# Review of Energy Management Systems in Microgrids
## 3 Energy Management Systems Applications in Microgrids
To lower generation costs brought on by the randomness of the load and the intermittent nature of the solar capacity, a multi-agent-based energy management system based on the differential evolution algorithm in the Java Tool Development Framework (JADE) was employed. The best solution was selected by considering critical loads, and this system also took grid price fluctuations into account. Energy management systems are also designed for microgrids containing homes and buildings [77]. Distributed generation management and coordination of demand response are part of the energy management system optimization process. The main purpose of the cost function is to meet the customer's energy and heat demands while reducing operating expenses. The Hypertext (HTPP) communication protocol forms the basis of the communication platform of agents. For DC microgrids, energy management systems using artificial intelligence-based algorithms and multi-agent systems to ensure supply-demand balance and power quality in the system can be used [78]. Additionally, a fully decentralized control approach based on multi-agent systems can also be applied. In Ref. [78], a microgrid design including photovoltaic modules, a wind turbine, a lithium-ion battery energy storage system, critical and non-critical DC loads, and a grid is proposed, and energy management of this microgrid system is obtained by using a multi-agent-based control structure. Distributed generation agents, battery agents, load agents, and grid agents are further components of the multi-agent system. These agents communicate with one another, share data (likepower, voltage, current, and charge level) between the units, and complete the tasks that have been delegated to them in the multi-agent system. Energy management systems based on multi-agent systems can also optimize energy from renewable resources by employing Maximum Power Point Tracking (MPPT) algorithms. An artificial neural network controller can be used to control the energy storage system in addition to the multi-agent system-based energy management system. This maximizes the charge and discharge of batteries [79]. The goal of the study is to balance the power within the microgrid. This study offers a flexible control to achieve this balance. MATLAB/Simulink is used to model all components of the designed microgrid. JADE is used to create agents for multi-agent systems on the system and design the communication and information sharing between the generated agents. The program, called MACSIMJX, facilitates the relationship and communication between JADE and MATLAB in this design. This ensures that the agents designed in JADE and the microgrid designed in MATLAB cooperate. In Ref. [80], an energy management system based on multiple agents is used. This system takes into account various load models and energy from distributed energy resources. They proposed a cutting-edge method that inspires customers to participate. Using JADE programming, this proposal was validated on interconnected grids. The management system provides customers with an attractive benefit-cost ratio and reduces peak consumption. Ref. [81] introduced a multi-agent hybrid energy management system that combines the best features of decentralized and centralized approaches to optimize the economic operation of the microgrid. A novel simulation platform for energy management systems