A metaheuristic approach to next-generation UGS university microgrid techniques:

It is another effective alternative for microgrid optimization. Metaheuristic methods seek suitable solutions using genetic algorithms, which combine statistical mechanisms and principles of biological evolution to achieve optimal control and operation of microgrid power. Model predictive control methods are currently employed to forecast power generation and to manage stored energy effectively. This approach classically links both control and stochastic scheduling. One of the most notable applications is the prediction of component degradation in the network, particularly in energy storage systems. Optimization techniques based on multi-agent approaches enable appropriate decentralized management of university microgrids. These multi-agents — covering diverse loads, renewable systems, storage, and distributed generators — interact to minimize overall microgrid costs.