Optimal Energy Scheduling of Digital Twins Based Integrated Energy System

Du, Jialu; Harsh, Pratik; Sun, Hongjian

Abstract

With global climate change and growing electricity demand, optimizing Integrated Energy Systems (IES) for low-carbon 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.

Citation

Du, J., Harsh, P., & Sun, H. (2024, December). Optimal Energy Scheduling of Digital Twins Based Integrated Energy System. Presented at The 4th International Conference on Smart City and Green Energy, Sydney

• Presentation Conference Type: Conference Paper (published)
• Conference Name: The 4th International Conference on Smart City and Green Energy
• Start Date: Dec 10, 2024
• End Date: Dec 13, 2024
• Acceptance Date: Nov 16, 2024
• Deposit Date: Dec 12, 2024
• Peer Reviewed: Peer Reviewed
• Keywords: Index Terms-Digital Twins; Integrated Energy System; Mixed Integer Linear Programming; Battery Energy Storage System
• Public URL: https://durham-repository.worktribe.com/output/3216494
• Publisher URL: https://icscge.org/