that make up a large portion of the Egyptian diet. Because of this, Egypt is the world’s largest wheat importer. As Egypt’s food production fails to keep pace with the needs of the growing population, it will rely more and more heavily on imports [21].
The risks of import reliance Relying heavily on trade to support domestic food supply exposes a nation to two vulnerabilities. First, global food prices have been highly volatile in recent years and shocks in world prices can feed into the domestic market. Second, for reliance on imported food to be sustainable beyond the short-term, a healthy fiscal position is required.
A major cause of the rise in Egypt’s food insecurity over the past decade has been exposure to global food price spikes, which have threatened domestic supply and pushed up prices [44].
When the average household already spends 40% of its income on food, sudden price spikes can be disastrous. Over 80% of households have reported having to resort to eating cheaper, less-nutritious staple foods to cope with higher food prices. If resource scarcity and import- dependence continue to push food prices upwards, more of the population will come to rely on food subsidies. This will add to the government’s fiscal burden and further put in danger the viability of the subsidy system.
The Egyptian Government struggled to maintain crucial grain stocks as economic conditions threatened its ability to pay for food imports. In early 2013, grain stocks fell to a record low of only three months supply, as foreign currency reserves plummeted. Only emergency financial support from Saudi Arabia, the United Arab Emirates and Kuwait saved the government from a balance of payments and food supply crisis [21].
Financing imports To ensure its food security as resource scarcity rises, Egypt will need to maintain the level of food imports without exacerbating the structural fiscal deficit. This will require sustained economic growth, but there are serious economic challenges inhibiting that growth. The 2011 revolution brought the Egyptian economy to a standstill, as limited policy flexibility and the prolonged transition to a new political regime created uncertainty and virtually eliminated capital inflow. Four years later, the economy is still suffering from a severe deceleration in its growth rate and the new government faces numerous obstacles [44].
29 Nile Basin Analysis
The central challenge will be to implement far-reaching reforms to address the structural weaknesses of the economy and attract private investment. Efforts to achieve this will be hampered by vested interests and public backlash against any austerity measures. Reforming these is a crucial step towards economic recovery.
Maintenance of political calm will assist investor confidence and probably allow the economy to hobble along (with support from the Gulf States) in the medium-term; which will improve immediate food insecurity issues. The strong action and wide-spread reform required to ensure inclusive, long-term prosperity and to support trade-based food security, however, will be hampered by the current political environment [40].
Securing food and water for the future Many of the Egyptian Government’s current strategies to improve food security are unsustainable, as they exacerbate entrenched inefficiencies and resource scarcity. This includes reclaiming desert areas for agricultural production and increasing subsidies for domestic food producers. The three key policy approaches required to improve Egypt’s food and water security prospects are: to cooperate with regional stakeholders on water allocations, restructure the food subsidy system and reduce wastage throughout the food and water supply chains [38].
Comprehensive waste reduction While Egypt’s domestic agricultural sector will never be capable of approaching national self-sufficiency, major gains can be made in the efficient use of agricultural inputs and waste reduction throughout the food system. To end unnecessary losses from Egypt’s food chain and water supply, a comprehensive effort is required to improve agricultural productivity and reduce waste. Wastage is endemic throughout Egypt’s water infrastructure and its food production and distribution systems. Its irrigated agriculture is plagued by water loss as a result of its outdated practices. In irrigation efficiency, Egypt ranks in the bottom 10% of countries in the region. In the food distribution chain, waste is also rife [21].
Programs to improve agricultural practices and better understand the specific points of supply chain loss, could drastically reduce pre- and post-harvest food loss and the wastage in water inputs. Further reform to the food subsidy system could also assist waste reduction.
30 Chapter 2
The Egyptian Government needs to act now to introduce a suite of initiatives to address resource scarcity, inefficiency and growth challenges if it is to avoid increasing rates of poverty and food and water insecurity.
Figure 6 – Egyptian virtual water imports in crops from (a) outside of the river basin, and (b) other Nile Basin states, FAO report
2.2.3 Energy
Egypt’s energy sector is currently facing a variety of conflicting and overlapping challenges. This is mainly seen in Egypt’s gruelling efforts to strike a balance between production, domestic consumption, and export revenue, while seeking to maintain internal political harmony. Despite the fact that Egypt is the largest non-OPEC oil producer in Africa and the second largest gas producer in the continent, as well as the vital role it plays in regional and global energy markets, the country’s energy status throughout the last four years (2010-14) reflects a reversal on all levels [49]. This crisis is as much the consequence of historical
31 Nile Basin Analysis
‘mal-planning’ as it is the short-term consequence of the country’s past three years of political turmoil following 2011 revolution [49].
However, starting from late 2014, investment and economic growth started picking up on the back of political stability. Fixed investment was set to be the primary driver of growth as a result of greater clarity and transparency in Egypt’s economic policy. Furthermore, the current government firmly displayed some decisive measures to further patch-up the energy sector. This is most likely disclosed under the pillars of the new energy strategy (Egyptian Minister of Petroleum 2015 [53]), including: security, by boosting and diversifying and improving energy efficiency; sustainability, by addressing debt build-up and phasing out of subsidies in a socially responsible manner and governance, by improving and modernizing the oil and gas sector’s governance and encouraging private sector investment.
The major features of the Egyptian Energy Sector Egypt’s energy sector has been recently characterized as being highly auspicious but peculiar. It is important to notice that energy production has been dominated largely by fossil fuels. Egypt has never been a major oil producer, however, it has traditionally been a net exporter of energy. Oil was exported until the late 1990s, when production had declined from its peak to roughly match local consumption. The discovery and exploitation of large reserves of natural gas meant Egypt became a significant exporter of gas, both by pipeline and as liquefied natural gas (LNG) [45].
Egypt now buys a large proportion of its foreign partners' share in crude oil, to meet increasing local consumption.
In principle, the hydrocarbon sector is a vital source of foreign currency and a key destination for foreign direct investment. Until the end of 2010 net energy exports generated a good surplus. The Egyptian energy sector has been considered to some extent partially liberal in comparison with other neighbouring countries. Egypt used to have close and successful partnerships and cooperation with some major international oil and gas companies. The country’s record in this regard has been positive for decades (until the uprisings by early 2011) [49]. In Egypt, there is a strong conviction that the full utilization of the country’s hydrocarbon reserves relies mainly on the nature and strength of the partnership between the government and international companies [34].
32 Chapter 2
Furthermore, Egypt has a pivotal role to play in international energy transit. The country’s potential as a major transit country has not yet been fully utilized. However, Egypt possesses a key role in regional and global energy markets. This mainly depends on its geographical proximity and strategic location at the crossroad of international trade of oil and gas, in addition to the control of two major transit routes: the Suez Canal and the Suez- Mediterranean Pipeline (SUMED).
Over the last decade, Egypt’s energy policies and energy sector have witnessed fundamental changes. Accordingly, several characteristics of the country’s energy sector could be identified. Egypt’s growing population and economic (industrial) development led to a significant rise in the demand for energy products in all sectors (residential, transportation and industrial). Subsequently the consumption of oil, gas and electricity has been boosted. As a result, in recent years domestic supplies have fallen short of demand, due to the ongoing increase in consumption, stagnation in production, and a very generous subsidy policy which heavily contributed to increasing consumption [35]. Further deepening the situation is that some oil and gas fields have reached maturity or are in decline, and there has been a withdrawal of foreign investment in the last four years which has prevented production growth. Thus, the economic consequences of this decline in production and fast growing consumption are seen in higher import volumes and a drop-off in natural gas exports, which have turned Egypt’s hydrocarbon trade balance negative over the course of fiscal year 2012- 2013 [35]. However, starting from late 2014 and shortly after new Egyptian president Abdel Fattah El-Sisi assumed power, investment and economic growth started picking up on the back of relative political stability. Fixed investment was set to be the primary driver of growth as a result of greater clarity and transparency in economic policy. As a result, the government is currently leaping towards attracting impressive FDI (Foreign Direct Investment) in the energy sector. Based on the latest Business Monitor International forecast, investor sentiment in Egypt has significantly improved as political stability returns and the government begins repayments owed to oil companies [52].
33 Nile Basin Analysis
Oil Sector Egypt’s oil sector witnessed a stark change beginning in the early 2000s. The country drifted away from being an oil exporter to a net oil importer, as domestic supplies have fallen short of demand. This was mainly due to different factors including: the enormous rise in population; growing economic development mainly in projects generated by fuel; and a drop in new investments in the oil sector (most likely as a repercussion of the energy subsidy system along with the political instability since 2011) [34]. This led to an increase in oil consumption in parallel with stagnation in production. Egypt’s oil consumption has clearly outpaced production since 2010 [33]. One major challenge in Egypt’s oil sector is how to maintain a minimum level of oil exports in order to maximize foreign exchange revenue. It has been known that any barrel consumed domestically, especially when consumption outpaced production, is a barrel not exported, depriving Egypt of foreign exchange. Like many other developing nations, Egypt is seeking to strike a balance between crude oil production, domestic consumption, and export revenue, while seeking to maintain internal political harmony [36].
Figure 7 – Oil Production and Consumption Trends, MOEE report
Despite declining production and becoming a net oil importer, Egypt’s crude oil exports have remained virtually flat over the past few years, mainly to secure a share in foreign currency. As a result, there is a lower volume of domestic crude oil available for domestic refineries, and Egypt must then compensate the difference by importing petroleum products and/or crude oil [37].
Egypt has the largest oil refinery capacity in Africa, though it operates below capacity. Egypt’s oil refinery capacity is around 700,000 bbl/day, while the country’s refined
34 Chapter 2
petroleum output is around 450,000 bbl/day [32]. The refinery output declined from 2009- 2013 by almost 28%, as some reports attribute this decline to Egypt’s policy that permits foreign oil companies to export crude oil as a repayment for the country’s financial debt. Subsequently, the supply of crude oil available for domestic refineries has declined, and Egypt started importing petroleum products (which reached almost 170,000 bbl/day in 2013) [34]. In recent years, there were plans to expand the country’s refining capacity through building more refineries in partnership with foreign companies, though they have not been realised yet. The petroleum industry is a key component of Egypt’s economic performance, thus the decline of oil exports in the last few years had a significant impact on the country’s economic development [37]. Some new oil discoveries have boosted Egypt’s reserve estimate over the past few years.
Egypt has maintained a sustainable level of exploration, with a number of oil discoveries made every year [37]. However, it is expected that Egypt will struggle to increase crude oil output due to limited overall oil potential. The majority of Egypt's below-ground hydrocarbon potential has proven to be gas, thus what new liquid product develops is likely to come from gas condensates and not traditional oil [41].
Natural Gas Since the early 2000s, Egypt has emerged as an important producer and exporter of natural gas. Egypt started turning to gas to replace oil in the domestic market, most importantly for fuelling heavy industries and electric power plants in order to save more crude oil for exports. Until 2011 about 18% of gas production was exported. Around 80% of Egypt’s natural gas reserves are in the Mediterranean and the Nile Delta, followed by smaller amounts in the Western Desert and the Gulf of Suez [34]. Since 2011 Egypt’s natural gas production has declined sharply and a stalled power sector started to become apparent. The decline was by an annual average of 3% from 2009-2013; meanwhile, Egypt’s total gas exports have declined substantially by an annual average of 30% from 2010-2013. This is ascribed not only to the decline of production, but also to the enormous rise in domestic demand, particularly in the power sector. Starting from the 1990s the government encouraged households, businesses and the industrial sector to consider natural gas as substitute for petroleum products. Therefore, the consumption increased by an annual average of 7%, and
35 Nile Basin Analysis
as a result Egypt started importing liquefied natural gas (LNG) to satisfy its consumption [37].
Figure 8 – Natural Gas Production and Consumption Trends, MOEE report
While invigorating foreign investment in the gas sector, Egypt realizes that such an objective could not be achieved in one fell swoop. The low gas price causes a bias in favour of gas export projects while at the same time reduces investors' interest in the upstream and downstream gas sector. This requires Egypt to revisit its gas allocation policy, especially if the government is trying to de-link investors' interest from domestic gas prices [46].
Electricity and Renewable Energy Power generation in Egypt is integrated by a Unified Power System, upon which all power stations of the national electricity grid are linked. Electricity demand is increasing at about 7% annually and is expected to continue to grow at this rate for the foreseeable future. Energy is essential for Egypt’s economic growth for two primary reasons: it is a direct driver of domestic development, and represents a source of foreign currency associated with fuel exports [47].
Egypt’s power generation capacity is around 27 GW per year, though only about 60-70% of its capacity is operating, mainly due to the country’s inability to import raw materials necessary to produce power [48][50]Errore. L'origine riferimento non è stata trovata.. Electricity generation is fuelled by around 80% natural gas and 20% oil and renewable sources [49]. The electricity sector is by far the largest gas consumer, accounting for over half of the total gas consumption in the country. Overall, Egypt’s power crisis is a result of a number of overlapping factors including: rising demand, natural gas supply shortages,
36 Chapter 2
aging infrastructure, political instability, and inadequate generation and transmission capacity [37].
Shortly after regaining political and economic stability in 2014, certain measures have been taken in this regard including: cutting gas supply to certain fertilizers and cement factories in order to increase gas supply for power plants; channelling huge investment towards the sector, as giant deals have been signed with international operators (mainly GE and Siemens) to further expand, develop and increase the capacity of existing power plants; implementing concrete reforms in energy subsidies, to pre-empt consumers such as factories, business, wealthy citizens from benefiting from the canopy of subsidies; and enhancing initiatives to instil energy efficiency and robustly eliminate overconsumption practices such as wasteful storage, daytime street lighting, and excessively late work hours (mainly in entertainment venues) [50].
As for renewable energy, Egypt is considered by many observers to be a country which has the right environment to meet a large proportion of its energy needs by utilizing wind and solar power.
However, the country’s potential in renewable energy is not properly utilized and it accounts for a minor share for Egypt’s energy mix. The wind conditions in Egypt are among the best in the world and almost 90% of its land is suitable for setting up wind turbines [34]. In the framework of expanding power production and overcoming the shortage of oil and gas supply, Egypt currently has a significant interest in promoting foreign investment in renewable energy projects to meet energy production shortfalls [37]. This trend has gained momentum lately, especially in the wake of increased political and economic stability and significant investment attracted at an economic development conference held in March 2015. In early 2015, Egypt’s New and Renewable Energy Authority (NREA) announced that almost 70 companies have been selected to take part in developing 4.3 GW of renewable energy projects in the country [51].
Despite the significant measures carried out by the government recently to further maximize the share of renewable energy in Egypt’s mix, Egypt has still a long way to go in this field. Striking a plausible balance between conventional energy sources and renewables in shaping the country’s energy map requires not only a robust political will, but also eclectic measures in developing and promoting the renewable energy industry.
37 Nile Basin Analysis
Policy framework The principal sources of policy in Egypt are the Ministry of Electricity and Energy (MOEE), which holds a monopoly over the distribution, transmission and generation of electricity, along with the Supreme Council for Energy (SCE), the latter reporting directly to the President. The Egyptian Electric Utility and Consumer Protection Agency (EEUCPA) is the industry watchdog and responsible for licensing and sector monitoring.
Development of the renewable energy industry has become a priority over recent years for the Egyptian government, but the events of 2011 and the subsequent political uncertainty have slowed progress in the renewable energy sector [20].
Egypt’s present energy strategy (adopted by resolution of the SCE in February 2008) aims at increasing the share of renewable energy to 20% of Egypt’s energy mix by 2020. This target is expected to be met largely by scaling-up of wind power projects with the share of wind power in total electricity generation being expected to reach 12% [20]. This translates to a wind power capacity of about 7200MW by 2020.
The Egyptian government has recognized the need for reform of the electricity sector in order to attract private sector investment in power generation, as it is accepted that the private sector will be instrumental to Egypt’s ability to deliver its renewable energy targets. One of the key models that the Government is pursuing is the competitive bidding approach whereby the Egyptian Electricity Transmission Company (EETC) will issue tenders requesting the supply of power from large-scale renewable energy resources for pre- determined sites on a build, own, operate (BOO) basis. It is expected that the competitive bidding approach will result in additional capacity of about 2500MW from the private sector [20].
The law contemplates a number of policies aimed at stimulating and supporting renewable energy generation. Importantly, the law contemplates the establishment of a feed-in tariff to reimburse producers for the cost of production not covered by electricity prices. Further, the new law also provides for the development of a “Fund for Development of Power Generation from Renewable Energy” which is intended to cover the deficit between the renewable energy costs and market prices as well as provide financial support to pilot projects.
38 Chapter 2
In late 2012, positive steps to kick-start the development of the renewable energy industry were taken [19]. The SCE announced exemptions from customs duty and sales taxes on parts for renewable energy systems. It was also announced that allocations of land belonging to the New and Renewable Energy Authority (NREA) would be made to private companies working in the wind and solar fields.
Concerning fossil fuels, Egypt subsidies in 2013 totaled the sum of $30 billion, equal to the 11% of GDP and absorbing around one fifth of total public spending [21]. Oil products have been the most heavily subsidized. In 2013, the rate of subsidization averaged 68% for oil
Figure 9 - Fossil fuel subsidies by fuel in Egypt, data from IEA 2014 Report products and 61% for fossil energy as a whole (Figure 9). Subsidies result from the government directly setting fuel prices, in most cases well below the full cost of supply. Fossil fuels subsidies have placed a heavy burden on the economy, for this reason in 2014 cuts were announced by the new government in the 2014/2015 budget allocation to fuel subsidies, together with a sharp increase of the price of gasoline. This cuts would reduce the budget deficit to 20% of GDP [21].
Many of the Egyptian Government’s current strategies to improve food security are unsustainable, as they exacerbate entrenched inefficiencies and resource scarcity [22]. This includes reclaiming desert areas for agricultural production and increasing subsidies for domestic food producers. The three key policy approaches required to improve Egypt’s food and water security prospects are: to cooperate with regional stakeholders on water allocations, restructure the food subsidy system and reduce wastage throughout the food and water supply chains [22].
39 Nile Basin Analysis
40
Chapter 3: Metodological Approach
This chapter focuses on the analysis of the structure of the model utilized for the research and aims to point out the relationships, both logical and algebraic, which link the branches of the model.
3.1 General overview
One of the major division among the taxonomic classifications of energy demand models is between ‘top-down’ models which are based on macro- economic social accounting matrices, and ‘bottom-up’ models which can describe in greater detail the expected impact of changes in technology or input costs within particular product markets. The analysis of the apparent discrepancy between predictions arising from these different approaches suggests that the class of bottom-up energy models can be sub-categorised as either supply- side (considering the impact of efficient technologies on the supply and conversion of energy) or demand-side(the impact of end-user lifestyles on energy consumption).
LEAP is an example of the second type. Arguably, bottom-up models are more appropriate for incorporating the immediate and direct impacts of specific energy-efficiency policies, which generally target saving sat a disaggregated level and cannot be readily incorporated into a top- down model.
When modeling energy savings, as distinct from energy demand growth, a different distinction occurs between top-down and bottom-up methods. Here the bottom-up approach starts from data at the level of a specific energy-efficiency improvement measure and then aggregate results from all the measures [54]. By contrast, top-down methods for evaluating savings use aggregated sectorial levels of energy savings as a starting point. These aggregated savings in turn rely on energy-efficiency indicators, which are themselves
41 Methodological Approach
calculated using bottom-up unit consumption indicators that are then aggregated to a sectoral level [55].
With regard to modeling of end-use behaviour, the approach to data has been relatively cautious, partly reflecting uncertainty over the amount and type of data which can realistically be collected, and the level of modeling resources which will be available. One particular bottom-up energy end-use model which Stockholm Environment Institute (SEI) has recently been evaluating Long range Energy Alternatives Planning system (LEAP) was chosen because the energy end-use branch structure with LEAP is not pre-defined and enables a number of alternative structures to be constructed and evaluated and because it allows energy demand forecasts using the system (LEAP) as a scenario planning tool.
