Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

By constructing digital twins (DT) of an integrated energy system (IES), one can benet 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 suer 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.
Original languageEnglish
Article number117899
Number of pages9
JournalApplied Energy
Volume305
Early online date29 Sep 2021
DOIs
Publication statusE-pub ahead of print - 29 Sep 2021

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