## 4 Microgrid Energy Management Approaches
### Other Energy Management Approaches  
Rule-based, artificial intelligence, fuzzy logic, game theory, and multi-agent approaches are also utilized for energy management of microgrids. Rule-based approaches, characterized by predefined \"if-else\" strategies, are extensively explored in the literature for energy management within microgrids. These methodologies often involve establishing a hierarchy among energy sources and storage systems based on user preference or availability. Representative rule-based decision-making in microgrid energy management is presented in Figure 7, widely used in the literature. If the solar and wind output are sufficient, the power is distributed to the load, and any excess power is stored in the BESS. If the renewable energy output is insufficient, power is drawn from the BESS if the battery level is adequate. If the BESS level is low, load shedding is implemented, or power is purchased from the electrical grid. Optionally, the battery discharge can be prioritized to ensure critical loads are powered during nighttime or periods without RES generation. In [56], a priority sequence is determined, allowing PV systems, BESS, or biomass to be selected as the primary energy source contingent upon the user's selection. Similarly, ref. [41] details a rule-based method wherein surplus energy from PV panels is initially stored in the BESS before being dispatched to the electrical grid. They illustrate how rule-based strategies can be adapted to manage diverse configurations of microgrid components, including PV-WT-BESS, PV-Hydrogen, PV-Pumped Hydroelectric, WT-BESS, and PV-BESS systems. Another exemplary implementation of rule-based management strategies is showcased in [109], which proposes a model for an isolated microgrid integrating PV, WT, a diesel generator, and the power grid.  
The study in [110] introduces a multi-objective EMS tailored for microgrids, focusing on minimizing overall operational expenses and carbon emissions. This system enhances the BESS management process by utilizing a fuzzy logic approach to manage uncertainties in microgrid operations. It determines the optimal timing and rate for charging or discharging the BESS, considering variables such as RES power production, demand load, and electricity costs. The method employs input membership functions to steer the inference engine in assessing the rules. Furthermore, the study in [111] presents an EMS that utilizes a fuzzy logic controller, which is developed and monitored using LabVIEW, for the regulation of a DC microgrid. This EMS employs fuzzy logic to manage the state of charge (SOC) of lithium-ion batteries, thereby enhancing their life cycle. The study in [112] introduces a strategy for a real-time decentralized demand-side management system in a grid-connected microgrid. Every client linked to the microgrid predicts their daily load demand within this system. Utilizing these predictions, the EMS partakes in a mixed-strategy non-cooperative game, proceeding until a Nash equilibrium is achieved. At this equilibrium, consumption forecasts are modified to aim for the lowest possible electricity cost. In [113], the study proposes a hierarchical control strategy for an isolated microgrid based on a multi-agent system, which focuses on maintaining stable voltage levels while optimizing the economic and environmental aspects of the system. In [114], an EMS model is presented for an isolated hybrid microgrid. The model utilizes PSO in combination with artificial neural networks to enhance energy management strategies. In [115], the authors introduce an EMS powered by recurrent neural networks and complement it with a multi-agent-based weather forecasting method. In [116], a reinforcement learning algorithm was applied to enhance the coordination among various ESS in a microgrid.