3.1.1 Bottom-up Models
The bottom-up approach includes all models which use input data from a hierarchal level less than that of the sector as a whole. Models can account for the energy consumption of individual end-uses, individual houses, or groups of houses and are then extrapolated to represent the region or nation based on the representative weight of the modeled sample. The variety of data inputs results in the groups and sub-groups of the bottom-up approach as shown in Figure 10.
Figure 10 – Top-down and bottom-up modeling techniques for estimating the regional or national residential energy consumption
42 Chapter 3
Statistical methods (SM) rely on historical information and types of regression analysis which are used to attribute dwelling energy consumption to particular end-uses. Once the relationships between end-uses and energy consumption have been established, the model can be used to estimate the energy consumption of dwellings representative of the residential stock. Engineering methods (EM) explicitly account for the energy consumption of end-uses based on power ratings and use of equipment and systems and/or heat transfer and thermodynamic relationships [56].
Common input data to bottom-up models include dwelling properties such as geometry, envelope fabric, equipment and appliances, climate properties, as well as indoor temperatures, occupancy schedules and equipment use. This high level of detail is a strength of bottom-up modeling and gives it the ability to model technological options. Bottom-up models have the capability of determining the energy consumption of each end-use and in doing so can identify areas for improvement [55]. As energy consumption is calculated, this approach has the ability of determining the total energy consumption of the residential sector without relying on historical data. The main drawback caused by this level of detail is that the input data requirement is greater than that of top-down models and the calculation or simulation techniques of the bottom-up models can be complex.
In all cases the bottom-up models must be extrapolated to represent the housing sector. This is accomplished using a weight factor for each modeled house or group of houses based on its representation of the sector. A notable capability of the bottom-up approach is its ability to explicitly account for the effect of occupant behaviour and ‘‘free energy’’ gains such as passive solar gains. Although free energy gains have historically been neglected during residential analysis, they are now a common design point as focus is placed on alternative energy technologies. Statistical methods attribute all of the measured energy consumption to end-uses and in doing so incorporate the occupant’s behaviour with regards to use and settings of appliances [64]. However, if all energy sources are not accounted for, the end- use energy consumption estimates are derated by this consumption difference. Based in its physical principle roots, the EM has the ability to capture the additional energy consumption level based on requirements, inclusive of free energy. However, occupant behaviour must be estimated which is difficult as it has been shown to vary widely and in unpredictable ways. The following sections examine the modeling techniques by reviewing published models. The applicability, basic methodology and major conclusions found by the
43 Methodological Approach
researchers are listed. There is a tendency towards chronological order to facilitate understanding of the modeling technique development stream and contributions by the authors. Certain techniques were found to follow a clear development stream (e.g. conditional demand analysis) while others contain a wide variety of techniques and are discontinuous. Emphasis is placed on modeling technique development and less on the simple application to a new region [57].
3.1.2 LEAP: Long range Energy Alternatives Planning system
The LEAP model has been developed by the Stockholm Environment Institute-Boston (SEI- B) and used to evaluate energy development policies [58]. The central concept of LEAP is an end-use driven scenario analysis. Additionally, the model includes the technology and environmental database (TED) to estimate environmental emissions of the energy utilization. LEAP is an integrated modeling tool that can be used to track energy consumption, production and resource extraction in all sectors of an economy. LEAP is a demand-driven tool, in that the user first describes current and future energy requirements for households, transport, industry, and other sectors, then uses LEAP to model processes such as electricity generation, coal mining, and other energy supply systems that provide fuels for final consumption. It can be used to account for both energy sector and non-energy sector greenhouse gas (GHG) emission sources and sinks [59].
In addition to tracking GHGs, LEAP can also be used to analyse emissions of local and regional air pollutants, making it well-suited to studies of the climate co-benefits of local air pollution reduction. Finally, LEAP can track the direct costs of fuels and resources, of devices and systems that use energy, and of energy supply infrastructure so as to estimate the relative costs of different approaches to providing energy for an economy.
44 Chapter 3
The diagram below shows the relationships (Figure 11 – LEAP structure and calculation flowFigure 11) of the different integrated elements of the LEAP structure, and of the calculation flows within LEAP.
Figure 11 – LEAP structure and calculation flow
Modeling Methodologies LEAP is not a model of a particular energy system, but rather a tool that can be used to create models of different energy systems, where each requires its own unique data structures. As a demand-driven tool, analysis of energy consumption and its associated costs and emissions in an area is organized into a flexible hierarchical tree structure of energy demand, which is typically organized by sector, subsector, end-use and device.
The structure created by the user can be detailed and end-use oriented, or highly aggregate (e.g. sector by fuel), and the level of detail can vary from sector to sector as appropriate to adjust to data availability and to model goals. LEAP supports a wide range of different modeling methodologies: on the demand side these range from bottom-up, end-use accounting techniques to top-down macroeconomic modeling. LEAP also includes a range of optional specialized methodologies including stock-turnover modeling for areas such as transport planning [58].
On the supply side, LEAP provides a range of accounting and simulation methodologies that are powerful enough for modeling electric sector generation and capacity expansion
45 Methodological Approach
planning, but which are also sufficiently flexible and transparent to allow LEAP to easily incorporate data and results from other more specialized models.
Table 5 - Simulation structure of the utilized model: sectorial energy demands are independently derived by products of key drivers, e.g. macro-economic growth, and sectoral energy intensities in the demand module. In the transformation module, the requirement of each fuel will be fulfilled with the production of existing capacities. Primary resource is withdrawal by the required feedstock during the transformation process. The entire energy system is balanced by exporting the surplus and importing the shortage energy [61].
On the energy supply side (conversion of resources or intermediate products into intermediate products or end-use fuels), the LEAP Transformation program provides for integrated analysis of energy conversion, transmission and distribution, and resource
46 Chapter 3
extraction. The Transformation program provides demand-driven, engineering-based simulations, and uses a basic hierarchy including “modules” (sectors, for example, electricity generation), each containing one or more “processes”, such as power plants or types of power plants. Each process can have one or more feedstock fuels and one or more auxiliary fuels. LEAP allows for simulation of both capacity expansion and process dispatch, and calculates imports, exports and primary resource requirements, as well as costs and environmental loadings.
Figure 12 – Flow of Data which links Input and Output [59]
A schematic of a generic Transformation “Module” is shown below (Figure 13), but any given module can be very simple (for example, modules can have just one input and one output, and a single process and conversion efficiency, or can have multiple input and output fuels, and many different processes or plants) or very complex, depending on the data available and the modeling choices made by the user.
For electricity generation in particular, although LEAP is not intended to be a full-fledged dispatch model, it does provide the capability to dispatch different electricity generation resources (“processes”) using criteria ranging from running to full capacity, to dispatch by share of capacity or historical output, merit order, or least-cost resource.
Plants are dispatched to meet both total demand (in MWh) as well as the instantaneous peak demand that can be defined to vary by hour, day and season. If peak demand is to be tracked,
47 Methodological Approach
Figure 13 – Generic Elements of Transformation Modules in LEAP [59] users can exogenously specify a load-duration curve and LEAP will dispatch plants by merit order. Alternatively, load shapes be specified for each demand device so that the overall system load is calculated endogenously. Thus the effect of demand-side management (DSM) or energy efficiency policies on the overall load shape can then be explored in scenarios. Plant dispatch can also then be varied by season (for example, to reflect how hydro dispatch may vary between wet and dry seasons).
LEAP’s modeling capabilities operate at two basic conceptual levels. At one level, LEAP's built-in calculations handle straightforward energy, emissions and cost-benefit accounting calculations. At the second level, users enter spreadsheet-like expressions that can be used to specify time-varying data or to create a wide variety of sophisticated multi-variable models, thus enabling econometric and simulation approaches to be embedded within LEAP’s overall accounting framework. As of 2013, LEAP also supports optimization modeling: allowing for the construction of least cost models of electric system capacity expansion and dispatch, potentially under various constraints such as limits on CO2 or local air pollution [61].
48 Chapter 3
Time Frame LEAP is intended as a medium to long-term modeling tool. Most of its calculations occur on an annual time-step, and the time horizon can extend for an unlimited number of years. Studies typically include both a historical period known as the Current Accounts, in which the model is run to test its ability to replicate known statistical data, as well as multiple forward looking scenarios. Typically, most studies use a forecast period of between 20 and 50 years. Some results are calculated with a finer level of temporal detail. For example, for electric sector calculations the year can be split into different user-defined “time slices” to represent seasons, types of days or even representative times of the day. These slices can be used to examine how loads vary within the year and how electric power plants are dispatched differently in different seasons.
Scenario Analysis LEAP is designed around the concept of long-range scenario analysis. Scenarios are self- consistent storylines of how an energy system might evolve over time. Using LEAP, policy analysts can create and then evaluate alternative scenarios by comparing their energy requirements, their social costs and benefits and their environmental impacts. Scenarios can be used to test policy assumptions and for sensitivity analysis.
3.2 Framework of Energy Demand Model
Demand analysis is a disaggregated, end-use based approach for modeling the requirements for final energy consumption in an area . You can apply economic, demographic and energy- use information to construct alternative scenarios that examine how total and disaggregated consumption of final fuels evolve over time in all sectors of the economy. You can also examine the costs and environmental implications of each scenario . Energy demand analysis is also the starting point for conducting integrated energy analysis, since all Transformation and Resource calculations are driven by the levels of final demand calculated in your demand analysis.
49 Methodological Approach
LEAP provides a lot of flexibility in how you structure your demand data. These can range from highly disaggregated end-use oriented structures to highly aggregate analyses. Typically a structure would consist of sectors including households, industry, transport, commerce and agriculture, each of which might be broken down into different subsectors, end-uses and fuel-using devices [59]. You can adapt the structure of the data to your purposes, based on the availability of data, the types of analyses you want to conduct, and your unit preferences. Note also that you can create different levels of disaggregation in each sector.
Similarly, you also have choices in the methodologies you can apply for energy demand analysis [59]. The following methodologies are available:
Activity Level Analysis, which itself consists of either Final Energy Demand Analysis, or Useful Energy Demand Analysis in which energy consumption is calculated as the product of an activity level and an annual energy intensity (energy use per unit of activity) Stock Analysis, in which energy consumption is calculated by analyzing the current and projected future stocks of energy-using devices, and the annual energy intensity of each device (defined as energy per device). Transport Analysis, in which energy consumption is calculated as the product of the number of vehicles, the annual average mileage and the fuel economy of the vehicles.
In each case, demand calculations are based on a disaggregated accounting for various measures of social and economic activity (number of households, vehicle-km of travel, tonnes of industrial production, commercial value added, etc.) These "activity levels" are multiplied by the energy intensities of each activity (energy per unit of activity). Each activity level and energy intensity can be individually projected into the future using a variety of techniques, ranging from applying simple exponential growth rates and interpolation functions, to using sophisticated modeling techniques that take advantage of LEAP's powerful built-in modeling capabilities.
Among these three strategies, the only one that will be discussed in this research is the Activity Level Analysis dew to the overall objective and the available data.
50 Chapter 3
3.2.1 Activity Analysis
In this, the default methodology, energy consumption is calculated as the product of an activity level and an annual energy intensity (energy use per unit of activity). Overall activities are defined as the products of the individual activities entered along a complete branch of the demand tree. Typically, activities are specified as a single absolute value (e.g. number of households) multiplied by a series of shares or saturations/penetrations (e.g. the percent share of urban households, the penetration of an end-use such as air conditioning), and the penetration of each technology that meets the end-use[59][61].
Total energy consumption is thus calculated by the equation:
푒푛푒푟푔푦 푐표푛푠푢푚푝푡𝑖표푛 = 푎푐푡𝑖푣𝑖푡푦 푙푒푣푒푙 푥 푒푛푒푟푔푦 𝑖푛푡푒푛푠𝑖푡푦
There are two basic variations to this methodology: in a Final Energy Demand Analysis you specify energy intensities at the device level as the amount of fuel used per unit of activity; in a Useful Energy Demand Analysis you specify useful energy intensities at the next highest branch level (typically the end-use level), and then specify the efficiencies of each device.
Final Energy Demand Analysis In a final energy demand analysis, energy demand is calculated as the product of the total activity level and energy intensity at each given technology branch. Energy demand is calculated for the Current Accounts year and for each future year in each scenario.
퐷푏,푠,푡 = 푇퐴푏,푠,푡 ∗ 퐸퐼푏,푠,푡
Where D is energy demand, TA is total activity, EI is energy intensity, b is the branch, s is scenario and t is year (ranging from the base year [0] to the end year). Note that all scenarios evolve from the same Current Accounts data, so that when t = 0, the above equation can be written as:
퐷푏,0 = 푇퐴푏,0 ∗ 퐸퐼푏,0
The energy demand calculated for each technology branch is uniquely identified with a particular fuel. Thus, in calculating all technology branches, LEAP also calculates the total final energy demand from each fuel.
51 Methodological Approach
The total activity level for a technology is the product of the activity levels in all branches from the technology branch back up to the original Demand branch.
푇퐴푏,푠,푡 = 퐴푏′,푠,푡 ∗ 퐴푏′′,푠,푡푏 ∗ 퐴푏′′′,푠,푡 ∗ …
Where Ab is the activity level in a particular branch b, b' is the parent of branch b, b'' is the grandparent, etc. Note that those branches marked as having "No data" as well as the top level "Demand" branch are treated as having an activity level of 1. The activity level values of other branches with percentage units (e.g. percent shares or percent saturations) are always divided by 100 to yield a fractional value from zero to one in the calculations.
Useful Energy Demand Analysis In a useful energy demand analysis, energy intensities are specified, not for a technology, but at one level up, at the category with aggregate energy intensity branch type.
In Current Accounts you specify final energy intensities for the category with aggregate energy intensity branch type and fuel shares and efficiencies for each technology branch below. These data are used calculate the overall useful energy intensity for the aggregate energy intensity branch and the activity shares for each technology as follows:
For each technology branch:
푈퐸푏,0 = 퐸퐼퐴퐺,0 ∗ 퐹푆푏,0푥 ∗ 퐸퐹퐹푏,0
푏 = 1, … , 퐵
Where EIAG,0 is the final energy intensity in aggregate energy intensity branch, UE is the useful energy intensity in a technology branch b, FS is its fuel share, EFF is its efficiency, and b is one of B technology branches.
The useful intensity of the aggregate energy intensity branch is the sum of the useful intensities for each technology branch:
푈퐸퐴퐺퐺,0 = 푆푢푚푏 = 1. . 퐵(푈퐸푏,0)
The activity share (i.e. the share of the number of technologies, rather than the fuel share) is the product of the fuel share and efficiency of each technology b:
52 Chapter 3
푈퐸푏,0 퐴푆푏,0 = 푈퐸퐴퐺퐺,0
AS is activity share.
In scenarios, you enter expressions to independently project the Current Accounts values calculated above for the useful energy intensity of the aggregate energy intensity branch, the technology activity shares and their efficiencies. The final energy intensity for each technology is given by:
퐸퐼푏,푠,푡 = 푈퐼퐴퐺퐺,푠,푡 ∗ 퐴푆푏,푠,푡/ 퐸퐹퐹푏,푠,푡
Overall energy demand for each technology is calculated in the same way as for a final energy demand:
퐷푏,푠,푡 = 푇퐴푏,푠,푡 ∗ 퐸퐼푏,푠,푡
3.3 Transformation Calculation
Transformation calculations are demand-driven, so the available data allow the modelling through four different levels of detail:
Level 1 (Simplest): Ignores capacity limits: dispatch specifies shares of each process.
Level 2: User controls what to build and when and fully controls the dispatch of processes (e.g. by percent share or in proportion to avail. capacity).
Level 3: User controls what to build but LEAP decides when (to meet a planning reserve margin). Dispatch e.g. by merit order to meet peak demands along a load‐duration or a seasonal/time‐of‐day load curve.
53 Methodological Approach
Level 4 (Most detailed): LEAP decides what to build and when, using least‐cost optimization. It can also reflect CO2 prices and other externalities, mitigation targets and there is the availability of OSeMOSYS with GLPK for optimization (linear or mixed integer programming).
Figure 14 – Relation between data requirements and independence degree of the time forecast
After deciding the level of detail there is the need of analysing the production system. This operation is made by following the scheme below [59][61].
Identify Modules to Include A module represents an energy industry or sector such as electricity generation, oil refining, district heating, charcoal production, or electricity transmission and distribution. You will want to include all current modules, and, depending on the time-scale of your analysis any planned or potential future modules, even those that may not yet be significant, such as cogeneration, biofuel production, and perhaps even hydrogen production. One starting point would be to examine the rows in the conversion section of an energy balance to identify the modules for the area you are studying. Alternatively, you can work from the module structure which exists in LEAP's default data set. By editing the tree in your LEAP area, you can change the default module structure. Modules can be added, deleted or changed to fit your study's requirements.
54 Chapter 3
Sequence Modules Arrange modules on the tree into a correctly ordered list. The ordering of the modules reflects their position in the sequence of energy flows through an area. Thus, transmission and distribution modules are normally placed near the top (close to Demand), conversion modules such as electricity generation are placed in the middle, and primary resource extraction modules are placed at the bottom of the list.
Set Module Properties In setting-up each module, it is important to consider how each module is to be represented. For example, whether you wish to model capacity constraints, or whether you want to assume that a module can always meet its requirements. Modules without capacity restrictions require less data and are generally easier to set-up and debug, but may not accurately reflect real-world situations.
Determine Level of Detail and Enter Data Another important consideration is the level of detail to include in each module. Each module is divided into one or more processes. A process describes an individual technology or a group of technologies. You need to decide whether you wish to include each individual technology (e.g. each power plant) or whether you want to model groups of similar technologies as a single process (e.g. all diesel peaking plants). While there are no particular limits on the number of processes you can include in each module, bear in mind that the more processes you include, the more complex your model will be to set-up, interpret and debug and the slower your calculations will be. For each process, you enter technical data such as capacities, efficiencies, and output fuels. You can also enter cost data, and describe the environmental loadings of feedstocks and auxiliary fuels.
Each module is operated to meet the demands that arise from domestic requirements and export demands (net of any minimum specified level of imports). For the first calculated module (the one closest to demands), domestic requirements are set equal to final demands (see Demand Calculations). After each module is calculated, domestic requirements are decremented by the outputs produced from the module, but incremented by the input fuels required by the module. Ultimately the calculation yields the requirements for primary resources (fossil and renewable), which can be compared to the amounts specified in your Resource branches.
55 Methodological Approach
The following flow chart shows the basic steps in the Transformation calculations (Figure 15).
Figure 15 – Transformation Caluculations basic steps [62]
56 Chapter 3
3.3.1 Endogenous Capacity Expansion Calculations
If you have specified a set of processes that will have their capacity added endogenously, LEAP calculates how much capacity is added as follows:
Existing capacity before the addition of endogenously calculated additions is calculated as follows for each process in each year:
퐶푎푝푎푐𝑖푡푦 퐵푒푓표푟푒 퐴푑푑𝑖푡𝑖표푛푠 = (퐸푥표푔푒푛표푢푠 퐶푎푝푎푐𝑖푡푦
+ 퐸푛푑표푔푒푛표푢푠 퐶푎푝푎푐𝑖푡푦 퐴푑푑푒푑 푃푟푒푣𝑖표푢푠푙푦) ∗ 퐶푎푝푎푐𝑖푡푦 푉푎푙푢푒
Where Endogenous Capacity Added Previously, is summed over the previous lifetime years, so as not to include endogenously added capacity that is now retired. Note that exogenously specified capacity is not automatically retired: any retirements of this type of capacity need to be entered explicitly as lower values [59]. The Capacity Value term is used to adjust the actual capacity value of intermittent renewable energy processes, which are assumed to have less capacity value than thermal power plants (thermal power plants typically have a capacity value of 100%).
