An adjustable Predictive&Prescriptive method for the RO-based optimal power flow problem
Traditionally before solving the optimal power flow considering uncertainty (OPF–U) problem, the predicted value of uncertainty parameters, such as wind power, e.g., is derived from data using a statistics approach or machine learning. Based on the predicted uncertainty parameters, the solution to t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023074984 |
| _version_ | 1797646590069964800 |
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| author | Liqin Zheng Xiaoqing Bai Xiaoqing Shi Yunyi Li Dongmei Xie Chun Wei |
| author_facet | Liqin Zheng Xiaoqing Bai Xiaoqing Shi Yunyi Li Dongmei Xie Chun Wei |
| author_sort | Liqin Zheng |
| collection | DOAJ |
| description | Traditionally before solving the optimal power flow considering uncertainty (OPF–U) problem, the predicted value of uncertainty parameters, such as wind power, e.g., is derived from data using a statistics approach or machine learning. Based on the predicted uncertainty parameters, the solution to the OPF-U problem can be obtained by the prescriptive analytics technique, such as robust optimization (RO). However, it is unclarified how the prediction error in predictive analytics affects solving the OPF-U problem in prescriptive analytics. We propose an adjustable framework method combining machine learning and RO for the OPF-U problem. The k-nearest neighbor is applied to obtain k samples around the predicted value from sufficient historical data. And the optimization results from a minimum volume ellipsoid set containing the k samples are applied to construct KMV set. Then a robust fluctuation region with an adjustable budget level is gained from the KMV set by a two-term exponential formula, which can be embedded into a two-stage RO model. Computational experiments under test cases of different uncertainty scales show the robustness and adjustability of the proposed fluctuation region are better than the state-of-the-art box and ellipsoidal sets. The solution of the proposed two-stage RO model is more economical than the state-of-the-art RO model. The out-of-sample simulation also demonstrates the proposed adjustable Predictive&Prescriptive method can reduce the computational burden as the scale of the system increases when predictive and prescriptive analytics are separated. |
| first_indexed | 2024-03-11T15:03:54Z |
| format | Article |
| id | doaj.art-76758d11313c4a80ab79b9b7ae42cec4 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-11T15:03:54Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-76758d11313c4a80ab79b9b7ae42cec42023-10-30T06:05:38ZengElsevierHeliyon2405-84402023-10-01910e20290An adjustable Predictive&Prescriptive method for the RO-based optimal power flow problemLiqin Zheng0Xiaoqing Bai1Xiaoqing Shi2Yunyi Li3Dongmei Xie4Chun Wei5Guangxi Key Laboratory of Power System Optimization and Energy Technology, College of Electrical Engineering, Guangxi University, Nanning 530004, China; State Grid Xiamen Electric Power Supply Company, Xiamen 361004, ChinaGuangxi Key Laboratory of Power System Optimization and Energy Technology, College of Electrical Engineering, Guangxi University, Nanning 530004, China; Corresponding author.Guangxi Key Laboratory of Power System Optimization and Energy Technology, College of Electrical Engineering, Guangxi University, Nanning 530004, ChinaGuangxi Key Laboratory of Power System Optimization and Energy Technology, College of Electrical Engineering, Guangxi University, Nanning 530004, ChinaGuangxi Key Laboratory of Power System Optimization and Energy Technology, College of Electrical Engineering, Guangxi University, Nanning 530004, ChinaZhejiang University of Technology, Zhejiang 310014, ChinaTraditionally before solving the optimal power flow considering uncertainty (OPF–U) problem, the predicted value of uncertainty parameters, such as wind power, e.g., is derived from data using a statistics approach or machine learning. Based on the predicted uncertainty parameters, the solution to the OPF-U problem can be obtained by the prescriptive analytics technique, such as robust optimization (RO). However, it is unclarified how the prediction error in predictive analytics affects solving the OPF-U problem in prescriptive analytics. We propose an adjustable framework method combining machine learning and RO for the OPF-U problem. The k-nearest neighbor is applied to obtain k samples around the predicted value from sufficient historical data. And the optimization results from a minimum volume ellipsoid set containing the k samples are applied to construct KMV set. Then a robust fluctuation region with an adjustable budget level is gained from the KMV set by a two-term exponential formula, which can be embedded into a two-stage RO model. Computational experiments under test cases of different uncertainty scales show the robustness and adjustability of the proposed fluctuation region are better than the state-of-the-art box and ellipsoidal sets. The solution of the proposed two-stage RO model is more economical than the state-of-the-art RO model. The out-of-sample simulation also demonstrates the proposed adjustable Predictive&Prescriptive method can reduce the computational burden as the scale of the system increases when predictive and prescriptive analytics are separated.http://www.sciencedirect.com/science/article/pii/S2405844023074984Machine learningTwo-stage robust optimizationUncertain fluctuation regionOptimal power flow |
| spellingShingle | Liqin Zheng Xiaoqing Bai Xiaoqing Shi Yunyi Li Dongmei Xie Chun Wei An adjustable Predictive&Prescriptive method for the RO-based optimal power flow problem Heliyon Machine learning Two-stage robust optimization Uncertain fluctuation region Optimal power flow |
| title | An adjustable Predictive&Prescriptive method for the RO-based optimal power flow problem |
| title_full | An adjustable Predictive&Prescriptive method for the RO-based optimal power flow problem |
| title_fullStr | An adjustable Predictive&Prescriptive method for the RO-based optimal power flow problem |
| title_full_unstemmed | An adjustable Predictive&Prescriptive method for the RO-based optimal power flow problem |
| title_short | An adjustable Predictive&Prescriptive method for the RO-based optimal power flow problem |
| title_sort | adjustable predictive prescriptive method for the ro based optimal power flow problem |
| topic | Machine learning Two-stage robust optimization Uncertain fluctuation region Optimal power flow |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023074984 |
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