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

TL;DR

This paper introduces a digital twin-based day-ahead scheduling approach for integrated energy systems that effectively manages uncertainties, reduces costs, and emphasizes the roles of electric vehicles and thermal storages in decarbonization.

Contribution

It proposes a novel digital twin framework combined with deep learning for uncertainty-aware energy system scheduling, achieving significant cost reductions.

Findings

Reduces IES operating costs by 63.5% compared to traditional methods.

Highlights the proactive roles of electric vehicles and thermal storages.

Demonstrates the effectiveness of digital twins in managing uncertainties.

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.