Peak system power requirements on the module are calculated as a function of the total energy requirements and the system load factor:
푃푒푎푘 푅푒푞푢𝑖푟푒푚푒푛푡 [푀푊] = 퐸푛푒푟푔푦 푅푒푞푢𝑖푟푒푚푒푛푡 [푀푊/ℎ푟]/(푀표푑푢푙푒 퐿표푎푑 퐹푎푐푡표푟 ∗ 8760 [ ℎ푟푠/푦푒푎푟])
Where the load factor is either entered as data, or calculated as the average height of a load curve. The reserve margin before the addition of endogenously calculated additions is calculated as follows:
푅푒푠푒푟푣푒 푀푎푟푔𝑖푛 퐵푒푓표푟푒 퐴푑푑𝑖푡𝑖표푛푠 = (퐶푎푝푎푐𝑖푡푦 퐵푒푓표푟푒 퐴푑푑𝑖푡𝑖표푛푠 − 푃푒푎푘 푅푒푞푢𝑖푟푒푚푒푛푡)/푃푒푎푘 푅푒푞푢𝑖푟푒푚푒푛푡
The amount of endogenous capacity additions required is calculated as follows:
57 Methodological Approach
퐸푛푑표푔푒푛표푢푠 퐶푎푝푎푐𝑖푡푦 퐴푑푑𝑖푡𝑖표푛푠 푅푒푞푢𝑖푟푒푑 = (푃푙푎푛푛𝑖푛푔 푅푒푠푒푟푣푒 푀푎푟푔𝑖푛 − 푅푒푠푒푟푣푒 푀푎푟푔𝑖푛 퐵푒푓표푟푒 퐴푑푑𝑖푡𝑖표푛푠) ∗ 푃푒푎푘 푅푒푞푢𝑖푟푒푚푒푛푡
Finally, endogenous capacity additions are calculated for each process by cycling through the processes listed on the Endogenous Capacity screen in the order listed on the screen. In each year, capacity continues to be added in the amounts specified on the screen in the Addition Size column, until the amount added is greater than or equal to the Endogenous Capacity Additions Required.
3.3.2. Process Dispatch Calculations
During Transformation calculations, LEAP dispatches the processes in each module to try and meet the requirements for energy on the module. Depending on the dispatch rule you, different calculation algorithms area used to calculate how much of each process is dispatched.
Two specialized dispatch rules ("Dispatched by Merit Order" and "Dispatched by Running Cost") are available for simulating the dispatch of processes (typically electric power plants) in order to meet both the peak power requirements and the energy requirements on a module. For information on the calculations for these two dispatch rules, see topic: Dispatching Processes on a Load Curve [58].
The rest of this section focuses on the remaining dispatch rules.
The first step in dispatching the process is to calculate the share of energy outputs for each process. The algorithms for this calculation are:
In Proportion to Base Year Output
퐵푎푠푒푌푒푎푟푂푢푡푝푢푡𝑖 푃푟표푐푒푠푠푆ℎ푎푟푒𝑖 = 푛 ∑𝑖=1 퐵푎푠푒푌푒푎푟푂푢푡푝푢푡
58 Chapter 3
In Proportion to Available Capacity
퐶푎푝푎푐𝑖푡푦𝑖 ∗ 푀퐶퐹𝑖 푃푟표푐푒푠푠푆ℎ푎푟푒𝑖 = 푛 ∑𝑖=1 퐶푎푝푎푐𝑖푡푦𝑖 ∗ 푀퐶퐹𝑖
Once the share of output from each process has been calculated, LEAP calculates the actual energy output from each process. In cases where you have chosen to capacity-constrain processes, LEAP will compare the proportions of output shares and available capacities (capacity x availability) in order to identify the process in each module that constrains overall output from the module. This information is in turn used to calculate the total capacity constrained outputs from each process.
Finally, this information is used to calculate the input fuels required by each process, and consequently the costs, benefits and environmental loadings of each process.
3.3.3 Dispatching Processes on a Load Curve
If the Module Dispatch Rule is set to "Dispatch by Merit Order", or "Dispatch by Running Cost", then during Transformation calculations, LEAP will simulate how processes are dispatched to meet both the power requirements specified by a cumulative annual load curve and the overall annual energy requirements on a module.
Merit orders are either specified explicitly on the Process Properties screen, or if the Dispatch Rule is set to "Dispatched by Running Cost", they are determined as follows:
퐹푢푒푙퐶표푠푡𝑖 푅푢푛푛𝑖푛푔퐶표푠푡𝑖 = 푉푎푟𝑖푎푏푙푒푂푀퐶표푠푡𝑖 + 퐸푓푓𝑖푐𝑖푒푛푐푦𝑖
To simulate the dispatch of processes, LEAP first makes a list of processes, sorted by their merit order. This information is used to calculate the available capacity of each group of processes with the same merit order. Next LEAP makes a discrete approximation of the load curve and divides it up into vertical "strips. The height of each strip is equal to the overall system peak load requirement multiplied by the average percentage of peak load of two
59 Methodological Approach
adjacent points on the specified load curve. The width of the strip is the difference in hours of those same two adjacent data points. Overall peak system load requirement is calculated from the energy requirements on the module, and the module's load factor:
퐸푛푒푟푔푦푅푒푞푢𝑖푟푒푚푒푛푡[푀푊ℎ푟] 푃푒푎푘푆푦푠푡푒푚푃표푤푒푟푅푒푞푢𝑖푟푒푚푒푛푡[푀푊] = 퐿표푎푑퐹푎푐푡표푟 ∗ 8760
Figure 16 –Example of Peack-Load Curve taken from exercises [60]
Each group of processes is dispatched in vertical "strips" in order to try and fill the area under the load curve. Base load plants are dispatched first at the bottom, followed by intermediate and peak load plants. To properly represent the average technical availability of each plant (allowing for periods when plants are unavailable because of planned or unplanned outages), the maximum height of each strip is the available capacity for each group (the sum of Capacity x Maximum Availability) for all processes in the group. Each group is dispatched in turn until the load curve strip is filled. In cases where the available capacity of the group exceeds the amount required, the actual amount of each process dispatched is reduced, so that each process is dispatched in proportion to its available capacity.
60 Chapter 3
3.3.4 Shortfall and Surplus Calculations
Once the outputs from each process in a module has been calculated, it uses the settings you specify on the Output Fuel Properties screen to simulate how LEAP deals with situations of shortfalls and surpluses in the production of each output fuel.
Under shortfall conditions requirements unmet by production can either be met with additional "gap-filling" imports, or they can remain unmet. Notice that this latter case does not necessarily imply that a requirement is unmet in the system as a whole. An upstream module that is simulated later on may produce the same output fuel.
Under surplus conditions additional production can either be exported or wasted.
3.3.5 Calculation of Systems with Feedback Flows
To accommodate more complex energy supply systems, the Transformation calculations allow for energy systems with energy flow feedbacks. For example, in the diagram shown below, an oil refinery might produce diesel for use in power generation, while the oil refinery itself might consume some of the generated electricity. These systems are solved by iterating the basic Transformation calculation, but with adjustments made to total demands after each iteration to reflect any unmet requirements for upstream energy flows.
61 Methodological Approach
Figure 17 – Iterations Example [60]
In the figure shown above, a final demand for 100 units of electricity is met by a power plant using diesel and operating at 33% efficiency. This, in turn, requires 300 units of diesel, which are produced by a simplified (one product) refinery operating at 94% efficiency. Thus, the overall demand for crude oil is 320 units. To produce the diesel, the oil refinery itself uses 5 units of electricity. In the first calculation iteration, these 5 units of electricity demand remain unmet by the system. In the second iteration, the 5 units of unmet requirements are added to the final energy demand that drives the calculation. This causes the power plant to generate 105 units of electricity using 315 units of oil. This, in turn, requires that the refinery uses 320 units of crude and 5.25 units of electricity. The third iteration begins with the generation of 105.25 units of electricity, and iterations continue until successive changes in the electricity requirements become small. At this point, the system has converged to a solution.
3.4 Scenario Analysis
At the heart of LEAP is the concept of scenario analysis. Scenarios are self-consistent story- lines of how a future energy system might evolve over time in a particular socio-economic setting and under a particular set of policy conditions. Using LEAP, scenarios can be built and then compared to assess their energy requirements, social costs and benefits and
62 Chapter 3
environmental impacts. You can use scenarios to ask an unlimited number of "what if" questions, such as: what if more efficient appliances are introduced, what if different electric generation capacity expansion plans are pursued, what if indigenous reserves of oil and gas are discovered, what if renewable energy technologies are introduced, etc.
All scenarios share a common set of Current Account data. Current Accounts data may either reflect a single set of base year data or may represent multiple years of historical data. Each scenario runs from the First Scenario Year to the End Year of the study. In cases where the Current Accounts is for only a single year, then the First Scenario Year will be the year immediately after the base year [59].
Scenarios in LEAP encompass any factor that can change over time, including those factors which may change because of particular policy interventions, and those that reflect different socio-economic assumptions. Variations, in these latter types of factors are normally referred to as a sensitivity. In LEAP, sensitivities are included in a scenario. Because of this, it is important in your cost-benefit analysis that you only compare scenarios with the same socio- economic assumption.
Expression Search Order Whenever LEAP calculates a value for a particular variable at a particular branch, scenario and region, the value of that variable is based on evaluating the expression defined for that branch, variable, scenario and region. LEAP uses a specified search order to look for these expressions. At each step in the search, LEAP looks to see if a non-blank expression has been explicitly entered. If the expression is found the search concludes successfully, and the expression is used to calculate a set of values for the currently active branch, variable, scenario and region. If no non-blank expression is found the search continues through any additional scenarios that these scenarios depend on.
When working in areas with multiple regions, the same basic search order described above is used. The only exception is that if a region is set to inherit expressions from another parent region then, at each step in the expression search process, LEAP will first look in the current active region, and then, if no expression is found, it will look in the parent region for the scenario currently being searched.
63 Methodological Approach
Expression Search Example One: A Simple Scenario Hierarchy Let's examine a simple example with just one baseline scenario and one mitigation scenario.
Suppose LEAP needs to find the expressions for a given branch and in the Mitigation Scenario. The following is the process used for Searching for an expression:
Figure 18 - Search Process for a Variable in the Mitigation Scenario, Simple Hierarchy [60]: Step 1: Current active scenario Step 2: Additional scenarios listed in the First Inherits (not this example) Step 3: Additional scenarios affecting those listed in Step 2 (not this example) Step 4: Finally walk backup main scenario tree First LEAP looks in the active scenario, in this case Mitigation (Step 1). If no expression is found LEAP walks up the main scenario tree (Step 4) firstly to the Baseline scenario and ultimately to the Current Accounts data until a non-blank expression is found [59].
This example is a typical situation of data sets in which you enter any historical or static data in the Current Accounts and then enter a complete description of how your energy system is likely to change over time under current policy conditions in the Baseline scenario. The data in the mitigation scenario only needs to specify how the mitigation scenario is likely to be different from the data already specified in the Baseline or Current accounts.
A Scenario Hierarchy with Multiple Inheritance Suppose that the mitigation scenario (example 1) is actually a package of 3 other scenarios: mitigation measures that could be conducted individually in the industry, buildings and transport sectors. This package of scenarios is constructed by specifying additional scenarios upon the mitigation scenario
64 Chapter 3
For these scenarios, the following expression search process is followed. LEAP first looks in the active scenario, which in this case is Mitigation (Step 1). If no expression is found, LEAP looks in each of the three additional scenarios (industry, buildings and transport) in the order shown on the Manage Scenarios screen (Step 2). LEAP then proceeds to walk up the main scenario tree, first to the Baseline scenario and then to Current Accounts (Step 4). Ultimately, if no expression is found then the default value for the variable is used
Figure 19 - Search Process for a Variable in the Mitigation Scenario, Package of Scenarios [60]: Step 1: Current active scenario Step 2: Additional scenarios listed in the First Inherits Step 3: Additional scenarios affecting those listed in Step 2 (not applicable in this example) Step 4: Finally walk backup main scenario tree. If other scenarios are built upon, the previous ones, the calculation process iterates following the same steps.
3.5 Optimization
LEAP includes the capability to automatically calculate least cost capacity expansion and dispatch of supply-side Transformation modules [62]. This capability works through integration with the Open Source Energy Modeling System (OSeMOSYS) which has been developed by a coalition of organizations including the Royal Technical University (KTH) in Sweden, SEI, the International Atomic Energy Agency (IAEA), and the UK Energy
65 Methodological Approach
Research Center. OSeMOSYS in turn depends on the GNU Linear Programming Kit (GLPK), a software toolkit intended for solving large scale linear programming problems by
Figure 20 – Interaction between Leap Model and OSeMOSYS, in detail in the image is highlighted the data flow, the image is taken from LEAP Optimization Guide [62] means of the revised simplex method. Both OSeMOSYS and GLPK are open source and freely distributed tools [62][63]. Both are included as part of LEAP's standard installation and both are fully integrated into LEAP's user interface.
Optimization can be used to calculate the least-cost expansion and dispatch of power plants for an electric system, where optimal is defined as the energy system with the lowest total net present value of the social costs of the system over the entire period of calculation (from the base year through to the end year).
In calculating the optimal system LEAP takes into account all of the relevant costs and benefits incurred in the system including:
• The capital costs for building new processes.
66 Chapter 3
• The salvage values (or decommissioning costs) for decommissioning processes • The fixed and variable operating and maintenance costs • The fuel costs • The environmental externality values (pollution damage or abatement costs).
LEAP and OSeMOSYS model calculate both capacity expansion pathways. One thing to remember with optimization calculations is that the model tells the user what future configuration of the energy system will yield the lowest overall cost to society. Such pathways may not necessarily represent realistic policy options in a particular country for many different reasons such as the social and environmental acceptability of certain technologies or the need to preserve diversity and energy security. On the other hand, with LEAP’s simpler accounting calculations the user tells the model what energy system to build. These pathways are not necessarily optimal in the sense of being least cost [63].
Note also that the optimality of a given pathway may be very sensitive to input assumptions such as future capital costs, future efficiency assumptions, future fuel costs or future GHG mitigation targets. A system that is optimal for a country under one set of assumptions (for example low growth in fuel prices) may be far from optimal under another set of assumptions (high growth in oil prices). Generally, the goal in energy planning is not to identify a single optimal solution, but rather to identify robust energy policies that work well under a range of plausible input assumptions. You can easily use LEAP’s scenario capabilities to calculate explore different optimal solutions under different sets of input assumptions
3.5.1 OSeMOSYS Formulation
OSeMOSYS model is constructed in terms of sets, parameters, variables, equations (some of which are constraints) and an objective function [63].
Each model application is defined by a data file. This indicates the number and type of technologies and fuels; which technologies produce or consume/use what fuels etc. The
67 Methodological Approach
model definition (in a broad sense) is programmed in the GNU Mathprog mathematical programming language
The model equations are divided into those used to meet capacity constraints (Equations CC1...), activity constraints (A1...), fuel production (FP1...), capital investment (CI1...), salvage values (S1...) operating costs (OC1...), and total discounted costs (TDC1...).
All model sets contain entries which are used as indexes to parameters and variables.
Year (y) - This set contains years over which the model is solved. Almost all variables and parameters are indexed by the model year for which it is solved. Investment year (v) – this set is used to track the years in which investments are made. Investment years are identical in number to model years. It is separate from the model year set as it will be used to index investments by their “investment year” over the modeling period. Technology (t) – this is the set of all technologies represented in the application.
Capacity Constrains The first variable used is NewCap and contains new investment in capacity by the model year in which the investment was made, for each technology
Supposing that I would like to change the indexes, replacing the model Year index for the
InvestmentYear index in a new variable, NewCapVDimv,t. Where NewCapVDim is the transposition of the NewCap variable, where entries indexed by model year set are moved to the corresponding vintage year set.
This is done by multiplying the elements of NewCapVDimy,t by a parameter (which I have
defined) called IDMatrixy,v. This parameter has indexes from the Year and Vintage sets and its values are similar to an identity matrix. Multiplication is done by multiplying each element from one variable with each element from the next variable, where the index corresponds.
Supposing that all of the elements with common Year indexes were summed and that the same procedure would be undertaken for all indexes in the Technology set, I would have a new variable with indexes from the Vintage and Technology sets. As an iterated expression this is represented as follows:
68 Chapter 3
퐸푞푢푎푡𝑖표푛 퐶퐶1: ∑ 푁푒푤퐶푎푝푦 , 푡 ∗ 퐼퐷푀푎푡푟𝑖푥 푦 , 푣 = 푁푒푤퐶푎푝푉퐷𝑖푚푣 , 푡 퐴푙푙 푦∈푌푒푎푟 ∀푣 ∈ 푉𝑖푛푡푎푔푒 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦
Equation CC1 serves only algebraic purposes, transposing the NewCap variable from the y (ModelYear) dimension to the v (Vintage) dimension and is part of a set of equations called capacity constraints. Equation CC2 derives the matrix of capacities invested in during the model period.
퐸푞푢푎푡𝑖표푛퐶퐶2 ∶ 푁푒푤퐶푎푝푉퐷𝑖푚푣 , 푡 ∗ 푉𝑖푛푡푎푔푒푀푎푡푟𝑖푥푦 , 푣 , 푡 = 푁푒푤퐶푎푝푆푡푟푢푐푡푢푟푒 푦 , 푣 , 푡 ∀ 푦 ∈ 푌푒푎푟 , 푣 ∈ 퐼푛푣푒푠푡푚푒푛푡푌푒푎푟 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦
Equation CC3 derives the TotalCapAnn variable, which is the sum of the residual capacity in each year with capacities invested in during the model period.
퐸푞푢푎푡𝑖표푛퐶퐶3: ∑ 푁푒푤퐶푎푝푆푡푟푢푐푡푢푟푒푦 ,푣 ,푡 + 푅푒푠𝑖푑푢푎푙퐶푎푝푎푐𝑖푡푦푦 ,푡 퐴푙푙 푣∈푉𝑖푛푡푎푔푒
= 푇표푡퐶푎푝퐴푛푛푦,푡 , ∀ 푦 ∈ 푌푒푎푟 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦
Equation CC4 is a constraint ensuring that total capacity (de-rated by its capacity factor for each load region) is always larger than the required activity
퐸푞푢푎푡𝑖표푛퐶퐶4 ∶ 푇표푡퐶푎푝퐴푛푛푦 , 푡 ∗ 퐶푎푝푎푐𝑖푡푦퐹푎푐푡표푟 푦 , 푡 ∗ 푂푛푒푀푎푡푟𝑖푥퐿푅푙 ≥ 퐴푐푡𝑖푣𝑖푡푦푦 , 푙 , 푡 ∀ 푦 ∈ 푌푒푎푟 , 푙 ∈ 퐿표푎푑푅푒푔𝑖표푛 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦
Each technology is able to accommodate an activity which uses, produces or both uses and produces energy. This activity (currently measured in terms of power, such as kW/year or GJ/year) should be less than the derated capacity of that technology. The technology capacity is derated by a capacity factor. (If the capacity factor is kept at 1 there is no derating.) The total capacity of a technology is the sum of new investments made during the modelling period (which have not been retired) plus the any residual capacity left over from before the modeling period. In order to account for stations, invested in during the modeling period, which may be retired during the modeling period the notion of a station's “investment year” is introduced. With CC1-4 is ensured that the capacity of each technology is greater than its activity for each load region.
69 Methodological Approach
Activity Constraints Equation A1: Ensures that the activity of a technology type through the year never exceeds its annual availability. As the model will choose the most economic time (load region) to reduce the activity of a technology (if the constraint is reached) this approximates planned outage.
퐸푞푢푎푡𝑖표푛 퐴1: ∑ 퐴푐푡𝑖푣𝑖푡푦푦 ,푙 ,푡 ∗ 푌푒푎푟푆푝푙𝑖푡 푦 ,푙 푙∈푇𝑖푚푒푆푙𝑖푐푒
≤ 푇표푡퐶푎푝퐴푛푛푦 ,푡 ∗ 퐴푣푎𝑖푙푎푏𝑖푙𝑖푡푦퐹푎푐푡표푟푦 ,푡 ∀ 푦 ∈ 푌푒푎푟 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦
In these constraints the activity of a technology is limited to a fraction of what its output would otherwise be – were it limited only by the capacity of the technology. Therefore it may run at the maximum level (derated by the capacity factor) for some time slices, but for other time slices its output would be below this. (This serves to approximate planned outage in a facility such as a power station for example. The model determines when the most economic time for this to happen.) This constraint is implemented by considering the activity rate for each load region and the length of the load region, the activity's annual availability as well as the total capacity of the technology in which the activity occurs. The total capacity determines the maximum potential activity during a load region [63].
