Accelerating Batched Power Flow on Heterogeneous CPU-GPU Platform
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6269 |
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| _version_ | 1850049826317664256 |
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| author | Jiao Hao Zongbao Zhang Zonglin He Zhengyuan Liu Zhengdong Tan Yankan Song |
| author_facet | Jiao Hao Zongbao Zhang Zonglin He Zhengyuan Liu Zhengdong Tan Yankan Song |
| author_sort | Jiao Hao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-319ea53bcea04287bb6db78d0b8eb4c7 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-319ea53bcea04287bb6db78d0b8eb4c72025-08-20T02:53:38ZengMDPI AGEnergies1996-10732024-12-011724626910.3390/en17246269Accelerating Batched Power Flow on Heterogeneous CPU-GPU PlatformJiao Hao0Zongbao Zhang1Zonglin He2Zhengyuan Liu3Zhengdong Tan4Yankan Song5Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518000, ChinaShenzhen Power Supply Bureau Co., Ltd., Shenzhen 518000, ChinaSichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, ChinaSichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, ChinaSichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, ChinaSichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, ChinaAs 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.https://www.mdpi.com/1996-1073/17/24/6269batch power flowheterogeneous CPU-GPUbus type conversionbinary data exchangeGPU acceleration |
| spellingShingle | Jiao Hao Zongbao Zhang Zonglin He Zhengyuan Liu Zhengdong Tan Yankan Song Accelerating Batched Power Flow on Heterogeneous CPU-GPU Platform Energies batch power flow heterogeneous CPU-GPU bus type conversion binary data exchange GPU acceleration |
| title | Accelerating Batched Power Flow on Heterogeneous CPU-GPU Platform |
| title_full | Accelerating Batched Power Flow on Heterogeneous CPU-GPU Platform |
| title_fullStr | Accelerating Batched Power Flow on Heterogeneous CPU-GPU Platform |
| title_full_unstemmed | Accelerating Batched Power Flow on Heterogeneous CPU-GPU Platform |
| title_short | Accelerating Batched Power Flow on Heterogeneous CPU-GPU Platform |
| title_sort | accelerating batched power flow on heterogeneous cpu gpu platform |
| topic | batch power flow heterogeneous CPU-GPU bus type conversion binary data exchange GPU acceleration |
| url | https://www.mdpi.com/1996-1073/17/24/6269 |
| work_keys_str_mv | AT jiaohao acceleratingbatchedpowerflowonheterogeneouscpugpuplatform AT zongbaozhang acceleratingbatchedpowerflowonheterogeneouscpugpuplatform AT zonglinhe acceleratingbatchedpowerflowonheterogeneouscpugpuplatform AT zhengyuanliu acceleratingbatchedpowerflowonheterogeneouscpugpuplatform AT zhengdongtan acceleratingbatchedpowerflowonheterogeneouscpugpuplatform AT yankansong acceleratingbatchedpowerflowonheterogeneouscpugpuplatform |