Impact of Bidding Zone Configuration on the French Electricity Network

Transcription

1 Impact of Bidding Zone Configuration on the French Electricity Network Alexandre Canon Degree project in Electric Power Systems Second Level, Stockholm, Sweden 2014 XR-EE-EPS 2014:005

2 KTH Royal Institute of Technology Master Thesis Impact of Bidding Zone Configuration on the French Electricity Network Author: Alexandre Canon Supervisor: M. Mahir Sarfati Examiner: Dr. Mohammad Reza Hesamzadeh RTE Supervisor: M. Vincent Protard A thesis submitted in fulfilment of the requirements for the degree of Master Thesis in the Electric Power Systems Electricity Market Research Group April 2014

3 Declaration of Authorship I, Alexandre Canon, declare that this thesis titled, Impact of Bidding Zone Configuration on the French Electricity Network and the work presented in it are my own. I confirm that: This work was done wholly or mainly while in candidature for a research degree at this University. Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated. Where I have consulted the published work of others, this is always clearly attributed. Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work. I have acknowledged all main sources of help. Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself. Signed: Date: 07/03/2014 i

4 KTH ROYAL INSTITUTE OF TECHNOLOGY Abstract Electricity Market Research Group Master Thesis Impact of Bidding Zone Configuration on the French Electricity Network by Alexandre Canon At the beginning, the first operation concern of electricity transmission networks is to ensure the security of power system. These interconnections are also used for the purpose of exchanging electricity from one country to another. They have a central role to achieve the integrated European electricity market by allowing electricity supplier to sell energy to a customer in another EU country. This enables market players to trade electricity depending on opportunities and prices in various bidding areas in Europe. The interconnections contribute therefore to the effectiveness of the European electricity market. The volume of trade is however limited by the physical limitations of the transmission lines, which are determined by the TSOs through cross border capacity calculations and assigned to the actors based on different market mechanisms (e.g. capacity allocation). The fast development of renewable energy sources has increased the imbalances between supply and demand. This further increases constraints on transmission lines, including the interconnections between neighboring countries. In order to manage this problematic situation, the modification of the bidding areas configuration is often considered as a solution. Different studies developed methods based on nodes aggregation or minimization of re-dispatching costs to define the price areas. However, the impact of this kind of measure on the overall system is not well studied. This master thesis work presents a general methodology to study the impact of a new bidding zone configuration in the French electrical network from a market point of view. In order to define a relevant bidding zone configuration in the system, physical flows on the lines are determined and the two ends of binding links are located in two different bidding zones. Then, electricity price, social welfare evolution and modification of the flows due to the new generation pattern are presented in order to evaluate and analyze the impact of the new bidding zone configuration on the market. The modelling limits are analyzed in order to evaluate the proposed approach.

5 Acknowledgements I would like to express my gratitude to all the professionals who have been available in order to answer my questions and help me during these six months within the French Transmission system Operator RTE Réseau de Transport d Electricité. Thus, I am particularly grateful for the assistance given by Mr Vincent Protard, Mr Lucian Balea and all members of the Cross Border Market Design department. Their collaboration and the valuable advice they gave me enabled me to fulfil this research study and to experience a rewarding internship. Finally I would like to thank my supervisor Mr Mahir Sarfati, PhD candidate in Electric Power systems, and my examiner Dr Mohammad Reza Hesamzadeh for their availability and their involvement. Their support throughout this project had been very helpful in the carrying out of this master thesis. iii

6 Contents Declaration of Authorship i Abstract ii Acknowledgements iii Contents List of Figures List of Tables iv vii ix 1 Introduction 1 2 Background and Literature Review Importance of the Exchanges between different Bidding Zones Social Welfare and Day-Ahead Social Welfare No Commercial Exchanges Interconnection between two areas: Importance of the Transmission Capacity Congestion Management Capacity Calculation and Allocation Capacity Calculation Capacity Allocation Bidding Zones Studies Bidding Zones A suitable context for Bidding Zone Configuration studies in Europe Market Splitting already implemented in some European countries Price signals considerations Loop Flows problematic THEMA Study Bidding Zones studies: an open topic Model Presentation Electricity Market Modelling Economic Dispatch iv

7 Contents v Monte-Carlo Simulation Interface Inputs Load Renewable Energy Sources Hydraulic Production Thermal Production Links Assumptions Market Assumptions Minimum Stable Power and Minimum up/down time Constraints Hurdle Costs Load Flow Simulations DC Load Flow on Antarès AC Load Flow with network model Methodology New Bidding Zone Configuration Congestions Redispatching Costs Choice of the Net Transfer Capacity Economic Impact Study Commercial Exchanges Evolution Price Convergence Indicator Price Divergence Indicator Social Welfare study Loop Flow study Data preparation Unscheduled Flows European Loop Flows Results Background Localisation of Congestions in France New Bidding Zone configuration: France North and France South Commercial Capacity Determination Modification of the European Commercial Exchanges Influence of the new Configuration Seasonality Exchanges Evolution when there is a congestion in France Economic Aspects Price Convergence Price Divergence Social Welfare evolution Loop Flows Study Limits of the study Impact on Congestion Management

8 Contents vi Data limitations Capacity Calculation Indicators used in the Loop Flows Study Conclusion 74 A Grid Transmission Capabilities Computation (Source: RTE) 76 B Impedance Computation Principle (Source: RTE) 77 C List of the performed simulations during the study 79 D Seasonality of the Commercial Exchanges in France 81 E Price Divergence Results for other European borders 83 F European Net Values for the four simulations used in Loop Flows study 85 Bibliography 90

9 List of Figures 2.1 Supply curve and demand curve for both isolated areas A and B (Source: RTE) Supply curve and demand curve for both areas A and B with large transmission capacity (Source: RTE) Supply curve and demand curve for both areas A and B in case of congestion (Source: RTE) Illustration of the difference between Scheduled Exchanges and Physical Flows (Source: RTE) Comparison of different Admissible Domains for NTC/ATC or Flow- Based Method Capacity Allocation Mechanisms (Source: RTE) Nordic System with Market Splitting in Sweden and Norway Loop Flow and Transit Flow definitions Activities in the Bidding Zone Review Process Antares Interface with the considered European areas in the Simulations French hourly Consumption (mean value over 50 Time-Series years) Intra Daily Modulation Parameter enables to smooth the Hydraulic Production (Source: RTE) Load Profile during the day (for the example purpose, completely fictitious) Marginal Prices throughout the day taking into account the constraints or not Operating Cost Evolution if the constraints are neglected (compared to the case with constraints) Marginal Price Evolution if the constraints are neglected (compared to the case with constraints) No Congestion situation, Physical Flow lower than the line capacity Congestion through the line, Redispatching example Modifications performed from the Base Case to obtain consistent Physical Flows Loop Flows due to Internal Exchanges in three Bidding Zones Areas used as starting point in the Study Mean values over 50 years of the hourly Physical Flows in France Statistics of Physical Flows France South-France North over 50 Monte- Carlo years vii

10 List of Figures viii 5.4 Commercial Exchanges (MWh/h) in initial Bidding Zone Configuration: mean values over 50 Monte-Carlo years; Statistics in both ways of the interconnection Commercial Exchanges (MWh/h) in new Bidding Zone Configuration (2500MW capacity): mean values over 50 Monte-Carlo years; Statistics in both ways of the interconnection Commercial Exchanges (MWh/h) in new Bidding Zone Configuration (4000MW capacity): mean values over 50 Monte-Carlo years; Statistics in both ways of the interconnection Commercial Exchanges from France South to France North (mean value of each hour 12am) Duration curves for borders with Switzerland when FR2-FR1 is constrained (solid line: 2 Bidding Zones configuration; dashed line: 1 Bidding Zone configuration) Switzerland used as New Path for Electricity Evolution of Price Convergence due to new Bidding Zone Configuration throughout European borders Price Divergence Indicator Evolution for the border FR1-FR Hourly European Social Welfare Evolution throughout Monte-Carlo synthesis year Hourly Consumer, Producer and Trading surpluses Evolution (mean value over 50 Monte-Carlo years) in the different Bidding Zones Loop Flows measure for one Bidding Zone configuration; for two Bidding Zones configuration - January 9am Loop Flows measure for one Bidding Zone configuration; for two Bidding Zones configuration - June 10am Commercial Exchanges and Physical Flows; Unscheduled Flows Indicator for one Bidding Zone configuration - January 9am Commercial Exchanges and Physical Flows; Unscheduled Flows Indicator for two Bidding Zones configuration - January 9am Commercial Exchanges and Physical Flows; Unscheduled Flows Indicator for one Bidding Zone configuration - June 10am Commercial Exchanges and Physical Flows; Unscheduled Flows Indicator for two Bidding Zones configuration - June 10am

11 List of Tables 2.1 Measures proposed by THEMA to deal with Loop Flows problematic List of the Power Plants with their characteristics Clusters used to implement the economic results in the AC Load Flow simulations List of the Simulated Countries and Abbreviations Structure of the New Bidding Zones Configuration in France Congestion Time rates depending on the capacity link between the two French Bidding Zones Colour Scale for net values in the different maps Evolution of the Congestion time rates in European interconnections when FR2-FR1 is constrained Evolution of the Congestion time rates in European interconnections when FR1-FR2 is constrained ix

12 Chapter 1 Introduction Interconnections have a significant role in the European electricity market since they increase the trading possibilities within the system and improve the overall security of supply. The capacity through the different interconnections is however a scarce resource due to physical limitations. Market mechanisms such as capacity calculation and allocation enable to determine the amount of electricity which can flow on the interconnections and how to assign it to the market players. Many challenges appeared with the increase of renewable energy sources in the system. The evolutions of the energy policies in each European country and the foreseen entry into force of the network code have also a significant impact on the electricity market. This master thesis report deals with a topical research field: the bidding zone configuration in a meshed electrical network. The current context in the European system is suitable for this kind of project to the extent that this evolution of the market is often seen as a possible solution to deal with different issues: congestion management, localisation price signals, balance between internal and cross-border flows... The main objective of this paper is to define a general methodology in order to define a relevant bidding zone configuration in France and study its impact on the European electricity market based both on physical and economic considerations. The present report is organized as follows: The second chapter gives an overview of the research field and a literature review of the studies related to this subject. This section enables to approach the main challenges at stake with bidding zones studies and the possible consequences of market splitting in Europe. 1

13 Chapter 1. Introduction 2 Then the models used for the simulations as well as the set of data are presented. The assumptions taken into account and the limitations of the study are also clarified. Chapter four corresponds to the general methodology to define a new bidding zone on the French territory and then, based on different indicators, the impact of such evolution is assessed. In this perspective, exchanges evolution, electricity prices and social welfare are of interest for economic purposes and indicators for loop flows study are defined. The results chapter gives the conclusions obtained when the methodology is applied for a case study with a median scenario at time horizon Congestion management considerations enable to define new bidding zones in France and the impact on the European system is presented. To finish the limits of the study and future possible studies are given.

14 Chapter 2 Background and Literature Review 2.1 Importance of the Exchanges between different Bidding Zones The European electricity network represents nowadays 35 interconnected countries with the same objectives: network safety, security of electricity supply and economic efficiency. This large meshed network improves the stability of the whole system (larger inertia of the system) and enables to increase the total social welfare within the electricity market Social Welfare and Day-Ahead Social Welfare The notion of social welfare in an electricity market can be defined in different ways according to the considered study. On the whole, this quantity enables to measure the overall market value for the involved actors based on different considerations. Thus market performances are taken into account but other aspects can also be involved such as ecological concerns (impact on the carbon dioxide emissions), impact on the society, social modification... In order to have an unbiased definition, the day-ahead social welfare is often used because it is only based on economic parameters of the overall system. The definition used for this report is the sum of the different actors surpluses within the market: Consumer surplus: difference between the amount a consumer is willing to pay minus the market price 3

15 Chapter 2. Background and Literature Review 4 Producer surplus: difference between the price a producer is actually selling his energy minus his bidding price on the market Congestion rent if there are interconnections in the system (definition given below) In this paper, social welfare implicitly refers to the day-ahead social welfare since other considerations depend on subjective interpretation and indicators No Commercial Exchanges Considering two areas, it is very easy to understand why such interconnections are essential. Assuming perfect competition, if the two areas are isolated, the price cross in each area corresponds to the intersection between the demand curve (aggregated loads with decreasing order prices) and the supply curve (aggregated offer with ascending order prices). Thus, only demand bids with a larger price than the spot price and offer bids with a lower price than the market price are accepted. This price maximizes the total surplus (for both producers and consumers) and only the cheapest power plants are started. Each area presents its own electricity price (figure 2.1). Figure 2.1: Supply curve (blue) and demand curve (red) for both isolated areas A and B (Source: RTE) Interconnection between two areas: Importance of the Transmission Capacity Now if the two areas are interconnected by a link which presents a certain transmission capacity, the total operation cost (overall price the producers have to pay to produce the needed generation) to satisfy the whole demand can be decreased. Assuming that area A presents a lower price than area B, it would be beneficial (from the Day-Ahead social Welfare point of view) to export energy from A to B. Price in A

16 Chapter 2. Background and Literature Review 5 would increase (more expensive power plants are started to cover the export power) and price in B would decrease (expensive groups are stopped since import helps cover the load). If the transmission capacity is large enough, the exchanges will occur until the prices in areas A and B are the same (this is the economic optimum) (figure 2.2). In the case where the transmission capacity is not enough to reach this optimum point, congestion will occur. Price in A will increase, price in B will decrease but a price difference between the two areas will remain (figure 2.3). This price difference generates a so-called congestion rent (2.1). The congestion rent is collected by the TSOs in order to reinforce the interconnections and increase the cross-border capacities. Congestion Rent = Amount transmitted through the line P rice Dif f erence (2.1) Figure 2.2: Supply curve (blue) and demand curve (red) for both areas A and B with large transmission capacity (Source: RTE) The principle explained above can be generalized for n areas. An efficient congestion management is one of the key to improve both technical and economical performances of the European network Congestion Management Inside a bidding zone, measures are taken in order to deal with congestion into the electrical system. Different methods are available depending on the considered timeframe. Thus, in short term, congestions can be solved with costly and non-costly measures.

