IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i15p8153-d598540.html
   My bibliography  Save this article

Two-Stage Physical Economic Adjustable Capacity Evaluation Model of Electric Vehicles for Peak Shaving and Valley Filling Auxiliary Services

Author

Listed:
  • Dunnan Liu

    (School of Economics and Management, North China Electricity Power University, Changping District, Beijing 102206, China)

  • Tingting Zhang

    (School of Economics and Management, North China Electricity Power University, Changping District, Beijing 102206, China)

  • Weiye Wang

    (School of Economics and Management, North China Electricity Power University, Changping District, Beijing 102206, China)

  • Xiaofeng Peng

    (State Grid Electric Vehicle Service Company, Xicheng District, Beijing 100032, China)

  • Mingguang Liu

    (School of Economics and Management, North China Electricity Power University, Changping District, Beijing 102206, China)

  • Heping Jia

    (School of Economics and Management, North China Electricity Power University, Changping District, Beijing 102206, China)

  • Shu Su

    (State Grid Electric Vehicle Service Company, Xicheng District, Beijing 100032, China)

Abstract

A large number of renewable energy and EVs (electric vehicles) are connected to the grid, which brings huge peak shaving pressure to the power system. If we can make use of the flexible characteristics of EVs and effectively aggregate the adjustable resources of EVs to participate in power auxiliary services, this situation can be alleviated to a certain extent. In this paper, a two-stage physical and economic adjustable capacity evaluation model of EVs for peak shaving and valley filling ancillary services is constructed. The main steps are as follows: with the help of the deep learning ability of the AC (Actor-Critic) algorithm, the optimal physical charging scheme of EV fleet is determined to minimize the grid fluctuation under the travel constraints of private EVs, and the optimized charging power is transferred to the second stage. In the second stage, load aggregators encourage users to participate in ancillary services by setting subsidy prices. In this stage, the model constructs a user decision model based on a logistic function to describe the probability of users accepting dispatching instructions. With the goal of maximizing the revenue of load aggregators, the wolf colony algorithm is used to solve the optimal solution of the time-sharing subsidy level, and finally the economic adjustable capacity of the EV fleet considering the subjective decision of users is obtained.

Suggested Citation

  • Dunnan Liu & Tingting Zhang & Weiye Wang & Xiaofeng Peng & Mingguang Liu & Heping Jia & Shu Su, 2021. "Two-Stage Physical Economic Adjustable Capacity Evaluation Model of Electric Vehicles for Peak Shaving and Valley Filling Auxiliary Services," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8153-:d:598540
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/15/8153/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/15/8153/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    2. Hahn, Tobias & Schönfelder, Martin & Jochem, Patrick & Heuveline, Vincent & Fichtner, Wolf, 2013. "Model-Based Quantification of Load Shift Potentials and Optimized Charging of Electric Vehicles," MPRA Paper 91613, University Library of Munich, Germany, revised 04 Jul 2013.
    3. Li, Xiao Hui & Hong, Seung Ho, 2014. "User-expected price-based demand response algorithm for a home-to-grid system," Energy, Elsevier, vol. 64(C), pages 437-449.
    4. Azadfar, Elham & Sreeram, Victor & Harries, David, 2015. "The investigation of the major factors influencing plug-in electric vehicle driving patterns and charging behaviour," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1065-1076.
    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. Chia-Hsuan Wu & Ching-Ming Lai & Tomokazu Mishima & Zheng-Bo Liang, 2021. "Simulation-Assisted Design Process of a 22 kW Wireless Power Transfer System Using Three-Phase Coil Coupling for EVs," Sustainability, MDPI, vol. 13(21), pages 1-15, November.
    2. Weimin Ma & Jiakai Chen & Hua Ke, 2021. "Electric Vehicle Assignment Considering Users’ Waiting Time," Sustainability, MDPI, vol. 13(23), pages 1-14, December.

    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. Schücking, Maximilian & Jochem, Patrick & Fichtner, Wolf & Wollersheim, Olaf & Stella, Kevin, 2017. "Charging strategies for economic operations of electric vehicles in commercial applications," MPRA Paper 91599, University Library of Munich, Germany.
    2. Barone, G. & Buonomano, A. & Calise, F. & Forzano, C. & Palombo, A., 2019. "Building to vehicle to building concept toward a novel zero energy paradigm: Modelling and case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 625-648.
    3. Erdinc, Ozan & Paterakis, Nikolaos G. & Pappi, Iliana N. & Bakirtzis, Anastasios G. & Catalão, João P.S., 2015. "A new perspective for sizing of distributed generation and energy storage for smart households under demand response," Applied Energy, Elsevier, vol. 143(C), pages 26-37.
    4. Kaschub, Thomas & Jochem, Patrick & Fichtner, Wolf, 2016. "Solar energy storage in German households: profitability, load changes and flexibility," Energy Policy, Elsevier, vol. 98(C), pages 520-532.
    5. Yasuaki Miyazato & Hayato Tahara & Kosuke Uchida & Cirio Celestino Muarapaz & Abdul Motin Howlader & Tomonobu Senjyu, 2016. "Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems," Sustainability, MDPI, vol. 8(12), pages 1-22, December.
    6. Sarah Ouédraogo & Ghjuvan Antone Faggianelli & Guillaume Pigelet & Jean Laurent Duchaud & Gilles Notton, 2020. "Application of Optimal Energy Management Strategies for a Building Powered by PV/Battery System in Corsica Island," Energies, MDPI, vol. 13(17), pages 1-20, September.
    7. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    8. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    9. Elma, Onur & Selamogullari, Ugur Savas, 2015. "A new home energy management algorithm with voltage control in a smart home environment," Energy, Elsevier, vol. 91(C), pages 720-731.
    10. Kuang, Yanqing & Chen, Yang & Hu, Mengqi & Yang, Dong, 2017. "Influence analysis of driver behavior and building category on economic performance of electric vehicle to grid and building integration," Applied Energy, Elsevier, vol. 207(C), pages 427-437.
    11. Colmenar-Santos, Antonio & Linares-Mena, Ana-Rosa & Borge-Diez, David & Quinto-Alemany, Carlos-Domingo, 2017. "Impact assessment of electric vehicles on islands grids: A case study for Tenerife (Spain)," Energy, Elsevier, vol. 120(C), pages 385-396.
    12. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
    13. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    14. Biresselioglu, Mehmet Efe & Demirbag Kaplan, Melike & Yilmaz, Barbara Katharina, 2018. "Electric mobility in Europe: A comprehensive review of motivators and barriers in decision making processes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 1-13.
    15. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    16. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    17. Alahäivälä, Antti & Heß, Tobias & Cao, Sunliang & Lehtonen, Matti, 2015. "Analyzing the optimal coordination of a residential micro-CHP system with a power sink," Applied Energy, Elsevier, vol. 149(C), pages 326-337.
    18. Illmann, Ulrike & Kluge, Jan, 2019. "Public Charging Infrastructure and the Market Diffusion of Electric Vehicles," IHS Working Paper Series 9, Institute for Advanced Studies.
    19. Jun Bi & Yongxing Wang & Shuai Sun & Wei Guan, 2018. "Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing," Energies, MDPI, vol. 11(5), pages 1-18, April.
    20. Jansen, Malte & Gross, Rob & Staffell, Iain, 2024. "Quantitative evidence for modelling electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(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:jsusta:v:13:y:2021:i:15:p:8153-:d:598540. 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.