Dynamic Stochastic Multi-objective Optimal Power Flow incorporating Solar-Wind Energy using Hybrid Gbest-guided Artificial Bee Colony–NSGA-II Algorithm

Integrating wind and solar resources into power systems has created significant variability and uncertainty, rendering traditional Optimal Power Flow (OPF) formulations insufficient for effective operational planning. This work advances existing research by incorporating dynamic load demand scenarios and renewable intermittency into a comprehensive Dynamic Stochastic Multi-Objective Optimal Power Flow (DS-MOOPF) framework, moving beyond the focus on deterministic or fixed stochastic OPF models. Renewable energy uncertainties are modeled with probability distributions: wind speed uses a Weibull distribution, solar irradiance follows a lognormal distribution, and dynamic load variations are represented by normal distributions to reflect real-time demand. An improved hybrid gbestABC–NSGA-II algorithm is introduced to tackle the complex optimization problem, merging the exploration-exploitation balance of the Artificial Bee Colony with the elitist non-dominated sorting and diversity-preserving features of NSGA-II. Diversity-Enhanced Tri-Stage Repair Constraint-Handling Method an effective constraint-handling method guarantees feasible decisions amid various system limits, while Pareto archive management with crowding-distance sorting improves convergence and maintains solution diversity. A modified TOPSIS-based decision analysis module is used to find the Best Compromise Solution (BCS), providing scalable and practical support for system operators. Experiments on IEEE 30, 57, and 118-bus test systems show that the proposed hybrid framework offers faster convergence, better diversity, and greater robustness than benchmark algorithms like MOEA/D, NSGA-III, MOABC, NSGA-II, and MOPSO. Statistical tests with Inverted Generational Distance (IGD), Hypervolume (HV), and the Wilcoxon rank-sum method confirm the approach’s superiority. The results show that the DS-MOOPF model is a scalable and resilient tool for managing large-scale uncertainty in power systems, supporting sustainable development and net-zero emission goals.