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Journal articles on the topic 'Electricity markets simulation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Foti, Magda, and Manolis Vavalis. "Intelligent Bidding in Smart Electricity Markets." International Journal of Monitoring and Surveillance Technologies Research 3, no. 3 (2015): 68–90. http://dx.doi.org/10.4018/ijmstr.2015070104.

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This paper has two aims. Firstly, to briefly present overall objectives and expected outcome of an on-going effort concerning design, implementation and the analysis of next generation energy systems based on anticipatory control and a set of ICT emerging technologies and innovations. Secondly, to describe an early proof-of-concept implementation and the associated experimentation of a simulation platform focused on holistic detailed studies of electric energy markets. The proposed platform allows us to elucidate issues related to the open and smart participation of producers and consumers on

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2

Ngujen Tat, Andrejus. "Electricity Price Forecasting Using Monte Carlo Simulation: The Case of Lithuania." Ekonomika 97, no. 1 (2018): 76–86. http://dx.doi.org/10.15388/ekon.2018.1.11780.

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The main purpose of this article is to determine the practical use of the Monte Carlo simulations in electricity markets for forecasting future prices. First, we review the structure of the electricity markets – how they work, what implications do they have and how they’ve evolved during the last decades. Second, we discover that there are only few researches that have been made on this topic as well as there haven’t being made any researches regarding the Lithuanian electricity market. Then, we will carry out an analysis on how to use a Monte Carlo simulation approach in electricity markets.

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3

Vale, Zita, Tiago Pinto, Isabel Praca, and Hugo Morais. "MASCEM: Electricity Markets Simulation with Strategic Agents." IEEE Intelligent Systems 26, no. 2 (2011): 9–17. http://dx.doi.org/10.1109/mis.2011.3.

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4

Mello, João, Cristina de de Lorenzo, Fco Alberto Campos, and José Villar. "Pricing and Simulating Energy Transactions in Energy Communities." Energies 16, no. 4 (2023): 1949. http://dx.doi.org/10.3390/en16041949.

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Extensive literature is available for modeling and simulating local electricity markets, often called P2P electricity markets, and for pricing local energy transactions in energy communities. Market models and pricing mechanisms provide simulation tools to better understand how these new markets behave, helping to design their main rules for real applications, and assessing the financial compensations of the internal energy transactions. As such, pricing mechanisms are often needed in energy management systems when centralized management approaches are preferred to market-based ones. First, th

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5

Morais, Hugo, Tiago Pinto, and Zita Vale. "Adjacent Markets Influence Over Electricity Trading—Iberian Benchmark Study." Energies 13, no. 11 (2020): 2808. http://dx.doi.org/10.3390/en13112808.

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This paper presents a study on the impact of adjacent markets on the electricity market, realizing the advantages of acting in several different markets. The increased use of renewable primary sources to generate electricity and new usages of electricity such as electric mobility are contributing to a better and more rational way of living. The investment in renewable technologies for the distributed generation has been creating new opportunities for owners of such technologies. Besides the selling of electricity and related services (ancillary services) in energy markets, players can particip

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Banal-Estañol, Albert, and Augusto Rupérez Micola. "Behavioural simulations in spot electricity markets." European Journal of Operational Research 214, no. 1 (2011): 147–59. http://dx.doi.org/10.1016/j.ejor.2011.03.041.

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7

Francisco, Silva, Teixeira Brígida, Pinto Tiago, Santos Gabriel, Vale Zita, and Praça Isabel. "Generation of Realistic Scenarios for Multi-Agent Simulation of Electricity Markets." Energy 116, Part 1, no. 1 December 2016 (2016): 128–39. https://doi.org/10.1016/j.energy.2016.09.096.

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Most market operators provide daily data on several market processes, including the results of all market transactions. The use of such data by electricity market simulators is essential for simulations quality, enabling the modelling of market behaviour in a much more realistic and efficient way. RealScen (Realistic Scenarios Generator) is a tool that creates realistic scenarios according to the purpose of the simulation: representing reality as it is, or on a smaller scale but still as representative as possible. This paper presents a novel methodology that enables RealScen to collect real e

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8

Hamagami, Tomoki. "Using Intelligent System Approaches for Simulation of Electricity Markets." IEEJ Transactions on Electronics, Information and Systems 126, no. 2 (2006): 156–60. http://dx.doi.org/10.1541/ieejeiss.126.156.

