Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II

Teo, Tiong Teck, Logenthiran, Thillainathan, Woo, Wai Lok, Abidi, Khalid, John, Thomas, Wade, Neal S., Greenwood, David M., Patsios, Charalampos and Taylor, Philip C. (2021) Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II. IEEE Transactions on Cybernetics, 51 (1). pp. 5375-5386. ISSN 2168-2267

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Official URL: https://doi.org/10.1109/tcyb.2020.3031109


This article proposes a fuzzy logic-based energy-management system (FEMS) for a grid-connected microgrid with renewable energy sources (RESs) and energy storage system (ESS). The objectives of the FEMS are reducing the average peak load (APL) and operating cost through arbitrage operation of the ESS. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state of charge of ESS, the power difference between load and RES, and electricity market price. The effectiveness of the fuzzy logic greatly depends on the membership functions (MFs). The fuzzy MFs of the FEMS are optimized offline using a Pareto-based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy-logic controller. A comparison with other control strategies with similar objectives is carried out at a simulation level. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, U.K.

Item Type: Article
Additional Information: Funding information: This work was supported by the Engineering and Physical Sciences Research Council under Grant EP/P001173/1 and Grant EP/N032888/1.
Uncontrolled Keywords: Energy storage management, membership function (MF) tuning, microgrid, multiobjective evolutionary\nobreak algorithm (MOEA)
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 17 Dec 2020 14:44
Last Modified: 22 Dec 2021 16:45
URI: http://nrl.northumbria.ac.uk/id/eprint/45037

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