Multi-Objective Day-Ahead Scheduling for Air Conditioning Load Considering Dynamic Carbon Emission Factor

The traditional optimal scheduling of air conditioning load has traditionally focused on improving user comfort and reducing electricity costs. However, research on carbon emissions generated during the operation of air conditioning is still in the developmental stage. Currently used average carbon emission factors in carbon-emission studies face challenges such as delayed data updates and difficulty in reflecting spatiotemporal variations. These issues contribute to the inaccurate quantification of carbon emissions, creating a challenging situation, which does not meet the development needs of green power systems under the “dual carbon goals”. Therefore, this paper proposes a multi-objective scheduling method for cooling-dominant air conditioning load considering the dynamic carbon emission factor (CEF), in conjunction with real-time spatiotemporal data from the electricity grid model, generates the electric carbon factor for each moment throughout the day. Firstly, while considering user comfort, the dynamic CEF-based carbon-emission cost and electricity cost are fused into the user’s comprehensive electricity cost, and a multi-objective optimization model for day-ahead scheduling of air conditioning loads is established. In addition, the above model is solved by the NSGA-II algorithm to obtain the Pareto front composed of non-dominated solutions. Then, the best compromise solution is objectively selected through gray target decision (GTD) to provide scientific decision-making for day-ahead scheduling of cooling-dominant air conditioning loads. Finally, four users with different air conditioning loads and room temperature requirements are designed to verify the effectiveness of the proposed strategy. The simulation results illustrate that compared with single-objective optimization and simple multi-objective decision-making methods, the proposed strategy possesses a stronger trade-off ability, which can greatly reduce the comprehensive electricity cost while ensuring user comfort.