Fuel Production Constraints Equation FP1: Multiplies the Activity of each technology to the ratio of fuel production to activity (OtptActvtyRatio). This gives the Production of each fuel from each technology.
퐸푞푢푎푡𝑖표푛 퐹푃1: ∑ 퐴푐푡𝑖푣𝑖푡푦푦 ,푙 ,푡 ∗ 푂푡푝푡퐴푐푡푣푡푦푅푎푡𝑖표푦 ,푡 ,푓 ∀푡∈푇푒푐ℎ푛표푙표푔푦
= 푃푟표푑푢푐푡𝑖표푛푦 ,푙 ,푓 ∀ 푦 ∈ 푌푒푎푟 , 푙 ∈ 푇𝑖푚푒푆푙𝑖푐푒 , 푓 ∈ 퐹푢푒푙
Equation FP2: Multiplies the Activity of each technology to the ratio of fuel used per unit of activity (InptActvtyRatio). This gives the “Consumption” or use of each fuel.
퐸푞푢푎푡𝑖표푛 퐹푃2: ∑ 퐴푐푡𝑖푣𝑖푡푦푦 ,푙 ,푡 ∗ 퐼푛푝푡퐴푐푡푣푡푦푅푎푡𝑖표푦 ,푡 ,푓 ∗ 푂푛푒푀푎푡푟𝑖푥퐿푅푙 ∀푡∈푇푒푐ℎ푛표푙표푔푦
= 퐶표푛푠푢푚푝푡𝑖표푛푦 ,푙 ,푓
70 Chapter 3
∀ 푦 ∈ 푌푒푎푟 , 푙 ∈ 푇𝑖푚푒푆푙𝑖푐푒 , 푓 ∈ 퐹푢푒푙
Equation FP3: Simply constrains the system so that production is always bigger than consumption or use and the endogenous demand for each fuel
퐸푞푢푎푡𝑖표푛 퐹푃3: 푃푟표푑푢푐푡𝑖표푛 푦 , 푙 , 푓 ≥ 퐷푒푚푎푛푑 푦 , 푙 , 푓 ∗ 푈푠푒푦 , 푙 , 푓 ∀ 푦 ∈ 푌푒푎푟 , 푙 ∈ 푇𝑖푚푒푆푙𝑖푐푒 , 푓 ∈ 퐹푢푒푙
There are several possible variations on this constraint which is the “main driver” of the model. To start with, for each fuel an exogenous demand can be specified in terms of power consumed per time slice (e.g. In GJ/year or kW/year). This demand can be met from the activity of a technology. A technology's fuel production is related to its activity by an “output activity ratio”. If a technology's activity, and output activity ratio for a given fuel is greater than zero it produces that fuel. Similarly, the consumption or use of a fuel by a technology is related by its “input activity ratio”. The model is constrained to produce more fuel than is used by energy technologies plus fuel that is exogenously demanded. The flexibility that comes with defining an input and output activity ratio allows the user to relate the capacities and activity in terms of the input of fuel (using an input activity ratio of 1, and an output activity ratio equal to the technology's efficiency (if only one fuel is produced)). The capacity may be related to the technology's output by having an output activity ratio of 1 and an input activity ratio equal to the reciprocal of the efficiency (if only one fuel is production).
Capital Costs Equation CI1: Calculates the capital investment required for each year for each technology
퐸푞푢푎푡𝑖표푛퐶퐼1 ∶ 퐶푎푝𝑖푡푎푙퐶표푠푡 푦 , 푡 ∗ 푁푒푤퐶푎푝푦 , 푡 = 퐶푎푝𝑖푡푎푙퐼푛푣푒푠푡푚푒푛푡푦 , 푡 ∀ 푦 ∈ 푌푒푎푟 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦 Equation CI2: Discounts the capital investment to the annual discounted capital investment for each technology
퐸푞푢푎푡𝑖표푛퐶퐼2 ∶ 퐶푎푝𝑖푡푎푙퐼푛푣푒푠푡푚푒푛푡 푦 , 푡 ∗ 퐷𝑖푠푐표푢푛푡퐹푎푐푡표푟 푦 , 푡
= 퐷𝑖푠푐퐶푎푝𝑖푡푎푙퐼푛푣푒푠푡푚푒푛푡 푦 , 푡 ∀ 푦 ∈ 푌푒푎푟 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦
The capital costs are calculated by multiplying capacity requirements by their respective capital costs. They are then discounted. These costs are recorded for each technology type
71 Methodological Approach
as well as for the year in which the investment took place. They are the total NPV (Net Present Value) capital expenditure for each year and each technology.
These values are stored in the variable: DiscCapitalInvestment.
Salvage Value Equation S1: Multiplies the investment costs with the salvage factor. The salvage factor is calculated outside the model and is a function of the life of the plant at the end of the model period and the technology specific discount rate. It is also a function of the method of depreciation used. In the mock-up interface, the salvage factor is calculated using shrinking fund depreciation method. The depreciated value of the technology as a fraction of the initial capital cost is discounted to the last model year yielding the salvage factor.
퐸푞푢푎푡𝑖표푛 푆1 ∶ 퐶푎푝𝑖푡푎푙퐶표푠푡푦 , 푡 ∗ 푁푒푤퐶푎푝푦 , 푡 ∗ 푆푎푙푣푎푔푒퐹푎푐푡표푟 푦 , 푡 = 푆푎푙푣푎푔푒푉푎푙푢푒 푦 , 푡 ∀ 푦 ∈ 푌푒푎푟 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦
At the end of the modeling period, the stock of equipment which was invested in during the period has value which could be salvaged. This is dependent on (amongst other things) how it is depreciated, the life and the time in which it is invested. (The NPV salvage value is calculated, and for simplicity is accounted for (by technology) in the year in which the new investment took place – rather than in the last year of the modeling period.)
These values are stored in the variable: SalvageValue.
Operating Costs Equation OC1: Calculates the undiscounted annual variable operating cost (VarOpCost) for each technology, load region and year. The variable cost (VariableCost) is multiplied by the fraction of the year accounted for in each load region (Year Split).
퐸푞푢푎푡𝑖표푛푂퐶1: 퐴푐푡𝑖푣𝑖푡푦 푦 , 푙 , 푡 ∗ 푉푎푟𝑖푎푏푙푒퐶표푠푡 푦 , 푡 ∗ 푌푒푎푟푆푝푙𝑖푡 푦 , 푙 = 푉푎푟푂푝퐶표푠푡 푦 , 푙 , 푡 ∀ 푦 ∈ 푌푒푎푟 , 푙 ∈ 푇𝑖푚푒푆푙𝑖푐푒 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦
Equation OC2: the variable cost (VarOpCost) for each time slice is then summed over all the load regions in the year, to establish the annual variable operating (AnnVarOpCost) cost by hear and technology.
퐸푞푢푎푡𝑖표푛푂퐶2 ∶ ∑ 푉푎푟푂푝퐶표푠푡 푦 ,푙 ,푡 = 퐴푛푛푉푎푟푂푝퐶표푠푡푦 ,푡 ∀푙∈퐿표푎푑푅푒푔𝑖표푛
72 Chapter 3
∀ 푦 ∈ 푌푒푎푟 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦 Equation OC3: The product of the per unit fixed costs (fixedCost) with total installed capacity (TotCapAnnual) determines the annual fixed cost for each technology. In this equation, that is added to the annual variable cost (AnnVarOpCost) to derive the total operating costs per year for each technology (OperatingCost).
퐸푞푢푎푡𝑖표푛푂퐶3: 푇표푡퐶푎푝퐴푛푛 푦 , 푡 ∗ 퐹𝑖푥푒푑퐶표푠푡 푦 , 푡 + 퐴푛푛푉푎푟푂푝퐶표푠푡 푦 , 푡
= 푂푝푒푟푎푡𝑖푛푔퐶표푠푡 푦 , 푡 푓표푟 푎푙푙 푦 ∈ 푌푒푎푟 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦 Equation OC4: Finally the operating costs per technology are discounted by multiplying themby the discount factor by year
퐸푞푢푎푡𝑖표푛푂퐶4 ∶ 푂푝푒푟푎푡𝑖푛푔퐶표푠푡 푦 , 푡 ∗ 퐷𝑖푠푐표푢푛푡퐹푎푐푡표푟 푦 , 푡 = 퐷𝑖푠푐푂푝푒푟푎푡𝑖푛푔퐶표푠푡푦 , 푡 ∀ 푦 ∈ 푌푒푎푟 , 푡 ∈ 푇푒푐ℎ푛표푙표푔푦 Operating costs are calculated by summing the variable and fixed operating costs. The variable costs are a function of the technology's activity level during a time slice and the length of that time slice. These costs associated with each load region are summed to give an annual operating cost. The fixed cost is determined by the capacity of technology in the system. These costs are calculated for each technology, for each year.
These values are stored in the variable: DiscOperatingCosty
Total Discounted Costs Equation TDC1: The total discounted cost for each technology is calculated by add in the (discounted) operating costs, capital cost minus the salvage value for each technology.
퐸푞푢푎푡𝑖표푛푇퐷퐶1
∶ 퐷𝑖푠푐푂푝푒푟푎푡𝑖푛푔퐶표푠푡 푦 , 푡 + 퐷𝑖푠푐퐶푎푝𝑖푡푎푙퐼푛푣푒푠푡푚푒푛푡 푦 , 푡
− 푆푎푙푣푎푔푒푉푎푙푢푒 푦 , 푡 = 푇표푡푎푙퐷𝑖푠푐퐶표푠푡 푦 , 푡
∀ 풚 ∈ 풀풆풂풓 , 풕 ∈ 푻풆풄풉풏풐풍풐품풚
Objective Function The final equation that is going to be minimized due to the GLPK linear solver.
73 Methodological Approach
The model is set up so that all costs incurred are attributed to a technology. Later, cost penalties will be introduced for under production (cost of energy not served) or over production of either energy or a commodity such as an emission.
퐸푞푢푎푡𝑖표푛푀푂1 ∶ ∑∀ 푦∈푌푒푎푟 ∑,∀푡∈푇푒푐ℎ푛표푙표푔푦 푇표푡푎푙퐷𝑖푠푐퐶표푠푡푦 ,푡 [63]
74
Chapter 4: Model Structure
4.1 Introduction
For realistically simulating the Egyptian energy system a great amount of input data is needed. Information on demand, the energy mix and the corresponding plant parameters as well as transmission capacities, fuel prices and economic growth in Egypt are important data for the model.
A completely accurate simulation of the Egyptian energy system is not achievable as it would require an enormous amount of very accurate data which, as stated in the previous analysis, in some cases are impossible to find. Due to some hypothesis and some esteems, a simplified but accurate version of the system is modelled, where the parameters are a good representation of the reality but with a lower degree of complexity.
When evaluating future scenarios an extra factor of uncertainty is added to the difficulties of accurate simulation. Input data concerning the future are then based on extrapolations of trends and grounded predictions. In some cases required input data can be inaccessible for the public, in this case estimates based on the best available information and assumptions are used to approach reality.
Before the beginning of the model realization, a long work of data gathering and analysis have been done in order to find the most recent year with the required amount of data. Then once yearly datasets have been built, a preliminary analysis has been done in order to find the most accurate one, for each year I couldn’t gather the same amount of data.
Due to some factors, such as Egyptian’s Institutions most recent data publications, the chosen year to be used as base year is 2012. The other data gathered are used, if the statistical significance is proved, in the time forecast process.
75 Scenario Simulation and Results
In the following section the main input parameters that are used in the model are described along with their assumptions and simplifications. These data are aimed to be as accurate as possible and includes a minimum of guesses. An explanation is also given on how hydro power and other renewable resources are modelled.
The Model description is divided in three main parts:
Data and Assumptions description Demand setting, Demand analysis and Demand forecast analysis Transformation Branch setting
After the model description each scenario will be described and analysed.
4.2 Input Data and Assumptions
In this section the input data for the Base Year and the Reference Scenario are described (REF scenario at page n. 103). The data sources are mentioned and input assumptions are described.
4.2.1 Socioeconomic Variables
Key assumptions are defined as variables which, if not specifically treated, are not utilized in the calculation process of demand, transformation and resources branches. I inserted in this branch some socio-economic and demographic data and some user variables.
76 Chapter 5
I did a statistical analysis of the historical trends to identify the best fitting growth equations. Historical trends have been used to make the regression and are reported in Appendix II. Results of the statistical analysis are reported in Table 6 and underline the validity of the analysis.
Table 6- Key Assumptions description and diagram, full data in Appendix II, main source http://data.worldbank.org/country/egypt-arab-republic - R2is the coefficient of determination and indicates how well data fit a statistical model, the standard error (SE) is the standard deviation of the sampling distribution.
Type of time Variable Base Year Value - 2012 R2 Std. error forecast
Income 240,32 Billion $ Linear forecast 0,9985 1,4292
Exponential Population 80,72 Million people 0,9999 0,02569 forecast
Households size 5 constant - -
Population/5 Million Household - - - houses
GDP 262,82 Billion $ Linear forecast 0,9936 4,3603
End Year Yearly growth 43% 0,9999 0,013 Urbanization 0,2388%
The forecast of these variables is in line to other studies found in literature (FAO, IEA) and other sources [75][76].
Results underline an exponential growth of the population which is the main critical aspect that fuels the crisis of WEF nexus. The Urban percentage of households grows of 2,87% points from 2013 to 2040 as shown in Figure 22 and Figure 21 (the difference through historical data and time forecast ones is done by a blue vertical line).
77 Scenario Simulation and Results
Table 7 – Results of the forecast from Appendix II
End Urban Rural Income Population GDP Year Households Households Households Urban Billion Million Million Million Million Billion $ % $ people houses houses houses 264,77 82,112 294,28 43,105 16,422 7,079 9,344 2013 1 428,30 92,368 481,261 43,83 18,474 8,097 10,377 2020 9 661,93 109,28 748,377 44,888 21,856 9,811 12,045 2030 5 895,56 129,289 1015,493 45,972 25,858 11,887 13,97 2040 1
Figure 22- Historical trend and time forecast of Population
Figure 21 – Historical trend and time forecast of the number of Households, divided through the growth of the End year percentage of Urban Households
78 Chapter 5
4.2.2 Boundaries and Assumptions
After the realisation of Key Assumptions variables I needed to identify some data that would be used as starting point to shape up Egyptian energy system and to identify the relations among each productive sector. I decided to use Egyptian energy balance from International Energy Agency (IEA) dataset as the basis.
From the balance (Table 8) and my previous analysis 2.2.3 Energy) it is clear that Egypt energy system relies strongly on energy sources that are produced internally except for coal, which is all imported (but it’s not used in the electricity production process).Volumes of
Table 8 – Egypt Energy Balance of 2012 from IEA report, data are reported in thousand tonnes of oil equivalent (ktoe)
79 Scenario Simulation and Results
resources imports and exports in comparison to the production quantities are nearly negligible. These facts led me to identify the goal as an analysis of domestic production and this implied the necessity to limit the oscillatory trends of import and export branches. This and the volume relative difference led me to consider import and export target volumes as constants unless the trend is clearly decreasing or increasing.
Electricity transboundary transmission capacity limits are not implemented directly in the model but are inserted as maximum of electricity imports and export sum.
Table 9 – Relative volumes of resources Import and Export Crude Oil Oil Products Natural Gas Electricity
[percentage of production]
Import 8,48 % 14,17 % 0,00 % 0,06 %
Export 19,20 % 8,27 % 11,46 % 0,16 %
4.2.3 Resources
Analysing Egypt’s resources I had to consider both fossil fuels and renewable sources, trying to quantify reservoirs for fossils and the availability of renewable sources. Among fossils I found that there are no proven reservoirs of coal.
Last proved values are considered constants due to the fact that it is unreasonable to consider historical trends to foresee when other reservoirs will be found.
Crude oil Crude oil proved reservoirs value are related to governmental reports [71]. Proved reserves are those quantities of petroleum which, by analysis of geological and engineering data, can
80 Chapter 5
be estimated with a high degree of confidence to be commercially recoverable from a given date forward, from known reservoirs and under current economic conditions.
Figure 23 – Evolution of proved Crude Oil reservoirs
Table 10 – Crude Oil proved reservoirs, Billion of bbl
2002-2003 2004-2005 2006 2008 2010 2013
[billion barrels of oil equivalent] Egypt 3,308 2,7 2,6 3,7 4,3 4,4
Natural Gas Natural Gas proved reservoirs are taken from governmental reports [72].
Table 11- Natural Gas proved reservoirs,million cubic meters,2186000mm^3 are 12866,3Gtoe
2002-2004 2005-2010 2011-2013 2015
[million cubic meters] Egypt 1264000 1657000 2186000 3036000
Renewables The hydro power in Egypt is a substantial part of the energy mix. A few smaller power plants are modelled as run-of-river, while the biggest "Aswan High Dam" has a reservoir. The reservoir hydro plant is described by the weekly water inflow in the reservoir. This is determined by the yearly variations of the Nile current and based on information given by
81 Scenario Simulation and Results
EEHC. A simulation over the whole year (52 weeks) is done in which the amount of water used per week is highlighted. The maximum load of the High Aswan Dam is found in and is 2300 MW. The minimum daily generation can also be found and amounts to only 12% of its maximum daily generation. The hourly minimum generation is then set to 10 in order not to stop the current in the Nile River. For the run-of-river plants the generation profile is fixed. This is a simplification of reality as the production of these hydro plants is highly related to the Aswan dam's generation profile. As the run-of-river plants are relatively small in capacity, not modelling them accurately is not expected to be a big limitation of the model. Hydro resource yearly availability is set 1272 ktoe as reported by EEHC.
4.2.4 Fuel Prices
About fuel prices the only data available were about base year (2012) [67] and in absence of historical values I had to refer my forecast to other studies [68]. Prices are based on the IEA New Policies Scenario that assumes the low carbon agreements of G20 are being implemented [69]. For natural gas, regional price difference exists as NG needs to be transported by gas pipes. The natural gas prices were assumed to start at the production price in 2012 and converging to the European price by 2030. The fuel prices are given in Table 12. For natural gas and coal a distribution costs of 0.50 and 1.00 $/GJ respectively are added to these prices.
Table 12 – Fuel Costs per type of fuel in Egypt
2012 2015 2020 2025 2030 2035 2040 Fuel Type $/GJ $/GJ $/GJ $/GJ $/GJ $/GJ $/GJ Natural Gas 9,1 9,6 10,4 11,6 12,9 13,7 14,7 Crude Oil 18,2 18,3 18,6 19,1 19,9 20,1 20,6 Coal Bituminous 4,0 4,1 4,2 4,3 4,4 4,5 4,6 Electricity 18,4 18,4 18,4 18,4 18,4 18,4 18,4 Residual Fuel Oil 21,0 21,2 21,5 22,0 23,0 23,2 23,7 Diesel 25,5 25,8 26,1 26,8 27,9 28,2 28,8 Kerosene 23,9 24,1 24,4 25,0 26,1 26,4 26,9 Jet Kerosene 22,7 22,9 23,2 23,8 24,8 25,1 25,6
82 Chapter 5
Gasoline 27,1 27,3 27,7 28,4 29,6 29,9 30,6 LPG 9,7 9,76 9,89 10,1 10,6 10,7 10,9 Naphtha 7,2 7,3 7,4 7,59 7,91 8 8,17 Sun ------Wind ------Water ------
4.3.5 Startup costs and fuel use of power plants
In order to implement proper unit commitment in LEAP model, it is necessary to characterize the power plants included in the model. The parameters needed for unit commitment modelling are:
Start-up costs: given in $ per MW of installed capacity Fixed fuel use: given as share of the fuel used at full capacity, changes the efficiency to a new marginal efficiency.
These data are given for each generation facility.
The biggest hydro power generator is also subject to a minimum generation limit.
