梯级水电站群并行多目标优化调度方法

发布时间：2018-04-23 08:30

本文选题：梯级水电站群 + 优化调度　；参考：《水利学报》2017年01期

【摘要】：为保障梯级水电站群多目标优化调度问题的计算效率和求解精度,提出了基于Fork/Join多核并行框架的并行多目标遗传算法。该方法以多目标遗传算法为基础,引入多种群异步进化策略保证种群间个体多样性;采用迁移机制保障子种群的信息有机互馈,提升算法收敛性和解集多样性;利用并行技术实现子种群在各内核的同步求解,提高计算效率。针对问题特点,耦合个体实数串联编码方法、混沌初始化种群策略和约束Pareto占优机制等,进一步提升方法寻优性能。澜沧江流域梯级水电站群多目标优化调度结果表明,所提方法可充分利用多核资源,提升模型计算效率与求解精度,并能获得分布均匀、合理可行的调度方案集,为水电系统多目标高效决策提供科学依据。
[Abstract]:In order to ensure the computational efficiency and accuracy of multi-objective optimal dispatching problem of cascade hydropower stations, a parallel multi-objective genetic algorithm based on Fork/Join multi-core parallel framework is proposed. Based on multi-objective genetic algorithm, multi-population asynchronous evolution strategy is introduced to ensure individual diversity among populations, and migration mechanism is used to ensure the information of sub-populations to feed each other, to improve convergence and diversity of solution sets. The parallel technique is used to solve the subpopulation synchronously in each kernel, and the computational efficiency is improved. According to the characteristics of the problem, coupled individual real number serial coding method, chaotic initialization population strategy and constrained Pareto dominance mechanism are used to further improve the optimization performance of the method. The results of multi-objective optimal dispatching of cascade hydropower stations in Lancang River basin show that the proposed method can make full use of multi-core resources, improve the efficiency and accuracy of model calculation, and obtain a set of well-distributed and reasonable and feasible dispatching schemes. It provides scientific basis for multi-objective and high-efficiency decision-making of hydropower system.
【作者单位】：大连理工大学水电与水信息研究所;
【基金】：国家自然科学基金重大国际合作项目(51210014) 国家重点基础研究发展计划(973计划)项目(2013CB035906)
【分类号】：TV737

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