IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v356y2024ics030626192301677x.html
   My bibliography  Save this article

Joint chance-constrained program based electric vehicles optimal dispatching strategy considering drivers' response uncertainty

Author

Listed:
  • Zhang, Kaizhe
  • Xu, Yinliang
  • Sun, Hongbin

Abstract

Electric vehicles (EVs) bring both opportunities and challenges to the distribution network due to their features of random behavior, storage capacity and charging flexibility. EV aggregator (EVA) can serve to unlock the dispatching potential of large scale of EVs and participate in the joint energy and reserve electricity market. In this paper, the improved EVA model for ancillary services is firstly established. Then, the dispatching potential of EVs considering drivers' response willingness and its uncertainty through EVA is explored. To obtain the optimal incentive price considering the driver response uncertainty, the joint chance-constrained program (JCCP) model is proposed. Due to the nonlinearity and nonconvexity of probability constraints, the developed JCCP model is intractable. A Monte Carlo based sequential convex approximation (SCA) algorithm is further developed to achieve the tractability and solve the JCCP model effectively. Case studies show that the proposed incentive program can reduce 15.1% net cost of EVA compared to uncoordinated charging. Besides, the proposed SCA algorithm can solve the JCCP problem with more accurate and less conservative results compared to the existing method, while achieving a decent computational efficiency for the day-ahead dispatching.

