ACE Research Area:
Agent-Based Electricity Market Research

Last Updated: 7 May 2013

Site Maintained By: Leigh Tesfatsion Professor of Economics and Mathematics Iowa State University Ames, IA 50011-1070 http://www.econ.iastate.edu/tesfatsi/ tesfatsi AT iastate.edu General Resources on Electricity Restructuring Electricity Market Open-Source Software ACE Website

Table of Contents

• Introduction
• Basic Issues
• Surveys and Overviews
• Other Readings
• Software, Toolkits, and Demos
• Related Resource Sites and Groups
• Some Early Individual Researchers

Introduction

The U.S. electricity industry is currently undergoing substantial changes in both its structure (ownership and technology aspects) and its architecture (operational and oversight aspects). These changes involve attempts to move the industry away from highly regulated markets with administered cost-based pricing and towards competitive markets in which prices more fully reflect supply and demand forces. The goal of these changes is to provide industry participants with better incentives to control costs and introduce innovations. The process of enacting and implementing policies and laws to bring about these changes has come to be known as restructuring.

This restructuring process has been controversial. The meltdown in the restructured California wholesale power market in the summer of 2000 has shown what can happen when market designs are implemented without sufficient pre-testing. Following the California crisis, many energy researchers have eloquently argued the need to combine sound physical understanding of electric power and transmission grid operation with economic analysis of incentives in order to develop electricity markets with good real-world performance characteristics.

The goal of this resource site is to encourage the study of restructured electricity systems from a perspective that adequately addresses both economic and engineering concerns. In line with this goal, stress is placed on research making use of powerful new agent-based computational modeling tools. These tools permit restructured electricity systems to be modeled as commercial networks of strategically interacting traders and regulatory agencies learning to operate through time over realistically rendered transmission grids.

A comprehensive repository of general resources related to electricity restructuring is also available.

Basic Issues

• What form should electricity restructuring take? To what extent should regions be permitted to design their own systems rather than adhere to a centralized design -- e.g., FERC's wholesale power market platform (aka standard market design) in the U.S.?
• How should transmission grid constraints and congestion be properly accounted for in electricity market design? What role might "financial transmission rights" play?
• How important is demand responsiveness for the effective functioning of electricity markets?
• To what extent can strategic participants "game the system" under currently proposed/implemented electricity market designs?
• To what extent should independent system operators and other regulatory agencies intervene in electricity markets in an attempt to mitigate market power and inefficiency problems?
• Are the goals of "market efficiency" and "reliable flow of electricity to consumers" necessarily at odds with each other?
• How can electricity markets be protected against hackers, insider trading, and other illegal disruptive activities as these markets come to depend more heavily on Internet transactions?

Surveys and Overviews

• Massoud Amin, Restructuring the Electric Enterprise: Simulating the Evolution of the Electric Power Industry with Intelligent Adaptive Agents (pdf,450KB), Chapter 3 in Market Analysis and Resource Management, edited by A. Faruqui and K. Eakin, Kluwer Publishers, March 2002.

  Abstract: The author discusses the development of the Simulator for Electrical Power Industry Agents (SEPIA) sponsored by the Electric Power Research Institute (EPRI). SEPIA uses autonomous adaptive agents to represent possible industrial components (e.g., generation units, transmission system, load) and the corporate entities (e.g., GenCos and LoadCos) that own these components. Objectives are: (1) to develop a high-fidelity scenario-free modeling and optimization tool to use for gaining strategic insight into the operation of the deregulated power industry; (2) to show how networks of communicating and cooperating intelligent software agents can be used to adaptively manage complex distributed systems; and (3) to investigate how collections of agents (agencies) can be used to buy and sell electricity and participate in the electronic market place and, ultimately, to create self-optimizing and self-healing capabilities for the electric power grid and the interconnected critical infrastructures.

• Albert Banal-Estanol and Augusto Rupérez Micola, "Are Agent-Based Simulations Robust? The Wholesale Electricity Trading Case" (pdf, 410KB), Working Paper, March 2010.

  Abstract: The authors explore the consistency of some of the standard techniques used in agent-based computational economics, particularly learning representations, with a special focus on prevailing wholesale electricity trading simulation methods.

• Eric Guerci, Mohammad Ali Rastegar, and Silvano Cincotti, "Agent-Based Modeling and Simulation of Competitive Wholesale Electricity Markets", pp. 241-286 in Handbook of Power Systems II, Springer, 2010.

  Abstract: "This paper sheds light on a promising and very active research area for electricity market modeling, i.e., Agent-Based Computational Economics. The intriguing perspective of such research methodology is to succeed in tackling the complexity of the electricity market structure, thus the fast-growing literature appeared in the last decade on this field. This paper aims to present the state-of-the-art in this field, by studying the evolution and by characterizing the heterogeneity of the research issues, of the modeling assumptions and of the computational techniques adopted by the several research publications reviewed."

• Vladimir S. Koritarov, "Real-World Market Representation with Agents" (pdf,2MB), IEEE Power and Energy Magazine, 2004, 39-46

  Abstract: "The electric power industry around the world is undergoing an extensive restructuring process. In many countries the traditional vertically integrated power utilities are being unbundled and replaced with a number of separate business entities dealing with the generation, transmission, and distribution of electric power. One of the most significant features of the restructuring process is the introduction of electricity markets, aimed at providing competitive electricity service to consumers. As power markets are relatively new and still continue to evolve, there is a growing need for advanced modeling approaches that simulate the behavior of electricity markets over time and how market participants may act and react to the changing economic, financial, and regulatory environments in which they operate. (This paper argues that a) new and rather promising approach is to model the electricity market as a complex adaptive system using an agent-based modeling and simulation approach."

