IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i10p2809-d176614.html
   My bibliography  Save this article

A Bi-Level EV Aggregator Coordination Scheme for Load Variance Minimization with Renewable Energy Penetration Adaptability

Author

Listed:
  • Saad Ullah Khan

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Khawaja Khalid Mehmood

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Zunaib Maqsood Haider

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Muhammad Kashif Rafique

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Chul-Hwan Kim

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea)

Abstract

The provision of ancillary services by electric vehicles (EVs) such as load smoothing and renewable energy (RE) compensation in the form of an aggregated storage is more regulated in the smart grid context. As such, the presence of multiple EV aggregators in the distribution network requires adept supervision by the distribution system operator (DSO). In this paper, a coordination scheme of aggregators is proposed to smoothen the load profile of distribution networks by enacting EV discharging during peak load and off-peak charging, keeping in view the EV driving requirements. A bi-level on-line interaction procedure from the DSO to the aggregators and vice versa is devised to manage the aggregators based upon their energy capacity and requirements. The aggregators employ a water-filling algorithm in a two-step EV power allocation method. The proposed scheme operation is demonstrated on an medium voltage (MV) distribution feeder located in Seoul with its actual traffic density data. The results show the achievement of peak shaving and valley filling objectives under aggregator coordination and that the EVs are completely charged before departure. The effect of various EV penetration levels and adaptivity of the scheme to RE incorporation is also verified. Furthermore, a comparison with an existing peak shaving method shows the superior performance of the proposed scheme.

Suggested Citation

  • Saad Ullah Khan & Khawaja Khalid Mehmood & Zunaib Maqsood Haider & Muhammad Kashif Rafique & Chul-Hwan Kim, 2018. "A Bi-Level EV Aggregator Coordination Scheme for Load Variance Minimization with Renewable Energy Penetration Adaptability," Energies, MDPI, vol. 11(10), pages 1-28, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2809-:d:176614
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/10/2809/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/10/2809/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andersson, S.-L. & Elofsson, A.K. & Galus, M.D. & Göransson, L. & Karlsson, S. & Johnsson, F. & Andersson, G., 2010. "Plug-in hybrid electric vehicles as regulating power providers: Case studies of Sweden and Germany," Energy Policy, Elsevier, vol. 38(6), pages 2751-2762, June.
    2. Xu, Zhiwei & Hu, Zechun & Song, Yonghua & Zhao, Wei & Zhang, Yongwang, 2014. "Coordination of PEVs charging across multiple aggregators," Applied Energy, Elsevier, vol. 136(C), pages 582-589.
    3. Mingchao Xia & Qingying Lai & Yajiao Zhong & Canbing Li & Hsiao-Dong Chiang, 2016. "Aggregator-Based Interactive Charging Management System for Electric Vehicle Charging," Energies, MDPI, vol. 9(3), pages 1-14, March.
    4. Jia, Hongjie & Li, Xiaomeng & Mu, Yunfei & Xu, Chen & Jiang, Yilang & Yu, Xiaodan & Wu, Jianzhong & Dong, Chaoyu, 2018. "Coordinated control for EV aggregators and power plants in frequency regulation considering time-varying delays," Applied Energy, Elsevier, vol. 210(C), pages 1363-1376.
    5. San Román, Tomás Gómez & Momber, Ilan & Abbad, Michel Rivier & Sánchez Miralles, Álvaro, 2011. "Regulatory framework and business models for charging plug-in electric vehicles: Infrastructure, agents, and commercial relationships," Energy Policy, Elsevier, vol. 39(10), pages 6360-6375, October.
    6. 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.
    7. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    8. 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.
    9. Mehrdad Ehsani & Milad Falahi & Saeed Lotfifard, 2012. "Vehicle to Grid Services: Potential and Applications," Energies, MDPI, vol. 5(10), pages 1-15, October.
    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.
    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. Kyuho Maeng & Sungmin Ko & Jungwoo Shin & Youngsang Cho, 2020. "How Much Electricity Sharing Will Electric Vehicle Owners Allow from Their Battery? Incorporating Vehicle-to-Grid Technology and Electricity Generation Mix," Energies, MDPI, vol. 13(16), pages 1-25, August.
    2. Ming Tang & Jian Wang & Xiaohua Wang, 2020. "Adaptable Source-Grid Planning for High Penetration of Renewable Energy Integrated System," Energies, MDPI, vol. 13(13), pages 1-26, June.
    3. Saad Ullah Khan & Khawaja Khalid Mehmood & Zunaib Maqsood Haider & Muhammad Kashif Rafique & Muhammad Omer Khan & Chul-Hwan Kim, 2021. "Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders," Energies, MDPI, vol. 14(2), pages 1-16, January.
    4. Yilu Wang & Zixuan Jia & Jianing Li & Xiaoping Zhang & Ray Zhang, 2021. "Optimal Bi-Level Scheduling Method of Vehicle-to-Grid and Ancillary Services of Aggregators with Conditional Value-at-Risk," Energies, MDPI, vol. 14(21), pages 1-16, October.

