Abstract
The paper presents the Genoa Artificial Power Exchange, an agent-based framework for modeling and simulating power exchanges implemented in MATLAB. GAPEX allows creation of artificial power exchanges reproducing exact market clearing procedures of the most important European power-exchanges. In this paper we present results from a simulation performed on the Italian PEX where we have reproduced the Locational Marginal Price Algorithm based on the Italian high-voltage transmission network with its zonal subdivisions and we considered the Gencos in direct correspondence with the real ones. An enhanced version of the Roth-Erev algorithm is presented so to be able to consider the presence of affine total cost functions for the Gencos which results in payoff either positive, negative and null. A close agreement with historical real market data during both peak- and off-peak load hours of prices reproduced by GAPEX confirm its direct applicability to model and to simulate power exchanges.
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References
Bagnall, A., Smith, G.: A multi-agent model of the UK market in electricity generation. IEEE Transactions on Evolutionary Computation 9(5), 522–536 (2005)
Ball, P.: The earth simulator. New Scientist 2784, 48–51 (2010)
Bower, J., Bunn, D.W.: Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and wales electricity market. Journal of Economic Dynamics & Control 25, 561–592 (2001)
Bunn, D.W., Oliveira, F.: Agent-based simulation: an application to the new electricity trading arrangements of England and wales. IEEE Transactions on Evolutionary Computation 5(5), 493–503 (2001)
Camerer, C., Ho, T.: Experience-weighted attraction learning in normal-form games. Econometrica 67, 827–874 (1999)
Cau, T.D.H., Anderson, E.J.: A co-evolutionary approach to modelling the behaviour of participants in competitive electricity markets. In: IEEE Power Engineering Society Summer Meeting, vol. 3, pp. 1534–1540 (2002)
Cincotti, S., Guerci, E., Raberto, M.: Agent-based simulation of power exchange with heterogeneous production companies. Computing in Economics and Finance 2005, Society for Computational Economics 334 (2005)
GME: Official web site (2010), http://www.mercatoelettrico.org/En/Default.aspx
GME: Uppo auction module user manual, appendix a - market splitting auction algorithm. Tech. rep., GME (2010), http://www.mercatoelettrico.org/En/MenuBiblioteca/Documenti/20100429MarketSplitting.pdf
Gode, D.D.K., Sunder, S.: Allocative efficiency of markets with zero intelligence traders. Market as a partial substitute for individual rationality. J. Polit. Econ. 101(1), 119–137 (1993)
Gode, D.D.K., Sunder, S.: Double auction dynamics: structural effects of non-binding price controls. Journal of Economic Dynamics and Control 28(9), 1707–1731 (2004)
Guerci, E., Rastegar, M., Cincotti, S.: Agent-based modeling and simulation of competitive wholesale electricity markets. Handbook of Power Systems 3(2), 241–286 (2010)
Guerci, E., Ivaldi, S., Raberto, M., Cincotti, S.: Learning oligopolistic competition in electricity auctions. Computational Intelligence 23(2), 197–220 (2007)
Jing, Z., Ngan, H., Wang, Y., Zhang, Y., Wang, J.: Study on the convergence property of roth-erev learning model in electricity market simulation. In: 8th International Conference on Advances in Power System Control, Operation and Management (APSCOM 2009), pp. 1–5 (November 2009)
Kirschen, D.S., Strbac, G.: Fundamentals of Power System Economics. Wiley (2004)
Migliavacca, G.: Srems: a short-medium run electricity market simulator based on game theory and incorporating network constraints. In: Power Tech, 2007 IEEE Lausanne, Switzerland, pp. 813–818 (July 2007)
Nicolaisen, J., Petrov, V., Tesfatsion, L.: Market power and efficiency in a computational electricity market with discriminatory double-auction pricing. IEEE Transactions on Evolutionary Computation 5(5), 504–523 (2001)
Nicolaisen, J., Smith, M., Petrov, V., Tesfatsion, L.: Concentration and capacity effects on electricity market power. In: Proceedings of the 2000 Congress on Evolutionary Computation, La Jolla, USA, vol. 2, pp. 1041–1047 (2000)
Rastegar, M., Guerci, E., Cincotti, S.: Agent-based model of the Italian wholesale electricity market. In: Proceedings of the 6th International Conference on the European Energy Market, EEM 2009 (2009)
Roth, A.E., Erev, I.: Learning in extensive form games: Experimental data and simple dynamic models in the intermediate term. Games Econ. Behav. 8(1), 164–212 (1995)
Ruperez Micola, A., Banal Estaol, A., Bunn, D.W.: Incentives and coordination in vertically related energy markets. Journal of Economic Behavior and Organization 67, 381–393 (2008)
Sun, J., Tesfatsion, L.: Dynamic testing of wholesale power market designs: An open-source agent-based framework. Comput. Econ. 30, 291–327 (2007)
TERNA S.p.A.: Individuazione della rete rilevante - italian version only. Tech. rep., TERNA S.p.A. (2008)
Watkins, C., Dayan, P.: Q-learning. Machine Learning 8(3-4), 279–292 (1992)
Weidlich, A., Veit, D.J.: Bidding in interrelated day-ahead electricity markets: Insights from an agent-based simulation model. In: Proceedings of the 29th IAEE International Conference, Potsdam (2006)
Weidlich, A., Veit, D.J.: A critical survey of agent-based wholesale electricity market models. Energy Economics 30, 1728–1759 (2008)
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Cincotti, S., Gallo, G. (2013). The Genoa Artificial Power-Exchange. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2012. Communications in Computer and Information Science, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36907-0_23
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DOI: https://doi.org/10.1007/978-3-642-36907-0_23
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