Linda Huber Tom Kessler. Congestion Management in Cross-Border Electricity Markets using Explicit Auctions. Semester Thesis

Transcription

1 eeh power systems laboratory Linda Huber Tom Kessler Congestion Management in Cross-Border Electricity Markets using Explicit Auctions Semester Thesis Department: EEH Power Systems Laboratory, ETH Zürich Expert: Prof. Dr. Göran Andersson, ETH Zürich Supervisor: Thilo Krause, ETH Zürich Zürich, August 25

2 Abstract Title: Congestion Management in Cross-Border Electricity Markets using Explicit Auctions Keywords: Spot Market, Transmission Capacity Market, Explicit Auctions, Agent Based Modeling, Q-Learning Algorithm, Rule Based Algorithm, Fixed Incremental Price Probing Algorithm In this thesis two electricity markets with a given interconnection are modeled. Additionally to the two electricity markets, a market for transmission capacity is implemented, where the market players, i.e. generators and loads in both areas are able to buy transmission capacity in order to buy or sell electricity in the other area. Which market players are able to buy transmission capacity for which price is determined by a so called explicit auction. To simulate the behavior of the market players and the development of electricity and transmission capacity prices, a multiagent system was used. The agents made their decisions according to the Q-Learning algorithm as well as the rule based algorithm. i

3 Contents 1 Introduction 1 2 Electricity Market The Two Basic Market Concepts Bilateral Markets Mediated Markets Types of Markets Forward Market and Future Market Spot Market Interaction between Forward Markets and Spot Markets The Spot Market in Detail The Clearing Procedure in a Spot Market The EEX Spot Market Transmission Capacity Market Cross-border Management Methods Capacity Allocation Methods Capacity Alleviation Methods Market Model Two Single Price Areas Assumptions and Simplifications Implementation Electricity Markets Assumptions and Simplifications Functionality of the Electricity Markets Implementation Transmission Capacity Market Assumptions and Simplifications Implementation Simulation with Multiagent Systems Introduction Reinforcement Learning ii

4 CONTENTS iii Introduction to Q-Learning Implementation Fixed Incremental Bid Probing Algorithm (FI Algorithm) Introduction to the FI Algorithm for the Transmission Capacity Market Modeling Willingness to Experiment Implementation Case Studies Q-Learning Case Studies Price Development and Bidding Strategies of Loads Bidding Strategies of Generators Bidding Strategies of Loads and Generators together Conclusions FI Algorithm Case Studies Bidding Strategies of Generators in Area A Bidding Strategies of Loads in Area B Bidding Strategies of Generators in A & B Conclusions Comparison between QL and FI General Comparison Bidding Strategies of Agents and Price Development Conclusions and Outlook Conclusions Outlook Literatur

5 Overview Goal of this thesis is to analyze the price structures as well as the behavior of the market players in two different electricity markets with a given interconnection. To simulate the electricity and the transmission capacity markets, a multiagent system is implemented in MATLAB. The single agents are able to develop their optimal bidding strategy according to two algorithms, the Q-Learning algorithm and rule based algorithm. Chapter 1, Introduction, is a brief explanation of the current situation in the electricity markets worldwide. How they have developed and in which direction they might further evolve. Chapter 2, Electricity Market, gives an introduction into the different types of electricity markets. Chapter 3, Transmission Capacity Market, familiarizes the reader with currently used cross-boarder congestion management methods and how the transmission capacity markets are organized. Chapter 4, Market Model, explains how the electricity and transmission capacity markets are implemented and how they interact. Chapter 5, Simulation with Multiagent Systems, gives a short introduction into agent based modeling. Afterwards the Q-Learning and the rule based algorithm are introduced and their implementation explained. Chapter 6, Case Studies, carries out various simulations with both, the Q-Learning and the rule based algorithm. The conclusions summarize the most interesting results. Chapter 7, Comparison between QL and FI, compares the two algorithms implemented in this thesis. It is analyzed how they differ and what results were obtained. Chapter 8, Conclusions and Future Work, summarizes the most important results of this work and gives an outlook into possible future work. iv

6 List of Figures 2.1 Bilateral and Mediated Electricity Markets The Classification of Spot Markets and Future Markets Clearing in a Spot Market Prices and Trading Volume at the EEX between Current Implementation of different Congestion Management Methods in Europe The Two Phases of One Market Cycle with the Markets TCM A/B (Transmission Capacity Market for the Transmission Line between A and B), EM A and EM B (Energy Market located in A resp. B) Cost Curve of a Generator Marginal Cost Curve of a Generator Value Curve of a Load Marginal Value Curve of a Load Clearing in the Energy Market Model Price Convention in Case Two Vertical Segments Intersect Market Clearing Prices over variable transmission capacity Market Clearing Prices over the iterations with a transmission capacity of TCP and price difference over variable transmission capacity Mean Market Clearing Prices over iterations with QL Mean TCP Price difference Smoothed mean TCP Smoothed price difference Load 1 in area A bidding (,1,2,3,4) Load 2 in area A bidding (,1,2,3,4) Load 1 in area B bidding (,1,2,3,4) Load 2 in area B bidding (,1,2,3,4) Load 1 in area B bidding (,5,1,15,2) Load 2 in area B bidding (,5,1,15,2) v