17 Chapter 2. Background and Literature Review 6 Figure 2.3: Supply curve (blue) and demand curve (red) for both areas A and B in case of congestion (Source: RTE) TSOs will first use non-costly measures like modification of the topology of the grid and Power Shifter Transformer (PSTs) taps changes. If these measures are not able to solve the constraint, TSOs can apply costly measures such as redispatching or counter-trading. Counter-trading corresponds to a transaction initiated by the TSO between two areas to relieve a congestion between these two areas. The location of the energy modification is based on the merit order or another independent method. Redispatching represents an increase and decrease of the level of production of specified unit in order to reduce a given constraint. The choice of the affected units is based on their sensitivity on the constraint. Redispatching can be internal (inside a country), external (measures in a country to relieve a constraint in another area) or cross-border (increase of production in one country and decrease in another one). For long-term solutions, TSOs reinforce the grid by building new lines and PSTs. Grid enforcements represent the ideal solution to the extent that the amount of congestions is decreased in the system. These investments are expensive and have to be balanced with the overall gain due to congestion reductions. In either solution, the grid can be exploited without any constraint. Inside the bidding zone, congestions are somehow ignored in order to freely trade electricity with a unique price. In Europe, such approach is impossible since the network is not strong enough, and will never be to solve all congestions. Alleviate all the network constraints does not represent the optimum solution in economic point of view. The management of congestions will therefore remain. Two mechanisms in order to manage capacity are used: first the capacity calculation which computes the volume of electricity to be traded

18 Chapter 2. Background and Literature Review 7 between areas and secondly the capacity allocation which allocates the available capacity to the market players. 2.2 Capacity Calculation and Allocation Capacity Calculation The transmission capacities between different bidding zones are very interesting from a market point of view. It is therefore of interest to use efficient and coordinated mechanisms to compute these limits given by the TSOs. The capacities cannot be defined arbitrarily by TSOs since it impacts other bidding zones, these capacities have to be agreed on the regional level. An objective and systematic method of computation is necessary. Two notions have to be distinguished: physical flows which are the actual flows on the grid and commercial exchanges that result from transactions between market players. The European electrical system cannot be considered as a unique copper plate since the network is meshed and the power flows are ruled by the Kirchhoff s laws. Thus, if the commercial exchange between France and Germany is increased by 100MW, it is impossible to get a physical flow of 100MW between those two countries. The power flow will follow different paths according to the network topology and system situation (figure 2.4). The TSO computes the interconnections capacities based on the possible situation during the exchanges. The main goal of the capacity calculation method is to convert the calculated available physical margins on power lines into commercial capacities for different timeframes (annual, monthly, weekly, D-2, D-1). NTC Method The first method used by RTE with the neighbouring bidding zones is based on Net Transfer Capacities (NTC). Based on a base case (topology, exchanges assumptions, consumption and production forecasts), for different fault hypothesis in the system (N, N-1... ) the physical margin (physical capacity minus the flow on the line for the given situation) is computed for the most impacted lines. The physical margin is then divided up between the different possible paths (cf. Kirchhoff s laws) in an equitable way (same margin for all the considered paths). In order to compute the commercial capacity for each interconnection, the notion of Power Transmission Distribution Factor (PTDF) is introduced. This factor shows how the flows on a line are modified when the exchange between two bidding zones is modified. For instance, if the commercial exchange between

19 Chapter 2. Background and Literature Review 8 Figure 2.4: Illustration of the difference between Scheduled Exchanges and Physical Flows (Source: RTE) two bidding zones A and B is increased by 100MW and the flow through a given line increases by 30MW; the PTDF for this border and branch is 0.3. This example shows that the new power flow has to be known in order to compute the PTDF matrix. It is necessary to determine which node will be modified when a change of import/export occurs through an interconnection. Generation Shift Keys (GSKs) are used to translate an incremental cross-border exchange into incremental nodal injections. By this method, it is possible to determine the power flow modifications in the electrical system due to changes in the commercial exchanges between bidding zones. Then, the PTDF matrix can be computed. P T DF k,ij = P trans k P ij = Changed Intensity on Line k Changed Exchange between Bidding Zones i and j (2.2) Once the margins are divided up and the PTDF matrix is computed, the commercial capacities are easily obtained by dividing the physical margins by the PTDF. For each interconnection, the commercial capacity is computed for relevant N-1 situations. Since the system has to remain safe in all N-1 situations, the final commercial capacity is defined by the lowest obtained value. Thus, all commercial capacities (for each interconnection) are simultaneously feasible.

20 Chapter 2. Background and Literature Review 9 Flow-Based Method Another method developed in order to compute the interconnection capacities is the so-called Flow-Based (FB) method, which is on the eve of a CWE go live [1]. This methodology takes into account the constraints on the most impacted lines in the system for all the considered outage scenarios. Based on this information and the PTDF matrix, a supply domain is defined in order to ensure the security of the network. The figure 2.5 shows the security domain for both market coupling models (ATC and FB) for a simple case composed of 3 bidding zones. The x-axis corresponds to the commercial exchanges from country A to country B and the y-axis the exchanges from country A to country C. The yellow area defines the admissible domain. It can be shown that the market possibilities are higher in the FB method since the whole security domain is available for the market. In the ATC/NTC method, the domain is limited due to TSOs choices (fixed commercial capacities). Figure 2.5: Comparison of different Admissible Domains for NTC/ATC or Flow- Based Method [1] Compared to the ATC/NTC method, which could be a choice made by the TSO, the FB method gives the security domain itself. In both ATC and FB Market Coupling,

21 Chapter 2. Background and Literature Review 10 the optimization problem presents the same objective function (maximize the total day ahead social welfare) but with different formulation of grid constraints. The trading opportunities are considerably increased in a FB model, since the net positions are market driven, the odds to get a price convergence between the different bidding zones is higher than with a simple ATC model. The model does not compute fixed limits for the commercial exchanges but gives a domain where the net positions (and therefore exchanges) can be optimized Capacity Allocation There are two main capacity allocation mechanisms used in the European Electricity Market: explicit auctions and implicit allocation. In the first one, the energy exchanges and the transmission capacity allocation are decoupled. Actors need to acquire the energy and the capacity separately. With implicit allocation, the energy and the corresponding transmission right are simultaneously traded; actors only need to take into account the energy trading since everything is coupled. The implicit allocation is the target model for the Day Ahead Integrated European Market [2]. In the different borders between France and its neighboring countries, the capacity allocation mechanisms are mostly as follows (figure 2.6): Figure 2.6: Capacity allocation mechanisms (Source: RTE) The long-term transactions are done through explicit auctioning: annual, monthly auctions. A secondary market begins where the different actors can trade the commercial capacity they acquired. At the beginning of D-1, the transmission rights have to be nominated in order to be effective. Then, the principle of Use It Or Sell It (UIOSI) is

22 Chapter 2. Background and Literature Review 11 applied: if an auction is not used, it has to be sold again, the actor will be financially compensated according to the Harmonized Auctions Rules [3]. Market players can nominate transmission rights in both ways of an interconnection. Netting is applied. For example if an actor nominates 100 MW from a bidding zone A to a bidding zone B and another 50 MW from B to A, the used capacity is only 50 MW from A to B. This method enables to take into account the actual exchanges program for each link between bidding zones in both ways in order to propose all the available capacity and therefore optimize the use of the interconnection. Indeed, the nominations performed on both ends can either use a share of the computed capacity or relieve a part of it if they are in the opposite direction. During the day-ahead market, either the explicit auction is used, or an implicit auction is applied. In the second case, the transmission capacities are traded with the energy. In the first case, the process is similar to the long-term market. The only difference is the principle of Use It Or Lose It (UIOLI): if an auction is not used, it is lost and the actor is not financially compensated for not using the capacity. The remaining netted capacity will then be offered to the intraday market with implicit or explicit auctions. In this context where the interconnections capacities are essential for an efficient development of integrated European electricity market, using the adequate capacity calculation method associated with an adequate allocation mechanism. The target model for the day ahead in a meshed network given in the network code on Capacity Allocation and Congestion Management (CACM) is Flow Based Market capacity calculation and market coupling for the allocation mechanism [2]. 2.3 Bidding Zones Studies Bidding Zones A bidding zone is the largest geographical area within which Market Participants are able to exchange energy without Capacity Allocation [4]. It is also assumed that there is not any constraint inside a bidding zone and in case of internal congestion the Transmission System Operator (TSO) is responsible to relieve the involved transmission line. Each bidding zone is therefore considered as a copper plate from the market point of view and the only possible congestions would occur between these bidding zones. Capacity calculation and allocation are used to manage exchanges through interconnections between bidding zones.

23 Chapter 2. Background and Literature Review A suitable context for Bidding Zone Configuration studies in Europe Market splitting as a whole and its impact on the electricity market has become a large subject of interest. Thus, the studies related to that domain are relatively limited and mainly confidential since most of them are internal investigation and research. Measures have to be taken in order to achieve an efficient congestion management. The study presented in [5] is the perfect example of such context. The author analyses the impact of zonal pricing on total system cost for different potential delimitations. The clustering method used is based on an algorithm (Ward s minimum variance method) which enable to aggregate areas with price which cause the minimum decrease in homogeneity quality of the cluster. Different cases are studied and compared in Europe according to the number of defined clusters. Although the study is only based on a total system prices comparison, it is possible to conclude that national borders are not necessary suitable clusters and it is very complicated to find an optimal solution. The development of this kind of study could enable to investigate different problematic in the current European market configuration: The balance between the amount of congestion management costs to reflect either in the market (different electricity spot prices in the bidding zones) or through the consumer price (redispatching costs have an impact on the consumer costs within a bidding zone) To send relevant long-term price signals to face the new market organization where the function of transmission is separated from the electricity generation and the large development of renewable energy sources Study the impact of both internal and cross-border transactions; this axis embracing loop and transit flows problematic. The reconfiguration seems to be the perfect solution for all the problems faced by the TSOs, but other solution can be found for each of the three issues: congestion, localisation and impact of internal congestion on other bidding zones. For example, development of new lines, introduction of new market design... all of these new solutions will help to deal with the increase of electricity trades, the important development of renewable sources and their integration into the market...

24 Chapter 2. Background and Literature Review Market Splitting already implemented in some European countries Market splitting is implemented in some countries in Europe. In this section, a brief overview of the reasons which led to modify the electricity market mechanisms and choose to create several bidding zones is presented. From one country to another, the causes for market splitting implementation are very different and strongly related to the market situation in the considered bidding zone. Norway, for instance, resorts to market splitting in case of structural congestions, to ensure the security of supply, to prevent the impact of important maintenance activities... The bidding zone configuration is very flexible and they can change the borders inside the country quite easily. The production in Sweden is mainly located in the North of the country whereas the consumption is in the South. When congestions occurred in the Swedish system, the export towards Öresund connection was limited and both losses for Danish customers and gains for Swedish producers used to happen. Four bidding zones have therefore been created (figure 2.7) since it was an interesting option to deal with internal bottlenecks without modifying the interconnection capacity with Denmark and without unreasonable countertrade commitments [6]. Figure 2.7: Nordic System with Market Splitting in Sweden and Norway [7] In Poland, the electrical system is facing several problems and congestions due to a weak transmission grid. A nodal energy market is currently of interest and could be implemented in the Polish system in order to improve the situation.

