Publication: Particle swarm optimised fuzzy controller for charging�discharging and scheduling of battery energy storage system in MG applications
Date
2020
Authors
Faisal M.
Hannan M.A.
Ker P.J.
Rahman M.S.A.
Begum R.A.
Mahlia T.M.I.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
Aiming at reducing the power consumption and costs of grids, this paper deals with the development of particle swarm optimisation (PSO) based fuzzy logic controller (FLC) for charging�discharging and scheduling of the battery energy storage systems (ESSs) in microgrid (MG) applications. Initially, FLC was developed to control the charging�discharging of the storage system to avoid mathematical calculation of the conventional system. However, to improve the charging�discharging control, the membership function of the FLC is optimised using PSO technique considering the available power, load demand, battery temperature and state of charge (SOC). The scheduling controller is the optimal solution to achieve low-cost uninterrupted reliable power according to the loads. To reduce the grid power demand and consumption costs, an optimal binary PSO is also introduced to schedule the ESS, grid and distributed sources under various load conditions at different times of the day. The obtained results proved that the robustness of the developed PSO based fuzzy control can effectively manage the battery charging�discharging with reducing the significant grid power consumption of 42.26% and the costs of the energy usage by 45.11% which also demonstrates the contribution of the research. � 2020 The Authors
Description
Battery management systems; Battery storage; Charging (batteries); Controllers; Cost reduction; Electric power transmission networks; Electric power utilization; Fuzzy control; Fuzzy logic; Membership functions; Microgrids; Particle swarm optimization (PSO); Scheduling; Secondary batteries; Temperature control; Battery energy storage systems; Battery temperature; Conventional systems; Distributed sources; Fuzzy logic controllers; Mathematical calculations; Particle swarm optimisation; Scheduling controllers; Electric power system control