IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v148y2018icp148-158.html
   My bibliography  Save this article

Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot

Author

Listed:
  • Ioakimidis, Christos S.
  • Thomas, Dimitrios
  • Rycerski, Pawel
  • Genikomsakis, Konstantinos N.

Abstract

The renewed interest in the deployment of electric vehicles promises enhanced environmental and social compatibility, higher energy efficiency, as well as effective power grid support through the vehicle-to-grid energy exchange mode. Focusing on a smaller scale, such as buildings with large parking lots for electric vehicles, the aim of the so-called vehicle-to-building concept is to regulate the power consumption of a building by either throttling the charging rate of electric vehicles or by delivering electricity (discharging) into the building when needed. In this paper, a mathematical model is implemented in MATLAB to peak-shave and valley-fill the power consumption profile of a university building by scheduling the charging/discharging process in an electric vehicle parking lot, using real-world data of power consumption and parking lot occupancy. The simulation of three scenarios with different number of parking spots reveal the feasibility of the proposed approach to effectively flatten the power consumption profile during daytime, which is particularly important for electricity customers that the energy cost depends on the contracted power capacity. In detail, the results indicate that the peak power consumption is reduced by approximately 3% and 20% for the scenarios with the minimum and maximum number of electric vehicle parking spots respectively.

