Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm
This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization tec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/17/3036 |
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| author | Mohamed A. M. Shaheen Hany M. Hasanien Said F. Mekhamer Mohammed H. Qais Saad Alghuwainem Zia Ullah Marcos Tostado-Véliz Rania A. Turky Francisco Jurado Mohamed R. Elkadeem |
| author_facet | Mohamed A. M. Shaheen Hany M. Hasanien Said F. Mekhamer Mohammed H. Qais Saad Alghuwainem Zia Ullah Marcos Tostado-Véliz Rania A. Turky Francisco Jurado Mohamed R. Elkadeem |
| author_sort | Mohamed A. M. Shaheen |
| collection | DOAJ |
| description | This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems. |
| first_indexed | 2024-03-10T01:34:04Z |
| format | Article |
| id | doaj.art-f13fdc395b3e4677ac8388d0a627ca14 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T01:34:04Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-f13fdc395b3e4677ac8388d0a627ca142023-11-23T13:37:13ZengMDPI AGMathematics2227-73902022-08-011017303610.3390/math10173036Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning AlgorithmMohamed A. M. Shaheen0Hany M. Hasanien1Said F. Mekhamer2Mohammed H. Qais3Saad Alghuwainem4Zia Ullah5Marcos Tostado-Véliz6Rania A. Turky7Francisco Jurado8Mohamed R. Elkadeem9Electrical Engineering Department, Future University in Egypt, Cairo 11835, EgyptElectrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, EgyptElectrical Engineering Department, Future University in Egypt, Cairo 11835, EgyptCentre for Advances in Reliability and Safety, Hong Kong, ChinaElectrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi ArabiaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, SpainElectrical Engineering Department, Future University in Egypt, Cairo 11835, EgyptDepartment of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, SpainElectrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta 31511, EgyptThis paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems.https://www.mdpi.com/2227-7390/10/17/3036machine learningprobabilistic optimal power flowrenewable energy sources |
| spellingShingle | Mohamed A. M. Shaheen Hany M. Hasanien Said F. Mekhamer Mohammed H. Qais Saad Alghuwainem Zia Ullah Marcos Tostado-Véliz Rania A. Turky Francisco Jurado Mohamed R. Elkadeem Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm Mathematics machine learning probabilistic optimal power flow renewable energy sources |
| title | Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm |
| title_full | Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm |
| title_fullStr | Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm |
| title_full_unstemmed | Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm |
| title_short | Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm |
| title_sort | probabilistic optimal power flow solution using a novel hybrid metaheuristic and machine learning algorithm |
| topic | machine learning probabilistic optimal power flow renewable energy sources |
| url | https://www.mdpi.com/2227-7390/10/17/3036 |
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