Abstract

This research presents an advanced multi-criteria optimisation strategy to address the complex challenges associated with many-objective optimal power flow (MOOPF). This study presents a hybrid algorithm that combines the Multiobjective Artificial Bee Colony (MOABC) algorithm with the Non-dominated Sorting Genetic Algorithm II (NSGA-II), optimising the balance between exploration and exploitation in the search space. The hybrid MOABC-NSGAII algorithm is rigorously evaluated using the IEEE CEC 2023 benchmark test instances, showcasing its robustness, efficiency, and ability to address complex optimisation challenges. Subsequent to the comprehensive benchmarking, the algorithm is implemented on the IEEE 118 bus system, to address complex real-time optimisation scenarios. This study aims to concurrently minimise the fuel costs of thermal generators, active power losses, and deviations in voltage magnitude. The research seeks to improve the economic efficiency, reliability, and environmental sustainability of the power system through the optimisation of three critical parameters. The findings from IEEE CEC benchmark test and MOOPF IEEE 118 bus system case study analysis confirm the effectiveness of the proposed hybrid algorithm, demonstrating notable enhancements in attaining a balanced and optimised power system operation. This investigation highlights the effectiveness of hybrid MOABC-NSGAII in addressing many-objective tasks with statistical validation of performance metrics proves its applications in large-scale power system management.

Authors

Abhishek Bajirao Katkar¹, Himmat Tukaram Jadhav²
Government Polytechnic, Kolhapur, India¹, SNDT Women’s University, India ²

Keywords

Multi-Objective Optimization, Optimal Power Flow, Hybrid ABC-NSGAII, CEC Benchmark Functions