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9

Xavier, Edgar B., David A. V. Goncalves, Bruno H. Dias, and Bruno S. M. C. Borba. "Electricity Auction Simulation Platform for Learning Competitive Energy Markets." IEEE Potentials 36, no. 5 (2017): 32–36. http://dx.doi.org/10.1109/mpot.2016.2563179.

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10

Praça, I., C. Ramos, Z. Vale, and M. Cordeiro. "Intelligent Agents for the Simulation of Competitive Electricity Markets." International Journal of Modelling and Simulation 24, no. 2 (2004): 73–79. http://dx.doi.org/10.1080/02286203.2004.11442289.

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11

Veiga, Bruno, Gabriel Santos, Tiago Pinto, Ricardo Faia, Carlos Ramos, and Zita Vale. "Simulation tools for electricity markets considering power flow analysis." Energy 275 (July 2023): 127494. http://dx.doi.org/10.1016/j.energy.2023.127494.

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12

Trigo, Paulo, Paulo Marques, and Helder Coelho. "(Virtual) Agents for running electricity markets." Simulation Modelling Practice and Theory 18, no. 10 (2010): 1442–52. http://dx.doi.org/10.1016/j.simpat.2010.04.003.

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13

Chassin, David P., Jason C. Fuller, and Ned Djilali. "GridLAB-D: An Agent-Based Simulation Framework for Smart Grids." Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/492320.

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Simulation of smart grid technologies requires a fundamentally new approach to integrated modeling of power systems, energy markets, building technologies, and the plethora of other resources and assets that are becoming part of modern electricity production, delivery, and consumption systems. As a result, the US Department of Energy’s Office of Electricity commissioned the development of a new type of power system simulation tool called GridLAB-D that uses an agent-based approach to simulating smart grids. This paper presents the numerical methods and approach to time-series simulation used b

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14

Bâra, Adela. "Mix-generation optimization for electricity market simulation." Scientific Bulletin of Naval Academy XXIII, no. 1 (2020): 180–85. http://dx.doi.org/10.21279/1454-864x-20-i1-023.

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Owning several types of generating units requires an optimized schedule to cover the negotiated bilateral contracts. This approach will lead to a better electricity market strategy and benefits for an electricity producer. In this paper, we will simulate the operation of five different generators including generators based on Renewable Energy Sources (such as wind turbines and photovoltaic panels) that belong to an electricity producer. The five generators are modelled considering the specificity of their type and primary energy source. For instance, for renewable energy sources, we will consi

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15

Zhou, Zhi, Wai Kin (Victor) Chan, and Joe H. Chow. "Agent-based simulation of electricity markets: a survey of tools." Artificial Intelligence Review 28, no. 4 (2007): 305–42. http://dx.doi.org/10.1007/s10462-009-9105-x.

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16

Xie, Yuting, Lin Tian, Jinqing Luo, et al. "Simulation of the Bidding Behavior of Electricity Retailers Based on the Q -Learning Algorithm." Wireless Communications and Mobile Computing 2022 (May 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/8007634.

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At present, the reform of electricity marketization has become a new trend in the development of the world’s electric power industry and electricity retailers have a huge role in further activating power market competition. Simulation of the bidding behavior of electricity sales companies is helpful to analyze the rationality of the transaction behaviors of electricity sales companies and predict the potential risks of market operation. Considering the complex and diverse behaviors of electricity retailers and the immaturity of existing simulation technologies in the power sales market, this p

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17

Wang, Ying, Chang Liu, Weihong Yuan, and Lili Li. "MRL-Based Model for Diverse Bidding Decision-Makings of Power Retail Company in the Wholesale Electricity Market of China." Axioms 12, no. 2 (2023): 142. http://dx.doi.org/10.3390/axioms12020142.

