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OPPORTUNITIES FOR FLEXIBILIZATION OPTIONS IN A SYSTEM WITH MORE IMPLEMENTATION OF RENEWABLE ENERGY SOURCES

Business case identification for different flexibilization options for the Dutch electricity system

Keje Spijkerman EES-2015-255 Master Programme Energy and Environmental Sciences, University of Groningen

Keje Spijkerman EES-2015-255 Master Programme Energy and Environmental Sciences, University of Groningen

Research report of Keje Spijkerman Report: EES-2015-255 Supervised by:

H. (Hans) van der Spek, FME Dr. R.M.J. (René) Benders, Center for Energy and Environmental Sciences, IVEM Prof. dr. H.C. (Henk) Moll, Center for Energy and Environmental Sciences, IVEM Prof. dr. ir. G.P.J. (Gerard) Dijkema, Center for Energy and Environmental Sciences, IVEM University of Groningen Energy and Sustainability Research Institute Groningen, ESRIG

Nijenborgh 4 9747 AG Groningen T: 050 - 363 4760 W: www.rug.nl/fwn/research/esrig

TABLE OF CONTENTS

Summary ................................................................................................................................................. 5

Samenvatting ........................................................................................................................................... 7

List of abbreviations ................................................................................................................................ 9

Introduction ................................................................................................................................... 11

1.1 Aim of the research ............................................................................................................... 11

1.2 Scope and boundary setting .................................................................................................. 12

1.3 Research questions ............................................................................................................... 13

1.4 Research methods ................................................................................................................. 14

Electricity markets ......................................................................................................................... 15

2.1 Current markets .................................................................................................................... 16

2.1.1 Long term contracts ...................................................................................................... 16

2.1.2 Day-ahead market ......................................................................................................... 17

2.1.3 Intraday market ............................................................................................................. 19

2.1.4 Imbalance market .......................................................................................................... 20

2.1.5 Comparison and trends different markets .................................................................... 23

2.2 Future/Needed markets ........................................................................................................ 24

2.2.1 Future local energy markets .......................................................................................... 25

2.2.2 Capacity control market mechanism ............................................................................. 25

2.2.3 Frequency control ......................................................................................................... 26

2.2.4 Opportunities for new business cases with these future markets................................ 27

Flexibilization options .................................................................................................................... 29

3.1 Overall SWOT analysis ........................................................................................................... 29

3.1.1 Strengths ....................................................................................................................... 30

3.1.2 Weaknesses ................................................................................................................... 31

3.1.3 Opportunities ................................................................................................................ 31

3.1.4 Threats ........................................................................................................................... 32

3.2 Energy storage ....................................................................................................................... 34

3.2.1 Strengths ....................................................................................................................... 37

3.2.2 Weaknesses ................................................................................................................... 37

3.2.3 Opportunities ................................................................................................................ 38

3.2.4 Threats ........................................................................................................................... 38

3.2.5 Position of Energy storage ............................................................................................. 38

3.3 Demand Side Management ................................................................................................... 39

3.3.1 Strengths ....................................................................................................................... 41

3.3.2 Weaknesses ................................................................................................................... 41

3.3.3 Opportunities ................................................................................................................ 41

3.3.4 Threats ........................................................................................................................... 42

3.3.5 Position of DSM options ................................................................................................ 42

3.4 Dispatchable power generation ............................................................................................ 42

3.4.1 Strengths ....................................................................................................................... 43

3.4.2 Weaknesses ................................................................................................................... 43

3.4.3 Opportunities ................................................................................................................ 44

3.4.4 Threats ........................................................................................................................... 44

3.4.5 Position of Dispatchable power generation .................................................................. 44

3.5 Interconnection between Markets ....................................................................................... 45

3.5.1 Strengths ....................................................................................................................... 46

3.5.2 Weaknesses ................................................................................................................... 47

3.5.3 Opportunities ................................................................................................................ 47

3.5.4 Threats ........................................................................................................................... 47

3.5.5 Position of Interconnection ........................................................................................... 48

3.6 Overall position of flex options ............................................................................................. 48

Flexibility investment model ......................................................................................................... 49

4.1 Aim of the model ................................................................................................................... 49

4.2 Methods ................................................................................................................................ 49

4.3 Model setup, structure and boundary settings ..................................................................... 49

4.4 Technical details .................................................................................................................... 51

4.4.1 CAPEX ............................................................................................................................ 51

4.4.2 OPEX .............................................................................................................................. 52

4.4.3 Network Costs ............................................................................................................... 52

4.4.4 Rewards ......................................................................................................................... 53

4.4.5 Annual profit .................................................................................................................. 55

4.4.6 Return on investment .................................................................................................... 56

4.5 Model validation ................................................................................................................... 56

4.5.1 Network costs ................................................................................................................ 56

4.5.2 Validation of the economic outcomes box ................................................................... 57

4.6 Electricity scenarios ............................................................................................................... 58

4.6.1 Trends in the electricity markets ................................................................................... 59

4.6.2 Day-ahead scenarios ..................................................................................................... 60

4.6.3 Intraday scenarios ......................................................................................................... 63

4.6.4 Imbalance market scenarios.......................................................................................... 63

4.6.5 Conclusions of different scenarios ................................................................................ 64

4.6.6 Position of scenarios in model ...................................................................................... 65

Case studies ................................................................................................................................... 67

5.1 Energy storage ....................................................................................................................... 67

5.2 Demand response ................................................................................................................. 68

5.3 CHP units ............................................................................................................................... 69

5.4 Electric boiler ......................................................................................................................... 70

5.5 Power2hydrogen ................................................................................................................... 70

5.6 Interconnection ..................................................................................................................... 71

5.7 Conclusions current technologies ......................................................................................... 71

Discussion ...................................................................................................................................... 75

6.1 The position of this study ...................................................................................................... 75

6.2 Data availability ..................................................................................................................... 75

6.3 Limitations ............................................................................................................................. 76

6.4 This study in comparison with other studies and countries ................................................. 77

Conclusion ..................................................................................................................................... 79

7.1 Electricity markets ................................................................................................................. 79

7.2 Flexibility investment model ................................................................................................. 80

7.3 Position of flexibility options ................................................................................................. 80

Recommendations ........................................................................................................................ 83

References ..................................................................................................................................... 85

Appendix A ............................................................................................................................................ 91

The Dutch energy system .................................................................................................................. 91

Physical network ........................................................................................................................... 92

Market system ............................................................................................................................... 94

Other stakeholders ........................................................................................................................ 95

Conclusions ........................................................................................................................................ 96

APPENDIX B ........................................................................................................................................... 97

Appendix C ............................................................................................................................................ 99

Appendix D .......................................................................................................................................... 101

Appendix E ........................................................................................................................................... 103

Scenario ‘Limited Development’ ..................................................................................................... 103

Scenario ‘Green and Flex’ ................................................................................................................ 104

Scenario ‘CHP phase-out’ ................................................................................................................ 105

Appendix F ........................................................................................................................................... 107

Appendix G .......................................................................................................................................... 109

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SUMMARY

Due to the finite nature of fossil fuels and the negative influences these fuels have on the climate, there is a growing interest in a sustainable energy supply. To make the electricity system more sustainable, the implementation of sustainable renewable energy technologies is rapidly increasing. However, the most implemented renewable electricity technologies (wind turbines and solar panels) have an unpredictable and volatile production pattern, increasing the production fluctuations in the complete electricity system. This, in combination with a growing electricity demand due to electrification (electric vehicles and heat pumps), increases the demand for flexibility in the Dutch electricity grid. This increasing flexibility demand offers opportunities for investors, interested to invest in technologies to provide flexibility to the grid. This study is carried out on behalf of the FME association, with the goal to inform, possible investors in flexibility options for the electricity grid, about the trends in the different electricity markets and the opportunities these trends offer for business cases for the four different flexibility providing options (energy storage, demand side management, dispatchable power generation and interconnection). This research analyzes the different electricity markets in the Dutch electricity system. Additionally, the possible opportunities and challenges to tender more flexibility to the grid are analyzed. Currently, there is an overcapacity of gas fired power plants in the Dutch electricity system, decreasing the demand for extra flexibility. This absence of extra demand for flexibility in the system, is currently translated in a low price volatility by the different electricity markets (Day-ahead, Intraday and Imbalance market). Due to this low price volatility, it is not interesting to invest in flexibility providing options at the moment. Beside the economic unattractiveness of flexibility options, there are multiple barriers preventing flexibility providing options from being implemented on a large scale. The biggest barriers are the Dutch network tariff structure and a lack of valuation of possible system services flexibility options can provide like, congestion prevention, a decreasing demand for back-up capacity and an improved business case for variable renewable electricity technologies. Due to these barriers there is no level playing field for all flexibility options to provide flexibility to the Dutch system. The flexibility investment model is developed in this study, to analyze the economic attractiveness of different flexibilization options for the Dutch electricity system. According to this model, only demand response is interesting to invest in, with a return on investment (ROI) period of 4.1 years in the current system. All other flexibility options face ROI values longer than 10 years, exceeding their lifetimes. To test the opportunities for other flexibility options and to test also the robustness of all investments, two other scenarios are included in the model. According to the ‘limited development’ scenario, not only demand response will be interesting the coming years, also electric boilers and lithium-ion batteries become more interesting, with ROIs of 4.8 and 7.9 years. Under the ‘green and flex’ scenario, only demand response and electric boilers (ROI = 4.9 years) have a positive business case. The main conclusion from this study is that currently only demand response is an interesting technology to invest in. The other flexibility providing options currently do not have positive business cases, since the demand for new flexibility options is not present. However, this demand for extra flexibility could increase quickly, when the total installed capacity of wind turbines and solar panels increases, the difficult position of gas fired power plants and CHP units will last for multiple years and the electricity demand will increase significantly the coming years. However, how these parameters will develop the coming years is highly uncertain. For a case to be positive, the case specific conditions are very important. As cases have specific requirements, high risks are involved in investments in flexibility options. For companies interested in investing in flexibility options, with the aim of reducing the total energy costs, it is wise to analyze the opportunities to install energy saving measures first. Since energy saving technologies most of the time have shorter ROI periods and the business cases of energy saving measures are more secure.

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SAMENVATTING

Door het eindige karakter van fossiele brandstoffen en de negatieve invloeden van deze brandstoffen op het klimaat, groeit de interesse in een duurzame energievoorziening. Om het elektriciteitssysteem te verduurzamen groeit de implementatie van duurzame hernieuwbare energietechnologieën. De meest geïmplementeerde duurzame technologieën (windturbines en zonnepanelen) hebben echter een onvoorspelbaar en fluctuerend productiekarakter. Hierdoor neemt de fluctuatie in productie toe. Deze toename in productie fluctuaties in combinatie met een groeiende elektriciteitsbehoefte door elektrificatie (elektrisch rijden en warmtepompen), vergroot de behoefte aan flexibiliteit in het elektriciteitsnet. Deze toenemende vraag naar flexibiliteit biedt kansen voor investeerders. Deze studie is uitgevoerd in opdracht van ondernemersorganisatie FME, met als doel mogelijke investeerders in flexibiliteitsopties voor het elektriciteitsnet te informeren over de trends in de verschillende elektriciteitsmarkten en de kansen die deze trends bieden voor business cases voor de vier verschillende flexibiliteitsopties (energieopslag, demand side management, op- en afschakelbare elektriciteitopwekkers en interconnectie). Dit onderzoek analyseert de verschillende Nederlandse elektriciteitsmarkten. Ook de mogelijke kansen en belemmeringen om meer flexibiliteit aan het systeem te kunnen geven worden onderzocht. Momenteel wordt het Nederlandse systeem gekenmerkt door zijn grote gasgestookte capaciteit. Hierdoor is er weinig vraag naar extra flexibiliteit in het net, wat zich momenteel vertaalt in een relatief kleine prijsvolatiliteit op de verschillende markten (Day-aheadmarkt, Intradaymarkt en Onbalansmarkt). Deze kleine prijsvolatiliteit maakt investeren in flexibiliteitsopties niet interessant. Echter zijn er, buiten het feit dat de markten nog niet gunstig zijn voor investeringen in flexibiliteit, nog een aantal belemmeringen voor de implementatie van flexibiliteittechnologieën in het Nederlandse systeem. De belangrijkste zijn de huidige netwerkkostenstructuur en een gebrek aan waardering voor de mogelijke systeemdiensten die flexibiliteitsopties kunnen leveren, zoals het voorkomen van congestie, het verminderen van de back-up capaciteit en het verbeteren van de business case van duurzame elektriciteitstechnologieën. Door deze belemmeringen is er geen ‘level playing field’ voor alle flexibiliteitsopties. Het ‘flexibility investment model’ is ontwikkeld in deze studie om de economische aantrekkelijkheid van verschillende flexibiliteitsopties te kunnen analyseren. Uit het model komt naar voren dat alleen demand response interessant is op dit moment. Dit komt door de terugverdientijd van 4,1 jaar in de huidige situatie. Alle andere flexibiliteitsopties hebben terugverdientijden langer dan 10 jaar. Deze terugverdientijden overschrijden de levensduur van veel technologieën en maken ze hierdoor oninteressant. Om de kansen voor andere flexibiliteitsopties te onderzoeken en om de robuustheid van alle investeringen te testen, zijn er twee scenario’s toegevoegd aan het model. Onder de ‘limited development’ scenario zijn naast demand response, ook lithium-ion batterijen en elektrische boilers interessant de komende jaren, met terugverdientijden van 4,8 en 7,9 jaar. Met het 'green and flex’ scenario hebben alleen demand response en elektrische boilers een positieve business case. De hoofdconclusie van dit onderzoek is dat demand response momenteel de enige interessante flexibiliteitsoptie is om in te investeren. Alle andere flexibiliteitsopties hebben momenteel geen positieve business case door de geringe behoefte aan extra flexibiliteit in het elektriciteitssysteem. Echter kan de vraag naar flexibiliteit sterk toenemen als het aandeel wind- en zonne-energie sterk toeneemt, de lastige positie van gascentrales en WKK installaties meerdere jaren blijft bestaan en de elektriciteitsbehoefte sterk toeneemt. Hoe deze variabelen zich de komende jaren zullen ontwikkelen is echter hoogst onzeker. Door de grote situatie afhankelijkheid bij investeringen in flexibiliteitsopties, is het investeringsrisico relatief hoog. Bedrijven die willen investeren in flexibiliteitsopties met als doel om de totale energiekosten te verkleinen doen er verstandig aan om eerst naar energiebesparende technologieën te kijken, aangezien deze technologieën vaak een kortere terugverdientijd hebben en de business cases van energiebesparende technologieën meer zekerheid kennen.

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LIST OF ABBREVIATIONS

APX – Power Spot Exchange Market CHP – Combined Heat and Power COP – Coefficient of performance DPG – Dispatchable power generation DSM – Demand side management DSO – Distribution system operator FOM – Fixed Operation and Maintenance cost ICM – Interconnection between markets OTC – Over the counter market PRP – Program Responsible Party ROI – Return on investment TSO – Transmission system operator VOM – Variable Operation and Maintenance cost VRE – Variable renewable energy

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INTRODUCTION

The relationship between the environment and human activities is increasingly stressed the last decades. Mankind has claimed large parts of nature and natural resources and has given polluting compounds back to the environment. This pollution has significant consequences for the climate and environment all over the globe. From an environmental point of view, especially CO2 emissions are emphasized nowadays. Most of the scientists agree on the fact that CO2 is an important greenhouse gas, causing climate change. One of the sectors contributing significant to the global CO2 emissions is the energy sector. To decrease the impact of the energy sector on the environment, policies are created on every thinkable governmental level. These policies are focusing on the one hand on decreasing the energy demand and on the other hand on implementing more renewable energy sources (RES; Sociaal Economische Raad, 2013). For the production of energy from renewable energy sources, most of the time variable energy producing technologies are used. More specifically, most policies are mainly focusing on solar PV and wind turbines to produce a large share of the electricity in a sustainable way (Sociaal Economische Raad, 2013). However, the energy production of these technologies is controlled by climate and weather patterns, making the production highly volatile and unpredictable. An increase in variable renewable energy (VRE) technologies increases the volatility in supply and demand and therefore, increases the risks for overloads or even black-outs (Eurelectric, 2014). This volatility in production can cause problems for the whole system when not dealt with correctly. Currently, the need for flexibility in the Netherlands is supplied by coal and gas thermal power plants, adapting their power generation to the changing demand (Hout et al., 2014). However, there are multiple options to deal with this fluctuating supply and demand patterns like; Energy storage, Demand side management, Dispatchable power generation and Interconnection capacity between markets (International Energy Agency, 2014a). A major part of these options are currently not mature nor economically viable and are most of the time not profitable (Lund et al., 2015). Nevertheless, these options have to be developed and implemented in the coming years to deal with the increasing share of variable renewable energy in the energy system. New business cases can emerge in the coming years, since the energy price volatility will increase, making flexible energy systems more valuable and profitable when implemented in the right timeslots (Ketterer, 2014). Flexibilization options can operate in the hours when the energy price is in favor of the technology, decreasing financial losses and optimizing the production/profit ratio. This study investigates the different flexibility options to see where the opportunities and barriers for flex options in the energy system are. These opportunities and barriers are combined in a developed tool, which can provide an indication to possible investors of the economic attractiveness of an investment in a flexibilization option.

1.1 Aim of the research

The main aim of this study is to investigate the possibilities for business cases for flexibility providing technologies in the Dutch electricity system. Which technologies are interesting to provide flexibility to the electricity system? To answer this question, a model is developed to analyze the economic attractiveness of the different flex providing technologies. To develop this model, this research has analyzed the current and near future energy system. The result gives an answer to the question where the flexibility is needed in the system. This is done to inform possible investors about the opportunities and pitfalls the current electricity system has. To inform the investors, an overview of the energy markets is provided, the barriers are identified and the main decision making parameters are included in the model. This model can be used by investors to test the robustness of the investment under different scenarios.

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1.2 Scope and boundary setting

The goal of this research is to inform companies about possible business cases for flexibilization options within the energy system. From a starting position this is done for companies affiliated to FME. FME is a business association representing the Dutch industry. However, when the results are useful for other sectors or companies not affiliated to FME, the expertise can be used by other interested parties as well. The business cases are not only interesting for the company that provides the flexibility to the grid. A significant share of the members affiliated to FME is developing and manufacturing the flexibility providing technologies. For these companies, this research can help selecting their strategies. The companies affiliated to FME are all based in the Netherlands. However, these companies do not only manufacture, develop and supply for the national market. A large number of companies is also active on the international market. Nevertheless, the implementation of flex options for the energy system is investigated for the Dutch energy system. This is done in context with the electricity markets in North West Europe, since opportunities, barriers and the possible business cases are heavily influenced by national and regional parameters. Especially national regulations and legislations are strongly influencing the energy system. Imbalances in the energy system are mainly dealt with on a national level. Therefore, flexibilization options have the main purpose to stabilize the national electricity grid (Hout et al., 2014). Nonetheless, the international context cannot be ignored completely, since national electricity grids and markets are interconnected. This research focusses on the integration of more flexibilization options in the Dutch energy system. The energy system, however, is a very broad concept and has to be defined specifically. The focus of this research is to investigate the Dutch electricity grid to identify the possibilities to make this grid more flexible, secure and safe. So in practice, only fluctuations in the electricity grid are investigated and not the flex options for other sectors, like the transport sector. There are four options to create more flexibility in the electricity system, as discussed in the introduction. These options are: Electricity storage (ES), Demand side management (DSM), Dispatchable power production (DPG) and Interconnection capacity between markets (ICM). All these options will be taken into account as possible flex options for the Dutch system. In this research, especially DSM needs boundary settings. This, since possible products produced by DSM technologies (e.g. Power2gas, Power2heat and Power2products) could create more flexibility in other parts of the energy system as well. Nevertheless, this study limits itself to the use of these outputs in the internal system or to the rewards earned with the trade in these products. What the purpose of the products is after selling is not taken into account, since this is out of the scope of the flexibilization technology itself. The rewards from these products will be compared with the value of the products produced in the traditional way. In this way, the additional value of electrification will be investigated, to see if the technology is economically attractive. This study defines Energy storage as Power2power storage. This means that the technology has to take up electricity and should be able to deliver electricity back to the grid without additional conversion steps. This means that the energy is kept in the electricity system. All other forms of flexibility where electric energy is converted in other forms of energy like, Power2gas and Power2heat, are seen as DSM options, since these options convert electricity into other energy forms and these forms of energy will leave the electricity system due to their use in industry or other sectors. When and how to produce these forms of energy can be decided by the asset owner. Power2products is an electricity price steered production process. The production will be increased when prices are low and will be decreased when prices are too high. This is similar to demand response, however, in power2products the process is optimized to be able to deal with price fluctuations. To analyze the economic attractiveness of flexibility providing technologies, a tool is developed. This tool is a model, which analyzes the possible rewards of the flexibility technology on the different electricity markets. With these rewards in combination with the total investment costs, the return on investment (ROI) period of the technology is calculated. The model is called the ‘flexibility investment model.’ This study tries to provide insights in the energy system and the opportunities for investors to

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find new business cases in the flexibilization of the electricity grid. The possible technologies investigated by the model are all currently available or near future technologies. This is done, since business cases are built on available knowledge and technology characteristics. This study investigates possible business cases for the period 2015 to 2023. This is done, since the current energy policy of the Netherlands is quantified for the period until 2023 (Sociaal Economische Raad, 2013). Business cases can only be identified under the current policy framework, since the parameters deciding if something is a business case are strongly influenced by policy frameworks. The goal of this study is to inform possible investors about the opportunities and challenges in tendering flexibility to the grid. However, beside the word ‘investor’ also the words company or end-user are used. These words all deal with the same party in the system, since companies are end-users of electricity and they can invest in flex options. The technology providing the flexibility also has many synonyms used throughout this report. Flex options, flexibility assets, flexibilization options and flex providing technologies are all describing the same technology.

1.3 Research questions

From the research aim an overall end goal can be identified. The goal is to provide information about the current electricity system and to arrive at a method to rank flexibilization technologies in order of economic attractiveness. In this way, this report enables parties in the energy system to respond to dynamics in the Dutch electricity grid and the price volatility in the electricity markets. To create this ranking method, different steps have to be taken. The first boundary setting used in this research is the focus on flexibilization options for the electricity grid. The electricity grid is chosen while it has to face major challenges the coming years (Slingerland et al., 2015). On the one hand, the electricity grid has to deal with more fluctuation in electricity production and on the other hand, there is more demand for electricity due to the electrification trend. This, while the share of VRE is increasing fast, together with an increasing demand for electricity (e.g. electric vehicles), endangering the traditional electricity system (ING, 2014; Lund et al., 2015). The other parts of the energy system (heat and transport) are less vulnerable for the implementation of renewables, since the renewable technologies used in these sectors are less depending on weather conditions, but more on climate aspects over a longer period (e.g. biomass production; Schaeffer et al., 2012). Nevertheless, heat is also partly taken into account, since a significant amount of flexibilization technologies deal with heat production and have opportunities to increase the electricity grid security (e.g. hybrid boilers and power2heat). With this scope and boundary setting, a main research question can be conducted. This research question is: Main research question: What is the position of flexibilization options in the current Dutch electricity system and in the period until 2023 and where can possible business cases be identified? Several sub-questions can be derived from the main research question to make it easier to answer the main research question. Sub-questions:

1. Which electricity markets are present in the Dutch electricity system?

2. Which flexibilization options are currently available and what are their positions in the market? 3. How to analyze the economic attractiveness of the different flexibility providing technologies?

4. Which technologies are of interest the coming years?

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1.4 Research methods

Several methods will be used in this research. To answer the first sub-question, the current electricity markets are investigated using a quantitative system analysis. By doing so, an overview is created how the current electricity markets work and what will be the trends in the coming years. Especially the trend of more variable renewable energy (VRE) in the electricity supply portfolio is interesting, as this increases the demand for flexibility. These trends and scenarios will be collected from different studies. With these trends and scenarios, the main drivers can be identified and can be used as input for the flexibility investment model that will be created. To answer the second sub-question a SWOT analysis is executed. With this SWOT analysis the position of the different flexibility providing technologies is analyzed. SWOT stands for Strengths, Weaknesses, Opportunities and Threats. First, the internal factors influencing the business cases, will be discussed in the Strengths and Weaknesses paragraphs. The Opportunities and Threats discuss the external parameters influencing the business cases of the different flex options. This SWOT analysis is executed for all the four different flex options (Energy storage, Demand Side Management, Dispatchable Power Generation and Interconnection Between Markets) to analyze their position on the market. A literature study together with expert interviews provides the data on relevant requirements for different investors. From this literature study, the relationships between the different parameters and requirements can be formulated and therefore be included in the flexibility investment model. This model will be created in Excel and will analyze the ROI period of a flex option using the possible rewards and costs of the technology. Multiple scenarios of different studies will be compared to see what are the most relevant parameters influencing the markets and to come up with scenarios which are realistic for the period till 2023. These scenarios will be incorporated into the model. A qualitative system analysis is executed to identify external factors influencing the system. The model is validated by incorporating existing business cases and by consulting experts. Several technologies will be evaluated with it and an economic ranking can be created. To test the robustness of these business cases a quantitative sensitivity analysis will be executed. This analysis identifies the strengths and weaknesses of these business cases, providing insights in when to invest and when not. This can be done by adjusting the different parameters inside the model.

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ELECTRICITY MARKETS

In this chapter, the different electricity markets will be discussed. How the markets work and how the demand for flexibility is translated in these markets will be analyzed. However, before we can discuss the role of the electricity markets in providing flexibility, we first have to define the term ‘Flexibility.’ Opposite to most other energy systems, the electricity system is very complex, due to the demand and supply that have to be in balance all the time. Electricity cannot be stored in the grid and has to be consumed directly after generation. Therefore, the balance between generation and consumption has to be monitored continuously. This can be done on multiple geographical levels. On a local level, balancing the grid is important to prevent congestion and is done by distribution system operators (DSOs). Congestion is mainly created by a too large supply which cannot be transported by the available transport capacity (TenneT, 2012). On a national level, balancing the grid is important to prevent black-outs (Eurelectric, 2014). This balancing on national level is managed by the transport system operator (TSO), which is TenneT in the Netherlands (Energie-Nederland and Netbeheer Nederland, 2011). Figure 2-1 gives a schematic representation of the power system. Here, the balancing principle is visualized.

