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Advisor(s)
Abstract(s)
Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find
the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the
obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in
detail.
Description
Keywords
Liberalized electricity markets Particle swarm optimization Price forecast Risk management
Citation
Publisher
IEEE