A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources

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The traditional optimal power flow problem (OPF) usually centers on thermal generators, which have limited fuel for power generation, while emissions from the network system are commonly overlooked. However, the rising appreciation of renewable energy sources, valued for their sustainability, abundance, and environmental friendliness, has sparked increasing interest in the power systems domain. Consequently, there is a growing trend of integrating renewable energy sources into the electrical grid. This work explores the adaptation of the standard IEEE-30 bus system by incorporating renewable energy sources as a case study. This involves the replacement of traditional thermal generators located on buses 5 and 11 with wind generators, while bus 13 is substituted with solar generators. Addressing the uncertainty and intermittence inherent in renewable energy sources (RES), Weibull and lognormal probability density functions are employed for RES availability estimation. Integrating RES into the optimal power flow problem is framed as a multi-objective optimization task. A novel meta-heuristic optimization approach termed the Memory-Guided Jaya algorithm (MG-Jaya), is specifically tailored to address diverse challenges in multi-objective optimal power flow (MOOPF) incorporating RES. A smart memory-based strategy is incorporated into the algorithm to enhance solution optimality, convergence properties, and exploitation capabilities. Furthermore, a suitable mechanism aimed at efficiently finding Pareto-optimal solutions is proposed to address the multi-objective optimization optimal power flow (MOOPF) problem. Besides, to further evaluate the performance of the proposed approach in addressing complex, larger-scale issues, the study opts to utilize another test system of greater magnitude, namely the IEEE-57 bus system. To assess its effectiveness, the approach is compared against 14 recently introduced metaheuristics, which have garnered significant citations. To ensure fair comparisons, parameter configurations for all algorithms are automated using the parameter tuning tool iterated racing (irace). The experimental outcomes are analyzed using various nonparametric statistical techniques, including the Hyper-volume test, the Wilcoxon signed-rank test, and the critical difference plot. Furthermore, three authenticity criteria - Generational Distance (GD), Spacing Parameter (SP), and Diversity Metric (DM) - are employed to analyze the obtained Pareto-optimal solutions. Simulation results show that the proposed MG-Jaya algorithm demonstrates competitive capabilities. It effectively handles multi-objective and non-convex optimization problems. When compared to other approaches, it outperforms them in terms of solution optimality and feasibility.