Artificial Immune System Applied to the Multi-stage Transmission Expansion Planning
Transmission expansion planning (TEP) is a complex optimization task to ensure that the power system will meet the forecasted demand and the reliability criterion, along the planning horizon, while minimizing investment, operational, and interruption costs. Metaheuristic methods have demonstrated the potential to find good feasible solutions, but not necessarily optimal. These methods can provide high quality solutions, within an acceptable CPU time, even for large-scale problems. This paper presents an optimization tool based on the Artificial Immune System used to solve the TEP problem. The proposed methodology includes the search for the least cost solution, bearing in mind investments and ohmic transmission losses. The multi-stage nature of the TEP will be also taken into consideration. Case studies on a small test system and on a real sub-transmission network are presented and discussed.
KeywordsTransmission Expansion Planning Artificial Immune System Power System Metaheuristics
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