IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i5p1287-d147163.html
   My bibliography  Save this article

Optimized Daily Dispatching Strategy of Building- Integrated Energy Systems Considering Vehicle to Grid Technology and Room Temperature Control

Author

Listed:
  • Zesen Wang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Yanmei Tang

    (State Key Laboratory for Security and Energy Saving, Beijing 100192, China)

  • Xiao Chen

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Xiangyang Men

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Jun Cao

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Haifeng Wang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

With the development of vehicle-to-grid (V2G) technology, electric vehicles (EVs) are not only a promising transportation solution, but also can be used as mobile energy storage and spinning reserves, which play an important role in building-integrated energy systems (BIESs) and can further promote energy efficiency. Considering the space-time characteristics of EVs and the room temperature demand, this study establishes a planning model using V2G technology to minimize daily dispatch cost. Four kinds of control modes were proposed, combining the technology of the resident-owned and staff-owned EVs, in combination with the internal heating and power supply system. In this paper, the operating state of the system and the interaction of the equipment are analyzed under different charging and discharging control modes of EVs. The economics of the microgrid of the comprehensive energy building under four control modes are also discussed. Simulation results indicate that the combined control mode of residential vehicle and office vehicle is optimal for building an integrated energy microgrid, and the room temperature requirements can also be used as an important income source for building the microgrid.

Suggested Citation

  • Zesen Wang & Yanmei Tang & Xiao Chen & Xiangyang Men & Jun Cao & Haifeng Wang, 2018. "Optimized Daily Dispatching Strategy of Building- Integrated Energy Systems Considering Vehicle to Grid Technology and Room Temperature Control," Energies, MDPI, vol. 11(5), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1287-:d:147163
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/5/1287/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/5/1287/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lu, Yuehong & Wang, Shengwei & Sun, Yongjun & Yan, Chengchu, 2015. "Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming," Applied Energy, Elsevier, vol. 147(C), pages 49-58.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jerzy Mikulik, 2018. "Energy Demand Patterns in an Office Building: A Case Study in Kraków (Southern Poland)," Sustainability, MDPI, vol. 10(8), pages 1-16, August.
    2. Saeid Bashash & Kai Lun Lee, 2019. "Automatic Coordination of Internet-Connected Thermostats for Power Balancing and Frequency Control in Smart Microgrids," Energies, MDPI, vol. 12(10), pages 1-23, May.
    3. Guozhong Liu & Li Kang & Zeyu Luan & Jing Qiu & Fenglei Zheng, 2019. "Charging Station and Power Network Planning for Integrated Electric Vehicles (EVs)," Energies, MDPI, vol. 12(13), pages 1-22, July.
    4. Monica Arnaudo & Monika Topel & Björn Laumert, 2020. "Vehicle-To-Grid for Peak Shaving to Unlock the Integration of Distributed Heat Pumps in a Swedish Neighborhood," Energies, MDPI, vol. 13(7), pages 1-13, April.
    5. Jesús Rodríguez-Molina & Pedro Castillejo & Victoria Beltran & Margarita Martínez-Núñez, 2020. "A Model for Cost–Benefit Analysis of Privately Owned Vehicle-to-Grid Solutions," Energies, MDPI, vol. 13(21), pages 1-38, November.
    6. Xinjia Gao & Ran Li & Siqi Chen & Yalun Li, 2023. "Potential Analysis and Optimal Management of Winter Electric Heating in Rural China Based on V2H Technology," Sustainability, MDPI, vol. 15(15), pages 1-21, July.

