梯级水电站群长期发电优化调度多核并行随机动态规划方法  

Parallel stochastic dynamic programming for long-term generation operation of cascaded hydropower stations

作　　者：王森[1] 程春田[1] 武新宇[1] 李保健[1] 

机构地区：[1]大连理工大学水电与水信息研究所,大连116024

出　　处：《中国科学：技术科学》2014年第2期209-218,共10页Scientia Sinica(Technologica)

基　　金：国家重点基础研究发展计划("973"计划)(批准号:2013CB035906);国家自然科学基金(批准号:51109024);国家杰出青年科学基金(批准号:51025934)资助项目

摘　　要：随机动态规划求解水电站群长期发电优化调度易产生"维数灾"问题,导致计算耗时急剧增加,求解效率降低.如何缓解维数灾和提高计算效率,一直是水库优化调度致力于研究的难点问题.在随机动态规划的并行性分析基础上,提出了基于Fork/Join并行框架的多核并行随机动态规划方法.该方法将单个时段内所有变量组合状态下的计算任务作为父任务,通过分治法递归分解为多个子任务,并平均分配到不同的内核同时计算实现细粒度并行求解.以澜沧江下游梯级水电站群为研究实例,建立了3个变量离散数不同的调度方案,并在多核环境下验证该方法的计算效率.结果表明,在2和4核环境下,该方法的计算耗时与串行方法相比,分别节省了约50%和70%,大幅度缩减计算耗时,可充分利用多核资源;同时,计算任务的规模越大,并行计算的耗时缩减幅度越大.因此,此方法为大规模水电系统优化调度提供了一种可行途径,其并行原理可为其他应用所借鉴.Stochastic Dynamic Programming （SDP） for long-term generation operation of cascaded hydropower stations will bring about the curse of dimensionality, resulting in the rapid increase of computational time and the decrease of computational efficiency. Therefore, alleviate the dimensionality problem and improve the computational efficiency are always difficult issues for long-term generation operation of cascaded hydropower stations. On the basis of the parallelism analysis for SDP, a parallel stochastic dynamic programming （PSDP） based on Fork/Join parallel framework was proposed. In this method, all computational tasks for the returns from all discrete combinations in one stage were taken as parent task, which was decomposed into several subtasks by divide-and-conquer method. After this, the decomposed subtasks were solved in different cores respectively for achieving fine-grain parallel computation. The proposed approach was implemented to long-term generation operation of cascaded hydropower stations located on Lancangjiang River, and 3 different schemes with different discrete number of variables were established for testing the computational efficiency in multi-core environment. The result shows that the computational time, compared with serial computation, decreased respectively about 50% in 2-core environment and 70% in 4-core environment, making full use of multi-core resources. In addition, the larger computational scale can reduce more computational time in multi-core environment. Hence, the proposed approach is effective for operation of large-scale hydropower system, and can provide guidance for other applications.

关 键 词：梯级水电站群 随机动态规划 并行 多核 优化调度 

分 类 号：TV737[水利工程—水利水电工程]