Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power

In 2022, global coal combustion contributed significantly to global pollution, producing 15.22 billion metric tons of carbon dioxide (CO2). This research addresses the urgent challenge of mitigating CO2 emissions in Indian coal power plants by strategically deploying solar photovoltaic (PV) systems and integrating demand response mechanisms. The imperative to reduce greenhouse gas emissions from coal-based electricity generation underscores the critical context of climate change. Emphasizing the vital role of integrating renewable energy-based distributed generators into the existing coal infrastructure, this study positions solar PV technology as a promising solution. Optimal solar PV system allocation is achieved through the implementation of the genetic algorithm technique. Factors such as solar resource availability, electricity demand patterns, and the CO2 intensity associated with coal power generation are considered in this process. The primary research objective is twofold: to minimize CO2 emissions and maximize the integration of solar PV systems while mitigating power losses. The proposed approach considers the intermittent nature of solar power and the dynamic characteristics of demand. Rigorous testing on an IEEE 33-bus system powered by the studied coal power plant reveals a substantial 29.31% reduction in CO2 generation following the implementation of the proposed strategy. This research represents a decisive step towards fostering a more sustainable and environmentally friendly energy landscape. Our study's outcomes offer valuable insights for policymakers and stakeholders in the energy sector, providing a robust foundation for the advancement of environmentally conscious practices within the coal power industry.