IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v356y2024ics0306261923017907.html
   My bibliography  Save this article

A market-based real-time algorithm for congestion alleviation incorporating EV demand response in active distribution networks

Author

Listed:
  • Menghwar, Mohan
  • Yan, Jie
  • Chi, Yongning
  • Asim Amin, M.
  • Liu, Yongqian

Abstract

The dynamic charging behavior of electric vehicles (EVs) is causing frequent line-overloading problems and serious power security issues. Controlled and smart charging mechanisms for EVs incorporating demand response (DR) may provide significant operational flexibility to the grid operators and reduce charging costs for EV users. However, controlling EV charging may cause inconvenience to individual EV users. A market-based mechanism is proposed in this research work that allows EV users and households to participate in the network congestion alleviation DR program online while maintaining the power quality. Consumers submit their charging requirements (e.g., charging power and charging deadlines) and load curtailment tolerances to the aggregator. The aggregator is an entity assumed to have a long-term contract with the distribution system operator (DSO) and regularly receives network congestion information on behalf of retail energy users. Lyapunov optimization (LO) framework is used to reschedule the EVs, and household loads using DC optimal power flow (DCOPF) to get the dynamic congestion cost signal (CCS). The developed strategy is tested on a modified IEEE 33-bus radial active distribution network (ADN). Simulation results illustrate that the designed algorithm is promising in mitigating network congestion by ensuring significantly fewer violations of network constraints (i.e., line limits) both in terms of capacity and frequency. It also results in less energy cost as compared to other benchmark algorithms, e.g., greedy algorithm, and provides a guarantee of meeting EV charging, and flexible household loads’ (FHL) delay tolerance constraints. The average curtailment ratio of FHL and the service delay for EV-charging requests are converged to the user-defined limits, i.e., 0.25 for curtailment ratio tolerance and 10 times-lots for EV service delay. Unlike other counterparts, the developed algorithm is especially suitable for real-time applications as the per-slot average computational cost is negligible, i.e., 0.55 s.

