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Lookup NU author(s): Tiong Teck Teo, Xiaoyan Feng, Dr Thillainathan Logenthiran, Dr Wai Lok Woo, Dr Khalid Abidi
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) published in its final definitive form in 2020. For re-use rights please refer to the publishers terms and conditions.
This paper proposes a fuzzy logic based energy management system (FEMS) for a grid-connected microgrid with renewable energy sources (RES) 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. The fuzzy membership functions of the FEMS are optimized offline using a Pareto based multi-objective 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. Index Terms-energy storage management, membership function tuning, microgrid, multiobjective evolutionary algorithm. I. INTRODUCTION Microgrid are small-scale power system that consist of renewable energy sources (RES) such as photovoltaic (PV), wind power and load. The intermittency of the load and RES of a microgrid possess a serious challenge to the stability and security of the power system. The energy storage system (ESS) is seen as one of the keys enabling technology to mitigate these challenges. However, large-scale operation of ESS remains an expensive option despite efforts and subsidies from the government. As such, the operation of a single ESS should provide multiple services to maximize its benefit [1]. Several control strategies have been proposed for the energy management system (EMS) to operate the ESS such as fuzzy logic-based energy management system (FEMS) [2], fuzzy logic controller (FLC) for wind power smoothing [3] and grid power smoothing [4]. These methods improve the operation of the ESS, the design process heavily rely on expert knowledge and optimization is not applied. Mathematical optimization methods such as mixed-integer linear programming (MILP), stochastic programming and convex optimization are also proposed. A day-ahead and week-ahead scheduling of ESS to maximize revenue is proposed in [5]. A bidding, scheduling and deployment of ESS solely for revenue maximization using stochastic programming is proposed in [6]. A daily cost minimization using convex optimization by considering a time-of-use tariff and day-ahead
Author(s): Teo TT, Feng X, Logenthiran T, Woo WL, Abidi K
Publication type: Conference Proceedings (inc. Abstract)
Publication status: In Press
Conference Name: 2020 IEEE PES General Meeting
Year of Conference: 2020
Acceptance date: 01/06/2020
Date deposited: 27/07/2020
