Analysis of equivalent energy storage for integrated electricity-heat system

As the low-carbon energy transition continues to advance, the integrated electricity-heat system (IEHS) has developed rapidly and become a promising option to promote renewable energy accommodation, due to its enormous operating flexibility supported by the thermal inertia of district heating systems (DHSs). The key to making full use of thermal inertia is a suitable and reasonable model of the DHS. Existing mechanistic models such as the node method require a large amount of information and are not suitable for practical applications, while equivalent models such as the thermo-electric analogy method are not intuitive enough to portray thermal inertia. Therefore, based on the virtual energy storage (ES) characteristics caused by thermal inertia, this paper proposes an equivalent ES model to equate the quasi-dynamic model of the DHS, so as to realize practical utilization and intuitive portrayal of thermal inertia. The proposed equivalent ES model utilizes a two-level architecture to match the different time scales of the electric power system (EPS) and DHS and adopts the timevarying parameters to intuitively portray the thermal inertia. Then, a hybrid machine learning model merging feature selection and regression prediction and the economic dispatch (ED) model of the IEHS are synthetically used to estimate the parameters of the equivalent ES. Based on the equivalent ES model, the equivalent ED of IEHS can be implemented by the real-existing grid dispatch center. Finally, the feasibility and validity of the proposed model and method are verified by the case studies conducted on two IEHSs. The results show that applying the equivalent ES to the ED of IEHS improves the computational performance by about 20 %.