Multi-objective Enhanced Artemisinin Optimization Approach for Maximizing DG Hosting Capacity in Unbalanced Distribution Networks

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Original abstract

This paper presents an integrated method to simultaneously determine the optimal DG location and size in distribution systems, while considering operational constraints.The integrated method aims to simultaneously consider multiple performance indices, including maximizing the peak hosting capacity (PHC), reducing total active power loss (TAPL), and enhancing the voltage stability index (VSI).The DG integration challenge proposed in this paper is formulated as a weighted-sum mixed-integer nonlinear optimization problem.To address this complex problem, the Quasi-Opposite Artemisinin Optimization algorithm (QOAO) is presented.In this algorithm, the artemisinin algorithm (AO) is enhanced through the addition of Opposition-Based Learning (OBL) and Quasi-Opposition-Based Learning (QOBL) with three strategies: random QOBL, selective QOBL, and hybrid QOBL to improve search capability and convergence accuracy.The suggested QOAO is able to simultaneously tackle a discrete and continuous DG integration problem in distribution systems.The performance of the QOAO is tested on the IEEE 123-Bus unbalanced distribution system and also validated by comparative analysis with selected comparative algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Orca Optimization Algorithm (OOA), Electric Eel Foraging Optimization (EEFO), Gray Wolf Algorithm (GWO), White Shark Optimizer (WSO), and Artemisinin Algorithm (AO).The simulation results demonstrate that the presented QOAO efficiently improves overall distribution system performance while maintaining acceptable operational constraints.Specifically, the QOAO achieves significant enhancement in PHC (i.e., 99.889%), voltage profile (i.e., the minimum voltage of 0.998 p.u phase-a), VSI (i.e., the minimum VSI of 0.839 p.u phase-a), and system loss reduction (i.e., 63.05%) relative to the base case, respectively.Furthermore, the efficiency and reliability of the presented QOAO are assessed using statistical performance metrics and non-parametric statistical tests (i.e., Friedman and Wilcoxon signed-rank).The statistical results reveal that QOAO outperforms the selected algorithms considered in this study, achieving the best mean value of 0.115, the lowest standard deviation of 0.0028 across 30 independent runs, a p-value less than 0.0001, and the lowest mean rank of 1.2667.

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