Steady-state deduction methods of a power system based on the prediction of large-scale wind power clusters
The integration of a high proportion of wind power has brought disorderly impacts on the stability of the power system. Accurate wind power forecasting technology is the foundation for achieving wind power dispatchability. To improve the stability of the power system after the high proportion of win...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1194415/full |
| _version_ | 1797830387679887360 |
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| author | Rongqiang Feng Rongqiang Feng Haiping Yu Haiping Yu Xueqiong Wu Xueqiong Wu Chenxi Huang Chenxi Huang Tianchi Du Wei Ding |
| author_facet | Rongqiang Feng Rongqiang Feng Haiping Yu Haiping Yu Xueqiong Wu Xueqiong Wu Chenxi Huang Chenxi Huang Tianchi Du Wei Ding |
| author_sort | Rongqiang Feng |
| collection | DOAJ |
| description | The integration of a high proportion of wind power has brought disorderly impacts on the stability of the power system. Accurate wind power forecasting technology is the foundation for achieving wind power dispatchability. To improve the stability of the power system after the high proportion of wind power integration, this paper proposes a steady-state deduction method for the power system based on large-scale wind power cluster power forecasting. First, a wind power cluster reorganization method based on an improved DBSCAN algorithm is designed to fully use the spatial correlation of wind resources in small-scale wind power groups. Second, to extract the temporal evolution characteristics of wind power data, the traditional GRU network is improved based on the Huber loss function, and a wind power cluster power prediction model based on the improved GRU network is constructed to output ultra-short-term power prediction results for each wind sub-cluster. Finally, the wind power integration stability index is defined to evaluate the reliability of the prediction results and further realize the steady-state deduction of the power system after wind power integration. Experimental analysis is conducted on 18 wind power farms in a province of China, and the simulation results show that the RMSE of the proposed method is only 0.0869 and the probability of extreme error events is low, which has an important reference value for the stability evaluation of large-scale wind power cluster integration. |
| first_indexed | 2024-04-09T13:35:42Z |
| format | Article |
| id | doaj.art-055951065e264338aa575ae106fb4693 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-09T13:35:42Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-055951065e264338aa575ae106fb46932023-05-09T10:20:37ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-05-011110.3389/fenrg.2023.11944151194415Steady-state deduction methods of a power system based on the prediction of large-scale wind power clustersRongqiang Feng0Rongqiang Feng1Haiping Yu2Haiping Yu3Xueqiong Wu4Xueqiong Wu5Chenxi Huang6Chenxi Huang7Tianchi Du8Wei Ding9NARI Group (State Grid Electric Power Research Institute) Co., Ltd., Nanjing, ChinaNARI-TECH Nanjing Control Systems Co., Ltd., Nanjing, ChinaNARI Group (State Grid Electric Power Research Institute) Co., Ltd., Nanjing, ChinaNARI-TECH Nanjing Control Systems Co., Ltd., Nanjing, ChinaNARI Group (State Grid Electric Power Research Institute) Co., Ltd., Nanjing, ChinaNARI-TECH Nanjing Control Systems Co., Ltd., Nanjing, ChinaNARI Group (State Grid Electric Power Research Institute) Co., Ltd., Nanjing, ChinaNARI-TECH Nanjing Control Systems Co., Ltd., Nanjing, ChinaCollege of Energy and Electrical Engineering, Hohai University, Nanjing, ChinaShandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaThe integration of a high proportion of wind power has brought disorderly impacts on the stability of the power system. Accurate wind power forecasting technology is the foundation for achieving wind power dispatchability. To improve the stability of the power system after the high proportion of wind power integration, this paper proposes a steady-state deduction method for the power system based on large-scale wind power cluster power forecasting. First, a wind power cluster reorganization method based on an improved DBSCAN algorithm is designed to fully use the spatial correlation of wind resources in small-scale wind power groups. Second, to extract the temporal evolution characteristics of wind power data, the traditional GRU network is improved based on the Huber loss function, and a wind power cluster power prediction model based on the improved GRU network is constructed to output ultra-short-term power prediction results for each wind sub-cluster. Finally, the wind power integration stability index is defined to evaluate the reliability of the prediction results and further realize the steady-state deduction of the power system after wind power integration. Experimental analysis is conducted on 18 wind power farms in a province of China, and the simulation results show that the RMSE of the proposed method is only 0.0869 and the probability of extreme error events is low, which has an important reference value for the stability evaluation of large-scale wind power cluster integration.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1194415/fulllarge-scale wind power clusterstability assessmentsteady deductioncluster divisionultra-short-term power cluster forecastingimproved GRU |
| spellingShingle | Rongqiang Feng Rongqiang Feng Haiping Yu Haiping Yu Xueqiong Wu Xueqiong Wu Chenxi Huang Chenxi Huang Tianchi Du Wei Ding Steady-state deduction methods of a power system based on the prediction of large-scale wind power clusters Frontiers in Energy Research large-scale wind power cluster stability assessment steady deduction cluster division ultra-short-term power cluster forecasting improved GRU |
| title | Steady-state deduction methods of a power system based on the prediction of large-scale wind power clusters |
| title_full | Steady-state deduction methods of a power system based on the prediction of large-scale wind power clusters |
| title_fullStr | Steady-state deduction methods of a power system based on the prediction of large-scale wind power clusters |
| title_full_unstemmed | Steady-state deduction methods of a power system based on the prediction of large-scale wind power clusters |
| title_short | Steady-state deduction methods of a power system based on the prediction of large-scale wind power clusters |
| title_sort | steady state deduction methods of a power system based on the prediction of large scale wind power clusters |
| topic | large-scale wind power cluster stability assessment steady deduction cluster division ultra-short-term power cluster forecasting improved GRU |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1194415/full |
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