Optimal Energy Scheduling of Digital Twins Based Integrated Energy System

With global climate change and growing electricity demand, optimizing Integrated Energy Systems (IES) for lowcarbon and economic transformation has become particularly important. By applying digital twin (DT) technology to create a virtual copy of the physical entities of the IES, real-time monitoring and prediction of the system operation can be realized to improve energy efficiency, save costs, and reduce carbon emissions. This paper proposes an optimal scheduling method for integrated energy systems (IES) using digital twin (DT) technology and a Mixed Integer Linear Programming (MILP) model to address the uncertainty of renewable energy output in real-world scenarios. The MILP model is used to co-optimize the power trading and battery energy storage system (BESS), adjusting the system's output status in real time to achieve a more flexible and reliable energy supply. The IES case study shows that the proposed DT-based approach can reduce the operating costs of IES by at least 13.7 % compared to the existing scheduling methods. It is also found that optimizing the BESS has a positive effect on maintaining optimal battery operation status.