## 1 Introduction  
The scarcity of fossil fuels and growing concerns about global warming have led to the emergence of renewable energy sources. Microgrids, which coordinate various renewable energy resources, distributed generators, energy storage systems, and electrical loads alongside the conventional power grid, are a promising technique to mitigate the depletion of fossil fuels and to reduce the associated carbon footprint. However, the high penetration of renewable energy resources, such as solar power and wind power, imposes new challenges in the operation of microgrids because of their intermittent nature. To address the uncertainties posed by these intermittent power sources, energy management systems have been increasingly developed to increase the reliability of microgrids and to yield better performance. Energy management is defined as an approach to optimize the utilization of controllable devices in a microgrid to minimize its operation cost, emissions, and other objectives. This work focuses on the resilient energy management of microgrids, where, in addition to generators and storage units, various switchable and controllable loads can be strategically scheduled to meet the electricity demand of critical loads under extreme environmental conditions, such as islands, border regions, and military bases. In these scenarios, access to the main power grid may be uncertain or prohibitively expensive. Introducing control over various switchable and controllable loads makes the optimization problem of energy management more difficult to solve, as it exhibits the typical characteristics of being large-scale, multi-objective, and multi-constraint. Therefore, developing effective solution algorithms is especially necessary. In recent years, diverse methods including mathematical programming-based optimization approaches and meta-heuristic algorithms have been proposed [1] for energy management. [PERSON] et al. [2] tackled the energy management problem by employing particle swarm optimization, taking into account the integration of photovoltaic systems and batteries while considering energy tariff rates. [PERSON] et al. [3] delved into a stochastic optimization model, focusing on curtailing net generation costs considering real-time market prices. [PERSON] and [PERSON] [4] introduced a supervisory energy management method that amalgamates fuzzy logic controllers with sliding mode control, ensuring a stable power supply and uninterrupted energy delivery. [PERSON] and [PERSON] [5] introduced an energy management approach that seamlessly integrates batteries with wind energy systems, placing significant importance on monitoring battery State of Charge (SoC) for effective battery charging and discharging. [PERSON] et al. [6] proposed a novel approach that leverages artificial neural networks in conjunction with hybrid whale optimization to minimize generation costs while forecasting renewable energy availability. [PERSON] et al. [7] developed an encompassing energy management system that optimizes the simultaneous operation of thermal and battery systems, resulting in improved frequency and voltage stability within microgrid networks. [PERSON] et al. [8] engaged in a comprehensive exploration of voltage control and energy management strategies, employing an exhaustive approach across both isolated and grid-connected microgrid systems. This approach addresses critical challenges, including generation failures, low power generation, and battery constraints. [PERSON] et al. [9] proposed an optimized energy management approach that harnesses biogeography-based modeling to minimize hydrogen fuel consumption within charging-station-based microgrid systems. [PERSON] et al. [10] devised an optimal scheduling strategy employing model predictive control to bolster system resilience in the face of renewable energy variability. This strategy finds practical application in multi-microgrid scenarios, especially under dynamic trading conditions. [PERSON] et al.