Generalized Normal Distribution Optimization Algorithm for Economic Dispatch with Renewable Resources Integration

Abstract

In an electric power system operation, the main goal of economic dispatch (ED) is to schedule the power outputs of committed generating units efficiently. This involves consideration of relevant system equality and inequality constraints to meet the required power demand at the lowest possible operational cost. This is a challenging optimization problem for power system operators that can be dealt with efficient meta-heuristic algorithms. This article uses a recent meta-heuristic approach named the generalized normal distribution optimization (GNDO) algorithm to achieve near-optimal solutions. The efficacy of the proposed GNDO algorithm is validated through experimentation on three distinct test power system networks: one with three thermal units, the second one with six thermal-unit, and the third one with ten thermal units. The algorithm's performance is also assessed on a power network with renewable energy sources. All analyses of the four test cases are conducted on the MATLAB/SIMULINK platform. Finally, this article also compares the obtained results with other literature-reported strategies, genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), flower pollination algorithm (FPA), and bald eagle search (BES) algorithm. It is evident from the simulated cases that the employed GNDO algorithm exhibits superior performance for two cases and competitive performance for the remaining cases in achieving the lowest operation costs and power losses.