Balancing the grid is a challenge, since the demand and supply of electricity are changing continuously. Consumers can switch off lights on a small scale, but also shut down big electricity consuming processes on a larger scale. These changes in demand create imbalances in the grid. The production has to be adapted to this new demand. The electricity system is historically steered by the demand. The generators have to adapt to the demanded amount of electricity (Klimstra, 2014). The definition of flexibility is provided by the International Energy Agency, (2011) as:

“Flexibility expresses the extent to which a power system can increase/decrease electricity production or consumption in response to variability” This variability is created historically by the change in demand, but will increase the coming years mainly due to the increase in VRE capacity. These variable electricity producing technologies only generate electricity if the conditions are right and in this way, increase the demand for back-up capacity (Hout et al., 2014). The required flexibility can be supplied by different technologies and on different scales. The different options can be clustered in four main groups; Energy storage, Demand side management, Dispatchable power production and Interconnection capacity between markets (International Energy Agency, 2014a). These options can bring flexibility into the system in various ways from, decreasing the demand to increasing the supply. The description of the different flex options will be done in the chapter 3. Currently, the Dutch electricity system has enough flexible capacity to balance the system. This, since there is a large capacity of gas fired power plants which can adjust their production relatively quickly to the change in demand (Hout et al., 2014; Slingerland et al., 2015). However, the traditional electricity producers have difficulties maintaining their profits and market shares (Peeters et al., 2014). Especially gas fired power plants have difficulties to survive, since

Figure 2-1: Schematic representation of the power system operational principle (Papaefthymiou et al., 2014)

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electricity prices have dropped, extra production capacity has come online and the gas prices were relatively high in comparison with coal prices the last years (CE Delft et al., 2015). This difficult position of gas fired power plants in combination with an increasing capacity of variable renewable energy technologies can create problems in the future (Hout et al., 2014). A more detailed description of the position of electricity producers is provided in Appendix A. Before we can discuss the different electricity markets, the current Dutch electricity system will briefly be discussed. A more detail description of the role of the different stakeholders is provided in Appendix A. Many stakeholders are involved in the Dutch electricity system. At the physical part of the system, transmission system operator (TSO) TenneT and multiple distribution system operators (DSOs) have the responsibility to ensure a stable, safe and reliable grid (Energie-Nederland and Netbeheer Nederland, 2011). The national grid will be capable of dealing with the transition towards more renewables. The transport capacity in the high voltage grid is large enough to prevent congestion problems (Slingerland et al., 2015). This however, is different in the case of the distribution grids. DSOs have to invest large amount of money the coming years, to prevent local congestion or overload problems. This, especially is the case in areas with a high number of solar PV installations as in areas where electric vehicles and heat pumps are popular (van Melle et al., 2014). Flexibility options can play an important role in the prevention of these DSO problems. Another stakeholder facing challenges in the coming years is the conventional electricity generator, since many variables are influencing the playing field of the generators like, fuel costs, competition, CO2 prices and governmental actions (Klimstra, 2014). Therefore, it is highly uncertain how much conventional capacity will remain the coming decade. This decreasing capacity of conventional production capacity in combination with an increase in production fluctuations from the implementation of more VRE capacity, increases the demand for extra flexibility capacity in the system to ensure the system will remain safe, reliable and affordable. This extra demand for flexibility will have its effects on the different electricity markets which will be discussed below and will be the main topic of this research.

2.1 Current markets

In this section, the different electricity markets in the Netherlands will be discussed. This includes the current available markets and possible future markets. In the paragraph of the current markets, the currently available electricity markets will be discussed to see what their position in the electricity system is. Their size and prices will be discussed to analyze the opportunities for flexibility technologies in these markets. In the possible future markets paragraph of this chapter, the need for flexibility in other parts of the system will be identified. These parts of the system can create their own markets in the future.

2.1.1 Long term contracts

The large share of the electricity trade is done using long term contracts (ECN et al., 2014). This long term contracts represented 34% and 29% of the consumed electricity in respectively 2012 and 2013 (ECN et al., 2014; Köhne, 2015). This market share fluctuates significantly over the year and therefore, trends are hard to identify. In this market, parties trade their demand and supply on a continuous basis. The long term contracts deal with demand and supply from one month up to three years ahead of the implementation date. The deals made by long term contracts can be fine-tuned one month ahead of implementation (Slingerland et al., 2015). The main reason why long term contracts represent a large part of the electricity trade is because it ensures a fixed price over a longer period. This enables involving parties to decrease the risks of dealing with highly volatile electricity prices (Slingerland et al., 2015). These long term contracts are not interesting for the identification of business cases for flexibility technologies, since the demand for flexibility is determined by the differences in demand and supply on in a shorter timeframe. Therefore, the long term contracts are not included in the rest of the report.

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2.1.2 Day-ahead market

Of all electricity demand in the Netherlands in 2014, 44.5 TWh was traded on the Day-ahead market (APX, 2014a; Köhne, 2015). This represented 44.9% of the total market. This volume and share fluctuated over the last five years and is therefore hard to predict. The market developments of the different markets is discussed more in detail in table 2-6. Electricity is traded between parties that need more or less electricity as mentioned in their long term contracts. This, since the demand and supply patterns are fine-tuned one month, week or day before the implementation date (Slingerland et al., 2015). This trading takes place at the APX power spot exchange market (APX). On the Day-ahead market the electricity supplying and demanding parties have to deliver their requested volumes and prices per hour, one day before the implementation date. To be more precise, parties have to deliver their requests one day ahead of implementation, before 12:00 PM (GMT +01:00; APX, 2015). Electricity can be traded in blocks of 0.1 MWh or a multiple of that. The APX aggregates all bids using the merit order method, which ranks the requested demand and supplies on the requested prices. In this way, the APX determines the electricity price and volume that will be traded. This price and volume are respectively called the Market Clearing Price and Volume (MCP and MCV; Slingerland et al., 2015). An example of the aggregated demand and supply curves created by the APX is depicted in figure 2-2. The red line represents all electricity generation bids, while the blue line is the aggregated demand line. All requested demand left from the intersection will be delivered by the tendered supply left from the intersection point. All requested and tendered electricity that passes the MCP point will not be sold. Where on the line a supply bid is positioned has to deal with the marginal cost of the production technology. Variable renewable energy technologies, like wind and solar, do not have a marginal cost and can therefore be offered for very low prices or even for free. In contrast to VRE, coal and gas fired power plants have fuel costs that have to be translated into their production price. The higher the marginal cost per MWh produced, the higher the offered bid in the Day-ahead market and other markets and therefore, the smaller the chance a technology is able to supply to the market. As discussed above, the MCP sets the price of which all electricity is sold. What can be deducted from figure 2-2 is that the production technology which has the highest bid but still is allowed to deliver, sets the price for all the electricity. In times of low demand, supply technologies which have low marginal costs will set the price and therefore, the overall electricity price is low. On the other hand, in periods of high demand or low sun and wind activity, supply technologies with high marginal costs will set the price (gas fired power plants) and therefore, the overall electricity price will be relatively high (Tieben et al., 2013). This method of merit order and marginal costs is present in each of the discussed markets.

Figure 2-2: Aggregation Day-ahead curve APX market (APX, 2015a)

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These aggregated curves are made for every hour of the day. In this way, the MCPs and MCVs are determined and can be set next to each other to create a daily price and volume pattern. Examples of a daily, weekly and yearly pattern are depicted in appendix B. The average price per MWh on the Day-ahead market over a complete year was 52 Euro/MWh in 2013 and 41 Euro/MWh in 2014 (CE Delft et al., 2015). The price developments of the last years are illustrated in figure 2-3. The price volatility has decreased the last years, due to the better market coupling mechanism with other countries in North West Europe. This damping effect on the price volatility was bigger than the increase of price volatility, due to the increase of VRE capacity from wind turbines and solar PV panels (CE Delft et al., 2015).

From the figures in appendix B and figure 2-3, several trends can be identified. The first trend is that electricity is cheaper in night hours than in daily hours. The second conclusion that can be made is that there is a dip in the electricity price during the weekend. The last conclusion that can be made is the relatively high prices during winter periods in comparison with summer months. All these trends can be explained with the differences in electricity demand. The low prices are in periods when the demand is relatively low (nights, weekends and summers) while, the higher price periods take place in periods of relatively high demand (days, weeks and winters). From these figures it can be concluded that the current system is highly demand steered. This however, can change in the future, when the prices will be steered by the power production. More specifically, the prices will mainly be controlled by the production patterns of VREs (CE Delft et al., 2015; Hout et al., 2014; Slingerland et al., 2015). Sometimes it is not in favor of a demanding party to bid on an hourly basis on the day ahead market. This, since the electricity will only be supplied when the price is right. When the price increases per hour there is a chance the demanding party will not be supplied when its bid was too low. This is especially risky when the stop of electricity delivery has influences on the production process of an industry. To reduce the chance on electricity exclusion, but to ensure a relatively low electricity price, a block bidding can be done. Block biddings are part of the Day-ahead market in which the electricity is traded a day before production and consumption (Slingerland et al., 2015). In these blocks, the demanding party demands electricity for multiple consecutive hours during the day. In this way, the electricity is delivered completely (or completely not when the offered price is too low) over the multiple hours, decreasing the risks (Slingerland et al., 2015). Table 2-1 shows the situation of the Day-ahead market over the last years (APX, 2014a). It shows the traded volumes in the Day-ahead market and also the total market values. The Day-ahead market has increased significantly the last years. In comparison to 2009 the Day-ahead market traded volume has grown with 62% by 2013 (Autoriteit Consument & Markt, 2014). However, the market has slightly decreased its traded volume in 2014 in comparison to 2012.

Figure 2-3: Price trends on the Dutch Day-ahead market (CE Delft et al., 2015)

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Table 2-1: Day-ahead market (APX, 2014a; Autoriteit Consument & Markt, 2014; ECN et al., 2014)

Year 2010 2012 2014

Traded volume (TWh) 33.4 49.6 44.45 Average price (€/MWh) 46 48 41 Total value market (€) 1,536,400,000 2,380,800,000 1,830,800,000

Table 2-1 depicts the Day-ahead market in 2010, 2012 and 2014. Last year, over 44.4 TWh was traded on the market with an average price of 41.18 euro/MWh. This makes a total market value of over 1.83 billion euros in 2014. Nevertheless, the price is highly volatile on the Day-ahead market. Figure 2-4 shows a price duration curve of the Day-ahead market of 2014. This curve shows the price per MWh over the amount of hours per year. This is the market clearing price that was reached that specific hour of the year.

2.1.3 Intraday market

Closer to real time, parties can trade electricity on the Intraday market. In this market, the parties can trade electricity ‘last minute’ in blocks of one hour. Similar to the Day-ahead market, the Intraday market deals in blocks of one hour and is facilitated by the APX power exchange market in the Netherlands (Slingerland et al., 2015). However, in contrast to the Day-ahead market, the Intraday market trades on a continuous basis up to five minutes before delivery. This market will become more important the coming years due to the increase in installed VRE capacity (ECN et al., 2014). The price volatility of the Intraday market is expected to be bigger in comparison with the Day-ahead market. This, because it is closer to real time, which limits the ramp up or down options. Nevertheless, as figure 2-5 shows, the price differences between the Day-ahead market and the Intraday market are very limited (Autoriteit Consument & Markt, 2014). This can also be concluded from data from the APX, which tells us that that the average Intraday price was 42.02 Euro per MWh in 2014, compared to 41.18 euro per MWh on the day ahead market (APX, 2015b). This is mainly due to the limited VRE capacity in the Netherlands. In Germany, where the VRE share in the electricity generation mix is significantly bigger, the price differences can be significantly. This has mainly to do with the accuracy of the weather predictions (TenneT, 2014). The volume traded on the Intraday market was 1.0 TWh in 2014 comparing with 44.5 TWh traded on the Day-ahead market and a total electricity demand of 99 TWh (APX, 2014b; Hout et al., 2014; Köhne, 2015). An overview of the Intraday market is depicted in table 2-2.

Figure 2-4: Day-ahead market 2014 - price duration curve (APX, 2014b)

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Table 2-2: Value Intraday market 2014 (APX, 2015b)

The Intraday market has increased significantly the last four years and does not show the small decrease in 2014 as the Day-ahead market showed (Autoriteit Consument & Markt, 2014). A possible explanation for the increase in traded volumes on the Intraday market can be the increase in VRE capacity in the Netherlands. This VRE capacity increase also makes the electricity production more sensitive to weather patterns. Therefore, there is an increasing focus on weather prediction accuracy (Lund et al., 2015).

2.1.4 Imbalance market

To ensure a stable grid, it has to be in balance constantly. As discussed earlier this report, TenneT is responsible for this balance. To balance the system TenneT has multiple options. The different options to balance the system are depicted in figure 2-6.

Primary Reserve This balancing method deals with the most real time data. The primary reserve is a frequency controlling mechanism positioned at power generating units. The power generating unit reacts automatically to the change in grid frequency, and therefore stabilizes the grid. The primary reserve is

Year 2010 2012 2014

Traded volume (TWh) 0.003 0.45 1.020 Average price (€/MWh) 50.78 53.23 42.02 Total value market (€) 1,414,659 24,100,400 42,020,000

Figure 2-5: Difference between Imbalance market and Day ahead/intraday markets (Autoriteit Consument & Markt, 2014)

Figure 2-6: Principle frequency deviation and subsequent activation of reserves (Ophuis, 2015)

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activated within 30 seconds after the frequency change (Ophuis, 2015). The minimum capacity that has to be provided to TenneT is 1 MW and this has to be available 100% of the time during the contracted time period (Ophuis, 2015). The minimum amount of available primary reserve is determined by the European Network of Transmission System Operators for Electricity (ENTSO-E). This required available capacity was 96 MW in the Netherlands in 2014 (Ophuis, 2015). The downside of tendering flexibility to TenneT as primary reserve is that the availability has to be 100% and TenneT itself determines when the production is activated. Since there is no ability to voluntary bid on this market and the reward is only a yearly capacity reward, this type of balancing the grid is not taken into account in this study (TenneT, 2013a). There is no opportunity to tender flexibility to the grid voluntarily. Regulating and reserve power Regulating power is tendered by PV parties or other stakeholders in the system and is tendered to TenneT. The tendered capacity has to be fully online within 15 minutes after the grid frequency change (Tennet, 2004). The owner of the regulating power capacity is free to tender its flexibility to the system. However, when it has offered its flexibility to the market its availability has to be 100% (Ophuis, 2015). The minimum capacity has to be 4 MW. The reward the flexibility provider gets is build up from a capacity reward and a delivery reward. So the flexibility provider is rewarded for the time it tenders its flexibility. The reward for the actual electricity production is determined on the Imbalance market. Reserve power is used to decrease the stress on the regulating power capacity and is activated later. The reserve power providing units are most of the time cheaper, but slower reacting power units (Tennet, 2004). The reward a flex provider gets is determined by the merit order of the Imbalance market. This merit order works similar as on the Day-ahead market, where the options with the lowest marginal costs will get the bid. The difference between regulating and reserve power is the fact who controls the production. TenneT contracts the regulating power and therefore controls the activation. Reserve power is tendered voluntarily and therefore has to be activated by its owner (Ophuis, 2015). Emergency power The last option TenneT has to balance the system when the primary reserve, regulating power and reserve power are not enough, there is emergency power. This emergency power is contracted by TenneT and is rewarded for its available capacity. This emergency capacity has a minimum of 20 MW and is activated automatically by TenneT when needed (TenneT, 2013b). Emergency power is only limitedly used in the Netherlands but when used, the owner gets a standard reward of 200 euro/MWh plus the APX price of that moment (TenneT, 2013b). So it can be interesting to tender flexibility on this market. However, the shorter the ramp rate of the technology, the better its position in the merit order of TenneT, increasing the chance of being selected to provide the flexibility (TenneT, 2013b). Therefore, it is preferable to have a very short ramp up/down speed. In this study when discussing the Imbalance market as an option to tender flexibility, the regulating and reserve power is meant. This is done since it is a market where flexibility can be tendered voluntarily. The capacity reward of the regulating power market is not taken into account since this study focuses on the markets flexibility can be tendered. In this case, this is the Imbalance market. Table 2-3: balancing mechanisms (Ophuis, 2015; TenneT, 2013b)

Primary control Regulating power Reserve power Emergency power

Contract Yes Yes No Yes Voluntary bid No Yes Yes No Availability fee Yes Yes No Yes Compensation/MWh No Yes Yes Yes €/MWh - Imbalance market Imbalance market 200 + APX price Minimum bid size 1 MW 4 MW 4 MW 20 MW

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The Imbalance market is controlled by the transmission system operator (TSO) and has the purpose to secure the balance between supply and demand in real time. TenneT does this by creating an electricity market for every 15 minutes. TenneT has the responsibility to supply electricity to every consumer whenever they want to have it (Slingerland et al., 2015). However, TenneT has partly shifted this responsibility towards other parties in the system. These are the so called Program Responsible Parties (PRP or PV parties) as discussed above. The job of the ‘PV party’ is to ensure a balanced energy portfolio between the electricity supplier and the consumer for every 15 minute time frame (Slingerland et al., 2015). The imbalances in the portfolios of the PV parties are corrected with each other and when needed, additional capacity is addressed. The TSO publishes the prices and imbalances on a 2 minute basis on their website, to provide the opportunity for producer or consumers to trade the electricity on a 15 minute basis. How this market will develop the coming years is highly uncertain. Some experts state that, due to more VRE capacity more imbalances will occur (Hout et al., 2014), while other studies state that most of this increase in imbalances will be absorbed by more accurate weather predictions (Lund et al., 2015). Also the available installed production capacity is an important unknown, since this creates the merit order and determines the price on the Imbalance market (TenneT, 2013b). Therefore, it is hard to predict the Imbalance market in the coming years, making the investments based on this market highly uncertain. The trends of the last few years are illustrated in table 2-4. The average demand on the Imbalance market was 27 MW per 15 minutes ramp up, while the maximum ramp up demand was 256 MW and the maximum ramp down demand was 181 MW in 2014 (Slingerland et al., 2015). The total amount of electricity traded on the Imbalance market is roughly 3.3% of the total electricity demand and equaled 3.27 TWh in 2014 (Köhne, 2015). The cost per MWh bought at the Imbalance market is much more volatile than on the Day-ahead and Intraday market (Köhne, 2015). In the period between 2009 and 2014, the average price per MWh on the Imbalance market was 107.69 Euro (Köhne, 2015). However, since the volume of the Imbalance market only represents 3.3% of the total market, the influence of the relatively high prices on the Imbalance market on the total electricity price consumers pay is relatively small, being 0.51 euro per MWh on average in the period between 2009 and 2014. In comparison with the average electricity price of 48.11 euro per MWh for the period of 2009-2014, it only increased the price consumers paid for their electricity with 1.06 % (Köhne, 2015). For the Imbalance market it is important to identify the different biddings. There are biddings for ramp up and ramp down capacity. These ramping capacities are differently valuated, discussed in table 2-4. This is the value of the Imbalance market, including the regulating and reserve power demand. Table 2-4: value of the Imbalance market 2013 (Köhne, 2015; Slingerland et al., 2015)

Year 2010 2012 2014

Average ramp up per 15 minutes (MW): 28.0 28.7 27.0 Maximum ramp up per 15 minutes (MW): 187 205 256 Maximum ramp down per 15 minutes (MW): 237 334 181 Total market ramp up (MWh): 1,731,014 1,557,766 1,531,671 Total value ramp up (€): 101,106,085 118,787,046 92,754,746 Total market ramp down (MWh): 1,799,121 1,621,990 1,740,761 Total value ramp down (€): 63,414,410 54,854,411 55,888,710 Total value market (€): 164,520,495 173,641,457 148,643,456

In 2013 and 2014 the differences between the average imbalance prices per MWh and the day ahead market were respectively 28.59 and 17.74 euro/MWh (Köhne, 2015). This means that the average difference between the day ahead market and the Imbalance market has decreased between 2013 and 2014. However, from this data cannot be concluded that is the price peaks decreased as well. 10% of the time the price per MWh was above 95 euro/MWh in 2013. The 10% cheapest hours had an average price of 14 euro/MWh in 2013. 6% of the time the electricity price was even negative on the Imbalance market in 2013 (Movares, 2014). The price duration curve of the Imbalance market in 2013 is shown in figure 2-7.

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Since TenneT monitors the imbalance on a minute basis, a positive imbalance can shift towards a negative imbalance in a minute of time. The expected reward can turn into a loss when the imbalance changes from a ramp up demand to a ramp down demand and vice versa. Therefore, the income generated by tendering flexibility on the Imbalance market on a 15 minute timescale is highly volatile, unpredictable and risky.

2.1.5 Comparison and trends different markets

After discussing all different electricity markets, table 2-5 provides an overview of all the different characteristics of these markets. The markets are ranked on the timescale of the electricity trading. Table 2-5: Types of flexibility and their corresponding markets (Slingerland et al., 2015)

Table 2-6 gives a summary of the different trends in the markets discussed above. These trends are seen the last years in the different markets. This can give an indication on how the system is likely to evolve the coming years. How the different parameters influence the different markets will be discussed more in detail in the chapter of the flexibility investment model.

Market Type of flexibility

Timescale Bidding period Organization Price volatility

Imbalance Short-term < 15 minutes 1 minute after imbalance occurs

TenneT Very high

Intraday Short/Mid-term

Hour During the day until 5 minutes before implementation

APX Medium

Day-ahead Mid-term Hour One day ahead APX Medium Long term contracts

Long-term Weeks/season Month before implementation date

Bilateral Very low

Figure 2-7: Price duration curve Imbalance market 2013 (Movares, 2014)

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Table 2-6: Characteristics and trends of the different markets (APX, 2015b, 2014a; Autoriteit Consument & Markt, 2014; Köhne, 2015)

Markets Market volume, share and price

2010 2012 2014 Volume trend Total consumption

104.4 TWh 101.9 TWh 99.1 TWh - (can increase when more electric vehicles are bought)

Day-ahead 33.4 TWh 32.0% 46 €/MWh

49.6 TWh 48.7% 48 €/MWh

44.5 TWh 44.9% 41 €/MWh

+/-

Intraday 0.03 TWh 0% 50.78 €/MWh

0.45 TWh 0.5% 53.23 €/MWh

1.02 TWh 1.0% 42.02 €/MWh

+

Imbalance 3.5 TWh 3.38% 58 €/MWh ramp up 35 €/MWh ramp down

3.2 TWh 3.12% 76 €/MWh ramp up 34 €/MWh ramp down

3.3 TWh 3.30% 61 €/MWh ramp up 32 €/MWh ramp down

+/- (depends on weather prediction accuracy)

Flexibilization options currently can tender their flexibility on three main markets, respectively the; Day-ahead market, Intraday market and the Imbalance market. Flexibilization options have to compete with other producers/consumers on these markets. Currently, for most flexibilization technologies it is hard to make a robust business case, since they have to compete with conventional power plants in the merit order. Depending on the risks allowed, the investor can tender its flexibility on the Day-ahead, Intraday or Imbalance market. The Day-ahead market is relatively good to predict and is therefore the market with the lowest risks involved. It is also dealing every hour per year, while the Intraday and Imbalance markets only deal when there are imbalances. However, the profits also increase with the risks involved and therefore, flexibilization options capable of dealing on the Intraday and Imbalance market can increase their profits. For investors which allow larger risks, the Intraday market can be interesting. The bidding period is closer to real time, however, after a match is found the delivery or production is ensured. This is not the case for the Imbalance market, since the goal of this market is to balance the complete system. Flex asset owners who consider to tender flexibility to the Imbalance market, are not ensured from delivery/production and therefore should have an asset which can deal with this. An example of a suitable process is the production of steam. Steam can be produced by electricity and gas. Therefore, the production of steam can be a continuous one, switching from electricity as feedstock to gas and vice versa depending on the prices. Another parameter important for the Imbalance market is the ramp up or down rate, deciding the speed the technology can balance the grid. This is one of the main parameters taken into account by TenneT in deciding who is allowed to deliver the flexibility to the grid on the Imbalance market. Therefore, for investors considering investments in flexibility assets, it is wise to evaluate the system the flex option has to fit in and the risks the system is allowed to have.

2.2 Future/Needed markets

Beside the different markets discussed above, there are other market mechanisms that can be implemented in the coming years. This paragraph discusses possible other market mechanisms that can be implemented to deal with the more decentralized, fluctuating energy production and consumption.

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2.2.1 Future local energy markets

Smart and micro grids can create more local energy markets with other characteristics than the conventional markets. The organization which has to deal with the more locally distributed electricity is the Distribution System Operator (DSO). The DSO deals with the medium and low voltage electricity grids (ING, 2014; Slingerland et al., 2015). Groups of prosumers can combine their production units and steer their demand to the produced electricity. Smart grids can be designed in several ways like, energy storage on household level or on district level. Another option to choose is to maintain the connection to the grid or to go completely off-grid. A smart grid will create its own market, based on the available electricity in the system. To make local markets profitable, the prices have to be lower than the national grid. With the current systems this will hard to manage. However, with decreasing prices of electricity storage this might be possible in the future. Another important factor determining the feasibility of local markets is the social factor. Do communities want to be self-sufficient and how much money is this allowed to cost (Slingerland et al., 2015)? Congestion markets: Another example of a local market is the so called congestion market. This market already exists in some places in the Netherlands. These markets are created by the TSO/DSOs to prevent local grid overloads. Since the system operators are obliged to connect every consumer/producer, it has to build the required infrastructure (TenneT, 2012). However, in the case of a fast increase of electricity production capacity or demand, the infrastructure will not be ready in time. Therefore, a market is created with the local stakeholders to decide who will produce/consume and how much exactly. In this way, the congestion is prevented and all stakeholders can have a share of the profits. Specific characteristics of these congestion markets are that they are temporary and local (TenneT, 2012). A congestion market is the last option to deal with local congestion, therefore it will be avoided as much as possible (TenneT, 2012). Because of all this, the congestion market will only be implemented on a very local scale and with a limited time span.

