Solving Energy Management Optimization Using Various Global Metaheuristics Methods

This paper proposes a new planning strategy based on a stochastic optimization method namely the Arithmetic optimization Algorithm (AOA), to solve the optimal power management problems such as the improvement of total cost, the minimization of the total power losses, the reduction of voltage deviation, and reducing the emission of thermal generators. A comparative study between the AOA and moth flame optimizer (MFO), the particle swarm optimization (PSO) and the genetic algorithm (GA) is elaborated. The particularity and efficiency of these selected metaheuristic algorithms in terms of convergence behaviors and the solution quality have been validated on the standard IEEE 30-bus electric system. The obtained results compared to other algorithms demonstrate that the proposed AOA is an effective algorithm and may be considered as an alternative tool for solving various practical power management optimization problems.