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Ahmed D, Ebeed M, Kamel S, Nasrat L, Ali A, Shaaban MF, Hussien AG. An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty. Sci Rep 2024; 14:15558. [PMID: 38969676 PMCID: PMC11226461 DOI: 10.1038/s41598-024-65867-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 06/25/2024] [Indexed: 07/07/2024] Open
Abstract
The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.
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Affiliation(s)
- Deyaa Ahmed
- Holding Company for Water and Wastewater (HCWW), Aswan, 81542, Egypt
| | - Mohamed Ebeed
- Faculty of Engineering, Sohag University, Sohag, 82524, Egypt
- Department of Electrical Engineering, University of Jaén, EPS Linares, 23700, Linares, Jaén, Spain
| | - Salah Kamel
- Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
| | - Loai Nasrat
- Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
| | - Abdelfatah Ali
- Department of Electrical Engineering, American University of Sharjah, 26666, Sharjah, United Arab Emirates
- Department of Electrical Engineering, South Valley University, Qena, 83523, Egypt
| | - Mostafa F Shaaban
- Department of Electrical Engineering, American University of Sharjah, 26666, Sharjah, United Arab Emirates
| | - Abdelazim G Hussien
- Department of Computer and Information Science, Linköping University, Linköping, Sweden.
- Faculty of Science, Fayoum University, Fayoum, Egypt.
- MEU Research Unit, Middle East University, 11831, Amman, Jordan.
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Jiang D, Lin Y, Zhu W, He Z. A parallel based evolutionary algorithm with primary-auxiliary knowledge. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Li W, Shi C, Xu Q, Huang Y. Dynamic Population Cooperative. INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022. [DOI: 10.4018/ijsir.313664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Particle swarm optimization (PSO) has attracted wide attention in the recent decade. Although PSO is an efficient and simple evolutionary algorithm and has been successfully applied to solve optimization problems in many real-world fields, premature maturation and poor local search capability remain two critical issues for PSO. Therefore, to alleviate these disadvantages, a dynamic population cooperative particle swarm optimization for global optimization problems (DPCPSO) is proposed. Firstly, to enhance local search capability, an elite neighborhood learning strategy is constructed by leveraging information from elite particles. Meanwhile, to make the particle easily jump out of the local optimum, a crossover-mutation mechanism is utilized. Finally, a dynamic population partitioning mechanism is designed to balance exploration and exploitation capabilities. 16 classic benchmark functions and 1 real-world optimization problem are used to test the proposed algorithm against with 6 typical PSO algorithms. The experimental results show that DPCPSO is statistically and significantly better than the compared algorithms for most of the test problems. Moreover, the convergence speed and convergence accuracy of DPCPSO are also significantly improved. Therefore, the algorithm is highly competitive in solving global optimization problems.
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Affiliation(s)
- Wei Li
- Jiangxi University of Science and Technology, China
| | - Cisong Shi
- Jiangxi University of Science and Technology, China
| | - Qing Xu
- Jiangxi University of Science and Technology, China
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