Multiobjective Energy Optimization Strategy for Source–Load–Storage Coordination in Intelligent Buildings Considering User Satisfaction

As the proportion of building energy consumption in total energy consumption continues to rise, traditional energy scheduling strategies and building load regulation methods are improved to reduce energy consumption and enhance the flexibility of building scheduling. In this study, a two-stage optimization strategy for energy-efficient buildings incorporating electric vehicles (EVs) based on user satisfaction is proposed. First, a source–load–storage coordinated energy optimization system for buildings, including photovoltaic (PV) generation, energy storage systems (ESSs), EVs, light-emitting diode (LED) lights, and heating, ventilation, and air conditioning (HVAC), is established. Second, the satisfaction levels of users with multiple flexible loads are used as indicators of comfort to dynamically adjust energy consumption in buildings. Then, a multiobjective energy optimization model is formulated to minimize daily operational costs while simultaneously maximizing user satisfaction, with an emphasis on balancing comfort and economic efficiency. Third, a two-stage energy optimization model of day-ahead and intraday is constructed to reduce the impact of source–load forecasting errors on the operation of building energy systems, and an incentive demand response strategy is introduced to guide users to participate in scheduling in the intraday stage. Finally, different cases are created to test the effectiveness of the proposed strategy. The overall simulation results validate the proposed approach with operational cost reduction of 12.9% while maintaining a user satisfaction level above 0.95 and grid volatility reduction of 7.56% as compared to the traditional energy optimization strategy.