2.2.2 Capacity control market mechanism

To ensure there is enough capacity to generate electricity when the sun is not shining and the wind is not blowing, a new market mechanism can be introduced. In this market mechanism, the power generating units that are available for the days when there is no production from VREs are rewarded for their presence. Several countries in Europe are experimenting with this form of ensuring back-up capacity. Figure 2-8 depicts the implementation of a capacity market mechanism in different countries in Europe. A benefit of a capacity control market mechanism is that the system is ensured to have enough peak generating capacity. For the Dutch case, a capacity mechanism could mean that gas fired power plants, which are currently mothballed and are not profitable, can survive and provide electricity in periods there is high demand and low supply (no sun and wind; Slingerland et al., 2015). However, there are also downsides to this market mechanism, since it has a bias towards the supply side of the system and favors conventional power plants above other flexiblitzation options (Slingerland et al., 2015). For the Dutch case, there is no need for a capacity control market mechanism the coming years, since there is enough existing flexibility capacity in the conventional power generating units to ensure a stable grid (Slingerland et al., 2015). Currently, there is no capacity mechanism in the Netherlands and therefore this market is not taken into account in this study.

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2.2.3 Frequency control

To ensure a stable grid, it is important to have a stable frequency in the grid. In North West Europe this frequency is determined to be 50.000 Hz (say fifty Hz; ENTSO-E, 2009). When the demand increases the frequency drops depending on the capacity of the power taken from the grid. When the demand is decreased the frequency increases again (Klimstra, 2014). The frequency is controlled using spinning turbines which adapt their rotation speed automatically when the frequency in the grid changes. By doing so the frequency is stabilized. When increasing the rotation speed, the frequency increases and vice versa (Klimstra, 2014). TenneT is responsible for the frequency balance in the grid in the Netherlands. However, TenneT is not allowed by law to invest in frequency or load balancing technologies and therefore, has to make deals with power suppliers to control the frequency (Ministerie van Economische Zaken, 1998). TenneT has made contracts with market parties on the different balancing markets discussed in paragraph 3.1.4. In the current situation, there is no problem with ensuring the grid frequency. However, when more intermitted renewable energy sources are introduced in the system this can become a problem, since these systems have a more fluctuating production pattern. This fluctuation in production influences the frequency in the grid (Klimstra and Hotakainen, 2011). However, VRE technologies can be adjusted/retrofitted to change their frequency output to stabilize the system. Currently, most VRE units are not equipped with these frequency adjusting systems, since most VRE systems are designed to maximize the output instead of balancing the frequency of the grid (Tarnowski and Kjær, 2010). When more VRE capacity pushes out the conventional power plants, less power plants can stabilize the grid frequency, making it more vulnerable for black-outs. Therefore, it is important to ensure that there are enough spinning reserves or enough frequency controlling systems in VRE technologies, to ensure a stable frequency (Klimstra, 2014). To ensure there are enough spinning reserves or other frequency controlling technologies to ensure the frequency of the grid, a special frequency controlling market can be created. Currently, this is not the case, since there are enough spinning reserves (Klimstra, 2014). However, policy makers should take into account that this could change due to the influence of VREs.

Figure 2-8: Capacity mechanisms: state-of-play in the European Union (Slingerland et al., 2015)

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2.2.4 Opportunities for new business cases with these future markets

The possible markets discussed in paragraph 2.2 are currently, all not present in the Netherlands, with exception of the congestion market, which is implemented on a small scale. The expectations are that the coming years these market mechanisms will not be implemented (Slingerland et al., 2015). However, the need for these markets highly depends on the implementation rate of extra VRE capacity. Currently, these markets are not interesting to take into account for the investigation of new business cases. The market mechanisms are also not included for the period until 2023, since studies show, the Dutch system is capable of dealing with the VRE capacity increase the coming decade (Slingerland et al., 2015). Nevertheless, these markets are mentioned since some are implemented in neighboring countries which are connected to the Dutch system. They are also included to illustrate the options to ensure a stable grid. The market approach the Dutch system has, is not the only option to ensure the grid is operating reliable. From the analysis above only the three real markets are valuated today and are therefore included in the rest of the report. However, this is not a given fact for the next decade. Depending on the demand for flexibility in the grid, other market models like capacity or frequency market mechanisms can be developed and implemented. However, the expectations are that the market system will not change in the coming years (Slingerland et al., 2015). This, since there simply is no need for extra flexibility in the near future.

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FLEXIBILIZATION OPTIONS

There are multiple methods to increase the flexibility of the electricity grid. These flexibility methods can be clustered into four main groups. More flexibility can be supplied by steering the demand or supply, by storing the energy and by more interaction with other networks and grids. These groups all are depicted in figure 3-1. How these flexibility methods work is described further in this chapter.

Energy can be stored in periods when there is a high production of electricity or when the prices are low. This stored electricity can be released when the production is low or expensive. In this study, energy storage is defined as Power2power storage. The energy is kept inside the electricity system. The second option to supply flexibility to the grid is by Dispatchable Power Generation (DPG). DPG means that power generation can be decreased when there is over-production and the generation can be ramped up when more power generation is needed. As there is an option for flexibilization on the supply side, there is also a possibility for flexibility on the demand side. The need for electricity can be steered in order to stabilize the system. This can be done on multiple levels from using washing machines when there is low demand for electricity to shutting down production units in the industry in times of over-demand. With demand side management the demand for electricity can be balanced with the production of electricity. The last option to balance the Dutch electricity grid is by importing electricity from other countries or areas when there is a need for electricity and to export electricity when there is an over-supply. The volumes in which this can be traded depends on the interconnection capacity between the different countries (Papaefthymiou et al., 2014). This chapter will discuss the different technology clusters and will describe the possible technologies, capable of supplying the need for flexibility in the grid. All technology clusters are analyzed using SWOT analyses. The SWOT analysis stands for Strengths, Weaknesses, Opportunities and Threats. The Strengths and Weaknesses investigate the internal factors of the technology, while the Opportunities and Threats discuss the external parameters, influencing the business case of flex options. Since some parameters have influences on every flex option, first an overall SWOT analysis is executed. This overall SWOT analysis discusses the points that play a role in the implementation of all flex options.

3.1 Overall SWOT analysis

In this paragraph, the overall SWOT analysis for flex options in the Dutch electricity system will be discussed. In figure 3-2 a summary of the analysis is provided. Each point in this figure will be discussed more in detail later on in this paragraph. The parameters discussed in this paragraph do have influences on all or at least most of the flexibilization options.

Figure 3-1: Categorization of system flexibility options (Papaefthymiou et al., 2014)

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3.1.1 Strengths

All flexibilization options can act on the different electricity markets (interconnection capacity makes use of electricity markets of neighboring countries). However, the business cases of flexibility providing technologies are not only determined by the rewards earned on the different electricity markets. The business cases can be improved when flex options can help to decrease the electricity grid connection capacity. When the connection capacity can be decreased, consumers have to pay less network costs to the DSOs (Stedin, 2014). Therefore, it could be beneficial for households to use flex options in a smart way to reduce their connection capacity. For households and other small consumers, the network tariff is fixed to the connection capacity. The prices are depicted in Appendix C. An example of a business case in this decrease in connection capacity, is the soft-6A concept of the company Flexicontrol. Here the households have a 6 Ampere connection in combination with an energy management system. These households directly save 200 Euros a year on their energy bill in comparison with a standard household with a 3 x 25 Ampere connection capacity (Balvers, 2014). When this capacity is decreased the consumer has to take possible electrification of its energy use into account. The connection capacity should facilitate the use of electric vehicles and heat pumps when these options will be implemented. The prices large consumers pay, vary according to the consumption of electricity and are also depicted in Appendix C. However, also for large consumers a smart electricity demand can significantly save network costs, improving the business case for flex options significantly.

Strengths- Decrease grid connection capacity

Weaknesses- Limited market access small flex options

- Households have no price incentive to invest in flex options

Opportunities- Increasing electricity price volatility

- Prevent local congestion

- Environmental benefits

Threats- Network costs

- overcapacity flex options

- Flex options cannot be used by DSOs

- Political uncertainty about policy

- Position conventional power plants

Figure 3-2: Summary overall SWOT analysis

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3.1.2 Weaknesses

The first general weakness focuses on small scale flexibility options. The weakness for small flex options can be found on the imbalance market (regulating and reserve power) and for emergency power. Small scale flex options are very capable to provide services to these markets. However, there is a minimal capacity threshold to enter these markets. As discussed in chapter 2 about the electricity markets, the regulating power market has a minimum of 4 MW that has to be offered as one bid, while the emergency power market has a threshold of 20 MW (Ophuis, 2015; TenneT, 2013b). Individual flex assets most of the time do not have these capacities, excluding them from these markets. A solution for this could be the aggregation of multiple small flex assets to overcome this threshold. Another problem with these markets is the 100% availability that is obliged by TenneT. To balance the system, TenneT has to have balancing power available 24/7. Not all flexibility options are capable of meeting this requirement. Since electricity storage technologies have to reload after electricity delivery and not all DSM options are online 24/7, they are not capable of ensuring 100% availability. Due to this problem, not all options are capable of meeting the requirements for regulating and emergency power services for the Dutch grid (Ophuis, 2015). Additional to the problem of minimal capacities on the different markets, it is also not possible for households and other small consumers to act on the different markets. The price they pay per MWh is determined by the supplier. This price is fixed and therefore, there is no price incentives for flex options on a household level. Currently, the differences between the night and day tariff for small users is very small, decreasing the motivation to supply flexibility to the grid (Gaslicht.com, 2013). Additional to this, electricity is a low involvement product according to Krebbekx et al., (2015) and therefore not interesting for households to deal with actively. Therefore, there is no incentive to tender flexibility to the grid on this scale (Krebbekx et al., 2015).

3.1.3 Opportunities

The position of flexibility providing technologies is not fixed in the coming years. There are some trends and developments that can improve the business cases of the different flex options. The first one is the increasing price volatility on the different electricity markets. Due to the implementation of more variable renewable energy capacity and the difficult position of gas fired power plants, the expectations are that the electricity prices will be more volatile in the coming years (CE Delft et al., 2015; Hout et al., 2014; Slingerland et al., 2015). With this increasing volatility, it becomes more interesting to provide electricity to the system in periods there are high price peaks and to take electricity from the grid when the prices are very low. These price peaks will occur more often the coming years, increasing the business case opportunities for flexibility options. Beside the market opportunities, flex assets can provide other electricity system services. These services are currently not valuated and therefore, do not improve the business cases for these flexibility providing technologies. However, when these services are valuated, these will improve the business cases significantly (TU Delft et al., 2015). The first example of a system service that can be provided by flex options is the prevention of local congestion. Flex options can balance the local grids by adjusting their production/consumption of electricity to the status of the grid. In this way, congestion problems can be prevented. This system service will become more important the coming years, since the expectations are that the electricity demand per household will increase significantly, due to the increasing use of electric vehicles and heat pumps (van Melle et al., 2014). Depending on the increase in peak demand, investments are required to secure the system on district level. Flex options can help to make the system less stressed and congestion problems can be prevented. By doing so, investments in extra transport capacity can be prevented, saving costs for DSOs and therefore, for the complete system. When a part of these prevented investments can be given back to the flexibility provider this can not only save costs for DSOs, but also improve the business cases for flex options.

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The last general opportunity of flex options is the prevention of CO2 emissions. With renewable energy sources, the goal is to reduce the CO2 and other greenhouse gas emissions. However, without flexibilization options, there still will be need for back-up capacity. This back-up capacity is most of the time delivered by fossil fuel driven technologies. With the introduction of relatively clean flexibilization options, the need for back-up capacity can be reduced, which can have beneficial consequences for the amount of CO2 emitted and therefore for the environment, when it substitutes the polluting power plants. However, flexibilization options cannot take away all demand for back-up capacity. In periods of no wind and lots of clouds, there should be enough capacity to deal with the demand. If the amount of flexibility capacity doubles, this would not lead to a reduction of back-up capacity of 50% (McKinsey & Company et al., 2015). Beside the CO2 reduction, there is another benefit of less demand for back-up capacity. When there are more options to deal with the demand for flexibility, there will be less demand for thermal power plants (McKinsey & Company et al., 2015). The lower demand for conventional thermal power plants can prevent investments being done on this electricity production capacity enlargement. These prevented investments can have positive effects for society. Currently, these system services are not valuated and are therefore not taken into account when investigating the business cases of different flex options.

3.1.4 Threats

The biggest market threat for flex options currently is the overcapacity of flexibility providing technologies in the Dutch electricity system. This has to do with the large gas fired production capacity that is mothballed (Slingerland et al., 2015). Due to this overcapacity, the price peaks are relatively low on the different markets. This small price volatility decreases the chances for flex options to provide flexibility, since the gas fired power plants can provide flexibility to the system relatively cheap when taken out of the mothballed situation (Hout et al., 2014). When more flexibilization options enter the markets and these technologies have a better position on the merit order of the market, it will be very hard for the other flexibility providing technologies to gain a good market share. As discussed in the opportunities section above, flex options can help to prevent congestion. However, to support the business cases of the flexibility technologies, currently, a prevention of investments in the electricity grid by a DSO or TSO will not help. This because, the Dutch DSOs and TSO are obliged by the Dutch electricity law to ensure the grid capacity is big enough to facilitate every desired demand (Ministerie van Economische Zaken, 1998). They are not allowed to fund a flexibilization option to prevent grid enlargement investments (Ministerie van Economische Zaken, 1998). This, since DSOs and TenneT are not allowed to interfere with the electricity markets (Ministerie van Economische Zaken, 1998; TU Delft et al., 2015). Implementing flexibilization options to prevent congestion does require market interactions and therefore is not an option for DSOs and TenneT. As mentioned by van Melle et al., (2014) energy storage can help DSOs preventing congestion problems. For TenneT the situation is different. Under the current policies, TenneT does not face problems in its system. Until 2023, the Dutch electricity system is capable of dealing with the transition towards more renewables (Sijm et al., 2015; Slingerland et al., 2015). There is enough back-up transport capacity to deal with the increasing amount of intermitted energy production from VREs. This means that flexibilization technologies will not get additional rewards from TenneT for the prevention of investments, since these investments will not be necessary the coming years. Beside the legal issues, another reason why DSOs and TSOs do not yet invest in flexibilization options is that it currently is, most of the time, cheaper to enlarge the grid, instead of investing in flexibilization options. This can be illustrated by an example from the southern part of the Dutch province of Zeeland. The local DSO Delta has compared the cost of a DSM system, which can control the peak load, with an extra cable to strengthen the local grid. This specific cable is stated to be one of the most complex and expensive ones, as it has to pass the Westerschelde (Delta Netwerkbedrijf B.V., 2015). Delta has

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investigated the installation of an electric boiler at DOW Chemical Company B.V. as a DSM option to decrease the peak loads in congestion periods. Table 3-1 gives an overview of the comparison made by the DSO Delta. Table 3-1: DSM congestion management vs grid expansion in Dutch province of Zeeland (Delta Netwerkbedrijf B.V., 2015)

€/year

Yearly Congestion management cost 323,731 Yearly total cost of ownership electric boiler 300,000 Yearly additional network costs DOW 905,868 +

Total yearly cost DSM 1,529,599 Yearly costs extra cable 1,050,000

From this table it can be concluded that in this case, the yearly cost for an extra cable is 479.599 euro cheaper than the DSM option. This, while the DSO has to pay the congestion management cost to the flex provider, has to provide the boiler to DOW and has to pay for the additional network costs. On first hand, the additional network costs are for DOW, since DOW uses the electricity. However, DOW will only accept the installation of a flex option to prevent grid investments, if these extra network costs will be paid by the DSO, since the DSO benefits from the installation of the flex option. To consider a flexibilization option instead of an extra cable, all these extra costs combined have to be lower than the costs of an extra cable. This example gives an indication of the costs a DSO has to take into account when considering a flex option to prevent congestion problems. Examples of other possible options are vehicle2grid or local energy storage projects. However, the exact costs are very case specific and therefore, other flex options could be beneficial in certain situations. Due to this case dependency, the example given above does not represent all congestion problems. Another threat for flex options on a household level as on a larger scale, is the Dutch network tariff structure. The additional network costs can have a big influence on the business case of flexibilization options. Flex options can help to decrease the consumption and the consumption peaks and therefore, can decrease the network costs. However, when the flex option increases the demand, the network tariffs will increase as well. Investors indicate that the additional network tariffs that have to be paid for flex options are a major obstacle to overcome the coming years to increase the attractiveness of different flexibilization options (Krebbekx et al., 2015; TU Delft et al., 2015). These additional network tariffs can specifically cause problems for prosumers. When a net producer decides to tender flexibility to the grid and thereby becomes a net consumer, this can result in a change in the contract with the DSO and therefore, can create more costs. Producers of electricity no not have to pay network tariffs (Autoriteit Consument & Markt, 2013). This policy is called the LUP (Landelijk Uniform Producententarief; national uniform producer’s tariff). Here the network tariff for net producers is reduced to zero. Only the annual connection costs have to be paid (Autoriteit Consument & Markt, 2013). This policy was created in the 1990s and was beneficial for the electricity system of those days. However, the system has changed significantly the last decades towards a more decentralized electricity system. Nevertheless, the network tariff structure has not changed with it (Van der Valk et al., 2015). To give each flexibilization option the same opportunities to make a business case, a level playing field for flexibility options has to be created (McKinsey & Company et al., 2015). Van der Valk et al., (2015) has suggested some possible solutions. A more level playing field can be reached by splitting the contracted capacity into a guaranteed capacity and an optional capacity that can be used in periods the grid is capable of transporting the electricity (Van der Valk et al., 2015). If the grid is capable of transporting the extra capacity is decided by the local DSO. In this way, flex options can act on the status of the grid and can use the grid more efficient.

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Another obstacle for especially small scale flexibility is the decreasing tax per unit of electricity when more electricity is consumed (ECN et al., 2014). This is the case, since the Netherlands has a decreasing energy tax tariff for consumers. Meaning that the tax a consumer pays over one MWh of electricity decreases when more MWhs are consumed (ECN et al., 2014). This makes small scale flexibility significantly more expensive in comparison with large scale flex options. For a good flexibility market to work it is very important to have a level playing field (EASE, 2015). Van der Valk et al., (2015) stated that a level playing field should include a change in the network tariff structure and tax system, so the consuming technologies can also tender flexibility to the grid. This in combination with, a flexibility market more focusing on the Intraday market. In this way, smaller flex options can also tender their flexibility on a market. This is in contrast with the Imbalance market, where TenneT has strict regulations about volumes and ramp rates. These adaptations of the system should be in line in whole Europe and prevent technology biases to come to the most economic optimal system (Van der Valk et al., 2015). For a good flexibility market to work it is very important to have a level playing field (EASE, 2015). As for energy storage, DSOs are not allowed to use DSM options to congestion prevention. The DSO is obliged by law to increase the transport capacity of the infrastructure for the prevention of congestion. All the threats and opportunities in this paragraph are part of a discussion; how to transform the current energy system to make it suitable for the transition towards more renewable and decentralized energy production? Due to this discussion, multiple policies are under debate. From a business perspective one of the biggest challenges is the big uncertainty about the future electricity system. Due to the changing policies (salderingsregeling), subsidies, technological developments, network tariff structure and consumer behavior, investors are hesitating to invest. Also the electricity markets are highly uncertain. Will there be more low electricity prices or will other flex technologies take these periods, increasing the prices (TU Delft et al., 2015)? To have the best business case it is important to step in at the right moment. However, when is this moment? This is exactly the unknown interested investors want to know. A part of this uncertainty can be taken away when there is a national long term vision on the energy transition, facilitating not only renewable energy technologies, but also searching for the most optimal (economic, energetic, environmental, social) energy system as a whole. The last threat to overcome is the current interest and position of conventional producers of electricity in the system (TU Delft et al., 2015). These producers have invested large amounts of money into the system over the years and see their market share decreasing due to the introduction of more decentralized renewable energy sources. Currently, these conventional producers deliver the required flexibility to the system and want to defend their position as an indispensable player in the system (TU Delft et al., 2015). However, currently there is an overcapacity of flexible steerable power plants in the Dutch electricity system. It therefore is highly uncertain how the position of the conventional power plants will develop the coming years in comparison to other power producing technologies and flexibility providing technologies. However, what is clear is that the fossil fuel sector will try to keep its market share.

3.2 Energy storage

The first option to provide more flexibility to the Dutch electricity system is energy storage. Only Power2power storage will be discussed in this section. Power2heat and Power2gas are seen as a form of demand side management, while the electricity is used to produce another products or forms of energy and this energy will leave the electricity system. In this study, energy storage is seen as a way to take excess volumes of electricity when there is overproduction, store this overproduction over time and use this electricity in times of over-demand or supply shortages. In economic terms this means that energy is stored in periods of low electricity prices and will be used or feed in to the grid in times of high prices.

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There is a wide range of technologies that can store electricity. These technologies all vary in size, capacity and price. Depending on the application, the right storage system can be chosen. The right technology does not only depends on the capacity but also on the energy density per kg and cm3 and the speed of one loading cycle (Pierie and Van Someren, 2015; Slingerland et al., 2015). Currently, pumped hydro power provides almost 95% of all installed energy storage capacity in the world (Grond et al., 2013). The biggest problem for expanding the capacity of pumped hydro power is its geographical limit. Especially in the Netherlands there are no mountains suitable for the build of large hydropower dams (Slingerland et al., 2015). Beside pumped hydro storage there are multiple other energy storing technologies. The most known form of energy storage is chemical energy storage in batteries. Since the introduction of Tesla’s Powerwall there is a hype on battery technologies storing electricity on a household and district level. However, not only Tesla is developing these electricity storing technologies, also BMW and Mercedes-Benz are developing storage technologies (Young, 2015). The technology primarily focused on by these producers is Lithium-ion (Young, 2015). Examples of other battery technologies are Lead acid or Redox flow batteries. There are more energy storing technologies, beside chemical battery technologies and pumped hydro storage. Examples of these technologies are compressed air storage, flywheels and capacitors. These technologies all use electricity and convert the electrical energy into another form of energy to store the energy. When there is a demand for the stored energy, the conversion is done the other way around to produce electricity. Depending on the characteristics of the storage technologies different applications can be determined. Pierie and Van Someren (2015) created an overview of the possible applications of different electricity and energy storage technologies shown in figure 3-3.

Beside the use of electricity storage devices on a large scale to balance the system, there is a growing interest in energy storage devices on a household level. This small scale energy storage in a household is called energy storage behind the meter. Behind the meter refers to energy management at the consumers/prosumers of electricity. Investments in flexibilization options can have big rewards for prosumers of electricity when they store their own produced electricity and use it in periods the prices

Figure 3-3: Suitability energy storage over different time periods (Pierie and Van Someren, 2015)

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are relatively high. By doing so, the prosumer uses its own power or the power from the grid when the electricity prices are low and also stores electricity when prices are low. When prices are high the prosumer can use the stored electricity to satisfy its own demand. In this way, he saves money compared to the electricity price of the grid at that moment in time. The asset owner can act on the different markets discussed above. So in practice, there will be no new market only more actors on the market. However, this can have consequences for the prices and volumes of the markets. As discussed above, there are numerous options to store electricity. However these options are not widely introduced in the Netherlands. What is the reason for this and what is the position of energy storage in the Dutch electricity system? This will be discussed in the analysis below. First the internal factors will be analyzed and secondly the external parameters are discussed in this SWOT analysis. An overview of the SWOT analysis is depicted in figure 3-4.

Strengths- Short ramp rate

- Suitable for frequency regulation, load following and off-grid applications

- Scalable

- Aggregatable

Weaknesses- Too big CAPEX costs

- Too long ROIs

- Environmental pollution chemical storage

- Geography not suitable for PHS

Opportunities- Combination with solar PV

Threats- Salderingsregeling

- No seperate asset class

Figure 3-4: Summary SWOT analysis Energy Storage

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3.2.1 Strengths

Electricity storage has multiple strong points, making it an interesting option to provide flexibility to the grid. The biggest strength of energy storage and especially chemical energy storage, is the short ramp rate of the technologies (Pierie and Van Someren, 2015). The technology can provide flexibility to the grid relatively fast, making it interesting to use as real time balancing option (Pierie and Van Someren, 2015). This is also underlined by the International Energy Agency, (2014c) which state that the asset of storage lies in the modularity, controllability and responsiveness of the technology, making the option primarily attractive for frequency regulation, load following and off-grid applications. Due to their good modularity and controllability, energy storage devices are relatively easy to scale up. Multiple chemical batteries can be clustered to create one big battery or multiple batteries behind the meter can be aggregated. When the aggregated capacity is big enough, the aggregator can tender the flexibility of the electricity storage units together to the different electricity markets, or can tender it to TenneT to balance the system. This aggregation of electricity storage units is called swarming (Nikolaus, 2015). Currently, swarming is not very integrated in the Dutch system, since there is no large capacity of electricity storage units present in the Netherlands. However, in the future, this swarming could provide an important addition to the balancing of the grid. The prosumers offering their systems to the aggregator could get an economic reward for this flexibility.

