Sustainable energy management in microgrids: a multi-objective approach for stochastic load and intermittent renewable energy resources

Integrating photovoltaic (PV) systems and wind energy resources (WERs) into microgrids presents challenges due to their inherent unpredictability. This paper proposes deterministic and probabilistic sustainable energy management (SEM) solutions for microgrids connected to the main power system. A prairie dog optimization (PDO) algorithm is utilized to optimize energy utilization, grid stability, and the integration of renewable energy resources (RERs). The algorithm addresses cost reduction, voltage profile improvement, and optimal sizing of RERs. Simulation results demonstrate that incorporating grid-connected PV units and wind turbines (WTs) strategically reduces total costs and enhances system performance. The PDO method outperforms other optimization algorithms, such as Equilibrium Optimizer (EO) and Whale Optimization Algorithm (WOA), in SEM for microgrids due to its consistent convergence properties. Under deterministic settings, optimal integration of RERs reduces overall costs by 52%, with a significant 7.49% improvement in voltage profile index. In probabilistic scenarios, a remarkable 63.68% reduction in overall costs and a 7.51% enhancement in voltage stability index are achieved. These findings demonstrate the effectiveness of PDO in addressing SEM challenges in microgrid environments, showcasing superior performance over existing optimization techniques.