IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v239y2019icp1294-1307.html
   My bibliography  Save this article

Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach

Author

Listed:
  • Abbasi, Mohammad Hossein
  • Taki, Mehrdad
  • Rajabi, Amin
  • Li, Li
  • Zhang, Jiangfeng

Abstract

As the number of electric vehicles (EVs) is steadily increasing, their aggregation can offer significant storage to improve the electric system operation in many aspects. To this end, a comprehensive stochastic optimization framework is proposed in this paper for the joint operation of a fleet of EVs with a wind power producer (WPP) in a three-settlement pool-based market. An aggregator procures enough energy for the EVs based on their daily driving patterns, and schedules the stored energy to counterbalance WPP fluctuations. Different sources of uncertainty including the market prices and WPP generation are modeled through proper scenarios, and the risk is hedged by adding a risk measure to the formulation. To obtain more accurate results, the battery degradation costs are also included in the problem formulation. A detailed case study is presented based on the Iberian electricity market data as well as the technical information of three different types of EVs. The proposed approach is benchmarked against the disjoint operation of EVs and WPP. Numerical simulations demonstrate that the proposed strategy can effectively benefit EV owners and WPP by reducing the energy costs and increasing the profits.

Suggested Citation

  • Abbasi, Mohammad Hossein & Taki, Mehrdad & Rajabi, Amin & Li, Li & Zhang, Jiangfeng, 2019. "Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach," Applied Energy, Elsevier, vol. 239(C), pages 1294-1307.
    • Handle: RePEc:eee:appene:v:239:y:2019:i:c:p:1294-1307
    DOI: 10.1016/j.apenergy.2019.01.238
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919301205
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.01.238?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Xiao, Yunpeng & Wang, Xifan & Wang, Xiuli & Dang, Can & Lu, Ming, 2016. "Behavior analysis of wind power producer in electricity market," Applied Energy, Elsevier, vol. 171(C), pages 325-335.
    3. Haque, A.N.M.M. & Ibn Saif, A.U.N. & Nguyen, P.H. & Torbaghan, S.S., 2016. "Exploration of dispatch model integrating wind generators and electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 1441-1451.
    4. Varkani, Ali Karimi & Daraeepour, Ali & Monsef, Hassan, 2011. "A new self-scheduling strategy for integrated operation of wind and pumped-storage power plants in power markets," Applied Energy, Elsevier, vol. 88(12), pages 5002-5012.
    5. Škugor, Branimir & Deur, Joško, 2016. "A bi-level optimisation framework for electric vehicle fleet charging management," Applied Energy, Elsevier, vol. 184(C), pages 1332-1342.
    6. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    7. Khalid, Muhammad & Aguilera, Ricardo P. & Savkin, Andrey V. & Agelidis, Vassilios G., 2018. "On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting," Applied Energy, Elsevier, vol. 211(C), pages 764-773.
    8. Sousa, Tiago & Morais, Hugo & Soares, João & Vale, Zita, 2012. "Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints," Applied Energy, Elsevier, vol. 96(C), pages 183-193.
    9. Perez-Diaz, Alvaro & Gerding, Enrico & McGroarty, Frank, 2018. "Coordination and payment mechanisms for electric vehicle aggregators," Applied Energy, Elsevier, vol. 212(C), pages 185-195.
    10. Mwasilu, Francis & Justo, Jackson John & Kim, Eun-Kyung & Do, Ton Duc & Jung, Jin-Woo, 2014. "Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 501-516.
    11. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, January.
    12. Katz, Jonas & Andersen, Frits Møller & Morthorst, Poul Erik, 2016. "Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system," Energy, Elsevier, vol. 115(P3), pages 1602-1616.
    13. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert & Veneroni, Marco, 2017. "Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several models," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 158-187.
    14. Al-Swaiti, Mustafa S. & Al-Awami, Ali T. & Khalid, Mohammad Waqas, 2017. "Co-optimized trading of wind-thermal-pumped storage system in energy and regulation markets," Energy, Elsevier, vol. 138(C), pages 991-1005.
    15. Wang, Mingshen & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Yu, Xiaodan & Qi, Yan, 2017. "Active power regulation for large-scale wind farms through an efficient power plant model of electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1673-1683.
    16. Exizidis, Lazaros & Kazempour, S. Jalal & Pinson, Pierre & de Greve, Zacharie & Vallée, François, 2016. "Sharing wind power forecasts in electricity markets: A numerical analysis," Applied Energy, Elsevier, vol. 176(C), pages 65-73.