3.2.2 Weaknesses

One of the biggest weaknesses of energy storage is that currently, the CAPEX costs are too high, especially for chemical energy storage (Pierie and Van Someren, 2015; Zakeri and Syri, 2015). These high investment costs in combination with a market reward that is too small due to the low price volatility of today, result in a situation where the return on investment period is too long to make energy storage an interesting flexibility option (TU Delft et al., 2015). However, this is the current situation. The expectations are that the investment costs will drop due to R&D and a production capacity increase (International Energy Agency, 2014c). Additional to the economic, market and legal issues (discussed in the overall SWOT analysis) preventing the implementation of electricity storage technologies, there are environmental issues influencing the business cases for electricity storage. The production of multiple electricity storage devices currently is not very environmentally friendly. For example, the production of chemical batteries requires large amounts of heavy metals, which can have a bad impact on the environment. The environmental costs are often not incorporated into the price of a technology (Zakeri and Syri, 2015). This is also the case for the current electricity system, while the price for CO2 does not represent all environmental problems caused by the use of fossil fuels. The environmental problems caused by the extraction and refining of the fossil fuels are not included in the CO2 price (European Environment Agency, 2007). To create a level playing field for all technologies, all the environmental costs (CO2, water and energy footprint, use of toxic elements, polluting side-products etc.) should be incorporated in the price of the technologies. When all environmental issues are taken into account, sustainable energy storage technologies can become more interesting, while cheap electricity is produced by wind and solar PV systems and expensive electricity is produced by gas fired power plants (CE Delft et al., 2015). Electricity storage can help the business case of VRE technologies and can get a bigger reward from the market, since the price peaks will increase due to the higher fuel prices. Pollution is not the only environmental aspect that has to be taken into account. Large energy storage technologies like pumped hydro storage (PHS) and Compressed Air Energy Storage (CAES) do need geographical elements. PHS is not possible in the Netherlands, since there are no mountains suitable for the build of large hydropower dams (Slingerland et al., 2015). The CAES technology requires empty gas fields to store the compressed air, making them not feasible for all locations in the Netherlands (Pierie and Van Someren, 2015).

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3.2.3 Opportunities

Next to the internal parameters influencing the business case of energy storage, there are external factors. The first opportunity for energy storage is the combination with solar PV panels, which makes energy storage behind the meter interesting. The own produced electricity will be kept in the household to be used later when the demand is bigger than the production. In this way, the household prevents the uptake from the grid, saving on energy taxes. Currently, it is not interesting to store the own produced electricity on side. This has to do with the ‘salderingsregeling.’ Due to this policy, there is no price incentive to store the electricity. This regulation will be explained more in detail in the Threats paragraph 3.2.4.

3.2.4 Threats

The external world does not only provide opportunities for energy storing technologies. There are also serval threats for energy storage. The first and biggest one, preventing energy storage behind the meter from being implemented, is the ‘Salderingsregeling.’ This policy includes that the overproduction of electricity by PV panels during sunny hours can be feed back into the grid for the same price as it is taken from the grid. The feed in tariff therefore, also includes taxes and fees which raises the reward for the producer. With this policy the Dutch government aims to increase the installation of PV panels in households. However, this ‘Salderingsregeling’ together with the fact that Dutch households have a fixed electricity price, makes it not only unattractive to tender flexibility on a small scale, but also increases the imbalance in the grid (Krebbekx et al., 2015). This, since electricity is feed back into the grid in periods the production is already high, while there are more households that have solar PV systems that feed back into the grid. The second threat for energy storage is the problem of categorization. Energy storage is a black spot in the Dutch energy policy, since it is not a consumer nor a producer of electricity. Due to this legal gap energy storing units do not profit from the benefits electricity producers have and have to pay network tariffs and taxes like consumers have to pay. This gap in the policy has to be solved to make energy storage more attractive in the coming years (Energy Storage Europe, 2015). This can be done by including energy storage as a separate asset class in the system, beside consumers and producers. With a beneficial tax regime and regulation, energy storage can become more attractive.

3.2.5 Position of Energy storage

Currently, the position of energy storage is difficult in the Dutch electricity system. Large scale electricity storage is not implemented in the Netherlands, since the return on investment periods of the technologies are too long making the investment not interesting. This long ROI period has to do with the high CAPEX costs involved with installing energy storing devices, and the small price volatility on the different electricity markets. This especially is the case for chemical energy storage devices. For energy storage behind the meter, the ‘Salderingsregeling’ makes the technology not interesting to invest in. This policy has as goal to increase the installation of PV systems in households. However, due to this policy there is no price incentive to store the energy on a local scale. Nevertheless, energy storing devices can provide important system services to the grid like the prevention of local congestion problems and a decreasing demand for conventional power plants. However, these services are currently not valuated, making it not possible to use them to improve the business case of energy storage in the Netherlands. The biggest threat for energy storage currently has to do with the overcapacity in the market and with regulation issues like network tariffs and asset classes. Despite all these challenges for energy storage the asset of energy storing devices lies in its fast ramp rate, modularity and controllability.

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3.3 Demand Side Management

The second option to provide more flexibility to the electricity grid is by using demand side management (DSM). This option steers the demand for electricity. The demand can be adjusted in two ways. The demand can be decreased by for example, stopping the energy consumption of household appliances like, fridges and washing machines or even by shutting down complete industrial processes. On the other hand can the demand be increased by increasing the production capacity in the industry or by steering household appliances like electric boilers, increasing the demand (Krebbekx et al., 2015). DSM is mainly price steered, meaning a DSM option will only be used to provide flexibility in periods the price is beneficial for the technology to operate (Krebbekx et al., 2015). There are different options to tender flexibility to the grid using Demand side management. These options will be discussed below before analyzing them with a SWOT analysis. Demand response: decreases the consumption of electricity when there is an imbalance in the system or the electricity prices are very high. Demand response can be done on every level from switching off fridges in households to shutting down production processes in the industry. If it is interesting to invest in demand response depends on the electricity price peaks on the different electricity markets. Demand response also represents increasing consumption in a smart way with installed technologies and processes, in times the prices are relatively low. Demand increasing DSM technologies: in contrast to demand response, there are other DSM technologies that can increase the electricity demand in periods the prices are low or the system has an over-supply of electricity. There are numerous options to increase the demand for electricity. The most important options are: Power2heat, Power2X, and Power2products. Power2heat is the DSM option that uses electricity for the production of heat. In this way, gas is substituted as an energy source. This option is beside a flexibilization option also used as an electrification option. Power2X represents the production of other products from electricity. This can be the production of another energy carrier like hydrogen or methane, but also chemicals. If this is interesting to produce using electricity, depends on the price of the electricity and the value of the product. Power2products is a term used to describe the flexible use of production capacity to balance the system.

Figure 3-5: Screening curves for each DSM technology (Dutch Ministry of Economic Affairs and Netherlands Enterprise Agency, 2015)

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There are numerous other technologies to create flexibility by using demand side management. However, this research focuses on the most economic viable ones and the method to compare them. For the selection of possible interesting flex options in DSM, figure 3-5 is used to see what the most economically attractive technologies are. This figure shows the costs of the technology per MWh over the full load hours per year. What can be concluded from this figure is that the technologies strongly vary in price and decrease in price per MWh when more full load hours are used. The most interesting technologies according to figure 3-5 for all full load hours are electric boilers, more flexible CHP units and heat pumps only in high full load hours. The business cases of these technologies will be investigated using the flexibility investment model that will be developed in chapter 4. After demand side management is explained above, it is time to analyze the position of this option to provide more flexibility to the system. The position of DSM options is analyzed using a SWOT analysis. The outcomes of the analysis are represented in figure 3-6.

Strengths- Many options --> Demand response, Power2heat, Power2X

- All scales

Weaknesses- Very case specific

- High COP value is it not a flex option anymore

- Energy policy in company does not allow DSM options -->network tariffs

- Rewards from Power2X often too low

Opportunities- Power2X --> make use of more cheap electricity hours

- Demand response --> more expensive price peaks

- Improved income VRE capacity

Threats- High organization and administration costs

Figure 3-6: Summary SWOT analysis DSM options

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3.3.1 Strengths

The biggest strengths of Demand side management is the big diversity in technologies. Demand response can be used on multiple scales to decrease the demand for electricity. This can be done on a household level by stopping electric appliances to stopping complete production processes in the industry. There is a large capacity that can be used for demand response in the Netherlands, providing a large part of the ramp down demand (Movares, 2014). Power2heat and Power2X can be used to increase the electricity demand. This can also be done on multiple scales from using an electric boiler in households to producing hydrogen from electricity. This divers mix of technologies is a strength of DSM, since for every situation there is a technology to provide more flexibility (Krebbekx et al., 2015).

3.3.2 Weaknesses

Due to the large number of DSM technologies and the different characteristics of the systems the technologies have to fit in, it is very case specific if the technology is suitable or not. Due to this case specific implementation it is very hard to come to implementation standards. Which technology is the most interesting for which situation depends on the costs of the technology, the capacity, the network costs, the energy policy of the investor and the physical size of the installation (Krebbekx et al., 2015). Also the internal energy policy of an end-user has influences on its options to tender flexibility. Electricity consumers do not like to change things in their core business. This is especially problematic for demand response, since this flex option changes the demand pattern significantly (Krebbekx et al., 2015). To tender flexibility to the system, it has to give a net reward which is higher than the loss of not taking the electricity from the grid. If energy does not represent a big share of the costs of an end-user, the focus is not to create flexibility or to save energy (Krebbekx et al., 2015). So in order to enlarge the flexibility in this part of the system other factors play an important role. These factors will be discussed in discussion chapter. Currently, the rewards are too low for most DSM options to make them interesting to invest in. For example, the production of Hydrogen from electricity is difficult due to the relatively low Hydrogen price. The production of Hydrogen from gas is cheaper than from Electricity making it not interesting to switch to electricity as an energy carrier (Mieog et al., 2014). However not only the value of the produced products is currently too low. Another reason why industries are not willing to invest in DSM is the high costs involved with administration and organization of the process (Klimstra and Hotakainen, 2011).

3.3.3 Opportunities

With the introduction of more wind and solar PV capacity, more low electricity price hours will occur on the different markets. These low electricity hours can be used for the production of other energy carriers or products using Power2X. The production costs of the products can decrease when the electricity is cheaper. This could improve the position of the produced product on the international market. With the increase in low electricity price hours an increase in high price hours can be expected due to the bigger demand for flexibility in the system. This increase in price peaks will improve the business case for demand response. Demand side management technologies can provide the same system services as energy storage like, the prevention of local congestion preventing investments in grid capacity enlargements. Other services are environmental benefits and less back-up requirements. Beside these services which are the same as for energy storage there is another service DSM options can provide. DSM options which increase the demand for electricity in periods the prices are low could help the business cases for variable renewable energy technologies. This is especially the case when the share of variable renewable energy increases significantly. This, since VREs most of the time have a marginal price of zero. In times the wind speed is high and the sun irradiation is strong, the high capacity of VRE could

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produce the entire demand. Since VREs are the only electricity producing units at that moment and they have a marginal costs of zero, the prices for the sustainably produced electricity are very low. This decreases the business case for these VRE technologies (CE Delft et al., 2015). Flexibilization options can act on these low prices and increase their demand. More flexibilization options in the system therefore means a higher electricity demand. This extra demand has an increasing effect on the electricity price (CE Delft et al., 2015; Van der Valk et al., 2015). This higher electricity price helps the business case for renewable energy technologies, helping the transition towards a more renewable energy production (Lund et al., 2015). Another benefit of flexibility options in case of a large VRE capacity is that it can prevent curtailment of VRE installations in times of high wind speeds and solar irradiance. In this way, flexibility options help to use the renewable energy capacity in the most optimal way (Slingerland et al., 2015).

3.3.4 Threats

In case of high demand, resulting in high prices, there are more opportunities for demand response (Movares, 2014). However, the time before implementation is very important for the potential of demand response. The potential doubles when the announcement time before implementation provided by TenneT or the market itself, increases from 5 minutes to 1 or 2 hour in front of the expected delivery time (Movares, 2014). This also increases the number of players in the market and therefore stresses the position of demand response in the merit order. For demand increasing DSM options, the implementation of more DSM options decreases the business case for all DSM options, since the electricity prices increase for each flex option.

3.3.5 Position of DSM options

Demand side management on a household level is currently not very interesting since there is no financial incentive for investors to do so. On an industry level, DSM can be an interesting option but, it highly depends on the situation it has to fit in, the characteristics of the technology and the change in network tariffs. This change in network tariffs is an important factor, since implementing DSM units can have a large influence on the electricity demand and supply pattern of an asset owner. This change in demand pattern could have influences on the height of the network cost an asset owner has to pay. Due to these network tariffs large energy consuming DSM options have troubles to find business cases. This is different for demand response. Since demand response decreases the demand for electricity it can also help to decrease the network costs, improving the business case. As for energy storage the extra system services that can be provided by DSM options are not valuated today and therefore cannot help to improve the business case.

3.4 Dispatchable power generation

Dispatchable power generation (DPG) is referring to flexibilization options which can adjust their power production to the change in electricity demand. The time span this flexibility can be provided depends on the ramp rate of the specific technology. Currently, most flexibility is offered by gas and coal fired power plants and decentralized CHP units (Hout et al., 2014). These units can ramp up and down relatively quickly and are therefore suitable to fulfil the task of balancing the electricity system. However, CHP and gas fired power plants are not the only technologies to provide flexibility to the electricity grid. Another DPG option to provide flexibility, is the curtailment of renewable energy technologies. Curtailment means that the power production is stopped in periods the prices are too low or when there is an overproduction in the system. In practice, this means that wind turbines are stopped, even if the wind speed is high enough to produce electricity. In the Netherlands this will not be needed the coming years since the VRE capacity is not big enough to cause overproductions. However this is already the case in Germany some hour per year (Slingerland et al., 2015).

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3.4.1 Strengths

The first strength discussed in figure 3-7 is that existing power plants can adjust their power generation and respond on the change in demand. In case the power plant is already in business, it is relatively cheap and easy to control the output. This is already the case since currently, almost all flexibility is provided by conventional power plants and CHP units (Hout et al., 2014). Another strength of DPG is the good market access. Since these production units are capable of producing 100% of the time, these DPG units can be used to balance the system more in real time on the imbalance markets and emergency markets as discussed in the overall SWOT analysis. These markets have relatively high rewards improving the business cases of flex providing technologies.

3.4.2 Weaknesses

In case the power plant is already in business it is relatively cheap and easy to control the output. However, in case a power plant has to run on part load to be available for ramp up demand, this highly influences the efficiency, fuel use and CO2 emission of the plant (Slingerland et al., 2015). The fuel efficiency can decrease with 10% up to 40% when power plants are running on part loads (Hout et al., 2014). This has significant effects on the business cases of DPG technologies.

Strengths- CHP units already present

- 100% availability if needed

Weaknesses- Most CHP units heat demand steered

- Low efficiency when running part load

Opportunities- CHP units can be made more flexible

- Prius principle

Threats- deteriorating spark spread -->

- Increasing gas price,

- Decreasing electricity price

Figure 3-7: Summary SWOT analysis Dispatchable power generation

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3.4.3 Opportunities

Another technology to provide flexibility is by retrofitting must-run CHP units to make them more flexible. Currently, CHP units are most of the time heat demand steered. When the heat can be stored or be produced from other sources, the CHP unit does not have to run in cheap electricity hours, improving the business case for CHP units (Peeters et al., 2014). These flexible CHP units however, remain available for the provision of flexibility to the system in times the system needs the flexibility or the prices are high enough. Another option to improve the business case of dispatchable power generating units is the Prius principle. In this concept, energy storing units are used to support combustion engines. This is done, since the efficiency of combustion engines is relatively low when used on low capacity (Hout et al., 2014). The efficiency will increase when the engine runs closer to full capacity. In the Prius principle energy storing units are added to the combustion engine system to provide the energy needed for running on low capacity. The combustion engine only starts when the demand for its capacity is high enough to have a high combustion efficiency. In this way, large costs on fuel can be saved which is also beneficial for the environment (U.S. Environmental Protection Agency and CHP partnership, 2015). Beside the options to improve the business case for dispatchable power generating units with additional hardware, the business case can be improved by the additional system services DPG units can provide to the electricity system. When these system services will be valuated and the difference between the electricity and gas price will increase, CHP units will have a significant better business case.

3.4.4 Threats

Due to the relative high fuel price (gas price) in comparison with the electricity and coal price, gas fired dispatchable power generation units have troubles to survive and are standing last on the merit order (Hout et al., 2014; Peeters et al., 2014; Sijm et al., 2013). This means they only produce when other technologies are not capable of satisfying the demand. Therefore, the number of hours the technology will produce electricity is limited. However, dispatchable power generation units can play a vital role in the electricity system by balancing the system when there is an unpredicted imbalance in the system. Dispatchable power generation units are very well suitable for this option since they can provide relatively large capacities relatively quickly (Slingerland et al., 2015). How the future will look like for DPG (more specific CHP) units highly depends on the merit order position of the units in comparison with other flexibilization technologies. Not only CHP units face challenges to provide flexibility to the Dutch electricity grid. Another option to provide more flexibility to the grid is by the curtailment of VRE technologies in periods there is an overproduction of electricity. The expectation are that this will not be needed in the Dutch system, since the VRE capacity is relatively small. In Germany curtailment will be needed in periods of low demand and high wind and solar production (Slingerland et al., 2015). The challenge with curtailment is that is can face social opposition since it prevents the use of renewable energy sources over conventional power plants.

3.4.5 Position of Dispatchable power generation

Dispatchable power generation units already exist on a large scale in the Dutch electricity system, this is mainly in the form of decentralized CHP units. Currently, the position of these CHP units is not beneficial for the technology. This difficult position has to do with the spark spread. This spark spread is the term describing the relative difference in price between gas and electricity. This spark spread currently is not in favor of CHP units, since gas is relatively expensive and electricity relatively cheap. Therefore, it is not interesting to produce electricity with gas. Dispatchable power generation is also

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be done by large thermal power plants. Gas fired power plants in combination with CHP units provide most of this flexibility in the Netherlands. However, these plants suffer from the low price volatility in the current markets and their difficult position on the merit order. The position of these dispatchable power generating units will improve if the spark spread will improve. However, there are also other options to improve the position of DPG units. Examples are the flexibilization of CHP units and the implementation of the Prius principle. The last option to provide flexibility in the market with power generators is the curtailment of VRE technologies. This currently is not done since the VRE capacity is relatively low and there is social opposition against the curtailment of renewable energy sources.

3.5 Interconnection between Markets

The last option to create more flexibility into the electricity grid is by making more interconnection with other areas and countries. As already mentioned in figure 2-1, the Dutch grid is connected to the grids of neighboring countries. If the Netherlands imports or exports electricity all depends on the electricity prices in the Netherlands in comparison with the interconnected countries. Electricity will be imported when the prices are lower in the neighboring countries and will be exported in case of higher prices in the surrounding countries. How much will be traded depends on the demand and interconnection capacity (TenneT, 2015). Table 3-2 depicts the current and future interconnection capacities between the Netherlands and its neighboring countries. Table 3-2: Available and planned interconnection capacity (TenneT, 2015)

Year Belgium GW

Germany GW

Norway GW

United Kingdom GW

Denmark GW

Total nominal1)

GW

Total after reduction2)

GW

2013 1.7 2.4 0.7 1.0 0 5.9 5.5 2014 1.7 2.4 0.7 1.0 0 5.9 5.5 2015 1.7 2.5 0.7 1.0 0 6.0 5.5 2018 2.43) 4.0 0.7 1.0 0 8.2 7.6 2021 2.4 4.54) 0.7 1.0 0.7 9.4 8.7

1) Without reductions 2) With reductions due to errors, revisions and loop flows due to wind power production oversupplies 3) Increasing capacity with 700 MW with Belgium in 2018, still has to be confirmed 4) Increasing capacity with 500 MW with Germany in 2019, still has to be confirmed

Interconnection can be enlarged on a (inter)national scale, but also on a more local scale. In case of a local scale, most of the time smart grids are mentioned. These smart grids use the locally produced electricity in the best physical or economic way. The system can be designed in such a way that it reduces the congestion as much as possible and is storing electricity in times there is a possibility of congestion (van Melle et al., 2014). In this way, the system is designed in a most grid efficient way. Another option to design a smart grid is to maximize the financial benefits from it. Energy is stored in cheap hours and will be used in expensive hours. This market model takes the possible congestion problems only limitedly into account. Which market model is developed depends on the type of investor. DSOs want to decrease the congestion, while households will make choices based on electricity prices. These types of interconnection, as all the other flex options, all have strong points and challenges to overcome. What the position of interconnection is will be analyzed by the SWOT analysis discussed below (see figure 3-8).

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3.5.1 Strengths

More interconnection can enlarge the flexibility in the grid significantly. This, while more power plants over a larger area can provide the flexibility and by selecting the cheapest option the overall cost can be decreased (Klimstra, 2014). With this in mind, the Netherlands can benefit from more cheap electricity periods in Germany, due to the larger VRE capacity installed in Germany, causing more low electricity prices (Sijm et al., 2013). With more interconnection capacity, the Netherlands can also use the Pumped Hydro Storage units in Norway to balance the system (Rooijers et al., 2014). This interconnection cable between the Netherlands and Norway is called the NorNed cable. By increasing the interconnection between countries, the most economic system can be designed. This can also improve the business case for Dutch conventional power plants, as these power plants can provide power to other counties when they need more demand for flexibility in their grid (TU Delft et al., 2015). Another strong point of interconnection infrastructures is their long lifetimes. In contrast to other flexibilization options, interconnection infrastructures have relative long lifetimes of around 40 years (van Melle et al., 2014). Therefore the investments needed to install this extra transport capacity can be spread out over more years, making the technology most of the time cheaper than other options to balance the system (Delta Netwerkbedrijf B.V., 2015).

Strengths- Long lifetime

- Most economic efficient use of system

Weaknesses- Energy losses when transportingover large distances

- Cannot take all demand for back-up capacity away

Opportunities- Benefiting from energy transition in neighboring countries

- Improved income VRE capacity

- Less back-up capacity required

Threats- Social resistance

- Increasing the dependency on other countries

Figure 3-8: Summary SWOT analysis Interconnection between Markets

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3.5.2 Weaknesses

However, more interconnection capacity will not be able to provide all flexibility, since climate patterns are not country restricted. This means that if wind speeds are low in the Netherland, Germany will most probably have low wind speeds as well and therefore, they both need back-up capacity (Klimstra and Hotakainen, 2011). This interconnection with other countries can also increase the prices in the Dutch electricity system. This since extra demand for flexibility in neighboring counties can be provided by Dutch gas fired power plants (Slingerland et al., 2015). By doing so, the electricity demand on the Dutch electricity markets increases and therefore the prices will increase as well. Another challenge for extra interconnection capacity is the losses related to the transport over large distances. It is energetically not favorable to transport electricity over very large distances, as a large part of the energy will be lost (Klimstra and Hotakainen, 2011). Due to these losses flexibilization options cannot be clustered in only one area or country.

3.5.3 Opportunities

Overcapacity of gas fired power plants can provide flexibility to neighboring countries. This especially can increase the business cases of the power plants with relatively high marginal costs, since the power plants will be used more often to supply flexibility to other parts of the North West Europe. However, as already discussed, due to more interconnection capacity the Dutch electricity system is also capable of benefiting from large VRE capacities in other countries like, Germany and Norway. Due to the import of cheap renewable electricity the electricity prices can drop in the Netherlands. In this way, the Dutch system benefits from the energy transition of neighboring countries. If this will happen highly depends on the market developments in the neighboring counties. However, as discussed as a weakness, more interconnection can lead to a higher demand and thereby a higher electricity price. For consumers this is not beneficial. However, for variable renewable energy technologies in the Netherlands, the higher prices can improve the business cases as discussed in the DSM paragraph. On the other side, interconnection can help to decrease the need for extra back-up capacity. The required flexibility can be provided by cheaper power plants in neighboring countries. How the system will develop highly depends on the system developments in all countries in North West Europe.

3.5.4 Threats

This interconnection sounds as the solution for most flexibility problems, while it connects the areas with flexibility technologies with the flexibility demanding areas. However, there are also some downsides to this interconnection. The first one is the social opposition towards these (large) interconnection cables. This problem is already present in Germany. There is large social opposition against the extra transport capacity that is planned between the large VRE capacity in the North of the country and the large consuming industries and available pumped hydro storage capacity in the South (Kwasniewski, 2014). This shows that social aspects have to be taken into account when implementing flex options. Another threat of interconnection has to do with dependency. With a more integrated energy market in North West Europe, the prices in the whole area are set by the demand and supply extremes in a few areas. An example of this is the high electricity price in the Netherlands in periods France faces cold weather periods. The high electricity demand from the French households for heating purposes, will have consequences for the electricity prices in whole North West Europe. This means more interconnection capacity can align electricity prices in Europe, but increases the range of events that has effects on the Dutch electricity prices (Essent, 2015).

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3.5.5 Position of Interconnection

Interconnection capacity helps to stabilize the Dutch and European grids. More flex options over a larger area can be used to stabilize the Dutch electricity system. Currently, it is one of the cheapest options to improve the flexibility of the Dutch electricity system, since the lifetime of interconnection infrastructure is much longer than most other flexibilization options. However, social resistance towards large infrastructures and the energy losses when transporting over large distances make that it will not be the only option for the provision of flexibility. Additional to these challenges, more interconnection cannot solve all imbalance problems in the system and more interconnection makes the national electricity prices more vulnerable for events from neighboring countries.

3.6 Overall position of flex options

What can be concluded from the SWOT analyses done on the different flex options is that increasing the interconnection capacity with other countries, currently is the most interesting option to provide flexibility, beside the conventional power plants that do provide the flexibility in the current system. All other flex options face many difficulties gaining a significant market share. Energy storage has difficulties entering the Dutch electricity system due to the ‘Salderingsregeling’ making it not interesting to invest in the technology on a household level. On a larger scale the electricity storing technologies often have too high CAPEX costs. DSM options suffer from low price volatility in the electricity markets in combination with high additional network costs. However, demand response can play a role in providing flexibility to the system, since network costs can be saved in this way improving the business case. The situation for CHP units currently is also not beneficial. The spark spread currently is not in favor of CHP units making it difficult to make profits on the different electricity markets. All flexibilization options can provide additional system services besides balancing the grid. Examples of these services are the prevention of local congestion, improved business cases for VRE technologies and lower back-up capacity requirements. However, these flex options are not valuated today and are therefore not improving the current business case for these flexibilization options. Despite the good position of interconnection, this flexibilization option is out of scope in the rest of this study, since TenneT is the only stakeholder allowed to invest in interconnection. Therefore, it is not interesting to investigate the business cases of interconnection capacity for individual stakeholders.