Due to a lack of data for the actual plants, the parameters are found from official data for the Irish power plants [72]Ireland was used as reference country as it had the most comprehensive data set in terms of unit commitment data. The use of Ireland data is taken from [77] that refers to other IEA studies in the Mediterranean Region [78]. The average was found per fuel type or turbine type for each of the parameters. The resulting values are given in Table 13. The assumptions in these data are that the generation facilities in Egypt have similar technical characteristics as in Ireland. It is likely that for the newly built power plants this assumption holds relatively well as the power plant markets are becoming more international and uniform.
83 Scenario Simulation and Results
Table 13 – Unit commitment parameters per fuel/turbine type
Start-up costs Min. generation Fixed fuel use
Fuel/ Turbine type [% of fuel use at [$/MW] [% of total capacity] full capacity]
Natural gas – CC 194.1 0,46 0.2
Natural gas – GT 97.9 0.34 0.17
Natural gas - ST 210.7 0.14 0.08
Fueloil 205 0.32 0.08
Lightoil 61.6 0.23 0.19
Water 0 0.38 0
4.2.6 Investments
When LEAP performs an investment optimization, the simulation runs the full 52 weeks, with aggregated time-steps and no unit commitment parameters.
Investments will only be allowed from the year 2018. The model is capable of investing both in generation capacity and in transmission capacity. For investing in generation LEAP can choose generation units from the technology catalogue I realized. The data given on the plant's efficiency and costs are found from the IEA energy outlook [70]. The investment policy is that 110% of peak load needs to be available in thermal capacity. For the investments in transmission capacity, the maximum increase in capacity is 4000 MW every 5 years on all existing lines.
84 Chapter 5
Table 14 – Technology catalogue for investments
Fixed Variable CAPEX Efficiency Lifetime Technology O&M O&M
[m$/MW] [k$/MW] [$/MWh] [%] [years]
Steam coal – subcritical 1.8 45 3.8 35 30
Steam coal – supercritical 2.2 63 5.3 40 30
CCGT 0.8 25 2.1 59 30
Gas Turbine 0.4 20 1.7 38 30
Medium speed Diesel 1.6 22 1.8 45 30
Low speed Diesel 2.4 10 0.8 46 30
Solar PV 1.9 24 2.0 14.5 25
Onshore wind 1.5 22 3.7 27.1 20
4.3 Energy Demand Setting and Forecast
This paragraph is divided in three main sections, in the first one are specified the technical data and the definitions used in setting up the demand branch of the model. In the second part is analysed the base year (2012) demand and in the last one are shown and analysed the results of the time forecast of the Energy Demand.
85 Scenario Simulation and Results
4.3.1 Demand Setting
The analysis of the energy demand is one important part, in fact being my model a demand driven one, production and transformation branches will try to match demand volumes and if they are not able to reach the target value, import of resources and then import of products will try to match the objective.
Following Table 8 I divided Energy demand in four branches: Industry, Transportation, Non Energy Use and Other, which is itself divided in other 4 sub-branches (Residential, Commercial and public services, Agriculture and forestry and Non specified). Each one is analysed for each fuel type and using key assumptions and historical trends I defined a growth law. All categories follow IEA classification which is referred to ISIC (International Standard Industrial Classification of All Economic Activitie) from United Nations Statistical Division [67][68].
Industry Industry consumption is specified as follows (energy used for transport by industry is not included here but is reported under transport):
Iron and steel industry [ISIC Group 241 and Class 2431]; Chemical and petrochemical industry [ISIC Divisions 20 and 21] excluding petrochemical feedstocks; Non-ferrous metals basic industries [ISIC Group 242 and Class 2432]; Non-metallic minerals such as glass, ceramic, cement, etc. [ISIC Division 23]; Transport equipment [ISIC Divisions 29 and 30]; Machinery. Fabricated metal products, machinery and equipment other than transport equipment [ISIC Divisions 25 to 28]; Mining (excluding fuels) and quarrying [ISIC Divisions 07 and 08 and Group 099]; Food and tobacco [ISIC Divisions 10 to 12]; Paper, pulp and print [ISIC Divisions 17 and 18]; Wood and wood products (other than pulp and paper) [ISIC Division 16];
86 Chapter 5
Construction [ISIC Divisions 41 to 43]; Textile and leather [ISIC Divisions 13 to 15]; Non-specified (any manufacturing industry not included above) [ISIC Divisions 22, 31 and 32].
In this branch I found a prevalence of linear trends during historical data analysis as listed below (Table 15). Another remarkable fact is the strong decreasing trend of coal.
Table 15 – Industry Branch, demand analysis for fuel type
Fuel type Base year value Forecast equation R2
LPG 25 kt Exponential forecast 0,9962
Kerosene 6 kt Constant -
Diesel 1445 kt Constant -
Residual Fuel - 2234 kt -4,4213% of annual growth Oil
Natural Gas 5983 ktoe +2,0349% of annual growth -
Electricity 3153 ktoe +5,2925% of annual growth -
Coal Bituminous 198 ktoe -10,8828% of annual growth -
Transport Consumption in transport covers all transport activity (in mobile engines) regardless of the economic sector to which it is contributing [ISIC Divisions 49 to 51], and is specified as follows:
Domestic aviation includes deliveries of aviation fuels to aircraft for domestic aviation - commercial, private, agricultural, etc. It also includes use for purposes other than flying, e.g. bench testing of engines, but not airline use of fuel for road transport. The domestic/international split should be determined on the basis of departure and landing locations and not by the nationality of the airline. Note that this may include journeys of considerable length between two airports in a country.
87 Scenario Simulation and Results
Road includes fuels used in road vehicles as well as agricultural and industrial highway use. Excludes military consumption as well as motor gasoline used in stationary engines and diesel oil for use in tractors that are not for highway use. Rail includes quantities used in rail traffic, including industrial railways. Pipeline transport includes energy used in the support and operation of pipelines transporting gases, liquids, slurries and other commodities such as the energy used for pump stations and maintenance of the pipeline. Energy for the pipeline distribution of natural or manufactured gas, hot water or steam (ISIC Division 35) from the distributor to final users is excluded and should be reported in energy industry own use, while the energy used for the final distribution of water (ISIC Division 36) to household, industrial, commercial and other users should be included in commercial/public services. Losses occurring during the transport between distributor and final users should be reported as losses. Domestic navigation includes fuels delivered to vessels of all flags not engaged in international navigation. The domestic/international split should be determined on the basis of port of departure and port of arrival and not by the flag or nationality of the ship. Fuel used for ocean, coastal and inland fishing and military consumption are excluded. Non-specified includes all transport not elsewhere specified
Table 16 - Transport Branch, demand analysis for fuel type
Fuel type Base year value Forecast equation R2
Gasoline 6079 kt Linear Forecast 0,9947
Jet Kerosene 590 kt Linear Forecast 0,9389
Diesel 8550 kt Linear Forecast 0,9587
Residual Fuel Oil 279 kt Exponential Forecast 0,8972
Natural Gas 404 ktoe Linear Forecast 0,8842
Electricity 40,45 ktoe Costant -
88 Chapter 5
Other Other covers residential, commercial and public services, agriculture/forestry, fishing and non-specified.
About Residential sub-branch and Commercial and Public Servicies there are no detailed information to add except a list of [ISIC Divisions 33, 36-39, 45-47, 52, 53, 55, 56, 58-66, 68-75, 77-82, 84 (excluding Class 8422), 85-88, 90-96 and 99].
Agriculture and Forestry covers deliveries to users classified as agriculture, hunting and forestry by the ISIC, and therefore it includes energy consumed by such users whether for traction (excluding agricultural highway use), power or heating (agricultural and domestic) [ISIC Divisions 01 and 02]. In it is included also Fishing sub-branch because before 2007 the data collected were aggregated so to use historical trends I had to work according IEA dataset. Fishing includes fuels used for inland, coastal and deep-sea fishing. Fishing covers fuels delivered to ships of all flags that have refuelled in the country (including international fishing) as well as energy used in the fishing industry [ISIC Division 03].
Non-specified includes all fuel use not elsewhere specified as well as consumption in the above-designated categories for which separate figures have not been provided. Military fuel use for all mobile and stationary consumption is included here (e.g. ships, aircraft, road and energy used in living quarters) regardless of whether the fuel delivered is for the military of that country or for the military of another country.
Table 17 – Other Branch, demand analysis for fuel type
Fuel type Base year value Forecast equation R2
Residential
Growth as Households Charcoal 32692 TJ - number
Growth as Households LPG 4221 kt - number
Growth as Households Kerosene 68 kt - number
89 Scenario Simulation and Results
Growth as Households Natural Gas 1144 ktoe - number
Growth as Households Electricity 4724 ktoe - number
Growth as Households Coal Bituminous 4 ktoe - number
Commercial and Public Services
Electricity 2667 ktoe Linear Forecast 0,897
Agriculture and Forestry
Kerosene 7 kt Constant -
Diesel 2080 kt Linear Forecast 0,9873
Residual Fuel Oil 83 kt Exponential Forecast 0,9342
Electricity 492 ktoe Linear Forecast 0,8991
Non Specified
Charcoal 814 ktoe Linear Forecast 0,9939
Non Energy Use Non-energy use covers those fuels that are used as raw materials in the different sectors and are not consumed as a fuel or transformed into another fuel. Non-energy use is shown separately in final consumption under the heading non-energy use.
Note that for biomass commodities, only the amounts specifically used for energy purposes (a small part of the total) are included in the energy statistics. Therefore, the non-energy use of biomass is not taken into consideration and the quantities are null by definition.
90 Chapter 5
Table 18 – Non Energy Use Branch, demand Analysis for fuel type
Fuel type Base year value Forecast equation R2
Naphtha 2856 ktoe Linear Forecast 0,9425
Natural Gas 4206 ktoe Exponential Forecast 0,8973
4.3.2 Demand Analysis
Analysing current demand help to find out the differences that occur through the identified branches. I must point out that this is the final demand, that is the demand of final users, so it doesn’t take into account the amount of fuel used through transformation processes. So this analysis focuses on the energy needed by the end user, which is the key parameter to let the transformation section of the model work properly. In Table 19 are reported the volumes of demand.
Total demand on the base year is 54,5 millions of tonnes of oil and is divided among the branches as can be seen in Figure 24.
Other is the branch that consumes the majority of the energy requirements, it consumes 32,5% of total demand. This category is, as said before, composed of four sub-branches and I found out that among them the one that requires the biggest energy volume is domestic sector.
The second branch in order of consumed volumes is Transport, which needs 16,7millions of tonnes of oil equivalent. Industrial sector is the third in terms of consumption while only 13% of the total demand can be assigned to Non Energy use branch.
91 Scenario Simulation and Results
Table 19 – Base year (2012) demand volumes per demand sector
Industry Transport Other Non Energy use Total Branch [millions of tonnes of oil equivalent]
2012 13,0 16,7 17,7 7,1 54,5
Figure 24 – Percentage of share of demand for each sector on the base year
After the description of the final energy demand in terms of branch, let’s describe it in terms of fuel type.
The most required fuel type is diesel, which covers 22,9% of the total. Diesel consumption, as resulted in the tables of chapter 4.3.1, is divided among all branches except for Non Energy use.
The second fuel in terms of consumption is electricity, 11,09 millions of tonnes of oil equivalent are required on the base year. Electricity finial demand covers 20,4 % of the final demand as can be seen in Figure 25.
Natural Gas demand is the third it terms of volume of final demand, but it is the first in terms of total demand. The amount of natural gas used in the transformation branch, mainly to
92 Chapter 5
produce electric energy, is not in reported in this analysis because it is not related to the end user (it can be found in Table 8).
Aggregated oil products final demand is 29,86ktoe and represents 54,80 % of the final demand. This fact underlines how the end user demand is strongly related to crude oil. Also crude oil demand, as part of the natural gas demand, is not reported directly in this graph because it goes through the process of oil refining (one of the processes in the transformation branch) to obtain oil products.
Table 20 – Base year (2012) demand volumes per type of fuel
Electricity Natural Gas Gasoline Diesel LPG Others Total Fuel type [millions of tonnes of oil equivalent]
2012 11,09 11,74 6,50 12,50 4,80 7,86 54,48
Figure 25 – Percentage of share of demand per each type of fuel on the base year
From this analysis is clear how the final energy demand is related directly to oil products and natural gas.
In the setting of transformation module will be underlined how in Egypt also electricity demand is indirectly related to fossil fuel sources.
93 Scenario Simulation and Results
4.3.3 Demand Forecast Analysis
The analysis in this chapter represents one of the results of this work, in fact the demand growth goes through the simulation process. Starting from the base year dataset, with the hypothesis and assumptions enounced in the Setting chapter, this demand forecast is realized.
In Table 21 are reported the results of the time forecast for each branch of the demand sector. Final energy demand grows from 54,5Mtoe in 2012 to 107,0Mtoe on the end year. So I must presume that the energy system shape will change substantially to meet the needs that nearly double in the forecast process.
Figure 26 – Demand growth for each branch from 2015 to the end year
Table 21 - Demand Forecast for each Branch
2012 2015 2020 2025 2030 2035 2040 2040 Branches [millions of tonnes of oil equivalent] [% of total]
Industry 13,0 13,6 14,9 16,7 19,1 22,1 26,0 24,3%
94 Chapter 5
Transport 16,7 19,7 24,3 29,0 33,7 38,4 43,1 40,2%
Other 17,7 18,9 21,0 23,2 25,4 27,8 30,3 28,4%
Non Energy use 7,1 6,9 6,8 6,9 7,0 7,3 7,6 7,0%
Total 54,5 59,1 67,0 75,7 85,2 95,6 107,0 -
Analysing the growth for each branch it can be seen in Figure 26 that the sector whose demand increases the most is Transport. Starting from a demand of 16,7Mtoe in 2012 it concludes in 2040 with a need of 43,1Mtoe. In the year 2040 Transport represents the 40,2% of the final energy demand. This huge gap leads me to underline how the transportation matter will be crucial in the future and how in the case of Egypt leads to the doubling of diesel demand and the growth by tree times of gasoline needs.
The energy demand of the Other sector started from a dominant position and in five year time loses this spot to Transport. Domestic needs (that is the main sub-branch of Other) grow as fast as population and this leads to an increase of 171 % the final demand of Other sector
Figure 27 – Demand forecast for type of fuel
95 Scenario Simulation and Results
through the forecast. Industrial sector needs grow faster than Other branch (but not faster than Transport), in 28 year time it’s final energy need doubles.
From a fuel type point of view I found out that the most required type of fuel is electricity, which starting from 11,1Mteo reaches 28,2Mtoe with a growth of 253,9 %. This increase in electricity demand is led by each branch of final users and led electricity to be the first final demand from 2036 as can be seen in Table 22 and Figure 27.
The second fuel in term of consumption is diesel, followed in terms of growth by gasoline. As stated before, the growth of the needs of these fuels led Transport to be the branch with highest demand. In 2040 the sum of the demand of these two fuels (43,1Mtoe) will represent 74,6 % of the demand of oil products. The final demand of oi products will be of 61,8Mtoe, representing the 57,8% of the final demand.
Table 22 - Demand forecast results for type of fuel
2012 2015 2020 2025 2030 2035 2040 2040 Fuels [% of [million tonnes of oil equivalent] total] Electricity 11,1 12,3 14,5 17,1 20,2 23,8 28,2 26,3 % Natural Gas 11,7 11,7 11,9 12,2 12,8 13,6 14,5 13,6 % Kerosene 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 % Gasoline 6,5 8,0 10,3 12,7 15,1 17,4 19,8 18,5 % Jet Kerosene 0,6 0,6 0,6 0,7 0,7 0,7 0,7 0,7 % Diesel 12,5 14,0 16,5 18,9 21,4 23,9 26,3 24,6 % Residual Fuel Oil 2,5 2,3 1,9 1,6 1,3 1,1 0,9 0,8 % LPG 4,8 5,0 5,5 6,0 6,5 7,0 7,6 7,1 % Coal Bituminous 0,2 0,1 0,1 0,05 0,03 0,02 0,01 0,0 % Charcoal 1,6 1,7 1,8 2,0 2,1 2,3 2,5 2,3 % Naphtha 2,9 3,2 3,8 4,4 5,0 5,7 6,3 5,9 % Total 54,5 59,1 67,0 76,0 85,2 95,6 106,9 -
The demand of natural gas will decrease in percentage of total demand, its value in 2040 is 123,68% of the bease year one. In this count it’s not considered the ammount of natural gas that is consumed in the transformation branch (mainly to produce electricity). So speaking of total demand, considering also the natural gas absorbed by transormation processes, it’s value reaches 50,33 Mtoe in 2040 and it’s second only to crude oil.
96 Chapter 5
Crude oil’s demand is not taken into account in these data, because it’s a row material without a final demand, in fact it is all absorbed by refinery sector to produce oil products. It’s demand can be esteemend by summing all oil products demands. It’s value is 61,74 Mteo representing 57,8% of the demand in 2040.
4.4 Transformation
As stated in Chapter 3 arranging transformation modules on the tree into a correctly ordered list is very important. The ordering of the modules reflects their position in the sequence of energy flows through an area. Thus, transmission and distribution modules are normally placed near the top (close to Demand), conversion modules such as electricity generation are placed in the middle, and primary resource extraction modules are placed at the bottom of the list.
Figure 28 - Flow of fuels through transformation processes as it is implemented in this model.
97 Scenario Simulation and Results
For all processes I assumed as costant their efficiency and consumptions except for Oil Refineries and Electricity plants.
4.4.1 Transformation Processes and Losses
Energy industry own use contains the primary and secondary energy consumed by transformation industries for heating, pumping, traction, and lighting purposes. These quantities are shown as negative figures. Included here are, for example, own use of energy in coal mines, own consumption in power plants (which includes net electricity consumed for pumped storage) and energy used for oil and gas extraction.
Gas works is treated similarly to electricity generation, with the quantity produced appearing as a positive figure in the gas column, inputs as negative entries in the coal, oil products and gas columns, and conversion losses appearing in the total column.
Transfers include interproduct transfers, products transferred and recycled products (e.g. used lubricants which are reprocessed).
Statistical differences, which are taken from IEA energy balance (Table 8), include the sum of the unexplained statistical differences for individual fuels, as they appear in the basic energy statistics. It also includes the statistical differences that arise because of the variety of conversion factors in the coal and oil columns. Due to an easy managing of this value, which is not a proper process, it has been inserted for each fuel in the resource branch.
Table 23 – characterizing parameters of processes and losses
Fuel type Losses [%] Consumption [%] Efficiency [%]
Transmission and Distribution
Electricity 11,13 - -
Natural Gas 1,4 - -
98 Chapter 5
Energy Industry own use
Electricity - 3,28 -
Natural Gas - 30,25 -
Diesel - 4,453 -
Residual Fuel Oil - 6,078 -
Gas Works
Coal 2,88 - -
Coal Transformation
Coal - - 46,28
Transfers
Crude Oil - - 89,5
Charcoal Production
Wood 20
4.4.2 Electricity Generation
In the annual EEHC report [10] all existing power plants are listed and categorized by production company. Based on this list the current generation portfolio was set up in the in LEAP. Some characterising parameters such as capacity, efficiency fuel type and type of turbine (gas (GT), steam (ST) or combined cycle (CC)) are also found in [9]. When no plant- specific data was available, the values in Table 24 were used for efficiency, the operation and maintenance (O&M) costs and the lifetime. The efficiency of the wind and solar and hydropower is based on the full load hours (FLHs).
99 Scenario Simulation and Results
Table 24 – Characterizing parameters per fuel/turbine type
Fixed O&M Variable O&M Fuel/ Turbine Efficiency Lifetime costs costs type [%] [k$/MW] [$/MWh] [years]
Natural gas – CC 59 25 2.1 30
Natural gas – GT 38 20 1.7 30
Natural gas – ST 35 45 3.8 30
Coal 35 45 3.8 30
Fueloil 45 45 3.8 30
Lightoil 46 20 1.8 30
Water Based on FLHs 46 3.3 50
Sun Based on FLHs 24 2.0 25
Wind Based on FLHs 22 3.7 20
100 Chapter 5
Figure 29 – First classification of power plants, from EEHC report 2012-2013 [74]
101 Scenario Simulation and Results
Figure 30 – Detailed description of power plants through some parameters, from EEHC report 2012- 2013 [74]
102 Chapter 5
4.4.3 Oil Refining
Oil refineries show the use of primary energy for the manufacture of finished oil products and the corresponding output. Thus, the total reflects transformation losses. In certain cases the data in the total column are positive numbers. This can be due either to problems in the primary refinery balance, or to the fact that the IEA uses regional net calorific values for oil products.
