IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v139y2017icp422-432.html
   My bibliography  Save this article

Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach

Author

Listed:
  • Aghajani, Saemeh
  • Kalantar, Mohsen

Abstract

With increasing the share of electric vehicles in electricity market, it is important to investigate their impact on electricity trading and their interactions with other market entities involved in the system. This paper provides a methodology to develop the interaction between parking lot and distribution system operator in energy and reserve market while considering load and wind power uncertainty. To this end, a bilevel approach is applied to model inherently conflicting objective between distribution system operator and parking lot and interactions between the two agents. In the proposed model, upper-level problem represents the total operation cost minimization from the distribution system operator’s perspective while the lower-level problem represents the scheduling energy and reserve from the parking lot owner’s point of view with the objective of minimizing the parking cost. The method is capable of finding the equilibrium point for decision making conflict between the objective of the upper and lower level. The proposed bilevel problem is reduced to a single level optimization problem by implementing dual theorem and the Karush–Kuhn–Tucker optimality conditions. The numerical results illustrate the effectiveness of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:139:y:2017:i:c:p:422-432
    DOI: 10.1016/j.energy.2017.08.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217313798
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.08.004?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. Guille, Christophe & Gross, George, 2009. "A conceptual framework for the vehicle-to-grid (V2G) implementation," Energy Policy, Elsevier, vol. 37(11), pages 4379-4390, November.
    2. Willis, K. G. & Garrod, G. D., 1997. "Electricity supply reliability : Estimating the value of lost load," Energy Policy, Elsevier, vol. 25(1), pages 97-103, January.
    3. 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.
    4. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Moradi-Dalvand, Mohammad & Zare, Kazem, 2017. "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, Elsevier, vol. 118(C), pages 1168-1179.
    5. 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.
    6. Nienhueser, Ian Andrew & Qiu, Yueming, 2016. "Economic and environmental impacts of providing renewable energy for electric vehicle charging – A choice experiment study," Applied Energy, Elsevier, vol. 180(C), pages 256-268.
    7. Soares M.C. Borba, Bruno & Szklo, Alexandre & Schaeffer, Roberto, 2012. "Plug-in hybrid electric vehicles as a way to maximize the integration of variable renewable energy in power systems: The case of wind generation in northeastern Brazil," Energy, Elsevier, vol. 37(1), pages 469-481.
    8. Parvizimosaed, M. & Farmani, F. & Monsef, H. & Rahimi-Kian, A., 2017. "A multi-stage Smart Energy Management System under multiple uncertainties: A data mining approach," Renewable Energy, Elsevier, vol. 102(PA), pages 178-189.
    9. 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.
    10. Nurre, Sarah G. & Bent, Russell & Pan, Feng & Sharkey, Thomas C., 2014. "Managing operations of plug-in hybrid electric vehicle (PHEV) exchange stations for use with a smart grid," Energy Policy, Elsevier, vol. 67(C), pages 364-377.
    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. 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.
    2. Kwag, Kyuhyeong & Shin, Hansol & Oh, Hyobin & Yun, Sangmin & Kim, Tae Hyun & Hwang, Pyeong-Ik & Kim, Wook, 2023. "Bilevel programming approach for the quantitative analysis of renewable portfolio standards considering the electricity market," Energy, Elsevier, vol. 263(PD).
    3. 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.
    4. Bostan, Alireza & Nazar, Mehrdad Setayesh & Shafie-khah, Miadreza & Catalão, João P.S., 2020. "An integrated optimization framework for combined heat and power units, distributed generation and plug-in electric vehicles," Energy, Elsevier, vol. 202(C).
    5. Mohammadi Landi, Meysam & Mohammadi, Mohammad & Rastegar, Mohammad, 2018. "Simultaneous determination of optimal capacity and charging profile of plug-in electric vehicle parking lots in distribution systems," Energy, Elsevier, vol. 158(C), pages 504-511.
    6. 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.
    7. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid, 2019. "Optimal Operational Scheduling of Reconfigurable Multi-Microgrids Considering Energy Storage Systems," Energies, MDPI, vol. 12(9), pages 1-23, May.
    8. Ali M. Jasim & Basil H. Jasim & Habib Kraiem & Aymen Flah, 2022. "A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    9. 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.
    10. 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.
    11. Subramanian, Vignesh & Feijoo, Felipe & Sankaranarayanan, Sriram & Melendez, Kevin & Das, Tapas K., 2022. "A bilevel conic optimization model for routing and charging of EV fleets serving long distance delivery networks," Energy, Elsevier, vol. 251(C).
    12. Ahmadian, Ali & Sedghi, Mahdi & Fgaier, Hedia & Mohammadi-ivatloo, Behnam & Golkar, Masoud Aliakbar & Elkamel, Ali, 2019. "PEVs data mining based on factor analysis method for energy storage and DG planning in active distribution network: Introducing S2S effect," Energy, Elsevier, vol. 175(C), pages 265-277.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Richardson, David B., 2013. "Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 247-254.
    6. Sharma, S. & Jain, Prerna, 2023. "Risk-averse integrated DR and dynamic V2G scheduling of parking lot operator for enhanced market efficiency," Energy, Elsevier, vol. 275(C).
    7. Popović Vlado & Jereb Borut & Kilibarda Milorad & Andrejić Milan & Keshavarzsaleh Abolfazl & Dragan Dejan, 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.
    8. 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.
    9. Hota, Ashish Ranjan & Juvvanapudi, Mahesh & Bajpai, Prabodh, 2014. "Issues and solution approaches in PHEV integration to smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 217-229.
    10. Zhong, Jin & He, Lina & Li, Canbing & Cao, Yijia & Wang, Jianhui & Fang, Baling & Zeng, Long & Xiao, Guoxuan, 2014. "Coordinated control for large-scale EV charging facilities and energy storage devices participating in frequency regulation," Applied Energy, Elsevier, vol. 123(C), pages 253-262.
    11. He, Yongxiu & Zhang, Qi & Pang, Yuexia, 2017. "The development pattern design of Chinese electric vehicles based on the analysis of the critical price of the life cycle cost," Energy Policy, Elsevier, vol. 109(C), pages 382-388.
    12. Einolander, Johannes & Lahdelma, Risto, 2022. "Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure," Energy, Elsevier, vol. 256(C).
    13. 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.
    14. Mubbashir Ali & Jussi Ekström & Matti Lehtonen, 2018. "Sizing Hydrogen Energy Storage in Consideration of Demand Response in Highly Renewable Generation Power Systems," Energies, MDPI, vol. 11(5), pages 1-11, May.
    15. Chaouachi, Aymen & Bompard, Ettore & Fulli, Gianluca & Masera, Marcelo & De Gennaro, Michele & Paffumi, Elena, 2016. "Assessment framework for EV and PV synergies in emerging distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 719-728.
    16. Kley, Fabian & Lerch, Christian & Dallinger, David, 2011. "New business models for electric cars--A holistic approach," Energy Policy, Elsevier, vol. 39(6), pages 3392-3403, June.
    17. Sirat, Ali Parsa, 2018. "Loss Minimization through the Allocation of DGs Considering the Stochastic Nature of Units," MPRA Paper 87636, University Library of Munich, Germany.
    18. Zhang, Qi & Mclellan, Benjamin C. & Tezuka, Tetsuo & Ishihara, Keiichi N., 2013. "A methodology for economic and environmental analysis of electric vehicles with different operational conditions," Energy, Elsevier, vol. 61(C), pages 118-127.
    19. 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).
    20. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).

    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:139:y:2017:i:c:p:422-432. 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.