Integrating Digital Twins and Positive Energy Districts: A Framework for Scalable Urban Energy Transformation

Positive Energy Districts (PEDs) offer a path to urban climate neutrality but present significant management complexity. Digital Twins (DTs), dynamic virtual replicas, provide powerful tools for monitoring, simulation, and control needed for PEDs. This paper proposes a structured conceptual and architectural framework for DT-PED integration, delineating functional layers (Data Acquisition, Virtual Modeling, Analytics Engine, Control, Stakeholder Interface) and their interdependencies. This framework provides a blueprint to leverage DTs for managing heterogeneous systems and data streams inherent in PEDs. Addressing key challenges in data, modeling, interoperability, and scalability, identified herein, is crucial for realizing robust, integrated DT-PED solutions needed to accelerate sustainable urban energy transformation.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-10-21
DOI
https://doi.org/10.1051/e3sconf/202565404007/pdf
Primary Topic
Integrated Energy Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Integrating Digital Twins and Positive Energy Districts: A Framework for Scalable Urban Energy Transformation

Per Sieverts Nielsen, Ramazan Sari, Xiufeng Liu, Xiaobing Zhang
Springer Link (Chiba Institute of Technology)
Integrated Energy Systems Optimization
article

Integrating Digital Twins and Positive Energy Districts: A Framework for Scalable Urban Energy Transformation

Per Sieverts Nielsen, Ramazan Sari, Xiufeng Liu, Xiaobing Zhang
article en

Abstract

Positive Energy Districts (PEDs) offer a path to urban climate neutrality but present significant management complexity. Digital Twins (DTs), dynamic virtual replicas, provide powerful tools for monitoring, simulation, and control needed for PEDs. This paper proposes a structured conceptual and architectural framework for DT-PED integration, delineating functional layers (Data Acquisition, Virtual Modeling, Analytics Engine, Control, Stakeholder Interface) and their interdependencies. This framework provides a blueprint to leverage DTs for managing heterogeneous systems and data streams inherent in PEDs. Addressing key challenges in data, modeling, interoperability, and scalability, identified herein, is crucial for realizing robust, integrated DT-PED solutions needed to accelerate sustainable urban energy transformation.

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 37%
Integrated Energy Systems Optimization
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