• Hongyan Li and Leigh Tesfatsion, "Development of Open Source Software for Power Market Research: The AMES Test Bed" (pdf preprint,628KB), Journal of Energy Markets, 2(2), Summer 2009, 111-128.

  Abstract: This study discusses potential benefits and drawbacks of developing open-source software for power market research, using the AMES Wholesale Power Market Test Bed for concrete illustration.

• Frank Sensfuß, Mario Ragwitz, Massimo Genoese, and Dominik Möst, Agent-Based Simulation of Electricity Markets: A Literature Review (pdf,485KB), Working Paper Sustainability and Innovation, No. S 5/2007, Fraunhofer Institute Systems and Innovation Research.

  Abstract: "Liberalization, climate policy, and promotion of renewable energy are challenges to players of the electricity sector in many countries. Policy makers have to consider issues like market power, bounded rationality of players, and the appearance of fluctuating energy sources in order to provide adequate legislation. Furthermore the interactions between markets and environmental policy instruments become an issue of increasing importance. A promising approach for the scientific analysis of these developments is the field of agent-based simulation. The goal of this article is to provide an overview of the current work applying this methodology to the analysis of electricity markets."

• Leigh Tesfatsion, "Agent-Based Modeling of Electric Power Markets" (pdf,2.5MB), July 2010.

  Abstract: This presentation first discusses the complexity of electric power systems, and the potential of agent-based modeling (ABMB) for handling this complexity. It then discusses the development of an ABM testbed ("AMES") for the performance study of U.S. restructured electric power markets. Experimental findings are used for concrete illustration.

• Leigh Tesfatsion, "Auction Basics for Wholesale Power Markets: Objectives and Pricing Rules" (pdf,504KB), Proceedings of the IEEE Power and Energy Society General Meeting, Calgary, Alberta, CA, July 26-30, 2009. Last Updated: February 2011.

  Abstract: Power systems have distinctive features that greatly complicate the development of auction designs. This study reviews the theory and practice of auction design as it relates specifically to U.S. restructured wholesale power markets, i.e., centrally-administered wholesale power markets with congestion managed by locational marginal prices. Basic auction concepts such as reservation value, net seller surplus, net buyer surplus, competitive market clearing, market efficiency, market pricing rules, supply offers, demand bids, strategic capacity withholding, and market power are explained and illustrated. Complicating factors specific to wholesale power markets are clarified, and recent advances in computational tools designed to address these complications are briefly noted.

• Anke Weidlich and Daniel Veit, "A Critical Survey of Agent-Based Wholesale Electricity Market Models" (doi/eneco), Energy Economics, 30(4), July 2008, 1728-1759.

  Abstract: "The complexity of electricity markets calls for rich and flexible modeling techniques that help to understand market dynamics and to derive advice for the design of appropriate regulatory frameworks. Agent-Based Computational Economics (ACE) is a fairly young research paradigm that offers methods for realistic electricity market modeling. A growing number of researchers have developed agent-based models for simulating electricity markets. The diversity of approaches makes it difficult to overview the field of ACE electricity research; this literature survey should guide the way through and describe the state-of-the-art of this research area. In a conclusive summary, shortcomings of existing approaches and open issues that should be addressed by ACE electricity researchers are critically discussed."

• Steve Widergren, Junjie Sun, and Leigh Tesfatsion, "Market Design Test Environments" (pdf,136KB), Proceedings of the IEEE Power Engineering Society General Meeting, Montreal, June 2006 (electronic).

  Abstract: "Power industry restructuring continues to evolve at multiple levels of system operations. At the bulk electricity level, several organizations charged with regional system operation are implementing versions of a Wholesale Power Market Platform (WPMP) in response to U.S. Federal Energy Regulatory Commission initiatives. Recently the Energy Policy Act of 2005 and several regional initiatives have been pressing the integration of demand response as a resource for system operations. These policy and regulatory pressures are driving the exploration of new market designs at the wholesale and retail levels. The complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance demand a flexible computational environment where designs can be tested and sensitivities to power system and market rule changes can be explored. This paper discusses the use of agent-based computational methods for the study of electricity markets at the wholesale and retail levels, and explores distinctions in problem formulation between these levels."

• Shun-Kun Yu and Jia-Hai Yuan, "Agent-Based Computational Economics: Methodology and Its Application in Electricity Market Research" (pdf,205KB), Proceedings of the 7th International Power Engineering Conference (IPEC), 2005.

  Abstract: "The restructured electricity market is a complex adaptive system and world-wide experiences show that market design is a complicated task. Recently, under the paradigm of agent-based computational economics (ACE), a new research focus is forming and a large number of literatures are springing up, but there is still no discussion on ACE’s theoretical value and insufficiency in the research of electricity market in a hierarchy of methodologies. Therefore the authors' research is an attempt in this aspect. By means of analyzing the evolution of economics methodology from mathematical deduction to simulation induction, and their inherent relevance, the unique superiority of ACE on the level of methodology is expounded. A further selective survey on existing literatures shows that with the ACE model the marketization process can be understood clearly in deeper level and wider scope. Finally, to give a reference to theoretical progress, the prospective application of ACE, especially its potential in China’s electric sector restructuring,is discussed."

• Zhi Zhou, Wai Kin Chan, and Joe H. Chow, "Agent-Based Simulation of Electricity Markets: A Survey of Tools" (pdf,800KB) Artificial Intelligence Review, Volume 28, 2007, 305-342.

  Abstract: "Agent-based simulation has been a popular technique in modeling and analyzing electricity markets in recent years. Themain objective of this paper is to study existing agentbased simulation packages for electricity markets.We first provide an overview of electricity markets and briefly introduce the agent-based simulation technique.We then investigate several general-purpose agent-based simulation tools. Next, we review four popular agent-based simulation packages developed for electricity markets and several agent-based simulation models reported in the literature. We compare all the reviewed packages and models and identify their common features and design issues. Based on the study, we describe an agentbased simulation framework for electricity markets to facilitate the development of future models for electricity markets."