7 LIST OF FIGURES vi 6.15 Load 1 in area B bidding (,16,17,18,19) Load 2 in area B bidding (,16,17,18,19) Generator 1 in area A bidding (,1,2,3,4) Generator 2 in area A bidding (,1,2,3,4) Generator 1 in area B bidding (,1,2,3,4) Generator 2 in area B bidding (,1,2,3,4) Generator 1 in area A bidding (,14,16,18,2) Generator 2 in area A bidding (,14,16,18,2) Generator 1 in area A bidding (,17,18,19,2) Generator 2 in area A bidding (,17,18,19,2) Generator 1 in area A bidding (,17,18,19,2) and transmission capacity of Generator 2 in area A bidding (,17,18,19,2) and transmission capacity of Generator 1 in area A bidding (,1,2,3,4) Generator 2 in area A bidding (,1,2,3,4) Generator 1 in area B bidding (,1,2,3,4) Generator 2 in area B bidding (,1,2,3,4) Load 1 in area A bidding (,1,2,3,4) Load 2 in area A bidding (,1,2,3,4) Load 1 in area B bidding (,1,2,3,4) Load 2 in area B bidding (,1,2,3,4) Bids of Generators located in A with Medium Fixed Bids Bid Distribution of the First Generator in A with Medium Fixed Bids Bid Distribution of the First Generator in A with Low Fixed Bids Bids of Generators located in A with High Fixed Bids Bid Distribution of the First Generator in A with High Fixed Bids Bid Distribution of the First Generator in A Bid Distribution of the Second Generator in A Distribution of the Transmission Capacity Price Load 1 in area A bidding according to FI Load 2 in area A bidding according to FI Load 1 in area B bidding according to FI Load 2 in area B bidding according to FI Mean Market Clearing Prices over iterations with FI

8 List of Tables 3.1 Congestion Management Methods Generators in area A & B to assess the price development with the Q-Learning algorithm Loads in area A & B to assess the price development with the Q-Learning algorithm Independent dispatch Market Clearing Prices in Area A & B Generators in area A & B to assess bidding strategies of loads Loads in area A & B to assess bidding strategies of loads Independent dispatch MCPs in area A & B for assessment of loads bidding strategies Average rewards for Load 1 & 2 in area B Generators in area A & B to assess bidding strategies of generators Independent dispatch MCPs in area A & B for assessment of generators bidding strategies Generators in Area A & B to Asses the Bidding Behavior of Generators in A Loads in Area A & B to Asses the Bidding Behavior of Generators in A Medium Value Bids of Generators in Area A Independent Dispatch Market Clearing Prices in Area A & B Low Value Bids of Generators in Area A High Value Bids of Generators in Area A Modes of the Generators Bids Generators in Area A & B Loads in Area A & B Independent Dispatch Market Clearing Prices in Area A & B Mean Transmission Capacity Bids of Loads from Area B Generators in Area A & B for Assessment of Bidding Behavior of Generators in Both Areas Loads in Area A & B for Assessment of Bidding Behavior of Generators in Both Areas vii

9 LIST OF TABLES viii 6.23 Mean Bids of Generators in Area A & B Average rewards for Load 1 & 2 in area B bidding according to FI

10 List of Acronyms ABM Agent Based Modeling DA Day-Ahead EEX European Energy Exchange FI Fixed Increment IEM Internal Electricity Market ISO Independent System Operator LMP Locational Marginal Pricing LPX Leipzig Power Exchange MCP Market Clearing Price SMP System Marginal Price SPA Single Price Area TCP Transmission Capacity Price TSO Transmission System Operator ix

11 Acknowledgments The authors of this thesis would like to thank Prof. Göran Andersson for allowing them to do their work at the Power Systems Laboratory at the ETH Zürich, as well as Thilo Krause, their supervisor, for his support and guidance. x

