IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i19p4989-d1493039.html
   My bibliography  Save this article

The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization

Author

Listed:
  • Taorong Jia

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Guoqing Yang

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Lixiao Yao

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

Abstract

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network’s carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations.

Suggested Citation

  • Taorong Jia & Guoqing Yang & Lixiao Yao, 2024. "The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization," Energies, MDPI, vol. 17(19), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4989-:d:1493039
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/19/4989/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/19/4989/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Zhaoqi & Zhang, Lu & Tang, Wei & Ma, Ziyao & Huang, Jiajin, 2024. "Equilibrium configuration strategy of vehicle-to-grid-based electric vehicle charging stations in low-carbon resilient distribution networks," Applied Energy, Elsevier, vol. 361(C).
    2. Polasek, Tomas & Čadík, Martin, 2023. "Predicting photovoltaic power production using high-uncertainty weather forecasts," Applied Energy, Elsevier, vol. 339(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Yongli & Wang, Huan & Meng, Xiao & Dong, Huanran & Chen, Xin & Xiang, Hao & Xing, Juntai, 2023. "Considering the dual endogenous-exogenous uncertainty integrated energy multiple load short-term forecast," Energy, Elsevier, vol. 285(C).
    2. Jinhwa Jeong & Dongkyu Lee & Young Tae Chae, 2023. "A Novel Approach for Day-Ahead Hourly Building-Integrated Photovoltaic Power Prediction by Using Feature Engineering and Simple Weather Forecasting Service," Energies, MDPI, vol. 16(22), pages 1-21, November.
    3. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
    4. Pavol Belany & Peter Hrabovsky & Zuzana Florkova, 2024. "Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations," Energies, MDPI, vol. 17(5), pages 1-34, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4989-:d:1493039. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.