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

• URL: http://arxiv.org/abs/2109.14423v1
• Date: Wed, 29 Sep 2021 13:58:01 GMT
• Title: Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties
• Authors: Minglei You and Qian Wang and Hongjian Sun and Ivan Castro and Jing Jiang
• Abstract summary: Digital twins (DT) of an integrated energy system (IES) can improve coordinations among various energy converters. Case studies show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%.
• Score: 14.946548030861866
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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, 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. 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.

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