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<p>The fast proliferation of renewable energy sources (RES) in modern power grids has increased the complexity of the Optimal Power Flow (OPF) problem, which is nonlinear, nonconvex, and uncertainty-driven. This study proposes a Renewable-integrated Multi-Objective Optimal Power Flow (MOOPF–RE) architecture that improves both techno-economic and environmental performance under wind-solar variability. We propose a hybrid algorithm <i>gbestABC–NSGA-II</i> that combines global-best guided Artificial Bee Colony exploration with NSGA-II’s elitist non-dominated sorting to achieve superior convergence, diversity preservation, and feasibility restoration. Stochastic wind and solar models based on Weibull and lognormal distributions, together with adaptive repair-based constraint handling and an adaptive grid-crowding archive, ensure realistic uncertainty representation and well-distributed Pareto fronts. An integrated Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) module identifies the best compromise solution in accordance with operator preferences. Extensive trials on IEEE 30-, IEEE 57-, and Indian 62-bus systems show considerable improvements, including reductions of up to <b>6.75%</b> in overall generation cost, <b>31.39%</b> in emissions, <b>14.67%</b> in active power loss, and <b>39.51%</b> in voltage deviation. Benchmarking against cutting-edge algorithms like Multi-Objective Adaptive Guided Differential Evolution algorithm (MOAGDE), Improved Multi-objective Manta Ray Firefly Optimization (IMOMRFO), Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), Multi-Objective Modified Imperialist Competitive Algorithm (MOMICA), Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and statistical validation with IGD, HV, and Wilcoxon-rank tests demonstrate the proposed framework’s resilience and scalability. The results demonstrate the hybrid MOOPF–RE approach as a resilient and sustainable decision-support tool, well linked with SDGs 7 and 13.</p>

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A Novel Hybrid Metaheuristic Approach to Analyse Techno-economic and Environmental Performance of Renewable-integrated Multi-objective Optimal Power Flow Framework

  • 期刊论文

  • 发表日期：

    2026年1月24日

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  • Abhishek Bajirao Katkar,
  • Himmat Tukaram Jadhav

摘要

The fast proliferation of renewable energy sources (RES) in modern power grids has increased the complexity of the Optimal Power Flow (OPF) problem, which is nonlinear, nonconvex, and uncertainty-driven. This study proposes a Renewable-integrated Multi-Objective Optimal Power Flow (MOOPF–RE) architecture that improves both techno-economic and environmental performance under wind-solar variability. We propose a hybrid algorithm gbestABC–NSGA-II that combines global-best guided Artificial Bee Colony exploration with NSGA-II’s elitist non-dominated sorting to achieve superior convergence, diversity preservation, and feasibility restoration. Stochastic wind and solar models based on Weibull and lognormal distributions, together with adaptive repair-based constraint handling and an adaptive grid-crowding archive, ensure realistic uncertainty representation and well-distributed Pareto fronts. An integrated Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) module identifies the best compromise solution in accordance with operator preferences. Extensive trials on IEEE 30-, IEEE 57-, and Indian 62-bus systems show considerable improvements, including reductions of up to 6.75% in overall generation cost, 31.39% in emissions, 14.67% in active power loss, and 39.51% in voltage deviation. Benchmarking against cutting-edge algorithms like Multi-Objective Adaptive Guided Differential Evolution algorithm (MOAGDE), Improved Multi-objective Manta Ray Firefly Optimization (IMOMRFO), Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), Multi-Objective Modified Imperialist Competitive Algorithm (MOMICA), Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and statistical validation with IGD, HV, and Wilcoxon-rank tests demonstrate the proposed framework’s resilience and scalability. The results demonstrate the hybrid MOOPF–RE approach as a resilient and sustainable decision-support tool, well linked with SDGs 7 and 13.