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Strategic bidding methodology for electricity markets using adaptive learning
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Adaptive learning Electricity markets Forecasting methods Intelligent agents Multiagent systems
Contexto Educativo
Citação
Editora
Springer Berlin Heidelberg