25 Chapter 2. Background and Literature Review 14 New bidding zone configuration is a considered solution in order to improve congestion management, prevent from impacts due to planned works in the system... Due to an important meshed network in Europe, other problematic such as price signals or loop flows can increase the interest in this new market design Price signals considerations Price signals are an essential point in order to properly develop the European network. These signals are considered good and useful if they accurately reflect the network s physical constraints. Modifying the bidding zone configuration in such way that congestion would be visible through congestion rent (price difference between 2 bidding zones due to congestion) is studied as an eventual congestion management method [8]. The investments would be considerably improved in case of relevant and accurate price signals in the network. The possible inconsistency between generation investments timeframes and bidding zones lifetimes represents a significant shortcoming. Secondly if a large amount of new generation units is built in the same area, the price will change and the signal as well. Other mechanisms, like relevant subsidies, are another solution Loop Flows problematic A commercial exchange realized between two bidding zones does not only affect the flow between these bidding zones. It has a significant influence on other zones: loop flows or transit flows. The examples below (figure 2.8) give the definitions of these different kinds of flows for a simple situation with three bidding zones involved: Areas A, B and C. Figure 2.8: Loop Flow and Transit Flow definitions

26 Chapter 2. Background and Literature Review 15 Loop Flow can be defined as the flow over bidding zones (areas B and C) caused by a scheduled exchange within another bidding zone (origin and destination in area A). Transit Flow can be defined as the flow over bidding zones (area B) caused by origin (in area A) and destination (in area C) in two different bidding zones. Loop flows and transit flows can decrease the available capacity of neighbouring countries even if they are not involved in the scheduled exchanges. Furthermore, loop flows are not controlled through capacity calculation and allocation, counter-trading or redispatching are therefore the only way to get rid of them in case they accentuate a constraint in the system. Creating new bidding zone could enable to transform loop flows into transit flows and take them into account in a flow based market coupling mechanism [9]. Transit and loop flows have to be handled with regional coordinated capacity calculation and allocation. In the European meshed network, the presence of unscheduled flows is bound to happen and sometimes completely unexpected. Loop flows or transit flows are not necessarily synonyms of constraints since these flows can both have a restraining and relieving effect on the grid [10]. The more exchanges are submitted to allocation mechanisms on a small area (i.e. small bidding zone), the more the system is controlled and predictable on the other hand a smaller area will introduce larger uncertainties for the consumption and renewable energy sources. Market splitting is therefore of interest in order to decrease the impact of unscheduled flows on the European electricity market. The bidding zones configuration issue is an important matter since unconstrained flows within a given area could cause unplanned flows and impact the neighbouring systems. As they are not constrained by any capacity calculation and allocation mechanisms; the flows have a priority access in the electricity network. Different studies have been performed to determine the impact of such exchanges in Europe based on statistical point of view using available database or model to compute the flows and compare them with the physical ones. The Flow-Based method combined with a new bidding zone configuration could be a solution. It is however very complicated to know who is responsible for a given unplanned flow (all flows which were not foreseen during the market processes) and every country should keep in mind that their own exchanges have also an impact on these flows. It is therefore more adequate to sum all the created flows instead of studying only one isolated bidding zone. A study has also been performed to show that loop flows are completely inevitable in the European meshed network and instead of modifying the bidding zone configuration they should be taken into account in the network codes. The problematic related to loop flows and bidding zone configurations has been approached in [11] [12] [13].

27 Chapter 2. Background and Literature Review 16 Table 2.1: Measures proposed by THEMA to deal with Loop Flows problematic Measures considered to deal with loop-flow issues (THEMA report) Modification of the topology of the grid (substation, PST) Short Term Measures Load/Generation Patterns Modification Capacities values reduction New Bidding Zone Configuration Medium Term Measures Flow-Based Market Coupling Market Signals for Renewable Sources Long Term Measures Grid Investments THEMA Study THEMA study in [14] emphasizes the need to find a solution about this phenomenon which provokes a decrease in the market efficiency, endanger the system and have a negative effect on the incentives effectiveness. Different measures for different timeframes are proposed (Table 2.1). THEMA used historical data for both market and physical flows for a quantitative analysis. Based on these results, an idea of the amount of unscheduled flows in Europe is presented. Then using a model on software GAMS, different solutions in order to decrease the amount of loop flows are studied. A physical grid model is needed to investigate the loop flows origin. THEMA did not have any physical model; it is therefore not possible to conclude on loop flows origin since the impedances are not taken into account. The consulting group proposed different solutions in order to decrease the impact of unplanned flows on the system. The conclusion reflects that Flow-Based Market Coupling combined with an adequate bidding zone configuration is a possible solution [14]. A coordinated grid development is also highly recommended Bidding Zones studies: an open topic The bidding zones configuration is therefore a highly topical issue since this network evolution could be of interest in different cases: bottlenecks management, price signals, loop flows... However, this research field is quite new and many considerations have to be taken into account. The current knowledge on market splitting is not enough to accurately understand the impact on the market liquidity, the competition for instance smaller bidding zones could increase the risk of market power... In this context, through the Network Code on Capacity Allocation and Congestion Management [15], Agency for Cooperation of Energy Regulators (ACER) invited ENTSO-E

28 Chapter 2. Background and Literature Review 17 to start a Bidding Zone Review process (figure 2.9). In order to study the influence of existing bidding zones on electricity market and their possible modifications, ACER and ENTSO-E cooperate and the Terms of Reference [9] were presented during the Regulatory electricity Forum in Florence in November The involved TSOs elaborated a technical report based on the current network situation [4]. This report mainly focuses on congestion issues and management based on different aspects: physical congestions in the network, security of supply, considered measures, indicators used to determine unscheduled flows... Once the review launched, different bidding zones configurations will be analyzed and compared taking into account both technical and economic aspects. Figure 2.9: Activities in the Bidding Zone Review Process [9]

29 Chapter 3 Model Presentation In this part, the different models used both for electricity market modelling and load flow simulations are presented. The content mainly focuses on the economic part which is the most used in the bidding zone configuration impact study. The inputs used in the simulations are also detailed. 3.1 Electricity Market Modelling The internally developed software used for the simulations called Antarès enables to combine both electricity market modelling (economic dispatch) via supply-demand equilibrium under constraints and Monte-Carlo simulations. The model is designed in order to simulate the market mechanisms inside a bidding zone but also to take into account the exchanges through interconnections. Thus, it is possible to simulate the whole European system Economic Dispatch Economic dispatch principle is based on the supply and demand mechanism [15]. In a given area, the electricity spot price is assumed to be determined by the intersection of the supply and demand curves. This trade enables to maximize the total surplus from a general point of view. Antarès uses the cheapest power plants to cover the load in the most efficient way considering the constraints. Thus, economic dispatch is based on a minimization of the overall cost in the system (3.1) subject to different constraints such as power plants availability, links properties, defined relations between different flows... The time span 18

30 Chapter 3. Model Presentation 19 used is of one year and the time resolution is one hour in order to be consistent with the resolution used in the French wholesale electricity market. Min(Ω) = t B(t, g, z)q(t, g, z) (3.1) G z Where: B(t,g,z) is the bid of the power plant G in zone z at hour t Q(t,g,z) is the generated power in power plant G in zone z at hour t In the model used in this study, the approach is composed of three different steps: A first optimization for hydro and thermal generation is run disregarding the unit commitment constraints. This enables to use only linear programming since the approximation gets rid of the integer variables. Then the thermal constraints (minimum stable power, minimum up/down time, must-run conditions...) are forced. To finish, knowing the committed thermal fleet for the given hour, a second linear optimization is run Monte-Carlo Simulation The main advantage of the software used in the study is the possibility to combine electricity market modelling with Monte-Carlo simulations. In the electricity market, many parameters are uncertain. There is a probability that a power plant is not available during a given hour due to maintenance work or failure in the system, renewable energy sources (wind speed, solar energy) depend on the considered year... By using different coherent set of data, typical years can be simulated. The software can also determine whether or not a power plant is available based on the outage rate and duration given as input... Moreover, the study is based on 2030 forecasts data, then, using Monte-Carlo simulations enables to obtain more realistic and accurate results. Monte-Carlo method is based on estimation of output variables by random observations [16]. Since it is completely impossible to know the 2030 characteristics regarding weather conditions, group availability and amount of consumption, Monte-Carlo represents an interesting alternative.

31 Chapter 3. Model Presentation 20 An estimate of the expected value of a random variable X can be obtained by computing the mean value (3.2) of n independent observations x i of the considered variable [16]. m X = 1 n n x i (3.2) i=1 This Monte-Carlo method, called simple sampling, is the one applied in this study. Different observations of uncertain input parameters are realized and then, the electricity market is simulated with these different values. The mean values of the output variables give a better estimate of the reality of the system. It can be shown that the variance of the estimate obtained with the simple sampling Monte-Carlo method is given by (3.3). V ar[m X ] = V ar[x] n (3.3) The number of observations has a significant impact on the results accuracy. It has been decided in the study to simulate 50 years of Monte-Carlo. This sample is historically large enough to obtain relevant results and it is possible to deal with the results via Excel Interface With Antarès software, an area is defined as a node in the interface. In this node, consumption and production data are given (please refer to section 3.2) and for each Monte-Carlo year simulated, the program randomly pick one the available time-series. In each area optimization is performed. Links between areas are also modelled and some constraints can be implemented to take more parameters into account. Figure 3.1 shows the graphical interface of the software used. The following countries are simulated: Austria, Belgium, France, Germany, Great Britain, Ireland, Italy, Luxembourg, Netherlands, Northern Ireland, Portugal, Spain and Switzerland. 3.2 Inputs The input data in electricity market studies are very important since they determine the time horizon and the purpose of the study. In this study, programmes of production and consumption realized by RTE for the time horizon 2030 are used. This choice enables

32 Chapter 3. Model Presentation 21 Figure 3.1: Antares Interface with the considered European areas in the Simulations (color orange for the 25 French areas) to be more flexible on the considered assumptions and gives one possible evolution of the future network expansion. The following assumptions are taken into account: Moderate consumption growth Slight decrease of the French nuclear installations Development of Renewable Energies The initial set of data available corresponds to different already-made time-series both for consumption and the different kind of productions for European countries. In France, the same data are available but for 25 areas. This enables to be more accurate on the production distribution inside the country and determine a new bidding zone configuration by aggregating smaller areas. The available information is presented in the following sections. In order to make decisions, a sensitivity analysis of the input/scenarios should be made, for example with the four scenarios of ENSTO-E Scenario Outlook and Adequacy Forecast [17].

33 Chapter 3. Model Presentation Load The load is obtained for each country based on historical data and forecasts at the time horizon 2030 with the assumption of a moderate growth. 100 time-series years (TS years) are available which means that for 100 years, the consumption for each of the 13 countries is known at a time resolution of one hour. The graph below (figure 3.2) gives the mean French consumption over 50 TS years. Figure 3.2: French hourly Consumption (mean value over 50 Time-Series years) Since more accurate data are needed in France, the consumption in each of the 25 areas in France must be known. Based on actual network situations, it is possible to compute the load in each area and compare it with the total consumption in France. Thus, a coefficient is defined for each area and every hour, the load is known in the 25 French areas Renewable Energy Sources The renewable energy sources data is also based on historical ones. 100 TS years are available for wind generation and 3 TS years are available for solar production. Thus, for each Monte-Carlo year simulated, the model will randomly choose one of the TS years for each type of production. It is necessary to make sure that for one type of production, the software always pick the same TS years for all the considered areas. Indeed, that way enables to obtain coherent and relevant production since a windy or sunny year will have an impact on many areas.

34 Chapter 3. Model Presentation 23 Wind and solar productions are obtained by using a time-series analyzer. This tool enables to find the most suitable parameters of the distribution based on historical data. The models defined as output of this function are therefore very close to the reality. For instance, if wind speed has to be simulated, a Weibull distribution would be the most suitable choice. Then, by analyzing the historical time-series, the parameters of the distribution, the daily profile, the spatial correlation are found to correspond to the real values. A simple turbine model could enable to transform these wind speed values into wind production. The data used in this study have been obtained through this process in order to directly find wind production. In this case, a beta distribution is an interesting alternative Hydraulic Production Hydraulic production is often the most problematic one in most of the electricity market models since it is very difficult to foresee the hydraulic generation when the electricity price are not known. Indeed, the production depends on the market situation since hydraulic power plants try to maximize their profit. Thus, the energy is saved in the reservoirs if the prices are low and the available power is used when it is the most profitable. The initial set of data for hydraulic production is composed of historical observations. 60 TS years are available with the overall amount of hydraulic energy to produce per month in each area. The share of Run-Of-river (ROR) production for each month in the different area is also known. The ROR energy to produce throughout a given month is known. Thus, the hourly ROR production in each considered area is obtained by dividing this value by the number of hours in the month. For the Storage Power (SP), the process is more complex. The historical data give the amount of energy to produce due to the storage for each area and for each month (60 TS years). The hourly production for a zone z is determined by the method presented below. First of all, a weighed load is computed for each area using equation (3.4). This value takes into account the net load of the considered area which represents the load minus the non-dispatchable generation. This net load is to be covered by hydraulic production or dispatchable generation. The weighed load considers also the fact that the hydraulic production in a given area does not necessarily depend only on the load in this considered area by using the allocation matrix A. For example, in the studied

35 Chapter 3. Model Presentation 24 system, there are 25 areas in France; the hydraulic production in a given area depends on the French consumption and not only on its own load. In the study, it is assumed that the hydraulic production in one country only depends on its own consumption. l weighed (z) = i A(i, z)l net (z) for every area i (3.4) A process called inter-daily generation breakdown enables to find the amount of energy to produce each day knowing the available energy throughout the month. First, target values which correspond to optimum energies to produce throughout the day are computed based on (3.5). This equation shows that there is not any linear relation between the load and the hydraulic production. Historical data show that the power factor is around 2. In the available data, β = 1.5 is chosen. The sum of all the target values has to be the already-known monthly production. h(i) h(j) = ( ) lweighed (i) β for all (i, j) (days) (3.5) l weighed (j) With: nb days i=1 h(i) = monthly production(i) (3.6) The amount of hydraulic energy to produce within the day in each area is computed by solving an optimization problem. This process minimizes a fictitious cost proportional to the difference between the daily energy and the target value presented above (computed by (3.5)), subject to constraints given in formulas (3.7)(3.8). 0 h (i) 24 P max (3.7) nb days i=1 h (i) = nb days i=1 h(i) (3.8) Then it is necessary to define a last parameter (given in (3.9)) which enables to limit the production peak during the day and therefore smooth the hydraulic generation throughout the day (figure 3.3). ( ) Daily peak in the day Intra Daily Modulation P arameter = max Mean power throughout the day (3.9) To conclude on the hydraulic production, the model combines both historical data and optimization in order to be as realistic as possible. The ROR production is easily

