Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/3398
Título: Strategic bidding in electricity markets: an agent-based simulator with game theory for scenario analysis
Autor: Pinto, Tiago
Praça, Isabel
Vale, Zita
Morais, H.
Sousa, Tiago
Palavras-chave: Decision-making
Electricity markets
Intelligent agents
Game theory
Multiagent systems
Scenario analysis
Data: 2013
Editora: IOS Press
Relatório da Série N.º: Integrated Computer-Aided Engineering; Vol. 20, Issue 4
Resumo: Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.
Peer review: yes
URI: http://hdl.handle.net/10400.22/3398
ISSN: 1875-8835
1069-2509
Versão do Editor: http://iospress.metapress.com/content/n7663hm471786636/
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