周华艳,周建中,何中政,冯仲恺,查〓港.基于烟花量粒子群算法的水库群联合优化调度[J].水电能源科学,2018,36(10):84-87
基于烟花量粒子群算法的水库群联合优化调度
Joint Optimal Operation of Reservoir Group Based on Hybrid Fireworks Quantum Particle Swarm Algorithm
  
DOI:
中文关键词:  烟花爆炸算法  量粒子群  梯级水库  优化调度
英文关键词:fireworks algorithm  quantum behaved particle swarm  cascade reservoir  optimal scheduling
基金项目:国家重点研发计划(2016YFC0402205)
作者单位
周华艳,周建中,何中政,冯仲恺,查〓港 华中科技大学 水电与数字化工程学院 湖北 武汉 430074 
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中文摘要:
      为寻求一种水库群联合优化调度的高效求解方法,在标准烟花爆炸算法(FA)寻优机制的基础上,通过引入可有效利用粒子位置信息的量粒子群算法进化机制提升算法性能,提出烟花量粒子群算法(FAQPSO),将其应用于溪洛渡-向家坝-三峡梯级电站四库联合优化调度问题中,发现在相同求解条件下,FAQPSO更易获得高质量结果,且收敛快、鲁棒性强。同时,溪向梯级和三峡梯级两个梯级单独调度和联合调度结果表明,上游溪向梯级对下游梯级进行电力补偿,提高了四库总体发电量,验证了溪向—三峡梯级电站联合调度的必要性。
英文摘要:
      To find efficient method for solving the reservoirs joint optimization scheduling problem, based on the standard FA optimization mechanism, the fireworks quantum behaved particle swarm (FAQPSO) was proposed by introducing the quantum behaved particle swarm algorithm evolutionary that can effectively use the inter particle information to improve the performance of the FA. And the FAQPSO was applied to the four reservoirs joint optimization scheduling problem of the Xiluodu Xiangjiaba(Xixiang) Three Gorges cascade reservoirs. Under the same solution conditions, the FAQPSO is more likely to obtain high quality results with fast convergence and strong robustness. By comparing the results of the separate scheduling and joint scheduling of the two cascades on the Xixiang cascade and the Three Gorges cascade, the upstream cascades can give downstream cascades power compensation to increase the total power generation of the four reservoirs. Thus, it verifies the necessity of joint scheduling of the Xixiang Three Gorges cascade reservoirs.
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