Abstract
The economics of integrated community energy system (ICES) dispatch schemes are influenced by the accuracy of the parameters of the different energy-conversion-equipment models. Traditional equipment efficiency correction models only take into account the historical load factors and variations in the environmental factors, ignoring the fact that the input data do not come from the actual operating data of the equipment, which affects the accuracy of the equipment models and therefore reduces the economics of ICES dispatch solutions. Therefore, this paper proposes an optimal scheduling of a community-integrated energy system based on twin data, considering a device-correction model that combines an energy hub model and a twin data correction model. Firstly, a dynamic energy hub (DEH) model with a correctable conversion efficiency is developed based on the twin data; secondly, a physical model of the system and a digital twin are established, with the prediction data as the input of the digital twin and the twin data as the output. Polynomial regression (PR) and a back propagation neural network (BPNNS) are used to process the twin data to accurately extract the equipment conversion efficiency. Considering the lack of accuracy of traditional prediction methods, a prediction model combining a long- and short-term-memory neural network and digital-twin technology is constructed for renewable energy generation and load prediction. The simulation results show that using twin data to correct the equipment efficiency reduces the average absolute error and average relative error by 4.6706 and 1.18%, respectively, when compared with the use of historical data. Compared with the actual total cost of the dispatch, the total cost of the dispatch after the equipment efficiency correction was reduced by USD 850.19.