Evolutionary Tristate PSO for Strategic Bidding of Pumped-Storage Hydroelectric Plant

作者：P. Kanakasabapathy, K. Shanti Swarup · 发表于：IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · 年份：2010 · DOI：10.1109/tsmcc.2010.2041229 · 被引用次数：52 · 研究领域：Electric Power System Optimization、Smart Grid Energy Management、Microgrid Control and Optimization

This paper develops bidding strategy for operating multiunit pumped-storage power plant in a day-ahead electricity market. Based on forecasted hourly market clearing price, the objective is to self-schedule and maximize the expected profit of the pumped-storage plant, considering both spinning and nonspinning reserve bids and meeting the technical operating constraints. Evolutionary tristate particle swarm optimization (ETPSO) based approach is proposed to solve the problem, combining basic particle swarm optimization (PSO) with tristate coding technique and genetics-based mutation operation. The discrete characteristic of a pumped-storage plant is modeled using tristate coding technique and mutation operation is used for faster convergence. The proposed model is adaptive for nonlinear 3-D relationship between the power produced, the energy stored, and the head of the associated reservoir. The proposed approach is applied for a practical utility consisting of four units. Simulation results for different operating cycles of the storage plant indicate the attractive properties of ETPSO approach with highly optimal solution and robust convergence behavior.