Journal of Shanghai Jiao Tong University ›› 2025, Vol. 59 ›› Issue (12): 1795-1804.doi: 10.16183/j.cnki.jsjtu.2024.262

• New Type Power System and the Integrated Energy • Previous Articles     Next Articles

Low-Carbon Energy Management in Active Distribution Networks Based on Dynamic Carbon Entropy

WU Dongge1,2, CHANG Xinyue1,2(), XUE Yixun1,2, HUANG Yuxi1,2, SU Jia1,2, LI Zening1,2, SUN Hongbin1,2,3   

  1. 1 College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
    2 Key Laboratory of Clean Intelligent Control of Coal Electricity of the Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China
    3 Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
  • Received:2024-07-02 Revised:2024-09-10 Accepted:2025-01-02 Online:2025-12-28 Published:2025-12-30
  • Contact: CHANG Xinyue E-mail:changxinyue@tyut.edu.cn

Abstract:

In the context of energy transition and “carbon peak and carbon neutrality” goal, active distribution networks in the new power system can achieve scalable energy conservation and emissions reduction by increasing the penetration of renewable energy and leveraging demand-side management. To this end, a two-stage low-carbon energy management strategy for active distribution networks based on dynamic carbon entropy theory is proposed, using carbon price as a price signal to guide flexible loads in participating in low-carbon demand response. First, the carbon entropy model is analyzed, and a carbon entropy model considering energy storage is established to refine the carbon emission characteristics on the demand side. Then, an evaluation index of node carbon potential is proposed to evaluate the cleanliness of carbon emission of the system. Next, a two-stage low-carbon optimization scheduling model is developed for active distribution networks based on the dynamic carbon entropy. By utilizing the time-of-use electricity prices and node carbon prices as guiding signals, the model promotes the renewable energy consumption, reduces system carbon emissions, and achieves a certain peak-shaving and valley-filling effect. Finally, multiple scenarios are set up in the IEEE 33-node system to validate the effectiveness and superiority of the proposed low-carbon energy management model. The results show that the proposed strategy can achieve low-carbon energy management in active distribution networks.

Key words: active distribution network, dynamic carbon entropy, demand response, low-carbon energy management

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