This category of processes which is composed by many operations, due to shortness of data, has been shaped as a unique plant that processes all crude oil required and as output gives all oil products in proportion to demand. So capacity expansion of the refining sector is built as growth of oil volume treatment capacity of this virtual refining plant.
Table 25 – Refining sector capacity and expansion plan, MOEE report
2012 2015 2016 2018
Historical production 195367.70 - - -
Capacity 256960.01 287985 309885 419385
103
Chapter 5: Scenario Simulation and Results
In the scenario simulation process I decided to build up a Reference scenario and three future scenarios all related to the main one. The hierarchy of these scenarios is shown below (Figure 31) and underlines how the data and settings of the Reference scenario are used also in the other scenarios, with some additions.
Starting from the analysis of Reference scenario, which represents Egypt’s current energy transformation capacity facing the growth of the demand I added new plants.
Figure 31 – Scenario hierarchy
5.1 Scenario Description
In the following sections are described in detail the scenarios, results will be analysed starting from the next section (5.2).
104 Chapter 5
5.1.1 REF: Reference Scenario
This is the basis scenario, in it I simulated the energy transformation system’s, in particular the electricity system’s reaction to the demand growth with the built in generation capacity. This scenario relies on the lowest amount of assumptions in fact it doesn’t take into account investments, capacity building, and fuel costs hypotheses (4.2 Input Data and Assumptions).
With REF scenario analysis I had the objective of analysing the critical issues of the energy system pointing out where to intervene. Another crucial aspect is that with this scenario I made a sensitivity analysis to value the error propagation of the model.
5.1.2 GOV: Governmental Investment Scenario
Governmental Investment scenario implements the Egyptian Electric Holding Company (EEHC) generation capacity expansion plan that has been elaborated according to the Ministry of Electricity of Electricity and Energy (MOEE). The strategy follows the steps in Table 26 till 2018 and goes further to 2030 (full table is reported in Appendix III).
The years in the table represent the predicted beginning of the building process of the power plants whose projects have been already approved. The other plants in the expansion plan reported in Appendix III need to go through the process of design competition.
The Governmental Investments can be divided in fossil fuel power plants and renewable power plants and as I can see for the 2013-2018 period Egyptian Government is aiming to invest strongly in wind energy.
105 Scenario Simulation and Results
Table 26 - Generation Expansion Plan (2013-2018) MOEE (Ministry of Electricity and Energy).
2013 2014 2015 2016 2017 2018 Plant Name [MW]
Wind (East-Weast Nile) 360 850 1100 540
Photovoltaic Cells 20 20
Abu Kir (steam) 1300
Ain Sokhna (steam) 1300
Banha (CC) 250 500
Giza North (CC) 500 1250 500
Dairout BOO (CC) 1000
Sueaz (steam) 650
Mini&Small Hydro units 30
Helwan South (steam) 1950
Bani Suif (steam)
Qena (steam)
Dananhur (steam) 1000
Cairo West (steam) 650
Assiut (steam) 650
Damitta West (GT) 500 250
El Sueif (CC) 500
Mahmoudia (CC) 300
El Shabab (CC) 500
106 Chapter 5
5.1.3 OPT: Optimized Investment Scenario
Even though the EEHC has a clear plan when it comes to the expansion of the generation mix of Egypt, it is interesting to examine what the electricity landscape would become when the generation is determined by an investment optimization.
In the Optimized Investment scenario therefore, all new generation from the EEHC plan is maintained but the build order and peak load distribution of the demand through the plants are decided by the model. The model is also allowed to invest in transmission capacity.
The optimization is done in order to obtain the minimum total cost through all the simulation period, and results are evaluated in terms of total net present value (NPV).NPV is determined by calculating the costs (negative cash flows) and benefits (positive cash flows) for each period of the investment and then its yearly total is summed and actualized.
5.1.4 REN: Renewable Scenario
The Renewable scenario is built on the GOV scenario, and adds to the EEHC expansion plan new plants fuelled by renewable resources. This has been done aiming strongly to achieve the Governmental objectives in terms of energy mix:
In 2020, 20% of electricity generation from renewable sources. This is conforming to the NREA plans for renewable energy. In 2025 this value rises to 25%. In 2030, 30% of electricity generated from solar sources.
107 Scenario Simulation and Results
Table 27 - Renewable capacity proposed expansion plan through 2020, the full plan is reported in Appendix III
2015 2016 2017 2018 2019 2020 Plant type [MW]
PV plant 275 450 2*400 500
Mini Hydro 30
Wind 700 950 950
100,0% 90,0% 80,0% 70,0% 60,0% 50,0% 40,0% 30,0% 20,0% 10,0% 0,0% 2000 2003 2006 2009 2012 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045
Figure 32 – Diffusion model of domestic photovoltaic
The technologies added in this scenario are similar to the ones in EEHC expansion plan, photovoltaic plants and wind farms, in terms of technical parameters and total capacity, this has been done in order to maintain a coherency trough the scenarios and to keep valuable the feasibility studies done by MOEE. Part of the plan is reported in Table 27, the full expansion is reported in Appendix III.
In this scenario I also added domestic photovoltaic plants. I used standard 5kW domestic photovoltaic systems. Then supposing the realization of a subsidy system for this kind of technology in rural areas I implemented an S-shaped diffusion model taken from literature [79][80].
108 Chapter 5
Table 28 – Scenarios summary – Every scenario implements the demand forecast; GOV, OPT, REN implement the EEHC generation expansion plan; OPT scenario uses the investment optimization; REN implements an extra expansion plan with a renewable focus reported in Appendix III
EEHC Demand Investment Renewable generation forecast optimization focus expansion plan
Reference x
GOV x x
OPT x x x
REN x x x
5.2 Reference Scenario
The REF scenario is considered the most likely based on information given by EEHC. No investments are performed in this scenario not in generation capacity nor in transmission lines. The transmission capacities remain constant over the years.
Analysing results of this scenario led me to identify a statistical difference among data from EEHC and IEA.
Egyptian Electric Holding Company’s reports stated that in the year 2012 were produced 13055ktoe of electric energy to meet the final demand. Running the model backwords I found that the final demand was of 11088ktoe. By using the model backwords I mean utilising the transformation module, starting from his historical production, to find the demand that can be satisfied taking into account the losses of the system. The difference between final demand and production is due to energy sector own consumption and losses.
109 Scenario Simulation and Results
These final demand data were in line with the MOEE and EEHC reports.but they didn’t match those from the IEA energy balance for the same year where electricity demand was reported to be 12061ktoe.
Starting from this difference in input data, I considered as correct the demand from EEHC reports and confirmed by the data of the Ministry of Electricity and Energy, and used the gap to realise a sensitivity test.
5.2.1 Sensitivity Test
As the input data used in the simulations is subject to unavoidable uncertainty, a sensitivity analysis is conducted for the main parameters to see the impact of deviations in these data. The test aimed to find out how much a perturbation in the demand data would have been reflected in the output, so how a difference in electricity demand on the base year would have changed the projections and how the model would have reacted.
As stated before I used the differences of data in the base year to set the test and I found out that a gap of 973ktoe on the base year, 8,07% of EEHC final demand which from here on will be considered the real value, through the forecast reaches a mean perturbation of 8,62% (all data are reported in Table 29).
This evaluation stated that the starting demand gap doesn’t expand in a high extent and this indicates that the assumptions made about these parameters are not likely to compromise the results significantly and the model is robust with respect to these parameters.
Table 29 – Sensitivity Test Input and Results
IEA EEHC Error ΔD/D ΔP/P Propagation % % Production Demand Production Demand [%] 2012 14028,98 12061,20 13055,78 11088,00 8,07 8,07 - 2013 14563,80 12487,10 13386,29 11479,71 8,07 9,43 10,26
110 Chapter 5
Mean - - - - 8,62 9,51 10,67
After the evaluation of the expansion of the demand gap I evaluated how the electricity production of the model responded to this gap.
The difference in the production of the two cases were in percentage very low, in fact with starting with 9,43%, through all the simulated years I found out that the difference in demand caused a relative difference of the production with a mean of 9,51%. This evaluation compared to the demand difference took me to underline a little growth in the data perturbation, a mean growth of 0,89% from the demand difference to the generation difference. This proved the consistency of the assumptions made in setting the transformation modules of the energy system, in fact the model showed good reaction to the input perturbations and also the absence of a relevant amplification of the gap.
The mean error propagation through the model is 10,67% on a 8,62% mean perturbation, with an absolute growth of the gap percentage of 2,05%. This value confirms what sated before about the model response and flexibility to input changes and guarantees the correctness of the hypotheses.
The results of this analysis are satisfying and are used in the next paragraph to define two boundaries projections of the already built generation capacity.
111 Scenario Simulation and Results
5.2.2 Generation Reaction to the demand forecast
Through the sensitivity test two boundaries of demand forecast where defined (Figure 33) and the difference among them has been evaluated. The response of the generation capacity
Demand Gap Forecast 35000,00
30000,00
25000,00
20000,00
15000,00
10000,00 Electricity Electricity demand [ktoe] 5000,00 2011 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041
IEA demand EEHC demand
Figure 33 – Reference scenario Demand forecast starting from two different values of electricity demand on the base year is shown in Figure 34 and has also been divided in two. The blue profile as a response to the growth of the demand from IEA demand forecast and the red profile responding to the forecast of the demand from EEHC report.
Generation Responce to Demand Gap
35000,00
30000,00
25000,00
20000,00
15000,00
10000,00
Electricity Electricity Production [ktoe] 5000,00 2011 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041
Electricity IEA Electricity EEHC
Figure 34 – Reference scenario Generation response to the demand gap shown in Figure 33
112 Chapter 5
As can be seen both of the generation profiles, when electricity production reaches the value of 17575,0ktoe stop their growth. This value is the maximum capacity of the built in generation plants and after reaching the maximum production, to meet the demand excess the model starts to import electricity in a massive way (reaching the limit of the transnational transmission capacity).
In Table 30 are reported the numerical results for both the scenarios, from which can be seen the reach of the maximum generation.
For the IEA boundary, where the starting demand, and so his forecast, is higher, the limit of electricity production is reached in 2018, while for the EEHC boundary it is reached three years later, in 2021.
Table 30 - Reference scenario results table, in this table is highlighted the year of reaching of the maximum generation capacity.
IEA EEHC Production Demand Production Demand [ktoe] 2012 14028,98 12061,20 13055,78 11088,00 2013 14563,80 12487,10 13386,29 11479,71 2014 15070,80 12922,80 13852,31 11880,37 2015 15591,20 13370,10 14330,75 12291,72 2016 16125,90 13829,50 14822,21 12714,26 2017 16675,40 14301,80 15327,33 13148,54 2018 17124,00 14787,40 15846,77 13595,12 2019 17575,00 15287,10 16381,21 14054,62 2020 17575,00 15801,40 16931,41 14527,65 2021 17575,00 16331,30 17289,00 15014,88 2022 17575,00 16877,30 17575,00 15517,01 2023 17575,00 17440,40 17575,00 16034,76 2024 17575,00 18021,20 17575,00 16568,92 2025 17575,00 18620,80 17575,00 17120,28
From here on I decided to use the EEHC demand forecast in the other scenarios, this has been done in order to achieve a clearer and less multifaceted approach. In order to make this possible I have already reported the sensitivity analysis to underline how much is the uncertainty that affects the results that will follow.
113 Scenario Simulation and Results
5.3 Governmental Investment Scenario
The Governmental Investment scenario implements the EEHC generation capacity expansion plan and has a key role in this work, his simulation and the evaluation of his results are fundamental for Egypt to decide if this developing strategy is good enough to meet its objectives and to find out which aspects need to be improved.
5.3.1 Electricity Production
The expansion plan goes through a yearly mean capacity addiction of 2540 MW per year of different kind of power plants and through the years has to meet the high growth of the demand. It has also to face growth the disposal of old plants at the end of their lifetime. Despite what previously said, in this scenario is if the expansion of the generation capacity meets the final user demand growth, and as can be seen in Figure 35 there are no unmet
35,00
30,00
25,00
20,00
15,00
10,00
5,00 Millions Oil of ofEquivalent Tonnes Millions
- 2015 2020 2025 2030 2035 2040
Electricity Demand Electricity Production
Figure 35 – Electricity Production and demand in GOV scenario
114 Chapter 5
requirements. The difference between electricity production and demand is due to the consumption of the transformation sector and the losses.
Achieved this first and fundamental goal for a well-built expansion plan the focus drifts through the type of fuels used in the power plants.
The fuels required to produce the needed amount of electric energy changed through the years in quantity and in type. The change in fuel required is due to the addition of these plants:
22 Steam turbine power plants for a total of 20550MW 21 Combined Cycle plants for a total of 20200MW Gas Turbine power plants for a total of 750MW 1 Small hydroelectric unit of 30MW Photovoltaic fields for a total of 40MW Eolic plants for a total of 2850 MW
As can be seen in Figure 36 from 2014 there is a growth in the renewable fuels usage, but it reaches its peak in 2018 and from this point no more renewable capacity is added. So the amount of renewable energy used in the electricity production is grown, but not significantly.
Figure 36 - Fuels consumed in the Electricity production processes
115 Scenario Simulation and Results
The usage of fossil fuels instead highly increases without reaching a peak. What can be highlighted is that the usage of oil products (mainly fuel oil, both the heavy and the light fractions) in thermal power plants decreases in proportion to natural gas.
5.3.2 Energy Mix
For energy mix I intend the proportion of fuels used in the energy system and in particular in the electricity production process.
Total energy consumption through the forecast more than doubles, starting from 13,1Mtoe (millions of tonnes of oil equivalent) and reaching 32,8Mtoe. This growth is nearly exponential, with a growth of 2,6Mtoe in the first five years period (2015-2020) and a gap of 5,1Mtoe from 2035 to 2040.
Talking about renewable sources, as can be seen in Table 31. Hydropower usage is constant, in fact as reported in the analysis of water sources Egypt is already utilising about the 98% of the available energy from water. Solar power is nearly unused, and no capacity is built to fill this gap, its absolute value in 2012 is about 0,00159Mtoe and reaches 0,00279Mtoe at the end of the simulation.
Table 31 - Energy requirements by fuel type
2012 2015 2020 2025 2030 2035 2040
Fuels [Mtoe]
Natural Gas 9,1 10,0 11,2 13,5 16,4 19,5 23,5
Fuel Oil 2,8 2,8 3,4 4,1 4,6 5,7 6,9
Hydro 1,1 1,1 1,1 1,1 1,1 1,1 1,1
Wind 0,1 0,5 1,3 1,3 1,3 1,3 1,3
116 Chapter 5
Solar 0,0 0,0 0,0 0,0 0,0 0,0 0,0
Total 13,1 14,3 16,9 19,9 23,5 27,7 32,8
Among all renewables, wind power is the most promising by increasing its usage from 0,1 to 1,3Mtoe in 2018, but after this year no more eolic plants are built.
In Figure 37 I can confront the energy mix of the base year with the one in 2020. The renewable sources percentage increases, starting from 9,4% and reaching 13,6%.
Despite this growth the percentage of renewables is really far from what the Ministry of Electricity and Energy declared as an objective that is 20% of electricity produced by
Figure 37 – Energy mix in the base year and in 2020 renewable sources in 2020. So the EEHC plan meets the demand growth, increases the renewable share but is really far from achieving the 2020 objective
If Egypt’s plan is far from reaching this objective, it is even further by reaching the 2030 objective. Representatives from the MOEE and from NREA (New and Renewables Energy Authority) declared on March 2016 that Egypt aims to produce 30% of electricity from solar energy by 2030.
From the actual EEHC plan the percentage of solar in 2030 is 0,3% and the renewable percentage decreases to 9,9%.
117 Scenario Simulation and Results
5.3.3 Resources Requirement
From the analysis of the internal production of primary resources, which is the production, usage or extraction of row material or natural sources done in order to meet the demand of the transformation processes, some important results about fossil fuels can be highlighted.
Figure 38 - Internal production of primary sources
Natural gas needs appear to be greater compare to what showed before. This fact is due to the high part of natural gas absorbed both by final users (final demand), and from transformation processes (that with the final demand constitutes the total demand).
In fact all the differences among the data in Table 32and Table 31 are due to all the resources absorbed by the transformation processes.
Natural gas consumption is highly absorbed by electricity generation processes, such as oil products. For the refinement of oil products high volumes of crude oil are needed.
118 Chapter 5
Table 32 - Internal production of primary resources
2015 2020 2025 2030 2035 2040
Type of source [Mtoe]
Natural Gas 45,0 48,6 53,9 61,4 69,9 76,9
Crude Oil 40,4 46,8 53,4 60,5 60,7 60,7
Hydro 1,1 1,1 1,1 1,1 1,1 1,1
Wind 0,5 1,3 1,3 1,3 1,3 1,3
Solar 0,2 0,3 0,3 0,5 0,5 0,5
Total 87,3 98,1 110,0 124,8 133,6 140,5
As can be seen in Figure 38 another key issue of Egyptian energy system is the refinery capacity. Through the forecast, the built in capacity starts to increase the refinement volumes and after the capacity addition in 2030 is reached a limit. From 2030 to the end of the simulation time the growth of the demand of oil products is met through import.
The prevision of extra refinery capacity addition must be taken into account in future energy strategies of Egypt’s Government.
119 Scenario Simulation and Results
5.4 Renewable Scenario
Renewable scenario is my proposal (according to TriNex project guidelines) that tries to integrate in the generation capacity expansion plan of EEHC more renewable sources.
In detail my proposal aims to invest on solar energy and wind energy by adding 14 Photovoltaic fields (through the years of the simulation) with a total capacity addition of 5225MW and 8 eolic plants for a total capacity addition of 7350MW.
The proposed capacity addition added to the capacity already inserted in the expansion plan reaches:
5265MW of photovoltaic capacity 10200MW of eolic capacity
In parallel with this initiative I implemented in the scenario a policy of incentives aimed to increase the diffusion of domestic photovoltaic systems in the rural areas.
5.4.1 Energy Mix Changes
With the renewable capacity addition proposed, the energy mix through the years change a lot. As can be seen in Table 33 the renewable energy consumed to produce electricity increase a lot through the years.
Solar energy consumption reaches 3,2Mtoe per year, starting from a value of 0,002Mtoe. This is achieved thanks to the contribution of both solar fields and domestic systems.
The percentage of share of solar rises from 0,001% in 2012 to a value of 9,5% in 2020 and 13,7% in 2030.
120 Chapter 5
Hydropower is constant in terms of absolute value, but obviously the growth of the production gradually reduces the percentage of share.
Wing generation raised also in the Governmental Investment scenario, but its growth stopped rally early, reaching the maximum installed capacity in 2017. In this scenario the capacity grows through all the forecast time and helps to create a strong renewable base.
Figure 39 - Renewable scenario energy mix
Looking Figure 39 I can conclude that the first Governmental objective is reached, in fact electricity produced by renewable sources is 27,8 % of the total in 2020.
This result shows how a higher effort in the renewable direction must be taken into account by EEHC capacity expansion programme.
About the second energy mix objective I can conclude that it is partially reached, in fact in 2030 the renewable percentage of share is 32,2% as was declared, but it is not all from solar source. The mean yearly addition of 200MW of photovoltaic fields and the diffusion of 5kW domestic systems is not enough to achieve 30% from solar.
The high gap of 16,3% that comes out by looking at the difference between the percentage of solar in Renewable scenario and the one that was declare by Ministry of Electricity and Energy as an objective state clearly that to reach the goal a massive expansion program must be done. The yearly investments in solar capacity must be doubled (and from EEHC must
121 Scenario Simulation and Results
be more then multiplied by a factor of 200, the expansion plan takes into account only 40MW of solar plants).
