Optimizing Cost and Emissions in a 10-Unit Power Generation System using Different Optimization Techniques with Wind Energy Integration

This paper explores the optimization of cost and emissions in a 10-unit power generation system using three advanced techniques: Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), and a Python-based Search Optimization Algorithm. The study integrates wind energy to assess its impact on economic efficiency and environmental sustainability. A unified computational model was developed to implement and compare these methods under identical conditions. The findings indicate that PSO demonstrates faster convergence and higher efficiency in scenarios without wind energy, whereas TLBO performs better regarding cost reduction and environmental benefits when wind energy is incorporated and PYSOA converges more slowly and less efficient compared to PSO and TLBO. It reveals that integrating wind energy significantly reduces fuel and emission costs, emphasizing the role of renewable energy in sustainable power generation. Specifically, without wind energy, PSO achieved lower fuel and emission costs compared to TLBO and PYSOA. However, with wind energy integration, TLBO outperformed the other techniques in minimizing costs and emissions. This study underscores the importance of optimization techniques in achieving sustainable and cost-effective power generation.