Integrating renewable energy and V2G uncertainty into optimal power flow: A gradient bald eagle search optimization algorithm with local escaping operator
Abstract Here, a new approach is proposed for solving the optimal power flow (OPF) problem in transmission networks using a Gradient Bald Eagle Search Algorithm (GBES) with a Local Escaping Operator (LEO). The method takes into account uncertainty of the renewable energy sources (wind energy and pho...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12874 |
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| _version_ | 1832579807350620160 |
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| author | Mohamed H. Hassan Salah Kamel José Luis Domínguez‐García Reagan Jean Jacques Molu |
| author_facet | Mohamed H. Hassan Salah Kamel José Luis Domínguez‐García Reagan Jean Jacques Molu |
| author_sort | Mohamed H. Hassan |
| collection | DOAJ |
| description | Abstract Here, a new approach is proposed for solving the optimal power flow (OPF) problem in transmission networks using a Gradient Bald Eagle Search Algorithm (GBES) with a Local Escaping Operator (LEO). The method takes into account uncertainty of the renewable energy sources (wind energy and photovoltaic systems) and Vehicle‐to‐Grid (V2G) in the stochastic OPF problem. To improve the efficiency of the proposed technique and enhance its local exploitation capability, the LEO method's selection features are utilized. Monte Carlo methods are employed to estimate the generation costs of the renewable sources and PEVs and study their feasibility. The uncertainty of the renewable sources and PEVs is represented by Weibull, lognormal, and normal probability distribution functions (PDFs). The GBES approach is experimentally compared with well‐known meta‐heuristics using twenty‐three different test functions, and the results indicate its superiority over BES and other recently developed algorithms. Furthermore, the proposed method's effectiveness is evaluated using IEEE 30‐bus test system under various scenarios, and the simulation results demonstrate that it can effectively address OPF issues considering renewable energy sources and V2G, providing superior optimal solutions compared to other algorithms. |
| format | Article |
| id | doaj-art-2a115ff371894b3ab4ecae91d8eaf68f |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-2a115ff371894b3ab4ecae91d8eaf68f2025-01-30T12:15:54ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118164119415210.1049/rpg2.12874Integrating renewable energy and V2G uncertainty into optimal power flow: A gradient bald eagle search optimization algorithm with local escaping operatorMohamed H. Hassan0Salah Kamel1José Luis Domínguez‐García2Reagan Jean Jacques Molu3Department of Electrical Engineering, Faculty of Engineering Aswan University Aswan EgyptDepartment of Electrical Engineering, Faculty of Engineering Aswan University Aswan EgyptIREC Catalonia Institute for Energy Research Barcelona SpainTechnology and Applied Sciences Laboratory, U.I.T. of Douala University of Douala Douala CameroonAbstract Here, a new approach is proposed for solving the optimal power flow (OPF) problem in transmission networks using a Gradient Bald Eagle Search Algorithm (GBES) with a Local Escaping Operator (LEO). The method takes into account uncertainty of the renewable energy sources (wind energy and photovoltaic systems) and Vehicle‐to‐Grid (V2G) in the stochastic OPF problem. To improve the efficiency of the proposed technique and enhance its local exploitation capability, the LEO method's selection features are utilized. Monte Carlo methods are employed to estimate the generation costs of the renewable sources and PEVs and study their feasibility. The uncertainty of the renewable sources and PEVs is represented by Weibull, lognormal, and normal probability distribution functions (PDFs). The GBES approach is experimentally compared with well‐known meta‐heuristics using twenty‐three different test functions, and the results indicate its superiority over BES and other recently developed algorithms. Furthermore, the proposed method's effectiveness is evaluated using IEEE 30‐bus test system under various scenarios, and the simulation results demonstrate that it can effectively address OPF issues considering renewable energy sources and V2G, providing superior optimal solutions compared to other algorithms.https://doi.org/10.1049/rpg2.12874heuristic programmingrenewable energy sourcesVehicle‐to‐Grid |
| spellingShingle | Mohamed H. Hassan Salah Kamel José Luis Domínguez‐García Reagan Jean Jacques Molu Integrating renewable energy and V2G uncertainty into optimal power flow: A gradient bald eagle search optimization algorithm with local escaping operator IET Renewable Power Generation heuristic programming renewable energy sources Vehicle‐to‐Grid |
| title | Integrating renewable energy and V2G uncertainty into optimal power flow: A gradient bald eagle search optimization algorithm with local escaping operator |
| title_full | Integrating renewable energy and V2G uncertainty into optimal power flow: A gradient bald eagle search optimization algorithm with local escaping operator |
| title_fullStr | Integrating renewable energy and V2G uncertainty into optimal power flow: A gradient bald eagle search optimization algorithm with local escaping operator |
| title_full_unstemmed | Integrating renewable energy and V2G uncertainty into optimal power flow: A gradient bald eagle search optimization algorithm with local escaping operator |
| title_short | Integrating renewable energy and V2G uncertainty into optimal power flow: A gradient bald eagle search optimization algorithm with local escaping operator |
| title_sort | integrating renewable energy and v2g uncertainty into optimal power flow a gradient bald eagle search optimization algorithm with local escaping operator |
| topic | heuristic programming renewable energy sources Vehicle‐to‐Grid |
| url | https://doi.org/10.1049/rpg2.12874 |
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