36 Chapter 3. Model Presentation 25 Figure 3.3: Intra Daily Modulation Parameter enables to smooth the Hydraulic Production (Source: RTE) obtained with the same production every hours of the same month. The SP data process gives the amount of energy to produce for each area and each day of the year. Then, this daily value is optimized at an hourly time span throughout the day taking into account the intra daily modulation parameter Thermal Production The thermal production modelling is of interest to the extent that this type of production will give the electricity spot price every hour in the system. Indeed, the marginal cost of renewable energies is assumed to be null. Thus the marginal price is fixed by the most expensive thermal power plant used to cover the load. Different clusters are defined for each area: Nuclear, Lignite, coal, Gas, Oil, Mixed Fuel, Miscellaneous dispatchable generation. Parameters are to be defined: Capacity of the power plants Number of units Minimum stable power Min up/down time: some power plant cannot be started for one hour only, they have to produce for a certain amount of time Marginal cost of production Start-up cost, fixed costs Market bid

37 Chapter 3. Model Presentation 26 Through Monte-Carlo simulations, random events are taken into account. It is therefore necessary to give outage rates and durations both for planned outages (maintenance work... ) and forced outages (failure). Formulas (3.10) and (3.11) shows the Forced Outage Rate (FOR) and the Planned Outage Rate (POR) definitions with F the number of hours in forced outage, P the number of hours in planned outage and A the number of hours where the power plant is available. The Overall Outage Rate (OOR) given in (3.12) which is of interest when simulating the Monte-Carlo years does not corresponds to the sum of the two previous rates. Based on the formulas below, equation (3.13) used in the model can be defined. OOR = F OR = P OR = OOR = F A + F P A + P F + P A + F + P F OR + P OR 2.F OR.P OR 1 F OR.P OR (3.10) (3.11) (3.12) (3.13) The model allows the definition of power plant capacity modulation. Thus, the efficiency of the power plants can be modified throughout the period of the year. This function is especially used for thermal power plants. Demand Side Management (DSM) is also modelled as a fictitious thermal power plant. In case of loss of balance between supply and demand of electricity, DSM consists of a temporary decrease of the consumption on a given location or for some actors in the system (compared to their regular load). This method can be seen as an alternative to the installation of new power plants by relieve the network in difficult situations such as a fault, a large consumption increase or to compensate the renewable energies intermittence. In the model used, DSM is not available from April to October. For each of the 50 Monte-Carlo years simulated in the study, the first step is a time-series generation where the availability of the power plants is randomly determined following the given parameters for each cluster. Then knowing the availability and the capacity of each power plant, the optimization can be launched. Regarding the minimum stable power constraints and the minimum up/down times, further information are given in section 3.3. Some types of production are not taken into account in the thermal clusters presented above. Thus, for Combined Heat and Power, Bio Mass, Bio Gas, Waste, Geothermal,

38 Chapter 3. Model Presentation 27 Other and Storage, only one year of historical data is available for each area (no optimization, no marginal cost). The storage management is disregarded; that represents a large approximation since pumped-storage hydro power plants have a significant influence on the market results Links On Antarès software, each area is modelled as a node and the electrical interconnections between two zones are defined as a simple link where different parameters can be given. The model used for these links and the assumptions - taken into account or not - have a significant impact on the results. Indeed, the output of the models can be completely different if the impedances are given or not, according to the transmission capacities definition... To simplify the model, only the 400kV network is considered in the simulations. Every other voltage levels are aggregated in the 400kV stations. For each link between areas, the following parameters can be defined: Transmission capacity which can be defined in both direct and indirect direction (in case the capacity is not symmetrical). If physical flows are of interest, Grid Transmission Capabilities GTC are used; in case of economic studies, Available Transfer Capacities ATC are defined. In the European model used in the study, ATC used abroad are based on historical data. In France, GTC have been computed by RTE; the methodology is briefly presented in Appendix A. Hurdle costs. These costs represent a fictitious fee for using a transmission line (see section 3.3). Constraints. This module enables to define conditions on the flows through links, relations between flows (Kirchhoff laws) Assumptions Based on the model used and its limits, some assumptions have to be done. The main decisions regarding the electricity market modelling are presented below.

39 Chapter 3. Model Presentation 28 Table 3.1: List of the Power Plants with their characteristics Power Capacity Bid Pmin Min up down Plant (MW) (euros/mwh) (MW) Time Nuclear No constraint Nuclear No constraint Thermal h Market Assumptions The market is supposed to be ideal. Thus the assumptions of perfect competition and perfect information are considered [16]. There is perfect competition in the market when some conditions are fulfilled: The players are assumed to be rational The players are free to trade with each other There is no market power The perfect information criterion is valid if all players have access to all relevant information to take their decisions. Regarding the price elasticity of the load, this assumption is not taken into account since the demand is assumed to be known every hour for each simulated year (section 3.2) Minimum Stable Power and Minimum up/down time Constraints As presented in part 3.2, different constraints regarding the thermal power plants can be taken into account in the optimization process. In this report, the minimum stable power and minimum up/down constraints have been disregarded. This choice is due to one of the limit of the used model. Indeed, in order to consider these constraints, the model will create four periods of six hours during the day. For each period, a test is performed to assess whether or not the constraint is fulfilled and the latter is forced if necessary. This can considerably modify the results to the extent that the marginal cost is computed by adding 1MWh/h of consumption in each area and the least expensive available power plant defines the price cross. This problematic can easily be seen on a simple example. Three power plants are considered in a given bidding zone. The characteristics of the generating fleet are given in Table 3.1. A load profile of the area is fictitiously determined (figure 3.4).

40 Chapter 3. Model Presentation 29 Figure 3.4: Load Profile during the day (for the example purpose, completely fictitious) Based on this very simple example, it is possible to compare the real case with the software behavior. Most of the time, the load is covered by the two nuclear power plants. If the consumption exceeds 100MWh/h, the thermal power plant is necessary, thus it is called hours 5, 7, 17 and 19. Depending on the considered assumptions, the following happens: Real case: since the group is called hour 5, 17 and has to remain on for three hours, the thermal power plant produces 30 MW from 5am to 7am and from 5pm to 7pm. With Antarès: since the thermal power plant is called hour 5, the model will check if the constraint is fulfilled from hour 1 to hour 6; the same process will occur for the four periods during the day. Thus, the thermal power plant produces 30 MW from 4am to 9am and from 4pm to 9pm. By taking into account the minimum stable power and min up/down time constraints on the model, the thermal power plant is producing twice longer than in reality. Due to the minimum stable power, during some hours a part of the load is covered by thermal generation instead of nuclear. The marginal prices obtained with Antarès are therefore very low. Indeed, the marginal price given in the model will be based on the nuclear operating cost if the nuclear power plants are not fully used. The economic dispatch loses

41 Chapter 3. Model Presentation 30 all meaning. The evolution of the marginal cost in the different cases is presented below (figure 3.5). The black dashed curve which represents real marginal cost is very different from the one obtained on the model with all constraints (red dashed curve). The blue one corresponds to the results obtained on the model by disregarding the constraints. Figure 3.5: Marginal Prices throughout the day taking into account the constraints or not If the minimum stable power and minimum up/down time constraints are neglected, the system is less accurate since this means that any thermal group can be fired for only one hour and produce the amount of power that is necessary. The obtained results will of course present differences compared to reality. Figure 3.6 and figure 3.7 give the mean value of the difference for operating costs and electricity spot prices when the constraints are neglected compared to the initial case taking into account all assumptions. It can be shown that the impact is more important in Italy. Regarding France, the operating cost and the marginal prices are slightly modified. Disregarding the minimum stable power and minimum up/down time constraints provokes an increase of the French operating cost (+5%) and the marginal price (around +10%). As a general remark, the impact is different according to the considered country but remains acceptable. However the purpose of this study is to study the impact of a new bidding zone configuration in France. Thus most of the results will be presented as comparison; if the assumptions are coherent in the different simulations, the results are relevant.

42 Chapter 3. Model Presentation 31 An economic impact study is possible if the economic dispatch results have a real sense. The decision to delete these assumptions enables to obtain marginal prices as relevant optimization process outputs. Figure 3.6: Operating Cost Evolution if the constraints are neglected (compared to the case with constraints) Hurdle Costs The hurdle costs are available parameters for the inter-area links modelling. They represent a small cost for using the interconnections capacity. They enable to avoid irrelevant flows into the system. Indeed, since the objective function of the optimization problem is to minimize the overall cost, only useful flows will appear in the results. The impact of these hurdle costs on the results is studied in the chapter 4 and solution to deal with them especially regarding marginal prices is presented. 3.4 Load Flow Simulations Two methods are used in this study to determine the physical flows into the European network. The first one based on Antarès enables to determine via a DC Load Flow

43 Chapter 3. Model Presentation 32 Figure 3.7: Marginal Price Evolution if the constraints are neglected (compared to the case with constraints) the equivalent physical flows between all simulated areas. This is possible by defining impedances for each links. For the loop flows study, a more advanced model based on AC Load Flow computation is run to obtain the physical flow in each line in Europe and therefore be able to compare them with the commercial exchanges obtained with Antarès DC Load Flow on Antarès The DC Load Flow method is used in the study in order to obtain the physical flows in France. Based on electricity market mechanisms, the model internally developed by RTE enables to compute the production plan in the different areas for each Monte-Carlo year with an hourly time span. In a meshed network, there are some loops into the system and therefore different possible parallel paths between two locations. A method to determine in which way the flows are distributed among those paths is necessary in case physical flows are of interest.

44 Chapter 3. Model Presentation 33 The assumptions of the DC Load Flow [18] and the use of impedances in the model represent a solution to compute the physical flows which occur throughout the links between areas since the physics of the electricity network are modeled. The power transmission through a power line can be defined by the relation below (3.14). P 12 = U 1U 2 X 12 sin(δ 1 δ 2 ) (3.14) Where: P 12 is the transmitted power through the line (node1 node2) X 12 is the line reactance U i is the voltage at node i δ i is the rotor angle at node i Load flow computation based on definition (3.14) leads to a non-linear problem which can be solved by iterative methods. In order to simplify the problem, the following assumptions are often accepted: The voltages are considered constant: U 1 = U 2 = 1pu The phase angle difference from one side to another of a transmission line can be disregarded: δ 1 δ 2 0 and the sinus function can be linearized: sin x x Relation (3.14) is therefore simplified and the transmitted power through the line between node 1 and node 2 can now be computed by (3.15): P 12 = δ 1 δ 2 X 12 (3.15) It can be shown [18] that based on formula (3.15), there is a relation between the transmitted power through all lines in the system P trans and the net production matrix P node for all areas (3.16). The matrix M could be seen as a PTDF matrix since it corresponds to the way the flows are modified through line when the area balances change. P trans = MP node (3.16) If equivalent impedances are needed to study physical flows between areas, the model allows defining Kirchhoff s laws. For each mesh into the European electricity network, the constraints below (3.17) are implemented with F i the amount of power transmitted

45 Chapter 3. Model Presentation 34 through the link and X i the equivalent impedance. The methodology used by RTE to compute those impedances is given in Appendix B. F i X i = 0 (3.17) i mesh By using this method, even if the model is initially based on economic considerations, a simplified load flow computation is performed for each hour. The approximated physical flows are determined since impedances and injections/consumptions are known and this computation can be done for each hour in combination with the economic dispatch AC Load Flow with network model Physical flows on the European electricity network are of interest to evaluate the flow resulting from the commercial exchanges with 2 bidding zones or one bidding zone for France (cf. chapter 4). In order to obtain these flows, the RTE load flow model is used. This model is based on an AC load flow algorithm. It takes into account the actual nodes, lines, transfers of the grid. The model has been used for relevant hours; for some typical hour, the production plan and the consumptions are defined. Based on the inputs (load, generation dispatch, exchanges...), the model determines the physical flows through the grid. The load flow computation used is based on a Newton-Raphson algorithm. This algorithm enables to determine the physical quantities of interest such voltage, active and reactive powers for each node.