Suggested Citation

  • Zhang, Kaizhe & Xu, Yinliang & Sun, Hongbin, 2024. "Joint chance-constrained program based electric vehicles optimal dispatching strategy considering drivers' response uncertainty," Applied Energy, Elsevier, vol. 356(C).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s030626192301677x
    DOI: 10.1016/j.apenergy.2023.122313
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192301677X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.122313?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. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2015. "Value of flexible electric vehicles in providing spinning reserve services," Applied Energy, Elsevier, vol. 157(C), pages 60-74.
    2. Heinisch, Verena & Göransson, Lisa & Erlandsson, Rasmus & Hodel, Henrik & Johnsson, Filip & Odenberger, Mikael, 2021. "Smart electric vehicle charging strategies for sectoral coupling in a city energy system," Applied Energy, Elsevier, vol. 288(C).
    3. Zhang, Qian & Wu, Xiaohan & Deng, Xiaosong & Huang, Yaoyu & Li, Chunyan & Wu, Jiaqi, 2023. "Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market," Applied Energy, Elsevier, vol. 341(C).
    4. Park, Sung-Won & Cho, Kyu-Sang & Hoefter, Gregor & Son, Sung-Yong, 2022. "Electric vehicle charging management using location-based incentives for reducing renewable energy curtailment considering the distribution system," Applied Energy, Elsevier, vol. 305(C).
    5. Dai, Ziyi & Liu, Haobing & Rodgers, Michael O. & Guensler, Randall, 2022. "Electric vehicle market potential and associated energy and emissions reduction benefits," Applied Energy, Elsevier, vol. 322(C).
    6. de la Torre, S. & Aguado, J.A. & Sauma, E., 2023. "Optimal scheduling of ancillary services provided by an electric vehicle aggregator," Energy, Elsevier, vol. 265(C).
    7. Wu, Chuantao & Chen, Cen & Ma, Yuncong & Li, Feiyu & Sui, Quan & Lin, Xiangning & Wei, Fanrong & Li, Zhengtian, 2022. "Enhancing resilient restoration of distribution systems utilizing electric vehicles and supporting incentive mechanism," Applied Energy, Elsevier, vol. 322(C).
    8. Visser, L.R. & Kootte, M.E. & Ferreira, A.C. & Sicurani, O. & Pauwels, E.J. & Vuik, C. & Van Sark, W.G.J.H.M. & AlSkaif, T.A., 2022. "An operational bidding framework for aggregated electric vehicles on the electricity spot market," Applied Energy, Elsevier, vol. 308(C).
    9. L. Jeff Hong & Guangwu Liu, 2009. "Simulating Sensitivities of Conditional Value at Risk," Management Science, INFORMS, vol. 55(2), pages 281-293, February.
    10. 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.
    11. Zheng, Yanchong & Yu, Hang & Shao, Ziyun & Jian, Linni, 2020. "Day-ahead bidding strategy for electric vehicle aggregator enabling multiple agent modes in uncertain electricity markets," Applied Energy, Elsevier, vol. 280(C).
    12. L. Jeff Hong & Yi Yang & Liwei Zhang, 2011. "Sequential Convex Approximations to Joint Chance Constrained Programs: A Monte Carlo Approach," Operations Research, INFORMS, vol. 59(3), pages 617-630, June.
    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. Zhang, Kaizhe & Xu, Yinliang & Sun, Hongbin, 2024. "Bilevel optimal coordination of active distribution network and charging stations considering EV drivers' willingness," Applied Energy, Elsevier, vol. 360(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, Kaizhe & Xu, Yinliang & Sun, Hongbin, 2024. "Bilevel optimal coordination of active distribution network and charging stations considering EV drivers' willingness," Applied Energy, Elsevier, vol. 360(C).
    2. Guo, Shiliang & Li, Pengpeng & Ma, Kai & Yang, Bo & Yang, Jie, 2022. "Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles," Applied Energy, Elsevier, vol. 325(C).
    3. Wang, Qi & Huang, Chunyi & Wang, Chengmin & Li, Kangping & Xie, Ning, 2024. "Joint optimization of bidding and pricing strategy for electric vehicle aggregator considering multi-agent interactions," Applied Energy, Elsevier, vol. 360(C).
    4. Xiangchu Xu & Zewei Zhan & Zengqiang Mi & Ling Ji, 2023. "An Optimized Decision Model for Electric Vehicle Aggregator Participation in the Electricity Market Based on the Stackelberg Game," Sustainability, MDPI, vol. 15(20), pages 1-26, October.
    5. Strobel, Leo & Schlund, Jonas & Pruckner, Marco, 2022. "Joint analysis of regional and national power system impacts of electric vehicles—A case study for Germany on the county level in 2030," Applied Energy, Elsevier, vol. 315(C).
    6. L. Jeff Hong & Zhaolin Hu & Liwei Zhang, 2014. "Conditional Value-at-Risk Approximation to Value-at-Risk Constrained Programs: A Remedy via Monte Carlo," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 385-400, May.
    7. Zheng, Yanchong & Wang, Yubin & Yang, Qiang, 2023. "Bidding strategy design for electric vehicle aggregators in the day-ahead electricity market considering price volatility: A risk-averse approach," Energy, Elsevier, vol. 283(C).
    8. Songhao Wang & Szu Hui Ng & William Benjamin Haskell, 2022. "A Multilevel Simulation Optimization Approach for Quantile Functions," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 569-585, January.
    9. Youssef Amry & Elhoussin Elbouchikhi & Franck Le Gall & Mounir Ghogho & Soumia El Hani, 2022. "Electric Vehicle Traction Drives and Charging Station Power Electronics: Current Status and Challenges," Energies, MDPI, vol. 15(16), pages 1-30, August.
    10. Pesenti, Silvana M. & Tsanakas, Andreas & Millossovich, Pietro, 2018. "Euler allocations in the presence of non-linear reinsurance: Comment on Major (2018)," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 29-31.
    11. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    12. Fachrizal, Reza & Shepero, Mahmoud & Åberg, Magnus & Munkhammar, Joakim, 2022. "Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance," Applied Energy, Elsevier, vol. 307(C).
    13. Silvana M. Pesenti & Pietro Millossovich & Andreas Tsanakas, 2023. "Differential Sensitivity in Discontinuous Models," Papers 2310.06151, arXiv.org.
    14. Diaz, Cesar & Ruiz, Fredy & Patino, Diego, 2017. "Modeling and control of water booster pressure systems as flexible loads for demand response," Applied Energy, Elsevier, vol. 204(C), pages 106-116.
    15. Motalleb, Mahdi & Thornton, Matsu & Reihani, Ehsan & Ghorbani, Reza, 2016. "A nascent market for contingency reserve services using demand response," Applied Energy, Elsevier, vol. 179(C), pages 985-995.
    16. Cleary, Kathryne & Palmer, Karen, 2020. "Encouraging Electrification through Energy Service Subscriptions," RFF Working Paper Series 20-09, Resources for the Future.
    17. da Costa, B. Freitas Paulo & Pesenti, Silvana M. & Targino, Rodrigo S., 2023. "Risk budgeting portfolios from simulations," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1040-1056.
    18. Juan Ma & Foad Mahdavi Pajouh & Balabhaskar Balasundaram & Vladimir Boginski, 2016. "The Minimum Spanning k -Core Problem with Bounded CVaR Under Probabilistic Edge Failures," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 295-307, May.
    19. Borgonovo, Emanuele & Gatti, Stefano, 2013. "Risk analysis with contractual default. Does covenant breach matter?," European Journal of Operational Research, Elsevier, vol. 230(2), pages 431-443.
    20. L. Jeff Hong & Zhiyuan Huang & Henry Lam, 2021. "Learning-Based Robust Optimization: Procedures and Statistical Guarantees," Management Science, INFORMS, vol. 67(6), pages 3447-3467, June.

    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:356:y:2024:i:c:s030626192301677x. 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.