Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer

Optimal energy management has become a challenging task to accomplish in today’s advanced systems. If is managed the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It possible operate microgrids under grid-connected, well isolated modes. The authors presented new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) current study elucidate operation that loaded with sustainable, unsustainable sources. With integration of non-Renewable Energy Sources (RES) microgrids, pollution reduced. proposes this hybrid algorithm avoid stagnation achieve premature convergence. Having been strategized bi-objective problem, ultimate aim model’s cut costs incurred upon operations reduce emission pollutants 24-h scheduling period. In study, considered Micro Turbine (MT) followed by Wind (WT), battery unit Fuel Cell (FC) storage devices. microgrid was assumed grid-connected mode. validated proposed three different scenarios establish former’s efficiency efficacy. addition these, results attained from technique were also compared techniques implemented earlier. According outcomes, it inferred OGGWO approach outperformed other methods terms cost mitigation reduction.