Deep Deterministic Policy Gradient Algorithm Based Wind-Photovoltaic-Storage Hybrid System Joint Dispatch
A deep reinforcement learning based wind-photovoltaic-storage system joint dispatch model is proposed. First, a joint dispatch model that fully considers the constraints of various wind and solar storage stations is established, where tracking dispatch plans, wind and solar curtailment, and energy s...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2023-02-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202107065 |
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| _version_ | 1850031806715265024 |
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| author | Shuxing ZHANG Chi MA Zhixue YANG Yao WANG Hao WU Zhouyang REN |
| author_facet | Shuxing ZHANG Chi MA Zhixue YANG Yao WANG Hao WU Zhouyang REN |
| author_sort | Shuxing ZHANG |
| collection | DOAJ |
| description | A deep reinforcement learning based wind-photovoltaic-storage system joint dispatch model is proposed. First, a joint dispatch model that fully considers the constraints of various wind and solar storage stations is established, where tracking dispatch plans, wind and solar curtailment, and energy storage operation costs are considered in the objective function. Then, the state variables, action variables and reward function under the reinforcement learning framework are defined. Later, a deep deterministic policy gradient algorithm is introduced, using its environmental interaction and strategy exploration mechanism to learn the joint scheduling strategy, so as to achieve the dispatch strategy tracking, reduce wind and solar abandonment, and reduce energy storage charging and discharging. Finally, the historical data of wind power, photovoltaic, and dispatch plan in a certain area of northwestern China are employed to train and analyze the model. The results of the case studies show that the proposed method can adapt well to the changes in the wind power and photovoltaic power in different periods, and the joint scheduling strategy can be obtained under given data of wind and photovoltaic. |
| format | Article |
| id | doaj-art-3f6cb4e5fa2d47139a3c84e500922893 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2023-02-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-3f6cb4e5fa2d47139a3c84e5009228932025-08-20T02:58:51ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492023-02-01562687610.11930/j.issn.1004-9649.202107065zgdl-56-2-zhangshuxingDeep Deterministic Policy Gradient Algorithm Based Wind-Photovoltaic-Storage Hybrid System Joint DispatchShuxing ZHANG0Chi MA1Zhixue YANG2Yao WANG3Hao WU4Zhouyang REN5China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen 518000, ChinaCGN New Energy Holdings Co., Ltd., Beijing 100084, ChinaState Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University), Chongqing 400044, ChinaChina Nuclear Power Technology Research Institute Co., Ltd., Shenzhen 518000, ChinaChina Nuclear Power Technology Research Institute Co., Ltd., Shenzhen 518000, ChinaState Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University), Chongqing 400044, ChinaA deep reinforcement learning based wind-photovoltaic-storage system joint dispatch model is proposed. First, a joint dispatch model that fully considers the constraints of various wind and solar storage stations is established, where tracking dispatch plans, wind and solar curtailment, and energy storage operation costs are considered in the objective function. Then, the state variables, action variables and reward function under the reinforcement learning framework are defined. Later, a deep deterministic policy gradient algorithm is introduced, using its environmental interaction and strategy exploration mechanism to learn the joint scheduling strategy, so as to achieve the dispatch strategy tracking, reduce wind and solar abandonment, and reduce energy storage charging and discharging. Finally, the historical data of wind power, photovoltaic, and dispatch plan in a certain area of northwestern China are employed to train and analyze the model. The results of the case studies show that the proposed method can adapt well to the changes in the wind power and photovoltaic power in different periods, and the joint scheduling strategy can be obtained under given data of wind and photovoltaic.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202107065wind-photovoltaic-storage hybrid systemjoint scheduling strategyuncertaintydeep reinforcement learningdeep deterministic policy gradient algorithm |
| spellingShingle | Shuxing ZHANG Chi MA Zhixue YANG Yao WANG Hao WU Zhouyang REN Deep Deterministic Policy Gradient Algorithm Based Wind-Photovoltaic-Storage Hybrid System Joint Dispatch Zhongguo dianli wind-photovoltaic-storage hybrid system joint scheduling strategy uncertainty deep reinforcement learning deep deterministic policy gradient algorithm |
| title | Deep Deterministic Policy Gradient Algorithm Based Wind-Photovoltaic-Storage Hybrid System Joint Dispatch |
| title_full | Deep Deterministic Policy Gradient Algorithm Based Wind-Photovoltaic-Storage Hybrid System Joint Dispatch |
| title_fullStr | Deep Deterministic Policy Gradient Algorithm Based Wind-Photovoltaic-Storage Hybrid System Joint Dispatch |
| title_full_unstemmed | Deep Deterministic Policy Gradient Algorithm Based Wind-Photovoltaic-Storage Hybrid System Joint Dispatch |
| title_short | Deep Deterministic Policy Gradient Algorithm Based Wind-Photovoltaic-Storage Hybrid System Joint Dispatch |
| title_sort | deep deterministic policy gradient algorithm based wind photovoltaic storage hybrid system joint dispatch |
| topic | wind-photovoltaic-storage hybrid system joint scheduling strategy uncertainty deep reinforcement learning deep deterministic policy gradient algorithm |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202107065 |
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