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

You, Minglei, Wang, Qian, Sun, Hongjian, Castro, Ivan and Jiang, Jing (2022) Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties. Applied Energy, 305. p. 117899. ISSN 0306-2619

[img] Text
Manuscript_APEN_D_21_04005_R2_CleanVersion.pdf - Accepted Version
Restricted to Repository staff only until 29 September 2022.
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (767kB) | Request a copy
Official URL: https://doi.org/10.1016/j.apenergy.2021.117899

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.

Item Type: Article
Additional Information: Funding information: This work was supported by the Department for Business, Energy & Industrial Strategy (BEIS), UK through the project of “Ubiquitous Storage Empowering Response (USER)” https://www.theuserproject.co.uk/.
Uncontrolled Keywords: Digital twins, Multi-vector energy system, Integrated energy system, Machine learning
Subjects: H600 Electronic and Electrical Engineering
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: John Coen
Date Deposited: 27 Sep 2021 14:48
Last Modified: 15 Oct 2021 13:15
URI: http://nrl.northumbria.ac.uk/id/eprint/47368

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics