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December 10, 2025EnergiesOpen Access

Digital Twin-Enabled Distributed Robust Scheduling for Park-Level Integrated Energy Systems

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Authors

XCXiao ChangSLShengwen LiQWQiang Wang

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Overview

The proposed digital twin model enhances scheduling accuracy in energy systems, reducing error rates and improving robustness.

Key Points

  • This research aims to enhance scheduling performance in Park Integrated Energy Systems by utilizing a digital twin model for photovoltaic output.
  • Developed a PV digital twin model for improved probability distributions
  • Employed LSTM neural network for output prediction
  • Integrated real-time data for dynamic corrections
  • Used Latin hypercube sampling and k-means clustering for scenario generation
  • Constructed a two-stage distributed robust optimization model
  • Digital twin model improved prediction accuracy by reducing root mean square error by 13.3%
  • Mean absolute error reduced by 10.81%
  • Significantly enhanced operational economics compared to traditional scheduling methods
  • Demonstrated robustness in PIES scheduling with practical application value

Cite This Study

Chang et al. (2025) studied this question.

synapsesocial.com/papers/69401d412d562116f28f8331https://doi.org/10.3390/en18246471
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