Genetic Algorithm based Fuzzy Logic Controller for Optimal Charging-Discharging of Energy Storage in Microgrid applications

Abstract

Microgrid (MG) concept with renewable technologies have the challenges of supplying reliable power considering the intermittent nature of the sources. Energy storage system (ESS) has become a viable solution to control the power fluctuation and thus providing the reliable power to the consumer. However, commonly used charging-discharging control techniques have the limitations of solving overcharging or over-discharging problem, fast charging capability, and rapid response time. To overcome these problems, fuzzy logic controller (FLC) has been proposed to control the charging-discharging due to its easy implementation, no mathematical calculation, and simplicity. However, existing FLC technologies have the limitations in considering the battery control parameters, and selecting the safe operating region (20% to 80%) of the battery state of charge (SOC). Therefore, this research proposes an improved FLC considering the available power from grid and distributed sources, load demand, battery SOC and temperature. To improve the performance of the controller, membership functions (MFs) of the FLC have been optimized by using genetic algorithm (GA). To prove the superiority of GA, another widely used optimization algorithm, particle swarm optimization (PSO) is applied with the same load variation. Obtained results show that, the minimum and maximum SOC level for fuzzy-GA only system has been improved compared to fuzzy only and fuzzy-PSO system. Therefore, it can be concluded that, the developed model works efficiently in controlling the charging and discharging of the battery. The authors are in progress to apply the controller system for MG connected waste water treatment plant.