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Publicação
Electricity Markets Portfolio Optimization using a Particle Swarm Approach
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Orientador(es)
Resumo(s)
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 simulates 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 the
market context. However, it is still necessary to adequately
optimize the player’s 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 the different
markets the player is acting on in each moment, and depending
on different contexts of negotiation, such as the peak and offpeak
periods of the day, and the type of day (business day,
weekend, holiday, etc.). The proposed approach is tested and
validated using real electricity markets data from the Iberian
operator – OMIE.
Descrição
Palavras-chave
Adaptive Learning Artificial Neural Network Electricity Markets Multi-Agent Simulation Particle Swarm Optimization Portfolio Optimization
Contexto Educativo
Citação
Editora
IEEE