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Agent-based simulation of power exchange with heterogeneous production companies

Author

Listed:
  • 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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    References listed on IDEAS

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    Cited by:

    1. Davies, G.J. & Kendall, G. & Soane, Emma & 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.

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    More about this item

    Keywords

    Agent-based simulation; power-exchange market; market power; reinforcement learning; electricity production costs;
    All these keywords.

    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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