Other Readings

• Anthony J. Bagnall and George D. Smith, "A Multi-Agent Model of the UK Market in Electricity Generation" (pdf,678KB), IEEE Transactions on Evolutionary Computation 9(5), 2005, 522-536.

  Abstract: This paper describes an agent-based computational economics approach for studying the effect of alternative structures and mechanisms on behavior in a simulated model of the pre-NETA England and Wales electricity market. Agents learn using hierarchical learning classifier systems. The authors test to see whether the agents: (a) are able to learn optimal strategies when competing against non-adaptive agents; (b) are able to learn strategies observable in the real world when competing against other adaptive agents; and (c) are able to evolve cooperative behaviors without explicit communication.

• John Bower and Derek Bunn, "Experimental Analysis of the Efficiency of Uniform-Price versus Discriminatory Auctions in the England and Wales Electricity Market", Journal of Economic Dynamics and Control Volume 25, March 2001, 561-592.

  Abstract: The authors develop an agent-based computational model of the wholesale market for electricity in England and Wales that allows them to compare market prices and the bidding strategies of individual generators under different trading arrangements. The authors use this framework to address several restructing issues under debate for the England and Wales market -- in particular, the efficacy of using a uniform-price versus a discriminatory-price auction format.

• Derek W. Bunn and Fernando S. Oliveira, "Agent-Based Simulation: An Application to the New Electricity Trading Arrangements of England and Wales", IEEE Transactions on Evolutionary Computation, Volume 5, Number 5, October 2001, 493-503.

  Abstract: This paper presents a large-scale application of a multi-agent evolutonary model of the proposed New Electricity Trading Arrangements (NETA) in the United Kingdom. The model is a detailed plant-by-plant model with an active specification of the demand side of the market. The model was able to provide pricing and strategic insights into the workings of the NETA prior to its actual introduction.

• Derek W. Bunn and Fernando S. Oliveira, "Evaluating Individual Market Power in Electricity Markets via Agent-Based Simulation" (WP pdf,505KB), Annals of Operations Research Volume 121, Numbers 1-4, 2003, 55-77.

  Abstract: The authors use agent-based simulation in a coordination game to analyse the possibility of market power abuse in a competitive electricity market. The context of this was a real application to the England and Wales electricity market as part of a Competition Commission Inquiry into whether two particular generators could profitably influence wholesale prices.

• Chengrui Cai, Pedram Jahangiri, Auswin George Thomas, Huan Zhao, Dionysios C. Aliprantis, and Leigh Tesfatsion, "Agent-Based Simulation of Distribution Systems with High Penetration of Photovoltaic Generation" (preprint,pdf,433KB), Proceedings of the IEEE Power and Energy Society General Meeting, Detroit, MI, 2011 (electronic).

  Abstract: This paper discusses the development of an agent-based test bed permitting the integrated study of retail and wholesale power markets operating over realistically rendered transmission and distribution systems. A key issue to be addressed using this test bed is the dynamic effect of increased penetration of consumer-owned distributed energy resources, such as PV generation, particularly when coupled with increased price-sensitivity of demand as realized through demand response, demand dispatch, and/or price-sensitive demand bidding.

• Jack Casazza and Frank Delea, Understanding Electric Power Systems: An Overview of the Technology and the Marketplace, IEEE Press/Wiley-Interscience, Piscataway, NJ, 2003.

  Abstract: "(This book) bridges the gap between technology, government policy, economics and finance, business arrangements, and the Internet - helping the reader to understand the interrelationship of the many aspects of the provision of electric power supply. ... For engineers, policymakers, and students alike, (this book) provides a high-level overview of how electric power is generated, transmitted, and controlled in the United States."

• H. Chen, K. P. Wong, H. M. Nguyen, and C. Y. Chung, "Analyzing Oligopolistic Electricity Markets Using Coevolutionary Computation (pdf,431KB), IEEE Transactions on Power Systems, 21(1), February 2006, 143-152.

  Abstract: This paper presents a new unified framework for electricity market analysis based on coevolutionary computation (CCEMB) for oligopoly electricity markets modeled as either one-shot or repeated games. The standard Cournot model and a new Pareto improvement model are explored. Both linear and constant elasticity demand functions are considered. A case study shows that CCEM is highly efficient and can handle nonlinear electricity market models that are difficult to handle by conventional methods.

• Robert Entriken and Steve Wan, "Agent-Based Simulation of an Automatic Mitigation Procedure" (pdf,203KB), Proceedings, 38th Hawaii International Conference on System Sciences, 2005.

  Abstract: This paper describes experiments using computer-based agents to simulate the impact of the California ISO's proposed Automatic Mitigation Procedure (AMP) for limiting the exercise of market power. The experimental results indicate that the AMP is effective in reducing market clearing prices under situations where they would otherwise reach the price cap. Other work by the same two authors using agent-based tools includes EPRI Report 1007733 on automatic mitigation procedures (pdf,791KB) and EPRI Report 1007755 on available capacity market designs (pdf,1.5MB) , both reports focusing on the California electricity market.

• Damien Ernst, Anna Minoia, and Marija Ilic, "Market Dynamics Driven by the Decision-Making Power Producers" (ps,215KB), IEEE preprint, downloaded 5/11/05.

  Abstract: The authors develop an electricity market model in which strategic power producers interact through a spot market over a 2-node transmission grid with congestion managed by locational marginal pricing. They investigate the effects on market outcomes of the line transfer capacity, the number and size of generators, and the presence or absence of generator coalitions.

• Eric Guerci and Mohammad Ali Rastegar, "From Uniform Auction to Discriminatory Auction: Assessment of the Restructuring Proposal for the Italian Electricity Day-Ahead Market" (pdf,497KB), EUI RSCAS working paper (RSCAS_2009_69), 2009.

