Strategic bidding methodology for electricity markets using adaptive learning

  • Authors:

  • Tiago Pinto;Zita Vale;Fátima Rodrigues;Hugo Morais;Isabel Praça

  • Affiliations:

  • GECAD-Knowledge Engineering and Decision-Support Research Center, Institute of Engineering, Polytechnic of Porto, Porto;GECAD-Knowledge Engineering and Decision-Support Research Center, Institute of Engineering, Polytechnic of Porto, Porto;GECAD-Knowledge Engineering and Decision-Support Research Center, Institute of Engineering, Polytechnic of Porto, Porto;GECAD-Knowledge Engineering and Decision-Support Research Center, Institute of Engineering, Polytechnic of Porto, Porto;GECAD-Knowledge Engineering and Decision-Support Research Center, Institute of Engineering, Polytechnic of Porto, Porto

  • Venue:
  • IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
  • Year:
  • 2011

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Abstract

The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players' strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players' models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.