Optimization of operational scheduling for a solar–wind hybrid power plant using a genetic algorithm

The intermittency of renewable energy sources poses significant challenges to maintaining operational stability and scheduling efficiency in hybrid power plants (HPPs). This study proposes a genetic algorithm (GA)-based scheduling strategy for a hybrid solar–wind power plant integrated with a battery energy storage system (BESS) to improve renewable energy utilization, maintain supply reliability, and reduce operational inefficiencies under varying operating conditions. Field measurements of solar radiation, wind speed, and electrical load demand were preprocessed and used as inputs to the scheduling optimization model. A multi-objective fitness function was formulated to maximize renewable energy utilization while minimizing operating cost, power mismatch, and an equivalent carbon-emission indicator, subject to technical and operational constraints. The proposed framework incorporates renewable energy availability limits, battery state of charge (SOC) constraints, and converter operating limits, with scheduling performance evaluated through convergence analysis. The results show that renewable energy utilization increased from approximately 62% to 82%, representing a 20-percentage-point increase, while the normalized operating-cost index and equivalent carbon-emission indicator decreased by approximately 20% and 30%, respectively. Average power mismatch decreased from approximately 8.5% to 3.0%, corresponding to a relative reduction of 64.7%. The optimized schedule-maintained battery SOC within the prescribed operating range of 40–90%, although some operational limitations persisted during periods of high load. These findings indicate that the GA-based scheduling framework offers a feasible approach to coordinating solar, wind, and battery operation under intermittent renewable energy conditions.