梯级水库短期优化调度模型的精细化与GPU并行实现
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摘要
目前制约梯级水库短期优化调度在实际工程中应用的主要瓶颈有:所构建的优化模型存在不合理的简化策略,所选择的求解算法无法保证解的质量以及模型的计算时间远超规定时长。为解决上述问题,本文首先构建精细至水电站各机组工作特性的优化调度模型,接着通过二重嵌套动态规划(DP)计算给定模拟精度下的高质量解,并针对算法固有的"维数灾"问题,一方面通过数据压缩与数据库技术降低程序的内存占用量,另一方面将GPU并行加速技术首次引入水库调度领域,通过OpenACC实现算法的GPU并行以减少计算时间。最后通过潘口、小漩梯级水库日优化调度的实例研究与对比分析得出:精细模型较传统模型能更好地贴合电站的实际工况,提高梯级系统的发电效益;内存占用缩减策略的引入能有效降低算法的空间复杂度;GPU并行较传统的CPU并行能大幅提升算法的求解速度。由此为短期优化调度的理论发展与算法"维数灾"的处理提供借鉴。
There are currently three main bottlenecks restricting the application of cascade reservoir shortterm optimal operation in practical engineering—unreasonable simplification strategies in an optimization model,difficulty in guaranteeing the quality of a solution and calculation time much longer than required. To address these problems,a precise optimal operation model is firstly developed in this paper,fining down to the operating characteristics of each generator set of a hydropower station,and then a high-quality solution with given simulation accuracy is worked out by double-nested Dynamic Programming(DP). The inherent "dimensionality curse" of the algorithm is also tackled. On the one hand,the memory footprint of the computation program is reduced by data compression and database technology. On the other hand,OpenACC is used to realize the GPU parallel acceleration of the algorithm,a technology first introduced into the field of reservoir operation,to shorten the computation time. Finally,the daily optimal operation of Pankou and Xiaoxuan cascade reservoirs is taken for case study,and the following conclusions are drawn.Compared with the traditional model,the precise model can better fit the actual operating conditions of the power stations and enhance overall power generation benefits;the space complexity of the algorithm can be effectively lowered by employing the approach of memory footprint reduction;and in contrast to the traditional CPU parallelism,GPU parallel acceleration can considerably improve the speed of solving the model.The research results provide some reference for the theoretical development of reservoir short-term optimal operation and the processing of the "dimensionality curse" issue.
There are currently three main bottlenecks restricting the application of cascade reservoir shortterm optimal operation in practical engineering—unreasonable simplification strategies in an optimization model,difficulty in guaranteeing the quality of a solution and calculation time much longer than required. To address these problems,a precise optimal operation model is firstly developed in this paper,fining down to the operating characteristics of each generator set of a hydropower station,and then a high-quality solution with given simulation accuracy is worked out by double-nested Dynamic Programming(DP). The inherent "dimensionality curse" of the algorithm is also tackled. On the one hand,the memory footprint of the computation program is reduced by data compression and database technology. On the other hand,OpenACC is used to realize the GPU parallel acceleration of the algorithm,a technology first introduced into the field of reservoir operation,to shorten the computation time. Finally,the daily optimal operation of Pankou and Xiaoxuan cascade reservoirs is taken for case study,and the following conclusions are drawn.Compared with the traditional model,the precise model can better fit the actual operating conditions of the power stations and enhance overall power generation benefits;the space complexity of the algorithm can be effectively lowered by employing the approach of memory footprint reduction;and in contrast to the traditional CPU parallelism,GPU parallel acceleration can considerably improve the speed of solving the model.The research results provide some reference for the theoretical development of reservoir short-term optimal operation and the processing of the "dimensionality curse" issue.
引文
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[4]冯仲恺,牛文静,程春田.大规模水电系统优化调度降维方法研究Ⅰ:理论分析[J].水利学报,2017,48(2):146-156.
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[8]史亚军,彭勇,徐炜.基于灰色离散微分动态规划的梯级水库优化调度[J].水力发电学报,2016,35(12):35-44.
[9]纪昌明,马皓宇,李传刚.基于可行域搜索映射的并行动态规划[J].水利学报,2018,49(6):649-661.
