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Agent-based simulation of power exchange with heterogeneous production companies

Author

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  • Silvano Cincotti

    () (University of Genoa DIBE)

  • Eric Guerci

Abstract

Since early nineties, worldwide production and distribution of electricity has been characterized by a progressive liberalization. The state-owned monopolistic production of electricity has been substituted by organized power exchanges (PEs). PEs are markets which aggregate the effective supply and demand of electricity. Usually spot-price market are Day Ahead Market (DAM) and are requested in order to provide an indication for the hourly unit commitment. This first session of the complex daily energy market collects and orders all the offers, determining the market price by matching the cumulative demand and supply curves for every hour of the day after according to a merit order rule. Subsequent market sessions (also online) operate in order to guarantee the feasibility and the security of this plan. The electric market is usually characterized by a reduced number of competitors, thus oligopolistic scenario may arise. Understanding how electricity prices depend on oligopolistic behavior of suppliers and on production costs has become a very important issue. Several restructuring designs for the electric power industry have been proposed. Main goal is to increase the overall market efficiency, trying to study, to develop and to apply different market mechanisms. Auction design is the standard domain for commodity markets. However, properties of different auction mechanism must be studied and determined correctly before their appliance. Generally speaking, different approaches have been proposed in the literature. Game theory analysis has provided an extremely useful methodology to study and derive properties of economic "games", such as auctions. Within this context, an interesting computational approach, for studying market inefficiencies, is the theory of learning in games. This methodology is useful in the context of infinitely repeated games. This paper investigates the nature of the clearing mechanism comparing two different methods, i.e., discriminatory and uniform auctions. The theoretical framework used to perform the analysis is the theory of learning in games. We consider an inelastic demand faced by sellers which use learning algorithms to understand proper strategies for increasing their profits. We model the auction mechanism in two different duopolistic scenario, i.e., a low demand situation, where one seller can clear all the demand, and a high demand condition, where both sellers are requested. Moreover, heterogeneity in the linear cost function is considered. Consistent results are achieved with two different learning algorithms

Suggested Citation

  • Silvano Cincotti & Eric Guerci, 2005. "Agent-based simulation of power exchange with heterogeneous production companies," Computing in Economics and Finance 2005 334, Society for Computational Economics.
  • Handle: RePEc:sce:scecf5:334
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    File URL: http://repec.org/sce2005/up.6571.1107194148.pdf
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    References listed on IDEAS

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    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    3. Denton, Michael J. & Rassenti, Stephen J. & Smith, Vernon L., 2001. "Spot market mechanism design and competitivity issues in electric power," Journal of Economic Behavior & Organization, Elsevier, vol. 44(4), pages 435-453, April.
    4. Guerci, E. & Ivaldi, S. & Pastore, S. & Cincotti, S., 2005. "Modeling and implementation of an artificial electricity market using agent-based technology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 69-76.
    5. A. Roth & I. Er’ev, 2010. "Learning in Extensive Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Run," Levine's Working Paper Archive 387, David K. Levine.
    6. Klemperer, Paul, 1999. " Auction Theory: A Guide to the Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 13(3), pages 227-286, July.
    7. Vega-Redondo,Fernando, 2003. "Economics and the Theory of Games," Cambridge Books, Cambridge University Press, number 9780521775908, December.
    8. von der Fehr, Nils-Henrik Morch & Harbord, David, 1993. "Spot Market Competition in the UK Electricity Industry," Economic Journal, Royal Economic Society, vol. 103(418), pages 531-546, May.
    9. James Nicolaisen & Valentin Petrov & Leigh Tesfatsion, 2000. "Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing," Computational Economics 0004005, University Library of Munich, Germany.
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    Cited by:

    1. Davies, G.J. & Kendall, G. & Soane, E. & Li, J. & Rocks, S.A. & Jude, S.R. & Pollard, S.J.T., 2014. "Regulators as agents: modelling personality and power as evidence is brokered to support decisions on environmental risk," LSE Research Online Documents on Economics 51229, London School of Economics and Political Science, LSE Library.
    2. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, vol. 30(4), pages 1728-1759, July.

    More about this item

    Keywords

    Agent-based simulation; power-exchange market; market power; reinforcement learning; electricity production costs;

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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