A data-driven model for power system operating costs based on different types of wind power fluctuations
The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operati...
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| Published in: | Journal of environmental management Vol. 351; p. 119878 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.02.2024
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| ISSN: | 0301-4797, 1095-8630, 1095-8630 |
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| Abstract | The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%–18% and 3%–13%, respectively, which verified the effectiveness of the method.
•Two-layer clustering strategy with variable time windows identifies wind power fluctuation types.•A simulation model of power system operating costs is established based on a data-driven approach.•Power system operating costs simulation accuracy varies with seasons and wind power fluctuations types. |
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| AbstractList | The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%–18% and 3%–13%, respectively, which verified the effectiveness of the method.
•Two-layer clustering strategy with variable time windows identifies wind power fluctuation types.•A simulation model of power system operating costs is established based on a data-driven approach.•Power system operating costs simulation accuracy varies with seasons and wind power fluctuations types. The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%-18% and 3%-13%, respectively, which verified the effectiveness of the method.The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%-18% and 3%-13%, respectively, which verified the effectiveness of the method. The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%–18% and 3%–13%, respectively, which verified the effectiveness of the method. |
| ArticleNumber | 119878 |
| Author | Wang, Bohong Zhang, Haoran Liu, Yongqian Yan, Yamin Han, Shuang Yan, Jie Liang, Chao Liu, Shan |
| Author_xml | – sequence: 1 givenname: Jie surname: Yan fullname: Yan, Jie organization: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Renewable Energy, North China Electric Power University, Beijing, 102206, China – sequence: 2 givenname: Shan surname: Liu fullname: Liu, Shan organization: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Renewable Energy, North China Electric Power University, Beijing, 102206, China – sequence: 3 givenname: Yamin surname: Yan fullname: Yan, Yamin email: yanym0910@163.com organization: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Renewable Energy, North China Electric Power University, Beijing, 102206, China – sequence: 4 givenname: Haoran surname: Zhang fullname: Zhang, Haoran organization: School of Urban Planning and Design, Peking University, Shenzhen 518055, Guangdong Province, China – sequence: 5 givenname: Chao surname: Liang fullname: Liang, Chao organization: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Renewable Energy, North China Electric Power University, Beijing, 102206, China – sequence: 6 givenname: Bohong surname: Wang fullname: Wang, Bohong organization: National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, Zhejiang Ocean University, No.1 Haida South Road, 316022, Zhoushan, China – sequence: 7 givenname: Yongqian surname: Liu fullname: Liu, Yongqian organization: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Renewable Energy, North China Electric Power University, Beijing, 102206, China – sequence: 8 givenname: Shuang surname: Han fullname: Han, Shuang organization: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Renewable Energy, North China Electric Power University, Beijing, 102206, China |
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| Keywords | Simulation of new power systems Deep neural network algorithm Power system operating costs Data-driven Wind power fluctuation pattern |
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| Title | A data-driven model for power system operating costs based on different types of wind power fluctuations |
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