Modified differential evolution algorithm to finding optimal solution for AC transmission expansion planning problem

Abstract

The transmission expansion planning (TEP) problem primarily aims to determine the appropriate number and location of additional lines required to meet the increasing power demand at the lowest possible investment cost while meeting the operation constraints. Most of the research in the past solved the TEP problem using the direct current (DC) model instead of the alternating current (AC) model because of its non-linear and non-convex nature. In order to improve the effectiveness of solving the AC transmission expansion planning (ACTEP) problem, a modified version of the differential evolution (DE) is proposed in this paper. The main idea of the modification is to limit the randomness of the mutation process by focusing on the first, second, and third-best individuals. To prove the effectiveness of the suggested method, the ACTEP problem considering fuel costs is solved in the Graver 6 bus system and the IEEE 24 bus system. Moreover, the result of each system is compared to the original DE algorithm and state-of-the-art methods such as the one-to-one-based optimizer (OOBO), the artificial hummingbird algorithm (AHA), the dandelion optimizer (DO), the tuna swarm optimization (TSO), and the chaos game optimization (CGO). The results show that the proposed algorithm is more effective than the original DE algorithm by 1.86% in solving the ACTEP problem.