46 Chapter 4 Methodology In this chapter, the methodology used for the study is presented based on the theoretical frame and the rationale for the model choices. Methods to determine the new bidding zone configuration are developed. Then, indicators are given to assess the impact of this market evolution. The section ends with methodology regarding a loop flows study. This part is quite different from the impact study based on economic considerations since it combines both physical flows and commercial exchanges at the same time. Based on Antarès economic dispatch results, both commercial exchanges and physical flows are of interest. Commercial exchanges are determined since they are only based on the electricity market nature. Electricity market modelling is performed following the process and assumptions given in chapter 3. For each hour, the production is determined and the exchanges between bidding zones are found in order to minimize the overall cost. If the Kirchhoff s laws are taken into account in the model, the impedances are defined and the physical flows are obtained. In case, there are not any constraints on the flows through the links between areas, the exchanges are only constrained by the transmission capacities. This leads to an important observation: the capacities defined as parameters for each line do not represent the same thing is the impedances are considered in the system or if they are disregarded. If Kirchhoff s laws are activated (physical flows obtained as outputs), these capacities are physical constraints; in the other case (commercial exchanges as outputs) they represent Available Transfer Capacities (ATC). 4.1 New Bidding Zone Configuration The new bidding zone configuration has to be defined according to an unbiased and rational methodology. In this study, the definition of the new bidding zones in France is based on internal congestions observations. 35

47 Chapter 4. Methodology 36 The system is simulated in order to get the physical flows in France. The impedances are therefore taken into account in the model. The transmission capacities which correspond to physical limits are known for the internal links between the 25 French areas. However, it has been decided that the system would be simulated without any transit limitations in France. By using unconstrained results, all possible congestions into the system are visible. Two methods can be applied: a configuration only based on congestions and a way of reasoning based on redispatching costs Congestions The first method in order to define the new bidding zone configuration is based on congestion observations in the system. The principle is to find the congested links and then define the new bidding zones in such way that both ends of the congested lines are in different zones. Thus, the exchanges which would occur throughout the congested line once the new configuration is implemented are submitted to the market mechanisms. The congestion is now visible through the results and could lead to different incentives in the electricity market Redispatching Costs Redispatching is a measure that the Transmission System Operator (TSO) can take to deal with internal congestions. A simple case is presented below to explain this congestion management. A fictitious system is composed by two areas A and B (with respectively λ A and λ B as electricity prices) and a transmission line with a capacity of 100MW. If the optimum situation in the market leads to a physical flow of 50MW through the line, there is not any congestion in the system and the prices are equal in both areas (figure 4.1). Figure 4.1: No Congestion situation, Physical Flow lower than the line capacity However if the same fictitious system would lead to a transfer of 150MW from area A to area B to maximize the overall social welfare, congestion occurs in the system. Only 100MW are transferred (physical flows limited by the capacity of the link between areas

48 Chapter 4. Methodology 37 A and B) and the price in area A remains lower than in area B. If the constraint is not taken into account during the capacity allocation, the TSOs could use redispatching to relieve the transmission line by trading the extra 50MW. That represents a cost for the TSO since it acts against the market to relieve the congestion (figure 4.2). Figure 4.2: Congestion through the line, Redispatching example For a given border between areas A and B, for each hour t the redispatching cost can be defined as (4.1) in case of congestion and null if there is not any congestion: Redispatching cost(a, B, t) = (P trans P lim ). λ A λ B (4.1) With: P trans : Transmitted power through the link to maximize the social welfare (in case there is not any physical constraint), at hour t P lim : Physical capacity of the link In this method, the new bidding zone configuration in France could be defined by aggregating small French areas in order either to minimize the overall redispatching cost or to regroup areas with close prices. Thus, a division of the country in small bidding zones would be necessary and then the configuration would be based on larger zones (results of the aggregation process). This method has not been used in this master thesis since the actual French redispatching process is different. Indeed, redispatching is applied on a national level and therefore, it is not necessarily power plants in areas A and B that would be used to deal with this bottleneck. In case of internal redispatching, redispatching costs for the TSO in France are obtained by considering both the electricity spot price in the country and the adjustment price of the needed group. Formula (4.1) would not represent the actual cost for the French TSO. More information regarding the power plants involved in redispatching measures in France are needed to obtain a more suitable model. Thus, the bidding zone configuration defined in this study is based on the occurrence of congestions through internal French transmission lines.

49 Chapter 4. Methodology Choice of the Net Transfer Capacity Once the new configuration chosen, the question of the commercial capacity of the link between the new bidding zones is of interest. Indeed, only one link between the new bidding zones remains and for all other equivalent transmission lines, the capacity is set to null. The value of the capacity on the remaining link has a significant impact on the obtained results (cf chapter 5). A capacity calculation should have been performed in order to get the value of the commercial capacity between the new bidding zones. The general method used to perform such computation is given in section 2.2. But in the study case presented in chapter 5, the critical branches and the PTDF matrix are not available. It is therefore not possible to perform any capacity calculation. Thus, the capacity is set by an arbitrary choice. It has been decided to consider both physical flows and commercial exchanges to define these values. The system is simulated taking into account the impedances of the links in France without any Grid Transmission Capability (GTC) in order to observe the unconstrained physical flows between the new bidding zones. Then, the system is run without any Kirchhoff s laws; the unconstrained commercial exchanges (commercial capacity set to infinite) through the link of interest are measured. The internal commercial capacity in France is then defined based on the unconstrained physical flows and commercial exchanges. Two different values have been chosen in order to perform a sensitivity analysis and emphasize the importance of the commercial capacity value on the results: a case with strong constraints on the link and another one slightly less constraining. 4.3 Economic Impact Study In this section the general methodology to study the impact of the new bidding zone configuration in France from an economic point of view is presented. These indicators enable to assess whether or not such measures are beneficial for the European electricity market. This method has been used for the study case presented in the results part. It can obviously be generalized for every bidding zone configuration of interest. The main goal of this part is to present a general methodology which can be applied to study the impact of market splitting and compare different possible solutions.

50 Chapter 4. Methodology Commercial Exchanges Evolution The commercial exchanges are known in Europe when France has only one bidding zone. If the market design and the bidding zone structure are modified, the European transactions will also change. The first step in an impact study is to observe the evolution of the commercial exchanges at the level of the different borders. The main tendencies can be computed by using results from the Monte-Carlo synthesis (for example the mean value of hourly commercial exchanges, computed for 50 Monte-Carlo years). More statistics can be used to be more specific and obtain more information regarding the way the interconnection is used and the exchange values in each direction. A deeper analysis is possible by using duration curve. These curves enable to show the amount of time the exchange is larger or equal to a given value. In this study, the exchanges values are given as a percent of the available GTC or ATC in the link. The 10 th and 90 th percentile are also specified on the graphs. This presentation is perfect to have a complete overview of an interconnection behaviour since it gives all necessary data: minima, maxima, shares in each direction Price Convergence Indicator Price convergence into the electricity market is often considered as a criterion of interest to define the overall efficiency of the interconnections management. Indeed, a large price convergence reflects a small occurrence of congestion. Based on this principle, an indicator has been created to compute the price convergence for each interconnection in the system. In section 3.3, hurdle costs are presented. These fictitious costs avoid irrelevant flows in the system but they also have an impact on the prices. A price convergence is considered if the price difference on both sides of an interconnection is null. Because of these hurdle costs, even if there is convergence, the price difference is not necessary null. Convergence criteria are to be defined in order to create a relevant indicator. Thus, the system is simulated with the copper plate model assumptions. The copper plate model corresponds to a system where the whole Europe is only one bidding zone. All capacities are set to infinite and therefore there are not any constraints. Thus, the electricity prices in all simulated areas are supposed to be exactly the same for each hour; there is full convergence. Based on these results, it is possible to determine the price difference due to the hurdle costs for each interconnection.

51 Chapter 4. Methodology 40 The indicator will therefore consider both the price difference in the studied simulation and the one obtained with the copper plate simulation. For each hour and each border, there is not convergence if the price spread in simulation V i is higher than the copper plate price difference (4.2). The final value corresponds to the time rate where there is convergence over the 50 Monte-Carlo years (4.3). P rice Convergence(V i, b, t) = 1 if ( λ V i b,t λcp 0 if ( λ V i b,t λcp b,t ) = 0 b,t ) > 0 (4.2) With: λ V i b,t : price difference throughout border b, at hour t, for simulation V i : price difference throughout border b, at hour t, for copper plate model λ CP b,t P rice Convergence Indicator(V i, b) = t P rice Convergence(V i, b, t) number of hours in the year (4.3) Due to the assumptions considered in the simulations and the time horizon 2030, the absolute value is not of interest. In order to study the impact of the new bidding zone configuration, the evolution of this indicator will be presented for the different interconnections (difference between the initial value where France is one bidding zone and the simulation with the new situation) Price Divergence Indicator This section also deals with electricity spot prices. The indicator presented above shows the impact on the price convergence for each interconnection: it is possible to know if the convergence has increased or on the contrary if the new bidding zone configuration provokes divergence on both sides of a link. The limit of this indicator is that all price divergences are considered the same way; the price divergence is only considered as an absence of convergence but the price difference is not given. A price divergence indicator has therefore been created to study the impact on the price difference. Once again, the hurdle cost problematic has to be considered and the copper plate simulation results are part of the computation. Formula (4.4) gives the relation used to compute the price divergence indicator. It is mainly based on a root-mean square deviation which enables to aggregate the magnitudes of difference between the considered simulation V and the copper plate model CP into a single measure. This equivalent distance is interesting to the extent that the whole

52 Chapter 4. Methodology 41 divergence on both ends of an interconnection is represented by a simple indicator which emphasizes the large difference and decreases the impact of small ones. Quantitative information is available regarding the electricity prices analysis thanks to this indicator. P rice Divergence Indicator(V, a 1, a 2 ) = t T 2 i=1 (mpv a i,t mpcp a i,t )2 2. T (4.4) The global divergence from the ideal copper plate model is determined. It can therefore been assessed if the electricity market evolution is beneficial by bringing the whole system closer to the fictitious target model where there is only one bidding zone in Europe Social Welfare study The social welfare in an electricity market is an abstract measure which gives a general overview of the quality of the market for all players: consumers, producers and TSOs. Different definitions are used in the scientific papers which deal with this indicator [19]. A clear definition of the social welfare used in this study is given by formula (4.8). For each bidding zone i in the system, the social welfare can be computed as the sum of: The consumer surplus (4.5): difference between the amount the consumers are willing to pay in area i for hour t and the price they really have to pay Consumer Surplus i,t = [ ˆ CD D i,t λ i,t D i,t ] (4.5) With: ˆ C D : Fictitious price which corresponds to the maximum price the consumer is ready to pay D i,t : Load in area i at hour t λ i,t : Electricity Spot Price in area i at hour t The producer surplus (4.6): difference between the income obtained by selling the electricity in area i and the operating cost P roducer Surplus i,t = [G i,t λ i,t OC i,t ] (4.6) With: G i,t = D i,t + j i P ij: Amount of energy produced in area i during hour t (load taking into account the transmitted powers with the neighbouring bidding zones)

53 Chapter 4. Methodology 42 λ i,t : Electricity spot price in area i at hour t OC i,t : Total operating cost in area i at hour t The trading surplus (4.7): corresponds to the congestion rent. It is assumed that this rent is equally shared between the two TSOs on both sides of an interconnection. T rading Surplus i,t = j i P ij,t 2 (λ j,t λ i,t ) (4.7) With: P i,j,t : Transferred power through the link between areas i and j, at hour t. (P i,j,t > 0 if export; P i,j,t < 0 if import) λ i,t : Electricity spot price in area i at hour t Thus, the social welfare is obtained with the relation below (4.8). It can be shown that the overall social welfare computed for all simulated bidding zones in the market is given by (4.9). δw i,t = [ ] CD ˆ D i,t λ i,t D i,t + [G i,t λ i,t OC i,t ] + P ij,t 2 (λ j,t λ i,t ) (4.8) j i δw total,t = ˆ C D D total,t OC total,t (4.9) In all simulation, the same load is kept for the 50 years of Monte-Carlo. The only unknown variable in the relation above is the price the consumers are willing to pay. Since the impact of the new bidding zone configuration is of interest, an evolution of the social welfare is presented as results of the study. The load being exactly the same each hour for all areas, the only assumption to do is to consider that the consumers are willing to pay the same price in all market configurations. The bidding zone creation or the modification of commercial capacities does not modify the value C ˆ D. The first term of the consumer surplus is simplified in the social welfare comparison. Another important aspect of the social welfare evolution is the transfer of surplus from one actor to another. Indeed, it can be very interesting to know for whom the market structure modification is beneficial. 4.4 Loop Flow study This section presents another part of the study regarding the loop flows problematic. The area pricing is nowadays seen as a possible solution in order to deal with this

54 Chapter 4. Methodology 43 Table 4.1: Clusters used to implement the economic results in the AC Load Flow simulations Clusters for the different kinds of power plants Hydro generation (Run-of-river, Storage) Nuclear Coal Fuel Gas (turbine, combined cycle) Wind generation Fictive power plants phenomenon. Indeed, by creating new bidding zones, some exchanges would be subjected to capacity calculation and allocation mechanisms and that could potentially have an impact on the unscheduled flows in the system. The methodology is based on both economic dispatch and load flow simulations. Indeed, the commercial exchanges as well as the physical flows between the bidding zones are needed to determine the amount of loop flows and transit flows (cf. definitions given in section 2.3) Data preparation The simulations run on Antarès software to obtain commercial exchanges, use a certain set of data based on 2030 forecasts. Different files for the physical network are available with the same time horizon both for peak demand and low consumption. First, it is necessary to do some modifications of the data in order to obtain consistent data in order to determine both commercial exchanges and physical flows. In France, each power plant and each load have been placed in the 25 areas. In Antarès it is aggregated by clusters but for the load flow simulation it has to be divided between each individual production units. The clusters used for the production are given in the Table 4.1: The economic dispatch is computed for the 50 Monte-Carlo years for different configurations in the system: initial case and new bidding zone configuration. Two characteristic hours have been chosen: Peak load in winter: January Peak load in summer: June

