Graph Computing Based Power Network Analysis Applications
Liu Guangyi1, 3, Dai Renchang1, Lu Yi1, 2, Liu Kewen3, Wang Zhiwei1, Yuan Chen1, Fan Hong1, Dai Jiangpeng1
1. Global Energy Interconnection Research Institute North America San Jose CA 95134 USA;
2. State Grid Sichuan Electric Power Company Chengdu 610041 China;
3. Global Energy Interconnection Research Institute In. Beijing 102209 China
This paper discussed the bottlenecks of current energy management system (EMS) for grid modernization and smart grid. To meet the requirements for the next generation EMS, a graph-based power system online parallel computing approach is proposed in this paper. In this approach, the power system is modeled as a graph and the power system analysis and computing are performed on the graph all in memory. To improve the computation efficiency, a suite of parallel computing algorithm based on graph is developed to solve power flow and state estimation equations. Taking the graph computing advantages on nodal and hierarchical parallelism, the graph-based state estimation, power flow, and contingency analysis applications are implemented and integrated in a developed EMS prototype. The case study on a real provincial system verified the advantages of graph computing and its computation efficiency is outperformed than commercial EMS. The potential applications on EMS using graph computing are presented at the end of the paper.
刘广一, 戴仁昶, 路轶, 刘克文, 王之伟, 袁琛, 范宏, 戴江鹏. 基于图计算的能量管理系统实时网络分析应用研发[J]. 电工技术学报, 2020, 35(11): 2339-2348.
Liu Guangyi, Dai Renchang, Lu Yi, Liu Kewen, Wang Zhiwei, Yuan Chen, Fan Hong, Dai Jiangpeng. Graph Computing Based Power Network Analysis Applications. Transactions of China Electrotechnical Society, 2020, 35(11): 2339-2348.
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