  Abstract: "In the context of the 2009 debate on reforming the Italian market, a realistic agent-based computational model of the day-ahead market session of the Italian wholesale electricity market is simulated to compare market performances between uniform-price and pay-as-bid clearing mechanisms. An empirical validation of computational results at a macro-level is performed to test for accuracy of simulated outcomes with historical ones. The level of prices are accurately reproduced except for few peak hours. As far as concerns pay-as-bid auction, the computational experiments point out that it results in higher market prices than the uniform-price auction. In the pay-as-bid mechanism, sellers’ endeavours to maximize their profits are more costly thus leading to higher price levels."

• Pedram Jahangiri, Di Wu, Wanning Li, Dionysios C. Aliprantis, and Leigh Tesfatsion, Development of an Agent-Based Distribution Test Feeder with Smart-Grid Functionality (preprint pdf,386KB), Proceedings of the IEEE Power and Energy Society General Meeting, San Diego, CA, July 22-26, 2012 (electronic).

  Abstract: This paper reports on the development of an agent-based distribution test feeder with smart-grid functionality. The test feeder is based on an actual distribution feeder with various additional features incorporated, including rooftop photovoltaic generation and price-responsive loads (e.g., plug-in electric vehicles and intelligent air-conditioning systems). This work aims to enable the integrated study of wholesale electric power markets coupled with detailed representations of the retail-side distribution systems.

• Koen Kok, Martin Scheepers, and René Kamphuis, "Intelligence in Electricity Networks for Embedding Renewables and Distributed Generation" (pdf,447KB), in R.R. Negenborn, Z. Lukszo, and J. Hellendoom, editors, Intelligent Infrastructures, Springer, 2009.

  Abstract: This paper provides a general introduction to PowerMatcher, a smart-grid agent-based technology developed by ECN in cooperation with industry and research partners. PowerMatcher permits the intelligent, multi-goal, coordinated management of electrical power systems consisting of commodity markets operating over physical infrastructure.

• Hongyan Li and Leigh Tesfatsion, "Co-Learning Patterns as Emergent Market Phenomena: An Electricity Market Illustration" (WP pdf,1.4MB), Journal of Economic Behavior and Organization, Vol. 82, Issues 2-3, 2012, 395-419. The published article is available here.

  Abstract: The definition of emergence remains problematic, particularly for systems with purposeful human interactions. This study explores the practical import of this concept within a specific market context: namely, a double-auction market for wholesale electric power that operates over a transmission grid with spatially located buyers and sellers. Each profit-seeking seller is a learning agent that attempts to adjust its daily supply offers to its best advantage. The sellers are co-learners in the sense that their supply offer adjustments are in response to past market outcomes that reflect the past supply offer choices of all sellers. Attention is focused on the emergence of co-learning patterns, that is, global market patterns that arise and persist over time as a result of seller co-learning. Examples of co-learning patterns include correlated seller supply offer behaviors and correlated seller net earnings outcomes. Heat maps are used to display and interpret co-learning pattern findings. One key finding is that co-learning strongly matters in this auction market environment. Sellers that behave as Gode-Sunder budget-constrained zero-intelligence agents, randomly selecting their supply offers subject only to a break-even constraint, tend to realize substantially lower net earnings than sellers that tacitly co-learn to correlate their supply offers for market power advantages.

• Hongyan Li and Leigh Tesfatsion, "ISO Net Surplus Collection and Allocation in Wholesale Power Markets Under Locational Marginal Pricing" [(WP pdf,822KB), (PPT slides,1.6MB)], IEEE Transactions on Power Systems, Vol. 26, Issue 2, 2011, 627-641. (DOI Location).

  Abstract: This study uses 5-bus and 30-bus test cases to explore ISO net surplus (congestion rent) collections and allocations in wholesale power markets with grid congestion managed by locational marginal prices (LMPs). Price-sensitivity of demand and generator learning capabilities are taken as experimental treatment factors. A key finding is that conditions resulting in greater generator capacity withholding, hence higher and more volatile LMPs, also result in greater ISO net surplus collections that can be substantial in size. A key conclusion is that ISO net surplus collections should be used pro-actively to mitigate the conditions encouraging generator capacity withholding and hence high and volatile LMPs rather than to provide ex post support for LMP payment offsets and LMP volatility risk hedging as is currently the norm.

• Hongyan Li and Leigh Tesfatsion, "The AMES Wholesale Power Market Test Bed: A Computational Laboratory for Research, Teaching, and Training" (pdf, 930KB), Proceedings of the IEEE Power and Energy Society General Meeting, Calgary, Alberta, CA, July 26-30, 2009 (electronic).

  Abstract: This study reports on the AMES Wholesale Power Market Test Bed (Version 2.02). AMES is an open-source agent-based computational laboratory designed for the systematic study of restructured wholesale power markets operating over AC transmission grids subject to congestion. The AMES traders have learning capabilities permitting them to evolve their trading strategies over time. The potential usefulness of AMES for research, teaching, and training purposes is discussed and illustrated.

• Hongyan Li, Junjie Sun, and Leigh Tesfatsion, "Testing Institutional Arrangements via Agent-Based Modeling: A U.S. Electricity Markert Example" (pdf,1.8MB), pp. 135-158 in H. Dawid and W. Semmler (eds.), Computational Methods in Economic Dynamics, Dynamic Modeling and Econometrics in Economics and Finance 13, Springer-Verlag, Berlin Heidelberg, 2011.