55 Chapter 4. Methodology 44 The loop flow study cannot be automated; this solution has therefore been decided in order to obtain two different configurations in the system regarding consumption and production plan. For each hour, the results are given by the mean values over the 50 simulated years to take into account the possible random events and get a representative sample. If needed with the chosen hours, demand-side management, storage, unsupplied energy or spilled energy are quantities to take into account in order to obtain perfect supplydemand equilibrium in AC Load Flow simulations. Demand-side management (DSM) is by definition a decrease of the consumption in case of load peak. It appears therefore very clear that the best way to consider DSM in the physical model is to decrease the load in the areas where the fictitious thermal power plants which represent this phenomenon on Antarès are producing. Regarding storage, it is also easier to use the same method as DSM. The network model for 2030 is not accurate enough for some kinds of production. The production plan found for solar power plants and combined heat and power (CHP) cannot be properly fulfilled. The only solution is to decrease the load in the concerned areas. This does not represent an important error since photovoltaic or thermal solar production are mainly decentralized and the amount of CHP is relatively small compared to the total production. Wind generation is partially modeled in the physical network. If the available maximum power in the area is large enough to cover the planned production (cf. economic dispatch), the production is completely taken into account in the simulation. If the installed fleet is too small, the wind generators produce their maximum capacity and the rest is deduced from the load. In France, production and load are exactly the obtained values in economic situations since the physical model is accurate enough. Abroad, the network is not so detailed. Rough production fleets are available; the net values are therefore the priority. Since the production in the base case is consistent, only the loads are modified in order to obtain the good balance in each country. Portugal, Northern Ireland, Ireland and Great Britain do not have any physical model. Their net values are therefore taken into account in the load of neighboring countries in order to ensure perfect supply-demand equilibrium for the whole Europe. In France, the HVDC links are represented and set to the commercial values. All modification applied to the physical base case are given in figure 4.3. Consistent economic dispatch and load flow simulations are finally available for two hours. This enables to determine both the results from the electricity market (production

56 Chapter 4. Methodology 45 Figure 4.3: Modifications performed from the Base Case to obtain consistent Physical Flows plan, commercial exchanges, electricity prices... ) and the network behavior (physical flows, all constraints... ) Unscheduled Flows The amount of unscheduled flows can be easily computed once the commercial exchanges as well as the physical flows are known for the exact same situation. Indeed, commercial exchanges represent all the scheduled transactions due to market mechanisms and the physical flows correspond to the power transfers which actually happen in the network. An intuitive indicator would be to compare these two quantities in order to compute all unscheduled flows on each interconnection (4.10). This value would be in the same way as the physical flows if more power is transferred compared to the market expectations and in the opposite way of the physical flows if the commercial exchanges are higher. U nscheduled F lows = P hysical F lows Commercial Exchanges (4.10)

57 Chapter 4. Methodology 46 By analyzing this indicator when France is only composed of one bidding zone and with the new configuration, the impact of the market structure modification is visible. This study gives a relevant overview of the differences between the economic situation and the physics in the system. However, it is completely impossible to determine which part of these unscheduled flows are loop flows. Transit flows which are flows through a bidding zone due to a commercial exchange between two other bidding zones are also taken into account in the indicator. The inherent meshed nature of the European network necessarily causes these transit flows. The results obtained should therefore be taken with a grain of salt European Loop Flows A Loop Flow is a physical flow through a bidding zone caused by commercial exchanges having origin and destination in one other bidding zone (cf. definition chapter 2). The main idea in this section of the loop flows analysis is to obtain a measure which gives the amount of the loop flows only. As explained earlier, transit flows due to commercial exchanges between the different bidding zones are inevitable. The principle is to deviate from the real situation by deleting the induced flows by all transactions between bidding zones in Europe and only consider internal exchanges. All net values have therefore to be null in Europe. When there is not any exchange between bidding zones, all the remaining flows are loop flows. In figure 4.4, only internal exchanges are considered in each bidding zone (solid arrows) and the dashed arrows represent the loop flows provoked by these exchanges. For instance the physical flow observed between areas A and B gives the amount of loop flows created by internal exchanges in A, B and C at the same time on this interconnection A-B. This study obviously leads to a fictitious market situation. However, if the methodology to balance the net values in each bidding zone is conserved from one simulation to another, the results could be compared and the impact of the new bidding zone configuration on the overall amount of loop flows can be determined. In order to set the net values of all bidding zones to zero, the load is modified in such way that there is perfect supply-demand equilibrium in each area.

58 Chapter 4. Methodology 47 Figure 4.4: Loop Flows due to Internal Exchanges in three Bidding Zones

59 Chapter 5 Results In this chapter, the impact of a new bidding zone configuration in France is studied and the results obtained by performing the methodology presented in chapter 3 are given. First, the general situation of the study case is presented. Then, the bidding zone configuration in France is determined and its influence on the European network both based on economic and physical considerations is of interest. Many simulations have been run in the study both to find a possible way to divide France in two bidding zones and to estimate the influence of this new market structure. This represents an application of the presented methodology and a first analysis of the obtained results. All simulations with the assumptions taken into account in each case are listed in Appendix C. 5.1 Background The aim of the study is to determine the impact of market splitting in France on the European Electricity system. The list of countries considered in the simulations of the European system is given in Table 5.1. The model used for France as a starting point is more accurate. Indeed, the available set of data used in this study corresponds to the 25 areas represented in figure 5.1. This organization of the French territory has been defined by RTE based on technical considerations. Indeed the area definition has been realized in order to gather together coherent transmission lines which present physical flows in the same direction. Thus, the obtained transmitted power flows between these 25 areas could represent relevant physical flows since they could be considered as physical flows on an equivalent transmission line (figure 5.1). 48

60 Chapter 5. Results 49 Table 5.1: List of the Simulated Countries and Abbreviations Country Austria Belgium France Germany Great Britain Ireland Italy Luxembourg Netherlands Northern Ireland Portugal Spain Switzerland Abbreviation AT BE FR DE GB IE IT LUX NL IE N PT ES CH In base case configuration, each European country corresponds to one bidding zone. France production and consumption is modeled by 25 small areas but all these areas are part of the same bidding zone. Thus, for each hour, there is a unique electricity price in France. The aim of the new bidding zone determination is to aggregate some of these small areas in order to define bidding zones in France based on congestion observation. For each bidding zone and each of the 25 French areas, the production and consumption data described in part 2 are available. The chosen time horizon is 2030 and for each simulation, 50 Monte-Carlo years are run. The set of data corresponds to the assumptions of the median scenario (Moderate consumption growth, Slight decrease of the French nuclear installations, Development of Renewable Energies). Different scenarios are available: median, new generation mix, large consumption, low consumption. The methodology is applied on the median one. Robustness of the defined bidding zone configuration and the methodology as a whole could be studied by using other set of data. The main objective of the study is to determine a new bidding zone configuration objectively defined thanks to congestion observation and then evaluate the impact of this market splitting based both on economic and technical considerations. It could give an overview of the whole European system behaviour in case France would be divided in several bidding zones.

61 Chapter 5. Results 50 Figure 5.1: Areas used as starting point in the Study 5.2 Localisation of Congestions in France New Bidding Zone configuration: France North and France South In order to determine a suitable bidding zone configuration in France, the congestions are of interest in the French electricity network. Thus, the European system is simulated taking into account the impedances between the different areas and infinite GTCs. It is therefore possible to observe all constraints in France. The obtained physical flows are then compared to the physical limits of the equivalent links and the congestions are visible. Figure 5.2 gives a rough overview of the main tendencies of the physical flows in France. The given quantities represent the mean values of the hourly physical flows (synthesis

62 Chapter 5. Results 51 computed based on simple sampling method throughout 50 Monte-Carlo years). Thus, arrows which represent the flows are colour-coded in order to observe the probability to have constraints on the considered links. If the arrow is red, the physical flows are higher than the GTC and therefore congestion appears on the link at least 10 percent of the time; the arrow is orange when the flows are higher than 90 percent of the physical limit at least 10 percent of the time; it is yellow with a threshold of 75 percent of the GTC. Green is used for links which do not reach the considered thresholds. Figure 5.2: Mean values over 50 years of the hourly Physical Flows in France (colorcoded congestion) Based on the results above, the new bidding zone configuration has been decided in order to obtain both ends of congested lines in different zones. The blue line represents the new border which divides France in two bidding zones. The congestions on links Area19-Area6, Area17-Area5, Area16-Area2, Area16-Area3 are considered. Then, to finish the division, the border between Area14 and Area10 has been chosen. Even if there is no congestion on this link, a large amount of power is transferred. It would also

63 Chapter 5. Results 52 Table 5.2: Structure of the New Bidding Zones Configuration in France Bidding Zone Abbreviation French areas France North FR1 Areas 1 to 10 France South FR2 Areas 11 to 25 be impossible to separate Paris from the northern areas to the extent that the network is designed in such way that production from Area9 is supposed to transit in Area10 to cover the load in areas 1 to 4. Thus, two bidding zones are created in France: France North and France South. Table 5.2 gives the structure of the two French bidding zones. Other constraints in France especially in the South have been disregarded. It could be possible to create three different bidding zones based on the dividing methodology. In the following study case, the two bidding zones solution is developed since it corresponds to the most probable evolution of the market structure from European point of view. At first sight, the new bidding zone configuration does not seem as optimal. Indeed, based on congestion study on the French territory, physical flows through this new border are not in the same direction. The commercial exchanges between FR1 and FR2 will be submitted to market mechanisms but this limitation does not necessarily imply an efficient congestion management since some physical flows in opposite directions could relieve the total transmission even if there are congestions. These congestion management considerations enable to define the new bidding zones. However, the impact of the new configuration on the French physical flows is not possible with the simulation model (limits of the model in section 5.7) Commercial Capacity Determination The new bidding zone configuration in France is now defined. Available Transfer Capabilities (ATCs) for all interconnections in Europe are needed for the Electricity market modelling. The commercial capacity determination between the two French bidding zones is decisive since it has a significant impact on the market behaviour. If the ATC is set to infinite, France is considered as one unique bidding zone and the new configuration has no impact on the market. In reality, a capacity calculation taking into account all critical branches in the network and relevant N-1 situations would be performed to find a suitable value of ATC between FR1 and FR2. In this study, this computation is not possible since the PTDF matrix and accurate network description are not available.

64 Chapter 5. Results 53 An observation of both unconstrained commercial exchanges and physical flows has been performed in order to define the internal French ATC. Two simulations have been run: Simulation 1: The impedances are taken into account and based on DC Load Flow theory, physical flows are found. Unconstrained physical flows are obtained in France by setting all GTCs to infinite. Figure 5.3 gives the distribution of the unconstrained hourly physical flows from France South to France North (FR2-FR1) over 50 Monte-Carlo years (i.e. synthesis computed by simple sampling method over the 50 simulated years at a time resolution of one hour). It can be noticed that the mean value of these flows through the border is around 900MW. Thanks to this curve, the percentage of time where there is physical congestion on the border for a given capacity can be found. Simulation 2: In this purely economic study, the Kirchhoff s laws are not defined. Unconstrained French commercial exchanges are observed since the commercial capacity between FR1 and FR2 is set to infinite. ATCs abroad are constraining. This market simulation is exactly the same as if France was only bidding zone. The only difference is the measure of these exchanges through the new French border. Figure 5.3: Statistics of Physical Flows France South-France North over 50 Monte- Carlo years

65 Chapter 5. Results 54 Table 5.3: Congestion Time rates depending on the capacity link between the two French Bidding Zones Symmetrical capacity Physical Flows Commercial Exchanges France North-France South (Time rate>capacity) (Time rate>capacity) % 88.31% % 79.06% % 69.86% % 60.51% % 51.50% % 43.14% % 35.74% % 29.27% In Table 5.3 below, the percentage of time where there is congestion is given for different values of capacities between FR1 and FR2. The symmetrical capacities represent different quantities depending on the nature of the simulation. For the physical flows, the values given in the first column are GTCs whereas for commercial exchanges, they are considered as ATCs. A sensitivity analysis has been performed by choosing two different values of commercial capacities. The chosen value should not be too high since the new configuration would have a very small impact on the market and not too small since we still need power transfer between the two French bidding zones. Thus, the two different ATCs for the new bidding zone configuration are: 2500 MW: constraining case with around 50% of time commercial congestion in the system 4000MW: less constraining than the previous value (only 30% of time in congestion) 5.3 Modification of the European Commercial Exchanges By modifying the bidding zone configuration in France, the commercial exchanges in Europe are modified. The first step in an impact study consists of an analysis of these transactions.

66 Chapter 5. Results 55 Table 5.4: Colour Scale for net values in the different maps Amount Colour Scale for Imports Colour Scale for Exports Minimum Minimum - 20% 20% - 40% 40% - 60% 60% - 80% 80% - Maximum Maximum Influence of the new Configuration The system is simulated for 50 Monte-Carlo years by taking into account the ATCs. The impedances are disregarded in order to obtain commercial exchanges in the European network. The impact of the new bidding zone configuration can be analyzed in a first approach thanks to a comparison of the main tendencies of these exchanges through the different interconnections. Figure 5.4 gives the results in the initial case where France is considered as only one bidding zone. On the left, the mean values of the flows over the 50 simulated years are given. The colours represent the net value of the bidding zone; the scales are given in Table 5.4. Since information is lost with a mean hourly value over 50 years, the figure to the right gives more information especially the time rates in both ways of use of the interconnection with the mean value in each case and the percentage of time where the link is not used. The legend for the time-split arrows given in the maps with flows statistics is given below, with the example of the interconnection between France and Spain on figure 5.4: The interconnection is used 43% of the amount of hours over 50 simulated years in the direction France-Spain. The mean flow in this direction is 3291MWh/h. The interconnection is used 52% of the amount of hours over 50 simulated years in the direction Spain-France. The mean flow in this direction is 3408MWh/h. The 0 -Flows rate is 4%. During this time, there is not any exchange through the interconnection.

