IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i15p5509-d875468.html
   My bibliography  Save this article

Hierarchical Stochastic Optimal Scheduling of Electric Thermal Hydrogen Integrated Energy System Considering Electric Vehicles

Author

Listed:
  • Shiduo Jia

    (Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China)

  • Xiaoning Kang

    (Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China)

  • Jinxu Cui

    (Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China)

  • Bowen Tian

    (Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China)

  • Shuwen Xiao

    (Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an 710000, China)

Abstract

After a large number of electric vehicles (EVs) are connected to the integrated energy system, disorderly charging and discharging of EVs will have a negative impact on the safe and stable operation of the system. In addition, EVs’ uncertain travel plans and the stochastic fluctuation of renewable energy output and load power will bring risks and challenges. In view of the above problems, this paper establishes a hierarchical stochastic optimal scheduling model of an electric thermal hydrogen integrated energy system (ETH-IES) considering the EVs vehicle-to-grid (V2G) mechanism. The EVs charging and discharging management layer aims to minimize the variance of the load curve and minimize the dissatisfaction of EV owners participating in V2G. The multi-objective sand cat swarm optimization (MSCSO) algorithm is used to solve the proposed model. On this basis, the daily stochastic economic scheduling of ETH-IES is carried out with the goal of minimizing the operation cost. The simulation results show that the proposed strategy can better achieve a win-win situation between EV owners and microgrid operators, and the operation cost of the proposed strategy is reduced by 16.55% compared with that under the disorderly charging and discharging strategy, which verifies the effectiveness of the proposed model and algorithm.

Suggested Citation

  • Shiduo Jia & Xiaoning Kang & Jinxu Cui & Bowen Tian & Shuwen Xiao, 2022. "Hierarchical Stochastic Optimal Scheduling of Electric Thermal Hydrogen Integrated Energy System Considering Electric Vehicles," Energies, MDPI, vol. 15(15), pages 1-23, July.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5509-:d:875468
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/15/5509/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/15/5509/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu Huang & Weiting Zhang & Kai Yang & Weizhen Hou & Yiran Huang, 2019. "An Optimal Scheduling Method for Multi-Energy Hub Systems Using Game Theory," Energies, MDPI, vol. 12(12), pages 1-20, June.
    2. Shiduo Jia & Xiaoning Kang, 2022. "Multi-Objective Optimal Scheduling of CHP Microgrid Considering Conditional Value-at-Risk," Energies, MDPI, vol. 15(9), pages 1-21, May.
    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. Nima Mirzaei Alavijeh & David Steen & Zack Norwood & Le Anh Tuan & Christos Agathokleous, 2020. "Cost-Effectiveness of Carbon Emission Abatement Strategies for a Local Multi-Energy System—A Case Study of Chalmers University of Technology Campus," Energies, MDPI, vol. 13(7), pages 1-23, April.
    2. Seyed Hasan Mirbarati & Najme Heidari & Amirhossein Nikoofard & Mir Sayed Shah Danish & Mahdi Khosravy, 2022. "Techno-Economic-Environmental Energy Management of a Micro-Grid: A Mixed-Integer Linear Programming Approach," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    3. Alaa Farah & Hamdy Hassan & Alaaeldin M. Abdelshafy & Abdelfatah M. Mohamed, 2020. "Optimal Scheduling of Hybrid Multi-Carrier System Feeding Electrical/Thermal Load Based on Particle Swarm Algorithm," Sustainability, MDPI, vol. 12(11), pages 1-21, June.
    4. Mohammad Hemmati & Mehdi Abapour & Behnam Mohammadi-Ivatloo & Amjad Anvari-Moghaddam, 2020. "Optimal Operation of Integrated Electrical and Natural Gas Networks with a Focus on Distributed Energy Hub Systems," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    5. Sobhani, Seyed Omid & Sheykhha, Siamak & Madlener, Reinhard, 2020. "An integrated two-level demand-side management game applied to smart energy hubs with storage," Energy, Elsevier, vol. 206(C).

    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:15:y:2022:i:15:p:5509-:d:875468. 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.