12 Chapter 1 Introduction Electricity markets worldwide are changing. A few decades ago, most of the consumers had to buy their electricity from one utility holding a monopoly in this specific area. These utilities were vertically integrated, producing, transmitting and distributing the energy, resulting in a lack of choice for consumers and therefore competition within the electricity production and supply business. Since these utilities were mostly run by governments and therefore subsidized, there was no incentive to break up these old structures and to open the markets for competition. During the 9s however several countries made first steps towards liberalizing their electricity markets. The expectation was that through open competition prices for electricity would decrease and the whole industry would work more economically. One of the first countries which was willing to adopt these new concepts was New Zealand. New Zealand decided already in the 198s to liberalize its electricity market and made fast progress. In October 1996, after corporatization of local supply and distribution authorities and removal of statutory monopolies, the New Zealand Electricity Market, a competitive wholesale market began to operate and by April 1999 the separation of transmission line and supply businesses was completed.[1] Several countries in the European Union were also amongst the ones who soon tackled the needed reforms. But with the Directive 96/92/EC [2] the European Parliament set up a project far bigger than just the liberalization in some single member countries. The aim of the Directive was to create one single integrated market for electricity in the whole EU, the so called Internal Electricity Market (IEM). Key issue is free choice for consumers. This should be achieved in several steps to give companies and countries time to adapt to changes. In a first step, competition was introduced in 1999 only for very large consumers. The markets opened then gradually for even smaller consumers and by July 27 every household should be able to choose from where to buy their electricity.[3],[4] These new structures and ongoing changes in the electricity markets led 1

13 CHAPTER 1. INTRODUCTION 2 and still lead to new requirements for the infrastructure. In particular the transmission grids, originally designed to meet the needs of a single, geographically limited market are subject of ongoing debates, since congested transmission lines still impede true competition. It is crucial for truly liberalized markets in order to work efficiently that every market player, consumer and generator has the right to access the transmission grid, free of discrimination. In addition the transmission capacity should be used in an efficient way, meaning that it is allocated to those who value it the most. The transmission system operator (TSO) who owns and runs a high voltage grid plays a vital role in this process. Not only must he dispatch the available capacity in a non-discriminatory and economical way, he also has to maintain and secure network operations and make the necessary investments in the grid.[5] There exist several different approaches to deal with the problem of congested transmission lines. In the IEM for example, the TSOs of the countries often use different methods and often a single country even has different methods in the trade with its neighbors. The need for harmonization is obvious and so congestion management methods are being analyzed and examined in order to find a solution that meets basic requirements defined by regulators. Basically these requirements ask for non-discriminatory, market-based and economically efficient solutions. Some of the most-used methods in the IEM are the priority list, prorata and explicit auctions. If a priority list is used, the capacity is allocated according to certain rules, often first come, first serve. This method is not market-based and can lead to the discrimination of new or small market players. With the pro rata rationing, the capacity is allocated according to size of the requests of the different users. Although this method is nondiscriminatory, it is not market-based and not economically efficient. Explicit auctions, the currently most used congestion management method in Europe provide a market-based, economically efficient and discriminatoryfree method for allocating transmission capacity. While other promising methods such as implicit auctions or hybrid (implicit and explicit) auctions are still evaluated, it is of great importance to understand the mechanisms of explicit auctions in order to maximize social welfare.[5],[6],[7]

14 Chapter 2 Electricity Market This chapter gives an overview of the various existing electricity markets. The traded commodity is power over a certain time period. The terms power trading and (electrical) energy trading are used interchangeably. In addition the term electricity trading is used, although it is in a strict physical sense not quite correct. The different markets are distinguished with respect to the way the trading takes place and to the moment electrical energy is traded in regard to the moment it will be delivered. 2.1 The Two Basic Market Concepts There are two basic ways of trading energy. If a seller and a buyer trade directly, they make a bilateral trade. If a supplier sells his electrical energy indirectly to a consumer, by selling it first to an intermediary who resells it to the consumer, the trade is called a mediated trade.[8] Many varieties of those two market concepts exist. An overview of them can be seen in figure (2.1). The entire electricity market is a composition of different subtypes. Market Concept Type of Market bilateral: Direct Search Bulletin Board Brokered mediated: Dealer Exchange Pool less organized more centralized Figure 2.1: Bilateral and Mediated Electricity Markets Following the figure in [8] 3

15 CHAPTER 2. ELECTRICITY MARKET Bilateral Markets In bilateral markets trading takes place through direct interaction between buyers and sellers. The trading parties can specify any contract terms, regarding price, quantity and the duration of the contract, as they like.[8] There is no official price. In direct search markets the market participants have to find their trading partners themselves, in contrast to brokered and bulletin-board markets. In a brokered market brokers do not actually buy or sell electrical energy but they arrange a trade between two parties for which they are paid a commission. The freedom in determining almost any desired contract terms makes bilateral markets extremely flexible. But this flexibility also holds disadvantages: Negotiating and writing such contracts may be time-consuming and therefore expensive.[8] Usually is takes days to weeks to conclude a bilateral contract.[9] Hence for short-term energy trading incentives exist moving towards more standardized and centralized trading models like some types of mediated markets Mediated Markets Mediated markets standardize and therefore quicken trading. They are usually more centralized in contrast to bilateral markets, which are less organized.[8] In every mediated market there are one or more intermediaries who buy and resell electrical energy and build hence the connection between the supplier and consumer. Again there exist different types of mediated markets, which have different degrees of centralization. One is the dealer market. It has a similar degree of centralization as the brokered market but a dealer buys electrical energy and holds it until he resells it. He is not paid a fee but earns the price difference between purchase price and sales price. The Power Exchange Another form of mediated markets, which is the most common one, is the power exchange. The power exchange is managed by a central authority, the market operator. By acting as a counterparty, a power exchange provides financial security for all traders. Exchanges utilize auctions and are sometimes called auction markets.[8] A power exchange can have lots of advantages over a bilateral market. According to [8] it can reduce trading costs, increases competition, and produces a publicly observable price. Due to the inflexibility of exchanges and the transparent market price they operate much faster than bilateral markets. That is why they can operate much nearer to real time and what makes them the clearly evident choice for the day-ahead, hour-ahead and real-time market.

