This paper presents an intelligent power management and unit commitment (UC) framework for hybrid renewable microgrids, employing adaptive neuro-fuzzy inference system (ANFIS) controllers to optimize energy dispatch and minimize operational costs. The proposed system integrates multiple distributed energy resources including PV, wind energy, fuel cells, a diesel generator, and a battery energy storage system with bidirectional power flow capabilities. The methodology encompasses detailed mathematical modeling of individual system components, and an ANFIS-based control architecture is developed for maximum power point tracking (MPPT), hydrogen flow optimization, battery management, and bidirectional converter management. The intelligent UC strategy employs multi-objective optimization considering economic operation, environmental impact, power balance, generation capacity limits, and system reliability constraints. The battery energy storage system utilizes advanced management algorithms with state-of-charge optimization to provide power balancing, grid stabilization, and energy arbitrage capabilities. The simulation validates system performance under diverse operational conditions. The ANFIS-based intelligent dispatch strategy demonstrates effective performance compared to conventional rule-based and economic baseline approaches, achieving a 25.3% cost reduction compared to rule-based dispatch and a 14.0% reduction compared to economic baseline dispatch. The system achieves 51.0% renewable energy penetration while maintaining the battery’s state-of-charge within an optimal 51.2% operating range. Environmental benefits include 65.2% reduction in CO 2 emissions compared to baseline operation, with total daily emissions of 42.9 kg versus 123.5 kg for conventional economic dispatch. The intelligent power management system successfully maintains power balance through coordinated operation of all generation sources, achieving a net energy export of 35.2 kWh to the grid while minimizing operational costs. The fuel cell system operates at 85% fuel utilization efficiency with stack efficiencies ranging from 45% to 55%, while the battery management system ensures safe operation within specified voltage, current, and temperature limits.
Ali Q. Al‐Shetwi (Fri,) studied this question.