Intelligent Soft Computing Techniques for Cost-Effective Load Distribution in Power Systems

Abstract

One of the paramount challenges in optimizing load distribution is the short-term operational process of coordinating multiple power plants to meet demand at the minimal possible cost while adhering to transmission and operational constraints. Usually, specialized software is used to tackle this issue, considering the transmission capacity and resource operational constraints. To develop better solutions for economic load dispatch, this study investigates an evolutionary method for combating the ELD problem, concentrating on Differential Evolution (DE) and Black Hole (BH) optimization techniques. The research employs both the BH and DE algorithms to optimize power flow, with the primary objectives being minimizing power losses and reducing fuel costs for generation. The efficacy of these algorithms is evaluated using the IEEE 30-bus system under varying load conditions of 200 MW, 220 MW, 230 MW, 250 MW, and 270 MW. The findings indicate that, concerning minimizing power losses, the DE algorithm surpasses the BH algorithm in performance.