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

arXiv: https://arxiv.org/abs/2109.14423 · HTML: https://mlsanity.com/paper/2109.14423/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Integration of an Energy Management Tool and Digital Twin for Coordination and Control of Multi-vector Smart Energy Systems](https://mlsanity.com/paper/2007.12129/) (2020)

## Built on by (most cited first, 4 of 4)

- [Towards the Human Digital Twin: Definition and Design -- A survey](https://mlsanity.com/paper/2402.07922/) (2024)
- [Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study](https://mlsanity.com/paper/2503.04757/) (2025)
- [Textual-Knowledge-Guided Numerical Feature Discovery Method for Power Demand Forecasting](https://mlsanity.com/paper/2312.00095/) (2023)
- [Carbon-Aware Orchestration of Integrated Satellite Aerial Terrestrial Networks via Digital Twin](https://mlsanity.com/paper/2510.17825/) (2025)

## Abstract (arXiv)

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.