    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. Saad Ullah Khan & Khawaja Khalid Mehmood & Zunaib Maqsood Haider & Muhammad Kashif Rafique & Muhammad Omer Khan & Chul-Hwan Kim, 2021. "Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders," Energies, MDPI, vol. 14(2), pages 1-16, January.
    2. 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.
    3. 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.
    4. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Optimal scheduling of distributed energy resources in smart grids: A complementarity approach," Energy, Elsevier, vol. 141(C), pages 2135-2144.
    5. Sovacool, Benjamin K. & Kester, Johannes & Noel, Lance & Zarazua de Rubens, Gerardo, 2020. "Actors, business models, and innovation activity systems for vehicle-to-grid (V2G) technology: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    6. Zhang, Xian & Chan, Ka Wing & Wang, Huaizhi & Zhou, Bin & Wang, Guibin & Qiu, Jing, 2020. "Multiple group search optimization based on decomposition for multi-objective dispatch with electric vehicle and wind power uncertainties," Applied Energy, Elsevier, vol. 262(C).
    7. Colmenar-Santos, A. & de Palacio-Rodriguez, Carlos & Rosales-Asensio, Enrique & Borge-Diez, David, 2017. "Estimating the benefits of vehicle-to-home in islands: The case of the Canary Islands," Energy, Elsevier, vol. 134(C), pages 311-322.
    8. 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.
    9. George S. Fernandez & Vijayakumar Krishnasamy & Selvakumar Kuppusamy & Jagabar S. Ali & Ziad M. Ali & Adel El-Shahat & Shady H. E. Abdel Aleem, 2020. "Optimal Dynamic Scheduling of Electric Vehicles in a Parking Lot Using Particle Swarm Optimization and Shuffled Frog Leaping Algorithm," Energies, MDPI, vol. 13(23), pages 1-26, December.
    10. Saleh Aghajan-Eshkevari & Sasan Azad & Morteza Nazari-Heris & Mohammad Taghi Ameli & Somayeh Asadi, 2022. "Charging and Discharging of Electric Vehicles in Power Systems: An Updated and Detailed Review of Methods, Control Structures, Objectives, and Optimization Methodologies," Sustainability, MDPI, vol. 14(4), pages 1-31, February.
    11. Weiller, C. & Neely, A., 2014. "Using electric vehicles for energy services: Industry perspectives," Energy, Elsevier, vol. 77(C), pages 194-200.
    12. 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.
    13. 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.
    14. Masiero, Gilmar & Ogasavara, Mario Henrique & Jussani, Ailton Conde & Risso, Marcelo Luiz, 2017. "The global value chain of electric vehicles: A review of the Japanese, South Korean and Brazilian cases," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 290-296.
    15. Xin Li & Xiaodi Zhang & Yuling Fan, 2019. "A Two-Step Framework for Energy Local Area Network Scheduling Problem with Electric Vehicles Based on Global–Local Optimization Method," Energies, MDPI, vol. 12(1), pages 1-17, January.
    16. Moses Amoasi Acquah & Sekyung Han, 2019. "Online Building Load Management Control with Plugged-in Electric Vehicles Considering Uncertainties," Energies, MDPI, vol. 12(8), pages 1-19, April.
    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. Jargstorf, Johannes & Wickert, Manuel, 2013. "Offer of secondary reserve with a pool of electric vehicles on the German market," Energy Policy, Elsevier, vol. 62(C), pages 185-195.
    19. Popović Vlado & Kilibarda Milorad & Andrejić Milan & Jereb Borut & Dragan Dejan & Keshavarzsaleh Abolfazl, 2018. "Electric Vehicles as Electricity Storages in Electric Power Systems," Logistics, Supply Chain, Sustainability and Global Challenges, Sciendo, vol. 9(2), pages 57-72, October.
    20. Sarabi, Siyamak & Davigny, Arnaud & Courtecuisse, Vincent & Riffonneau, Yann & Robyns, Benoît, 2016. "Potential of vehicle-to-grid ancillary services considering the uncertainties in plug-in electric vehicle availability and service/localization limitations in distribution grids," Applied Energy, Elsevier, vol. 171(C), pages 523-540.

    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:11:y:2018:i:10:p:2809-:d:176614. 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.