A Multi-Core Parallel Genetic Algorithm for the Long-Term Optimal Operation of Large-Scale Hydropower Systems

摘要

The hydropower has undertaken a rapid development in the past several decades in China. At present, China has become the largest hydropower country and has built several huge hydropower bases. A favorable long-term optimal scheduling scheme of large-scale hydropower systems (LHS) is very important for improving the efficiency of hydropower plants. As hydropower optimal operation is nonlinear and nonconvex, and the problem scale increased significantly with the expanding scale of hydropower stations, the necessity of improving the solving efficiency for optimal operation has been amplified by the growing of hydropower stations and the increasing frequent of extreme climate events. This article presented a multi-core parallel genetic algorithm (MPGA) to solve long-term optimal operation of LHS. This algorithm based on genetic algorithm (GA), it distributes individuals to several isolate subpopulations to maintain the diversity, use single circle migration model to exchange individuals between subpopulations to assure the astringency of the algorithm. At the same time, multi-core parallel computing is adopted to make better use of multi-core CPU and improve the computing efficiency. Case study of in the Hongshui River cascaded hydropower system in the south China shown that MPGA is effective and can make a significant reduction in computing time and get reasonable hydropower operation results, which is an effective algorithm in long-term optimal operation for hydropower system.