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FLEXIBILITY INVESTMENT MODEL

The different flexibility options have been described with their position in the Dutch electricity system. Despite the difficult position of most flex options, there are some opportunities for business cases for flexibilization options. In this chapter, a model is developed to identify the economic opportunities for flexibilization options and important parameters for the implementation of flex options are discussed. The model is called the flexibility investment model. First the aim of the model will be discussed. Secondly, the methods, model setup, structure and boundary settings used to build the model are explained and at last the technical description of the model is done. All these parts of the model will be discussed below.

4.1 Aim of the model

The aim of the flexibility investment model is to analyze the economic attractiveness of flexibility providing technologies in the Dutch electricity system. With the results of this model, the aim is to provide information to possible investors which technologies are interesting and which uninteresting. To test the economic attractiveness of a flex option, the return on investment (ROI) period is calculated by the model. This ROI value represents the time it will take to earn the invested money back. This is the most important factor for possible investors in flex options (Krebbekx et al., 2015). The model is a quantitative system analysis of the economic attractiveness of a flex option. It provides the interested party insights in the most important factors, influencing the business case of the specific flexibility providing technology.

4.2 Methods

Three scientific methods were applied to evaluate the main parameters driving investors into flexibilization options. First a literature study was done to investigate what the most important factors are for investors in flexibilization options. From this study it became clear that the most important factor for investors is economics (Berenschot et al., 2015a; Krebbekx et al., 2015). With this as a conclusion from the other studies, the decision was made to make a model analyzing the economic attractiveness of a flexibility providing technology. However, beside economics other parameters are also important to take into account. However, since the model is based on economics these parameters will be discussed in chapter 6. The second method used in this chapter is a quantitative system analysis. This analysis was executed to investigate the relationships between the different parameters. The results of this quantitative system analysis are used to set up the model. The third method used in this model is a scenario analysis. Multiple electricity price scenarios are included in the model to analyze the robustness of the investments. The influences of different parameters on the electricity prices are analyzed and in this way the most important parameters could be identified. The model was validated using accurate and real data from the Power2products project and LTO Glaskracht.

4.3 Model setup, structure and boundary settings

A general visualization of the flexibility investment model is provided in figure 4-1. As can be seen in figure 4-1 the model is build up from five main blocks. Four of them have to be filled in by possible investors, the economic outcomes box generates economic results, based on the input provided by the investor. First, the energy policy in company box has to be filled in. This box provides information of the possibility for flex options, under the current energy policy of the investor. It tests the ability of an asset owner to steer its system on the fluctuating electricity prices. Secondly, the investor requirements are needed. Here the maximum CAPEX, OPEX and capacity have to be filled in to compare this later on

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with the technology characteristics. Also the required size and maturity level (explanation in Appendix D) of the flex asset can be added, to be tested by the model. The results of the comparison of the investor requirements with the technology characteristics are depicted in the result excel datasheet. The third box that is required is the technology characteristics box. In this box, the characteristics of the investigated technology have to be filled in. These characteristic can be obtained directly from technology suppliers or otherwise can be estimated using databases and literature. The data in this box is the basis for the total CAPEX and OPEX costs of the technology and so provides data on the possible business cases. The last boxes which have to be filled in, are the network tariffs boxes. These boxes provide an indication of the possible extra network costs the implementation of flexibilization options can cost. The network tariffs boxes are linked to the network tariff excel datasheet. Here, the real network tariffs are included for the different end-users.

The performance of a technology is calculated under different scenarios. In the economic outcomes box the performances are calculated for three main parameters; the total reward per year, the total profit per year and the return on investment (ROI) time. The scenarios used in this model are discussed in the scenario paragraph of this chapter and are included in the scenario excel datasheet. The model structure and boundaries used in this study are depicted in figure 4-2. In this figure, the green line is the model boundary setting and the dotted boxes are inputs only used when they are present. Figure 4-2 explains what the most important parameters in the model are. As discussed above, interested investors have to fill in case specific information and technology characteristics. With these parameters, the model will be able to estimate the ROI period of the technology. This ROI is the end result of the model. To calculate the ROI, first the total investment costs have to be calculated (CAPEX). Beside the total CAPEX costs, there are annual costs involved when implementing flexibilization options. These annual costs can be divided in OPEX and Network Costs. These costs together represent the annual costs the flex option has to recoup to make a profit. After all costs are included in the model, the rewards can be taken into account. The Rewards are build up from the market rewards, the additional value of the provided flexibility and the value of the produced product. By subtracting the annual costs from the rewards, the annual profit is determined. This profit tells the investor if the rewards of the technology are higher than the annual cost. When this is the case, the CAPEX costs are divided by the annual costs to come to the return on investment period. This is the end result of the model. In the next paragraph, the different parameters of the model will be discussed more in detail and the technical details will be explained as well.

Figure 4-1: Overview of the evaluation model

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4.4 Technical details

In this paragraph, the different steps in the flexibility investment model will be described more in detail and the technical details behind each step will be explained. In this way, this paragraph provides information about the buildup of the model.

4.4.1 CAPEX

CAPEX is an abbreviation of CAPital EXpenditures meaning the total amount of money a technology cost to install (Sijm et al., 2015). This investment has to be earned back during the lifetime of the technology. The CAPEX costs of a flex option are mainly determined by the technology characteristics and grid capacity. The investment costs of the technology and the possible extra grid connection costs are included combined with the CRF of both investments. The grid connection can be an important factor in the CAPEX part of the model. In existing companies a certain grid connection capacity present. However, the capacity of this grid connection is determined for the peak demands of today. When a flexibilization option is installed, the peak demands can increase. This peak demand increase can cause an overload in the system and therefore, the grid connection has to be enlarged. This enlargement can have significant consequences for the business case of the flexibility option (TU Delft et al., 2015). The CRF is the Capital Recovery Factor and includes the lifetime (n) and interest rate (i) of the investment. The CRF is calculated using equation 4-1. The CRF determines the annual costs for a technology.

𝐶𝑅𝐹 =𝑖(1 + 𝑖)𝑛

(1 + 𝑖)𝑛 − 1

Equation 4-1: Capital Recovery Factor (Zakeri and Syri, 2015) The result of the CAPEX equation is the total invested money on the technology during the lifetime of the installation. The CAPEX costs can be reduced by subsidies. The height of the subsidies are determined by governments and are therefore, taken as an external input in the model (Krebbekx et al., 2015). Since not every investment will receive subsidies, this box is dotted. This CAPEX costs have to be earn back during the lifetime of the installation. The time it takes for a technology to earn itself back is determined in the Return on Investment section of this paragraph.

Figure 4-2: Model structure and boundary setting

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Equation 4-1 shows how the Capital Recovery Factor is calculated. However, the CRF value is only a part of the total CAPEX costs equation. The complete equation used in the flexibility investment model is depicted in equation 4-2.

Equation 4-2: Total CAPEX costs equation

4.4.2 OPEX

OPEX represents the total annual fixed operation and maintenance costs. This primarily is determined by the technology characteristics. The robustness and maturity of the technology partly determine the total fixed O&M cost, since the OPEX costs are determined by the number of checks and replacements a technology needs. For new technologies this can be significantly higher than more mature and robust technologies (Krebbekx et al., 2015). Nevertheless, the OPEX costs are also influenced by the production pattern of the installation. This, since some technologies cannot cope with a very fluctuating production pattern, causing higher annual maintenance costs. However, this is not incorporated in this model. In the flexibility investment model a fixed O&M cost is used. This is done since this model intends to be usable for every flex providing technology. Since not all technologies have troubles with fluctuating production patterns, this is not included in the model. Investors with a fluctuating electricity demand, who intend to install a technology which has troubles with fluctuating patterns, the OPEX costs can be increased manually to test the robustness of the business case. The OPEX costs are included in the economic outcomes box in ‘minimal required reward per year.’ Together with the Network costs this ‘minimal required reward per year represents the minimal reward the technology needs to get to be profitable. The exact equation will be discussed in the next section.

4.4.3 Network Costs

As discussed above, the model starts with the energy policy of the company box. This box is partly included in the model boundary settings, since the interested investor has to fill this in but, the box only tests the possibilities for business cases. This box is included since the type of flex option installed, can have significant consequences on the electricity demand pattern and therefore, have consequences on the network tariffs that have to be paid. The network tariffs are included in the excel datasheet network tariffs, and include the network tariffs of Enexis and Stedin of 2015 (Enexis, 2015; Stedin, 2014). The total amount of network costs that have to be paid, depends on the contracted capacity, peak demands, grid connection and the amount of electricity used. These variables are represented by the grid capacity, energy balance, and energy policy of the company of figure 4-2. What can be concluded from Appendix C is that it can save a lot of money when especially the peak demand can be decreased. For some flex options this can be the case (energy storage) for other flexibilization options the peak demand can even increase (power2heat). Since these costs can have a major effect on the possible business case, it is important to take these factors into account. There is also a special group for large end-users using less than 600 hours a year (Enexis, 2015). For these end-users it is important to stay in this special tariff system, as it saves a lot of money compared to the other categories. The Network costs are included in the flexibility investment model in two blocks. First, the interested investor needs to fill in the current situation. Here the exact network situation has to be filled in. According to the data provided by the investor, the model determines the right network tariff category, in the same way DSOs determine the tariffs. The second part of the network tariff block

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represents the new situation. Here the investor has to fill in the future situation. This to model the network tariffs for the new situation. The difference between the old en new situation is calculated. This difference is taken as additional network tariff cost that has to be paid annually, due to the installation of a flex option. This additional cost is taken together with the OPEX costs, discussed in the previous paragraph, in the ‘Minimal required return per year.’ This part of the model calculates the minimal reward needed to provide a profit, using equation 4-3. 𝑀𝑖𝑛𝑖𝑚𝑎𝑙 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝑟𝑒𝑡𝑢𝑟𝑛 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟 = 𝐹𝑖𝑥𝑒𝑑 𝑂𝑃𝐸𝑋 𝑐𝑜𝑠𝑡𝑠 𝑝𝑒𝑟 𝑀𝑊 ∗ 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 + 𝐴𝑑𝑑𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝑁𝑒𝑡𝑤𝑜𝑟𝑘 𝐶𝑜𝑠𝑡𝑠

Equation 4-3: Minimal required return per year equation

4.4.4 Rewards

The reward section of the model is built up from the annual profits on the different electricity markets, the technology characteristics, the additional flex reward and the product values of figure 4-2. First, the markets included in the model are the Day-ahead (DAM), Intraday (IDM) and Imbalance (IBM) markets. These markets are included under different scenarios. These scenarios are provided by CE Delft et al., (2015) and do not change when variables in the model change. In reality, this is not the case, since flexibility options influence the demand for or supply of electricity and therefore have an influence on the electricity price. Since the scenarios are not influenced by the model they are out of the scope of the model boundaries and only used as external input. Another parameter that is included in the scenarios is the gas price. This gas price has large influences on the business cases for especially power2heat and Dispatchable CHP units. The gas price is fixed to see the influences of the electricity price on the business case. However, in reality the gas price also fluctuates and therefore can have influences on the business cases. In the case studies, the gas price is variated to analyze the sensitivity of the business cases on the gas price. How the different flex options act on the market is also very important for the number of hours that a technology can tender its flexibility. Table 4-1 provides an overview of the market models of the different flex technologies included in the model. These market decision models determine the interesting hours for a technology to be active on the markets. These variables will be used in the different equations below as the determination of the number of hours a technology is interesting to use. The E in the table refers to the electricity price and VOM stands for the variable operation and maintenance costs which have to be paid for the technology per MWh produced or consumed. Table 4-1: Market decision models of the different flexibility options included in the model

With the market decision models discussed in table 4-1, the interesting hours to tender flexibility to the markets can be determined. The number of interesting hours is, together with the electricity prices, the most important factor influencing the rewards in the flexibility investment model. With these interesting hours as a start, the rewards of the different flexibilization options can be calculated on the different markets. The rewards on the different markets for the different flexibilization options are calculated using the equations below. For each flex option a different equation is included. The letter N in the different equations stands for the number of interesting hours, the technology has on the different markets. ɳ stands for the efficiency of a technology, the E is the electricity price and the C stands for the capacity (MW) of the technology. All prices (electricity and gas) are in Euro per MWh.

Flex option Market decision

Power2heat E > Efficiency new/efficiency old *gas price - VOM Demand response E > Reward product per MW input Energy storage E < Average electricity price Dispatchable power generation E > Gas price / efficiency + VOM Power2X E < Value produced product per MW input * efficiency + VOM

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Equation 4-4: Rewards Power2heat options

Equation 4-5: Rewards CHP units

Equation 4-6: Rewards electricity storage

Equation 4-7: Rewards demand response

Equation 4-8: Rewards Power2X options

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Energy storage consumes energy when the price is below the average and has a feed in when the price is above the average. This is one way of steering an energy storage unit (TU Delft et al., 2015). Other business models deal with predetermined prices or with learning behavior of the system (TU Delft et al., 2015). The average price method is used, since this is the least complex business model and will therefore minimize the risks for its investor. Variable O&M (VOM) are costs made by running the installation like, fuel costs and operation costs (Zakeri and Syri, 2015). The variable O&M costs often represent the marginal costs of a flexibility technology and are therefore setting the minimal price per MWh required. Due to this, the VOM often determines the place of a flex technology in the merit order (Sijm et al., 2013). The place in the merit order is very important for investors since this determines the amount of full load hours the installation can run (Alberici et al., 2014; TU Delft et al., 2015). The technology characteristics together with this VOM determine the ‘capacity of the technology to take hours in the different markets.’ This probably is the most important parameter of the Technology characteristics box. The ‘capacity to take hours from the market’ is a fraction, indicating the possibilities for flex options to deal on the markets. For the Day-ahead market this number is mainly influenced by the electricity demand pattern. When a flex asset can be active 24/7 on the Day-ahead market (DAM) this number can be relatively high 0.8/0.95 while investors with a 40 hour per week demand pattern can have significantly lower fractions. The reason for this, is that most low price peaks are during the nights and weekends (Appendix B; APX, 2014a). For the Intraday and Imbalance markets (IDM and IBM) this fraction highly depends on the place on the merit order of the technology. To give a more robust business case this fraction should not be higher than 0.1 for technologies that can ramp up or down immediately and lower for technologies with longer ramping rates. Another parameter where assumptions are required is the Intraday market and the number of hours the market is online. This is important since the Intraday market is measured in number of transactions and not in hours. The Intraday market had 21.623 transactions in 2013 which corresponded to 6190 hours with biddings from 0.1 till 700 MW (APX, 2015b). Therefore, the capacity to take hours decreases in this market with an increase in capacity. The assumption is made that in the ‘limited development’ scenario the hours increase to 8000 per year, while the ‘green and flex’ scenario has 8756 hours. For the Imbalance market only the quarters with a clear ramp up or down demand are taken into account. This corresponded to 42% of the price duration curve being ramp down demand and 42% ramp up demand in 2013 (TenneT, 2013c). Periods of both and no imbalances are not taken into account. The percentages do not change in the different scenarios, since the peaks in the price duration curves are of most interest to the investors. Additional to the market rewards, there are the additional value of flexibility and the value of the product produced. Since these inputs are not always present for all flexibility technologies these boxes are dotted. The additional value of flexibility currently is not valuated since the market is capable of stabilizing the system and DSOs are not capable of using flex options to prevent congestion (chapter 3). Therefore, this variable is currently set at zero Euro/MW flexibility (TU Delft et al., 2015). Nevertheless, the TSOs, DSOs and governments are evaluating the current system and therefore, there is an opportunity this value of local flexibility can be materialized in the future. Also the product value can be included in the total reward. This can be interesting for especially power2X cases. For these flex options the value of the produced product is determined on another market than the electricity market. Therefore, the product price can be included as additional value per MWh input.

4.4.5 Annual profit

After the annual costs and rewards are calculated by the model, the annual profits can be determined. For every flex option this is calculated using the same equation. This equation is depicted below as equation 4-9.

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𝐴𝑛𝑛𝑢𝑎𝑙 𝑃𝑟𝑜𝑓𝑖𝑡 = 𝑅𝑒𝑤𝑎𝑟𝑑𝑠 − 𝐴𝑛𝑛𝑢𝑎𝑙 𝑐𝑜𝑠𝑡𝑠 Equation 4-9: Annual profit equation The annual costs of equation 4-3 are represented by the ‘Minimal required return per year.’ The annual profit is the amount of money the flexibility providing technology earns per year. However, with this profit the CAPEX costs are not taken into account. This is done in the Return on Investment part of the model. This ROI part is discussed in the next section.

4.4.6 Return on investment

The return on investment (ROI) is the end result of the model. This ROI provides the investor an indication about the number of years the technology will earn itself back. The ROI is determined using equation 4-10 The ROI is the end result of this model since investors have indicated that this is the most important factor determining the investment decision (Krebbekx et al., 2015). The total investment costs are visualized in figure 4-2 as the CAPEX. The annual profit is already depicted in equation 4-9

𝑅𝑂𝐼 =𝑇𝑜𝑡𝑎𝑙 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑐𝑜𝑠𝑡

𝐴𝑛𝑛𝑢𝑎𝑙 𝑝𝑟𝑜𝑓𝑖𝑡

Equation 4-10: Return on investment period That the ROI is more important for large investors than for small investors can be concluded from the average and maximum allowed ROIs of these investors. Industrial companies have a maximum ROI which is most of the time not higher than five years (TU Delft et al., 2015), while households can have ROIs of over 10 years.

4.5 Model validation

To test the model on accuracy and validity, the model is tested using data from existing flex options. The data used for validating the network costs part of the model is provided by the project Power2products of ISPT (Berenschot et al., 2015b). The data used to validate the economic outcomes box is provided by the report of Schlatmann and Horstink (2015) in combination with data provided by LTO Glaskracht (Van der Valk, 2015). The data provided by Schlatmann and Horstink (2015) and Van der Valk (2015) are used to validate the CHP units. For the other flex options no data was available to validate the model.

4.5.1 Network costs

The network costs are validated using a lecture provided by AVEBE in the Power2products consortium (Berenschot et al., 2015b). In this lecture, a company was introduced which has a variating consumption pattern. The network costs for the company are investigated under two situations. The new and old situation are indicated in table 4-2. The data used in this validation shows the consequences of changing energy demand patterns. In the old situation the company had a yearly uptake of 5,500 MWh from the grid. This is done with a maximum peak of 10 MW which is reached only once a year. The in business hours are therefore 550 hours meaning it is positioned in the “below 600 hours” category. This 10 MW is also the contractual peak. The peak pattern that the company uses is one week a year 10 MW, 18 weeks 5 MW peak capacity and 1 week 1 MW. The rest of the year, there is no uptake from the grid. The average peak per year is therefore 1.94 MW/week. The company is categorized in the HS/MS group.

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In the new situation, the company installs a flexibilization technology of 5 MW, which increases the maximum peak demand to 13 MW. The annual uptake has increased to 8,000 MWh. The in business time has therefore increased to 615 hours. The peak pattern is now: 2 month 13 MW, 8 months 10 MW and 2 months of 3 MW. This has significant consequences for the network costs in table 4-2. Table 4-2: Network cost situation 1 (Berenschot et al., 2015b)

Tariff part Old situation New situation

Connection costs 2,760 2,760 kW contracted 82,200 213,590 kW peak 55,484 179,194 Total costs 140,444 395,544

The difference between the old and the new situation is 255,100 euro/year. If the company decides to install the flexibilization option anyway, there is an option to decrease these extra costs. This can be done by increasing the maximum peak demand and changing the peak demand pattern to 1 week 15 MW, 1 week 13 MW, 8 weeks 10 MW and 2 weeks 3 MW. Due to the increase in maximum peak demand from 13 to 15 MW the in business hours are decreased from 615 to 533. Therefore, the category changes again and the prices drop. The outcomes are depicted in table 4-3. Table 4-3: Network cost situation 2 (Berenschot et al., 2015b)

Tariff part Old situation New situation Costs optimization

Connection costs 2,760 2,760 2,760 kW contracted 82,200 213,590 123,300 kW peak 55,484 179.194 62,634 Total costs 140,444 395,544 188,694

The company still pays 48,250 euro more than in the old situation. However, it saves 206,850 euro in comparison with the first new situation. Therefore, it is wise to investigate the network costs before implementing a flexibilization asset. This case is very specific since most companies are way beyond 600 hours running time per year. However, it gives an indication about the possible extra costs and it showed the model worked as expected. The network costs outcomes of table 4-3 are equal to the prices indicated by AVEBE and therefore, the model is validated for this part of the system.

4.5.2 Validation of the economic outcomes box

Beside the network tariff boxes validated in the previous paragraph, the economic outcomes box has to be validated. This box is build up from five different flex options (Power2heat, DPG, Energy storage, Demand response and Power2X). However, only dispatchable power generation units are currently installed (CHP). Since this technology is installed, data is available to validate the system. For the other flexibility options there is a lack of data to validate the specific parts of the model. The systems are not active on the markets and therefore, do not have data that can act as input for the model validation. The dispatchable power generation part of the model is validated using data from a report of Schlatmann and Horstink (2015) and experts of LTO Glaskracht (Van der Valk, 2015). The CHP data provided by LTO Glaskracht stated that the CHP unit has to compete with gas heated boilers. Greenhouses have a heat demand that can also be provided with a gas fired boiler with an efficiency of 94% (Schlatmann and Horstink, 2015). In the case provided by LTO Glaskracht, the CHP unit consumes 2.67 MWh gas per hour to produce 1 MW of electricity. When this amount of gas is used in a gas fired boiler with an efficiency of 94% and the gas price is 20.41 €/MWh as it was in 2014 (Schlatmann and Horstink, 2015), the value of this heat is 51.22 euro. However, this 2.67 MWh gas is not used in a gas fired boiler but in a CHP unit. The CHP characteristics are depicted in table 4-4.

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Table 4-4: CHP unit characteristics (Schlatmann and Horstink, 2015; Van der Valk, 2015)

Parameter Unit Value

CAPEX €/MW 420,000 OPEX €MW 0 VOM €/MWh 10 Capacity electric MW 1 Efficiency electricity generator % 42 Efficiency thermal unit % 45

The value of the heat provided by the CHP unit can be calculated by multiplying the gas consumptions by the thermal efficiency and the gas price per MWh. With the gas price of 2014 of 20.41 €/MWh, the value of the heat is 2.67 MWh * 45% efficiency * 20.41 €/MWh = 24.52 €/MWh electricity output. The difference between the gas fired boiler is 51.22 – 24.52 = 26.70 Euro per hour. Together with the extra VOM costs of the CHP unit in comparison with the boiler of 10 €/MWh, the minimum reward for electricity has to be 26.70 + 10 = 36.70 Euro per MWh electricity. According to LTO Glaskracht this 36.70 €/MWh for electricity could be interesting during 4000 hours per year on the imbalance market, based upon the price duration curve of 2014 (Van der Valk, 2015). For the model the value of the heat has to be included in the VOM costs. To do so first the total gas demand per hour has to be calculated using the formula used in the model: gas price / efficiency electricity generator = 20.41 / 0.42 = 48.60 Euro gas/hour. This responds to a gas demand of 2.38 MWh. This is lower than the 2.67 MWh described by LTO Glaskracht. When this 2.38 MWh gas is multiplied by the thermal efficiency and gas price the value of the produced heat is 21.86 Euro. This 21.86 Euro per MWh has to subtracted from the 10 €/MWh VOM costs of the CHP unit itself. When this is done, the VOM costs are -11.86 Euro per MWh electricity produced. When using this as input for the model, the minimal electricity price is 36.73 €/MWh which is very close to the 36.70 Euro/MWh provided by LTO Glaskracht. When inserting this in the model it becomes clear that on the Imbalance market more than 4000 hours are interesting. However, this can be explained by the scenarios used in the model. LTO Glaskracht has based the 4000 hours on the Imbalance prices of 2014, while in the model the prices of 2013 are included. When comparing these years with the 36.73 euro per MWh it becomes clear that the model works as expected. In this chapter, the flexibility investment model is developed to analyze the economic attractiveness of different flexibility options. The buildup of the model is described above. As discussed in figure 4-2, multiple scenarios are used to test the robustness of the possible business cases tested by the model. In the next paragraph the different scenarios included in the model are described in combination with the most important parameters influencing the electricity markets.

4.6 Electricity scenarios

Since nobody can predict exactly how the energy system and markets will look like in eight years’ time, it is important to explore the possible pathways and so, identify possible opportunities and threats for the investigated business cases. With multiple scenarios included, a sensitivity analysis can be executed to see the robustness of the business cases investigated by the model developed in the paragraphs above. First, the trends and policies will be discussed, secondly, three different scenarios are chosen and explained why these scenarios represent the different pathways.

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4.6.1 Trends in the electricity markets

As discussed in chapter 2 there are multiple trends that can be identified in the current electricity system. The biggest trend is the implementation of renewables. On every policy level there is a clear focus on renewables and in the Netherlands this is materialized by creating the ‘Nationaal Energieakkoord voor duurzame groei’. The main targets for the Netherlands are to have 14% renewables by 2020 and 16% by 2023. To reach these goals, the Netherlands is focusing on offshore and onshore wind turbines in combination with solar PV systems (Sociaal Economische Raad, 2013). These two types of renewables have an intermitted production pattern, making the system more volatile and unpredictable and increasing the demand for flexibility (Hout et al., 2014; TU Delft et al., 2015). However, the question is how much will be installed exactly and what will be the influence of these renewables on the system? To answer these questions multiple scenarios are made. However, first an indication about the merit order of the different power generating technologies is depicted in figure 4-3.

