Massive Scenario Reduction Based Distribution-Level Power System Planning Considering the Coordination of Source, Network, Load and Storage
The integration of high-proportion renewable power generation has brought great challenges to the efficiency of distribution network planning methods and the economy of planning results. In order to solve the problem of coordination between the massive operation data of renewable power generation an...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2022-12-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202208020 |
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| _version_ | 1850069062174900224 |
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| author | Jinsen LIU Ning LUO Jie WANG Chang XU Yi Cao Zhiwen Liu |
| author_facet | Jinsen LIU Ning LUO Jie WANG Chang XU Yi Cao Zhiwen Liu |
| author_sort | Jinsen LIU |
| collection | DOAJ |
| description | The integration of high-proportion renewable power generation has brought great challenges to the efficiency of distribution network planning methods and the economy of planning results. In order to solve the problem of coordination between the massive operation data of renewable power generation and the coordinated planning of the source-network-load-storage, this paper proposes a coordinated planning method of the source-network-load-storage based on the massive scenario dimension reduction. Firstly, the dimensionality reduction clustering is carried out on the wind-light-load mass high-dimensional scenarios by the principal component Gaussian mixture clustering algorithm, and the typical scenario set of wind and power loads is obtained; then, a source-network-load-storage coordination planning model of distribution network for massive scenarios is constructed, and the second-order cone relaxation technique is adopted to convert the non-convex constraints to convex ones; finally, the effectiveness of the proposed massive scenario dimension reduction clustering method and distribution network planning model is verified on the Portugal 54-node distribution network. |
| format | Article |
| id | doaj-art-c5b2e17252be4bf2a8559b3e26bc9cee |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2022-12-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-c5b2e17252be4bf2a8559b3e26bc9cee2025-08-20T02:47:52ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492022-12-015512788510.11930/j.issn.1004-9649.202208020zgdl-55-12-liujinsenMassive Scenario Reduction Based Distribution-Level Power System Planning Considering the Coordination of Source, Network, Load and StorageJinsen LIU0Ning LUO1Jie WANG2Chang XU3Yi Cao4Zhiwen Liu5Power Grid Planning and Research Center, Guizhou Power Grid Co., Ltd., Guiyang 550003, ChinaPower Grid Planning and Research Center, Guizhou Power Grid Co., Ltd., Guiyang 550003, ChinaPower Grid Planning and Research Center, Guizhou Power Grid Co., Ltd., Guiyang 550003, ChinaPower Grid Planning and Research Center, Guizhou Power Grid Co., Ltd., Guiyang 550003, ChinaEnergy Research Institute of China Southern Power Grid Co., Ltd., Guangzhou 510663, ChinaEnergy Research Institute of China Southern Power Grid Co., Ltd., Guangzhou 510663, ChinaThe integration of high-proportion renewable power generation has brought great challenges to the efficiency of distribution network planning methods and the economy of planning results. In order to solve the problem of coordination between the massive operation data of renewable power generation and the coordinated planning of the source-network-load-storage, this paper proposes a coordinated planning method of the source-network-load-storage based on the massive scenario dimension reduction. Firstly, the dimensionality reduction clustering is carried out on the wind-light-load mass high-dimensional scenarios by the principal component Gaussian mixture clustering algorithm, and the typical scenario set of wind and power loads is obtained; then, a source-network-load-storage coordination planning model of distribution network for massive scenarios is constructed, and the second-order cone relaxation technique is adopted to convert the non-convex constraints to convex ones; finally, the effectiveness of the proposed massive scenario dimension reduction clustering method and distribution network planning model is verified on the Portugal 54-node distribution network.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202208020distribution networkprincipal component analysis methodgaussian mixed clusteringsource-network-load-storagecoordinated planning |
| spellingShingle | Jinsen LIU Ning LUO Jie WANG Chang XU Yi Cao Zhiwen Liu Massive Scenario Reduction Based Distribution-Level Power System Planning Considering the Coordination of Source, Network, Load and Storage Zhongguo dianli distribution network principal component analysis method gaussian mixed clustering source-network-load-storage coordinated planning |
| title | Massive Scenario Reduction Based Distribution-Level Power System Planning Considering the Coordination of Source, Network, Load and Storage |
| title_full | Massive Scenario Reduction Based Distribution-Level Power System Planning Considering the Coordination of Source, Network, Load and Storage |
| title_fullStr | Massive Scenario Reduction Based Distribution-Level Power System Planning Considering the Coordination of Source, Network, Load and Storage |
| title_full_unstemmed | Massive Scenario Reduction Based Distribution-Level Power System Planning Considering the Coordination of Source, Network, Load and Storage |
| title_short | Massive Scenario Reduction Based Distribution-Level Power System Planning Considering the Coordination of Source, Network, Load and Storage |
| title_sort | massive scenario reduction based distribution level power system planning considering the coordination of source network load and storage |
| topic | distribution network principal component analysis method gaussian mixed clustering source-network-load-storage coordinated planning |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202208020 |
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