Learning from Past Bids to Participate Strategically in Day-Ahead Electricity Markets

We consider the process of bidding by electricity suppliers in a day-ahead market context where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers' bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals' bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals' production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent prices

Learning from past bids to participate strategically in day-ahead electricity markets

We consider the process of bidding by electricity suppliers in a day-ahead market context, where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers' bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals' bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals' production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent pricesFirst author draf

A review of the identification of market power in the liberalized electricity markets

The liberalization of the electricity market aimed to promote competition, innovation, and fair pricing for consumers. However, as with any imperfect system, certain loopholes exist. Some major players in the electricity market have taken advantage of these loopholes to benefit from their market power. This research examines various methods for detecting market power in the liberalized electricity market and proposes a combination of detection methods that effectively address the issue of market power abuse. Two approaches to market power detection were identified and analyzed. The first approach involves the use of structural indices and analysis, including Concentration Ratio (Crn), Herfindahl-Hirschman Index (HHI), Pivotal Supplier Indicator(PSI), Residual Supply Index(RSI), Structure Conduct Performance Model, and Residual Demand Analysis. The second approach utilizes simulation models such as Linear Optimization, Supply Function Equilibrium, Cournot- Nash Framework, Agent-Based Model, and New Empirical Industrial Organization. The research findings indicate that combining market simulation approaches, such as the linear optimization model, with other methods like residual demand analysis, concentration ratios, and agent-based models, provides a comprehensive approach to market power detection. The linear optimization model can identify potential discrepancies by comparing marginal costs and prices, thereby indicating possible market power abuse. By incorporating residual demand analysis, a deeper understanding of the demand side of the market can be gained. Additionally, considering concentration ratios and employing agent-based models to capture strategic choices and behaviors of market participants can enhance the accuracy of market power detectio

Price capping in partially monopolistic electricity markets

In this paper we consider an oligopolistic market in which one firm can be monopolist on her residual demand function and derive implications on the shape of her profit function, which we show may not be concave in price. We propose a simple price-capping rule that induce the pivotal operator to compete for quantity instead of taking advantage of her monopoly. Then, we analyze the bidding behaviour of the dominant electricity producer oper- ating in the Italian wholesale power market (IPEX). This firm is vertically integrated and in many instances she acts as a monopolist on the residual demand. We find that, contrary to expectations, this pivotal firm refrains to exploit totally her unilateral market power and, therefore, bids at levels well below the cap. We discuss such a behaviour and derive implications for the setting of the price cap.Electricity auctions, capacity constraints, price cap, optimal bidding

Information requirements for strategic decision making: energy market

Over the last two decades, the electricity sector has been involved in a challenging restructuring process in which the vertical integrated structure (monopoly) is being replaced by a horizontal set of companies. The growing supply of electricity, flowing in response to free market pricing at the wellhead, led to increased competition. In the new framework of deregulation, what characterizes the electric industry is a commodity wholesale electricity marketplace. This new environment has drastically changed the objective of electricity producing companies. In the vertical integrated industry, utilities were forced to meet all the demand from customers living in a certain region at fixed rates. Then, the operation of the Generation Companies (GENCOs) was centralized and a single decision maker allocated the energy services by minimizing total production costs. Nowadays, GENCOs are involved not only in the electricity market but also in additional markets such as fuel markets or environmental markets. A gas or coal producer may have fuel contracts that define the production limit over a time horizon. Therefore, producers must observe this price levels in these other markets. This is a lesson we learned from the Electricity Crisis in California. The Californian market\u27s collapse was not the result of market decentralization but it was triggered by other decisions, such as high natural gas prices, with a direct impact in the supply-demand chain. This dissertation supports generation asset business decisions -from fuel supply concerns to wholesale trading in energy and ancillary services. The forces influencing the value chain are changing rapidly, and can become highly controversial. Through this report, the author brings an integrated and objective perspective, providing a forum to identify and address common planning and operational needs. The purpose of this dissertation is to present theories and ideas that can be applied directly in algorithms to make GENCOs decisions more efficient. This will decompose the problem into independent subproblems for each time interval. This is preferred because building a complete model in one time is practically impossible. The diverse scope of this report is unified by the importance of each topic to understanding or enhancing the profitability of generation assets. Studies of top strategic issues will assess directly the promise and limits to profitability of energy trading. Studies of ancillary services will permit companies to realistically gauge the profitability of different services, and develop bidding strategies tuned to competitive markets

Oligopolistic and oligopsonistic bilateral electricity market modeling using hierarchical conjectural variation equilibrium method

