基于并行遗传算法的水电站群中期优化调度

Midterm optimal operation based on parallel genetic algorithm for hydropower stations

从水火电协调调度角度出发,提出了水电站群中期调峰出力最大模型。采用指数罚函数将模型目标的极大极小问题转化为可直接求解的无约束规划问题;结合遗传算法多种群的并行计算优势,构建粗粒度并行遗传算法,以提高求解效率和精度。乌江流域8座水电站的计算结果表明,所提模型能够在保证水电调峰的同时减少系统负荷变化对火电启停的影响,且粗粒度并行遗传算法能显著提高求解效率。

A midterm maximum peak load regulation model of hydropower stations is proposed from the perspective of coordinative operation of thermal and hydraulic power generation. The exponential penalty function is used to convert the minimax problem into the unconstrained programming problem to be solved directly,and the parallel computing advantage of multi-population genetic algorithm is integrated in the coarse-grained parallel genetic algorithm to increase the calculation efficiency and accuracy. The eomputative results for 8 hydropower stations in Wujiang river basin show that the proposed model mitigates the effect of system load change on the start-up and shut-down of thermal power plants while ensures the peak load regulation of hydropower stations. The coarse-grained parallel genetic algorithm improves the calculation efficiency significantly.