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Data Modeling and Synchronization Method to Align Power Trading Rules for Integrated Energy Management Systems

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  • Yingya Zhou

    (State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China)

  • Chin Hao Chong

    (School of Management, Guilin University of Aerospace Technology, Guilin 541004, China)

  • Weidou Ni

    (State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China)

  • Zheng Li

    (State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China)

  • Xiaoyong Zhou

    (School of Management, Guilin University of Aerospace Technology, Guilin 541004, China)

  • Linwei Ma

    (State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China)

Abstract

Integrated energy systems (IESs) couple multiple energy sources to promote clean energy and reduce emissions. IESs need to participate in business activities, such as power trading, aided by automated data-driven systems to achieve optimal and economical operation. However, challenges arise due to the lack of unified data model standards and the semantic ambiguity of rules, on top of the difficulty of synchronizing data across heterogeneous subsystems of integrated energy management systems (IEMSs). Previous research on power trading data models was limited to certain application scenarios and heterogeneities. This study pivots from proposing model standards to a standard neutral way to align power trading rules across IEMS subsystems. The method features a framework for power trading rules and a software platform called the power trading rule synchronizer. The proposed approach can minimize semantic ambiguity and ensure the automated rule synchronization across subsystems of IEMSs. A case study demonstrated the application of the proposed method, saving an estimated 672 man-days in implementing the rules in 16 subsystems of the IEMS, contributing directly and indirectly to reducing emissions. This study provides a foundation for aligning other rules in IEMSs, such as carbon and gas trading rules.

Suggested Citation

  • Yingya Zhou & Chin Hao Chong & Weidou Ni & Zheng Li & Xiaoyong Zhou & Linwei Ma, 2024. "Data Modeling and Synchronization Method to Align Power Trading Rules for Integrated Energy Management Systems," Sustainability, MDPI, vol. 16(20), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:20:p:9073-:d:1502455
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    References listed on IDEAS

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    1. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Xu, Xiandong & Yu, Xiaodan, 2016. "Optimal day-ahead scheduling of integrated urban energy systems," Applied Energy, Elsevier, vol. 180(C), pages 1-13.
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    Cited by:

    1. Biyun Chen & Tianwang Fu & Liming Wei & Rong Zheng & Zhe Lin & Haiwei Liu & Zhijun Qin, 2025. "Data-Driven Risk Warning of Electricity Sales Companies in the Whole Business Process," Sustainability, MDPI, vol. 17(9), pages 1-22, April.

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