    17. Kristoffersen, Trine Krogh & Capion, Karsten & Meibom, Peter, 2011. "Optimal charging of electric drive vehicles in a market environment," Applied Energy, Elsevier, vol. 88(5), pages 1940-1948, May.
    18. Peng, Chao & Zou, Jianxiao & Lian, Lian & Li, Liying, 2017. "An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits," Applied Energy, Elsevier, vol. 190(C), pages 591-599.
    19. Xiong, Yingqi & Wang, Bin & Chu, Chi-cheng & Gadh, Rajit, 2018. "Vehicle grid integration for demand response with mixture user model and decentralized optimization," Applied Energy, Elsevier, vol. 231(C), pages 481-493.
    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. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    2. Khaki, Behnam & Chu, Chicheng & Gadh, Rajit, 2019. "Hierarchical distributed framework for EV charging scheduling using exchange problem," Applied Energy, Elsevier, vol. 241(C), pages 461-471.
    3. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Optimal operation value of combined wind power and energy storage in multi-stage electricity markets," Applied Energy, Elsevier, vol. 235(C), pages 1153-1168.
    4. Xiao, Yunpeng & Wang, Xifan & Wang, Xiuli & Wu, Zechen, 2016. "Trading wind power with barrier option," Applied Energy, Elsevier, vol. 182(C), pages 232-242.
    5. Morteza Nazari-Heris & Mehdi Abapour & Behnam Mohammadi-Ivatloo, 2022. "An Updated Review and Outlook on Electric Vehicle Aggregators in Electric Energy Networks," Sustainability, MDPI, vol. 14(23), pages 1-24, November.
    6. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    7. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    8. Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
    9. Sousa, Tiago & Morais, Hugo & Soares, João & Vale, Zita, 2012. "Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints," Applied Energy, Elsevier, vol. 96(C), pages 183-193.
    10. Shi You & Junjie Hu & Charalampos Ziras, 2016. "An Overview of Modeling Approaches Applied to Aggregation-Based Fleet Management and Integration of Plug-in Electric Vehicles †," Energies, MDPI, vol. 9(11), pages 1-18, November.
    11. Varone, Alberto & Heilmann, Zeno & Porruvecchio, Guido & Romanino, Alessandro, 2024. "Solar parking lot management: An IoT platform for smart charging EV fleets, using real-time data and production forecasts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    12. Zhang, Xiaochun & Ma, Chun & Song, Xia & Zhou, Yuyu & Chen, Weiping, 2016. "The impacts of wind technology advancement on future global energy," Applied Energy, Elsevier, vol. 184(C), pages 1033-1037.
    13. Bonou, Alexandra & Laurent, Alexis & Olsen, Stig I., 2016. "Life cycle assessment of onshore and offshore wind energy-from theory to application," Applied Energy, Elsevier, vol. 180(C), pages 327-337.
    14. Mehrdad Tarafdar-Hagh & Kamran Taghizad-Tavana & Mohsen Ghanbari-Ghalehjoughi & Sayyad Nojavan & Parisa Jafari & Amin Mohammadpour Shotorbani, 2023. "Optimizing Electric Vehicle Operations for a Smart Environment: A Comprehensive Review," Energies, MDPI, vol. 16(11), pages 1-21, May.
    15. Li, Yang & Vilathgamuwa, Mahinda & Choi, San Shing & Farrell, Troy W. & Tran, Ngoc Tham & Teague, Joseph, 2019. "Development of a degradation-conscious physics-based lithium-ion battery model for use in power system planning studies," Applied Energy, Elsevier, vol. 248(C), pages 512-525.
    16. Liu, Hui & Huang, Kai & Wang, Ni & Qi, Junjian & Wu, Qiuwei & Ma, Shicong & Li, Canbing, 2019. "Optimal dispatch for participation of electric vehicles in frequency regulation based on area control error and area regulation requirement," Applied Energy, Elsevier, vol. 240(C), pages 46-55.
    17. Iria, José & Soares, Filipe & Matos, Manuel, 2019. "Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets," Applied Energy, Elsevier, vol. 238(C), pages 1361-1372.
    18. Dapeng Chen & Zhaoxia Jing & Huijuan Tan, 2019. "Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model," Energies, MDPI, vol. 12(7), pages 1-19, April.
    19. Wang, Shuoqi & Guo, Dongxu & Han, Xuebing & Lu, Languang & Sun, Kai & Li, Weihan & Sauer, Dirk Uwe & Ouyang, Minggao, 2020. "Impact of battery degradation models on energy management of a grid-connected DC microgrid," Energy, Elsevier, vol. 207(C).
    20. Andre Leippi & Markus Fleschutz & Michael D. Murphy, 2022. "A Review of EV Battery Utilization in Demand Response Considering Battery Degradation in Non-Residential Vehicle-to-Grid Scenarios," Energies, MDPI, vol. 15(9), pages 1-22, April.

    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:eee:appene:v:239:y:2019:i:c:p:1294-1307. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.