Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5909
Título: Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio
Autor: Pinto, Tiago
Vale, Zita
Sousa, Tiago
Sousa, Tiago
Morais, Hugo
Praça, Isabel
Palavras-chave: Multi-agent based simulation
MASCEM
ALBidS
Data: 2014
Editora: Springer
Relatório da Série N.º: Communications in Computer and Information Science;Vol. 430
Resumo: Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.
Peer review: yes
URI: http://hdl.handle.net/10400.22/5909
DOI: 10.1007/978-3-319-07551-8_41
Versão do Editor: http://link.springer.com/chapter/10.1007/978-3-319-07767-3_25
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