67 Chapter 5. Results 56 The situation is therefore obtained for 2030 if the market structure remains unchanged. This simulation represents the base case and the impact will be computed in comparison with these results. Figure 5.4: Commercial Exchanges (MWh/h) in initial Bidding Zone Configuration: mean values over 50 Monte-Carlo years (left); Statistics in both ways of the interconnection (right) The ATCs are conserved abroad and between the two French areas the capacity is set to 2500MW or 4000MW (cf. Section 5.2). In the initial case where France is only one bidding zone, the following links had an infinite commercial capacity: Area14-Area10, Area16-Area2, Area16-Area3, Area17-Area5, Area19-Area6. To model the two bidding zones, all the commercial capacities of the interconnections given above have been set to zero except for Area16-Area2 which presents the defined ATC (FR1-FR2) as characteristic. Figure 5.5 and figure 5.6 give the commercial exchanges results once the new bidding zone configuration is introduced for the two different values of ATC between France North and France South. First of all, the results given above show a relatively low impact of the new configuration on the European commercial exchanges. Indeed, the main tendencies of commercial exchanges in Europe are unchanged and some values are slightly modified. It can be noticed that the use of interconnections with Switzerland has increased with the new French bidding zones. The sensitivity analysis performed on the commercial capacity FR1-FR2 gives results that could have been intuitively foreseen. The larger the capacity is chosen, the lower the impact is on the system. In case the ATC between FR1 and FR2 is 2500MW, the

68 Chapter 5. Results 57 Figure 5.5: Commercial Exchanges (MWh/h) in new Bidding Zone Configuration (2500MW capacity): mean values over 50 Monte-Carlo years (left); Statistics in both ways of the interconnection (right) Figure 5.6: Commercial Exchanges (MWh/h) in new Bidding Zone Configuration (4000MW capacity): mean values over 50 Monte-Carlo years (left); Statistics in both ways of the interconnection (right)

69 Chapter 5. Results 58 commercial exchanges evolution is larger than with a choice of 4000MW. A constraining capacity in France decreases the overall European power transfer between north and south and therefore modifies the initial market operations Seasonality Seasonality of the commercial exchanges through the link between France North and France South is of interest since it gives information on the way the interconnection is used during the year. During the year, the mean values of commercial exchanges from France South to France North over 50 Monte-Carlo years have been computed at noon. Figure 5.7 gives the evolution of these quantities over the year for both one (blue curve) and two (red curve) bidding zones configurations in France. The seasonality of the exchanges in France for peak load situation is deduced from this study. Figure 5.7: Commercial Exchanges from France South to France North (mean value of each hour 12am) The new bidding zone configuration limits the flows through the link since its capacity is set to 2500MW but the overall seasonality remains unchanged. Thus, the market structure modification does imply a limitation of the flows but there is not any modification of the use of the French territory during the year. Similar studies for the hour 4am and 4pm are given in Appendix D and show the same conclusions.

70 Chapter 5. Results 59 Table 5.5: Evolution of the Congestion time rates in European interconnections when FR2-FR1 is constrained Constraints on France South France North Border Congestion time rate Congestion time rate evolution at direct capacity evolution at indirect capacity FR2-CH 24.58% -1.01% CH-FR % -7.01% CH-DE 0.95% -1.64% FR2-ES 1.25% % FR2-IT 1.53% -0.42% BE-FR1 0.00% % DE-FR1 1.53% -9.49% Another comment on these graphs is the fact that the French electrical network is mainly used from South to North during summer and in the other way during winter. This can be explained by the strong solar production in summer and the large correlation between French consumption and temperatures Exchanges Evolution when there is a congestion in France The commercial exchanges are slightly modified with the new bidding zone configuration in France but the overall behaviour remains unchanged. The main impact of the creation of two zones in France is a decrease of the north-south European power flow. Since the exchanges are limited in France, it would be interesting to determine whether some borders are used as alternative paths in Europe. The following study compares the percentage of time where there is congestion through all European borders of interest while the link France South France North (Table 5.5) or France North France South (Table 5.6 ) is congested. The values given in the tables below correspond to the congestion time rates difference between the two bidding zones configuration (commercial capacity 2500MW) and the initial market structure. This indicator gives a rough idea of which paths the electricity is willing to take when the French transmission system is limited. The results above demonstrate that European borders are effectively affected by the French commercial limit. Depending on the way the French link is congested, the impact can be either positive or negative. If France South France North is constrained the two borders between French bidding zones and Switzerland are more constrained. Since the flows between South and North are decreased in Europe the following links are relieved: from Spain to France, from France to Belgium and from France to Germany.

71 Chapter 5. Results 60 Table 5.6: Evolution of the Congestion time rates in European interconnections when FR1-FR2 is constrained Constraints on France North France South Border Congestion time rate Congestion time rate evolution at direct capacity evolution at indirect capacity FR2-CH % 11.89% CH-FR1-0.85% 24.99% CH-DE 0.00% 10.80% FR2-ES % 5.38% FR2-IT % 3.42% BE-FR1-0.52% 2.96% DE-FR % 1.39% Similarly, if France North France South is constrained Swiss borders are more loaded. Since the flows between North and South are decreased in Europe the exports from France towards Spain and Italy are smaller and the import from Germany decreases. The borders involving Switzerland seems to be the alternative paths for the commercial exchanges which are not able to flow through France. A deeper analysis of these links has been performed based on duration curves (cf. figure 5.8). These curves determine the percentage of time an hourly commercial exchanges is lower or equal to a given value over 50 Monte-Carlo years. For instance the solid blue line reflects that around 50% of the time, the exchange from FR2 to Switzerland is lower or equal to the ATC FR2-CH value. If the border is congested in one way or the other, the plots reach a plateau and the amount of time under constraints can be determined. The results below give in dashed line the situation for three Swiss links in one bidding zone configuration and in full line for the new market splitting when France South France North is constrained; the duration curves are computed considering only the flows during the hour of French congestion. When France is split into two zones, all duration curves are translated to the left. That means that the considered borders are more used in the direct way. It can be noticed that if the France South France North connection is congested, the exchanges from France South to Switzerland, from Switzerland to France North and from Switzerland to Germany are higher than the unconstrained case. The new path in this situation can therefore be defined as in figure 5.9: the red arrow corresponds to the unconstrained case where the needed commercial exchanges flows from FR2 to FR1 and the blue ones reflects the limited transfer in France and the path though Switzerland. The exact opposite situation is obtained when France North France South is constrained.

72 Chapter 5. Results 61 Figure 5.8: Duration curves for borders with Switzerland when FR2-FR1 is constrained (solid line: 2 Bidding Zones configuration; dashed line: 1 Bidding Zone configuration) Figure 5.9: Switzerland used as New Path for Electricity

73 Chapter 5. Results Economic Aspects In this section, the economic impact of the new bidding zone configuration is assessed by using the different indicators presented in the methodology part. For the different computed quantities, the evolution from the initial bidding zone configuration to the new one is given. Indeed, the absolute results are not of interest to the extent that the study is performed with a time horizon 2030 with strong assumptions. Since the parameters in all simulations are conserved, a comparison of the prices and the social welfare is completely relevant. This impact study corresponds to the insertion of two bidding zones in the European electricity market with the definition of a commercial capacity. It is assumed that this new configuration does not affect the other European capacities: all ATCs abroad remain constant Price Convergence As explained in the methodology, due to hurdle costs, a convergence criterion is defined for each hour over the 50 simulated years. For each hour and for each simulation, we measure the number of hours where there is convergence throughout the different European borders. Then the convergence rates are compared to obtain the impact of the new bidding zone configuration (cf. figure 5.10): Two bidding zone configuration with 2500MW of commercial capacity between FR1 and FR2 minus the initial market structure (in blue) Two bidding zone configuration with 4000MW of commercial capacity between FR1 and FR2 minus the initial market structure (in red) The impact on the price convergence is relatively low: the maximum variation is around 8%. The comparison realized between France and Switzerland is not exploitable since the quantities are different due to the dividing. Indeed, it is not possible to compare a price convergence between Switzerland and France as one bidding zone and then the convergence with the two French areas. As a general remark, the borders between France South and Spain or Italy present a higher convergence. The situation is more critical in between France North Belgium and Switzerland Germany. Price convergence is often seen as a relevant measure of the effectiveness of the interconnections. The impact of the new bidding zones in Europe

74 Chapter 5. Results 63 Figure 5.10: Evolution of Price Convergence due to new Bidding Zone Configuration throughout European borders varies from one border to another and based on this indicator, only local conclusions can be drawn Price Divergence The price convergence measures the time rate where electricity prices on both ends of an interconnection are equal. The main issue with this kind of measure is that price divergences are always taken into account the same way. The indicator used in this section enables to determine the degree of divergence throughout a given border. Thus, a price difference of a few cents would not be considered as a large spread reaching large amount (more than 100e/MWh). During the simulations it could happen that a part of the load is not covered. This energy not served has a cost called Value of Lost Load (VoLL). This VoLL value has a significant impact on the obtained value with the divergence indicator computed by (4.4). To conserve a certain consistency in the results, the price of offers at all cost in France has been chosen: 3000e/MWh. Once again the focus of this analysis should be on the evolution and not on the absolute results. Figure 5.11 gives the monthly evolution of the price divergence criterion computed on both ends of the interconnection between France North and France South. Each hour, the

75 Chapter 5. Results 64 indicator is computed for the initial market configuration and the two new situations (two bidding zones with 2500MW or 4000MW of inter-zonal link capacity). Then monthly averages are calculated taking into account the 50 Monte-Carlo years to obtain plausible reference year. Similar results for other European borders are available in Appendix E. Figure 5.11: Price Divergence Indicator Evolution for the border FR1-FR2 The price divergence indicator gives a measure of the overall divergence compared to the ideal copper plate model. Large values reflects pronounced divergence compared to the fictitious case where Europe would be one large bidding zone (all ATCs set to infinite) and larger spreads on both ends of the interconnection. As expected, the bidding zone creation in France provokes a larger divergence between FR1 and FR2. The impact is larger during winter where the commercial exchanges reach their maximum values. The 4000MW configuration is a transitional state between the constraining situation (2500MW) and the one bidding zone configuration. Same observations are visible for other European countries Social Welfare evolution In many studies dealing with electricity market, the social welfare corresponds to the most suitable indicator to assess the impact of a measure on the system. The same method as the one used in price analysis is applied for social welfare comparison. For

76 Chapter 5. Results 65 each hour over 50 Monte-Carlo years, the social welfare is computed in the different bidding zones and monthly mean values are finally found. Figure 5.12: Hourly European Social Welfare Evolution throughout Monte-Carlo synthesis year The results given in figure 5.12 represent the evolution of the hourly average social welfare values (mean values for each month) in Europe when France is divided by the new configuration. Indeed, the value of social welfare in itself does not represent a concrete quantity but its evolution quantifies the influence of the measure taken in the system. The impact is negative in Europe since a net decrease of the total social welfare is observed. Once again the influence is larger during winter since the capacity introduced in France provokes more constraints when the exchanges are high. The other European capacities have not been modified. Thus, the new bidding zone configuration only introduces constraints in the system: this social welfare evolution is therefore consistent with the expectations. Figure 5.13 gives the evolution of the producer, consumer and trading surpluses for each bidding zone. These values are obtained by comparison of the mean hourly surpluses over 50 Monte-Carlo years for the initial market situation and the constraining bidding zones configuration (2500MW of commercial capacity). Since the load and the amount the consumers are willing to pay are assumed to be constant in the two different bidding zone configurations, the consumer surplus represents

77 Chapter 5. Results 66 Figure 5.13: Hourly Consumer, Producer and Trading surpluses Evolution (mean value over 50 Monte-Carlo years) in the different Bidding Zones in this case a measure of the electricity price evolution. The new bidding zone configuration slightly decreases the Italian spot prices and increases the other European prices. Thus, consumer surplus is negative in all European bidding zones except in Italy. The new bidding zone configuration provokes a transfer of surplus from the consumers to the producers. France, Germany and Spain would be the most impacted countries. Trading surplus significantly appears in France and Switzerland total social welfare due to new congestions for the borders involving these countries (including the FR1-FR2 congestions). In Italy the market structure evolution would be beneficial for consumers. The results given above do not exactly reflect the reality of the system since a new bidding zone configuration in France would change the European commercial exchanges and some commercial capacities would be modified. The two French bidding zones have necessarily an impact on the capacity calculation and therefore on the ATCs values. The capacity calculation is not conceivable in this study since the PTDF and critical branches are not available. In order to illustrate the influence of this capacity calculation, some ATCs should be increased in Europe.