Most renewable energy technologies (excluding biomass) have a marginal costs that is zero and can therefore produce electricity against every price (Papaefthymiou et al., 2014). When large capacities of these renewables are installed it is possible these renewables will produce all demanded electricity. As discussed in chapter 2 a merit order is made on the APX spot market and the last producing unit determines the price. Therefore, in the case when only renewables are producing electricity, the price can be zero. The amount of hours the price is this low, highly depends on the total installed VRE capacity. Not only the share of intermitted renewables will have effects on the electricity system in the Netherlands and abroad. Another parameter influencing the system is the change in electricity demand. This does not only include the volume of the demand, but also the demand patterns. Large peaks in demand can significantly increase the demand for flexibility in the grid (van Melle et al., 2014). On one side, the Netherlands is focusing on increasing energy efficiency and decreasing the energy demand of the country by putting energy saving goals (Sociaal Economische Raad, 2013). On the other hand, the Dutch government is promoting electrification of the energy system, mainly by promoting the use of electric vehicles (Sociaal Economische Raad, 2013). The way these electric vehicles will be deployed in the Netherlands and the way they are charged, will have significant influences on the demand for flexibility in the system on a local and national scale (van Melle et al., 2014), since people use the electric car to drive to work and reload the car when they are at work or at home. Since most people come back from work around the same hours, the peak in electricity demand will increase significantly when more electric vehicles are bought (van Melle et al., 2014). In the scenarios, different pathways are explored to investigate how the system will react on this trend.

Figure 4-3: Dutch electricity merit order in 2013 and 2020 (Van der Hoofd, 2014)

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The third variable included in the scenarios will be the share of electricity produced by CHP units in the Netherlands. The Dutch electricity system currently is characterized by the large installed CHP capacity, producing a significant part of the electricity demand (ECN et al., 2014). Currently, most of these CHP units are heat demand steered, meaning that they produce electricity when there is demand for heat. This means they do not react on the electricity prices and can be considered must-run units. They produce electricity whatever the price is. When the situation is present, these must-run units are only producing electricity together with variable renewables, which have a marginal cost of zero, the electricity price will remain zero. However, the current situation is not in favor of must-run CHP units, since the electricity prices are relatively low in comparison with the gas price (Peeters et al., 2014). Therefore, there is a trend of transforming these CHP units. Some of them will be phased out, while others will be made flexible and only operate in the periods the price is right and there is a profit (Peeters et al., 2014). The total capacity must-run CHP will therefore reduce. However, how much will remain or made flexible is part of the discussion. However, when a large part of the CHP units is made flexible it can provide a large share of the requested flexibility in the future (CE Delft et al., 2015). The scenarios made by CE Delft et al. (2015) for the Power2products consortium are build up from the three variables discussed above. The position of each scenario is depicted in figure 4-4.

The trends discussed above are not the only trends influencing the electricity prices. Many parameters are influencing the energy system of today and in the future. Internal parameters like planned closure of conventional power plants and extra interconnection capacity are taken into account in the scenarios (CE Delft et al., 2015). However there are also other parameter influencing the electricity prices. Examples of these parameters are: governmental actions and regulations, TSO and DSO rules, CO2 prices and public opinion (CE Delft et al., 2015; Klimstra, 2014). Changes in these parameters are not taken into account. This is done to have a ‘ceteris paribus’ situation. This means that you can only change one parameter to see the influence on the system. When more parameters are changed simultaneously the relationships cannot be conducted.

4.6.2 Day-ahead scenarios

The scenarios described in this paragraph are produced by CE Delft et al. (2015) for the Power2products study consortium. Three main scenarios are created to investigate how the Day-ahead market reacts on the different parameters. The assumptions made to create the scenarios are depicted in table 4-5 and the consequences on the electricity prices are visualized in figure 4-5, 4-6 and 4-7. To investigate the relationship of the fuel prices on the different scenarios, three price ranges are defined. These ranges are depicted in table 4-6. All scenarios use a CO2 price of 15 €/ton.

Figure 4-4: Main variables the scenarios are build up from (CE Delft et al., 2015)

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Table 4-5: summary scenarios (CE Delft et al., 2015)

The different scenarios are executed under the three different fuel price ranges. At the end of this paragraph, the two most extreme scenarios will be chosen to be included in the model. This is done to give the range the market can change the coming decade. The results of the different scenarios is also quantified in Appendix F. The input for the three scenarios developed by CE Delft et al., (2015) is depicted in table 4-5. Table 4-6: Fuel price ranges used in the different scenarios (CE Delft et al., 2015)

Fuel price ranges Gas price (€/MWh) Coal price (€/ton)

Mid/mid 25 75 High/low 35 60 High/high 35 90

With these inputs for the different scenarios, the effects on the electricity prices can be investigated. To do this, CE Delft et al., (2015) made three graphs with the price duration curves of each scenario under different fuel prices. These price duration curves are depicted below. On the X-axes the number of hours per year is depicted, while the Y-axes shows the price of electricity in Euro/MWh. For a detailed description why de prices increase or decrease per scenario, Appendix E is included. Here the influences of each variable on the electricity prices is described in detail.

Scenario Capacity VRE in NL New demand categories CHP position

Limited developments

- 3 GW onshore wind - 1 GW offshore wind - 2GW solar PV

- 100,000 Electric vehicles - No Power2heat

Closure of economic unattractive units

Green and flex

- 6 GW onshore wind - 5 GW offshore wind - 7 GW solar PV

- 400,000 Electric vehicles - Power2heat capacity: 4 GW

winter, 2.5 GW summer

CHP units all made flexible

CHP phase-out

- 6 GW onshore wind - 4 GW offshore - 6 GW solar PV




Nationaal energie-akkoord

- 6 GW onshore wind - 4.5 GW offshore wind - 6 GW solar PV

Figure 4-5: Limited development scenario under different fuel prices (CE Delft et al., 2015)

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The scenarios used in the flexibility investment model are the most extreme scenarios. These are the ‘limited development’ (high/high) scenario and the ‘Green and Flex’ (mid/mid) scenario. The ‘limited development’ scenario represents the relatively long and high price peaks and the ‘Green and Flex’ scenario represents the long periods of low electricity prices and a relatively low number of high price peaks. What can be concluded from the different scenarios is that the number of very low electricity price hours is mainly determined by the installed VRE capacity and the flexibility of CHP units. The middle part of the price duration curve is mainly determined by the marginal costs of coal power plants. This marginal cost of coal fired power plants is not only determined by the price of coal but also by the CO2 price (CE Delft et al., 2015). A significant increase in the CO2 price will therefore mainly have an influence on the middle part of the price duration curve. The number and height of the price peak hours is mainly determined by the gas price. Gas fired power plants have the highest marginal costs and will therefore be at the end of the merit order (Van der Hoofd, 2014). This since gas fired power plants will provide a part of the electricity demand and will therefore set the price.

Figure 4-6: Green and flex scenario under different fuel prices (CE Delft et al., 2015)

Figure 4-7: CHP phase-out scenario under different fuel prices (CE Delft et al., 2015)

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4.6.3 Intraday scenarios

Beside the extensive explanation of the Day-ahead market scenarios there are also scenarios used for the Intraday market. The scenarios are based on the assumption that the Intraday market will grow the coming years since the real time trading will become more important due to more VRE in the system (CE Delft et al., 2015). This can be seen in figure 4-8 in which the number of transactions on the market is plotted against the price per MWh traded on this market.

The two Intraday scenarios created in this study are shown in figure 4-9. These scenarios are created using the market trends of the Day-ahead market scenarios, which are translated into the Intraday scenarios. The first assumption made in the two scenarios is that the total amount of transactions will increase due to more VRE capacity. This is also the opinion of most experts (CE Delft et al., 2015; Hout et al., 2014). How strong the Intraday will increase in volume and price volatility is related to the increase in VRE capacity. Also the size of the positive and negative peaks is determined by this. With the Day-ahead scenarios in mind, two new Intraday scenarios with different extremes are created. In figure 4-9, the Intraday market of 2013 is included with the two created scenarios to give an overview of the differences between the scenarios.

4.6.4 Imbalance market scenarios

In this study, also different scenarios of the Imbalance market are taken into account. This since the Imbalance market can be an important market for investors in flexibility. The Imbalance market is the market with the highest volatility and therefore profit margins. However, it is also a very unpredictable market and therefore, very hard to model. Because of this, there are no existing models or scenarios

Figure 4-8: Intraday development 2013-2014 (CE Delft et al., 2015)

-200

-100

0

100

200

300

400

0 10000 20000 30000 40000 50000

Euro

/MW

h

Transactions per year

APX 2013

Scenario limited development

Scenario Green and Flex

Figure 4-9: Different intraday scenarios

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found for the Imbalance markets in the Netherlands for the coming years. Therefore, two scenarios are created for the ramp up and ramp down Imbalance market. These scenarios are based on the current Imbalance market and the Day-ahead market. This is done since the middle part of the current Imbalance market price duration curve is positioned slightly higher than the Day-ahead market (APX, 2014a; TenneT, 2013c). The peaks are made more extreme and less extreme to simulate the differences in VRE production predictability. This is done since the predictability of weather patterns highly influences the need for flexibility and more specifically the price of balancing the system. The predictability of extra flexibility demand in the system has influences on the electricity prices, since some of the cheap flexibilization options have a relatively long ramp up speed. A better prediction gives these options more time to react and therefore the prices can be lower (Slingerland et al., 2015). The price duration curves of the different Imbalance markets are depicted in figure 4-10.

4.6.5 Conclusions of different scenarios

From the different scenarios, the influences of the different trends (discussed in figure 4-4) on the electricity prices in the different markets can be concluded. The trends discussed below all have the same consequences for each market and therefore are only described once. The first trend is the amount of VRE capacity installed. According to the scenarios, the installed VRE capacity mainly influences the number of low electricity price periods. The more VRE capacity is installed the more low electricity price hours there are. This has to deal with the low marginal costs VRE technologies have. The prices can become very low in periods VRE technologies are only technologies producing electricity. The installed CHP capacity is working on both sides of the price duration curves. The amount of must-run CHP capacity has influences on the low electricity price periods. The more must-run CHP capacity, the more low electricity price hours there are. This since must-run units do not set the price while they will produce whatever the price is. Flexibilization of the CHP units decreases the number low electricity price hours, as they are only used when there is not enough supply and then the CHP unit can set the price. On the demand side, the installation of power2heat and other flexibilization options has limited influences on the overall price. However, power2heat increases demand in periods of low electricity prices, and thereby raises the prices. Therefore, there will be less very low price periods. Electric vehicles have a minor influence on the overall electricity prices since they increase the demand over the whole year. However, CO2 prices have a bigger influence on the electricity price (CE Delft et al., 2015).

Figure 4-10: Imbalance market price duration curve

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The main drivers of the electricity price on the short run are the fossil fuel prices and the CO2 prices. This since these parameters have direct influences on the electricity prices (CE Delft et al., 2015; Sijm et al., 2013). The gas price is the parameter that has the biggest influence on the price volatility. The reason for this is that gas currently is used to supply electricity when there is high demand and low supply from other technologies and therefore will set the price on the market. A low gas price will have low influences on the average electricity price, while a high gas price increases the price significantly (CE Delft et al., 2015). From the different scenarios and trends in the Dutch electricity system several, consequences for the price peaks can be identified. Some consequences are good for specific flex options, while this consequence can have negative effects on the business cases of other flexibility providing technologies. The impact of these different parameters on the business cases of different flexibilization options is discussed in figure 4-11. Here the different types of flexibility are discussed with the biggest parameters influencing the current electricity prices. For each flex option the positive parameters are included in figure 4-11.

4.6.6 Position of scenarios in model

The scenarios used in this study are only a few of the numerous scenarios made for the different electricity markets. Since not every study agrees with the fact that the average electricity price will decrease the coming years (Hout et al., 2014), as it does in most of the scenarios made by CE Delft et al., (2015), the model is made in such a way that the included scenarios can be substituted. In this way, the model is capable of investigating the business cases under every electricity price scenario. Another important factor determining the business cases for flexibilization options is the number of flexibility suppliers present on the markets. What can be concluded from the scenarios with power2heat options included, is that more power2heat options decrease the number of low electricity price hours. Therefore, it is wise to install a flexibility technology in time, as the frontrunners will have a bigger chance the installation will meet the required ROI. When entering the market too late the prices are not profitable any more. However, the position on the merit order is the essential parameter which determines the business case opportunities. To test if the technology still has a positive business case when the number of players on the market increases, the ‘capacity to take hours on market’ variable is included in the model. By decreasing the capacity to take hours variable, the model decreases the number of hours the technology is able to tender flexibility to the market. This simulates the deteriorating position on the merit order when more flexibility providers enter the market. The consequences of the scenarios on the ROIs of different flex options will be discussed in the chapter 5.

Figure 4-11: Parameters which have positive effects on the business cases of the different flex options

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CASE STUDIES

In this chapter, several examples of flexibility providing technologies are investigated using the flexibility investment model. The most important parameters influencing the outcomes of each flexibility option are identified. By identifying these parameters, opportunities can be identified and main drivers can be analyzed to see what the most optimal conditions for each flex option are. In all cases, the assumptions are made that the network costs will not change, there is no subsidy available, the interest rate is 3.5% (Actuelerentestanden.nl, 2015; these loans are all tailor made, therefore the assumption is made that the interest rate is represented by the 10 year mortgage rates + 1% extra interest) and there is no additional reward for tendering flexibility to the grid. The model analyses the ROI period of each technology on the different markets. Here the assumption is made that the flex asset is capable of dealing on only one single market at the same time. In practice, this is not always the case, but since each flex option has its own market acting decisions, it is not possible to investigate the possibilities for all flex options individually. The capacity to take hours are all estimated relatively low, since this factor highly depends on the place of the technology in the merit order. Since this is not known yet, the risks are smaller when the capacity to take hours is estimated relatively low. All ROI periods for all investigated technologies under all market scenarios are summarized in Appendix G.

5.1 Energy storage

The first technology investigated in this chapter is the Lithium-ion battery. This is a form of energy storage. Lithium-ion is investigated since, currently there is a hype around Lithium-ion batteries. This has to deal with the introduction of Tesla’s Powerwall in spring 2015, making energy storing devices more available for individual consumers. However, there is also some criticism around the hype introduced by Tesla. The main arguments are that it still is too expensive and can only provide services for a few hours, not tackling the problems of long term imbalances in demand and supply (Jenal, 2015). In table 5-1 the current parameters of a big scale Lithium-ion battery are depicted. Table 5-1: Parameters Lithium-ion battery (McKinsey & Company et al., 2015)


CAPEX €/MW 375,000 FOM €/MW 10,000 VOM €/MWh 2 Capacity MW 5 Lifetime Years 12.5 Efficiency % 85

With this data as input for the model, the rewards, profits and ROIs can be calculated. The capacity to take hours is set relatively low since batteries have to reload after providing electricity to the system. With a capacity to take hours of 50%, 10% and 10% for respectively the Day-ahead, Intraday and Imbalance market, the following outcomes are calculated by the model. The Day-ahead market is only interesting under the limited development scenario. In this scenario the ROI is 8 years. For the other scenarios of the Day-ahead market the ROIs are 20 and 50 years. The interesting ROI for the limited development scenario has to deal with the relatively large number of high electricity price hours in this scenario. On the Intraday market the ROI periods vary between 12 and 53 years. The Imbalance market provides ROIs of 15 up to 31 years under the assumption of 10% capacity to take hours. From these outcomes the conclusion can be made that the current position of large scale Lithium-ion batteries is difficult. The risks are high and the investments are large. The risks can be decreased when the benefits of different markets can be stacked and if the other system services would be valuated. However, as discussed earlier, this valuation of system services is not expected to happen in the coming years. On a small scale, currently there is not a business case, since households are not financially attracted to

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the battery due to the ‘salderingsregeling’. When the CAPEX and OPEX costs decrease with 40% the coming years, the ROI values would decrease with 40% as well. This would lead to a ROI of 5 years in the Day ahead market in the limited development scenario. This all under the assumption that the trading decision is based on the average electricity price of that year (as discussed in table 4-1). The connection capacity is not decreased, since the capacity should be big enough to provide electricity to the grid.

5.2 Demand response

For demand response the business case is very case specific. This, since the economics involved in using demand response to tender flexibility are highly influenced by the system it has to fit in. Is the system allowed to be stopped if this saves money? The example investigated in this case is demand response in the Chlorine production process of AkzoNobel. This production process uses large amounts of electricity (200 up to 250 MW; Berenschot et al., 2015a). For the production of Chlorine, large amounts of electricity are used and therefore, electricity costs represent 43-45% of the total production costs of Chlorine (Egenhofer et al., 2014). Therefore, preventing consumption in very expensive hours can be beneficial relatively quickly. AkzoNobel made the production process more flexible by installing extra production and buffer capacity. The buffer will be filled in periods of low electricity prices and will be used to keep the output stable in periods when the electricity prices are high and the production process is decreased to provide the requested flexibility to the system. With this extra production and buffering capacity installed at the cost of 1.1 million Euro, the total response capacity is 73.5 MW (Berenschot et al., 2015c). This equals an investment of 15.000 euro per MW flexible capacity. The rest of the characteristics of the flex option are depicted in table 5-2.

Table 5-2: Parameters demand response (Berenschot et al., 2015c)


CAPEX €/MW 15,000 FOM €/MW 0 VOM €/MWh 0 Capacity MW 73.5 Lifetime Years 10 Efficiency Ton chlorine/MWh 0.28 Value Chlorine Euro/ton 474 (Kok et al., 2011)

The FOM and VOM are set to zero, since the operation and maintenance costs are already taken into account in the process cost itself. Therefore, making the process more flexible does not require extra costs for operation and maintenance. With an value of 474 Euro/ton Chlorine and an electricity share of 45% of the total production costs, the maximum price of electricity that still provides a profit is: 474 * 0.28 * 0.45 = 59.72 Euro per MWh. Taking this price per MWh as the decision price when to shut down the production process, in combination with a capacity to take hours of 20%, 1% and 1% on respectively the Day-ahead, Intraday and Imbalance markets, the ROIs under the current conditions are respectively 28, 51 and 4.1 years. The capacity to take hours factor is set low, since the main purpose of the company is to produce Chlorine and it will not earn money from sitting idle for long periods of time. Under the limited development scenario the ROI periods decrease further to 1.1 and 3.6 on the Day-ahead and Imbalance market. This has to do with the more extreme high electricity price peaks. From this case analysis it becomes clear that demand response can already be interesting in some cases, but it highly depends on the process characteristics. The value of the produced product in combination with the share of the electricity costs on the total production costs decides the attractiveness of demand response. In the example provided above, when the electricity costs would increase to 55%, the price decision would be around 73 euro per

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MWh. The ROI periods would then decrease only on the Imbalance market. The ROI period would decrease to 3.7 years under the current conditions and 3.2 in the limited development scenario. This decrease in ROI has to do with the relatively high prices AkzoNobel would get for the peak hours. The other markets have lower electricity prices and therefore, much less hours can be taken when the shutdown electricity prices increase significantly.

5.3 CHP units

The main dispatchable power generating technology, currently present in the Netherlands is combined heat and power (CHP). This technology is relatively cheap in comparison with other DPG units, which run on oil based products (Sijm et al., 2013). Due to the large number of CHP units present in the Netherlands, the data provided in table 5-3 was relatively easy to get to. Table 5-3: Parameters CHP unit (Schlatmann and Horstink, 2015)


CAPEX €/MW 420,000 FOM €/MW 0 VOM €/MWh 9 Capacity MW 1.5 Lifetime Years 10 Efficiency power generation % 42 Efficiency heat generation % 45

The gas price for large consumers was 20.41 €/MWh in 2014 (Schlatmann and Horstink, 2015). To produce 1 MWh of electricity with CHP units, 1 / 42% = 2.38 MWh gas is required. With a heat efficiency of 45%, the produced heat has the value of 21.86 € per MWh electricity output. When subtracting this from the VOM costs (as discussed already in paragraph 4.5.2), the ROI of the technology currently is 7.7 years and is 11 years under the limited development scenario in the Day-ahead market. This is when all heat and electricity can be used during all hours per year. Since the heat demand is highly related to the weather conditions, the estimation is that 70% of all interesting hours can be used (mostly during the winter; Van der Valk, 2015). When using this as input for the Day-ahead market, the most optimal scenario gives a ROI of 11 year. This is longer than the lifetime of the technology and makes it uninteresting to invest in CHP units. For the Intraday and Imbalance markets it is much harder to ensure a production period. This since the demand for electricity does not always match the heat demand of the investor. Due to this limitation, a cautious estimation of the hours is taken into account. When the CHP units can take 10% of the interesting Intraday and Imbalance hours to produce electricity, the most interesting ROIs still are 11 and 10 years on these markets. All results of the model with the input of table 5-3 are provided in Appendix G and these results provide an overview of the current position of the CHP units in the Netherlands. It is clear that the technology has difficulties to survive. This is also mentioned by the report of Peeters et al., (2014). The most important parameter for CHP units to decrease the Return on investment period is the spark spread. When the difference between the gas price and the electricity price increases (gas price lower or electricity price higher) the business case of CHP units will improve significantly. This can be illustrated when we decrease the current gas price from 20.41 to 10 Euro per MWh in the model. In this case, the ROI period decreases from 10.5 years to 2.5 years on the Day ahead market. When the gas price is 15 Euro per MWh the business case has a ROI of 4.1 year on the Day-ahead market. What can be concluded is that the gas price is crucial for a good business case. An increasing gas price decreases the business case significantly.

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5.4 Electric boiler

There are numerous options to produce heat with electricity. In this paragraph, the conventional electric boiler is used to heat up water. This technology itself has an efficiency of 99% (Rooijers et al., 2014). The rest of the characteristics of this technology are depicted in table 5-4. Table 5-4: Parameters electric boiler (Rooijers et al., 2014)


CAPEX €/MW 130,000 FOM €/MW 1,100 VOM €/MWh 0.5 Capacity MW 3 Lifetime Years 20 Efficiency % 99

With these parameters used as input in the model, together with the assumption that this technology will be able to trade 10% of the interesting hours on the Intraday and Imbalance market, the ROIs highly diverge. On the Day-ahead market of 2014 with a capacity to take hours of 100%, the ROI is 117 years, while the other scenarios provide ROIs of around 5 years. The low ROIs in the limited development and green and flex scenario have to do with the relative high gas prices in these scenarios (35 and 25 €/MWh) in combination with the large number of cheap electricity hours. On the Intraday and Imbalance markets the best ROIs are respectively 30 and 10 years. When the gas price would decrease to 15 Euro per MWh in the limited development scenario, the ROI value has increased with 4 times to 21 years. From this can be concluded that also for Power2heat business cases, the gas price is highly important. Despite the high ROIs of electric boilers, power2heat still can be interesting. When the efficiency increases from 99% to 200% (using heat pumps) the ROIs are all under 4.2 years in the case of the Day-ahead market, making them interesting to implement. This radical change in ROI has to do with the price difference between gas and electricity. With a COP of 2, electricity becomes cheaper than gas for most of the year, highly increasing the full load hours. With this increase in interesting hours, another problem arises. When it is cheaper to run the technology for most of the year, the technology is not interesting anymore to use as a flexibilization option. The technology causes electrification of the heat demand, but does not provide flexibility and will increase the demand for flexibility even more.

5.5 Power2hydrogen

The case used to investigate the use of electricity for the production of products (Product2X), is the production of hydrogen. Currently, most hydrogen is produced from methane, but it can also be produced by electrolyzing water (Mieog et al., 2014). To do so, large amounts of electricity are required. The characteristics of the production of hydrogen from electricity are visualized in table 5-5. Table 5-5: Power2hydrogen characteristics (Mieog et al., 2014)


CAPEX €/MW 2,000,000 FOM €/MW Not found = set on zero VOM €/MWh 0 Capacity MW 5 Lifetime Years 10 Hydrogen production per MWh kg 21.73 Availability per year % 90 (Grond et al., 2013)

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With a hydrogen price of 1.50 €/kg (Mieog et al., 2014), the value of the produced hydrogen per MWh electricity input is 32.60 Euro. Without taking the FOM and VOM costs into account, the installation is only profitable with an electricity price below the 32.60 Euro/MWh. These O&M costs are not taken into account, since no data was provided by Mieog et al. (2014) and there is even no business case without O&M costs. With the capacity to take hours of 90% for the Day-ahead market and 10% on the Intraday and Imbalance market, all the ROI values exceed the 55 years. Making them not interesting for tendering flexibility to the market. When the market value of the produced hydrogen increases to 3.00 euro/kg the technology becomes more interesting since the maximum allowed electricity price increases in that case from 32.60 to 65.19 Euro per MWh. With this in mind the ROI values decrease to 12 years minimum. However, this still is longer than the 10 years lifetime making the technology still too expensive to install.

5.6 Interconnection

For the case of more interconnection capacity, it is very hard to make generalized conclusions. This, since all transport capacities have their own case specific costs. Over what distance does the cable has to be installed and does the other hardware facilitate the new capacity, or does this hardware has to be replaced as well? Despite the high situation dependency, some general numbers are depicted in table 5-6. Only the MS cable is taken into account, since the high voltage grids are currently not suffering from overloads and therefore do not need extra investments (Slingerland et al., 2015). Table 5-6: Average prices grid enlargement (van Melle et al., 2014)

Material Specification Costs (€)

MS-cable (per meter) 3 x 240 AL 80 3 x 1 630 AL 140

Transformer MS/LS (per piece) 250 kVA 8,500 400 kVA 10,500 630 kVA 14,000 > 630 kVA 35,000 Connection < 630 kVA 8,350

> 630 kVA 3,500 Transformer HS/MS (per kW) General 55

The infrastructure shown in table 5-6 have a lifespan of 40 years, which is significantly longer than the 10 years most flex options have (van Melle et al., 2014). Therefore, the costs for the prevention of congestion are cheaper to solve with extra transport capacity instead of installing other flex options. These other flex options have to be overhauled three times in the same timespan as the extra cables should be replaced. Therefore, extra transport capacity is most of the time cheaper than other flex options to prevent congestion.

5.7 Conclusions current technologies

In this chapter, five different business cases are evaluated. The end result of the analyses was to come up with ROI values for each flexibility providing technology under different scenarios. The outcomes of the analyses are summarized in figure 5-1. To make the differences between the different ROIs more clear, the Y-axis is set to 50 years. Due to this small scale the ROIs of power2hydrogen fall of the scale. The exact values of the ROIs are included in Appendix G. What can be concluded from figure 5-1 is that currently only Demand response is interesting to invest in. This since the ROIs of the other technologies exceed the lifetime of the technologies, meaning that the technology has not been paid back before it needs a major overhaul. Figure 5-1 is conducted using the lowest ROI of one of the three electricity markets included in the model. Which market is chosen for which technology is shown in Appendix G.