  Abstract: "Many critical goods and services in modern-day economies are produced and distributed through complex institutional arrangements. Agent-based computational economics (ACE) modeling tools are capable of handling this degree of complexity. In concrete support of this claim, this study presents an ACE test bed designed to permit the exploratory study of restructured U.S. wholesale power markets with transmission grid congestion managed by locational marginal prices (LMPs). Illustrative findings are presented showing how spatial LMP cross-correlation patterns vary systematically in response to changes in the price responsiveness of wholesale power demand when wholesale power sellers have learning capabilities. These findings highlight several distinctive features of ACE modeling: namely, an emphasis on process rather than on equilibrium; an ability to capture complicated structural, institutional, and behavioural real-world aspects (miccro-validation); and an ability to study the effects of changes in these aspects on spatial and temporal outcome distributions."

• Hongyan Li, Junjie Sun, and Leigh Tesfatsion, "Separation and Volatility of Locational Marginal Prices in Restructured Wholesale Power Markets" (download site), ISU Economics Working Paper #09009, June 2009.

  Abstract: This study uses the AMES Wholesale Power Market Test Bed to investigate separation and volatility of locational marginal prices (LMPs) in an ISO-managed restructured wholesale power market operating over an AC transmission grid. Particular attention is focused on the dynamic and cross-sectional response of LMPs to systematic changes in demand-bid price sensitivities and supply-offer price cap levels under varied learning specifications for the generation companies. Also explored is the extent to which the supply offers of the marginal (price-determining) generation companies induce correlations among neighboring LMPs.

• Ross Little and Bruce Sawhill, "Market Feedback Replaces Regulation: Adaptation in the Electric Power Industry", Complexity, Vol. 3/No. 4, March/April 1998, 46-50.

• Charles M. Macal and Michael J. North, "Validation of an Agent-Based Model of Deregulated Electric Power Markets" (pdf,66KB), presented at the North American Association for Computational and Social Organization (NAACSOS) Conference, Notre Dame, Indiana, June 26-28, 2005.

  Abstract: EMCAS (Electricity Market Complex Adaptive SysteMB) is an agent-based simulation model of the electric power market designed to investigate market restructuring and deregulation and to understand the implications of a competitive power market on electricity prices, availability, and reliability. Model validation is an essential part of the model development process if models are to be accepted and used to support decision making. This paper describes the validation process for the EMCAS model and its results for use in practical decision making. The validation process also is an initial attempt to establish a general and practical framework for agent-based model validation.

• Vishnuteja Nanduri and Tapas K. Das, "A Reinforcement Learning Model to Assess the Market Power Under Auction-Based Energy Bidding" (pdf,734KB), IEEE Transactions on Power Systems 22(1), February 2007, 85-95.

  Abstract: This paper develops a nonzero sum stochastic game theoretic model and a reinforcement learning (RL)-based solution framework that allow assessment of market power in double-auction (DA) electricity markets. Since there are no available methods to obtain exact analytical solutions of stochastic games, an RL-based approach is utilized, which offers a computationally viable tool to obtain approximate solutions. These solutions provide effective bidding strategies for the DA market participants. The market powers associated with the bidding strategies are calculated using well-known indexes like Herfindahl–Hirschmann index and Lerner index and two new indices, quantity modulated price index (QMPI) and revenue- based market power index (RMPI), which are developed in this paper. The proposed RL-based methodology is tested on a sample network.

• James Nicolaisen, Valentin Petrov, and Leigh Tesfatsion, "Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing" (pdf,367KB), IEEE Transactions on Evolutionary Computation, Vol. 5, No. 5, October 2001, pp. 504-523. [ (pdf with colored figures,162KB), (pdf presentation,116KB)].

  Abstract: This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modifed Roth-Erev individual reinforcement learning algorithm to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained, and that market microstructure is strongly predictive for the relative market power of buyers and sellers independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier electricity study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms.

• Hyungna Oh and Timothy D. Mount, "Using Software Agents to Supplement Tests Conducted by Human Subjects", pp. 29-56 in H. Dawid and W. Semmler (eds.), Computational Methods in Economic Dynamics, Dynamic Modeling and Econometrics in Economics and Finance 13, Springer-Verlag, Berlin Heidelberg, 2011.

  Abstract: "The objective of this paper is to test whether or not software agents can match the observed behavior of human subjects in laboratory tests of markets. For this purpose, one set of tests uses four software agents and two human subjects to represent six suppliers in three different market situations: no forward contracts; fixed price forward contracts; and renewable forward contracts. An identical set of tests is conducted using software agents to represent all suppliers. The results show that software agents were able to replicate the behavior of human subjects effectively in the experiments, and have the potential to be used effectively in testing electricity auctions, doing additional sensitivity tests, and supplementing results obtained using human subjects."

• Georgios Papageorgiou, "Modelling of Electricity Markets Using Software Agents" (pdf,672KB), a dissertation submitted to the University of Manchester Institute of Science and Technology for the degree of Master of Science in Electrical Power Engineering, Department of Electrical Engineering and Electronics, UMIST, 2002.

  Abstract: This paper develops an agent-based computational model to investigate the bidding behavior of generating companies in a short-term bilateral market. Each generating company is modelled with specific strategic and operational objectives and bounded reasoning abilities based on principles of reinforcement learning. Price and market power effects are explored under a number of different treatments (e.g., settlement procedures, demand conditions, number of generators).

• Morteza Rahimiyan and Habib Rajabi Mashhadi, "Evaluating the efficiency of divestiture policy in promoting competitiveness using an analytical method and agent-based computational economics" (pdf,495KB), Energy Policy, Volume 38, 2010, 1588-1595.

  Abstract: Choosing a desired policy for divestiture of dominant firms’ generation assets has been a challenging task and open question for regulatory authority. To deal with this problem, in this paper, an analytical method and agent-based computational economics (ACE) approach are used for ex-ante analysis of divestiture policy in reducing market power.

• Morteza Rahimiyan and Habib Rajabi Mashhadi, "An Adaptive Q-Learning Algorithm Developed for Agent-Based Computational Modeling of Electricity Market" , IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews, Vol. 40, No. 5 (2010), 547-556.

