Optimal Scheduling of Renewable Sources Based Micro grid with PV and Battery Storage Using Giant Trevally Optimizer

Abstract

This paper proposes a Giant trevally optimizer (GTO) for the optimal scheduling of a hybrid power system. This hybrid power system encompasses multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines (WT), fuel cells (FC), micro turbines (MT), and a battery energy storage system (BESS). The primary objective of this method is to minimize the overall operating cost of a grid-connected micro grid while enhancing the accuracy and efficiency of the energy management system. GTO method is used to analyses the generation, storage, and response load options in order to resolve the economic dispatch difficulties. To manage with the optimal energy management of the grid connected micro grid with a high degree of uncertainties, a GTO algorithm is employed.  Subsequently, the performance of this proposed technique is assessed using the MATLAB Simulink platform and compared against several existing methodologies. Notably, the proposed method yields a cost value of 270, which is notably lower than the costs associated with other existing approaches.