16 CHAPTER 2. ELECTRICITY MARKET 5 The Pool Market The most centralized form of a mediated market is the pool market. Competitive electricity pools are often created on the basis of collaborative pools created by monopoly utility companies with adjacent service territories.[1] A pool market works similar to a power exchange with one of the main differences that a pool market also takes network constraints into account while clearing. So the market operator is also assigned the function of an independent system operator.[11] Whereas in a power exchange the market operator and the Independent System Operator (ISO) are distinct entities, though in constant cooperation. Another main difference is that pools provide a mechanism for reducing the scheduling risk of generators by side payments. If a generator is only partially accepted by the market clearing based on simple bids like in a power exchange, it can come into the situation that keeping the plant on-line would be a financial loss. But shutting the plant down would create costs at a later time. This generator would probably increase his price offer next time to cover his losses. If the generator on the other hand would trade in a pool, the rules of this pool and its complex bid structure would ensure that such a generator is also paid for his no-load or start-up expenses. That means a pool can pay different prices to different suppliers at the same time and location. [8] In practice, it is still not clear if the reduced risk for generators lowers energy prices or if the increased complexity of the bid format and the pool rules and therefore the reduced transparency of the pool market encourage price manipulations and therefore raises energy prices.[1] After classifying the electricity market into submarkets according to their different trading procedure, the next chapter is going to categorize the electricity market into different market types according to the time horizon. 2.2 Types of Markets Focusing on the temporal aspect of trading, electrical energy can be dealt either for immediate delivery as in the spot market or for delivery at sometime in the future at a price agreed now as in the forward market Forward Market and Future Market In the forward market electrical energy is traded to be delivered at a future date. Forward contracts are personalized and long-term between parties and thus are infrequently exchange traded. So a forward market is a general term used to describe the informal market by which these contracts are entered and exited and is in this definition referred to as bilateral market.

17 CHAPTER 2. ELECTRICITY MARKET 6 Future contracts are standardized, exchange-traded forward contracts. Electricity futures typically cover a month of power delivered during on-peak hours and are sold up to a year or two in advance. [8] Since future contracts are standardized they attract speculators to participate in the future market. The presence of those speculators increases the number of market participants significantly which makes it easier to find trading counterparties in the electricity market. The increased liquidity, defined as the ability of an asset to quickly be converted into cash and which is one of the most important characteristics of a good market, leads to a higher market price balance Spot Market In a spot market in its original sense, the seller delivers immediately and the buyer pays on the spot. While in liberalized electricity markets not only real-time markets but usually also day-ahead and hour-ahead markets are called spot markets. The European Energy Exchange (EEX) and other day-ahead power exchanges call day-ahead trade spot trade (see [12], [13]). Some authors differ from this definition as for example [8] and consider only the real-time market as a spot market. Therefore they refer day-ahead and hour-ahead markets already as forward markets. This work will use the first approach in distinguishing between forward and spot markets because day-ahead, hour-ahead as well as real-time electricity contracts are generally linked to obligatory physical delivery and consummation in contrast to weekly, monthly and yearly forward and future contracts. The classification chosen by the authors is displayed in figure (2.2). Future Markets Spot Markets yearly, monthly weekly contracts day - ahead hour - ahead real - time Figure 2.2: The Classification of Spot Markets and Future Markets [11] Electricity spot markets are usually either designed as power exchanges or pool markets. However, spot markets in a sense of day-ahead markets can be designed either way, bilateral or mediated and centralized which causes a great deal of controversy.[8]

