AI Summary & Key Takeaways
AI InsightThe proposed method seems to be a promising approach for ELD problems based on the solution quality and the computational efficiency.
📌 3 Key Takeaways
Abstract
This paper presents an Improved Differential Evolution (IDE) algorithm to solve Economic Load Dispatch (ELD) problem with non-smooth fuel cost curves con- sidering transmission losses, power balance and capacity constraints. The proposed IDE varies from the Standard Differential Evolution (SDE) algorithm in terms of three basic factors. The initial population in IDE is generated through the concept of Opposition Based Learning (OBL), applies tournament based mutation and uses only one population set throughout the optimization process. The performance of the proposed algorithm is investigated and tested with two standard test systems, the IEEE 30 bus 6 unit system and the 20 unit system. The experiments showed that the searching ability and convergence rate of IDE is much better than the SDE. The results of the proposed approach were compared in terms of fuel cost, computational time, power loss and individual generator powers with existing SDE and other meta-heuristics in literature. The proposed method seems to be a promising approach for ELD problems based on the solution quality and the computational efficiency.