梯级小水电群动态多目标优化调度  

Dynamic multi-objective optimal scheduling method for cascade small hydropower group

作　　者：王万良[1] 张羽方 徐新黎[1] 李笠[1] 

机构地区：[1]浙江工业大学计算机科学与技术学院,浙江杭州310023

出　　处：《浙江工业大学学报》2016年第6期591-600,共10页Journal of Zhejiang University of Technology

基　　金："十二五"国家科技支撑计划:农村小水电高效发电技术(2012BAD10B01);国家自然科学基金资助项目(61379123;61203371)

摘　　要：针对农村梯级小水电群多目标优化调度问题,建立了考虑发电量、生态需水和灌溉需水的多目标调度模型,不同于以往的固定多目标制定,提出了基于来水量和需水量关系的动态多目标选择机制,使得各调度时间段内的调度目标更加合理.同时,提出新的基于动态拥挤距离的最大最小适应度函数并引入改进的强度Pareto进化算法中的环境选择与配对选择思想,设计了一种新的多目标混合粒子群算法,最后应用于江西泸水河流域梯级小水电群调度,调度结果验证了该模型和算法的有效性和可行性.For the optimization scheduling problem of rural cascade small hydropower group, a multi-objective scheduling model was built, which involved of energy output of hydropower station, ecological water requirement and irrigation water requirement. Different from previous fixed goal setting, dynamic multi-objective selection mechanism based on the relationship between runoff and water demand was proposed, where the scheduling objectives were appropriately determined at different scheduling period by the selective factor. Then, the maximi, function based on dynamic crowding distance was proposed and the environment selection a^d pairing selection in improving the strength Pareto evolutionary algorithm were introduced, find a new hybrid multi-objective particle swarm optimization algorithm was designed. Finally, the proposed model and algorithm were applied to solve the multi-0bjective scheduling of cascade h^dropower stations of Lushui River in JiangXi province. The scheduling results illustrate that the the model and algorithm are effective and feasible.

关 键 词：小水电群 多目标优化调度 动态多目标 多目标粒子群算法 

分 类 号：TV697[水利工程—水利水电工程]