18 CHAPTER 2. ELECTRICITY MARKET Interaction between Forward Markets and Spot Markets The participation in forward markets originates from the desire to reduce being exposed to price fluctuations which are common in spot markets. Because electricity can not be stored, the production and consumption of electrical power has to be balanced anytime. To ensure this, power plants and big consumers try to forecast the total load as precise as possible. But there will always be deviations from these schedules. Therefore the spot market provides a platform to trade these unpredictable differential amounts of electrical energy. Hence, the volume of trades in the spot market represents only a small part of the volume traded in the entire electricity market. Nevertheless, the spot market price affects the prices in the other markets. If the spot market price is increasing over a long period, the prices in the other markets will increase as well. 2.3 The Spot Market in Detail This chapter focuses on the spot market. Section describes how a spot market is cleared and explains how much the accepted market participants are charged resp. paid. A real example of a spot market is introduced in section The Clearing Procedure in a Spot Market This section describes the operation of a spot market, focusing on the dayahead market, because specific features regarding the hour-ahead and realtime market are not taken into account. The day-ahead market is designed as a power exchange rather than a pool. Therefore the market clearing mechanism does not factor any congestion within the network. It will be explained how the market clearing price (MCP) for a certain market period is calculated and which suppliers respectively consumers are accepted to produce resp. consume electrical energy. Electricity sellers submit offers to supply a certain amount of power at a certain price per megawatt-hour during a certain period the next day. The offers are then ranked in order of increasing price and aggregated to a curve, which shows the bid price as a function of cumulative bid quantity.[1] This curve is named the market supply curve. On the other hand, electricity buyers submit bids to buy a certain amount of power for a certain price per megawatt-hour during a certain period the next day. The bids can be now ranked in order of decreasing price and aggregated to the market demand curve. Because suppliers as well as consumers place bids resp. offers this form of auction is called a two-part auction. The intersection of the market supply curve and the market demand curve is the equilibrium point

19 CHAPTER 2. ELECTRICITY MARKET 8 of demand and supply for this certain period of time (see figure (2.3)). The price at the equilibrium defines the Market Clearing Price (MCP) for that certain period. All offers submitted at a price lower or equal to the MCP are accepted and are instructed to produce the amount of energy corresponding to their accepted offers.[1] On the other side, all bids submitted at a price higher or equal to the MCP are accepted and are instructed to consume the amount of energy corresponding to their accepted bids. The market clearing price is equal to the system marginal price (SMP). The system marginal price is defined as the offer price of the cheapest generation unit required to meet the next increment of demand. Price per Mwh Future Markets demand curve Spot Markets day - ahead hour - ahead real - time MCP supply curve total amount of allocated energy Quantity in Mwh Figure 2.3: Clearing in a Spot Market The Pricing Rule Buyers accepted by the clearing process of the spot market pay the MCP for every bought megawatt-hour, whereas accepted sellers receive the MCP for every sold megawatt-hour, apart from the offers and bids they submitted. This form of auction where the same clearing price applies to all participants is referred to as uniform auction. Another pricing rule would be to charge the accepted buyers with their bid price resp. to pay the sellers the price of their actual offers, like in pay-as-bid auctions. Economists call a pay-as-bid auction a discriminatory auction because prices are discriminated among the participants.[11] One could argue that pay-as-bid auctions would lower prices because the MCP is only paid to the most expensive accepted producer and all other producers are paid a price lower than that. But according to [8] the pay-as-bid auction is not incentive compatible. That means it does not

20 CHAPTER 2. ELECTRICITY MARKET 9 fully encourage the participants to tell the truth. A producer may want to maximize his profit by submitting higher offers than his true cost is for producing a certain amount of electricity. He would try to foresee the MCP and would set his price offer to the estimated MCP or slightly below. Hence, gaming would occur. And gaming is always a mechanism, which increases prices in a market! The four Stages in a Day-ahead Spot Market Four stages can be specified in a Day-Ahead (DA) spot market using auctions to clear the market:[8],[11] 1. Bidding: A day is divided into periods. Usually these periods are set to one hour (at the spot market of EEX, [12]) or even to half an hour (at the spot market of the UK Power Exchange, [14]). Offers and bids accompanied by the possible capacity the in case of a supplier or desired quantity the in case of a consumer can be made for every single period separately until a fixed deadline. At that fixed deadline, one day before the physical delivery takes place, the order book is closed. 2. Clearing: After the order book is closed the market operator clears the market by overlaying the market supply curve and the market demand curve for each period. The intersection of these two curves represents the market equilibrium. The price for a megawatt-hour is set to the price at the equilibrium point, the MCP. 3. Physical delivery: Every supplier produces the volume of energy corresponding to his accepted offers, whereas every consumer uses the volume of energy corresponding to his accepted bid. 4. Settlement: Every consumer has to pay the spot market operator the MCP times the amount of power, which he consumed in a certain period. Whereon the spot market operator pays the MCP times the amount of power, which he produced in a certain period The EEX Spot Market The European Energy Exchange is a German power exchange and is located in Leipzig. It has emerged 22 from the merger of the Leipzig Power Exchange (LPX) and the European Energy Exchange located in Frankfurt. EEX is a DA spot market. One day ahead power can be sold or bought for every interval of one hour length. Figure (2.4) shows the mean MCP of each day for base load (-24h) and for peak load (9-2h) and the traded volume over the last three months. The large price fluctuations common in a spot market are apparent.

21 CHAPTER 2. ELECTRICITY MARKET 1 Figure 2.4: Prices and Trading Volume at the EEX between [12] In addition futures for years, quarters or months are traded at the EEX. For more details the reader is referred to [12].

