IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i4p1090-d501918.html
   My bibliography  Save this article

Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models

Author

Listed:
  • Charilaos Latinopoulos

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2BU, UK)

  • Aruna Sivakumar

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2BU, UK)

  • John W. Polak

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2BU, UK)

Abstract

The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments.

Suggested Citation

  • Charilaos Latinopoulos & Aruna Sivakumar & John W. Polak, 2021. "Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models," Energies, MDPI, vol. 14(4), pages 1-27, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1090-:d:501918
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/4/1090/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/4/1090/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Raha Akhavan-Tabatabaei & Manuel Bolívar & Jorge Hincapie & Andrés Medaglia, 2014. "On the optimal parking lot subscription policy problem: a hybrid simulation-optimization approach," Annals of Operations Research, Springer, vol. 222(1), pages 29-44, November.
    2. Hongmin Li & Woonghee Tim Huh, 2011. "Pricing Multiple Products with the Multinomial Logit and Nested Logit Models: Concavity and Implications," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 549-563, October.
    3. Garrett van Ryzin & Jeff McGill, 2000. "Revenue Management Without Forecasting or Optimization: An Adaptive Algorithm for Determining Airline Seat Protection Levels," Management Science, INFORMS, vol. 46(6), pages 760-775, June.
    4. Shokrzadeh, Shahab & Ribberink, Hajo & Rishmawi, Issa & Entchev, Evgueniy, 2017. "A simplified control algorithm for utilities to utilize plug-in electric vehicles to reduce distribution transformer overloading," Energy, Elsevier, vol. 133(C), pages 1121-1131.
    5. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "A cooperative game theoretic analysis of electric vehicles parking lot in smart grid," Energy, Elsevier, vol. 137(C), pages 129-139.
    6. Vickrey, William S, 1969. "Congestion Theory and Transport Investment," American Economic Review, American Economic Association, vol. 59(2), pages 251-260, May.
    7. Hetrakul, Pratt & Cirillo, Cinzia, 2014. "A latent class choice based model system for railway optimal pricing and seat allocation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 68-83.
    8. Shafie-khah, M. & Heydarian-Forushani, E. & Golshan, M.E.H. & Siano, P. & Moghaddam, M.P. & Sheikh-El-Eslami, M.K. & Catalão, J.P.S., 2016. "Optimal trading of plug-in electric vehicle aggregation agents in a market environment for sustainability," Applied Energy, Elsevier, vol. 162(C), pages 601-612.
    9. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    10. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.
    11. Quddus, Md Abdul & Shahvari, Omid & Marufuzzaman, Mohammad & Usher, John M. & Jaradat, Raed, 2018. "A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid," Applied Energy, Elsevier, vol. 229(C), pages 841-857.
    12. José Guadix & Luis Onieva & Jesús Muñuzuri & Pablo Cortés, 2010. "An overview of revenue management in service industries: an application to car parks," The Service Industries Journal, Taylor & Francis Journals, vol. 31(1), pages 91-105, February.
    13. DeForest, Nicholas & MacDonald, Jason S. & Black, Douglas R., 2018. "Day ahead optimization of an electric vehicle fleet providing ancillary services in the Los Angeles Air Force Base vehicle-to-grid demonstration," Applied Energy, Elsevier, vol. 210(C), pages 987-1001.
    14. Sehar, Fakeha & Pipattanasomporn, Manisa & Rahman, Saifur, 2017. "Demand management to mitigate impacts of plug-in electric vehicle fast charge in buildings with renewables," Energy, Elsevier, vol. 120(C), pages 642-651.
    15. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(C).
    16. Zheng, Yanchong & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2018. "A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid," Applied Energy, Elsevier, vol. 217(C), pages 1-13.
    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. Velaz-Acera, Néstor & Álvarez-García, Javier & Borge-Diez, David, 2023. "Economic and emission reduction benefits of the implementation of eVTOL aircraft with bi-directional flow as storage systems in islands and case study for Canary Islands," Applied Energy, Elsevier, vol. 331(C).
    2. Albert Y. S. Lam & Bogusław Łazarz & Grzegorz Peruń, 2022. "Smart Energy and Intelligent Transportation Systems," Energies, MDPI, vol. 15(8), pages 1-3, April.
    3. Rob Shipman & Rebecca Roberts & Julie Waldron & Chris Rimmer & Lucelia Rodrigues & Mark Gillott, 2021. "Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic," Energies, MDPI, vol. 14(21), pages 1-16, November.

