IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v321y2022ics0306261922007097.html
   My bibliography  Save this article

Quantifying the real-time energy flexibility of commuter plug-in electric vehicles in an office building considering photovoltaic and load uncertainty

Author

Listed:
  • Yu, Zhenyu
  • Lu, Fei
  • Zou, Yu
  • Yang, Xudong

Abstract

The widespread use of fluctuating renewable electricity has introduced challenges in balancing power systems. This can be alleviated by using flexible resources on the demand side to provide ancillary services at various timescales (e.g., day-ahead planning or real-time operation). However, practitioners lack a unified framework for measuring energy flexibility in the real-time operation frame. This study considers a control structure for clustered commuter plug-in electric vehicles (PEVs) in an office building with both day-ahead scheduling and real-time dispatch and proposes a generic quantification framework for assessing the capability of PEVs to manage the uncertainties of photovoltaic output and inflexible building load. Our evaluation considered the flexibility shortage in terms of power, energy, and control strategy, and the Monte Carlo method was used for probabilistic analysis. We conducted a sensitivity analysis to identify the key factors determining real-time flexibility. The results suggest that 1.5–2 times the number of PEVs is required to meet the defined level of real-time flexibility rather than planning flexibility. Possible conflicts in flexibility enhancement at different timescales and directions were observed under various day-ahead scheduling strategies; prediction information can be used to mitigate conflicts in scheduling strategies and improve the overall control performance.

Suggested Citation

  • Yu, Zhenyu & Lu, Fei & Zou, Yu & Yang, Xudong, 2022. "Quantifying the real-time energy flexibility of commuter plug-in electric vehicles in an office building considering photovoltaic and load uncertainty," Applied Energy, Elsevier, vol. 321(C).
    • Handle: RePEc:eee:appene:v:321:y:2022:i:c:s0306261922007097
    DOI: 10.1016/j.apenergy.2022.119365
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922007097
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119365?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. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    2. Schuller, Alexander & Flath, Christoph M. & Gottwalt, Sebastian, 2015. "Quantifying load flexibility of electric vehicles for renewable energy integration," Applied Energy, Elsevier, vol. 151(C), pages 335-344.
    3. Zhang, Shicong & Jiang, Yiqiang & Xu, Wei & Li, Huai & Yu, Zhen, 2016. "Operating performance in cooling mode of a ground source heat pump of a nearly-zero energy building in the cold region of China," Renewable Energy, Elsevier, vol. 87(P3), pages 1045-1052.
    4. Wang, Jiawei & You, Shi & Zong, Yi & Cai, Hanmin & Træholt, Chresten & Dong, Zhao Yang, 2019. "Investigation of real-time flexibility of combined heat and power plants in district heating applications," Applied Energy, Elsevier, vol. 237(C), pages 196-209.
    5. Wu, Di & Radhakrishnan, Nikitha & Huang, Sen, 2019. "A hierarchical charging control of plug-in electric vehicles with simple flexibility model," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2021. "Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: An integrated chance-constrained and," Energy, Elsevier, vol. 232(C).
    7. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).
    8. 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.
    9. 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.
    10. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Uncertainty Characterization via Scenarios," International Series in Operations Research & Management Science, in: Decision Making Under Uncertainty in Electricity Markets, chapter 0, pages 63-119, Springer.
    11. Dallinger, David & Gerda, Schubert & Wietschel, Martin, 2013. "Integration of intermittent renewable power supply using grid-connected vehicles – A 2030 case study for California and Germany," Applied Energy, Elsevier, vol. 104(C), pages 666-682.
    12. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    13. Zhang, Cong & Greenblatt, Jeffery B. & MacDougall, Pamela & Saxena, Samveg & Jayam Prabhakar, Aditya, 2020. "Quantifying the benefits of electric vehicles on the future electricity grid in the midwestern United States," Applied Energy, Elsevier, vol. 270(C).
    14. Tang, Hong & Wang, Shengwei, 2022. "A model-based predictive dispatch strategy for unlocking and optimizing the building energy flexibilities of multiple resources in electricity markets of multiple services," Applied Energy, Elsevier, vol. 305(C).
    15. Li, Sihui & Peng, Jinqing & Zou, Bin & Li, Bojia & Lu, Chujie & Cao, Jingyu & Luo, Yimo & Ma, Tao, 2021. "Zero energy potential of photovoltaic direct-driven air conditioners with considering the load flexibility of air conditioners," Applied Energy, Elsevier, vol. 304(C).
    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. Liu, Xiaochen & Fu, Zhi & Qiu, Siyuan & Zhang, Tao & Li, Shaojie & Yang, Zhi & Liu, Xiaohua & Jiang, Yi, 2023. "Charging private electric vehicles solely by photovoltaics: A battery-free direct-current microgrid with distributed charging strategy," Applied Energy, Elsevier, vol. 341(C).
    2. Liu, Xiaochen & Fu, Zhi & Qiu, Siyuan & Li, Shaojie & Zhang, Tao & Liu, Xiaohua & Jiang, Yi, 2023. "Building-centric investigation into electric vehicle behavior: A survey-based simulation method for charging system design," Energy, Elsevier, vol. 271(C).
    3. Yao, Zhaosheng & Wang, Zhiyuan & Ran, Lun, 2023. "Smart charging and discharging of electric vehicles based on multi-objective robust optimization in smart cities," Applied Energy, Elsevier, vol. 343(C).
    4. Zhi, Yuan & Yang, Xudong, 2023. "Scenario-based multi-objective optimization strategy for rural PV-battery systems," Applied Energy, Elsevier, vol. 345(C).

