Collaborative Optimization Strategy for Coal Blending and Dispatching in Power Systems Under Dual Carbon Goals

Under China's dual carbon goals of achieving peak carbon emissions by 2030 and carbon neutrality by 2060, reducing carbon emissions in the power industry is critical. This study proposes a collaborative optimization strategy for coal blending and dispatching in coal-fired power systems to address the economic and environmental impacts of mixed coal quality. A linear coal blending model is developed, incorporating boiler coal quality constraints, pollutant emissions, and carbon tax costs, aiming to minimize total system costs. Additionally, a synergistic framework integrates coal blending optimization with lowcarbon economic dispatch, formulated as a mixed-integer secondorder cone programming model, solved via GUROBI on MATLAB. Case studies on an improved IEEE39 system with four operational scenarios demonstrate that the collaborative strategy reduces carbon emissions by 19.1 % and total costs by 292,000 yuan compared to non-integrated approaches. Results highlight the effectiveness of incorporating carbon tax costs into optimization objectives, enabling dynamic adjustments of coal ratios (e.g., increasing low-carbon coal usage) to enhance system sustainability. This work provides a viable pathway for balancing economic efficiency and environmental performance in power systems under dual carbon constraints, with implications for future integration of multi-energy systems and AI-driven optimization.