    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. Luiz Almeida & Ana Soares & Pedro Moura, 2023. "A Systematic Review of Optimization Approaches for the Integration of Electric Vehicles in Public Buildings," Energies, MDPI, vol. 16(13), pages 1-26, June.
    2. Raha Akhavan-Tabatabaei & Manuel Bolívar & Jorge Hincapie & Andrés Medaglia, 2014. "On the optimal parking lot subscription policy problem: a hybrid simulation-optimization approach," Annals of Operations Research, Springer, vol. 222(1), pages 29-44, November.
    3. 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.
    4. Homa Rashidizadeh-Kermani & Hamid Reza Najafi & Amjad Anvari-Moghaddam & Josep M. Guerrero, 2018. "Optimal Decision-Making Strategy of an Electric Vehicle Aggregator in Short-Term Electricity Markets," Energies, MDPI, vol. 11(9), pages 1-20, September.
    5. Yingjie Lan & Huina Gao & Michael O. Ball & Itir Karaesmen, 2008. "Revenue Management with Limited Demand Information," Management Science, INFORMS, vol. 54(9), pages 1594-1609, September.
    6. 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).
    7. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2017. "Integrating renewable energy sources by electric vehicle fleets under uncertainty," Energy, Elsevier, vol. 141(C), pages 2145-2153.
    8. Quddus, Md Abdul & Shahvari, Omid & Marufuzzaman, Mohammad & Usher, John M. & Jaradat, Raed, 2018. "A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid," Applied Energy, Elsevier, vol. 229(C), pages 841-857.
    9. Francesca Ceglia & Elisa Marrasso & Carlo Roselli & Maurizio Sasso, 2021. "Small Renewable Energy Community: The Role of Energy and Environmental Indicators for Power Grid," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    10. Michael O. Ball & Maurice Queyranne, 2009. "Toward Robust Revenue Management: Competitive Analysis of Online Booking," Operations Research, INFORMS, vol. 57(4), pages 950-963, August.
    11. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    12. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach," Energy, Elsevier, vol. 139(C), pages 422-432.
    13. Zhou, Yong-Wu & Lin, Xiaogang & Zhong, Yuanguang & Xie, Wei, 2019. "Contract selection for a multi-service sharing platform with self-scheduling capacity," Omega, Elsevier, vol. 86(C), pages 198-217.
    14. Price, Ilan & Fowkes, Jaroslav & Hopman, Daniel, 2019. "Gaussian processes for unconstraining demand," European Journal of Operational Research, Elsevier, vol. 275(2), pages 621-634.
    15. Shuang Gao & Jianzhong Wu & Bin Xu, 2019. "Controllability Evaluation of EV Charging Infrastructure Transformed from Gas Stations in Distribution Networks with Renewables," Energies, MDPI, vol. 12(8), pages 1-20, April.
    16. Felix Papier, 2016. "Supply Allocation Under Sequential Advance Demand Information," Operations Research, INFORMS, vol. 64(2), pages 341-361, April.
    17. Lan, Yingjie & Ball, Michael O. & Karaesmen, Itir Z. & Zhang, Jean X. & Liu, Gloria X., 2015. "Analysis of seat allocation and overbooking decisions with hybrid information," European Journal of Operational Research, Elsevier, vol. 240(2), pages 493-504.
    18. Larry Weatherford, 2016. "The history of forecasting models in revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 212-221, July.
    19. Subramanian, Vignesh & Das, Tapas K., 2019. "A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes," Energy, Elsevier, vol. 167(C), pages 1266-1277.
    20. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2019. "Two-stage stochastic optimization for cost-minimal charging of electric vehicles at public charging stations with photovoltaics," Applied Energy, Elsevier, vol. 242(C), pages 769-781.

    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:14:y:2021:i:4:p:1090-:d:501918. 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.