optimal operation and management of energy resources in microgrids in the presence of renewable resources and energy storage by modified grey wolf optimization algorithm

This paper delves into the meticulous optimization of distributed energy resources and their storage within a conventional microgrid framework. the optimization endeavor leverages an array of cutting-edge technologies including photovoltaic, wind, fuel cells, micro-turbines, and batteries, with the dual objectives of curtailing operational expenses and fortifying system reliability. to attain these objectives, the article employs a refined algorithm derived from the grey wolf optimization technique. furthermore, simulations are executed under two distinct scenarios. in the first scenario, the presumption is that all distributed energy resources within the microgrid are exploitable, whereas in the second scenario, spatial constraints necessitate the exclusion of photovoltaic and wind turbine resources. simulation outcomes evince that post-implementation of energy management via metaheuristic algorithms, there is a discernible reduction in the operational costs of the microgrid alongside an enhancement in system reliability. additionally, the elimination of photovoltaic and wind resources results in escalated costs and grid blackout within the microgrid. in summary, the simulation findings affirm the superior efficacy of the proposed modified grey wolf algorithm in addressing energy management quandaries in comparison to the particle swarm optimization algorithm.