    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. Zhang, Haifeng & Tian, Ming & Zhang, Cong & Wang, Bin & Wang, Dai, 2021. "A systematic solution to quantify economic values of vehicle grid integration," Energy, Elsevier, vol. 232(C).
    2. Ensslen, Axel & Ringler, Philipp & Dörr, Lasse & Jochem, Patrick & Zimmermann, Florian & Fichtner, Wolf, 2018. "Incentivizing smart charging: Modeling charging tariffs for electric vehicles in German and French electricity markets," MPRA Paper 91543, University Library of Munich, Germany, revised 17 Feb 2018.
    3. Raveendran, Visal & Alvarez-Bel, Carlos & Nair, Manjula G., 2020. "Assessing the ancillary service potential of electric vehicles to support renewable energy integration in touristic islands: A case study from Balearic island of Menorca," Renewable Energy, Elsevier, vol. 161(C), pages 495-509.
    4. Tang, Hong & Wang, Shengwei, 2022. "Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services," Renewable Energy, Elsevier, vol. 201(P1), pages 35-45.
    5. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    6. Zhi, Yuan & Yang, Xudong, 2023. "Scenario-based multi-objective optimization strategy for rural PV-battery systems," Applied Energy, Elsevier, vol. 345(C).
    7. Masa-Bote, D. & Castillo-Cagigal, M. & Matallanas, E. & Caamaño-Martín, E. & Gutiérrez, A. & Monasterio-Huelín, F. & Jiménez-Leube, J., 2014. "Improving photovoltaics grid integration through short time forecasting and self-consumption," Applied Energy, Elsevier, vol. 125(C), pages 103-113.
    8. Schwarz, Marius & Auzépy, Quentin & Knoeri, Christof, 2020. "Can electricity pricing leverage electric vehicles and battery storage to integrate high shares of solar photovoltaics?," Applied Energy, Elsevier, vol. 277(C).
    9. Lin, Haiyang & Fu, Kun & Wang, Yu & Sun, Qie & Li, Hailong & Hu, Yukun & Sun, Bo & Wennersten, Ronald, 2019. "Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model," Energy, Elsevier, vol. 188(C).
    10. 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.
    11. Alexandra Märtz & Uwe Langenmayr & Sabrina Ried & Katrin Seddig & Patrick Jochem, 2022. "Charging Behavior of Electric Vehicles: Temporal Clustering Based on Real-World Data," Energies, MDPI, vol. 15(18), pages 1-26, September.
    12. Paterakis, Nikolaos G. & Gibescu, Madeleine, 2016. "A methodology to generate power profiles of electric vehicle parking lots under different operational strategies," Applied Energy, Elsevier, vol. 173(C), pages 111-123.
    13. Morales-España, Germán & Martínez-Gordón, Rafael & Sijm, Jos, 2022. "Classifying and modelling demand response in power systems," Energy, Elsevier, vol. 242(C).
    14. Lauvergne, Rémi & Perez, Yannick & Françon, Mathilde & Tejeda De La Cruz, Alberto, 2022. "Integration of electric vehicles into transmission grids: A case study on generation adequacy in Europe in 2040," Applied Energy, Elsevier, vol. 326(C).
    15. Tang, Hong & Wang, Shengwei, 2023. "Life-cycle economic analysis of thermal energy storage, new and second-life batteries in buildings for providing multiple flexibility services in electricity markets," Energy, Elsevier, vol. 264(C).
    16. Tang, Hong & Wang, Shengwei, 2023. "Game-theoretic optimization of demand-side flexibility engagement considering the perspectives of different stakeholders and multiple flexibility services," Applied Energy, Elsevier, vol. 332(C).
    17. Staudt, Philipp & Schmidt, Marc & Gärttner, Johannes & Weinhardt, Christof, 2018. "A decentralized approach towards resolving transmission grid congestion in Germany using vehicle-to-grid technology," Applied Energy, Elsevier, vol. 230(C), pages 1435-1446.
    18. Li, Mengyu & Lenzen, Manfred & Wang, Dai & Nansai, Keisuke, 2020. "GIS-based modelling of electric-vehicle–grid integration in a 100% renewable electricity grid," Applied Energy, Elsevier, vol. 262(C).
    19. Graber, Giuseppe & Calderaro, Vito & Mancarella, Pierluigi & Galdi, Vincenzo, 2020. "Two-stage stochastic sizing and packetized energy scheduling of BEV charging stations with quality of service constraints," Applied Energy, Elsevier, vol. 260(C).
    20. Christoph M. Flath & Sebastian Gottwalt, 2016. "Price-based load coordination revisited: augmenting open-loop coordination approaches," Business Research, Springer;German Academic Association for Business Research, vol. 9(1), pages 157-178, 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:321:y:2022:i:c:s0306261922007097. 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.