IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v364y2024ics030626192400535x.html
   My bibliography  Save this article

EV-observing distribution system management considering strategic VPPs and active & reactive power markets

Author

Listed:
  • Ebrahimi, Mahoor
  • Ebrahimi, Mahan
  • Shafie-khah, Miadreza
  • Laaksonen, Hannu

Abstract

The growing deployment of new flexible resources, renewable energy resources (RES), and Electric Vehicles (EV) in the distribution system necessitates new methods to manage the distribution system operation optimally. In this regard, our paper, by deploying the concept of Virtual Power Plants (VPPs) as the aggregation of multiple agents and local power markets that are known as important tools for future power systems presents a management framework for the distribution systems with high penetration of EVs. To this end, the interaction of the DSO and VPPs is studied based on their strategic behaviour through the local active and reactive power markets. This way, a bilevel optimization approach is proposed where the DSO aims to minimize its operational cost by setting the operation point of its own facilities and determining the hourly active and reactive power prices for VPPs considering the distribution system congestion in the upper level. At the lower level, VPPs try to minimize their cost by scheduling their assets based on the local active and reactive power prices set by the DSO. The results show how nodal pricing in local markets could improve the distribution system operation. In addition, it is indicated that Reactive Power Support (RPS) from VPP-owned EVPLs can decrease the VPPs’ cost by gaining profit in the reactive power market and facilitating their participation in the active power market.

