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Article type: Research Article
Authors: Bayat, Peyman | Afrakhte, Hossein; *
Affiliations: Department of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
Correspondence: [*] Corresponding author. Hossein Afrakhte, Electrical Engineering Department, Faculty of Engineering, University of Guilan, Rasht, Iran. E-mail: [email protected].
Abstract: Integration of several single microgrids (MGs) into the conventional grids which is tends to form multi-microgrid (MMG) systems can enhance the complexity of the operation. On the other hand, the fault occurrence in the uncertain place of system has a notable influence on the energy scheduling results of each operation interval. Thus, in order for MG systems to emerge on a large scale it is necessary to design an efficient and reliable energy management strategy. From this point of view, this paper proposed a novel problem formulation along with a modified optimization algorithm to perform optimized energy management of MMG systems. In the proposed problem formulation, the probability of the failure has been taken into consideration and decentralized multi-agents determine their optimum energy management for both faulted and normal operation modes using variable weighted multi objective function (VWMOF). In the proposed optimization algorithm which is called purpose-oriented shuffled complex evolution (POSCE), it has been tried to modify the shuffled complex evolution (SCE) algorithm to make it become an appropriate optimization method for multi-objective problems with higher coverage rate and higher success rate of search. For this purpose, all data processing and simulation results were made in MATLAB software environment and the effectiveness of the proposed method has been demonstrated on a modified reliability based case study with considering different optimization methods.
Keywords: Energy management strategy, multi-microgrid (MMG), optimization, purpose-oriented shuffled complex evolution (POSCE), reliability
DOI: 10.3233/JIFS-190666
Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 2021-2038, 2020
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