  Abstract: This study develops a fuzzy Q-learning (FQL) method to model a power supplier’s strategic bidding behavior in a computational electricity market. In the simulation framework, the power supplier uses FQL to select its bidding strategy conditional on the supplier's past experiences, risk preferences, and QL learning parameters that are adaptively changing to reflect the supplier's current market power opportunities. The application of the proposed FQL methodology for a power supplier in a multi-area power system shows the performance improvement in comparison to QL with fixed parameters.

• Stephen J. Rassenti, Vernon L. Smith, and Bart J. Wilson, "Using Experiments to Inform the Privatization/Deregulation Movement in Electricity", The Cato Journal 21 (3), Winter 2002, 515-544.

• Stephen J. Rassenti, Vernon L. Smith, and Bart J. Wilson, "Discriminatory Price Auctions in Electricity Markets: Low Volatility at the Expense of High Price Levels", Journal of Regulatory Economics 23(2), 2003, 109-123.

• Stephen J. Rassenti, Vernon L. Smith, and Bart J. Wilson, "Controlling Market Power and Price Spikes in Electricity Networks: Demand-Side Bidding", Proceedings of the National Academy of Sciences 100(5), March 4, 2003, 2998-3003.

• Steve Silberman, "The Energy Web" (html), Wired, July 2001.

  Abstract: Silberman provides ideas on how to use multi-agent systems techniques to distribute electric power. "The best minds in electricity R&D have a plan: Every node in the power network of the future will be awake, responsive, adaptive, price-smart, eco-sensitive, real-time, flexible, humming - and interconnected with everything else."

• Abhishek Somani and Leigh Tesfatsion, "An Agent-Based Test Bed Study of Wholesale Power Market Performance Measures" (pdf,2.8MB), IEEE Computational Intelligence Magazine, 3(4), November 2008, 56-72.

  Abstract: Wholesale power markets operating over transmission grids subject to congestion have distinctive features that complicate the detection of market power and operational inefficiency. This study uses a wholesale power market test bed with strategically learning traders to experimentally test the extent to which market performance measures commonly used for other industries are informative for the dynamic operation of restructured wholesale power markets. Examined measures include the Herfindahl-Hirschman Index (HHI), the Lerner Index, the Residual Supply Index, the Relative Market Advantage Index, and the Operational Efficiency Index. It is also shown that the objective function commonly used to manage these markets deviates systematically from the standard economic measure of market efficiency when grid congestion is present.

• Junjie Sun and Leigh Tesfatsion, "Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework" [ (pdf,2.2MB), (ppt,625KB)], Computational Economics, 30(3), 2007, 291-327. This article is an abridged version of ISU Economics Working Paper No. 06025

  Abstract: In April 2003 the U.S. Federal Energy Regulatory Commission proposed a complicated market design - the Wholesale Power Market Platform (WPMP) -- for common adoption by all U.S. wholesale power markets. Versions of the WPMP have been implemented in New England, New York, the mid-Atlantic states, the Midwest, the Southwest, and California. Strong opposition to the WPMP persists among some industry stakeholders, however, due largely to a perceived lack of adequate performance testing. This study reports on the model development and open-source implementation (in Java) of a computational wholesale power market organized in accordance with core WPMP features and operating over a realistically rendered transmission grid subject to congestion effects. The traders within this market model are strategic profit-seeking agents whose learning behaviors are based on data from human-subject experiments. Our key experimental focus is the complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance. Findings for a dynamic 5-node transmission grid test case are presented for concrete illustration. It is shown for this example that generators easily learn to implicitly collude on higher-than-true marginal costs when the demand bids of load-serving entities take the form of fixed loads.

• Auswin George Thomas, Chengrui Cai, Dionysios C. Aliprantis, and Leigh Tesfatsion, Effects of Price-Responsive Residential Demand on Retail and Wholesale Power Market Operations (preprint pdf,218KB), Proceedings of the IEEE Power and Energy Society General Meeting, San Diego, CA, July 22-26, 2012 (electronic).

  Abstract: This paper describes a computational platform for studying the effects of price-responsive residential demand for air-conditioning (A/C) on integrated retail and wholesale power market operations. The physical operations of the A/C system are represented by means of the physics-based equivalent thermal parameter model. Residential A/C energy usage levels are determined by means of a stochastic dynamic-programming optimization in which the daily comfort attained by the resident is optimally traded off against his daily energy costs, conditional on retail energy prices, environmental conditions, and A/C operational constraints. An example is provided to illustrate the dynamic feedback loop connecting residential A/C load, the energy prices determined at wholesale conditional on A/C load, and the retail energy prices offered to residential A/C consumers by wholesale energy buyers.

• Quynh Chi Trinh, Marcelo Saguan, and Leonardo Meeus, "Experience with Electricity Market Test Suite: Students Versus Computational Agents" (pdf,1.8MB), IEEE Transactions on Power Systems, 2013, to appear.

  Abstract: "This paper applies two experimental economics methods (i.e., agent-based modeling and laboratory experiment) to a market test suite that is based on a fictional European wholesale electricity market. Quantitative results of generators' strategic behavior in this market context are separated between generators played by human subjects (i.e., master students) in a laboratory experiment and generators represented by computational agents in an agent-based model. The behavior is measured through offers that students or agents make when participating in the electricity trading auction and the market outcomes under both methods are discussed in order to illustrate the difference between the behavior of human and computational agents. The paper also identifies the improvements that would need to be made to the market test suite to allow for a more conclusive comparison in future experiments."

• Daniel J. Veit, Anke Weidlich, Jian Yao, and Shmuel Oren, "Simulating the Dynamics in Two-Settlement Electricity Markets via an Agent-Based Approach" (pdf,1.4MB), International Journal of Management Science and Engineering Management, 1(2), 2006, 83-97.

