Research on Data-Driven Optimal Scheduling of Power System
The uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid. Based on the proximal policy optimization (PPO) algorithm, a safe and economic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/6/2926 |
| _version_ | 1797612037892734976 |
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| author | Jianxun Luo Wei Zhang Hui Wang Wenmiao Wei Jinpeng He |
| author_facet | Jianxun Luo Wei Zhang Hui Wang Wenmiao Wei Jinpeng He |
| author_sort | Jianxun Luo |
| collection | DOAJ |
| description | The uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid. Based on the proximal policy optimization (PPO) algorithm, a safe and economical grid scheduling method is designed. First, constraints on the safe and economical operation of renewable energy power systems are defined. Then, the quintuple of Markov decision process is defined under the framework of deep reinforcement learning, and the dispatching optimization problem is transformed into Markov decision process. To solve the problem of low sample data utilization in online reinforcement learning strategies, a PPO optimization algorithm based on the Kullback–Leibler (KL) divergence penalty factor and importance sampling technique is proposed, which transforms on-policy into off-policy and improves sample utilization. Finally, the simulation analysis of the example shows that in a power system with a high proportion of renewable energy generating units connected to the grid, the proposed scheduling strategy can meet the load demand under different load trends. In the dispatch cycle with different renewable energy generation rates, renewable energy can be absorbed to the maximum extent to ensure the safe and economic operation of the grid. |
| first_indexed | 2024-03-11T06:36:17Z |
| format | Article |
| id | doaj.art-d020bd18138442eab66f6fffd3a55eb5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T06:36:17Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d020bd18138442eab66f6fffd3a55eb52023-11-17T10:52:52ZengMDPI AGEnergies1996-10732023-03-01166292610.3390/en16062926Research on Data-Driven Optimal Scheduling of Power SystemJianxun Luo0Wei Zhang1Hui Wang2Wenmiao Wei3Jinpeng He4School of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaSchool of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaDepartment of Electrical Engineering, Shandong University, Jinan 250061, ChinaAutomation Academy, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaThe uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid. Based on the proximal policy optimization (PPO) algorithm, a safe and economical grid scheduling method is designed. First, constraints on the safe and economical operation of renewable energy power systems are defined. Then, the quintuple of Markov decision process is defined under the framework of deep reinforcement learning, and the dispatching optimization problem is transformed into Markov decision process. To solve the problem of low sample data utilization in online reinforcement learning strategies, a PPO optimization algorithm based on the Kullback–Leibler (KL) divergence penalty factor and importance sampling technique is proposed, which transforms on-policy into off-policy and improves sample utilization. Finally, the simulation analysis of the example shows that in a power system with a high proportion of renewable energy generating units connected to the grid, the proposed scheduling strategy can meet the load demand under different load trends. In the dispatch cycle with different renewable energy generation rates, renewable energy can be absorbed to the maximum extent to ensure the safe and economic operation of the grid.https://www.mdpi.com/1996-1073/16/6/2926grid dispatching optimizationproximal policy optimization algorithmimportance samplingdeep reinforcement learning |
| spellingShingle | Jianxun Luo Wei Zhang Hui Wang Wenmiao Wei Jinpeng He Research on Data-Driven Optimal Scheduling of Power System Energies grid dispatching optimization proximal policy optimization algorithm importance sampling deep reinforcement learning |
| title | Research on Data-Driven Optimal Scheduling of Power System |
| title_full | Research on Data-Driven Optimal Scheduling of Power System |
| title_fullStr | Research on Data-Driven Optimal Scheduling of Power System |
| title_full_unstemmed | Research on Data-Driven Optimal Scheduling of Power System |
| title_short | Research on Data-Driven Optimal Scheduling of Power System |
| title_sort | research on data driven optimal scheduling of power system |
| topic | grid dispatching optimization proximal policy optimization algorithm importance sampling deep reinforcement learning |
| url | https://www.mdpi.com/1996-1073/16/6/2926 |
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