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Power retail companies in the electricity market make profits through buying and selling power energy in the wholesale and retail markets, respectively. Traditionally, they are assumed to bid in the wholesale market with the same objective, i.e., maximize the profit. This paper proposes a multiagent reinforcement learning (MRL)-based model to simulate the diverse bidding decision-making concerning various operation objectives and the profit-sharing modes of power retail companies in China’s wholesale electricity market, which contributes to a more realistic modeling and simulation of the retai

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18

Santos, Gabriel, Tiago Pinto, Hugo Morais, et al. "Multi-agent simulation of competitive electricity markets: Autonomous systems cooperation for European market modeling." Energy Conversion and Management 99 (July 2015): 387–99. http://dx.doi.org/10.1016/j.enconman.2015.04.042.

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19

Kalczynski, Pawel, and Dawit Zerom. "Price forecast valuation for the NYISO electricity market." Kybernetes 44, no. 4 (2015): 490–504. http://dx.doi.org/10.1108/k-08-2014-0174.

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Purpose – Following the deregulation of electricity markets in the USA, independent power producers operate as for-profit entities. Their profit depends on the price of electricity and an accurate forecast is critical in making bidding decisions on the electricity and reserve markets or engaging in bilateral contracts. Competing price forecasts have their accuracy expressed in statistical terms but producers need to determine the long-term value of using a given forecast. The purpose of this paper is to address this issue by presenting a method of electricity price forecast valuation which com

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20

Wyrwoll, Lothar, Moritz Nobis, Stephan Raths, and Albert Moser. "Evolution of Fundamental Price Determination within Electricity Market Simulations." Energies 14, no. 17 (2021): 5454. http://dx.doi.org/10.3390/en14175454.

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Electricity prices are the key instrument for coordinating electricity markets. For long-term market analyses, price determination based on fundamental unit commitment simulations is required. Within the European wholesale market, electricity prices result from a market clearing, which finds a welfare-optimal price–quantity tuple considering a coupling of multiple market areas with limited transmission capacity. With increasing exchange capacities in Europe, the precise modeling of the market coupling is required. Many market simulation models use multi-stage approaches with a separation of ma

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21

Zhou, Zhi, Wai Kin Victor Chan, and Joe H. Chow. "Bidding behaviors in duopoly electricity markets with aspirant market share goals." SIMULATION 89, no. 11 (2013): 1369–87. http://dx.doi.org/10.1177/0037549713499248.

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22

Gabriel, Santos, Pinto Tiago, and Vale Zita. "Ontologies to Enable Interoperability of Multi-Agent Electricity Markets Simulation and Decision Support." Electronics 10, no. 11 (2021): 1270. https://doi.org/10.3390/electronics10111270.

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This paper presents the AiD-EM Ontology, which provides a semantic representation of the concepts required to enable the interoperability between multi-agent-based decision support systems, namely AiD-EM, and the market agents that participate in electricity market simulations. Electricity markets’ constant changes, brought about by the increasing necessity for adequate integration of renewable energy sources, make them complex and dynamic environments with very particular characteristics. Several modeling tools directed at the study and decision support in the scope of the restructured

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23

Narajewski, Michał, and Florian Ziel. "Estimation and Simulation of the Transaction Arrival Process in Intraday Electricity Markets." Energies 12, no. 23 (2019): 4518. http://dx.doi.org/10.3390/en12234518.

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We examine the novel problem of the estimation of transaction arrival processes in the intraday electricity markets. We model the inter-arrivals using multiple time-varying parametric densities based on the generalized F distribution estimated by maximum likelihood. We analyse both the in-sample characteristics and the probabilistic forecasting performance. In a rolling window forecasting study, we simulate many trajectories to evaluate the forecasts and gain significant insights into the model fit. The prediction accuracy is evaluated by a functional version of the MAE (mean absolute error),

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24

Pinto, Tiago, Zita Vale, Tiago M. Sousa, Isabel Praça, Gabriel Santos, and Hugo Morais. "Adaptive learning in agents behaviour: A framework for electricity markets simulation." Integrated Computer-Aided Engineering 21, no. 4 (2014): 399–415. http://dx.doi.org/10.3233/ica-140477.

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25

Silva, Francisco, Brígida Teixeira, Tiago Pinto, Gabriel Santos, Zita Vale, and Isabel Praça. "Generation of realistic scenarios for multi-agent simulation of electricity markets." Energy 116 (December 2016): 128–39. http://dx.doi.org/10.1016/j.energy.2016.09.096.