22 Chapter 3 Transmission Capacity Market With the advancing liberalization of electricity markets it became possible to trade flexibly electrical energy not only nationally but also internationally. This increased trading over frontiers causes problems: Transmission lines connecting different national electricity networks were first and foremost conceptualized to improve network stability. Hence, with those enhanced energy amounts flowing on these transmission lines transmission capacity bottlenecks arise. To prevent such capacity overloads several concepts exist. This chapter will provide an overview of these cross-border congestion management methods. 3.1 Cross-border Management Methods Historically transmission system operators (TSOs) designed the interconnections between their networks with the objective of stabilizing their system by smaller exchanges. Cross-border transmission lines are not primary build to support international power trade for economic reasons. Therefore nowadays the demand of power transmission can exceed the capabilities of the transmission network. For thermal and stability reasons measures have to be taken to limit the power flows through the cross-border interconnections.[15] Basically there are two basic approaches to deal with this problem: On one hand actions can be taken to increase the transmission capacities, be it by investment in new network facilities or by optimization and harmonization of operating standards to better utilize the networks. [7] On the other hand measures can be applied to manage the existing transmission capacities. These measures are so-called cross-border congestion management methods. They attribute limited transmission capacity to the interested market players and determine its utilization price. To assess congestion management methods five criteria were stated at the Florence Regulators 11

23 CHAPTER 3. TRANSMISSION CAPACITY MARKET 12 meeting of November 1999:[16],[17] 1. fair and non-discriminatory: Each market participant should be treated equally. They should pay the same price for the same service. 2. economically efficient: Incentives should guide the generators, demands and TSOs behaviors in such a way that the system optimum will be reached. 3. transparent and non-ambiguous: The method should be clear to all parties and should be widely resistant against gaming. 4. feasible: Congestion management must always be possible in the available time frame. 5. compatible: The method should be compatible with different types of trade and contracts. Within the congestion methods [11] distinguishes capacity allocation methods such as priority list, pro-rata rationing, explicit auctioning, market splitting (implicit auctioning) and nodal pricing and capacity alleviation methods such as generation redispatching and counter trading. Capacity allocation methods assist to allocate transmission capacity to the interested market players mostly before the physical delivery of the energy takes place.[11] Whereas capacity alleviation methods rather denote a way of congestion relief. Market participants can trade as if no congestion exists and the Transmission System Operator (TSO) covertly takes these remedying actions to circumvent an upcoming congestion. Capacity alleviation methods are mainly applied for real-time congestion relief.[11] Table 3.1 lists the most important congestion management methods which are explained in section and Capacity Allocation Methods Capacity Alleviation Methods Priority List Pro-rata Rationing Explicit Auctioning Market Splitting Nodal Pricing Redispatching Counter Trading Table 3.1: Congestion Management Methods Regarding chronology congestion management is usually organized as a sequence of four phases:[7] 1. Determination: First of all the available transmission capacity has to be determined by the TSO. This depends among others on the forecasted network condition and the considered time period.

24 CHAPTER 3. TRANSMISSION CAPACITY MARKET Allocation: The available transmission capacities now allocated among the interested loads and generators by means of a capacity allocation method. Therefore the competitors have to submit bids resp. reservations. 3. Forecast: After the transmission capacity is allocated and the (day-ahead) energy market is cleared, the TSO examines, based on the dispatch schedules and recent information about the network condition, if a congestion will occur and the network security limits will be injured. 4. Relief: If in phase 3 a congestion is foreseen, the TSO has to take measures to relief the network. Capacity alleviation methods are one possibility Capacity Allocation Methods Priority List Transmission capacity is allocated to interested parties in a priority order until the available capacity is used up. The most common priority criteria is the chronological order.[16] This gives the first come, first served method. The first come, first serve method allocates capacity according to the order in which the transmission requests are submitted at the TSO. Beginning with the first one submitted, one request after another is granted until the available capacity is fully allotted. The implementation of a priority list is very simple compared with other mechanisms.[6] The problem posed by this method is that it is discriminatory and not market-based. Its selection is based on certain priorities assigned and not of necessity on economic efficiency. So it also fails to meet the criteria of being economically efficient. Pro-rata Rationing With pro-rata rationing all requests for transmission capacity are partially granted. This means that every interested market player is allocated a fixed percentage of his requested capacity amount. This fixed percentage is given by the ratio of the available capacity to the requested capacity. The transmission rights a market player i obtains for a certain interval where n market players request transmission capacity, TC, is: TC obtained by i = TC available /(TC requested by TC requested by n ) (3.1) Like priority list, pro-rata rationing also lacks of being non-discriminatory and marked-based. It is also vulnerable to abuse and gaming. Market participants who apply for transmission rights can ask for much more transmission

