A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin

With the growing percentage of the intermittent renewable power generation, the energy system is under increasing pressure in balancing the supply and the demand. As a major part of urban energy consumptions, buildings can provide considerable regulation flexibility for the energy system by actively managing their energy demands. For the building energy flexibility (BEF) provided by thermostatically controlled loads (TCL), its dispatch performance is vulnerable to the building thermal parameter errors, and in some cases, occupants need to provide the critical information related to the indoor temperature state and the occupancy state to the energy management system outside buildings, which decreases the availability of the BEF and raises privacy concerns. For these issues in the BEF utilization, this paper proposes a data-model fusion dispatch strategy based on the digital twin (DT). The proposed strategy is capable of parameter fault tolerance and privacy protection by combining the model-free advantage of the data-driven method with the analytical optimization advantage of the model-driven method. Firstly, a DT-based BEF dispatch framework is proposed. Secondly, the building DT is established by combining the building thermal dynamics (BTD) data-driven model and the TCL operation mechanism model. And the building response deduction is carried out based on the DT. Finally, under the rolling optimization framework, the data-model fusion dispatch strategy is devised by uniting the DT deduction and the optimization constructed by the BTD mechanism model, in which the multi-dimensional modeling of the BTD is carried out from the state dimension and the energy dimension. The simulation results show that the optimization result can reach 98.4% of the ideal result under the scenario with 15% parameter random error, and 98.3% of the ideal result under the scenario with 15% random state noise injection.