[10]万新宇,王光谦.基于并行动态规划的水库发电优化[J].水力发电学报,2011,30(6):166-170,182.
[11] LI Xiang,WEI Jiahua. A parallel dynamic programming algorithm for multi-reservoir system optimization[J].Advances in Water Resources,2014,67:1-15.
[12]蒋志强,纪昌明,孙平.多维动态规划三种并行模式的对比分析[J].中国农村水利水电,2015(3):168-173,176.
[13] JEFFREY C NEAL,TIMOTHY J. FEWTRELL. A comparison of three parallelisation methods for 2D flood inundation models[J]. Environmental Modelling and Software,2010,25:398-411.
[14] ZHANG Shanghong. Parallel computation of a dam-break flow model using openACC applications[J]. Journal of Hydraulic Engineering,2017,143:1-10.
[15]侯精明,王润,李国栋.基于动力波法的高效高分辨率城市雨洪过程数值模型[J].水力发电学报,2018,37(3):40-49.
[16]覃金帛,曾志强,梁藉. GPU并行优化技术在水利计算中的应用综述[J].计算机工程与应用,2018,54(3):23-29,63.
[17]何沧平. OpenACC并行编程实战[M].北京:机械工业出版社,2017.
[18] JUCKELAND Guido. OpenACC for Programmers:Concepts and Strategies[M]. Addison-Wesley Professional,2017.
[19] ROB Farber. Parallel Programming with OpenACC[M]. Morgan Kaufmann,2017.
[2] LABADIE J W. Optimal operation of multireservoir systems:State-of-the-art review[J]. Journal of Water Resources Planning and Management,2004,130:93-111.
[3] LI Xiang,WEI Jiahua. Knowledge-based approach for reservoir system optimization[J]. Journal of Water Resources Planning and Management,2013,140:1-10.
[4]冯仲恺,牛文静,程春田.大规模水电系统优化调度降维方法研究Ⅰ:理论分析[J].水利学报,2017,48(2):146-156.
[5]冯仲恺,牛文静,程春田.大规模水电系统优化调度降维方法研究Ⅱ:方法实例[J].水利学报,2017,48(3):270-278.
[6]郭生练,陈炯宏,刘攀.水库群联合优化调度研究进展与展望[J].水科学进展,2010,21(4):496-503.
[7]冯仲恺,程春田,牛文静.均匀动态规划方法及其在水电系统优化调度中的应用[J].水利学报,2015,46(12):1487-1496.
[8]史亚军,彭勇,徐炜.基于灰色离散微分动态规划的梯级水库优化调度[J].水力发电学报,2016,35(12):35-44.
[9]纪昌明,马皓宇,李传刚.基于可行域搜索映射的并行动态规划[J].水利学报,2018,49(6):649-661.
[10]万新宇,王光谦.基于并行动态规划的水库发电优化[J].水力发电学报,2011,30(6):166-170,182.
[11] LI Xiang,WEI Jiahua. A parallel dynamic programming algorithm for multi-reservoir system optimization[J].Advances in Water Resources,2014,67:1-15.
[12]蒋志强,纪昌明,孙平.多维动态规划三种并行模式的对比分析[J].中国农村水利水电,2015(3):168-173,176.
[13] JEFFREY C NEAL,TIMOTHY J. FEWTRELL. A comparison of three parallelisation methods for 2D flood inundation models[J]. Environmental Modelling and Software,2010,25:398-411.
[14] ZHANG Shanghong. Parallel computation of a dam-break flow model using openACC applications[J]. Journal of Hydraulic Engineering,2017,143:1-10.
[15]侯精明,王润,李国栋.基于动力波法的高效高分辨率城市雨洪过程数值模型[J].水力发电学报,2018,37(3):40-49.
[16]覃金帛,曾志强,梁藉. GPU并行优化技术在水利计算中的应用综述[J].计算机工程与应用,2018,54(3):23-29,63.
[17]何沧平. OpenACC并行编程实战[M].北京:机械工业出版社,2017.
[18] JUCKELAND Guido. OpenACC for Programmers:Concepts and Strategies[M]. Addison-Wesley Professional,2017.
[19] ROB Farber. Parallel Programming with OpenACC[M]. Morgan Kaufmann,2017.