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26

Álvarez-Uribe, Karla C., Santiago Arango-Aramburo, and Erik R. Larsen. "Forward contracts in electricity markets and capacity investment: A simulation study." Utilities Policy 54 (October 2018): 1–10. http://dx.doi.org/10.1016/j.jup.2018.07.003.

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27

Valencia-Salazar, I., C. Álvarez, G. Escrivá-Escrivá, and M. Alcázar-Ortega. "Simulation of demand side participation in Spanish short term electricity markets." Energy Conversion and Management 52, no. 7 (2011): 2705–11. http://dx.doi.org/10.1016/j.enconman.2011.02.005.

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28

Parus, Ye V., and V. A. Evdokimov. "EVALUATION OF THE RESULTS OF SIMULATION OF ELECTRICITY MARKET COUPLING IN THE "DAY AHEAD" MARKET." Tekhnichna Elektrodynamika 2024, no. 1 (2024): 69–76. http://dx.doi.org/10.15407/techned2024.01.069.

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The publication is devoted to the method of preliminary analysis of the results of the electricity market coupling in the "day-ahead" market segment. The relevance of the task of developing methods for preliminary evaluation of the results of the coupling of the "day-ahead" market of Ukraine with European interstate electricity exchanges is substantiated. The theoretical basis for the procedure for the selection of a separate external factor, with the aim of numerically evaluating the influence of such a factor that affects the change in the market equilibrium, is presented. The main component

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29

Tiago, Pinto, M. Sousa Tiago, Morais Hugo, praça Isabel, and Vale Zita. "Metalearning to support competitive electricity market players' strategic bidding." Energy 135, June 2016 (2016): 27–34. https://doi.org/10.1016/j.epsr.2016.03.012.

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Electricity markets are becoming more competitive, to some extent due to the increasing number of players that have moved from other sectors to the power industry. This is essentially resulting from incentives provided to distributed generation. Relevant changes in this domain are still occurring, such as the extension of national and regional markets to continental scales. Decision support tools have thereby become essential to help electricity market players in their negotiation process. This paper presents a metalearner to support electricity market players in bidding definition. The propos

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30

Liu, Yu Jiao, Chuan Wen Jiang, and Jing Shuang Shen. "Energy Management of Battery Switch Station of Electric Vehicles in Two Settlement Electricity Market." Advanced Materials Research 608-609 (December 2012): 1533–36. http://dx.doi.org/10.4028/www.scientific.net/amr.608-609.1533.

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Battery switch station (BSS) can resolve the conflict between life and charging time of batteries of electric vehicles by offering quick replacement services though pre-charging with small power. This paper proposed an energy management model for BSS in two settlement electricity markets. The present program focused on how to satisfy demands of electric vehicles with lowest cost in electricity markets, which are the key issue of lowering operating costs of BSS. The program adopted the mean-variance theory to control financial risks of uncertainties of prices, and simulation results showed the

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31

Mabea, Geoffrey. "Simulating Generalised Locational Marginal Pricing for Power Markets in East Africa." International Journal of Energy Economics and Policy 13, no. 5 (2023): 450–60. http://dx.doi.org/10.32479/ijeep.14578.

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There is an enormous opportunity for adopting a wholesale electricity market under locational marginal pricing. Besides ensuring competition and accounting for network congestion, the price signal reflects a space-time electricity economy. This paper examines the prospects of introducing a single East African electricity market in five selected partner states. It reports on a pioneering effort to simulate the coupling of power markets, estimate locational marginal prices, and determine the economic benefits of cross-border power trade and competition across these five countries. The nodes repr

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32

Santos, Gabriel, Tiago Pinto, and Zita Vale. "Ontologies to Enable Interoperability of Multi-Agent Electricity Markets Simulation and Decision Support." Electronics 10, no. 11 (2021): 1270. http://dx.doi.org/10.3390/electronics10111270.

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This paper presents the AiD-EM Ontology, which provides a semantic representation of the concepts required to enable the interoperability between multi-agent-based decision support systems, namely AiD-EM, and the market agents that participate in electricity market simulations. Electricity markets’ constant changes, brought about by the increasing necessity for adequate integration of renewable energy sources, make them complex and dynamic environments with very particular characteristics. Several modeling tools directed at the study and decision support in the scope of the restructured wholes

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33

Bobo, Lucien, Lesia Mitridati, Josh A. Taylor, Pierre Pinson, and Jalal Kazempour. "Price-region bids in electricity markets." European Journal of Operational Research 295, no. 3 (2021): 1056–73. http://dx.doi.org/10.1016/j.ejor.2021.03.024.

