Abstract This paper introduces a Intelligent Hybrid Metaheuristic Multi-objective Optimal Power Flow framework for Renewable-integrated Power Systems (MOOPF–RE), aimed at improving Techno-economic and Environmental performance in uncertain operating conditions. The proposed framework introduces a hybrid Modified Artificial Bee Colony–NSGA-II (MOABC–NSGA-II) algorithm to tackle ongoing research gaps like poor probabilistic renewable modeling, weak constraint-handling robustness, and biased post-Pareto decision analysis. The hybridization merges ABC’s adaptive exploration with NSGA-II’s elitist non-dominated sorting, enhanced by a Stricter Constraint-Dominance Principle (SCDP) for feasibility and a Decomposition-Based Archive Approach (DAA) to preserve Pareto front diversity. Wind speed and solar irradiance uncertainties are modeled with Weibull and lognormal distributions, while a post-Pareto AHP-TOPSIS decision-support mechanism identifies the Best Compromise Solution (BCS). Simulations on IEEE 30- and 57-bus test systems confirm the algorithm’s effectiveness under varying wind and solar conditions. The proposed hybrid achieved a significant 0.85% reduction in the Levelized Cost of Electricity (LCOE) and a 12.44% decrease in emissions compared to reported OPF techniques, Comparative evaluation with advanced metaheuristics like MOSGA, MOALO, NSGA-II, and MOGOA showed better convergence, stability, and Pareto diversity, supported by performance indices such as HV, SP, and Friedman Ranking metrics. The stability and robustness analysis using SR, MCT, and MFE shows the proposed methodology’s reliability and consistency in nonlinear and constraint-heavy scenarios. The proposed MOABC–NSGA-II framework shows excellent scalability, efficiency, and resilience for multi-objective power flow optimization amid renewable uncertainty. The findings support global sustainability goals by enhancing SDG 7 and SDG 13 with cost-effective, low-emission optimal power flow.
Katkar et al. (2026) studied this question.