| 巨型水电站群短期联合调度双层并行优化方法及其应用 |
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| 引用本文： | 唐海华,郑慧涛,梅亚东,黄春雷,王峰. 巨型水电站群短期联合调度双层并行优化方法及其应用[J]. 水电能源科学, 2013, 31(11): 45-48,167 |
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| 作者姓名： | 唐海华 郑慧涛 梅亚东 黄春雷 王峰 |
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| 作者单位： | 南京南瑞集团公司 水利水电技术分公司， 江苏 南京 211106；;武汉大学 水资源与水电工程科学国家重点实验室， 湖北 武汉 430072;武汉大学 水资源与水电工程科学国家重点实验室， 湖北 武汉 430072;南京南瑞集团公司 水利水电技术分公司， 江苏 南京 211106；;南京南瑞集团公司 水利水电技术分公司， 江苏 南京 211106； |
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| 基金项目： | 国家电网公司科技基金资助项目(SG10011) |
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| 摘 要： | 鉴于巨型水电站群短期联合调度计算的高精度和高时效要求，在多核PC机群环境下，设计了基于水电站群拓扑结构与优化算法寻优轨迹的双层并行优化方法，并采用MPI+OpenMP混合细粒度模型，实现了多进程与多线程的同步并行计算，并结合不同电站数和PC机群规模，对并行优化算法的性能进行了测试和对比分析。实例应用结果表明，该算法在确保精度的前提下可大幅缩减耗时，显著提高计算效率。
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| 关 键 词： | 水电站群； 短期联合调度； 并行优化； 多核PC机群 |
Double Parallel Optimization Method of Short-term Joint Dispatch of Giant-scale Hydropower Station Groups |
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| TANG Haihu,ZHENG Huitao,MEI Yadong,HUANG Chunlei and WANG Feng. Double Parallel Optimization Method of Short-term Joint Dispatch of Giant-scale Hydropower Station Groups[J]. International Journal Hydroelectric Energy, 2013, 31(11): 45-48,167 |
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| Authors: | TANG Haihu ZHENG Huitao MEI Yadong HUANG Chunlei WANG Feng |
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| Affiliation: | Water Resources & Hydropower Company, NARI Group Corporation, SGEPRI, Nanjing 211106, China;State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, Wuhan 430072, China;State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, Wuhan 430072, China;Water Resources & Hydropower Company, NARI Group Corporation, SGEPRI, Nanjing 211106, China;Water Resources & Hydropower Company, NARI Group Corporation, SGEPRI, Nanjing 211106, China |
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| Abstract: | For the high accuracy and timeliness requirements of short-term joint dispatch of giant-scale hydropower station groups, double parallel optimization method based on topological structure of hydropower station groups and optimization trajectory of algorithm is designed with multi-core PC clusters. Multi-process and multi-thread synchronous parallel computing are implemented with mixed fine-grained model of MPI and OpenMP. Finally, this article analyzes the calculation performance of algorithm with different numbers of hydropower station and process. The instance results show that the calculation time is reduced greatly on the premise of ensuring optimization accuracy, and the computing efficiency is improved significantly. |
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| Keywords: | hydropower station groups short-term joint dispatch parallel optimization multi-core PC clusters |
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