Suggested Citation

  • Ebrahimi, Mahoor & Ebrahimi, Mahan & Shafie-khah, Miadreza & Laaksonen, Hannu, 2024. "EV-observing distribution system management considering strategic VPPs and active & reactive power markets," Applied Energy, Elsevier, vol. 364(C).
    • Handle: RePEc:eee:appene:v:364:y:2024:i:c:s030626192400535x
    DOI: 10.1016/j.apenergy.2024.123152
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192400535X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.123152?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. Rawat, Tanuj & Niazi, K.R. & Gupta, Nikhil & Sharma, Sachin, 2022. "A linearized multi-objective Bi-level approach for operation of smart distribution systems encompassing demand response," Energy, Elsevier, vol. 238(PC).
    2. Li, Qiang & Wei, Fanchao & Zhou, Yongcheng & Li, Jiajia & Zhou, Guowen & Wang, Zhonghao & Liu, Jinfu & Yan, Peigang & Yu, Daren, 2023. "A scheduling framework for VPP considering multiple uncertainties and flexible resources," Energy, Elsevier, vol. 282(C).
    3. Zhao, Jinli & Zhang, Mengzhen & Yu, Hao & Ji, Haoran & Song, Guanyu & Li, Peng & Wang, Chengshan & Wu, Jianzhong, 2019. "An islanding partition method of active distribution networks based on chance-constrained programming," Applied Energy, Elsevier, vol. 242(C), pages 78-91.
    4. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    5. Zhang, Tianhan & Qiu, Weiqiang & Zhang, Zhi & Lin, Zhenzhi & Ding, Yi & Wang, Yiting & Wang, Lianfang & Yang, Li, 2023. "Optimal bidding strategy and profit allocation method for shared energy storage-assisted VPP in joint energy and regulation markets," Applied Energy, Elsevier, vol. 329(C).
    6. Khaledi, Arian & Saifoddin, Amirali, 2023. "Three-stage resilience-oriented active distribution systems operation after natural disasters," Energy, Elsevier, vol. 282(C).
    7. Wooyoung Jeon & Sangmin Cho & Seungmoon Lee, 2020. "Estimating the Impact of Electric Vehicle Demand Response Programs in a Grid with Varying Levels of Renewable Energy Sources: Time-of-Use Tariff versus Smart Charging," Energies, MDPI, vol. 13(17), pages 1-22, August.
    8. Bhuiyan, Erphan A. & Hossain, Md. Zahid & Muyeen, S.M. & Fahim, Shahriar Rahman & Sarker, Subrata K. & Das, Sajal K., 2021. "Towards next generation virtual power plant: Technology review and frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    9. Jayachandran, M. & Rao, K. Prasada & Gatla, Ranjith Kumar & Kalaivani, C. & Kalaiarasy, C. & Logasabarirajan, C., 2022. "Operational concerns and solutions in smart electricity distribution systems," Utilities Policy, Elsevier, vol. 74(C).
    10. Lei, Xiang & Yu, Hang & Shao, Ziyun & Jian, Linni, 2023. "Optimal bidding and coordinating strategy for maximal marginal revenue due to V2G operation: Distribution system operator as a key player in China's uncertain electricity markets," Energy, Elsevier, vol. 283(C).
    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. Sun, Lingling & Li, Haibin & Jia, Qingquan & Zhang, Gong, 2024. "Optimization method of dynamic reconfiguration in virtual power plants," Renewable Energy, Elsevier, vol. 228(C).
    2. Li, Qiang & Zhou, Yongcheng & Wei, Fanchao & Li, Shuangxiu & Wang, Zhonghao & Li, Jiajia & Zhou, Guowen & Liu, Jinfu & Yan, Peigang & Yu, Daren, 2024. "Multi-time scale scheduling for virtual power plants: Integrating the flexibility of power generation and multi-user loads while considering the capacity degradation of energy storage systems," Applied Energy, Elsevier, vol. 362(C).
    3. Wang, Dongxue & Fan, Ruguo & Yang, Peiwen & Du, Kang & Xu, Xiaoxia & Chen, Rongkai, 2024. "Research on floating real-time pricing strategy for microgrid operator in local energy market considering shared energy storage leasing," Applied Energy, Elsevier, vol. 368(C).
    4. Guo, Tianyu & Guo, Qi & Huang, Libin & Guo, Haiping & Lu, Yuanhong & Tu, Liang, 2023. "Microgrid source-network-load-storage master-slave game optimization method considering the energy storage overcharge/overdischarge risk," Energy, Elsevier, vol. 282(C).
    5. Alain Aoun & Mehdi Adda & Adrian Ilinca & Mazen Ghandour & Hussein Ibrahim, 2024. "Optimizing Virtual Power Plant Management: A Novel MILP Algorithm to Minimize Levelized Cost of Energy, Technical Losses, and Greenhouse Gas Emissions," Energies, MDPI, vol. 17(16), pages 1-23, August.
    6. Ioannis Karakitsios & Dimitrios Lagos & Aris Dimeas & Nikos Hatziargyriou, 2023. "How Can EVs Support High RES Penetration in Islands," Energies, MDPI, vol. 16(1), pages 1-17, January.
    7. Ewelina Kochanek, 2021. "Evaluation of Energy Transition Scenarios in Poland," Energies, MDPI, vol. 14(19), pages 1-13, September.
    8. Cui, Jingshi & Wu, Jiaman & Wu, Chenye & Moura, Scott, 2023. "Electric vehicles embedded virtual power plants dispatch mechanism design considering charging efficiencies," Applied Energy, Elsevier, vol. 352(C).
    9. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    10. Lau, Jat-Syu & Jiang, Yihuo & Li, Ziyuan & Qian, Qian, 2023. "Stochastic trading of storage systems in short term electricity markets considering intraday demand response market," Energy, Elsevier, vol. 280(C).
    11. Madani, Seyyedreza & Pineau, Pierre-Olivier, 2024. "Investment in vehicle-to-grid and distributed energy resources: Distributor versus prosumer perspectives and the impact of rate structures," Utilities Policy, Elsevier, vol. 88(C).
    12. Yaçine Merrad & Mohamed Hadi Habaebi & Siti Fauziah Toha & Md. Rafiqul Islam & Teddy Surya Gunawan & Mokhtaria Mesri, 2022. "Fully Decentralized, Cost-Effective Energy Demand Response Management System with a Smart Contracts-Based Optimal Power Flow Solution for Smart Grids," Energies, MDPI, vol. 15(12), pages 1-27, June.
    13. Daniel Sousa-Dias & Daniel Amyot & Ashkan Rahimi-Kian & John Mylopoulos, 2023. "A Review of Cybersecurity Concerns for Transactive Energy Markets," Energies, MDPI, vol. 16(13), pages 1-32, June.
    14. Tang, Qinghu & Guo, Hongye & Zheng, Kedi & Chen, Qixin, 2024. "Forecasting individual bids in real electricity markets through machine learning framework," Applied Energy, Elsevier, vol. 363(C).
    15. Hou, Langbo & Tong, Xi & Chen, Heng & Fan, Lanxin & Liu, Tao & Liu, Wenyi & Liu, Tong, 2024. "Optimized scheduling of smart community energy systems considering demand response and shared energy storage," Energy, Elsevier, vol. 295(C).
    16. Luwen Pan & Jiajia Chen, 2024. "Optimal Energy Storage Configuration of Prosumers with Uncertain Photovoltaic in the Presence of Customized Pricing-Based Demand Response," Sustainability, MDPI, vol. 16(6), pages 1-18, March.
    17. Yi Hao & Zhigang Huang & Shiqian Ma & Jiakai Huang & Jiahao Chen & Bing Sun, 2023. "Evaluation Method of the Incremental Power Supply Capability Brought by Distributed Generation," Energies, MDPI, vol. 16(16), pages 1-17, August.
    18. Neska, Ewa & Kowalska-Pyzalska, Anna, 2022. "Conceptual design of energy market topologies for communities and their practical applications in EU: A comparison of three case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    19. Yuan, Wenlin & Wang, Xinqi & Su, Chengguo & Cheng, Chuntian & Liu, Zhe & Wu, Zening, 2021. "Stochastic optimization model for the short-term joint operation of photovoltaic power and hydropower plants based on chance-constrained programming," Energy, Elsevier, vol. 222(C).
    20. Daniel Sousa-Dias & Daniel Amyot & Ashkan Rahimi-Kian & John Mylopoulos, 2024. "Enhancing Trust in Transactive Energy with Individually Linkable Pseudonymous Trading Using Smart Contracts," Energies, MDPI, vol. 17(14), pages 1-20, July.

    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:364:y:2024:i:c:s030626192400535x. 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.