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityAn electricity market is very complex and different in its nature, when compared to other commodity markets. The introduction of competition and restructuring in global electricity markets brought more complexity and major changes in terms of governance, ownership and technical and market operations. In a liberalized electricity market, all market participants are responsible for their own decisions; therefore, all the participants are trying to make profit by participating in electricity trading. There are different types of electricity market, and in this research a bilateral electricity market has been specifically considered. This thesis not only contributes with regard to the reviewing UK electricity market as an example of a bilateral electricity market with more than 97% of long-term bilateral trading, but also proposes a dual aspect point of view with regard to the bilateral electricity market by splitting the generation and supply sides of the wholesale market. This research aims at maximizing the market participants’ profits and finds the equilibrium point of the bilateral market; hence, various methods such as equilibrium models have been reviewed with regard to management of the risks (e.g. technical and financial risks) of participating in the electricity market. This research proposes a novel Conjectural Variation Equilibrium (CVE) model for bilateral electricity markets, to reduce the market participants’ exposure to risks and maximize the profits. Hence, generation companies’ behaviors and strategies in an imperfect bilateral market environment, oligopoly, have been investigated by applying the CVE method. By looking at the bilateral market from an alternative aspect, the supply companies’ behaviors in an oligopsony environment have also been taken into consideration. At the final stage of this research, the ‘matching’ of both quantity and price between oligopolistic and oligopsonistic markets has been obtained through a novel-coordinating algorithm that includes CVE model iterations of both markets. Such matching can be achieved by adopting a hierarchical optimization approach, using the Matlab Patternsearch optimization algorithm, which acts as a virtual broker to find the equilibrium point of both markets. Index Terms-- Bilateral electricity market, Oligopolistic market, Oligopsonistic market, Conjectural Variation Equilibrium method, Patternsearch optimization, Game theory, Hierarchical optimization metho

Strategic Bidding of Offer Curves: An Agent-Based Approach to Exploring Supply Curve Equilibria

We model a market in which suppliers bid step-function offer curves using agent-based modeling. Our model is an abstraction of electricity markets where step-function offer curves are given to an independent system operator that manages the auctions in electricity markets. Positing an elementary and computationally accessible learning model, Probe and Adjust, we present analytic results that characterize both the behavior of the learning model and the properties of step-function equilibria. Thus, we have developed a framework for validating agent-based models prior to using them in situations that are too complicated to be analyzed using traditional economic theory. In addition, we demonstrate computationally that, by using alternative policies, even simple agents can achieve monopoly rewards for themselves by pursuing more industry-oriented strategies. This raises the issue of how participants in oligopolistic markets actually behave

Eliminating the Flaws in New England's Reserve Markets

New England’s wholesale electricity market has been in operation, since May 1, 1999. When the market began it was understood that the rules were not perfect (Cramton and Wilson 1998). However, it was decided that it was better to start the market with imperfect rules, rather than postpone the market for an indefinite period. After several months of operation, we now have a sense of the extent market imperfections have resulted in observed problems. Here we study the three reserve markets—ten-minute spinning reserve (TMSR), ten-minute non-spinning reserve (TMNSR), and thirty-minute operating reserve (TMOR); we also discuss the closely related operable capability (OpCap) market. The paper covers the first four months of operation from May 1 to August 31, 1999. It is based on the market rules and their implementation by the ISO, and the market data during this period, including bidding, operating, and settlement information. Since that data are confidential, we have presented only aggregate information in the tables and figures that follow. Although this paper will cover only the reserves markets, we have studied the data from the energy, AGC, and capacity markets as well. Since all of the NEPOOL markets are interrelated, one cannot hope to understand one market without having an understanding of the others.Auctions, Electricity Auctions, Multiple Item Auctions

Evolving game theory based decision making systems for NETA power market modelling, analysis and trading strategy development

In this thesis, current work carried out on analyzing the strategic behaviours in electricity trading is first reviewed. An intelligent decision-making and support technique, game theory, is often used in the market practice. Game theory is a discipline concerned with how individuals make decisions when they are partly aware of how their action might affect each other and when each individual might take this into account. Deficiencies and limitations of traditional game theory based methods developed for decision-making in electricity trading are also investigated. This research then explores to discover the impact of intelligent systems based trading strategies in the UK power markets. To model these behaviours and the New Electricity Trading Arrangements (NETA) system of the UK, traditional competitive and cooperative game theory strategies are taken into account in the work reported in this thesis. An improved methodology, “trigger price strategy”, is introduced to simulate power generation companies’ enhanced gaming strategies. Such modelling problem is, however, intractable and hence an extra-numerical search technique, Evolutionary Computation, is employed to solve the game theory based system modelling problem. An encoded Genetic Algorithm based technique is developed to search for an effective model for the complex decision-making process and to help decision-makers evaluate their strategies and bidding parameters. A novel and effective electricity trading simulation model is thus developed, where its design features are close to the NETA. The model scale is as close as possible to NETA. A complex and more realistic two-sided transaction mechanism with demand fully incorporated is incorporated in this model. These are a world first in this research area