IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i8p3207-d1374038.html
   My bibliography  Save this article

Two-Layer Optimization Strategy of Electric Vehicle and Air Conditioning Load Considering the Benefit of Peak-to-Valley Smoothing

Author

Listed:
  • Sichen Shi

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Peiyi Wang

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Zixuan Zheng

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Shu Zhang

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

Abstract

To satisfy the interests of multiple agents and those of comprehensive indicators such as peak-to-valley differences and load fluctuations occurring on the network side, this paper presents a flexible load demand-side response optimization method that considers the benefits of peak-to-valley smoothing. First, load aggregation modelling of air conditioning and electric vehicles was conducted, and the complementarity of the power consumption behavior of different types of flexible loads was used to improve the responsiveness of the load aggregator. Second, considering demand-side responses and taking into account the interests of both supply and demand, the load fluctuation and peak-to-valley difference on the network side are reduced, and a flexible load double-layer optimization model incorporating the peak-to-valley smoothing benefit is established. Finally, the effectiveness of the proposed optimization model is verified by using the KKT condition and the big M method to evaluate this two-layer optimization problem as a single-layer optimization problem. Comparative examples show that the proposed two-layer optimization method can take advantage of the complementarity of air conditioning and electric vehicles to improve the income of load aggregators. Moreover, the proposed method can effectively reduce the load peak-to-valley difference and load fluctuation of the distribution network by introducing the peak-to-valley smoothing benefit model.

Suggested Citation

  • Sichen Shi & Peiyi Wang & Zixuan Zheng & Shu Zhang, 2024. "Two-Layer Optimization Strategy of Electric Vehicle and Air Conditioning Load Considering the Benefit of Peak-to-Valley Smoothing," Sustainability, MDPI, vol. 16(8), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3207-:d:1374038
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/8/3207/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/8/3207/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Qi & Huang, Chunyi & Wang, Chengmin & Li, Kangping & Xie, Ning, 2024. "Joint optimization of bidding and pricing strategy for electric vehicle aggregator considering multi-agent interactions," Applied Energy, Elsevier, vol. 360(C).
    2. Shang, Yitong & Li, Sen, 2024. "FedPT-V2G: Security enhanced federated transformer learning for real-time V2G dispatch with non-IID data," Applied Energy, Elsevier, vol. 358(C).
    3. Qifen Li & Yihan Zhao & Yongwen Yang & Liting Zhang & Chen Ju, 2022. "Demand-Response-Oriented Load Aggregation Scheduling Optimization Strategy for Inverter Air Conditioner," Energies, MDPI, vol. 16(1), pages 1-15, December.
    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. Nitin Kumar Singh & Masaaki Nagahara, 2024. "LightGBM-, SHAP-, and Correlation-Matrix-Heatmap-Based Approaches for Analyzing Household Energy Data: Towards Electricity Self-Sufficient Houses," Energies, MDPI, vol. 17(17), pages 1-32, September.
    2. Yongjie Yang & Yulong Li & Yan Cai & Hui Tang & Peng Xu, 2024. "Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System," Energies, MDPI, vol. 17(15), pages 1-20, July.
    3. Minan Tang & Changyou Wang & Jiandong Qiu & Hanting Li & Xi Guo & Wenxin Sheng, 2024. "Short-Term Load Forecasting of Electric Vehicle Charging Stations Accounting for Multifactor IDBO Hybrid Models," Energies, MDPI, vol. 17(12), pages 1-19, June.
    4. Abdellatif Soussi & Enrico Zero & Alessandro Bozzi & Roberto Sacile, 2024. "Enhancing Energy Systems and Rural Communities through a System of Systems Approach: A Comprehensive Review," Energies, MDPI, vol. 17(19), pages 1-43, October.
    5. Francesco Lisi & Ismail Shah, 2024. "Joint Component Estimation for Electricity Price Forecasting Using Functional Models," Energies, MDPI, vol. 17(14), pages 1-18, July.
    6. Hristo Ivanov Beloev & Stanislav Radikovich Saitov & Antonina Andreevna Filimonova & Natalia Dmitrievna Chichirova & Oleg Evgenievich Babikov & Iliya Krastev Iliev, 2024. "Prediction of Pipe Failure Rate in Heating Networks Using Machine Learning Methods," Energies, MDPI, vol. 17(14), pages 1-16, July.
    7. Afshin Tatar & Amin Shokrollahi & Abbas Zeinijahromi & Manouchehr Haghighi, 2024. "Deep Learning for Predicting Hydrogen Solubility in n-Alkanes: Enhancing Sustainable Energy Systems," Sustainability, MDPI, vol. 16(17), pages 1-24, August.
    8. Fangfang Zheng & Xiaofang Meng & Lidi Wang & Nannan Zhang, 2023. "Power Flow Optimization Strategy of Distribution Network with Source and Load Storage Considering Period Clustering," Sustainability, MDPI, vol. 15(5), pages 1-14, March.

    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:jsusta:v:16:y:2024:i:8:p:3207-:d:1374038. 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.