  Abstract: This paper studies the dynamics in two-settlement electricity markets. In these markets, energy producers sign strategic forward contracts in the forward market, and engage in spatial oligopolistic competition in the spot market. We develop an agent-based model for simulating the outcomes of such markets. Numerical simulations imply that the access to the forward market leads to more competitive behaviors of the suppliers in the spot market, and thus to lower spot energy prices.

• P. Visudhiphan and Marija D. Ilic, "An Agent-Based Approach to Modeling Electricity Spot Markets", 2001 IFAC-SME Meeting Proceedings, Austria, September 6-8, 2001.

• Phillip Wild, Paul Bell, and John Foster, "The Impact of Carbon Pricing on Wholesale Electricity Prices, Carbon Pass-Through Rates and Retail Electricity Tariffs in Australia" (pdf,1.1MB), Working Paper, Department of Economics, University of Queensland, April 2012.

  Abstract: "The purpose of this article is to investigate the impact that the introduction of a carbon price signal will have on wholesale electricity prices, carbon-pass-through rates and retail electricity rates in the states making up the Australian National Electricity Market (NEM). In order to assess this, we employ an agent based model of the NEM called the ANEM model which contains many of the salient features of the NEM: intra-state and inter-state transmission branches, regional location of generators and load centres and accommodation of unit commitment features. A DC OPF algorithm is used to determine optimal dispatch of generation plant and wholesale prices within the ANEM model. We utilise ANEM model scenario runs to examine the impact of carbon prices on wholesale prices and carbon passthrough rates. This information is then used to assess the impact on retail electricity tariff rates and shares of cost components making up residential retail tariff rate structures for different states in the NEM."

• David Young, Stephen Poletti, Oliver Browne, "Can Agent-Based Models Forecast Spot Prices in Electricity markets?: Evidence from the New Zealand Electricity Market" (pdf,655KB), Working Paper, Electric Power Research Institute (EPRI), Palo Alto, CA, January 2012.

  Abstract: "Modelling price formation in electricity markets is a notoriously difficult process, due to physical constraints on electricity generation and flow. This difficulty has inspired the recent development of bottom-up agent-based models of electricity markets. While these have proven quite successful in small models, few authors have attempted any validation of their model against real-world data in a more realistic model. In this paper, we take one of the most promising algorithms, the modified Roth and Erev algorithm, and apply it to a 19-node simplification of the New Zealand electricity market. Once key variables such as water storage are accounted for, we show that our model can mimic short-run (weekly) electricity prices at these 19 key nodes quite closely."

• Tao Zhang and William J. Nuggall, "Evaluating Government's Policies on Promoting Smart Metering Diffusion in Retail Electricity Markets via Agent-Based Simulation" (pdf,754KB), Journal of Production Innovation Management, 28, 2011, 169-186.

  Abstract: "In this paper, we develop an agent-based model of a market game in order to evaluate the effectiveness of the U.K. government's 2008-2010 policy on promoting smart metering in the U.K. retail electricity market. We break down the policy into four possible policy options. With the model, we study the impact of the four policy options on the dynamics of smart metering diffusion and suggest policy implications. The context of the paper is a practical application of agent-based simulation to the retail electricity market in the United Kingdom. The contributions of the paper are both in the areas of policymaking for the promotion of innovation diffusion in the electricity market and in methodological use of agent-based simulation for studying the impact of policies on the dynamics of innovation diffusion."

Software, Toolkits, and Demos

• Open-Source Software for Electricity Market Research, Teaching, and Training

• ACEGES: Agent-Based Computational Economics of the Global Energy System (Java, Open Source)

  Developed by Vlasios Voudouris, ACEGES is a decision-support tool for energy policy by means of controlled computational experiments. The ACEGES tool is designed to be the foundation for large custom-purpose simulations of the global energy system. The ACEGES tool is written in Java and runs on Windows, Mac OS, and Linux platforms. It is based on a discrete-event multiagent simulation library (MASON), an evolutionary computation toolkit (ECJ), the R project for statistical computing, the GAMLSS framework for statistical modeling of agent behavioral rules, and (in certain specific cases) the Mathematica kernel.

• AMES Wholesale Power Market Test Bed (Java, Open Source)

  The AMES Wholesale Power Market Test Bed, developed entirely in Java by an interdisciplinary team of researchers at Iowa State University, is a modular and extensible agent-based computational laboratory for studying the dynamic efficiency and reliability of wholesale power markets restructured in accordance with guidelines issued by the U.S. Federal Energy Regulatory Commission. AMES is an acronym for Agent-based Modeling of Electricity Systems.

  AMES models traders with learning capabilities interacting over time in an ISO-managed wholesale power market operating over a transmission grid subject to congestion effects. Congestion on the grid is managed by means of locational marginal prices derived from optimal power flow solutions.

  AMES is a free open-source tool suitable for research, teaching, and training applications. It is designed for the intensive experimental study of small to medium-sized systems (2-500 nodes). A graphical user interface permits the creation, modification, analysis and storage of scenarios, parameter initialization and editing, specification of behavioral rules (e.g. learning methods) for market participants, and output reports through table and chart displays.

• EMCAS: Electricity Market Complex Adaptive System (Educational Version)

  Argonne National Laboratory is now offering (upon request) an educational version of EMCAS, its agent-based power market analysis tool. Contact Guenter Conzelmann (guenter@anl.gov) for more information.

• PowerMatcher (Commercial)

  PowerMatcher is a smart-grid agent-based technology developed by ECN in cooperation with industry and research partners. PowerMatcher permits the intelligent, multi-goal, coordinated management of electrical power systems consisting of commodity markets operating over physical infrastructure.