Table 33 - Renewable scenario Electric energy production by input fuel
2015 2020 2025 2030 2035 2040
Fuels [Million Tonnes of Oil Equivalents]
Natural Gas 9,6 9,2 10,1 12,0 15,3 19,3
Residual Fuel Oil 3,2 3,0 3,3 3,9 4,8 5,9
Hydro 1,1 1,1 1,1 1,1 1,1 1,1
Wind 0,4 2,0 2,8 3,2 3,2 3,2
Solar 0,0 1,6 2,7 3,2 3,2 3,2
Total 14,3 16,9 19,9 23,5 27,7 32,8
122 Chapter 5
5.4.2 Energy Savings
At the end of the analysis of Renewable scenario results I made a comparison in terms of resources used to produce electricity between it and the Governmental Investment scenario.
Figure 40 – Used resources to produce electricity differences between Renewable scenario and Governmental Investment scenario
The results are shown in Figure 40 and highlight a high saving of fossil fuel through the years.
In detail from 2020 a rising quantity of renewable substitute fuel oil and natural gas. At the beginning the saving are in the order of 2Mtoe and then this volume doubles through the years. At the end of the forecast time, the total volumes of fuel saved is about 94, 3Mteo with a mean of 3,3Mteo year. In detail 23,7Mtoe of crude oil have been saved and 70,7Mtoe of natural gas. It means that through the 28 year time forecast are saved enough fossil fuels to cover the demand (that more than doubled from the beginning) of the year up to come.
123 Scenario Simulation and Results
5.5 Cost Comparison
Analysing the optimized scenario alone would have been of limited interest, due to his similarities with the Government Investment scenario, so I decided to build up a comparison in terms of costs between it and the other two scenarios.
The most significant results of the cost analysis are shown in Table 34. The table represents the differences of Investment Optimized scenario and the Renewable scenario compared to the Governmental Investment scenario.
Investment Optimized scenario shows little differences compared to GOV scenario, the most important are a reduction in resource expenditure, a decrease of energy imports and a little increase of electricity production. These are all due to a better management of the plants through the distribution of load and peak demand.
Table 34 - Cost Analysis: differences from Governmental Investment scenario in terms of Net Present Value calculations
Discounted at 5,0% to year 2012. Governmental Investment Renewable Units: Billion 2012 U.S. Dollar Investment Optimized
Demand - - -
Transformation - 3,24 8,61 Transmission and Distribution - - -
Energy Industry Own Use - -0,89 -
Coal Transformation - - - Electricity Generation - 4,13 8,61
Oil Refining - - -
124 Chapter 5
Resources - -2,30 -16,19
Production - -3,81 -16,15 Imports - -2,33 -0,04
Exports - 3,84 -
Unmet Requirements - - - Net Present Value - 0,94 -7,59
Renewable scenario instead faces higher investments in generation plants that from a cash flow point of view are not compensated by the reduction of usage of renewable sources.
From this results I can conclude that Optimized scenario is, in terms of cash flow a better scenario than Governmental Investment scenario, in fact it has a higher Net Present Value.
Net Present Value is the difference between the present value of cash inflows and the present value of cash outflows. NPV is used in capital budgeting to analyse the profitability of a projected investment.
The following is the formula for calculating NPV:
Where Ct = net cash inflow during the period t, Co = total initial investment costs, r =discount rate, and t = number of time periods In terms of Cumulate total costs instead (results reported in Figure 41) the situation is lightly different.
The best actualized NPV of the INV scenario comes through higher investments in the initial years and so also total costs appear higher in the first period (INV total cost goes through a linear optimization). Governmental Investment scenario has the higher total cost and Renewables scenario obtains the lower total cost.
125 Scenario Simulation and Results
This is a different approach than the one in the table, and takes into account all possible costs, not only cash flows.
Figure 41 – Cumulate Total cost differences through the scenarios
The cost analysis highlighted that EEHC plan has a higher profitability than Renewable scenario and that MOEE must optimize his plan to increase its NPV. Renewable approach despite having a lower profitability has a lower total cost in the long term.
5.6 Analysis of Nile Water Reduction Perspective
After the analysis of the pure energetic problem I tried to link it to issues related to the vulnerability of water management in Egypt. In fact, as highlighted in Chapter 2, water management is a key variable in obtaining a sustainable development in Egypt.
The Nile River supplies about 97% of the annual renewable water resources in Egypt. Out of the Nile’s average natural flow of 84,0푘푚3/yr reaching Aswan, a share of 55,5Billion
126 Chapter 5
푚3/yr is allocated for Egypt according to the Nile Water Agreement (1959). This account for an average per capita share of about 760푚3/cap/yr as of year 2010.
To analyse the perspective of the reduction of Nile’s flow I compared some literature forecasts of Global Climate Models with forecast of the Egyptian Ministry of Public Works and Water Resources. This analysis was done in order to identify the changing variables and implement them in my model. From the comparison emerged that:
A decrease in rainfall due to climatic change is expected. The worst case scenario foresees a reduction of 50% of rainfall in Ethiopia and Uganda [83][84]. Most of the forecasted scenarios foresee a high decrease in the Egyptian’s Nile flow as can be seen in Figure 42 [81][85]. The analysis of the Egyptian Ministry of Public Works and Water Resources expects an increase of 10-14% of water demand from the agricultural sector in 2025 (which consumes 89% of Egyptian water in 2012) [82]. There is a high uncertainty about how actions on the river taken by upstream riparians can modify the flow of the river, especially in the dry season [81][86].
Adding all these uncertainties I decided to implement a reduction in the water availability for the energy sector of nearly 22% and analyse its impact.
Figure 42 - Global Climatic Models, Forecast of the flow of the Nile in Egypt
127 Scenario Simulation and Results
5.6.1 Results of the analysis
A reduction in the withdrawable quote of water as expected influences deeply the energy mix. With other renewable sources already running at full capacity, this leads to an increase of the fossil fuels consumption.
Both in 2030 and 2040 can be seen that, in a Water Reduction perspective, to fill the electricity production gap, as reported in Table 35 and Table 36, almost no renewable sources are available, so the model activates other thermal plants.
2030 80,00% 70,00% 60,00% 50,00% 40,00% 30,00% 20,00% 10,00% 0,00% Natural Gas Residual Fuel Oil Hydro Wind Solar
GOV GOV WR REN REN WR
Figure 43 – Energy mix differences among scenarios in 2030
Table 35 - Primary fuel used to produce electric energy in 2030
GOV GOV WR REN REN WR [Mtoe] 2030 Natural Gas 16,13 16,40 11,98 12,23 Residual Fuel Oil 5,03 5,12 3,92 4,01 Hydro 1,12 0,87 1,12 0,87 Wind 1,14 1,14 3,25 3,25 Solar 0,08 0,08 3,22 3,22
128 Chapter 5
This leads to an increase in the percentage of share of fossil fuels in energy mix as can be seen in Figure 43 and Figure 44.
Governmental investment scenario, in terms of energy mix was already missing his objectives and in this perspective goes further. The percentage of electricity produced by renewables goes down to 8,40%( from 9,94% of a perspective with no water flow reduction).
Renewable scenario doesn’t worsen the situation about the energy mix objective in 2020, with a 30,88% (from a 32,29% of a perspective without water reduction) of electricity produced by renewables, even with the reduction of the Nile flow, this scenario doesn’t miss the target. For REN scenario in WR perspective are valid the same considerations made before (without Nile flow reduction), the percentage is on target but the objective is to produce 30% from solar.
2040 80,00% 70,00% 60,00% 50,00% 40,00% 30,00% 20,00% 10,00% 0,00% Natural Gas Residual Fuel Oil Hydro Wind Solar
GOV GOV WR REN REN WR
Figure 44 – Energy mix differences among scenarios in 2040
Table 36 – Primary fuel used to produce electric energy in 2040
GOV GOV WR REN REN WR
[Mtoe] 2040
Natural Gas 23,27 23,63 19,26 19,52
Residual Fuel Oil 7,21 7,32 5,91 5,99
129 Scenario Simulation and Results
Hydro 1,12 0,87 1,12 0,87
Wind 1,11 1,12 3,25 3,25
Solar 0,08 0,08 3,25 3,25
The results of a compared total costs analysis are reported in Figure 45 and shows that in a flow reduction perspective each scenario costs more, due to an increase in fossil fuel production and consumption.
6300
5300
4300
3300 Milliondollars
2300
1300 2015 2020 2025 2030 2035 2040
GOV GOV WR REN REN WR
Figure 45 – Total costs differences among scenarios
From this analysis I can conclude that with an integrated approach some aspects, like the one discussed in this paragraph, are highlighted and confirm what said before about scenarios. Egypt needs to make a bigger effort in renewable sources’ direction.
This kind of effort can, in the long period, guarantee energetic security and relive water security problem.
130 Conclusions
Conclusions
In this thesis work, I have addressed important aspects related to the role played by uncertainties in the strategy evaluation process. In particular, I have proposed a combined approach, using forecasting methods and WEF analysis which allowed the quantification of key parameters such as energy security in the analysis of development scenarios. One of the major features of the framework is the realization of a model of Egyptian energy system to evaluate policies with a really low uncertainty propagation rate. The construction of this model framework has mainly involved the following activities:
The modeling of an energy system through LEAP The implementation of statistical forecasting analysis to evaluate the impact of energy demand growth on the process of electricity production and through that the evaluation of development strategies The Water Energy and Food approach usage in the evaluation of energy systems and the integration of this approach with forecasting methods.
With regards to the first activity, the main results show that Long range Energy Alternatives Planning System is a solid software tool to build energy systems, it is really flexible to the input data and supports a wide range of different modeling methodologies. Due to this features, though, the software requires high understanding of its functioning and options achievable through a long time practice and a continuous work of improvement on the model done by changing variables according to the results. The model of Egypt’s energy system I realized through this thesis is really accurate and has a low degree of uncertainty, as shown by the sensitivity analysis. It proved to be helpful in the process of policy and strategies analyses and as a decision support system.
The implementation in the model of statistical forecasting has proved itself fundamental in the process of scenario building and analyses. Through this activity of data and trend analysis the model assumed the capability to show changes in energy demand, transformation capacity and resource requirements. After this improvement the model was completed and
131 Conclusions
was used to make assessments of policy scenarios achieving appreciable results. The Egyptian Electricity Holding Company’s capacity expansion plan showed a good performance in meeting energy demand’s growth for every fuel type but displayed an incapacity to fulfil the objectives about the energy mix. The Governmental plan showed also that there is a scope of improvement in the balance between peak and load demand from a cost reduction perspective. The proposed improvements of the capacity expansion plan instead, more focused on renewable energy, were able to fulfil 2020 goal of 20% of electricity from renewable sources, but not to fulfil totally the 2030 objective (it produced more than 30% of electricity from renewables, but not from only solar sources) showing that a deeper focus on solar source usage in electricity production must be taken into account. Forecast analysis proved to be a really good method in scenario analysis and confirmed his utility in the cost analysis, in fact it highlighted that the renewable improvement of the capacity expansion plan, that required more investments in the early years, reached a lower level of cumulated total costs in the last 7 years of the forecast. This kind of results couldn’t have been obtained without this approach.
The WEF approach has been used in this work at first to help to identify the crucial aspects of Egyptian development problem and its relations with the other countries of the Nile Basin (as shown in Chapter 2), then it was added to the model and integrated with the statistical forecasting approach and it confirmed the results of the scenario analyses. In fact the results underlined how, in a Nile flow reduction perspective , the integration of a renewable focus in EEHC capacity expansion plan is crucial in terms of energy security, water security and in terms of total costs. The addition of the WEF approach to the model and highlighted also the need of Egypt’s Government of an accurate planning of water resource management and a collaboration with other riparian countries
Other improvements of this work have been tried but aren’t reported due to some problems that stopped their development or the inconsistency of the obtained results. The first improvement tried was the extension of the model to other riparian countries and the main complication found was the shortage of data. Solved the problematic issues this improvement could lead to a better integrated planning with the implementation of flows of resources through the countries. The second way tried was an Input-output analysis that utilising Egyptian input output tables would have linked the food security problem to water shortage and energy needs. This, due to the necessity of modifying old tables because I
132 Conclusions
hadn’t the license to access to recent ones, led to a high amount of hypotheses that reflected in the results as a high statistical uncertainty mining the significance of the analysis. This kind of improvement is the best suited for improving the WEF nexus approach. The last improvement tried was the usage of WEAP (Water Evaluation and Planning) a software tool for water resources planning developed by Stockholm Environment Institute. The problem of using WEAP was the impossibility to obtain the licence. This improvement is highly recommended due to the high integration with LEAP and the possibility to have them to work integrated to realize and evaluate in integrated energy and water management development plan.
133 Conclusions
134
List of Figures
Figure 1 - Nile Basin scheme - State of the Nile River Basin 2012 Report NBI, chapter 2, The Water Resources of the Nile Basin ...... 9
Figure 2 - Electrification rates in Nile Basin countries scheme - State of the Nile River Basin 2012 Report NBI, chapter 6, Hydropower Potential and the Region’s Rising Energy Demand ...... 13
Figure 3 - Hydropower potential in Nile Basin countries scheme - State of the Nile River Basin 2012 Report NBI, chapter 6, Hydropower Potential and the Region’s Rising Energy Demand ...... 14
Figure 4 - Nile Decision Support Tool (DST) for River Simulation and Reservoir Operation (RS-RO) module for water resources management. It includes 14 existing or planned reservoirs, 20 existing or planned hydropower plants, 13 inflow nodes, 16 river nodes, and 15 demand nodes - FAO Synthesis Report, Information Products for Nile Basin Water Resources Management, Rome 2011 ...... 15
Figure 5 - Population Growth and Water Availability, data from World Bank and FAO report ...... 22
Figure 6 – Egyptian virtual water imports in crops from (a) outside of the river basin, and (b) other Nile Basin states, FAO report ...... 31
Figure 7 – Oil Production and Consumption Trends, MOEE report ...... 34
Figure 8 – Natural Gas Production and Consumption Trends, MOEE report ...... 36
Figure 9 - Fossil fuel subsidies by fuel in Egypt, data from IEA 2014 Report ...... 39
Figure 10 – Top-down and bottom-up modeling techniques for estimating the regional or national residential energy consumption ...... 42
Figure 11 – LEAP structure and calculation flow ...... 45
Figure 12 – Flow of Data which links Input and Output [59]...... 47
Figure 13 – Generic Elements of Transformation Modules in LEAP [59] ...... 48
Figure 14 – Relation between data requirements and independence degree of the time forecast ...... 54
Figure 15 – Transformation Caluculations basic steps [62]...... 56
Figure 16 –Example of Peack-Load Curve taken from exercises [60]...... 60
Figure 17 – Iterations Example [60] ...... 62
135 List of Figures
Figure 18 - Search Process for a Variable in the Mitigation Scenario, Simple Hierarchy [60]: ...... 64
Figure 19 - Search Process for a Variable in the Mitigation Scenario, Package of Scenarios [60]: ...... 65
Figure 20 – Interaction between Leap Model and OSeMOSYS, in detail in the image is highlighted the data flow, the image is taken from LEAP Optimization Guide [62] ...... 66
Figure 21 – Historical trend and time forecast of the number of Households, divided through the growth of the End year percentage of Urban Households ...... 78
Figure 22- Historical trend and time forecast of Population ...... 78
Figure 23 – Evolution of proved Crude Oil reservoirs ...... 81
Figure 24 – Percentage of share of demand for each sector on the base year ...... 92
Figure 25 – Percentage of share of demand per each type of fuel on the base year ...... 93
Figure 26 – Demand growth for each branch from 2015 to the end year ...... 94
Figure 27 – Demand forecast for type of fuel ...... 95
Figure 28 - Flow of fuels through transformation processes as it is implemented in this model...... 97
Figure 29 – First classification of power plants, from EEHC report 2012-2013 [74] ...... 101
Figure 30 – Detailed description of power plants through some parameters, from EEHC report 2012-2013 [74] ...... 102
Figure 31 – Scenario hierarchy ...... 104
Figure 32 – Diffusion model of domestic photovoltaic ...... 108
Figure 33 – Reference scenario Demand forecast starting from two different values of electricity demand on the base year ...... 112
Figure 34 – Reference scenario Generation response to the demand gap shown in Figure 33 ...... 112
Figure 35 – Electricity Production and demand in GOV scenario ...... 114
Figure 36 - Fuels consumed in the Electricity production processes ...... 115
Figure 37 – Energy mix in the base year and in 2020 ...... 117
Figure 38 - Internal production of primary sources ...... 118
Figure 39 - Renewable scenario energy mix ...... 121
Figure 40 – Used resources to produce electricity differences between Renewable scenario and Governmental Investment scenario...... 123
136 List of Figures
Figure 41 – Cumulate Total cost differences through the scenarios ...... 126
Figure 42 - Global Climatic Models, Forecast of the flow of the Nile in Egypt ...... 127
Figure 43 – Energy mix differences among scenarios in 2030 ...... 128
Figure 44 – Energy mix differences among scenarios in 2040 ...... 129
Figure 45 – Total costs differences among scenarios ...... 130
Figure 46 - Water balance in the Nile Basin - FAO Synthesis Report, Information Products for Nile Basin Water Resources Management, Rome 2011 ...... 151
Figure 47 - Water balance in the Nile Basin by country - FAO Synthesis Report, Information Products for Nile Basin Water Resources Management, Rome 2011 ...... 152
137 List of Figures
138
List of Tables
Table 1 - Per capita GDP, HDI index and% of area covered by the Nile for each Nile Basin Country ...... 8
Table 2 - Nile basin countries water balances - Virtual water ‘flows’ of the Nile Basin, 1998-2004: A first approximation and implications for water security, M. Zeitouan, J.A. Allan, Y. Mohieldeen, Global Environmental Change 20 ( 2010) ...... 10
Table 3 - Water requirement by crops in Nile Basin countries balances - Virtual water ‘flows’ of the Nile Basin, 1998-2004: A first approximation and implications for water security, M. Zeitouan, J.A. Allan, Y. Mohieldeen, Global Environmental Change 20 ( 2010) ...... 11
Table 4 – Production of food crops in the Nile Basin, data from FAO report 2010, unit tons...... 26
Table 5 - Simulation structure of the utilized model: sectorial energy demands are independently derived by products of key drivers, e.g. macro-economic growth, and sectoral energy intensities in the demand module. In the transformation module, the requirement of each fuel will be fulfilled with the production of existing capacities. Primary resource is withdrawal by the required feedstock during the transformation process. The entire energy system is balanced by exporting the surplus and importing the shortage energy [61]...... 46
Table 6- Key Assumptions description and diagram, full data in Appendix II, main source http://data.worldbank.org/country/egypt-arab-republic - R2is the coefficient of determination and indicates how well data fit a statistical model, the standard error (SE) is the standard deviation of the sampling distribution...... 77
Table 7 – Results of the forecast from Appendix II...... 78
Table 8 – Egypt Energy Balance of 2012 from IEA report, data are reported in thousand tonnes of oil equivalent (ktoe)...... 79
Table 9 – Relative volumes of resources Import and Export ...... 80
Table 10 – Crude Oil proved reservoirs, Billion of bbl ...... 81
Table 11- Natural Gas proved reservoirs,million cubic meters,2186000mm^3 are 12866,3Gtoe ...... 81
Table 12 – Fuel Costs per type of fuel in Egypt ...... 82
Table 13 – Unit commitment parameters per fuel/turbine type ...... 84
Table 14 – Technology catalogue for investments...... 85
Table 15 – Industry Branch, demand analysis for fuel type ...... 87
139 List of Tables
Table 16 - Transport Branch, demand analysis for fuel type ...... 88
Table 17 – Other Branch, demand analysis for fuel type ...... 89
Table 18 – Non Energy Use Branch, demand Analysis for fuel type ...... 91
Table 19 – Base year (2012) demand volumes per demand sector ...... 92
Table 20 – Base year (2012) demand volumes per type of fuel ...... 93
Table 21 - Demand Forecast for each Branch ...... 94
Table 22 - Demand forecast results for type of fuel ...... 96
Table 23 – characterizing parameters of processes and losses ...... 98
Table 24 – Characterizing parameters per fuel/turbine type ...... 100
Table 25 – Refining sector capacity and expansion plan, MOEE report ...... 103
Table 26 - Generation Expansion Plan (2013-2018) MOEE (Ministry of Electricity and Energy)...... 106
Table 27 - Renewable capacity proposed expansion plan through 2020, the full plan is reported in Appendix III ...... 108
Table 28 – Scenarios summary – Every scenario implements the demand forecast; GOV, OPT, REN implement the EEHC generation expansion plan; OPT scenario uses the investment optimization; REN implements an extra expansion plan with a renewable focus reported in Appendix III ...... 109
Table 29 – Sensitivity Test Input and Results ...... 110
Table 30 - Reference scenario results table, in this table is highlighted the year of reaching of the maximum generation capacity...... 113
Table 31 - Energy requirements by fuel type ...... 116
Table 32 - Internal production of primary resources ...... 119
Table 33 - Renewable scenario Electric energy production by input fuel ...... 122
Table 34 - Cost Analysis: differences from Governmental Investment scenario in terms of Net Present Value calculations ...... 124
Table 35 - Primary fuel used to produce electric energy in 2030 ...... 128
Table 36 – Primary fuel used to produce electric energy in 2040 ...... 129
Table 37 – Key Assumptions Historical data and time forecast, the black line divides historical data from time forecast ...... 153
Table 38 – EEHC, Egypt Electricity Holding Company generation expansion plan ...... 155
140 List of Tables
Table 39 – REN Renewable scenario generation capacity expansion plan...... 157
141 List of Tables
142
Acronyms, Abbreviations
WEF Water Energy Food
NBI Nile Basin Initiative
HDI Human Development Index
GDP Gross Domestic Production
CFA Cooperative Framework Agreement
UNDP United Nations Development Programme
CIDA Canada International Development Agency
TECCONILE Technical Cooperation Committee for the Promotion of the Development and Environmental Protection of the Nile Basin
Nile-COM Council of Ministers for Water Affairs of all the Nile Basin
WB World Bank
Nile-TAC Technical Advisory Committee
Nile-SEC Nile Basin Initiative Secretariat
SVP Shared Vision Program
SAPs Subsidiary Action Programs
NELSAP Nile Equatorial Lakes Subsidiary Action Program
ENSAP Eastern Nile Lakes Subsidiary Action Program
CAADP Comprehensive African Agricultural Development Program
NEPAD Under the African Union’s New Partnership for Africa’s Development
143
OPEC Organization of the Petroleum Exporting Countries
LNG liquefied natural gas
SUMED Suez-Mediterranean Pipeline
FDI Foreign Direct Investment
MOEE Ministry of Electricity and Energy
SCE Supreme Council for Energy
EEUCPA Egyptian Electric Utility and Consumer Protection Agency
EETC Egyptian Electricity Transmission Company
BOO Build, Own and Operate
BOOT Build, Own, Operate and Transfer
NREA New and Renewable Energy Authority
SEI-B Stockholm Environment Institute-Boston
LEAP Long range Energy Alternatives Planning system
SM Statistical Methods
EM Engineering Methods
TED technology and environmental database
GHG greenhouse gas
OSMOSYS Open Source energy Modeling System
DSM demand-side management
144
Bibliography
[1] - United Nations Economic Commission for Africa, 2004 [2] - Salman and Salman, The Nile Basin Cooperative Framework Agreement:a peacefully unfolding African spring?, Water International, 38, 2013 [3] Brunnée, Jutta, Toope, The changing Nile Basin regime: does law matter?, Harvard International Law Journal 43, 2002 [4] Eckstein, Water scarcity, conflict and security in a climate change world: challenges and opportunities for international law and policy, Wisconsin International Law Journal 27, 2009. [5] Awulachew, Smakhtin, Moldem and Peden, The Nile River Basin: Water Agriculture, Governance and Livelihoods, Routledge, London 2012 [6] State of the Nile River Basin 2012, Synthesis, NBI report [7] Farming Report, Synthesis of the Country Reports at level of the Nile Basin, FAO,2011 [8] Hoekstra and Hung, Globalisation of water resources: International virtual water flows in relation to crop trade, Global Environmental Change 15, 2005 [9] Allan, J.A., 2003. Virtual water eliminates water wars? In: Hoekstra, A.Y. (Ed.), International Expert Meeting on Virtual Water Trade, IHE Delft, Netherlands, December 12-13, 2002 [10] WWF, 2008. UK Water Footprint: The Impact of The UK’s Food and Fibre Consumption on Global Water Resources, Worldwide Fund for Nature, Ashok Chapagain and Stuart Orr, London [11] Chapagain, A.K., Hoekstra, A.Y., 2004a. Water Footprints of Nations, Volume 1: Main Report. Value of Water Research Report Series No. 16. UNESCO-IHE Delft, Delft, Netherlands [12] Zeitoun, Allan and Mohieldeen, Virtual water ‘flows’ of the Nile Basin, 1998-2004: A first approximation and implications for water security, Global Environmental Change 20, 2010 [13] FAO-Nile Basin Project GCP/INT/945/ITA 2004 to 2009, Synthesis Report, Rome 2011 [14] Paisley and Henshaw, Transboundary governance of the Nile River Basin: Past, present and future, Environmental Development 7, 2013 [15] www.water-technology.net, last viewed 27/12/2015 [16] Todaro and Smith, Economic development eleventh edition, Pearson, 2011. [17] About the NBI, http://www.nilebasin.org/index.php/about-us/nile-basin-initiative, last viewed 11/08/2015 [18] Carrol, Past and Future Legal Frameworks of the Nile River Basin, 1999.
