Twin delayed deep deterministic policy gradient based stochastic optimal dispatch of hydro and wind power considering the uncertainty of wind power in power-time dimensions
The randomness, fluctuation and uncertainty of wind power brings great challenges to the dispatch and control of the power system. In order to raise the economy of hydro and wind power optimal dispatch, a stochastic optimal dispatching model considering the uncertainty of wind power in both time and...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524005490 |
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| _version_ | 1846162788829888512 |
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| author | Yuhong Wang Xu Zhou Yunxiang Shi Chenyu Zhou Qiliang Jiang Zongsheng Zheng |
| author_facet | Yuhong Wang Xu Zhou Yunxiang Shi Chenyu Zhou Qiliang Jiang Zongsheng Zheng |
| author_sort | Yuhong Wang |
| collection | DOAJ |
| description | The randomness, fluctuation and uncertainty of wind power brings great challenges to the dispatch and control of the power system. In order to raise the economy of hydro and wind power optimal dispatch, a stochastic optimal dispatching model considering the uncertainty of wind power in both time and power dimensions is proposed. The versatile distribution is used to describe the uncertainty of wind power in different power-time intervals, providing foundation for positive and negative reserves of hydro power. Moreover, to solve the multi-objective stochastic optimal dispatching model, the twin delayed deep deterministic policy gradient (TD3) algorithm is utilized, which can avoid falling into local optimum and has stronger search ability when dealing with high-dimensional multi-objective optimization problem. Simulations in a wind farm and hydro power station of western China show that the proposed model can accurately describe the uncertainty of wind power, and TD3 algorithm can find the global optimal solution more effectively than the traditional intelligent algorithms and other deep reinforcement learning algorithms. |
| format | Article |
| id | doaj-art-bc9c254ba33b4e349a4a36d220fc2033 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-bc9c254ba33b4e349a4a36d220fc20332024-11-20T05:06:13ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152024-12-01163110326Twin delayed deep deterministic policy gradient based stochastic optimal dispatch of hydro and wind power considering the uncertainty of wind power in power-time dimensionsYuhong Wang0Xu Zhou1Yunxiang Shi2Chenyu Zhou3Qiliang Jiang4Zongsheng Zheng5College of Electrical Engineering, Sichuan University, Chengdu, 610065, Sichuan, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu, 610065, Sichuan, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu, 610065, Sichuan, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu, 610065, Sichuan, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu, 610065, Sichuan, ChinaCorresponding author.; College of Electrical Engineering, Sichuan University, Chengdu, 610065, Sichuan, ChinaThe randomness, fluctuation and uncertainty of wind power brings great challenges to the dispatch and control of the power system. In order to raise the economy of hydro and wind power optimal dispatch, a stochastic optimal dispatching model considering the uncertainty of wind power in both time and power dimensions is proposed. The versatile distribution is used to describe the uncertainty of wind power in different power-time intervals, providing foundation for positive and negative reserves of hydro power. Moreover, to solve the multi-objective stochastic optimal dispatching model, the twin delayed deep deterministic policy gradient (TD3) algorithm is utilized, which can avoid falling into local optimum and has stronger search ability when dealing with high-dimensional multi-objective optimization problem. Simulations in a wind farm and hydro power station of western China show that the proposed model can accurately describe the uncertainty of wind power, and TD3 algorithm can find the global optimal solution more effectively than the traditional intelligent algorithms and other deep reinforcement learning algorithms.http://www.sciencedirect.com/science/article/pii/S0142061524005490Stochastic optimal dispatchingImproved versatile distributionWind power uncertaintyPower-time dimensionTwin delayed deep deterministic policy gradient (TD3) |
| spellingShingle | Yuhong Wang Xu Zhou Yunxiang Shi Chenyu Zhou Qiliang Jiang Zongsheng Zheng Twin delayed deep deterministic policy gradient based stochastic optimal dispatch of hydro and wind power considering the uncertainty of wind power in power-time dimensions International Journal of Electrical Power & Energy Systems Stochastic optimal dispatching Improved versatile distribution Wind power uncertainty Power-time dimension Twin delayed deep deterministic policy gradient (TD3) |
| title | Twin delayed deep deterministic policy gradient based stochastic optimal dispatch of hydro and wind power considering the uncertainty of wind power in power-time dimensions |
| title_full | Twin delayed deep deterministic policy gradient based stochastic optimal dispatch of hydro and wind power considering the uncertainty of wind power in power-time dimensions |
| title_fullStr | Twin delayed deep deterministic policy gradient based stochastic optimal dispatch of hydro and wind power considering the uncertainty of wind power in power-time dimensions |
| title_full_unstemmed | Twin delayed deep deterministic policy gradient based stochastic optimal dispatch of hydro and wind power considering the uncertainty of wind power in power-time dimensions |
| title_short | Twin delayed deep deterministic policy gradient based stochastic optimal dispatch of hydro and wind power considering the uncertainty of wind power in power-time dimensions |
| title_sort | twin delayed deep deterministic policy gradient based stochastic optimal dispatch of hydro and wind power considering the uncertainty of wind power in power time dimensions |
| topic | Stochastic optimal dispatching Improved versatile distribution Wind power uncertainty Power-time dimension Twin delayed deep deterministic policy gradient (TD3) |
| url | http://www.sciencedirect.com/science/article/pii/S0142061524005490 |
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