# Review of Energy Management Systems in Microgrids
## 3 Energy Management Systems Applications in Microgrids
[48] studied distributed energy management for networked microgrids embedded in modern distribution systems using mixed-integer linear programming. An alternating direction method of the multiplier-based distributed framework was developed for the scheduling of networked microgrids embedded in contemporary distribution systems by iteratively adjusting nodal price signals, taking into account the diverse ownership of microgrids, distributed energy resources that interface directly with utilities, and responsive loads. A contemporary distribution system made up of numerous networked microgrids, dispersed energy resources that communicate directly with utilities, and responsive loads is used to validate the suggested approach using numerical simulation results. A summary of classical method-based energy management systems applications in microgrids is given in Table 1.  
In Ref. [49], a two-layer energy management system was showcased for distant microgrids. An innovative scheduling algorithm that takes battery lifetime into account was put forth, and it should lower microgrid operating costs. The technique was based on goal programming, which gives varying weights to the cost of using batteries and fuel. Findings indicated that extending the battery life could lower the microgrid's overall operating costs despite an increase in fuel consumption. Over relatively large variations in battery costs for this case study, a wide range of weights proved effective in lowering the operational cost. Despite the analysis being restricted to lead-acid batteries, the technique is sufficiently universal to be applied to other kinds of batteries. To minimize the expenses associated with managing distributed energy resources, [50] proposed the use of memory-based genetic algorithms to optimize power management in grid-connected microgrids. The proposed method outperforms genetic algorithms and particle swarm optimization with a constriction factor and an inertia factor. A study on microgrid optimization using the particle swarm algorithm, which can run an isolated or connected microgrid, was presented by [51]. The suggested method takes into account the variations in the microgrid's load demands and renewable resource supply, and it provides suitable advance (24 h) forecasts to mitigate these variations. Ref. [52] suggested a system subject to demand, wind energy, electric vehicle costs, and electricity prices. In the optimization procedure of this investigation, the gray wolf algorithm is employed. The microgrid system's timing and operation are optimized utilizing this enhanced algorithm. Reduced general operating costs are a result of the system. Efficient management of the grid's integration of energy storage technologies,demand response programs, renewable energy sources, and other emerging technologies results in this cost reduction. Ref. [53] presented an adaptive modified particle swarm algorithm approach based on the hybridization of chaotic particle swarm algorithm and fuzzy self-adaptive particle swarm algorithm to optimize the multi-objective energy management system model of a grid-connected microgrid. The objective is to lower the operating costs and emissions of microgrids. The developed algorithm outperforms fuzzy self-adaptive and chaotic particle swarm algorithms. A novel approach to optimizing an interconnected microgrid was introduced by [54]. It combines a fuzzy logic expert system with a meta-heuristic grey wolf optimization. With this approach, the costs of the power plants as well as the emissions from fossil fuel sources are kept to a minimum. By taking into account the batteries' optimal capacity and minimizing the use of fossil fuels, this strategy lowers microgrid costs. In a stand-alone microgrid, ref. [55] introduced a genetic algorithm-based technique for determining the best location for renewable energy generation and batteries. The suggested multi-objectives include energy disposal and a decrease in life cycle and operational expenses.