Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (4): 228-231.

• 工程与应用 • Previous Articles     Next Articles

Shuffled differential evolution algorithm based on optimal scheduling of cascade hydropower stations

LI Yinghai1, MO Li2, ZUO Jian1   

  1. 1.College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang, Hubei 443002, China
    2.College of Hydroelectric and Digitalization Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2012-02-01 Published:2012-04-05

基于混合差分进化算法的梯级水电站调度研究

李英海1,莫 莉2,左 建1   

  1. 1.三峡大学 水利与环境学院,湖北 宜昌 443002
    2.华中科技大学 水电与数字化工程学院,武汉 430074

Abstract: For the complicated problem of cascade hydropower stations optimal dispatch, a novel shuffled differential evolution algorithm is proposed by hybridizing Differential Evolution algorithm(DE) and Shuffled Frog Leaping algorithm(SFL). In the proposed algorithm, the population is periodically executes grouping and shuffling operations, and the individuals are updated according to the differential evolution strategy in each group. The algorithm is applied to a case of cascade hydropower stations mid-long term optimal scheduling. The results show it is feasible and more efficient than dynamic programming.

Key words: differential evolution algorithm, shuffled frog leaping, cascade hydropower stations, optimal scheduling

摘要: 针对梯级水电站优化调度的复杂问题,结合差分进化算法和混合蛙跳算法各自优势,提出一种新的混合差分进化算法。该算法将差分进化策略嵌入到混合蛙跳算法框架中,对整个群体循环进行分组进化与混合操作,而在每个分组内部按照差分进化策略对个体不断进行更新。数值实验表明该算法具有较强的全局搜索能力,克服了基本差分进化算法易早熟收敛的缺点。将该算法应用于梯级水电站中长期优化调度实例,并与传统动态规划法进行比较分析,进一步验证了其可行性与有效性。

关键词: 差分进化算法, 混合蛙跳算法, 梯级水电站, 优化调度