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In the limited development scenario all flex options have lower ROI values. This has to do with the longer low electricity price periods on the one side, making Electric boilers and Power2hydrogen more interesting. On the other hand Demand response, Energy storage and CHP units have a better business case due to the more extreme price peaks in this scenario. The green and flex scenario is characterized by lower extreme price peaks in comparison with the limited development scenario. As discussed in the scenario paragraph, this has to do with the high flexible CHP capacity, decreasing the must run capacity and therefore the low electricity price hours and the high back-up capacity of these CHP units, decreasing the high price peaks. Due to these lower price peaks the business cases are less positive as the limited development scenario. However, due to the more low price periods in comparison with the APX prices of 2014, Electric boilers and Power2hydrogen have lower ROIs.

What can be concluded from the case studies analyzed above is that there are some parameters highly influencing the business cases of different flexibility options. As expected, is the CAPEX highly important, since the CAPEX is divided by the yearly profit to get the ROI period. A CAPEX half the size automatically means a ROI which is divided by two. Beside the CAPEX costs, the variable O&M costs are highly important. This since the VOM does not only influences the profit the asset gets, it also has influences for the position on the merit order. Meaning a low VOM results in a better position on the merit order and a better option to get the interesting price hours. Beside the factors important for all technologies, there are some specific parameters having big influences for specific flex options. These parameters are discussed below. For power2heat options, the COP value is very important to take into account. This since in this option, gas is substituted by electricity. When the COP value of the technology increases, the technology becomes interesting for most hours of the year. This results in an option that will be used to save energy or as an electrification step instead of providing flexibility to the grid. Under the current conditions, this is already the case when the technology has a COP of 2. For Power2X, the market value of the produced product is the main parameter influencing the business case. For the investigated case of producing Hydrogen with electricity, the value of the hydrogen is too small to make an interesting business case. For demand response, the main value of interest is the electricity price in comparison

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Figure 5-1: ROI periods for different flex options according to the flexibility investment model

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with the value of the produced product per MWh electricity input. When more value is created by using the electricity than that is saved when the uptake of electricity is stopped, the demand response is not profitable. For CHP units the spark spread currently is not in favor of the technology. When the gas prices decrease in comparison with the electricity prices, CHP units will become more interesting. To make CHP units more interesting, the value of the heat in combination with the value of the produced electricity should be higher than the heat value that should be produced by conventional boilers, which have high heat efficiencies. Only when this is the case, CHP units are more interesting than conventional boilers. From these case studies the interesting technologies can be identified. Currently, interconnection is the cheapest option to prevent congestion. This has to do with the relatively long lifetime of the infrastructure in comparison with other congestion preventing options. On the different electricity markets, currently, only demand response is interesting in some cases to provide flexibility to the system. This however, is very case specific and depends on the process that is used to provide flexibility. Under the limited development scenario, the average electricity prices increase but the high and low peaks will also become more extreme. This in combination with a higher gas price significantly improves the business cases of all flex options with the exception of CHP units. This exception has to do with the relative high gas price. In the case of the green and flex scenario, the electricity prices are lower and the peaks less extreme. This has to do with the high capacity of flexible CHP units in this scenario. Minimizing the high price peaks. The gas price (25 Euro/MWh) is only slightly higher than today (20.41 Euro/MWh). Here the ROI periods will decrease only for Power2X. To make Power2X interesting the technology has to run a relatively high number of hours per year. With a decreasing average electricity price during the year, the green and flex scenario is interesting for Power2X. The less extreme price peaks makes the green and flex scenario a less interesting scenario for the other flex options.

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DISCUSSION

6.1 The position of this study

The last years, there is a growing interest in renewable energy technologies. This growing interest can be seen in every governmental and societal level and is mainly focusing on variable renewable energy sources (VREs) like wind turbines and solar PV systems. Together with the growing share of VREs, the challenge of balancing the electricity grid has raised more attention. Balancing the grid is important since it is very hard to store electricity on a large scale. The growing attention for flexibility can be seen in households which are triggered by large popular companies, like Tesla and BMW to install energy storing batteries in their houses, to industries investigating the opportunities for flexibilization of their production processes. This study investigated the opportunities and possible pitfalls to tender flexibility to the market. One of the first findings was the big interests of conventional power plants in the flexibility market. Currently, almost all flexibility is supplied by gas and coal fired power plants. Possible investors in flexibility (industry, households, DSOs, greenhouse owners etc.) have a smaller share in the market and have to fight against the big players and their interests on the markets. This study tried to give practical information for interested investors in flexibilization options. What are the main drivers for the need for flexibility and how will these parameters change in the coming years? The outcomes of this study do not provide a silver bullet or a roadmap for flexibility, however, it does provide indications of business cases in a changing electricity system.

6.2 Data availability

Possible interesting technologies for the coming years are identified using currently available data. The current CAPEX and OPEX costs are used to identify the economic attractiveness of flex options for possible investors. How the costs will develop the coming years highly depends on the amount of R&D spent on it, the breakthrough done on these technologies and the fuel price trends (oil, gas and coal prices). Breakthroughs however, can happen always and everywhere in the system. This means that the expected costs for the technologies can be materialized much faster or slower than expected. Therefore, the future CAPEX and OPEX costs are highly uncertain. To keep the flexibility investment model also valid when there are technological breakthroughs, the CAPEX and OPEX costs can be adjusted in the model. Another point where data is not available, is the exact periods a market has price peaks or dips. Therefore, the opportunities for flex options to deal in certain periods cannot be quantified exactly. The knowledge of certain timeframes is especially important for investors who want to do DSM, but have an energy demand which has peaks in working days and hours, and have a small consumption during nights and weekends. This, since the price peaks are present during the week and day hours. Small electricity storage units which can only take up electricity until they are completely loaded, also have interests in better predictability. Better predictability of the price peaks will highly benefit the business cases of these flexibilization options. In this study, the fact that the periods of price peaks are unpredictable is tackled using the ‘capacity to take hours’ parameter in the flexibility investment model. This parameter can be set low when an investor thinks he will not be able to take most of the interesting hours and he can in this way test the robustness of the technology in his system. The last but biggest point of discussion in this report is the fact that parts of the model are not validated using real data. This has to do with the fact that most flex options are not implemented yet and there are no other models to validate the flexibility investment model with. The absence of other models has to do with the relatively new interest in the topic of flexibility in the grid. Due to the limited implementation of most flex options today, market conditions of these options are not quantified yet.

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When using the model to identify interesting flexibilization options, the lack of validation has to be kept in mind. When flex options are implemented on large scale and on the different markets, this data can be used to validate the other parts of the model. However, to support the conclusions drawn from the not validated flexibility investment model, the conclusions will be compared with other studies. This will be done in paragraph 6.4.

6.3 Limitations

This study has limited itself to the investigation of business cases until 2023. This is done, since the Dutch renewable energy policy (National Energieakkoord) has limited itself to the period until 2023. Due to this limitation, the assumption is made that the valuation of the system services, flex options can provide (discussed in paragraph 3.1.3), does not take place. This however, can change in the period after 2023, since the transition towards renewables will continue. However, the model is developed in such a way that it can include the valuation of these system services and therefore, still will be valid if this valuation takes place. This is also the case for the electricity prices included in the different scenarios. In the period after 2023, there will be more changes in electricity prices due to more VRE capacity, increasing demand or external parameters like changing CO2 prices. The changes in electricity prices however, can be included in the model by changing the included scenarios. In this way, the flexibility investment model still will be usable for the period after 2023. Beside parameters influencing the electricity prices and therefore the different scenarios, there are parameters that can influence other parts of the electricity system. With a changing electricity system the business cases for flex options can change as well. Examples of these parameters are; changing legislation, social opposition against certain flex options and cooling international relationships. The consequences of these parameters are not included in the model and therefore cannot be analyzed by the flexibility investment model. However, changes in these external parameters can have significant influences on the opportunities for flex options. For example, when CHP units are not allowed anymore due to strict environmental policies, the business case is completely gone. Another limitation of the flexibility investment model itself is the separate approach of the different electricity markets. The business cases are identified on one market at the time, while flex options can be active on multiple markets during the year. By being active on multiple markets, the business case can be improved significantly. When analyzing the business cases more in detail, the options on the different markets have to be analyzed to determine a more precise ROI. However, since this study intended to provide information of interesting flex options, the business cases will only improve, when multiple markets are included in one business case and therefore do not alter the conclusions. The focus of this study was on the economic attractiveness of flex options in the Dutch electricity grid. However, beside economics there are other parameters important for interested investors in flex options, which are not or only limitedly included in this study. The first one is the physical implementation of the technology. Does the flex asset fits inside the investor’s system and is the capacity not too large or too small? This in combination with the risks involved, has a big influence on the investment decision (Krebbekx et al., 2015). Beside the physical requirements it is also important to analyze the position of the flex technology in the system before implementation. What will be its place in the merit order and how quickly can the technology ramp up or down to stabilize the system? The third parameter important for investors is the required knowledge to operate the technology (Krebbekx et al., 2015). If it is very hard to operate, investors will search for other options. Beside all these parameters, the social acceptance of the technology is important to take into account, especially for households. Since most of these parameters are hard to quantify and the model was economically oriented, they are not included in the model. However, they have to be analyzed before the technology is implemented.

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The last limitation of this study it that it is focusing on the electricity market alone. The business cases for flexibilization options in the electricity markets are identified. However, a more integrated study has to be done to investigate the options for a more integrated energy system. This has to be done, since the heat, electricity and transport sector are highly interconnected nowadays (Dutch Ministry of Economic Affairs and Netherlands Enterprice Agency, 2015). As an example of a limitation of this study, compared to a more interrelated approach, is the fact that the gas price is fixed in the model. This however, is not the case in reality, as the electricity is traded on the APX spot market, gas is traded on the ENDEX spot market (Autoriteit Consument & Markt, 2014). There possibly is a relationship between the electricity and gas prices in certain periods during the year. This has influences on the business cases of some flex options like CHP and power2heat. Also does this study not identify the additional value of different flexibility options to the Dutch electricity system, discussed in chapter three. With a more integrated approach, flexibility options can be rewarded for the extra services they provide. Also the consequences of installing more flexibility in the system to the electricity prices is not incorporated in the flexibility investment model. This however, should be taken into account since more flexibility options in the system, deteriorate the business cases for themselves and other flexibility options.

6.4 This study in comparison with other studies and countries

The results of the flexibility investment model can be compared with other studies investigating the different flexibilization options. Cochran et al., (2014) indicated the relative costs per flexibility asset. These technologies are positioned in the merit order Cochran et al., (2014) estimated they would be applied (figure 6-1). The differences between the different technologies does not visualize the real differences in costs (Cochran et al., 2014). The figure created by Cochran et al. (2014) depicts the current situation. This does not mean that this cannot change the coming years. Therefore, it is important to investigate all options for flexibilization of the electricity grid. Since flex technology costs highly depend on the situation it has to fit in, figure 6-1 is not a blueprint for all situations.

Figure 6-1: Relative costs of flexibility options (Cochran et al., 2014)

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What can be concluded from the study of Cochran et al., (2014) is that the prevention of imbalances is the cheapest option to prevent flexibilization costs. However, when looking for options to solve imbalances in the system, industrial demand response is the cheapest option to provide flexibility to the grid. This is the same conclusion as what can be made from figure 5-1. In the study of Cochran et al., (2014) Thermal storage (power2heat) is cheaper than the use of gas turbines and CHP units. This is also the case for the results of the flexibility investment model for the limited development and green and flex scenarios. However, this study has focused on the Dutch electricity system, which is dominated by coal and gas fired power plants. The demand for flexibility is different in other countries and parts of the world, creating other return on investment periods for flex options in these countries. In Germany the demand for flexibility has increased significantly the last years. This is mainly due to the large increase in (subsidized) VRE capacity (Agora Energiewende, 2014). However, as in the Netherlands, the currently present conventional power plants will be capable of balancing the system (Agora Energiewende, 2014). Nevertheless, due to a higher VRE capacity installed, the system services that additional new flexibility options can provide can have a significant benefit for the system (Agora Energiewende, 2014). Therefore, the business case can be more robust as in the Netherlands. To improve the business cases in Germany as in the Netherlands, new flexibility options have to become one of the tools to ensure a stable and cost-efficient system. The system has to be designed in such a way, flexibility options have equal opportunities (Agora Energiewende, 2014). The business case is very different in the U.S. The U.S. have a bad grid infrastructure in some parts of the country (International Energy Agency, 2014c). Therefore, more flexibility in the grid can have significant benefits also in other parts of the system. Local storage and dispatchable power generating units can increase the power reliability significantly, ensuring a more stable grid (International Energy Agency, 2014c). Another benefit for flex options is the more suitable climate for especially solar PV systems, increasing the stress on the system. More local use and storage of electricity therefore can be very interesting. The U.S. energy legislation is also more in favor of multiple flexibility options, since there is a better level playing field for these options than in the Dutch situation. For example, energy storage is used as a tool to prevent congestion. Also demand side management has a more free entrance to the markets in comparison to the Dutch situation (International Energy Agency, 2014c). The business case for flexibilization options using high electricity price peaks is significantly different in the Belgian case. This has to do with the lack of production capacity, due to the closure of multiple nuclear power plants (Dichtbij, 2015). These power plants provide base load electricity for the Belgian electricity system. Due to this lack of production capacity, the electricity prices are relatively high and the system suffers from extreme high price peaks. On the 22nd of September 2015, the Belgium system even faced day ahead prices of 448 Euro per MWh (Dichtbij, 2015). Demand response, energy storage and CHP units can benefit from these extreme price peaks improving the business cases significantly. Since the Netherlands has an overcapacity of coal and gas fired power plants these price peaks will not occur in the Dutch situation.

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CONCLUSION

The main aim of this study was to investigate the possibilities for business cases for flexibility providing technologies in the Dutch electricity system. Which technologies are interesting to provide flexibility to the electricity system? The findings of this study can be grouped in the three paragraphs discussed below, first the electricity market conclusions will be discussed than the flexibility investment model is explained and at last, the position of the different flexibilization options is concluded.

7.1 Electricity markets

Currently, there are three markets where flexibility can be tendered. These markets are the Day-ahead, Intraday, Imbalance market. These markets deal with different time intervals. The Day-ahead market is dealing with the electricity trade one day before implementation. The price volatility in this market has decreased in the last years. The average price has also decreased the last years, to 41 euro/MWh in 2014. The Intraday has increased significantly in volume the last years and the expectations are that this market will continue to increase the coming years. Despite the more real time trading, the price volatility still is relatively small in this market and the prices are close to the Day-ahead market prices (42 €/MWh in 2014). The Imbalance market has remained stable over the last years (61 €/MWh ramp up and 32 €/MWh ramp down) and the estimations for the future Imbalance market are highly uncertain and contradictive. Some experts estimate the Imbalance market will increase in volume and price volatility, due to more unpredictable VRE capacity in the system. Other experts estimate most VRE production fluctuations can be absorbed by more and better weather predictions. Due to this better predictability, electricity can be traded on the Day-ahead market instead of the more expensive Imbalance market. Despite the high uncertainty in these markets, there are three major parameters that will influence the market prices. The main parameters are the amount of variable renewable energy capacity that will be installed the coming years, the installed flexible CHP capacity and the total demand increase. Starting with the VRE capacity which has an effect on the low price periods in the markets, due to the very low marginal costs VRE technologies have. Therefore, these technologies can tender their electricity very cheap to the markets. Another important parameter included in the different scenarios is the CHP production capacity that is (made) flexible. CHP units can provide a large share of the flexibility required in the system. However, as discussed in the scenario chapter, this is only possible when the must-run CHP units are made flexible. This has effects on the low electricity prices, since there is less must-run capacity and it affects the high electricity prices, since CHP units can ramp up when demand is high. The last major parameter included is the increasing demand. This has effects on the electricity prices, since a higher demand means most of the time higher prices. The intersection on the merit order shifts to the right, increasing electricity prices at that moment. The amount of flexibilization options like DSM and energy storage installed can have a significant effect on the price volatility and therefore on their own business cases. Beside the three electricity markets discussed above, there are other market mechanisms that can ensure a stable and secure grid in the coming years. Examples of these market mechanisms are a capacity market mechanism, frequency control and local (smart grid) markets. Despite the introduction of these markets in neighboring countries, the expectations are that these markets will not be implemented in the Netherlands soon. This, due to the good condition of the Dutch grid. Beside the possible market value of the flexibility assets, these options can also provide other services to the electricity grid. One of the biggest values of flex options for the grid is the prevention of congestion. The different scales of the system have their own demand for extra flexibility. The transmission system operator (TSO) TenneT is dealing with the high voltage grid. TenneT still has enough transport capacity left so there is no additional need for flexibility in this part of the system. The distribution system operators (DSOs) however, do have problems of congestion in their medium and low voltage grids. More flexibility on a local level could help to prevent congestion problems for DSOs. However, DSOs

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are currently obliged by law to enlarge the transport capacity, meaning extra cables have to be installed. DSOs and TenneT are not allowed to install other flex options to prevent congestion, since they are not allowed to deal on the different markets. Due to this regulation, valuation of the system service provided by flex options is currently not present. Other services flex options could provide to the system, which are also not valuated today, are: decreased grid connections, environmental benefits, less back-up capacity required and an improved income for renewable energy technologies. When these services are rewarded in the future, this would significantly improve the business case for different flexibility assets.

7.2 Flexibility investment model

To analyze the position of the different flexibilization options, the flexibility investment model was developed in this study. The model analyzes different flexibilization options on their economic attractiveness for investors. An economic analysis was executed, since investors indicated that economics are the most important factor, influencing their investment decision. To analyze the economic attractiveness, the return on investment period is calculated by the model. This provides information to the interesting investor about the time it will take to earn the invested money back. The most important parameters included in the model are the total investment costs (CAPEX), the operation and maintenance costs (OPEX), the network costs and the rewards. The CAPEX and OPEX costs are based on the technology characteristics. This is an external input, since every technology has its own costs and specifications. The network tariffs are included, since these costs can highly influence the business case for flex options. When flex options alter the electricity consumption pattern of an investor, this can result in higher network costs, decreasing the business case for flex options. The rewards included in the model are build up from the prices on the different electricity markets. Multiple scenarios are included to test the robustness of the flex options under different price ranges. The scope of this study was on the period until 2023. However, the model is built in such a way that the scenarios and network tariffs can be adjusted relatively easy and therefore the model can still be useful for the period after 2023, or in the situation where the electricity prices develop in a different direction as expected in the included scenarios. Beside the economic attractiveness of a flex option other parameters are important to take into account when analyzing a flex option. The physical installation of the system, the risks involved, the required knowledge level and the social aspects also have to be analyzed before implementing a flexibilization option. Due to the high case specific information needed to analyze the business case, there is no ‘silver bullet’ for the best investment in technologies improving the flexibility of the Dutch electricity grid. With this in mind and with the uncertainty about the scenarios and the Intraday and Imbalance market trends, it can be wise to invest in energy saving technologies instead of taking high risks in dealing on the volatile markets. This, since energy saving technologies do have ROIs lower than most flex options.

7.3 Position of flexibility options

In this report the different options to provide more flexibility to the electricity system are discussed. These options can be clustered into four main groups respectively; Demand side management (DSM), Energy storage (ES), Dispatchable power generation (DPG) and Interconnection between markets (ICM). All these options have their own opportunities and challenges to overcome. Energy storage has multiple challenges to overcome. Therefore, it currently is not interesting to invest in this technology. The unattractiveness has to do with the competitive position of electricity storing devices. The price of storing electricity still is higher than the price of current flex options (gas turbines and CHP units) and the ROIs are still above ten years. Energy storage will become more interesting

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when the price peaks are more extreme as is demonstrated in the limited development scenario. Here the ROI of Lithium-ion batteries dropped to 8 years. The high investment costs in combination with high network costs make large scale energy storage not interesting in the Netherlands. The network costs have to be paid, since electricity storage is not a separate asset class in the Dutch electricity law and therefore, storage has not the benefits electricity producers have. Storage behind the meter can become more interesting when the ‘salderingsregeling’ will be changed. Due to this regulation the price differences between uptake from and delivery to the grid are not present, making electricity storage not interesting. When this ‘salderingsregeling’ would be changed electricity storage can become more interesting, especially for prosumers which can benefit from their cheaply produced electricity. Demand side management is a technology that can adapt its electricity demand to stabilize the grid or to react on price incentives. Some technologies can ramp up their electricity demand (electric boilers, power2hydrogen) while other technologies decrease their demand in the moments this is needed or profitable (stop cooling in expensive hours, up to stopping production processes in industry). Implementing DSM technologies is challenging on all levels. On a household level, the price incentives are too small, due to the small price difference between day and night tariffs. On an industrial level, DSM has to deal with a network tariff system that increases when the peaks in the demand pattern increase. This in combination with high administrative and organization costs makes the business case for Demand side management currently not very interesting. In this study, three cases of DSM are investigated using the flexibility investment model (power2heat, power2hydrogen and demand response). The conclusion from the model is that currently only demand response is interesting in specific cases (ROI = 4.1 year) to provide the requested flexibility. Power2heat is interesting with technologies with a COP value higher than two. With a higher COP value the technology will become profitable for most hours of the year. With this increase in interesting hours a problem will arise. Power2heat will not be used as a flexibilization option, but as an electrification or energy saving measure. Power2hydrogen suffers from a low Hydrogen price, making the investment not interesting. In 2013, 22.3% of all electricity produced in the Netherlands was produced with decentralized gas units, mainly CHP units. These CHP units face difficulties today, due to the low electricity prices in combination with the relative high gas price, it is hard for them to make a profit. Currently, the ROI period of CHP units is 11 years in the Day-ahead market, making them not interesting to invest in. Therefore, the trend is to make must-run CHP units more flexible. In this way, CHP units only have to produce electricity when the electricity prices are high enough. Since these units are already present, they can provide large parts of the flexibility demand. Therefore, this is a major parameter to keep in mind when analyzing the market developments and more specific, the need for flexibility. If the technology will have the best place on the merit order in comparison with other flex options and therefore, will provide most of the requested flexibility, depends on the spark spread. One of the most important services local flex options can provide is the prevention of congestion. With the prevention of congestion, large investments in extra infrastructure can be prevented, saving large amounts of money. This prevention of congestion is often brought up as the biggest benefit of local flex options. Despite the technical opportunities to use flex options to prevent congestion, DSOs are currently not allowed by law to use them. DSOs have to prevent congestion by installing extra distribution capacity. Beside the legal challenges, installing extra cables also is the cheapest option to prevent congestion. The reason for this is the long lifetime (40 years) of this infrastructure in comparison with other flex options (lifetimes around 10 years). This means that the use of flex options currently is not interesting for the prevention of congestion problems. Interconnection with neighboring countries will have significant effects on the Dutch electricity markets. In general the average price will decrease. More periods of low prices will be available in the Netherlands, mainly due to the higher VRE implementation in neighboring countries. Due to the flow-

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based market mechanism introduced on the 20th of May 2015, the electricity prices decreased significantly in the Netherlands. Despite the fact that interconnection can help to balance the system, it cannot provide all demand for flexibility. This since weather patterns occur on continental scale and weather conditions in the Netherlands do not only affect the Dutch electricity system, but also the electricity system of its neighboring countries. Interconnection with other parts of Europe or the world is also not an option, since this has to deal with high costs and energy losses. Beside all technology specific challenges, there are also some overall barriers preventing other flex options from gaining market shares. The biggest challenges are the Dutch network tariff structure, the fact that flexibility technologies with a small capacity find difficulties entering the market, the system benefits are not all valuated and environmental effects are only limitedly taken into account. Due to these challenges, there is no level playing field for all flexibilization options in the Dutch electricity system. The current electricity system in the Netherlands is designed from an electricity generators perspective, who provides the current demand for flexibility. To give all flexibilization options an equal chance of gaining market shares, a level playing field is required. However, since the current market is relatively safe, reliable and affordable it is questionable if this level playing field will be implemented in the coming years. The main conclusion from this study is that currently only interconnection and demand response are interesting. However, the business case of demand response is highly case specific. For the period up to 2023, demand side management can become interesting when the number of low electricity price hours increases. The business case of CHP highly depends on the spark spread, while electricity storage benefits from a higher price volatility. How the business cases of flex options will look like in 2023 highly depends on how the markets will develop the coming years.

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RECOMMENDATIONS

To improve the findings of this study and to improve the investigation of business cases for flexibilization options, multiple follow up studies can be executed. This chapter will discuss the options to improve the conclusions and the possibilities for follow up studies. The options for new studies involve the modelling of the Intraday and Imbalance markets, and the best policy design to provide a level playing field. To improve the conclusions of this study more parts of the model need to be validated. These points of improvement will be discussed below. In this report, the different scenarios are explained from a Day-ahead market point of view. This is done since the data found on electricity market scenarios is focusing on the Day-ahead market. The scenarios made for the Intraday and Imbalance market are created based on the data provided by the Day-ahead scenarios. More research on underlying internal and external mechanisms influencing the Intraday and Imbalance markets in combination with price scenarios of these markets will improve the accuracy of the outcomes of the flexibility investment model on these markets. To do so, a quantitative analysis of these markets can reveal the underlying trends and mechanisms in these markets. The information of these mechanisms can help to make investments in flex options, operating on the Intraday and Imbalance markets, more robust. Another option to improve this study and the energy system as a whole, is the investigation of an optimal design for a level playing field for flex options. How can the Dutch electricity and energy system be designed in such a way that all flexibility options have the same chances. This study has to be done in combination with a study on what is the most cost-efficient system. To reach this, the electricity system should be incorporated in the complete energy system, since electricity is an energy carrier that can substitute, but also be substituted by other energy carriers. Electricity is an energy carrier that can be produced relatively easy from renewable energy sources. Therefore, the use of renewable electricity is one of the easiest options to make the energy system more sustainable. However, when electricity is used to make the energy system more sustainable, the demand for flexibility will increase significantly. Therefore, a system integrated study on the most optimal energy system design and the relationships with its internal and external environment is required to come to a system that remains safe, reliable and affordable in the coming decades. To improve the outcomes of the flexibility investment model developed in this report, more parts of the model need to be validated. To do this, more business cases of flex options have to be compared to real life cases. Currently, this is difficult since only CHP units and interconnection are used to provide flexibility to the system. Energy storage and demand side management are not implemented on a large scale yet. Demonstration sides where these technologies are implemented can be installed to provide cases to test the technical and economic suitability of the technology in the system. From these demonstration cases, case specific data can be obtained that can be used to validate parts of the flexibility investment model.