Suggested Citation

  • Ioakimidis, Christos S. & Thomas, Dimitrios & Rycerski, Pawel & Genikomsakis, Konstantinos N., 2018. "Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot," Energy, Elsevier, vol. 148(C), pages 148-158.
    • Handle: RePEc:eee:energy:v:148:y:2018:i:c:p:148-158
    DOI: 10.1016/j.energy.2018.01.128
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218301567
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.01.128?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. Honarmand, Masoud & Zakariazadeh, Alireza & Jadid, Shahram, 2014. "Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-grid concept and battery condition," Energy, Elsevier, vol. 65(C), pages 572-579.
    2. Vishnupriyan, J. & Manoharan, P.S., 2017. "Demand side management approach to rural electrification of different climate zones in Indian state of Tamil Nadu," Energy, Elsevier, vol. 138(C), pages 799-815.
    3. Wang, Tingting & Chen, Cynthia, 2012. "Attitudes, mode switching behavior, and the built environment: A longitudinal study in the Puget Sound Region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1594-1607.
    4. Hedegaard, Karsten & Ravn, Hans & Juul, Nina & Meibom, Peter, 2012. "Effects of electric vehicles on power systems in Northern Europe," Energy, Elsevier, vol. 48(1), pages 356-368.
    5. Hoehne, Christopher G. & Chester, Mikhail V., 2016. "Optimizing plug-in electric vehicle and vehicle-to-grid charge scheduling to minimize carbon emissions," Energy, Elsevier, vol. 115(P1), pages 646-657.
    6. Smith, William J., 2010. "Can EV (electric vehicles) address Ireland’s CO2 emissions from transport?," Energy, Elsevier, vol. 35(12), pages 4514-4521.
    7. Amirioun, Mohammad Hassan & Kazemi, Ahad, 2014. "A new model based on optimal scheduling of combined energy exchange modes for aggregation of electric vehicles in a residential complex," Energy, Elsevier, vol. 69(C), pages 186-198.
    8. Vassileva, Iana & Campillo, Javier, 2017. "Adoption barriers for electric vehicles: Experiences from early adopters in Sweden," Energy, Elsevier, vol. 120(C), pages 632-641.
    9. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2018. "Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule," Applied Energy, Elsevier, vol. 210(C), pages 1188-1206.
    10. Guelpa, Elisa & Barbero, Giulia & Sciacovelli, Adriano & Verda, Vittorio, 2017. "Peak-shaving in district heating systems through optimal management of the thermal request of buildings," Energy, Elsevier, vol. 137(C), pages 706-714.
    11. Kipping, A. & Trømborg, E., 2017. "Modeling hourly consumption of electricity and district heat in non-residential buildings," Energy, Elsevier, vol. 123(C), pages 473-486.
    12. Nezamoddini, Nasim & Wang, Yong, 2016. "Risk management and participation planning of electric vehicles in smart grids for demand response," Energy, Elsevier, vol. 116(P1), pages 836-850.
    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. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M. & Lund, Peter D., 2019. "Energy integration and interaction between buildings and vehicles: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    2. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    3. Singh, Kamini & Singh, Anoop, 2022. "Behavioural modelling for personal and societal benefits of V2G/V2H integration on EV adoption," Applied Energy, Elsevier, vol. 319(C).
    4. Nagel, Niels Oliver & Jåstad, Eirik Ogner & Martinsen, Thomas, 2024. "The grid benefits of vehicle-to-grid in Norway and Denmark: An analysis of home- and public parking potentials," Energy, Elsevier, vol. 293(C).
    5. 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.
    6. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2019. "Double layer home energy supervision strategies based on demand response and plug-in electric vehicle control for flattening power load curves in a smart grid," Energy, Elsevier, vol. 167(C), pages 312-324.
    7. Liu, Nian & Chen, Zheng & Liu, Jie & Tang, Xiao & Xiao, Xiangning & Zhang, Jianhua, 2014. "Multi-objective optimization for component capacity of the photovoltaic-based battery switch stations: Towards benefits of economy and environment," Energy, Elsevier, vol. 64(C), pages 779-792.
    8. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    9. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    10. Evgeny Nefedov & Seppo Sierla & Valeriy Vyatkin, 2018. "Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings," Energies, MDPI, vol. 11(8), pages 1-18, August.
    11. Shaukat, N. & Khan, B. & Ali, S.M. & Mehmood, C.A. & Khan, J. & Farid, U. & Majid, M. & Anwar, S.M. & Jawad, M. & Ullah, Z., 2018. "A survey on electric vehicle transportation within smart grid system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1329-1349.
    12. Gough, Rebecca & Dickerson, Charles & Rowley, Paul & Walsh, Chris, 2017. "Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage," Applied Energy, Elsevier, vol. 192(C), pages 12-23.
    13. Raslavičius, Laurencas & Starevičius, Martynas & Keršys, Artūras & Pilkauskas, Kęstutis & Vilkauskas, Andrius, 2013. "Performance of an all-electric vehicle under UN ECE R101 test conditions: A feasibility study for the city of Kaunas, Lithuania," Energy, Elsevier, vol. 55(C), pages 436-448.
    14. Soares, N. & Martins, A.G. & Carvalho, A.L. & Caldeira, C. & Du, C. & Castanheira, É. & Rodrigues, E. & Oliveira, G. & Pereira, G.I. & Bastos, J. & Ferreira, J.P. & Ribeiro, L.A. & Figueiredo, N.C. & , 2018. "The challenging paradigm of interrelated energy systems towards a more sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 171-193.
    15. Zhou, Yuekuan & Liu, Xiaohua & Zhao, Qianchuan, 2024. "A stochastic vehicle schedule model for demand response and grid flexibility in a renewable-building-e-transportation-microgrid," Renewable Energy, Elsevier, vol. 221(C).
    16. Mirzaei, Mohammad Javad & Kazemi, Ahad & Homaee, Omid, 2014. "Real-world based approach for optimal management of electric vehicles in an intelligent parking lot considering simultaneous satisfaction of vehicle owners and parking operator," Energy, Elsevier, vol. 76(C), pages 345-356.
    17. Personal, Enrique & Guerrero, Juan Ignacio & Garcia, Antonio & Peña, Manuel & Leon, Carlos, 2014. "Key performance indicators: A useful tool to assess Smart Grid goals," Energy, Elsevier, vol. 76(C), pages 976-988.
    18. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2017. "A flexible control strategy of plug-in electric vehicles operating in seven modes for smoothing load power curves in smart grid," Energy, Elsevier, vol. 118(C), pages 197-208.
    19. 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.
    20. He, Lifu & Yang, Jun & Yan, Jun & Tang, Yufei & He, Haibo, 2016. "A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles," Applied Energy, Elsevier, vol. 168(C), pages 179-192.

    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:energy:v:148:y:2018:i:c:p:148-158. 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.journals.elsevier.com/energy .

    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.