• Annotated list of pointers to ACE/CAS General Software and Toolkits

• Annotated list of pointers to ACE/CAS Computational Laboratories and Demonstration Software

Related Resource Sites and Groups

• General Resources on Electricity Restructuring

• A research team at Iowa State University under the direction of Leigh Tesfatsion and Dionysios Aliprantis is undertaking a project titled Integrated Retail and Wholesale (IRW) Power System Operations with Smart-Grid Functionality.

  The primary goal of this IRW project is the development and implementation of an agent-based testbed for the study of integrated IRW electric power markets operating over transmission and distribution grids with smart-grid functionality. The IRW testbed seams together two existing testbeds: (i) the AMES Wholesale Power Market Testbed, developed by a group of researchers at Iowa State University; and (ii) GridLAB-D, an electric energy distribution platform developed by the U.S. Department of Energy at Pacific Northwest National Laboratory (PNNL). The IRW testbed is being used to conduct systematic experimental studies focusing on the IRW effects of three key developments: introduction of dynamic-price contracting for retail customers; increased penetration of variable generation (e.g., wind, PV); and increased penetration of plug-in electric vehicles (PEV).

• A group of researchers at the London Metropolitan Business School under the direction of Dr. Vlasios Voudouris has embarked on a project titled the ACEGES Project. ACEGES refers to "Agent-based Computational Economics of the Global Energy System." The overall aim of this project is to develop, test, and disseminate an agent-based computational laboratory for the systematic experimental study of the global energy system.

• A group of researchers at the Argonne National Laboratory (ANL) has developed the EMCAS (Electricity Markets Complex Adaptive Systems) Model. EMCAS is an agent-based computational framework that permits the exploratory study of a wide variety of electricity market designs via systematic computational experiments.

• The Commonwealth Scientific and Industrial Research Organization (CSIRO) based in Australia is a diverse scientific organization focusing on the economic and social performance of many of Australia's leading industry sectors. One of the research areas under exploration by David Batten, George Grozev, and other CSIRO staff members is the agent-based modeling of Australia's National Electricity Market, referred to as NEMsim.

• Joseph Roop and Eihab Fathelrahaman, staff researchers at the Pacific Northwest National Laboratory, are using an agent-based approach to investigate to contract arrangements that determine the degree of responsiveness of residential households to changes in electricity prices.

• A group of researchers at Sandia National Laboratories has developed an agent-based simulation laboratory Next-Generation Agent-Based Economic Laboratory (NABLE) (pdf,170KB) for analyzing the economic factors, feedbacks, and downstream effects of infrastructure interdependencies, including (e.g.,) the effects of electric power outages. NABLE is a revised and restructured version of an earlier framework developed at Sandia, the Aspen Electricity Enhancement Model (Aspen-EE).

• Raimo P. Hämäläinen (Systems Analysis Lab, Helsinki University, Finland) has used agent-based modeling tools to explore energy, natural resources, and environmental issues. Stress is placed on the use of agent-based modeling tools as part of a larger process of participatory decision-making among stakeholders and researchers.

• Daniel Veit (Business School, University of Mannheim), Mario Ragwitz (Fraunhofer Institute for Systems and Innovation Research), and Wolf Fichtner (University of Karlsruhe) collaborated on the PowerAce Project from 2004-2007. This project used an agent-based computational framework to examine the effects of a carbon dioxide emission allowance trading scheme on future power plant structures, investment decisions, and greenhouse gas emissions in a liberalized power market. A key motivation for this project was that an emissions trading system with obligatory participation was currently being launched in the European Union. Project publications are available for downloading at the above project site.

Some Early Individual Researchers

• David Batten (CSIRO Agent-Based Modeling Working Group, Australia): Agent-based modelling of social, economic, and (bio)physical phenomena; Urban, regional, and evolutionary economics; Industrial ecology (energy, water, waste, and transportation).

• Derek Bunn (Decision Sciences, London Business School): Business forecasting, decision technology, electricity and energy economics.

• Silvano Cincotti (Department of Biophysical and Electronic Engineering, U of Genoa): Development of an agent-based computational framework for the study of electricity markets; Computational market design.

• Tapas Das (Department of Industrial and Management Systems, U of South Florida,Tampa): Modeling and Design of Deregulated Electric Power Markets; Impact of Auction Based Pricing in Energy and Financial Transmission Rights (FTR) Markets.

• Marija Ilic (Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh): Agent-based modeling of electricity spot markets; Large-scale systems modeling and simulation; Power systems control and pricing algorithms; Critical infrastructures and interdependencies.

• Vishnu Nanduri (Engineering and Applied Science, U of Wisconsin-Milwaukee): Stochastic optimization; Simulation-based optimization, and game-theoretic modeling.

• Augusto Rupérez Micola (Universitat Pompeu Fabra, Barcelona): Simulation methods; Applied econometrics; Energy markets; European political economy.

• Gerald B. Sheblé (Quanta Technology, LLC, Raleigh, North Carolina): Agent-based computational modelling of electric power markets; Electric power auction market training simulator; Electric power strategy selector via genetic algorithms; Electric power futures contract market simulator.

• Leigh Tesfatsion (Department of Economics, Iowa State University, Ames, Iowa): Market power and efficiency in computational electricity markets with auction pricing mechanisms; Role of learning v. structure in determining outcomes in restructured electricity markets; Agent-based computational economics.

• Daniel Veit (Business Administration and Information Systems, University of Mannheim, Germany): Economic and technical coordination and cooperation in markets for non-storable goods; E-Business (design of mechanisms for electronic markets); Electricity market design (PowerACE project).

• Anke Weidlich (SAP AG, Germany): Restructured electricity markets; Two-settlement systems; C02 emissions trading systems; German electricity industry; PowerACE; Agent-based computational economics.

Copyright © 2013 Leigh Tesfatsion. All Rights Reserved.