Intelligent battery charge-discharge control in off-grid microgrids using hybrid GEP-fuzzy techniques

Abstract

Effective management of battery energy assets is of high significance in stand-alone microgrids (MGs), where the intermittent behavior of renewable energy sources brings huge challenges in maintaining power reliability and operational efficiency. This study investigates the improvement of battery control strategies for the Kololo MG that will have an estimated daily load demand of 4096.82 kWh. Traditional battery management practices have a tendency to achieve sub-optimal charge and discharge cycles, thereby accelerating battery degradation and increasing energy costs. To alleviate these concerns, an innovative hybrid control system is proposed that couples gene expression programming (GEP) with fuzzy logic control (FLC). This procedure involves a series of comparative simulations of 3 distinct control strategies: Conventional, FLC, and GEP-FLC executed over a 24-h period with historical generation and load data. GEP supplements the fuzzy control structure by evolving membership functions and rule parameters for the optimal system response. The outcomes indicate that the GEP-FLC strategy has better performance in all the metrics considered, with a final state of charge equal to 65.8%, while that of FLC is 63.5% and that of conventional controllers is 59.8%, respectively. The GEP-FLC method also minimizes battery current spikes and improves energy efficiency through optimal allocation of excess and shortage intervals. The findings presented in this research illustrate the potential for data-driven, intelligent controllers to improve battery health, MG resilience, and sustainable energy transitions. Future research must expand this framework to include real-time control variables that integrate predictive forecasting and multi-objective optimization.