文化算法及其在梯级水电站长期优化调度中的应用

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Abstract In order to effectively dispose multi-variable and multi-dimention problems of long-term scheduling of cascade hydro-plant. Cultural algorithm (CA) is applied to the optimization problem for the first time. By using of evolutionary programming (EP) model in population space, the situation knowledge and the normative knowledge in belief space are constitued by refining and reasoning the experiences of excellent population that transmitted by the accept() function, and through injecting small disturbed signal, the possible coming problem of local convergence was solved in belief space. which in turn to guide population evolution by influence function in population space. A detailed long-term scheduling mathematical model based on annual maximum electric power output of cascaded Hydro-Plant is constructed, which includes mathematical model of characteristic water head and reservoir water volume. The simulation result for three hydro-plants demonstrates that CA has more powerful global, local searching ability and more fast convergence velocity than adaptive weight particle swarm optimization algorithm I (AWPSO-I), adaptive weight particle swarm optimization algorithm I (AWPSO-II) and chaotic particle swarm optimization algorithm (CPSO). The scheduling results can increase 2.32 and 0.51 hundred million kW· h compared to AWPSO-II and CPSO. The CA can offer a new optimization method and thought for large-scale cascaded Hydro-Plant scheduling.

Key words： Cultural algorithm population space belief space cascaded Hydro-Plant adaptive weight particle swarm optimization algorithm chaotic particle swarm optimization algorithm

Received: 12 January 2009 Published: 07 March 2014

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	Wu Jiekang
	Kong Fannie

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Wu Jiekang,Kong Fannie. Cultural Algorithm and Its Application in Long-Term Optimization Scheduling of Cascaded Hydro-Plants[J]. Transactions of China Electrotechnical Society, 2011, 26(3): 182-190.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2011/V26/I3/182

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