145 Bibliography
[19] NBI Secretariat, Strategic Plan 2012-2016. [20] Croker, Renewable Energy in Egypt: hydro, solar and wind, 2013 [21] L.Power, Death on the Nile: Egypt’s Burgeoning Food and Water Security Crisis, July 2014 [22] International Energy Association, World Energy Outlook 2014, 2014, Paris. [23] Assefa M. Melesse,Wossenu Abtew,Shimelis G. Setegn , Nile River Basin:Ecohydrological Challenges, Climate Change and Hydropolitics, Springer International Publishing Switzerland 2014 [24] A.Y.Hoekstra ,Virtual water trade: Proceedings of the International Expert Meeting on Virtual Water Trade, February 2003, Value of Water Research Report Series No. 12 [25] Mark Zeitoun , J.A.Allan, Yasir Mohieldeen, Virtual water ‘flows’ of the Nile Basin, 1998–2004: A first approximation and implications for water security, Global Environmental Change 20 (2010) 229–242 [26] African Union, Assembly of the Aftican Union , July 2003 Maputo, Mozambique [27] REPUBLIC OF RWANDA Ministry of Agriculture and Animal Resources, Strategic Plan for the Transformation of Agriculture in Rwanda, Final Report, February 2009 [28] Republic of Kenya, Kenya vision 2030: A Globally Competitive and Prosperous Kenya, First Medium Term Plan, 2008-2012 [29] Falkenmark, M, and C Widstrand. Population and Water Resources: A Delicate Balance. Population Bulletin, Population Reference Bureau, 1992.
[30] Magdy Hefny, Salah El-Din Amer, Egypt and the Nile Basin, Aquatic Science 67 (2005) 42–50 [31] Henri J. Dumont, The Nile, Origin, Environments, Limnology and Human Use, 2009 Springer Science
[32] Arab Republic of Egypt Ministry of Petroleum, http://www.petroleum.gov.eg/en/Pages/default.aspx, last checked online 17/2/2016 [33] Pascal Devaux, Egypt. Crisis in the Energy Sector, 2013, last checked online at: economic-research.bnpparibas.com.
[34] Gawdat Bahgat, “Egypt’s Energy Outlook: Opportunities and Challenges,” Mediterranean Quarterly, vol. 24,no.1, 2013, p.13. [35] Pascal Devaux, “Egypt. Crisis in the Energy Sector,” 2013, last checked online at: economic-research.bnpparibas.com. [36] Nazli Choucri, Christopher Heye and Michael Lynch, “Analyzing Oil Production in Developing Countries: A Case Study of Egypt,” The Energy Journal, vol. 11, no.3, 1990. [37] U.S. Energy Information Administration, “Country Analysis Brief: Egypt,” 2014, p.4. [38] Victor R. Squires, Hugh M. Milner , Katherine A. Daniell, River Basin Management in the Twenty-first Century Understanding People and Place, 2015 by Taylor & Francis Group, LLC
146 Bibliography
[39] Okoth-Owiro, THE NILE TREATY, State Succession and International Treaty Commitments:A Case Study of The Nile Water Treaties, Konrad Adenauer Stiftung and Law and Policy Research Foundation 2004
[40] FAO 2011, Agricultural Water Use Projections in the Nile Basin to 2030: Comparison with Food For Thought Scenarios, Information Products for Nile Basin Water Resources Management, http://www.fao.org/nr/water/faonile [41] Business Monitor International, Egypt's underexplored Western Desert offers oil production upside, 2015, last checked online at: http://www.businessmonitor.com/news-and-views/egypts-underexplored- westerndesert-offers-oil-production-upside#sthash.sO8ZbsNx.dpuf. [42] MWRI (Ministry of Water and Irrigation), Strategic framework for development and management of water resources in Egypt to 2050, 2010, Report prepared by MWRI [43] MWRI (Ministry of Water and Irrigation), Integrated Water Resource Management, December 2010, Report n. 18 [44] Karen Ellis, Green Growth Opportunities and Requirements in Egypt, deutsche Gesellschaft für internationale Zusammenarbeit (GiZ), November 2011 [45] Florian Zickfeld, Aglaia Wieland, Desert Power: Getting Started, The manual for renewable electricity in MENA, Full report, June 2013, Dii GmbH [46] Hossein Razavi, Natural Gas Pricing in Countries of the Middle East and North Africa, The Energy Journal, vol. 30, no.3, 2009. [47] Egypt, Policy and Regulatory Overviews,Clean Energy Information Portal, lastly checked online on 12/1/2015, http://www.reegle.info/policy-and-regulatory- overviews [48] Nick Butler, Egypt’s looming energy crisis,Financial Times, 2014, last checked online at: http://blogs.ft.com/nick-butler/2014/08/03/egypts-loomingenergy-crisis/. [49] Karim Hegazy, Egypt’s Energy Sector:Regional Cooperation Outlook and Prospects of Furthering Engagement with the Energy Charter, Energy Charter Secretariat Knowledge Centre 2015, ISSN: 2406-6087 [50] Tarek Shafey, Why Integrated Policies are vital for Egypt’s Energy Future,” Economywatch, 2012, last checked online at: http://oilprice.com/Energy/Energy- General/Why-Integrated-Policies-are-Vital-for-Egypts-Energy-Future.html [51] Jonathan Gifford, Huge interest in Egyptian renewable energy tender,last checked online at: http://www.pv-magazine.com/news/details/beitrag/huge-interest-in- egyptian-renewable-energytender_100017661/#ixzz3OAPGU0b4 [52] Business Monitor International BMI, Egypt Defense & Security Report, 2015, lastly checked online 13/02/2016, http://www.bmiresearch.com/ [53] Herif Ismail, Unlocking Egypt’s Energy Potentials, Egyptian Minister of Petroleum, Egypt’s Economic Development Conference, 2015 [54] Thomas, S., Boonekamp, P., Vreuls, H., Broc, J.S., Bosseboeuf, D., Lapillone, B., Labanca, How much energy saving is 1% per year? I still don’t know, but I know better how to find out, 2009,Summer Study Nice 1–6 June 2009
147 Bibliography
[55] Canes, Economic Modelling of Climate Change Policy, M.E., 2002,International Council for Capital Formation, Brussels [56] Huntington,Weyant, Modeling energy markets and climate change policy, J.P., 2004 In: Cleveland, C.J. (Ed.), Encyclopaedia of Energy, Vol. 1–6. Elsevier [57] Tol, Modelling the costs of emission reduction: different approaches, R.S.J., 2000. Pacific and Asian Journal of Energy 10 (1), 1–7 [58] Heaps, C.G., Long-range Energy Alternatives Planning (LEAP) system. [Software version 2015.0.16] Stockholm Environment Institute. Somerville, MA, USA,www.energycommunity.org [59] SEI; LEAP Web Help,lastly checked on 3/04/2016 http://www.energycommunity.org/WebHelpPro/LEAP.htm [60] SEI, LEAP Training Exercise, October 2014 [61] Joost Siteur, The Long-range Energy Alternatives Planning model (LEAP) and Wood Energy Planning Overview and Exercises,2014,RWEDP [62] Heaps, C.G., LEAP Quick Start Guide for Using Optimization, 2014, SEI. [63] Mark Howells, Documentation for OSMOSYS Model, 2009 [64] L.G. Swan, Renewable and Sustainable Energy Reviews 13, V.I. Ugursal (2009) 1819–1835 [65] IEA, http://www.iea.org/statistics/resources/balancedefinitions/, lastly checked on 27/02/2016 [66] UN statistics, http://unstats.un.org/unsd/cr/registry/, lastly checked on 23/3/2016 [67] Global Petrol Pricies, http://www.globalpetrolprices.com/Egypt/, lastly checked on 1/04/2016 [68] Nina Dupont, Wind power integration in Egypt and its impacts on unit commitment and dispatch , 2015,Technical University of Denmark, Department of Energy Management [69] IEA, World Energy Outlook 2015, November 2015 [70] IEA, World Energy Outlook 2014, November 2014 http://www.worldenergyoutlook.org/weomodel/investmentcosts/ [71] IndexMundi, http://www.indexmundi.com/, lastly checked online on 02/1/2015 [72] CER., Validation of market simulation software in sem to end 2012,Technical report, CER, 2012. [73] Carbou, Empowering egypt: Challenges http://www.carboun.com/energy/empowering-egypt/l. [74] Egyptian Electric Holding Company, Annual Report year 2012-2013, December 2015 [75] WorldMeters, http://www.worldometers.info/world-population/egypt-population/, lastly checked online on 18/2/2016 [76] WorldBank, http://www.worldbank.org/en/country/egypt, lastly checked online on 05/04/2016
148 Bibliography
[77] Nina Dupont, Wind power integration in Egypt and its impacts on unit commitment and dispatch, Technical University of Denmark, 2015 [78] IEA, World Energy Report 2014, November 2014 [79] Geroski, P.A, Models of technology diffusion, London Business School, Sussex Place, 2012, Regent’s Park, London NW 4SA, UK [80] Mahajan, V., Peterson,R.A., Models of innovation diffusion, Models of innovation diffusion, the International Professionals Publishers, London 2011 [81] El Din, M., Omar, M., Moussa, A.M.A, Water management in Egypt for facing the future challenges, Nile Research Institute, National Water Research Centre (NWRC), Cairo, Egypt 2015 [82] El-Sadek, A.; VirtualWater Trade as a Solution for Water Scarcity in Egypt, Water Resour Manage (2010) 24:2437–2448 [83] Attia1, B.B., Assessment of Vulnerability and Adaptation of Water Resources to Climate Change in Egypt , Springer 2015 [84] Setegn, G.S., Melesse, M.A., Climate Change Impact onWater Resources and Adaptation Strategies in the Blue Nile River Basin, Springer 2016 [85] Melesse, M.A., Abtew, M., Landscape Dynamics, Soil and Hydrological Processes in Varied Climates, Springer 2016 [86] Ouda, S., Major Crops and Water Scarcity in Egypt Irrigation Water Management under Changing Climate, Springer 2016
149 Bibliography
150
Appendix I - Water balance in the Nile Basin
Figure 46 - Water balance in the Nile Basin - FAO Synthesis Report, Information Products for Nile Basin Water Resources Management, Rome 2011
151
Figure 47 - Water balance in the Nile Basin by country - FAO Synthesis Report, Information Products for Nile Basin Water Resources Management, Rome 2011
152
Appendix II - Socioeconomic variables Forecast Results
Table 37 – Key Assumptions Historical data and time forecast, the black line divides historical data from time forecast
End Year Urban Rural Income Population GDP Households Urban Households Households Billion Million Million Billion $ % Million houses Million houses $ people houses 2007 - 74,230 130,479 42,491 14,846 6,308 8,538 2008 146,922 75,492 162,818 42,593 15,098 6,431 8,668 2009 172,114 76,775 188,982 42,695 15,355 6,556 8,799 2010 196,206 78,075 218,880 42,797 15,615 6,683 8,932 2011 216,766 79,393 236,001 42,899 15,879 6,812 9,067 2012 240,324 80,722 262,824 43,002 16,144 6,942 9,202 2013 264,771 82,112 294,280 43,105 16,422 7,079 9,344 2014 288,134 83,504 320,992 43,208 16,701 7,216 9,485 2015 311,496 84,920 347,704 43,311 16,984 7,356 9,628 2016 334,859 86,360 374,415 43,414 17,272 7,499 9,773 2017 358,222 87,824 401,127 43,518 17,565 7,644 9,921 2018 381,584 89,313 427,838 43,622 17,863 7,792 10,071 2019 404,947 90,828 454,550 43,726 18,166 7,943 10,222 2020 428,309 92,368 481,261 43,830 18,474 8,097 10,377 2021 451,672 93,934 507,973 43,935 18,787 8,254 10,533 2022 475,035 95,527 534,685 44,040 19,105 8,414 10,691 2023 498,397 97,146 561,396 44,145 19,429 8,577 10,852 2024 521,760 98,794 588,108 44,251 19,759 8,743 11,015 2025 545,122 100,469 614,819 44,356 20,094 8,913 11,181
153
2026 568,485 102,172 641,531 44,462 20,434 9,086 11,349 2027 591,847 103,905 668,243 44,568 20,781 9,262 11,519 2028 615,210 105,666 694,954 44,675 21,133 9,441 11,692 2029 638,573 107,458 721,666 44,781 21,492 9,624 11,867 2030 661,935 109,280 748,377 44,888 21,856 9,811 12,045 2031 685,298 111,133 775,089 44,996 22,227 10,001 12,226 2032 708,660 113,017 801,801 45,103 22,603 10,195 12,409 2033 732,023 114,933 828,512 45,211 22,987 10,392 12,594 2034 755,385 116,882 855,224 45,319 23,376 10,594 12,783 2035 778,748 118,864 881,935 45,427 23,773 10,799 12,974 2036 802,111 120,879 908,647 45,535 24,176 11,009 13,167 2037 825,473 122,929 935,359 45,644 24,586 11,222 13,364 2038 848,836 125,013 962,070 45,753 25,003 11,440 13,563 2039 872,198 127,133 988,782 45,862 25,427 11,661 13,765 2040 895,561 129,289 1015,493 45,972 25,858 11,887 13,970
154
Appendix III - Generation Expansion Plans
Table 38 – EEHC, Egypt Electricity Holding Company generation expansion plan
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Plant Name [MW]
Wind (East-Weast Nile) 360 850 1100 540
Photovoltaic cells 20 20
Abu Kir (steam) 1300
Ain Sokhna (steam) 1300
Banha (CC) 250 500
Giza North (CC) 500 1250 500
Dairout BOO (CC) 1000 1250
Sueaz (steam) 650
Mini&Small Hydro units 30
Helwan South (steam) 1950
Bani Suif (steam) 1500 750
Qena (steam) 650 650
155
Dananhur (steam) 1000 500
Cairo West (steam) 650
Assiut (steam) 650
Damitta West (GT) 500 250
El Sueif (CC) 500 250
Mahmoudia (CC) 300 150
El Shabab (CC) 500
Safaga (steam) 650 1300
Alex East (CC) 1500 750
Sidi Krir 2 (CC) 750
Cairo South (steam) 1300 650
Oyoun Mousa ext (steam) 650
Kafer EL-Dawar (steam) 650
Abo Kir (steam) 650
Steam Units 1300 1950 1200
Combined Cycle Units 1000 2250 1500 1250 1500 1250 1500
156
Table 39 – REN Renewable scenario generation capacity expansion plan
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Plant Name [MW]
PV plant 275 450 2*400 500 250 250 250 250 300 300 400 400 400 400
Mini Hydro 30
Wind 700 950 950 950 950 950 950 950
157