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Recently, the energy demand has increased dramatically on a global scale, driving the need to expand existing electrical systems to ensure service coverage, efficiency, and reliability. However, this expansion poses both technical and economic challenges. To make expansion projects economically feasible and attractive to investors, optimal conductor selection and phase balancing are essential. In this paper, a novel methodology is introduced that simultaneously addresses both problems through a leader–follower strategy, combining the Chu & Beasley Genetic Algorithm (CBGA) with the method of successive approximations for power flow analysis. To assess the effectiveness of the proposed strategy, two test systems under varying demand scenarios are employed, and their economic and statistical results are compared with those obtained using three metaheuristic optimization techniques: the hurricane-based optimization algorithm, the salp swarm optimization algorithm, and the sine cosine algorithm. According to the findings, the CBGA outperformed the other techniques, producing costs of USD 125,348.487 and USD 94,423.130, processing times of 11.766 s and 94.494 s, and standard deviations of 0.161% and 0.199% in the 8- and 25-node test systems, respectively. These results underscore the methodology's efficiency, responsiveness, and comprehensive approach to the optimal conductor selection and phase balancing problems in electrical systems. |
