多策略混合搜索的变时段人工蜂群算法及应用

杨舟; 刘招; 吕嘉玮; 雷晓辉

引用本文:	杨舟,刘招,吕嘉玮,等.多策略混合搜索的变时段人工蜂群算法及应用[J].灌溉排水学报,0,():-.
	YANG Zhou,LIU Zhao,LV Jiawei,et al.多策略混合搜索的变时段人工蜂群算法及应用[J].灌溉排水学报,0,():-.

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多策略混合搜索的变时段人工蜂群算法及应用
杨舟¹, 刘招¹, 吕嘉玮², 雷晓辉³
1.长安大学水利与环境学院;2.中国能源建设集团山西省电力勘测设计院有限公司山西太原;3.中国水利水电科学研究院北京

摘要:

【目的】为研究非汛期洪水、中小洪水调度和发电调度模拟中时段步长和约束条件在较长系列上的融合优化,提升水库调度综合效益。【方法】建立了多策略混合搜索的变时段的人工蜂群算法,构建了长系列可变时段的多约束下发电量最大的水库优化调度模型,基于对万安水库实例应用。【结果】非汛期年均发电量提高了1423.05kW.h；水量利用率提高了2.8%,加强了对洪水资源的利用；多策略混合搜索的人工蜂群优化算法缓解了多约束条件优化模拟计算时,时段步长选取与运算精度和收敛速度之间的矛盾,有效提升了标准人工蜂群算法的寻优效率与开发能力。【结论】研究成果可为非汛期洪水、中小洪水调度和发电调度综合效益的提升提供思路。

关键词: 水库优化调度；多策略；混合搜索；人工蜂群算法；变时段步长

DOI：

分类号:TV697.1 TV9

基金项目:陕西省重点研发计划项目(2019SF-237)；中央高校基本科研业务费资助项目（300102299206, 300102269201）；西安市建设科技计划项目（SJW2017-11)

Variable Period Artificial Bee Colony Algorithm Based on Multi Strategy Hybrid Search and I’ts Application

YANG Zhou¹, LIU Zhao¹, LV Jiawei², LEI Xiaohui³

1.School of Water and Environment,Chang’an University,Xi’an;2.China Energy Construction Group Shanxi Electric Power Survey and Design Institute Co,LTD;3.China Institute of water resources and hydropower

Abstract:

【Objective】In order to study the integration and optimization of time step size and constraints in long series in non flood season flood, medium and small flood regulation and power generation dispatching simulation, and to improve the comprehensive benefits of reservoir operation. 【Method】The artificial bee colony algorithm with Multi Strategy hybrid search and variable time interval was established, and the optimal operation model of the reservoir with the largest power generation under multiple constraints in a long series of variable time periods was constructed. 【Result】In non flood season, the average annual power generation increased by 1423.05 kW . h; the water utilization rate increased by 2.8%, which enhanced the utilization of flood resources; the artificial bee colony optimization algorithm with Multi Strategy hybrid search alleviated the contradiction between the selection of time step size and the calculation precision and convergence speed in the multi constraint optimization simulation calculation, and effectively improved the optimization efficiency and opening of standard artificial bee colony algorithm Ability. 【Conclusion】 The research results can provide ideas for improving the comprehensive benefits of non flood season flood, medium and small flood regulation and power generation dispatching.

Key words: Optimal reservoir operation; Many strategies; Hybrid search; Artificial bee colony algorithm; Change the step length of time