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34

Cheng, Lefeng, Pengrong Huang, Mengya Zhang, Ru Yang, and Yafei Wang. "Optimizing Electricity Markets Through Game-Theoretical Methods: Strategic and Policy Implications for Power Purchasing and Generation Enterprises." Mathematics 13, no. 3 (2025): 373. https://doi.org/10.3390/math13030373.

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This review proposes a novel integration of game-theoretical methods—specifically Evolutionary Game Theory (EGT), Stackelberg games, and Bayesian games—with deep reinforcement learning (DRL) to optimize electricity markets. Our approach uniquely addresses the dynamic interactions among power purchasing and generation enterprises, highlighting both theoretical underpinnings and practical applications. We demonstrate how this integrated framework enhances market resilience, informs evidence-based policy-making, and supports renewable energy expansion. By explicitly connecting our findings to reg

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35

Sun, Jian, Fan Wu, Mingming Shi, and Xiaodong Yuan. "Coordination of Renewable Energy Integration and Peak Shaving through Evolutionary Game Theory." Processes 12, no. 9 (2024): 1995. http://dx.doi.org/10.3390/pr12091995.

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This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations a

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36

Malo, P., and A. Kanto. "Evaluating Multivariate GARCH Models in the Nordic Electricity Markets." Communications in Statistics - Simulation and Computation 35, no. 1 (2006): 117–48. http://dx.doi.org/10.1080/03610910500416033.

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37

Vespucci, M. T., E. Allevi, A. Gnudi, and M. Innorta. "Cournot equilibria in oligopolistic electricity markets." IMA Journal of Management Mathematics 21, no. 2 (2009): 183–93. http://dx.doi.org/10.1093/imaman/dpp004.

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38

Vargas, Johandre, Carlos Jaime Franco, and Maritza Jimenez. "Electricity Pricing for Renewable Markets- A Simulation Approach for the Colombian Case." IEEE Latin America Transactions 19, no. 12 (2021): 1995–2002. http://dx.doi.org/10.1109/tla.2021.9480140.

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39

Shan, Y., C. Lo Prete, G. Kesidis, and D. J. Miller. "A simulation framework for uneconomic virtual bidding in day-ahead electricity markets." ACM SIGMETRICS Performance Evaluation Review 44, no. 3 (2017): 30. http://dx.doi.org/10.1145/3040230.3040238.

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40

Esmaeili Aliabadi, Danial, Murat Kaya, and Guvenc Sahin. "Competition, risk and learning in electricity markets: An agent-based simulation study." Applied Energy 195 (June 2017): 1000–1011. http://dx.doi.org/10.1016/j.apenergy.2017.03.121.

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41

Wang, Yiqi, Yang Yang, Junping Ji, Qisheng Huang, and Daojing He. "Simulation of synergistic trading in medium- and long-term power and carbon markets based on a multiagent model and reinforcement learning algorithm." Journal of Physics: Conference Series 2826, no. 1 (2024): 012003. http://dx.doi.org/10.1088/1742-6596/2826/1/012003.

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Abstract This paper simulates the transactions of thermal power plants participating in medium- and long-term power markets and carbon markets simultaneously. The bidding adjustment function in bilateral contracts can realistically reflect the adjustment of participants’ bids and ask prices. This paper introduces the carbon assets management module to improve the flexibility of transactions. The thermal power plants’ bidding strategies affect the market clearing prices in the centralized trading markets, which in turn will affect their profits. We implement a reinforcement learning algorithm t

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42

Aaltonen, Harri, Seppo Sierla, Rakshith Subramanya, and Valeriy Vyatkin. "A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage." Energies 14, no. 17 (2021): 5587. http://dx.doi.org/10.3390/en14175587.