    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. Tang, Rui & Li, Hangxin & Wang, Shengwei, 2019. "A game theory-based decentralized control strategy for power demand management of building cluster using thermal mass and energy storage," Applied Energy, Elsevier, vol. 242(C), pages 809-820.
    2. Eissa, M.M., 2018. "First time real time incentive demand response program in smart grid with “i-Energy” management system with different resources," Applied Energy, Elsevier, vol. 212(C), pages 607-621.
    3. Xiaoyu Xu & Chun Chang & Xinxin Guo & Mingzhi Zhao, 2023. "Experimental and Numerical Study of the Ice Storage Process and Material Properties of Ice Storage Coils," Energies, MDPI, vol. 16(14), pages 1-18, July.
    4. Behzadi, Amirmohammad & Holmberg, Sture & Duwig, Christophe & Haghighat, Fariborz & Ooka, Ryozo & Sadrizadeh, Sasan, 2022. "Smart design and control of thermal energy storage in low-temperature heating and high-temperature cooling systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    5. Luca Brunelli & Emiliano Borri & Anna Laura Pisello & Andrea Nicolini & Carles Mateu & Luisa F. Cabeza, 2024. "Thermal Energy Storage in Energy Communities: A Perspective Overview through a Bibliometric Analysis," Sustainability, MDPI, vol. 16(14), pages 1-27, July.
    6. Najafi, Arsalan & Falaghi, Hamid & Contreras, Javier & Ramezani, Maryam, 2016. "Medium-term energy hub management subject to electricity price and wind uncertainty," Applied Energy, Elsevier, vol. 168(C), pages 418-433.
    7. Jallal, Mohammed Ali & González-Vidal, Aurora & Skarmeta, Antonio F. & Chabaa, Samira & Zeroual, Abdelouhab, 2020. "A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction," Applied Energy, Elsevier, vol. 268(C).
    8. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    9. Bargos, Fabiano Fernandes & Lamas, Wendell de Queiróz & Bilato, Gabriel Adam, 2018. "Computational tools and operational research for optimal design of co-generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 507-516.
    10. Gianluca Valenti & Aldo Bischi & Stefano Campanari & Paolo Silva & Antonino Ravidà & Ennio Macchi, 2021. "Experimental and Numerical Study of a Microcogeneration Stirling Unit under On–Off Cycling Operation," Energies, MDPI, vol. 14(4), pages 1-14, February.
    11. Huang, Pei & Wu, Hunjun & Huang, Gongsheng & Sun, Yongjun, 2018. "A top-down control method of nZEBs for performance optimization at nZEB-cluster-level," Energy, Elsevier, vol. 159(C), pages 891-904.
    12. Gruber, J.K. & Huerta, F. & Matatagui, P. & Prodanović, M., 2015. "Advanced building energy management based on a two-stage receding horizon optimization," Applied Energy, Elsevier, vol. 160(C), pages 194-205.
    13. Hou, Langbo & Tong, Xi & Chen, Heng & Fan, Lanxin & Liu, Tao & Liu, Wenyi & Liu, Tong, 2024. "Optimized scheduling of smart community energy systems considering demand response and shared energy storage," Energy, Elsevier, vol. 295(C).
    14. Golpîra, Hêriş & Khan, Syed Abdul Rehman, 2019. "A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty," Energy, Elsevier, vol. 170(C), pages 1113-1129.
    15. Yang Zhang & Yuehong Lu & Changlong Wang & Zhijia Huang & Tao Lv, 2021. "Reward–Penalty Mechanism Based on Daily Energy Consumption for Net-Zero Energy Buildings," Sustainability, MDPI, vol. 13(22), pages 1-18, November.
    16. Chai, Jiale & Huang, Pei & Sun, Yongjun, 2019. "Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions," Energy, Elsevier, vol. 185(C), pages 176-189.
    17. Fabio Magrassi & Adriana Del Borghi & Michela Gallo & Carlo Strazza & Michela Robba, 2016. "Optimal Planning of Sustainable Buildings: Integration of Life Cycle Assessment and Optimization in a Decision Support System (DSS)," Energies, MDPI, vol. 9(7), pages 1-15, June.
    18. Deng, Na & Cai, Rongchang & Gao, Yuan & Zhou, Zhihua & He, Guansong & Liu, Dongyi & Zhang, Awen, 2017. "A MINLP model of optimal scheduling for a district heating and cooling system: A case study of an energy station in Tianjin," Energy, Elsevier, vol. 141(C), pages 1750-1763.
    19. Wang, Chengshan & Jiao, Bingqi & Guo, Li & Tian, Zhe & Niu, Jide & Li, Siwei, 2016. "Robust scheduling of building energy system under uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 366-376.
    20. Huang, Pei & Sun, Yongjun, 2019. "A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level," Energy, Elsevier, vol. 174(C), pages 911-921.

    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:11:y:2018:i:5:p:1287-:d:147163. 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.