Synergy of Binary Artificial Bee Colony Swarm Intelligent Optimizer and NSGA-II Evolutionary Algorithm for Renewable-Integrated Multi-Objective Profit-Based Unit Commitment

The environmentally constrained Profit-Based Unit Commitment Problem (PBUCP) is a large-scale, nonlinear, mixed-integer, multi-objective optimization problem essential for operational planning in deregulated power systems. It determines optimal unit commitment and dispatch schedules to simultaneously maximize profit and minimize emissions under technical and market constraints. With increasing renewable penetration and stricter environmental regulations, conventional approaches that treat emissions merely as constraints fail to support comprehensive multi-objective decision-making. Renewable variability further introduces stochasticity and nonlinearity, making traditional optimization techniques less effective. This paper proposes a hybrid Binary Artificial Bee Colony–NSGA-II (BABC–NSGA-II) framework for a renewable-integrated multi-objective PBUCP. The BABC component efficiently handles discrete ON/OFF scheduling using adaptive neighborhood exploration, while NSGA-II optimizes continuous dispatch variables to generate a well-distributed Pareto front. The model explicitly considers profit maximization and emission minimization under constraints including minimum up/down time, ramp rate limits, spinning reserve, renewable uncertainty, power balance, and generation limits. The proposed method is validated on three benchmark systems: a 10-unit system with 20% renewable penetration, a 26-unit system with 30% wind–solar integration, and a 54-unit large-scale system with 40% renewable penetration over a 24-hour horizon. Comparative analysis against Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Multi-Objective Artificial Bee Colony (MOABC), and standard NSGA-II demonstrates superior performance. The proposed approach achieves 6.8%–9.4% higher profit, 11.2%–15.7% lower emissions, and approximately 12% improvement in hypervolume index, confirming enhanced convergence and diversity. The framework provides a scalable and robust decision-support tool for environmentally sustainable power system operation.