Building and Investigating Generators' Bidding Strategies in an Electricity Market
Building and Investigating Generators' Bidding Strategies in an Electricity Market

Author: Xia Yin

Publisher:

Published: 2010

Total Pages: 326

ISBN-13:

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In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings.

Understanding Strategic Bidding in Restructured Electricity Markets
Understanding Strategic Bidding in Restructured Electricity Markets

Author: Ali Hortaçsu

Publisher:

Published: 2005

Total Pages: 50

ISBN-13:

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We examine the bidding behavior of firms competing on ERCOT, the hourly electricity balancing market in Texas. We characterize an equilibrium model of bidding into this uniform-price divisible-good auction market. Using detailed firm-level data on bids and marginal costs of generation, we find that firms with large stakes in the market performed close to theoretical benchmarks of static, profit-maximizing bidding derived from our model. However, several smaller firms utilized excessively steep bid schedules that deviated significantly from our theoretical benchmarks, in a manner that could not be empirically accounted for by the presence of technological adjustment costs, transmission constraints, or collusive behavior. Our results suggest that payoff scale matters in firms' willingness and ability to participate in complex, strategic market environments. Finally, although smaller firms moved closer to theoretical bidding benchmarks over time, their bidding patterns contributed to productive inefficiency in this newly restructured market, along with efficiency losses due to the close-to optimal exercise of market power by larger firms.

Market Performance and Bidding Behaviors in Deregulated Electricity Market
Market Performance and Bidding Behaviors in Deregulated Electricity Market

Author: Zhigang Liao

Publisher:

Published: 2012

Total Pages: 484

ISBN-13:

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The objective of this research is to investigate the impact of different pricing rules on the economic performance of a deregulated electricity market. In particular, the influence on the bid prices and profits of generators, total dispatch cost, and the volatility of these values will be examined.Given the debate, over the past two decades, regarding the selection of the best pricing rules, the applicability of the Revenue Equivalence Theorem in the deregulated electricity market is revisited in this research. This theorem has been adopted in the literature as a theoretical support for deciding pricing rule in the market settlement process. In this research, it is hypothesized that the Revenue Equivalence Theorem may not hold. Therefore, it is appropriate to analyze market performance when different pricing rules are imposed to govern the market. Furthermore, this research also highlights the importance of generators bidding strategies in the competitive electricity market. The different methods used to design generators bidding strategies as presented in the literature have been reviewed.In order to investigate the different impact of pricing rules on market performance, this research employs a computer simulation method. A simulation platform is used to imitate the business activities in the electricity auction market, mainly in relation to the bidding, scheduling and dispatching processes. An agent-based approach is adopted for this purpose. The competing generators in the electricity market are modeled as agents. Bid stacking is used to model the optimization process of the Independent System Operator. The generator agents bidding strategies are designed using the Q-Learning algorithm. A Q-Learning-driven generator agent enables it to actively learn from the historical and market information leading to more realistic and practical results. Different pricing rules under both maximum quantity bidding and variable quantity bidding are studied separately. The influence on the bid prices and profits of generators, total dispatch cost, and the volatility of these values are analyzed accordingly.

Network and Temporal Effects on Strategic Bidding in Electricity Markets
Network and Temporal Effects on Strategic Bidding in Electricity Markets

Author: Youfei Liu

Publisher: Open Dissertation Press

Published: 2017-01-27

Total Pages:

ISBN-13: 9781361470282

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This dissertation, "Network and Temporal Effects on Strategic Bidding in Electricity Markets" by Youfei, Liu, 劉有飛, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: of thesis entitled "Network And Temporal Effects On Strategic Bidding In Electricity Markets" Submitted by Youfei Liu for the Degree of Doctor of Philosophy at the University of Hong Kong in February 2006 The global deregulation of power industries has given rise to many fascinating research topics. This thesis addresses issues of strategic bidding by power generators. The problem of strategic bidding is to optimize an individual power generation bid by maximizing profits, based on production cost, expectation of rival behavior and system demand. Electrical power flow over different links is governed by the physical law (Kirchhoff law). As a result, electrical power flow cannot be independently determined and the electricity transmission system has global network effects. One major contribution of this study is to investigate the network effects of electricity transmission on strategic bidding and analyze the network-constrained electricity market equilibria. A three-node electricity system is used for investigation. The decision space of generators is divided into the congestion-on region and congestion-off region, and the optimal response curves of generators in each region are then derived. The market equilibrium is located as the intersection of these optimal response curves. It is analytically shown that this may consist either of a unique unconstrained market equilibrium, a unique constrained market equilibrium, multiple-equilibria, or no pure Nash equilibrium. Subsequently, the interaction between transmission rights holding and market power exercising is addressed. It is shown that in the situation with a positive PTDF, holding transmission rights mitigate market power, and produce an improvement in market efficiency, while in other situations, the reverse is true. Furthermore it is demonstrated that a possible allocation of transmission rights to generators can be found to achieve maximum efficiency. Another unique characteristic of electricity markets is their notable temporal ii effects. In other words, electricity prices have significant volatilities because of the non-storability of power energy and the large variations of system demand. The second part of this study investigates the temporal effects of the electricity market on strategic bidding. A periodic dynamic feedback system is proposed to model the generation competition process. With the developed system dynamics, an optimal control problem is formulated to study the multi-period optimization behavior (called the 'advanced' strategy) of a generator, and the state-feedback control rule is then derived via a sweeping method. It is demonstrated that the generator with optimal control can obtain more profits, and a sensitivity analysis is provided to locate the market factors that affect the performance of optimal control. Next, system uncertainties are included, and a stochastic optimal control problem for generation decision is formulated and solved. Two interesting problems are investigated, namely the effect of the generator's 'advanced' strategic behavior on market efficiency, and the way in which an individual's payoff evolves with other generators' 'advanced' strategic behavior. It is shown that the 'advanced' strategic behavior of generators will improve market efficiency, while an individual's payoff evolution resembles a 'Prisoner Dilemma'. An analysis of risk management of generation decisi...

