Optimizing energy and load management in island microgrids for enhancing resilience against resource interruptions

The increasing integration of distributed renewable energy sources (RES), energy storage systems (ESS), electric vehicle (EV) charging stations, and demand response (DR) mechanisms has significantly enhanced microgrid deployment. However, the operational complexity and vulnerability of islanded microgrids to disruptions, especially during renewable energy fluctuations, pose critical challenges. Existing approaches primarily focus on minimizing operational costs or emissions but fail to simultaneously address load curtailment, voltage stability, and resilience under uncertain conditions. In this paper, we propose a novel resilience-oriented energy and load management framework for island microgrids, integrating a multi-objective optimization function that explicitly minimizes load curtailment, energy losses, voltage deviations, emissions, and energy procurement costs while maximizing the utilization of renewable energy sources. Unlike conventional models that separately optimize demand-side management (DSM), distributed generation (DG), EV charging/discharging, and ESS scheduling, our approach incorporates a coordinated control strategy that jointly optimizes these elements alongside reactive power compensation devices such as capacitors and shunt reactors. To effectively solve this high-dimensional, nonlinear problem, we employ the Multi-objective Moth Flame Algorithm (MOMFA), an enhanced metaheuristic evolutionary algorithm designed to handle complex trade-offs between cost, reliability, and resilience. The superiority of MOMFA over conventional optimization techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) is validated through simulations on a realistic 33-node microgrid under various renewable energy outage scenarios. The results demonstrate that the proposed framework achieves a 60% reduction in voltage deviation, an 81% decrease in energy losses, and an 86% reduction in CO₂ emissions, while ensuring zero load curtailment, even under severe outage conditions. The proposed method offers a scalable, real-time implementable solution for microgrid operators seeking to enhance resilience against renewable energy intermittency and optimize energy utilization. This work significantly advances state-of-the-art microgrid energy management by providing a holistic, multi-objective, and resilience-driven optimization strategy.