Accelerating Batched Power Flow on Heterogeneous CPU-GPU Platform

Jiao Hao, Zongbao Zhang, Zonglin He, Zhengyuan Liu, Zhengdong Tan and Yankan Song ()
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Jiao Hao: Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518000, China
Zongbao Zhang: Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518000, China
Zonglin He: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China
Zhengyuan Liu: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China
Zhengdong Tan: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China
Yankan Song: Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China

Energies, 2024, vol. 17, issue 24, 1-14

Abstract: As the scale of China’s interconnected power grid continues to expand, traditional serial computing methods are no longer sufficient for the rapid analysis and computation of electrical networks with tens of thousands of nodes due to their small scale and low efficiency. To enhance the capability of online grid analysis, this paper introduces an accelerating batched power flow calculation method based on a heterogeneous CPU-GPU platform. This method, based on the fast decoupled method, combined with the tremendous parallel computing capability of GPUs with the multi-threaded parallel processing of CPUs, efficiently resolves the exceeding bus type conversion issues in GPU-batched power flow calculation and improves the accuracy of the power flow calculations. Then, a binary-based power flow data exchange format was introduced, which utilizes a single binary file for data exchange. This format significantly minimizes I/O time overhead and reduces file size, further enhancing the method’s efficiency. Case studies on real-world power grids demonstrate its high accuracy and reliability. Compared to the traditional single-threaded power flow calculation method, this method dramatically reduces time consumption in batch power flow calculations. It proves the significant advantages of dealing with large-scale power flow calculations.

Keywords: batch power flow; heterogeneous CPU-GPU; bus type conversion; binary data exchange; GPU acceleration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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