Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties

You, Minglei; Wang, Qian; Sun, Hongjian; Castro, Ivan; Jiang, Jing

Abstract

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 and cost saving.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.much lower long-term operating costs than those of 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.

Citation

You, M., Wang, Q., Sun, H., Castro, I., & Jiang, J. (2022). Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties. Applied Energy, 305, Article 117899. https://doi.org/10.1016/j.apenergy.2021.117899

• Journal Article Type: Article
• Acceptance Date: Sep 7, 2021
• Online Publication Date: Sep 29, 2021
• Publication Date: Jan 1, 2022
• Deposit Date: Sep 7, 2021
• Publicly Available Date: Sep 30, 2023
• Journal: Applied Energy
• Print ISSN: 0306-2619
• Electronic ISSN: 1872-9118
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 305
• Article Number: 117899
• DOI: https://doi.org/10.1016/j.apenergy.2021.117899
• Public URL: https://durham-repository.worktribe.com/output/1242149