Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China's carbon peaking and carbon neutrality goals. Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system, a multi-time scale optimal scheduling strategy based on model predictive control (MPC) is proposed under the consideration of load optimization. First, load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature, and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost. Second, considering inter-day to intra-day source-load prediction error, an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption. Finally, taking an office building on a summer work day as an example, the effectiveness of the proposed scheduling strategy is verified. The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%, improves the carbon emission reduction by 7.99%, and the photovoltaic consumption rate reaches 98.57%, improving the system's low-carbon and economic performance.