Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/1426
Título: Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning
Autor: Pinto, Tiago
Vale, Zita
Rodrigues, Fátima
Praça, Isabel
Morais, H.
Palavras-chave: Bidding strategies
Electricity markets
Multiagent simulation
Reinforcement learning
Simulated annealing
Data: 2011
Editora: IEEE
Resumo: Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.
URI: http://hdl.handle.net/10400.22/1426
ISBN: 978-1-4577-0809-1
978-1-4577-0808-4
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6082167
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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