Grasshopper Optimization Algorithm For Optimizing Microgrid Networks To Achieve Economic Dispatch

Abstract

In recent years, energy management has become an essential issue in the growing integration of microgrids, enabling more efficient and sustainable energy use. This research focuses on optimizing microgrid networks to achieve economic dispatch using the Grasshopper Optimization Algorithm (GOA). Economic dispatch aims to minimize total generation costs while meeting load demand. Traditional methods such as Particle Swarm Optimization (PSO), Priority List (Merit Order), and the Lagrange method have been widely used. Still, GOA offers superior performance potential in cost efficiency and computational effectiveness. This study compares the efficacy of PSO, GOA, Priority List, and the Lagrange method in optimizing microgrid operations through a series of simulations. Key performance metrics include total generation costs, convergence rates, and computation time. The results showed that GOA was superior to PSO and Priority List in reducing generation costs, with differences of $3.8 per hour of generation compared to PSO and $39.8 per hour of generation compared to Priority List. Additionally, GOA has a more adaptive convergence rate. However, the Lagrangian method does not apply to all objective functions, making it less effective for solving economic dispatch problems. In conclusion, GOA could significantly improve the economic efficiency of microgrid systems, contributing to more sustainable and cost-effective energy management. The combination of various optimization methods, such as GOA, PSO, and others, offers a more comprehensive approach to facing future energy management challenges.