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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


Minglei You

Ivan Castro

Jing Jiang


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 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.


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.

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
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 305
Article Number 117899


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