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APPENDIX A

The Dutch energy system

The Dutch electricity system is one of the most liberalized ones in the World. The Dutch electricity act of 1998 gave the producers and consumers more freedom to trade electricity (Tanrisever et al., 2013). Before the act, the Dutch electricity market was dominated by a small number of big players on the market. After the liberalization, smaller producers and suppliers have integrated into the Dutch electricity system, making the market more divers (Tanrisever et al., 2013). To identify stakeholders in the Dutch energy system to make the system more flexible, all links in the electricity system with their addressing stakeholders will short be introduced by Energie-Nederland and Netbeheer Nederland, (2011). The gas infrastructure of figure 10-1 will not be discussed since the scope of this research is on the electricity grid. The links and their corresponding stakeholders are discussed below per subgroup of the system:

The links in the system have different characteristics. Some of the stakeholders deal with the physical delivery of electricity while others participate on the markets or even the external framework. The stakeholders are clustered and discussed in the different groups below.

Figure 10-1: Schematic overview of the Dutch energy system (Energie-Nederland and Netbeheer Nederland, 2011)

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Physical network

First, the physical delivery of power will be discussed. All stakeholders taking part in the physical delivery part of the system are connected with blue lines in figure 10-1. The stakeholders which are connected to the blue lines and transport electricity will be discussed in this paragraph. Here the position of the system is discussed as is the need for flexibility in the system. Where in the system is demand for flexibility and who is responsible for this flexibility? Electricity generation: electricity is produced mainly in large power plants, owned by large energy companies. As discussed above, the energy generation market is completely liberalized, resulting in a situation where a major part of the power plants positioned in the Netherlands is owned by non-Dutch companies. Examples of foreign companies producing electricity in the Netherlands are Vattenfall, Dong and RWE (Tieben et al., 2013). These central power plants produced 63% of the total Dutch electricity in 2012 (Tieben et al., 2013). The rest was produced decentralized, mainly with CHP units (ECN et al., 2014). The power production by decentralized generators will increase in the coming years mainly due to the increase of VRE technologies (ING, 2014). Therefore, the number of market players will increase as well. Also the import of electricity is part of this link in the chain. In 2012 roughly 20% of all electricity was imported, mainly from Germany (Tieben et al., 2013). The role of the electricity generator is influenced by many factors depicted in figure 10-2.

What can be concluded from figure 10-2 is that there are many parameters influencing the current electricity system and the electricity generators in addition. Most of these parameters are discussed in the rest of this report. However what can be concluded already is that the role of the electricity generator is becoming more and more difficult nowadays. The traditional electricity producers have difficulties maintaining their profits and market shares (Peeters et al., 2014). This can create problems in the future, as a major part of the flexibility demand is currently provided by conventional power plants (Hout et al., 2014).

Figure 10-2: The many boundary conditions of an electricity generator in a free market (Klimstra, 2014)

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National transport grid: the stakeholder responsible for the national high voltage grid is called a transmission system operator (TSO) and this is the company TenneT in the Netherlands, of which the Dutch government is 100% shareholder. TenneT has a monopoly as a Dutch TSO. The role of TenneT is to secure a balanced electricity grid and the enabling of interconnection capacity with other electricity grids in Europe (Energie-Nederland and Netbeheer Nederland, 2011). To ensure a stable electricity grid, TenneT is responsible for the near-real time imbalance market. The second role of TenneT is enabling electricity trade with other countries. TenneT is doing this by building interconnection cables between the neighboring countries of the Netherlands. To deal with the energy trade with other countries in the EU, the ENTSO-E has made standardized rules to which each TSO in North West Europe has to apply (ENTSO-E, 2009). TenneT only deals with the high voltage grid. The middle and low voltage grids are managed by the distribution system operators (DSOs). Of all the electricity grids in the Netherlands, 4% is high voltage grid and therefore managed by TenneT (Tieben et al., 2013). As discussed in the chapter 2, congestion might occur in some parts of the system. However, the national transmission grid will be capable of dealing with more VRE. There is enough capacity to cope with large fluctuations in the grid (Slingerland et al., 2015). The problems might occur on other levels of the grid. Regional grids: are operated by distribution system operators (DSO). A DSO has the responsibility for the medium and low voltage grids. Most of DSOs belong to the traditional electricity suppliers but, they are legally independent from the suppliers. This is done to ensure grid access for other players (Energie-Nederland and Netbeheer Nederland, 2011). There are eight DSOs currently operating in the Netherlands: Cogas, Delta, Endinet, Enexis, Liander, Rendo, Stedin and Westland Infra. These DSOs are

monopolies in their working areas. Figure 10-3 shows the areas where the DSOs have a monopoly. The DSOs, as the TSO, is obliged by law to ensure every consumer has a connection capacity that is big enough to supply the requested demand (Ministerie van Economische Zaken, 1998). In practice, for DSOs and TenneT this means they are obliged to install enough transportation capacity for every consumer, even if this is socially not the most preferable option (Ministerie van Economische Zaken, 1998). The DSO and TenneT generate income via network tariffs. The network tariffs are part of the electricity bill and go the DSOs and TSO. The exact height of the network

tariffs is regulated by the ‘Autoriteit Consument en Markt’ (ACM; Tieben et al., 2013). The amount of the network tariff a DSO is allowed to pass on to the end-users is determined by the ACM using the ‘yardstick’ principle. All DSOs make costs they have to recoup. The ‘yardstick’ principle works by adding all costs of all DSOs together to create one total cost for the sector per year (Groot and Jobse, 2013). This is what the DSOs have to recoup as a sector. The ACM divides this total cost by the amount of connections and volumes transported by the DSOs and TSO, to come to a maximum network tariff. This price is equal for all DSOs and so the income of the DSO only depends on the number of consumers. Due to this principle it is beneficial to invest only in the required investments and not to waste money, since extra costs for an individual DSO does not mean an equal higher income (Groot

Figure 10-3: DSOs in the Netherlands (ECN et al., 2014)

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and Jobse, 2013). How the network tariffs are calculated is discussed in the chapter 4. In contrast to the TSO, DSOs will have to invest in new grids to ensure the grid is stable. The expectations are that especially on a district level congestion problems will occur. This is mainly due to local PV panels and an increase in electric vehicles (van Melle et al., 2014). Therefore DSOs are faced with the challenge how to design the system in the most optimal way. In this system, flexibility on a local scale could potentially prevent investments in grid expansion creating extra value for flexibility options. Several conclusions can be made for the physical part of the Dutch electricity system. First, the conventional electricity generators have difficulties to survive. Especially gas fired power plants are not profitable. Nevertheless, gas fired power plants are very important in the current system to provide flexibility. How the installed capacity will develop the coming years has large influences on the electricity prices and back-up capacity. The second conclusion is that the transition towards more VRE has very little influences on the high voltage grid. TenneT has enough capacity to deal with the fluctuations in production. Nevertheless, DSOs will have challenges to ensure the stability of the grid. DSOs will have to invest in grid expansion and or flexibility options to ensure the medium and low voltage grids are capable of dealing with the changing demand and production patterns. Especially districts with a high solar PV density will encounter congestion problems. Since flexibilization options might be able to manage the congestion problems, DSOs can reward flexibilization options more when bigger grid expansion investments can be prevented.

Market system

Secondly, the electricity market system will be discussed. In this group, all stakeholders involved in the electricity trade will be analyzed and so the important players in the market can be identified. The stakeholders directly connected to the different electricity markets as the connected stakeholders will be discussed. APX-ENDEX: is the exchange spot market for electricity and gas in which supplying and demanding parties are connected to each other to trade energy. In this chapter, only the APX is interesting, since this is the electricity market, while the ENDEX is the gas market (Krebbekx et al., 2015). The APX is not a physical market, biddings have to be send via internet towards the APX and here the optimal price is determined. There are different markets where electricity is traded. Which markets are present and the exact working of the APX is discussed in the market chapter. Traders: try to make business of the margins between supply and demand. They try to buy electricity when the price is low to be able to sell it when the prices rise (Energie-Nederland and Netbeheer Nederland, 2011). These traders act the same as on other markets like the stock market. Suppliers: are the contact between the electricity markets and the end-users. They are the commercial and administrative link between the consumer and the producer (Energie-Nederland and Netbeheer Nederland, 2011). A supplier delivers the electricity to the end-users, this is done by buying the electricity on the different electricity markets from the producers and sell it to the consumers. A supplier can also produce the power itself when it is owner of a power plant. The end-user and supplier have a contract that ensures both parties from a certain price or market access for a longer period. The supplier is the only stakeholder that can charge an end-user (ECN et al., 2014). The electricity bill comes from the supplier. In this bill also the network costs of the TSO and DSO are included. A supplier is also responsible for the consumption data of each consumer (ECN et al., 2014). This is of its interest, since this data is the basis of its charge to the consumer. There are 13 traditional suppliers in the Netherlands, however, this number is growing fast. Examples of traditional suppliers are Essent and Nuon while examples of new suppliers are Qurrent and VandeBron. More small suppliers are entering the markets. To step in as a supplier licenses have to be obtained. These are distributed by the ACM when all requirements are met (Tieben et al., 2013).

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Brokers: bring the supply and demand players together but does not have a position in the energy system itself in contrast to suppliers (Energie-Nederland and Netbeheer Nederland, 2011). Customers: are the end-users of electricity and are free to select their own supplier. Costumers or also called consumers or end-user can be categorized in two groups; small consumers and large consumers. A consumer is called a small consumer when its grid connection is smaller or equal to 3x80 Ampère (Ministerie van Economische Zaken, 1998). Every customer with a connection with a bigger capacity is automatically categorized as a large consumer. Consumers use the electricity delivered by their supplier and will pay the price for the consumption of the electricity. Also the network tariffs described above are paid by the consumer of the electricity. The price a customer has to pay for the electricity and network tariffs highly depends on the capacity of the connection and the total consumption (Enexis, 2015). So all income in the system comes from the end-user. Balance responsibles: or in the Netherlands abbreviated as ‘PV parties’ (programma verantwoordelijken) are responsible for the securing the balance in the system. Each PV party has to balance its demand and supply portfolio on every moment of the day (Fudura Enexis, 2012). A broad range of supply and demand options, therefore is beneficial for PV parties since this makes balancing their portfolios easier. This role can be played by everybody but in practice most of the PV parties are also suppliers or traders. This PV party collects the demands and supplies of all their costumers and tries to combine these as good as possible. In this way, the PV parties have to balance their demand and supply (Fudura Enexis, 2012). The differences between, long term demand and supply contracts in the portfolio of the PV party and the more real time demand and supply patterns, will be bought on the APX market. The price on the APX is higher than the long term contracts so it is wise to make the prediction as good as possible and to have a balanced demand and supply portfolio. The PV party also has to have a balanced portfolio on a 15 minute timescale. When this is not the case TenneT deals with the imbalance but puts the bill at the PV party. TenneT is responsible for the 15 minute balancing since an imbalance on this timescale can have major impacts on the complete system and TenneT is responsible for the complete system. In general, the imbalance market is even more expensive. So also in this case, it is very wise to balance your demand and supply as good as possible (Fudura Enexis, 2012). Aggregators: an aggregator is the link between prosumers (consumers of electricity which also produce electricity, mostly via PV panels) and the PV parties. The aggregator bundles the flexibility of multiple small prosumers together to be able to tender this to the PV parties as a possible flex option. In this way, small flex options can be used to balance the system (Van der Zee, 2015). The aggregator can make a portfolio of different flex providers. These small providers are bundled together to provide a safe and secure portfolio. When tendered as emergency power this portfolio is also called emergency power pool. The providers of this emergency power are called pool participants (TenneT, 2013a). It is up to the aggregator to reward the pool participants for their tendered flex options. From this part of the system can be concluded that there are multiple stakeholders involved in the market part of the system. The markets themselves are called the APX spot market and Imbalance market and here the suppliers and PV parties are active to balance their electricity portfolio. This all is done to maximize profits and to ensure the customer has a relatively low electricity price.

Other stakeholders

Beside the physical and market based stakeholders there are other stakeholders involved in the Dutch electricity system. The two most important stakeholders not discussed already are the ACM and the metering responsibles. These two stakeholders are discussed below.

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ACM: is the Autoriteit Consument en Markt (Authority Consumer and Market) and is responsible for the supervision on the energy system. The ACM is a supervisor created by the merge of the former Consumentenautoriteit, Nederlandse Mededingingsautoriteit (NMa) and the Onafhankelijke Post en Telecommunicatie Autoriteit (OPTA). Since the first of April 2013 the ACM has the supervision on the complete energy sector (Autoriteit Consument & Markt, 2015). The ACM controls the network tariffs DSOs and TSO are allowed to charge and supply licenses, stakeholders need to act on the electricity markets (Tieben et al., 2013). The ACM supervises and controls the system and punishes stakeholders when the rules are violated (Groot and Jobse, 2013). Metering responsibility: is the one that installs and maintains meters to see what is actually consumption of electricity. This information is passed on to the suppliers to be able to create the electricity bill. Therefore the role of the metering responsibility is very limited in the electricity system as it only checks the monitors (Energie-Nederland and Netbeheer Nederland, 2011).

Conclusions

In this appendix the current electricity system in the Netherlands is discussed. What can be concluded is that there are many stakeholders involved in the system. At the physical part of the system TSO TenneT, and multiple DSOs have the responsibility to ensure a stable, safe and reliable grid. The main conclusion from this appendix is that the national grid will be capable of dealing with the transition towards more renewables. The transport capacity in the high voltage grid is large enough to prevent congestion problems. This however, is different in the case of the distribution grids. DSOs have to invest large amount of money the coming years to prevent local congestion or overload problems. This, especially is the case in areas with a high number of solar PV installations as in areas where electric vehicles are popular. Flexibility options can play an important role in the prevention of these DSO problems. However, the DSOs are currently obliged to enlarge the grid. Another stakeholder facing challenges in the coming years is the conventional electricity generator. This since many variables are influencing the playing field of the generators. Therefore, it is highly uncertain how much conventional capacity will remain the coming decade. On the market side of the electricity system multiple stakeholders try to make profits. The profits can be made on the APX market in which the electricity is traded. Suppliers are present in the markets to connect generators with consumers. They are the administrative link in the delivery of electricity. To prevent large imbalances on the markets, balance responsibles or ‘PV parties’ are obliged to deliver a balanced demand and supply portfolio to the market. By doing so, the total demanded and supplied amount of electricity is in balance. Most of the time a balance responsible is also a supplier. There are also other players on the market trying to make profits on the fluctuating markets. The system is controlled by an external organization called the Autoriteit Consument en Markt (ACM). This organization supervises the system and can penalize stakeholders transgressing the system rules. The ACM also provides licenses required to operate on the markets as it determines the network tariffs that DSOs and TenneT are allowed to pass on to the end-users.

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APPENDIX B

Figure 10-4: Daily MCP and MCV at day-ahead market (APX, 2015a)

Figure 10-5: Weekly average MCP and MCV at the day-ahead market (APX, 2015a)

Figure 10-6 Yearly average MCP and MCV at the day-ahead market (APX, 2015a)

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APPENDIX C

Table 10-1: Network tariffs of Stedin DSO for small consumers (Stedin, 2014)

Connection capacity Standing charge tariff Including taxes Capacity tariff Including taxes

€/year €/year €/year €/year Low voltage 0.54 0.65 1.57 1.90 > 1 x 6 t/m 3 x 25A 18.00 21.78 125.59 151.97 > 3 x 25 t/m 3 x 35A 18.00 21.78 627.97 759.85 > 3 x 35 t/m 3 x 50A 18.00 21.78 941.96 1,139.77 > 3 x 50 t/m 3 x 63A 18.00 21.78 1,255.95 1,519.70 > 3 x 63 t/m 3 x 80A 18.00 21.78 1,569.94 1,899.62

Table 10-2: Network tariffs of Enexis for large consumers (Enexis, 2015)

Transport category based on contractual capacity

Standing charge transport services €/year

Contractual capacity €/kW/year

Peak demand €/kW/month

Reactive power €/kWArh

kWh normal use €/kWh

kWh low use €/kWh

Contractual transport capacity until 1500 kW LS (till 50 kW) 18.00 5.30 - 0.0067 0.0320 0.0168 MS/LS (50 – 125 kW) 411.00 22.21 1.44 0.0067 0.0083 0.0083 MS-D (125-1500 kW) 411.00 14.39 1.44 0.0067 0.0083 0.0083 Contractual transport capacity bigger than 1500 kW MS-D 411.00 14.39 1.44 0.0067 0.0083 0.0083 MS-T 411.00 12.00 1.16 0.0067 0.0045 0.0045 HS/MS 2,760.00 16.43 1.60 0.0067 - - TS 2,760.00 12.99 1.05 0.0067 - - Contractual transport capacity bigger than 1500 kW with maximum in business time of 600 hours/year HS/MS 2,760.00 8.22 0.55

(kW/week) 0.0067 - -

TS 2,760.00 6.50 0.36 (kW/week)

0.0067 - -

- Normal/low use hours= www.enexis.nl - LS = Low voltage, until 1 kV - MS-D = Medium voltage distribution grid (1-20 kV) - MS-T = Medium voltage transportation grid (1-20 kV) - TS = Intermediate voltage (30-50 kV) - HS/MS = High voltage (above 50 kV) towards medium voltage (1-20 kV)

http://www.enexis.nl/

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APPENDIX D

The different TRL maturity levels. The definitions are created by the European Commission, (2014).

- TRL 1 – basic principles observed - TRL 2 – technology concept formulated - TRL 3 – experimental proof of concept - TRL 4 – technology validated in lab - TRL 5 – technology validated in relevant environment - TRL 6 – technology demonstrated in relevant environment - TRL 7 – system prototype demonstration in operational environment - TRL 8 – system complete and qualified - TRL 9 – actual system proven in operational environment

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APPENDIX E

In paragraph 4.6, the different electricity price scenarios are discussed. In this Appendix, the consequences of the different parameters on the electricity prices will be described more in detail. More precisely, the Day-ahead scenarios of CE Delft et al. (2015) for the Power2products consortium will be investigated more closely. These scenarios are build up with data provided in table 10-3. Table 10-3: input different Day-ahead scenarios (CE Delft et al., 2015)

Beside the assumptions made for the different amounts of VRE capacity installed, additional demand and flexible CHP units, different fuel prices are taken into account in the different scenarios. These fuel price ranges are depicted in table 10-4. All scenarios use a CO2 price of 15 €/ton. The influences of the parameters on the electricity prices will be discussed in the next paragraphs. Table 10-4: Fuel price ranges used in the different scenarios (CE Delft et al., 2015)

Fuel price ranges Gas price (€/MWh) Coal price (€/ton)

Mid/mid 25 75 High/low 35 60 High/high 35 90

Scenario ‘Limited Development’

The first scenario discussed in this study is called ‘limited development.’ In this scenario, the goals made in the national energy agreement (Nationaal Energieakkoord) are not achieved. The main conclusions are that less wind energy is installed (4 GW) as was the goal in the national energy agreement and the electrification of the energy demand is only partly reached (100.000 electric vehicles). This relatively small transition still has influences on the electricity prices on the Day-ahead market. The outcomes of the modelling done by CE Delft are represented the price duration curve in figure 10-7.

Scenario Capacity VRE in NL New demand categories CHP position

Limited developments

- 3 GW onshore wind - 1 GW offshore wind - 2GW solar PV

- 100,000 Electric vehicles - No Power2heat


Green and flex

- 6 GW onshore wind - 5 GW offshore wind - 7 GW solar PV



CHP units all made flexible

CHP phase-out

- 6 GW onshore wind - 4 GW offshore - 6 GW solar PV




Nationaal energie-akkoord

- 6 GW onshore wind - 4.5 GW offshore wind - 6 GW solar PV

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From figure 10-7 and the data behind it, some conclusions can be made. The average electricity price is lower than the APX price in 2013. This is mainly due to the (small) increase in VRE capacity and the phase out of unprofitable CHP units decreasing the must-run capacity (CE Delft et al., 2015). From the demand side the number of electric vehicles and power2heat options is limited and so the increase in demand is limited. The price increase due to higher demand is therefore limited (CE Delft et al., 2015). The steep edges in the figure are created by the fact that there is less CHP capacity and most of the demand will be provided by coal fired power plants which have lower marginal costs. At 4200 hours there is a sharp edge in the electricity prices in the high/low and high/high scenarios. This is since the gas fired power plants are setting the price in these hours. With a relatively high gas price this has large influences on the electricity prices. In the high/low scenario the coal price is relatively low and therefore the hours where coal sets the price will be lower. The high/high scenario has higher coal prices and therefore also the hours where coal fired power plants will set the price will see price increases. The mid/mid scenario is cheaper in the expensive hours due to the lower gas prices.

Scenario ‘Green and Flex’

The second scenario is considered to be a ‘green and flex’ scenario. In this scenario, renewable energy will get a real boost and its capacity will increase significantly. Onshore 6 GW of wind capacity will be installed while offshore also 5 GW is installed. Solar PV will also increase significantly to 7 GW installed. Besides the relatively big capacity of VREs most CHP units will be made flexible. This decreases the must-run capacity significantly decreasing the low electricity price periods. On the demand side more demand is created by the 400,000 electric vehicles present in the Netherlands in 2023 and the use of power2heat in low price periods (4 GW in winter and 2.5 GW in summer). The electricity prices under different fuel prices are summarized in figure 10-8. Electricity prices in €/MWh are depicted on the y-axes while the number of hours per year are positioned on the x-axes.

Figure 10-7: Limited development scenario under different fuel prices (CE Delft et al., 2015)

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In this scenario, the average price is lower than the APX price in 2013. This is partly due to the high number of low electricity periods. This can be explained by the large VRE capacity, which produces electricity with no marginal costs. However, the number of hours the price is very low is limited by the flexibilization of CHP units (CE Delft et al., 2015). There is less CHP must-run capacity, decreasing the cheap supply capacity. Another reason why the number of low electricity price hours is limited is the installation of power2heat options (4 GW in winter + 2.5 GW in summer). This increases the demand together with the demand increase by electric vehicles. However, the electric vehicles increase the overall demand, while power2heat options only increase the demand in cheap electricity hours. This, while currently, the load pattern of electric vehicles is determined by the period the car parked and plugged in and not the period the price is low. This has to do with the behavior of households. People want to have a loaded vehicle, since they want to have a long travel range at any time (van Melle et al., 2014). Therefore, electric vehicles increase the electricity price over the whole price duration curve. However, CE Delft et al. (2015) state that the effect of 400,000 electric vehicles on the electricity prices is limited.

Scenario ‘CHP phase-out’

The third scenario is called the ‘CHP phase-out’ scenario. In this scenario, the capacity of VREs is increasing significantly like the ‘green and flex’ scenario. The total installed VRE capacity is: 6 GW onshore wind, 4 GW offshore and 6 GW solar PV. The big difference with the green and flex scenario is the amount of must run capacity. Additional to the ‘green and flex’ scenario, 3 GW of must-run CHP is closed. Decreasing the electricity supply capacity. The results of this are showed in figure 10-9. Because of the increase in VRE capacity the number of low electricity price hours is increasing in comparison with 2013. However, the number of hours is smaller in comparison with the ‘green and flex’ scenario since there is less must-run capacity. Due to this decrease in capacity, the high price peaks will be higher in comparison with 2013 (CE Delft et al., 2015). The complete price duration curve will shift to the right you can say.

Figure 10-8: Green and flex scenario under different fuel prices (CE Delft et al., 2015)

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The scenarios used in the model developed in this report, are the most extreme scenarios. These are the ‘limited development’ (high/high) scenario and the ‘Green and Flex’ (mid/mid) scenario. The ‘limited development’ scenario represents the relatively long and high price peaks and the ‘Green and Flex’ scenario represents the long periods of low prices and a relatively low high peak. What can be concluded from the different scenarios is that the number of very low electricity price hours is mainly determined by the installed VRE capacity and the flexibility of CHP units. The middle part of the price duration curve is mainly determined by the marginal costs of coal power plants. This marginal cost of coal fired power plants is not only determined by the price of coal but also by the CO2 price (CE Delft et al., 2015). A significant increase in the CO2 price will therefore mainly have an influence on the middle part of the price duration curve. The number and height of the price peak hours is mainly determined by the gas price. Gas fired power plants have the highest marginal costs and will therefore be at the end of the merit order (Van der Hoofd, 2014). This, since gas fired power plants will provide a part of the electricity demand and will therefore set the price.

Figure 10-9: CHP phase-out scenario under different fuel prices (CE Delft et al., 2015)

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APPENDIX F

Table 10-5: Price table different scenarios (CE Delft et al., 2015)

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APPENDIX G

Table 10-6: Return on investment periods (in years) according to the model

Market and Scenario Lithium-ion

CHP units

Electric boiler

Power2 hydrogen

Demand response

Day ahead scenario "APX 2014" 50 11 117 214 28

Day ahead scenario "Limited development"

7.9 16 4.8 91 1.1

Day ahead scenario "Green and Flex"

20 24 4.9 55 20

Intraday scenario "APX 2014" - 53 - 2,628 51

Intraday scenario "Limited development"

12 11 47 462 64

Intraday scenario "Green and Flex" 54 15 30 332 62

Imbalance market scenario "APX 2014"

20 11 17 199 4.1

Imbalance market scenario "Limited development"

15 10 10 134 3.6

Imbalance market scenario "Green and Flex"

31 15 32 312 5.8

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