Nash Strategies with Adaptation and Their Application in the Deregulated Electricity Market
Nash Strategies with Adaptation and Their Application in the Deregulated Electricity Market

Author: Xiaohuan Tan

Publisher:

Published: 2006

Total Pages: 166

ISBN-13:

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Abstract: The strategic bidding problems in the deregulated electricity market have been of keen interest to researchers and policy makers in recent decades. Given the conflicting nature of the problem, the theory of noncooperative games seems to be the right framework to investigate the problems. Although much efforts has been devoted to this topic, many issues remain unsolved, in which private information often keeps modelers from formulating and solving the games using available research results. Motivated by strategic bidding problems, this dissertation focuses on the adaptivity and sensitivity analysis of a class of nonzero-sum games with incomplete information. We first construct an infinite-horizon discrete-time linear quadratic N-person nonzero-sum game with complete information and perfect state information pattern, and then we consider the case with incomplete information. We first propose an adaptation mechanism for a player who lacks complete information of the game and prove convergence of the parameter estimates to the true values of the parameters under the conditions of persistent excitation. We then slightly modify the model to study the sensitivity of the costs of a game with respect to the embedded unknown parameter using multi-modeling method and we derive bounds for the cost deviation. Finally, we investigate the effect of market power by considering a market with lagged customer response to prices. These analyses are of fundamental importance if one wants to effectively apply game theory to strategic bidding problems.

Dynamic Noncooperative Game Models for Deregulated Electricity Markets
Dynamic Noncooperative Game Models for Deregulated Electricity Markets

Author: Jose Behar Cruz

Publisher: Nova Science Pub Incorporated

Published: 2009

Total Pages: 105

ISBN-13: 9781607410782

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The deregulated electricity markets are expected to be perfectly competitive, yet they remain oligopolistic in which the market participants are able to exercise market power to "game" the markets. Game theory, by its nature, is considered as the appropriate framework to study the interactive behaviours of decision makers with conflict of interest. Substantial research has been devoted to study gaming behaviour in the deregulated electricity market using game theory. However, most of the modelling of the markets is static and this type of model leads to non-optimal results for long-term strategic planning due to the inherent dynamic nature of the market. This book formulates and describes the gaming behaviour in the deregulated electricity market from a dynamic point of view, considering long-term interests in a changing environment. It starts with a review of the current situation of deregulation and a brief review of near-term energy issues. The book includes the latest results on bidding dynamic strategies for such markets.

Operation of Restructured Power Systems
Operation of Restructured Power Systems

Author: Kankar Bhattacharya

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 323

ISBN-13: 1461514657

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Deregulation is a fairly new paradigm in the electric power industry. And just as in the case of other industries where it has been introduced, the goal of deregulation is to enhance competition and bring consumers new choices and economic benefits. The process has, obviously, necessitated reformulation of established models of power system operation and control activities. Similarly, issues such as system reliability, control, security and power quality in this new environment have come in for scrutiny and debate. In this book, we attempt to present a comprehensive overview of the deregulation process that has developed till now, focussing on the operation aspects. As of now, restructured electricity markets have been established in various degrees and forms in many countries. This book comes at a time when the deregulation process is poised to undergo further rapid advancements. It is envisaged that the reader will benefit by way of an enhanced understanding of power system operations in the conventional vertically integrated environment vis-a-vis the deregulated environment. The book is aimed at a wide range of audience- electric utility personnel involved in scheduling, dispatch, grid operations and related activities, personnel involved in energy trading businesses and electricity markets, institutions involved in energy sector financing. Power engineers, energy economists, researchers in utilities and universities should find the treatment of mathematical models as well as emphasis on recent research work helpful.