78 Chapter 5. Results Loop Flows Study This study is completely different from the previous sections developed earlier in the impact study since it combines both economic results and load flow computations. Two hours have been defined for this analysis in order to obtain different and representative situations. These two hours have been chosen to be consistent with the evolution of the load during the year in Europe and in France and then the mean results over the 50 hours obtained in each Monte-Carlo year have been determined: January 9am June 10am Appendix F gives the European net values for the 4 simulations used in this study. The loop flow analysis is therefore to consider carefully since only two hours are studied. For the economic impact study, all hours during 50 Monte-Carlo years are taken into account. In each case, the system is simulated both when France is only composed by one bidding zone and with the new bidding zone configuration (2500 MW of commercial capacity). Thus, it is possible to analyze the evolution of the indicators due to market splitting in two completely different situations regarding production and consumption purposes. Figure 5.14 and figure 5.15 give the loop flows measurement realized by setting all net values in Europe to zero. All transactions through interconnections being null, there are not any transit flows in the system. This method enables to determine only loop flows by computing the physical flows through the links between bidding zones. All the net values have been set to zero. This modifies the generation unit pattern and the loop flows created by the exchanges inside a bidding zone are changed. Moreover, France is the only country in the simulation with the actual generation units and consumption dispatch to a node level. For the other countries an equivalent network is used and only the net positions of the bidding zones are accurate. A rough overview of loop flows through French interconnections is available though. The phenomenon appears to be more distinct in summer. It seems also that the new bidding zone configuration increases the amount of loop flows in the system for France North-Belgium and France South-Italy and decreases them for France-Switzerland and France-Germany.

79 Chapter 5. Results 68 Figure 5.14: Loop Flows measure for one Bidding Zone configuration (left); for two Bidding Zones configuration (right) - January 9am Figure 5.15: Loop Flows measure for one Bidding Zone configuration (left); for two Bidding Zones configuration (right) - June 10am

80 Chapter 5. Results 69 From figure 5.16 to figure 5.19, the commercial exchanges and the physical flows are given on the left and the unscheduled flow indicator is presented on map to the right. The unscheduled flows are the difference of the physical flows and the commercial cross border exchanges. This indicator enables to measure the amount of flows which were not foreseen in the market mechanisms through European interconnections. Figure 5.16: Commercial Exchanges and Physical Flows (left); Unscheduled Flows Indicator (right) for one Bidding Zone configuration - January 9am In winter, the amount of unscheduled flows for borders France North Belgium and France South Italy increases due to the new French bidding zones and decreases for France Switzerland and France North Germany. In summer, the situation is completely the opposite with an increase of unscheduled flows with Italy and Belgium and larger values with Germany and Switzerland. The new bidding zone configuration defined in this study does not represent a viable solution since its impact on the unscheduled flows depends on the period of the year. The robustness of the market configuration is determinant and, based on these results, market splitting in France would not improve the unscheduled flows problematic in Europe. Furthermore the unscheduled flow indicator does not represent a suitable quantity to evaluate the loop flows and transit flows. By considering unscheduled flows, it is impossible to accurately determine the source to sink of the problems.

81 Chapter 5. Results 70 Figure 5.17: Commercial Exchanges and Physical Flows (left); Unscheduled Flows Indicator (right) for two Bidding Zones configuration - January 9am Figure 5.18: Commercial Exchanges and Physical Flows (left); Unscheduled Flows Indicator (right) for one Bidding Zone configuration - June 10am

82 Chapter 5. Results 71 Figure 5.19: Commercial Exchanges and Physical Flows (left); Unscheduled Flows Indicator (right) for two Bidding Zones configuration - June 10am 5.6 Limits of the study This section presents the different limits of the model chosen in this master thesis. The consequences of these particularities are analyzed and solutions to address these shortcomings are proposed Impact on Congestion Management First of all, the model chosen cannot define impedances once the new bidding zone configuration is applied. Indeed, if several bidding zones appears in France, the Kirchhoff s laws definition is not possible anymore. Only one link between the two bidding zones remain functional and all other lines capacities are set to null. A new computation of the equivalent impedances would be necessary. Since this process is mainly based on historical network snapshots, a new set of Kirchhoff s laws cannot be obtained. The study of the impact on congestion management in France is not conceivable Data limitations In the electricity market modelling, the model used for European countries is not as accurate as the French one. Indeed, France is modeled by 25 small areas whereas all

83 Chapter 5. Results 72 other bidding zones are only represented by one node. For Load Flow computation, the available network is also more detailed in France. There is a significant lack of consistency between the data used to determine the commercial exchanges and the ones for physical flows. Many assumptions have to be taken into account on the pattern of the generation units for example. The study performed in this paper would lead to more reliable results with a complete set of data for whole Europe. Other market splitting such as a combination between German and French bidding zones configuration could be performed and compared. A robustness analysis is needed. Many scenarios are possible for For example ENTSO-E is studying 4 scenarios for this time horizon [17]. The projects that will be deeply analyzed are the ones needed in the four scenarios. For a bidding zone reconfiguration, the same robustness has to be performed before making any decision Capacity Calculation The implementation of the European bidding zone configuration needs to be combined with completely coordinated capacity calculation. Indeed, the commercial capacity has been chosen based on the constraining level involved and the importance of the European capacities modification has been demonstrated. An accurate computation based on the network critical branches and the different N-1 situations should be performed to obtain more relevant results. The model could be slightly modified to take into account new European market mechanisms such as Flow Based. Indeed, this new methodology would completely modify the obtained results and it would make sense to perform a study for the time horizon 2030 taking it into account Indicators used in the Loop Flows Study The loop flow measure where all net values in Europe are set to zero deviates from the reality of the system since it is necessary to delete all exchanges between bidding zones. Thus, the obtained results give a rough overview of the amount of loop flows without giving the ex act value. The unscheduled flow indicator presents also some limitations [4]: The scheduled flow is only supposed to flow through the border which is impossible in a meshed network.

84 Chapter 5. Results 73 The actor can choose several paths to schedule their exchanges. In the study case, when there is not enough capacity between FR1 and FR2, the actors are scheduling their exchanges through Switzerland. The indicator cannot take into account the impact of all commercial cross border exchanges on all borders. Another indicator could be used in order to assess the impact of the new bidding zone configuration on the amount of transit and loop flows into the European system. The PTDF Flow indicator enables to evaluate the difference between the measured physical flows through interconnections and the computed flows between bidding zones based on the net values and the PTDF matrix. This approach closer to the Flow-Based methodology compares a measured physical flow with a value which is closer to a result of capacity allocation where the influence of all exchanges on all interconnections is considered. Since the PTDF matrix is not available for this study case and the time horizon 2030, this computation was not possible.

85 Chapter 6 Conclusion The impact of the modification of the bidding zone configuration in France has been performed in this master thesis report. In the base case configuration, France corresponds to only one bidding zone. Based on congestion management considerations, a new bidding zone configuration has been defined and France has been divided into two different bidding zones (France North and France South). A general methodology both to define this new configuration and to study its impact on the European electricity system has been developed. The implementation of a new bidding zone configuration in France appears to be quite negative as a general point of view. A coherent division of the country is quite impossible considering the physical flows distribution in France. The new border defined by the two bidding zones configuration chosen in this paper is crossed by large flows in opposite directions. Thus, the efficiency of this new market configuration on internal French congestions is therefore certainly challenged. Regarding economic considerations, market splitting introduces constraints into European system and decreases the overall performances. The impact on the commercial exchanges is quite low and new paths are found in order to bypass the market limitations. The importance of the European ATCs evolution has been proved and a capacity calculation should be performed to obtain more accurate results and determine the real impact on the electricity prices and social welfare. In this study, the impact on the European social welfare is negative because of the constraints introduced by the new bidding zone configuration. The influence on commercial capacities for other borders could not be assessed. 74

86 Chapter 6. Conclusion 75 The loop flows problem might be the main reason which leads to market splitting in Europe. Based on the results obtained in this study, a new bidding zone configuration in France is not robust enough to deal with this phenomenon and only local conclusions can be drawn. This paper presented a general methodology in order to define a relevant bidding zone configuration and study its impact on the electricity market based on both physical and economic considerations. Based on this study, an optimal zonal configuration regarding the number of zones in France and the topology of the system could be found. The set of data used in this study presents some limitations and the pertinence of the results obtained would be higher with more consistent inputs for both market modelling and load flow simulations. Future studies could be performed to develop further this very important research field. The implementation of capacity calculation and Flow Based methodology into the simulation models could increase the pertinence of the results. Other possible solutions can be investigated to compete with market splitting such as grid investments, HVDC link, more coordination through interconnections management, cost sharing... A comparison of these methodologies and their impact on the European system could enable obtain the optimal solution in the current European market development.

87 Appendix A Grid Transmission Capabilities Computation (Source: RTE) The Grid Transmission Capability (GTC) is a parameter defined for each link between the 25 French areas. In order to see the congestions which occur into the French electrical network, it is necessary to define equivalent physical limits between these zones. This appendix gives the general method used by RTE in order to compute the GTCs used in the study. If there is only one line between two areas, the GTC is defined as 70% of the capacity of the line. In case there are several lines between two areas, the process is slightly different. A base case is simulated, all physical flows between areas (through the equivalent links) are known. Then the method used in order to define the GTCs is a simplified capacity calculation (general methodology given in chapter 2). The GTCs are obtained by increasing the physical flows between two areas for each N-1 situation (a N-1 situation occurs when there is a failure on one line in the system) until a constraint is hit on one of the critical branch in the system. This process uses the notion of PTDF in order to know how is influenced the flows through a line when the transmission between two areas is modified. The minimum value computed is chosen as the GTC in order to withstand any N-1 situation. 76

88 Appendix B Impedance Computation Principle (Source: RTE) 25 areas have been defined in France. In order to find the new bidding zone configuration, a statistical study of the physical flows inside the country is of interest. In order to apply the DC Load Flow theory presented in chapter 4, impedances of the equivalent links are needed between the areas. This appendix shortly present the way of reasoning of RTE in order to compute such impedances. First of all, for the considered system, network snapshots s are available for different 2030 scenarios (large or small consumption, large amount of renewable energies... ). Then an optimization process (given below) is performed in order to define the impedances in such way that the difference between the already known physical flows of the snapshots and the computed physical flows (based on these impedances) is minimized. Thus, an admittance matrix is defined in order to obtain theoretical physical flows which are as close as possible to the real ones. This computation is realized for all snapshots; equivalent impedances are obtained and they are valid for every possible network evolutions. The validity of these impedances is analyzed with an indicator which compares the difference between the theoretical physical flows and the real ones for each situation. 77

89 Appendix B. Impedance Computation Principle (Source: RTE) 78 Variables: Y, T 0 (and therefore T i,j,s ) Parameters: F i,j,s, θ s, I s min Y,T0 (F i,j,s T i,j,s ) 2 s,i,j Subject to: Y θ s = I s s T i,j,s = Y i,j (θ is θ js ) + T 0ij s, i, j T i,j,s : obtained physical flows with admittance matrix Y between areas i and j for simulation s θ s : phase matrix, simulation s I s : injection matrix, simulation s T 0ij : flows when there is not any exchange in the system, between areas i and j

90 Appendix C List of the performed simulations during the study Simulation 1 Impedances taken into account Hurdle Costs GTC infinite in France (no constraints in France), constraints in Europe (between bidding zones) No Minimum Up/Down Time Simulation 2 : Copper Plate Model No Impedances Hurdle Costs GTC infinite in Europe (only one BZ) No Minimum Up/Down Time Simulation 3 : 1 Bidding Zone configuration in France No Impedances Hurdle Costs 79

91 Appendix C. List of the performed simulations during the study 80 GTC infinite in France (only one bidding zone), constraints abroad No Minimum Up/Down Time Simulation 4 : 2 Bidding Zones configuration in France No Impedances Hurdle Costs GTC infinite in France North and France South (2 bidding zones in France), constraints abroad Commercial capacity infinite between the two French bidding zones No Minimum Up/Down Time Simulation 5 : 2 French bidding zones - high constraints through FR1-FR2 No Impedances Hurdle Costs GTC infinite in France North and France South (2 bidding zones in France), constraints abroad Commercial capacity between the two French bidding zones: 2500 MW No Minimum Up/Down Time Simulation 6 : 2 French bidding zones - low constraints through FR1-FR2 No Impedances Hurdle Costs GTC infinite in France North and France South (2 bidding zones in France), constraints abroad Commercial capacity between the two French bidding zones: 4000 MW No Minimum Up/Down Time

92 Appendix D Seasonality of the Commercial Exchanges in France Commercial Exchanges (mean values for 4am during a year) from France South to France North 81

93 Appendix D. List of the performed simulations during the study 82 Commercial Exchanges (mean values for 4pm during a year) from France South to France North

94 Appendix E Price Divergence Results for other European borders Price Divergence Evolution [two bidding zones configuration (2500MW capacity) minus one bidding zone configuration] in Europe 83

95 Appendix E. Price Divergence Results for other European borders 84 Price Divergence Evolution [two bidding zones configuration (2500MW capacity) minus one bidding zone configuration] in Europe

96 Appendix F European Net Values for the four simulations used in Loop Flows study 85

97 Appendix F. European Net Values for the four simulations used in Loop Flows study86 Net values in MWh/h (mean values over 50 Monte-Carlo years) - 1 Bidding Zone configuration January, 9am

98 Appendix F. European Net Values for the four simulations used in Loop Flows study87 Net values in MWh/h (mean values over 50 Monte-Carlo years) - 2 Bidding Zones configuration January, 9am

99 Appendix F. European Net Values for the four simulations used in Loop Flows study88 Net values in MWh/h (mean values over 50 Monte-Carlo years) - 1 Bidding Zone configuration June, 10am

100 Appendix F. European Net Values for the four simulations used in Loop Flows study89 Net values in MWh/h (mean values over 50 Monte-Carlo years) - 2 Bidding Zones configuration June, 10am