  By constructing digital twins (DT) of an integrated energy system (IES), one
can benefit from DT's predictive capabilities to improve coordinations among
various energy converters, hence enhancing energy efficiency, cost savings and
carbon emission reduction. This paper is motivated by the fact that practical
IESs suffer from multiple uncertainty sources, and complicated surrounding
environment. To address this problem, a novel DT-based day-ahead scheduling
method is proposed. The physical IES is modelled as a multi-vector energy
system in its virtual space that interacts with the physical IES to manipulate
its operations. A deep neural network is trained to make statistical
cost-saving scheduling by learning from both historical forecasting errors and
day-ahead forecasts. Case studies of IESs show that the proposed DT-based
method is able to reduce the operating cost of IES by 63.5%, comparing to the
existing forecast-based scheduling methods. It is also found that both electric
vehicles and thermal energy storages play proactive roles in the proposed
method, highlighting their importance in future energy system integration and
decarbonisation.
