Abstract

Economic emission dispatch (EED) is an important optimization problem in today's power systems with significant renewable energy integration. This work presents a hybrid particle swarm optimization and grey wolf optimization (PSO-GWO) approach for the multi-objective economic emission dispatch (EED) problem with solar and wind integration. The hybrid algorithm improves the exploration-exploitation trade-off by incorporating the social learning behaviour of particle swarm optimization and the hierarchical hunting behavior of grey wolf optimization. The uncertainty of renewable energy is modeled using probability distributions to enhance dispatch reliability. A weighted multi-objective approach is adopted to optimize fuel cost and emissions. The proposed approach is tested on a benchmark 10-unit and 6 IEEE generating units interconnected power system. The proposed method exhibits better convergence and performs better than the traditional PSO, genetic algorithm and other benchmark methods. The proposed approach reduces cost by 8.3% and emissions by 12.6% and is suitable for sustainable and efficient power system operation.