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Battery storages are an essential element of the emerging smart grid. Compared to other distributed intelligent energy resources, batteries have the advantage of being able to rapidly react to events such as renewable generation fluctuations or grid disturbances. There is a lack of research on ways to profitably exploit this ability. Any solution needs to consider rapid electrical phenomena as well as the much slower dynamics of relevant electricity markets. Reinforcement learning is a branch of artificial intelligence that has shown promise in optimizing complex problems involving uncertainty

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43

Li, T. "Market power analysis in electricity markets using supply function equilibrium model." IMA Journal of Management Mathematics 15, no. 4 (2004): 339–54. http://dx.doi.org/10.1093/imaman/15.4.339.

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44

Li, Yang, Ni Fang, Shengming He, et al. "Two-Stage Stochastic Scheduling of Cascaded Hydropower–Wind–Photovoltaic Hybrid Systems Considering Contract Decomposition and Spot Market." Sustainability 16, no. 3 (2024): 1093. http://dx.doi.org/10.3390/su16031093.

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With the advancement of China’s electricity markets and the continuous development of renewable energy sources (RESs), it is of great importance to investigate the strategic behavior of RESs in electricity markets. In this paper, a two-stage stochastic optimization model is proposed for a hybrid energy system composed of cascade hydropower plants, wind farms, and photovoltaic stations. Firstly, typical scenarios are generated based on Latin hypercube sampling (LHS) and the K-means clustering algorithm to represent uncertainties of wind–photovoltaic power outputs. Then, with an analysis of Chin

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45

Moret, Fabio, Pierre Pinson, and Athanasios Papakonstantinou. "Heterogeneous risk preferences in community-based electricity markets." European Journal of Operational Research 287, no. 1 (2020): 36–48. http://dx.doi.org/10.1016/j.ejor.2020.04.034.

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46

Boomsma, Trine Krogh, Nina Juul, and Stein-Erik Fleten. "Bidding in sequential electricity markets: The Nordic case." European Journal of Operational Research 238, no. 3 (2014): 797–809. http://dx.doi.org/10.1016/j.ejor.2014.04.027.

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47

Köberlein, Jana, Lukas Bank, Stefan Roth, et al. "Simulation Modeling for Energy-Flexible Manufacturing: Pitfalls and How to Avoid Them." Energies 15, no. 10 (2022): 3593. http://dx.doi.org/10.3390/en15103593.

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Due to the high share of industry in total electricity consumption, industrial demand-side management can make a relevant contribution to the stability of power systems. At the same time, companies get the opportunity to reduce their electricity procurement costs by taking advantage of increasingly fluctuating prices on short-term electricity markets, the provision of system services on balancing power markets, or by increasing the share of their own consumption from on-site generated renewable energy. Demand-side management requires the ability to react flexibly to the power supply situation

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48

Golmohamadi, Hessam, and Amin Asadi. "Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets." Energies 13, no. 18 (2020): 4874. http://dx.doi.org/10.3390/en13184874.

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This paper proposes a novel approach to optimize the main energy consumptions of heavy oil refining industries (ORI) in response to electricity price uncertainties. The whole industrial sub-processes of the ORI are modeled mathematically to investigate the joint power-heat flexibility potentials of the industry. To model the refinery processes, an input/output flow-based model is proposed for five main refining units. Moreover, the role of storage tanks capacity in the power system flexibility is investigated. To hedge against the electricity price uncertainty, an uncertain bound for the whole

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49

Pinto, Tiago, Nathalia Boeno, Zita Vale, and Everthon Sica. "Multiagent Simulation of Demand Flexibility Integration in Local Energy Markets." E3S Web of Conferences 239 (2021): 00010. http://dx.doi.org/10.1051/e3sconf/202123900010.

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Overcoming the issues associated with the variability of renewable generation has become a constant challenge in power and energy systems. The use of load flexibility is one of the most promising ways to face it. Suitable ways to incorporate flexibility in the electricity market, in addition to the already challenging integration of distributed generation primary sources, are therefore crucial. The integration of prosumers and consumers flexibility in the market is, however, not straightforward, as current wholesale and retail market structures are not prepared to deal with the current and fut

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50

Ford, Andrew. "Cycles in competitive electricity markets: a simulation study of the western United States." Energy Policy 27, no. 11 (1999): 637–58. http://dx.doi.org/10.1016/s0301-4215(99)00050-6.

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