Renewable Penetration Energy on Cost-Optimal Dispatch of PV–Wind–BESS Microgrids: A Comparative Study for Urban India

This study presents an optimized operational framework for a grid-connected microgrid serving the Bhopal region, India, incorporating photovoltaic (PV) systems, wind generation, and a battery energy storage system (BESS). The primary objective was to minimize daily operational costs while ensuring reliable dispatch amidst fluctuating renewable availability. A comparative optimization approach utilizing Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) is employed to ascertain the optimal power scheduling strategy. The methodology integrates real load and renewable generation profiles with operational constraints, facilitating a realistic assessment of microgrid flexibility. The results indicate that PSO achieved the lowest operational cost of ₹0.298 lakh/day, surpassing GA (₹0.2985 lakh/day) and SA (₹0.320 lakh/day). Additionally, PSO demonstrated the highest renewable penetration at 42%, followed by GA (40%) and SA (38%), highlighting its superior capability to leverage intermittent resources and coordinate BESS operations. The studied microgrid comprises a 1.5 MW PV system, a 1.0 MW wind turbine, and a 2 MWh battery energy storage system (BESS) designed to supply a representative urban load in Bhopal. These findings underscore the practical relevance of swarm intelligence methods in reducing reliance on grid imports and enhancing economic efficiency. The study contributes a validated optimization framework tailored to Indian microgrid conditions, where renewable variability and cost sensitivity are predominant factors. Future research may extend the model to include probabilistic forecasting, multi-day optimization horizons, and demand-response programs to further bolster system resilience.