Cost-effective and low-emission load dispatch incorporating wind-solar energy and plug-in electric vehicles via sine cosine algorithm

Abstract Fossil fuel-based power plants are major contributors to greenhouse gas emissions within the power sector. To improve both the cost-effectiveness and environmental sustainability of electricity generation, this study presents a multi-objective Economic Emission Load Dispatch (EELD) model that integrates renewable energy sources (RES), such as wind and solar plants, and plug-in electric vehicles (PEVs). The proposed model incorporates realistic, nonlinear constraints to enhance system reliability, including the arrival, departure, and waiting times of PEVs, as well as the underestimation and overestimation costs associated with RES generation. A Sine Cosine algorithm (SCA) is employed to address the multi-objective optimization problem, offering solutions from various operational perspectives. The model’s performance is evaluated using four test cases involving systems with ten and twenty thermal power plants with RES and PEVs. Comparative analysis with existing state-of-the-art methods demonstrates the proposed approach’s superior accuracy, computational efficiency, and ability to handle complex system dynamics and uncertainties.