25 CHAPTER 3. TRANSMISSION CAPACITY MARKET 14 capacity with the intent to receive the total desired amount as everybody only receives a fixed percentage of the requested amount. Explicit Auctioning Participants in an explicit auction not only submit a request for a certain amount of transmission capacity but also declare how much they are willing to pay for it. The bids are then ordered by descending price. Transmission capacity is allocated, beginning with the highest bid, until the available capacity is used up.[7] These auctions for capacity may be designed as uniform or pay-as-bid auctions.[11] In uniform auctions the price applied for all accepted participants is principally set to the bid price of the lowest accepted bid. Explicit auctions can be established for different time periods (days, weeks, months, years). Explicit auctioning is a non-discriminatory and market-based method of congestion management. Capacity is distributed in reference to the interested parties valuation of it. The concept of explicit auctioning separates the wholesale energy market from the transmission capacity market. According to [15] this can on one hand be an advantage in a sense that liberalization of the electricity industry generally calls for disentanglement. On the other hand this can be a disadvantage as separating electricity trading and obtaining transmission capacity increases the complexity of cross-border trade. And this fact might obstruct trading activities of market participants. In most instances explicit auctioning meets the five assessment criteria listed above with provisos regarding economic efficiency in practice aroused by market imperfections. See [7] for details. Market Splitting (Implicit Auctioning) [15],[11] Market splitting is a form of implicit auctioning. Implicit in contrast to explicit auctioning links the distribution of transmission capacity with the wholesale energy market. Transmission capacity allocation is managed implicitly by power exchanges. Market splitting requires a power exchange on either side of the congested line. Generators and loads submit their purchase or sale bid for energy in the zone where they wish to generate or consume. Then the exchanges are cleared as if there is no interconnection. The market operator of the exchange which has the higher market price starts to buy electricity from the other exchange. He buys just as much electricity so that the capacity of the line is fully utilized. The congestion charge, the difference between the clearing price in both sides of the congested line, is collected by the market operator and may be used to invest into the grid or may be allocated among the participants.[11] Implicit auctioning and therewith also market splitting is market-based

26 CHAPTER 3. TRANSMISSION CAPACITY MARKET 15 and non-discriminatory. The market clearing is based on the participants valuation of electricity. High value participants willing to buy at high prices or sell at low prices are cleared in preference to low value participants and thereby have a higher priority in obtaining transmission rights. According to [7] implicit auctioning can increase economic efficiency by eliminating the information lag between transmission capacity and wholesale energy markets. This reduces the possibility for exercising market power. Implicit auctioning is well suited for capacity allocation in combination with electrical energy trading in power exchanges. In the bilateral electricity market on the other hand capacity allocation can not be made implicitly. Therefore [7] recommends aiming for the implementation of a so called hybrid implicit/explicit auctioning method for the allocation of cross-border. This means capacity is allocated implicitly between power exchanges and explicitly for bilateral contracts. Nodal Pricing [11],[18] Nodal pricing which is also called locational marginal pricing (LMP) can be seen according to [19] as fully coordinated implicit auction. This method is applied in combination with a mediated and centralized energy market. By the market clearing process a number of locations in the transmission grid, called nodes, are each attached their specific market clearing price. These nodes represent the physical location in the grid where generators inject their produced power resp. loads withdraw the demanded power.[18] The market clearing prices are not only determined by the economic optimization as in power exchanges but also by the existing constraints in the transmission grids. Furthermore the prices incorporate costs resulting from expected electrical losses in the grid during transmission. These losses again are different high depending where the power is injected and where it is withdrawn. This as well as the congestion in certain parts of the transmission grids result in different node price. To clear a market according to the nodal pricing concept there is a lot of information needed regarding the specific features of the transmission lines, the generators and the loads. This is why the nodal pricing method is above all suitable for integrated market structures such as pool markets. [11] Capacity Alleviation Methods Capacity alleviation methods can be regarded as remedial actions.[16] These methods do not directly give an incentive to market participants not to overload the interconnection as capacity allocation methods do. On the other hand the TSOs get incentive to reduce congestion as they are fully responsible to relief congestion and have to pay the upcoming costs. Capacity alleviation methods are mostly used for last-minute corrections. Sections 3.1.2

27 CHAPTER 3. TRANSMISSION CAPACITY MARKET 16 and will briefly introduce the two capacity alleviation methods redispatching and counter trading. Redispatching Market participants trade as if no congestion exists. This may result in a prospective electricity flow across the transmission line exceeding its capacity. In case of occurring congestion the TSO redispatches generation in such a way that the electricity flow matches the available transmission capacity. He will command the generators downstream of the line to increase their production and the generators upstream to reduce their energy generation. Additionally interruptible loads may be used for redispatching as well.[7] The TSOs need to co-operate closely and have the authority to dispatch generators directly. [15] Dispatched generators have to be reimbursed by the TSO. Thus the TSO has to pay congestion costs and as a consequence he has an incentive to expand capacity if congestion costs get out of hand. On the other hand generators and loads do not receive any signals regarding congestion. Counter Trading Counter trading is a market-based form of redispatching.[7] Instead of dispatching generators directly, the TSO has to enter the market to buy electrical energy downstream of the line and sell it upstream. As with redispatching cost accrue with counter trading. Buying electricity downstream at a higher price and reselling it upstream at a lower price results in a financial loss for the TSO. He will ponder if it is not favorable to invest into the grid capacity.[11] Finally figure 3.1 shows a general overview of the congestion management methods currently applied in the European cross-border interconnections:

