Concise Comparative Study of Economic Dispatch of Electrical Power Systems

Abstract

Economic Dispatch (ED) is currently essential for operating electrical power systems globally, including offline Microgrids (MGs). ED ensures the stability and continuity of the electrical power system supply while keeping generating costs as low as possible. Identifying a generic solution for ED is usually complicated, as the operating conditions vary from one power system to another. Numerous solutions for ED have been documented in the literature involving conventional computation techniques, metaheuristic optimizations, and Artificial Intelligence (AI) techniques. A general review of such literature is lacking; a review could guide the researchers and engineers in the field of ED. This article comprehensively reviews ED while identifying the most prominent parameters, including transmission losses, emissions, renewable energies, and operating constraints. The article basically clarifies the research gap while comparing the different reported solutions, starting from standard techniques up to AI approaches, in a simple and concise fashion. Several comparisons are provided in the article to direct and guide the interested reader straightforwardly.  For the proposed case study, Teaching-Learning-Based Optimization (TLBO) achieves the lowest total cost, outperforming Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) by 0.01% and 1.05%, respectively. It also exhibits markedly improved robustness, with reductions of 96.11% and 99.99% in the confidence interval, and 96.13% and 99.97% in the standard deviation, relative to PSO and GA.