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Enhanced coati optimization algorithm-based optimal power flow including renewable energy uncertainties and electric vehicles

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  • Hasanien, Hany M.
  • Alsaleh, Ibrahim
  • Alassaf, Abdullah
  • Alateeq, Ayoob

Abstract

Power systems now face new issues due to incorporating electric vehicles (EVs) and renewable energy resources (RERs). This paper proposes a novel Enhanced Coati Optimization Algorithm (ECOA) for obtaining the optimal solution of the probabilistic optimal power flow (POPF) problems. The ECOA is a metaheuristic optimization algorithm that is robust and efficient for solving complex problems. It is used to tackle the OPF problem, which considers the stochastic characteristics of RERs. Moreover, EVs are included in the presented power systems in this paper. The novel approach is tested and verified on the IEEE-57 and IEEE-118 networks. The effectiveness of the proposed method is demonstrated by making a comparison with other metaheuristic-based methods. To obtain a practical study, real data of wind speed, solar irradiance, and electric vehicles profile are incorporated in the dynamic analyses. The simulation results show that the ECOA is robust and efficient for solving the OPF problem. It can also improve the performance of power systems with RESs and EVs. The findings of this research demonstrate that the suggested approach is promising for power system optimization problems, including RERs and EVs.

Suggested Citation

  • Hasanien, Hany M. & Alsaleh, Ibrahim & Alassaf, Abdullah & Alateeq, Ayoob, 2023. "Enhanced coati optimization algorithm-based optimal power flow including renewable energy uncertainties and electric vehicles," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223024635
    DOI: 10.1016/j.energy.2023.129069
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    References listed on IDEAS

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    1. Roald, Line A. & Pozo, David & Papavasiliou, Anthony & Molzahn, Daniel K. & Kazempour, Jalal & Conejo, Antonio, 2023. "Power systems optimization under uncertainty: a review of methods and applications," LIDAM Reprints CORE 3257, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Kamani, D. & Ardehali, M.M., 2023. "Long-term forecast of electrical energy consumption with considerations for solar and wind energy sources," Energy, Elsevier, vol. 268(C).
    3. Mohamed A. M. Shaheen & Hany M. Hasanien & Rania A. Turky & Martin Ćalasan & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2021. "OPF of Modern Power Systems Comprising Renewable Energy Sources Using Improved CHGS Optimization Algorithm," Energies, MDPI, vol. 14(21), pages 1-21, October.
    4. Li, Hang & Hou, Kai & Xu, Xiandong & Jia, Hongjie & Zhu, Lewei & Mu, Yunfei, 2022. "Probabilistic energy flow calculation for regional integrated energy system considering cross-system failures," Applied Energy, Elsevier, vol. 308(C).
    5. Mohamed S. Hashish & Hany M. Hasanien & Haoran Ji & Abdulaziz Alkuhayli & Mohammed Alharbi & Tlenshiyeva Akmaral & Rania A. Turky & Francisco Jurado & Ahmed O. Badr, 2023. "Monte Carlo Simulation and a Clustering Technique for Solving the Probabilistic Optimal Power Flow Problem for Hybrid Renewable Energy Systems," Sustainability, MDPI, vol. 15(1), pages 1-25, January.
    6. Mangipinto, Andrea & Lombardi, Francesco & Sanvito, Francesco Davide & Pavičević, Matija & Quoilin, Sylvain & Colombo, Emanuela, 2022. "Impact of mass-scale deployment of electric vehicles and benefits of smart charging across all European countries," Applied Energy, Elsevier, vol. 312(C).
    7. Benedetto-Giuseppe Risi & Francesco Riganti-Fulginei & Antonino Laudani, 2022. "Modern Techniques for the Optimal Power Flow Problem: State of the Art," Energies, MDPI, vol. 15(17), pages 1-20, September.
    8. Yu, Hang & Niu, Songyan & Shang, Yitong & Shao, Ziyun & Jia, Youwei & Jian, Linni, 2022. "Electric vehicles integration and vehicle-to-grid operation in active distribution grids: A comprehensive review on power architectures, grid connection standards and typical applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    9. Lauvergne, Rémi & Perez, Yannick & Françon, Mathilde & Tejeda De La Cruz, Alberto, 2022. "Integration of electric vehicles into transmission grids: A case study on generation adequacy in Europe in 2040," Applied Energy, Elsevier, vol. 326(C).
    10. Sumair, Muhammad & Aized, Tauseef & Aslam Bhutta, Muhammad Mahmood & Siddiqui, Farrukh Arsalan & Tehreem, Layba & Chaudhry, Abduallah, 2022. "Method of Four Moments Mixture-A new approach for parametric estimation of Weibull Probability Distribution for wind potential estimation applications," Renewable Energy, Elsevier, vol. 191(C), pages 291-304.
    11. Xiao, Hao & Pei, Wei & Wu, Lei & Ma, Li & Ma, Tengfei & Hua, Weiqi, 2023. "A novel deep learning based probabilistic power flow method for Multi-Microgrids distribution system with incomplete network information," Applied Energy, Elsevier, vol. 335(C).
    12. Huang, Kangdi & Liu, Pan & Kim, Jong-Suk & Xu, Weifeng & Gong, Yu & Cheng, Qian & Zhou, Yong, 2023. "A model coupling current non-adjustable, coming adjustable and remaining stages for daily generation scheduling of a wind-solar-hydro complementary system," Energy, Elsevier, vol. 263(PB).
    13. Mimica, Marko & Dominković, Dominik F. & Kirinčić, Vedran & Krajačić, Goran, 2022. "Soft-linking of improved spatiotemporal capacity expansion model with a power flow analysis for increased integration of renewable energy sources into interconnected archipelago," Applied Energy, Elsevier, vol. 305(C).
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    1. Hasanien, Hany M. & Alsaleh, Ibrahim & Alassaf, Abdullah, 2024. "Impact of electric vehicles and wave energy systems on OPF of power networks using hybrid Osprey-PSO approach," Energy, Elsevier, vol. 308(C).
    2. Ozkaya, Burcin, 2024. "Enhanced growth optimizer algorithm with dynamic fitness-distance balance method for solution of security-constrained optimal power flow problem in the presence of stochastic wind and solar energy," Applied Energy, Elsevier, vol. 368(C).
    3. Kaijie Xu & Xiaochen Zhang & Lin Qiu, 2025. "Explainable Warm-Start Point Learning for AC Optimal Power Flow Using a Novel Hybrid Stacked Ensemble Method," Sustainability, MDPI, vol. 17(2), pages 1-18, January.
    4. Hasanien, Hany M. & Alsaleh, Ibrahim & Tostado-Véliz, Marcos & Zhang, Miao & Alateeq, Ayoob & Jurado, Francisco & Alassaf, Abdullah, 2024. "Hybrid particle swarm and sea horse optimization algorithm-based optimal reactive power dispatch of power systems comprising electric vehicles," Energy, Elsevier, vol. 286(C).
    5. Bekir Emre Altun & Enes Kaymaz & Mustafa Dursun & Ugur Guvenc, 2024. "Hyper-FDB-INFO Algorithm for Optimal Placement and Sizing of FACTS Devices in Wind Power-Integrated Optimal Power Flow Problem," Energies, MDPI, vol. 17(23), pages 1-25, December.

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