28 CHAPTER 3. TRANSMISSION CAPACITY MARKET 17 Figure 3.1: Current Implementation of different Congestion Management Methods in Europe [6]

29 Chapter 4 Market Model 4.1 Two Single Price Areas Assumptions and Simplifications In this work electricity markets for two areas (area A and area B) are modeled. Each electricity market is implemented as a stand-alone spot market. The spot market can be regarded as a day-ahead (DA) market as issues related to the operation of hour-ahead and real-time markets are neglected. The DA spot market is further designed as a power exchange. The clearing process takes place as described in The spot markets are cleared separately. From this it follows that each spot market has its own market clearing price. This is why the overall market model is called Two Single Price Areas. Besides it is assumed that there is no way to trade energy outside of the two spot markets. Bilateral contracts are excluded in this model. Section 4.2 describes further assumptions relating to the electricity markets and their participants. It also explains how the electricity markets are concretely implemented in MATLAB. The two single price areas are connected with one single interconnection. The only way for power to flow between area A and area B is this single interconnection. Generators in one area selling electrical energy in the other area s power exchange will need to obtain transmission capacity to deliver the energy along the transmission line to the other area. Loads might also want to buy in the other areas s power exchange and therefore need to secure transmission capacity as well, in order to receive the energy along the transmission line. Any generator or load which did not obtain transmission capacity will not be allowed to enter into the other area s power exchange! In this model capacity is allocated by explicit auctioning of transmission capacity. For this purpose an auction platform is designed which operates separately from the two power exchanges. In this transmission capacity market interested market players can acquire rights to transmit a certain amount of power flow along the interconnector during a certain market pe- 18

30 CHAPTER 4. MARKET MODEL 19 riod. The length of this market period is set equal to the length of energy market s period. To the transmission rights a so-called must-participate rule is attached. It commits on its holder to endeavor to use his right, i.e. to participate in the other areas electricity market. This restriction is chosen in order to inhibit that market participants just obtain transmission capacity with the aim to block price changes. Other features of the explicit auction are specified in section 4.3. It also shows how the transmission market is cleared and explains its implementation in MATLAB. Figure (4.1) displays the two phases of one entire market cycle. This cycle will be passed through several times back-to-back for the simulations. Phase 1: bids from generators/loads from area A and B available transmission capacity TCM A/B transmission capacity allocation Phase 2: Bids/offers from generators/loads A bids/offers from generators/loads A which obtained transmission capacity EM A electrical energy dispatch in A EM B electrical energy dispatch in B bids/offers from generators/loads B which obtained transmission capacity bids/offers from generators/loads B Figure 4.1: The Two Phases of One Market Cycle with the Markets TCM A/B (Transmission Capacity Market for the Transmission Line between A and B), EM A and EM B (Energy Market located in A resp. B)

31 CHAPTER 4. MARKET MODEL Implementation two SPA simulation() is the overall function which simulates all market cycles and the corresponding bidding behavior of generators and loads from area A and area B in the transmission capacity market. In the version two SPA simulation QL() the market players follow the Q-Learning algorithm to determine their bids for the transmission market. Whereas in two SPA simlation FI() the simulation is run with the fixed incremental bid probing (FI) algorithm. The functionality and the concrete implementation of the Q-Learning algorithm and of the FI algorithm will be explained later in chapter 5. two SPA simulation QL() This function runs the entire simulation of all market cycles. It takes the number of market cycles and the available transmission capacity as parameters and returns all relevant values calculated over all market cycles, e.g. bids made in the transmission capacity market or the market clearing prices. two SPA simulation QL() basically calls up subfunctions and consists of the following steps: 1. create agents QL() is called up: The generators and loads for both areas A and B are created and initialized. 2. select FI bid() is called up: The generators and loads bids for the transmission capacity market are calculated according to the QL algorithm. 3. capacity clearing() is called up: Generators and loads submit their bid for transmission capacity and the transmission capacity market is cleared. 4. The agents supplies/demands for/in the energy market A resp. B will be set according to the outcome of step 3. For example, the supply of a generator i from area A in the energy market A, is calculated as follows: and for the energy market B: A PG i = P Gimax TC i (4.1) B PG i = TC i (4.2) P Gi,max stands for the the maximal supply capability of generator i and TC i for the obtained transmission capacity.