Suggested Citation

  • Menghwar, Mohan & Yan, Jie & Chi, Yongning & Asim Amin, M. & Liu, Yongqian, 2024. "A market-based real-time algorithm for congestion alleviation incorporating EV demand response in active distribution networks," Applied Energy, Elsevier, vol. 356(C).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017907
    DOI: 10.1016/j.apenergy.2023.122426
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923017907
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.122426?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. Pegah Alaee & Julius Bems & Amjad Anvari-Moghaddam, 2023. "A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management," Energies, MDPI, vol. 16(9), pages 1-28, April.
    2. Zhu, Dafeng & Yang, Bo & Liu, Qi & Ma, Kai & Zhu, Shanying & Ma, Chengbin & Guan, Xinping, 2020. "Energy trading in microgrids for synergies among electricity, hydrogen and heat networks," Applied Energy, Elsevier, vol. 272(C).
    3. Yan, Jie & Menghwar, Mohan & Asghar, Ehtisham & Kumar Panjwani, Manoj & Liu, Yongqian, 2019. "Real-time energy management for a smart-community microgrid with battery swapping and renewables," Applied Energy, Elsevier, vol. 238(C), pages 180-194.
    4. Nawaz, Arshad & Zhou, Min & Wu, Jing & Long, Chengnian, 2022. "A comprehensive review on energy management, demand response, and coordination schemes utilization in multi-microgrids network," Applied Energy, Elsevier, vol. 323(C).
    5. Zhang, XiaoWei & Yu, Xiaoping & Ye, Xinping & Pirouzi, Sasan, 2023. "Economic energy managementof networked flexi-renewable energy hubs according to uncertainty modeling by the unscented transformation method," Energy, Elsevier, vol. 278(PB).
    6. 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.
    7. Ninoslav Holjevac & Tomislav Baškarad & Josip Đaković & Matej Krpan & Matija Zidar & Igor Kuzle, 2021. "Challenges of High Renewable Energy Sources Integration in Power Systems—The Case of Croatia," Energies, MDPI, vol. 14(4), pages 1-20, February.
    8. Konstantinos Oureilidis & Kyriaki-Nefeli Malamaki & Konstantinos Gallos & Achilleas Tsitsimelis & Christos Dikaiakos & Spyros Gkavanoudis & Milos Cvetkovic & Juan Manuel Mauricio & Jose Maria Maza Ort, 2020. "Ancillary Services Market Design in Distribution Networks: Review and Identification of Barriers," Energies, MDPI, vol. 13(4), pages 1-44, February.
    9. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    10. Gu, Chenghong & Yan, Xiaohe & Yan, Zhang & Li, Furong, 2017. "Dynamic pricing for responsive demand to increase distribution network efficiency," Applied Energy, Elsevier, vol. 205(C), pages 236-243.
    11. Lee, Henry & Clark, Alex, 2018. "Charging the Future: Challenges and Opportunities for Electric Vehicle Adoption," Working Paper Series rwp18-026, Harvard University, John F. Kennedy School of Government.
    12. Eissa, M.M., 2018. "First time real time incentive demand response program in smart grid with “i-Energy” management system with different resources," Applied Energy, Elsevier, vol. 212(C), pages 607-621.
    13. Akbari, Ehsan & Mousavi Shabestari, Seyed Farzin & Pirouzi, Sasan & Jadidoleslam, Morteza, 2023. "Network flexibility regulation by renewable energy hubs using flexibility pricing-based energy management," Renewable Energy, Elsevier, vol. 206(C), pages 295-308.
    14. Babagheibi, Mahsa & Jadid, Shahram & Kazemi, Ahad, 2023. "An Incentive-based robust flexibility market for congestion management of an active distribution system to use the free capacity of Microgrids," Applied Energy, Elsevier, vol. 336(C).
    15. Haider, Sajjad & Rizvi, Rida e Zahra & Walewski, John & Schegner, Peter, 2022. "Investigating peer-to-peer power transactions for reducing EV induced network congestion," Energy, Elsevier, vol. 254(PB).
    16. Dini, Anoosh & Hassankashi, Alireza & Pirouzi, Sasan & Lehtonen, Matti & Arandian, Behdad & Baziar, Ali Asghar, 2022. "A flexible-reliable operation optimization model of the networked energy hubs with distributed generations, energy storage systems and demand response," Energy, Elsevier, vol. 239(PA).
    17. Yuan, Quan & Ye, Yujian & Tang, Yi & Liu, Yuanchang & Strbac, Goran, 2022. "A novel deep-learning based surrogate modeling of stochastic electric vehicle traffic user equilibrium in low-carbon electricity–transportation nexus," Applied Energy, Elsevier, vol. 315(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. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    2. Lv, Chaoxian & Liang, Rui & Zhang, Ge & Zhang, Xiaotong & Jin, Wei, 2023. "Energy accommodation-oriented interaction of active distribution network and central energy station considering soft open points," Energy, Elsevier, vol. 268(C).
    3. Liang, Hejun & Pirouzi, Sasan, 2024. "Energy management system based on economic Flexi-reliable operation for the smart distribution network including integrated energy system of hydrogen storage and renewable sources," Energy, Elsevier, vol. 293(C).
    4. Artis, Reza & Shivaie, Mojtaba & Weinsier, Philip D., 2024. "A flexible urban load density-dependent framework for low-carbon distribution expansion planning in the presence of hybrid hydrogen/battery/wind/solar energy systems," Applied Energy, Elsevier, vol. 364(C).
    5. 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).
    6. Sina Parhoudeh & Pablo Eguía López & Abdollah Kavousi Fard, 2023. "Stochastic Coordinated Management of Electrical–Gas–Thermal Networks in Flexible Energy Hubs Considering Day-Ahead Energy and Ancillary Markets," Sustainability, MDPI, vol. 15(13), pages 1-26, July.
    7. Matija Kostelac & Lin Herenčić & Tomislav Capuder, 2022. "Planning and Operational Aspects of Individual and Clustered Multi-Energy Microgrid Options," Energies, MDPI, vol. 15(4), pages 1-17, February.
    8. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    9. Jimmy Gallegos & Paul Arévalo & Christian Montaleza & Francisco Jurado, 2024. "Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review," Sustainability, MDPI, vol. 16(2), pages 1-33, January.
    10. Rodriguez, Mauricio & Arcos-Aviles, Diego & Guinjoan, Francesc, 2024. "Simple fuzzy logic-based energy management for power exchange in isolated multi-microgrid systems: A case study in a remote community in the Amazon region of Ecuador," Applied Energy, Elsevier, vol. 357(C).
    11. Theofilos A. Papadopoulos & Kalliopi D. Pippi & Georgios A. Barzegkar-Ntovom & Eleftherios O. Kontis & Angelos I. Nousdilis & Christos L. Athanasiadis & Georgios C. Kryonidis, 2023. "Validation of a Holistic System for Operational Analysis and Provision of Ancillary Services in Active Distribution Networks," Energies, MDPI, vol. 16(6), pages 1-27, March.
    12. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    13. Tan, Bifei & Chen, Simin & Liang, Zipeng & Zheng, Xiaodong & Zhu, Yanjin & Chen, Haoyong, 2024. "An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles," Applied Energy, Elsevier, vol. 356(C).
    14. Kalyani Makarand Kurundkar & Geetanjali Abhijit Vaidya, 2023. "Stochastic Security-Constrained Economic Dispatch of Load-Following and Contingency Reserves Ancillary Service Using a Grid-Connected Microgrid during Uncertainty," Energies, MDPI, vol. 16(6), pages 1-25, March.
    15. Liu, Jiejie & Li, Yao & Ma, Yanan & Qin, Ruomu & Meng, Xianyang & Wu, Jiangtao, 2023. "Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy," Energy, Elsevier, vol. 285(C).
    16. Akhlaque Ahmad Khan & Ahmad Faiz Minai & Rupendra Kumar Pachauri & Hasmat Malik, 2022. "Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review," Energies, MDPI, vol. 15(17), pages 1-29, August.
    17. Chen, Yuanyi & Hu, Simon & Zheng, Yanchong & Xie, Shiwei & Hu, Qinru & Yang, Qiang, 2024. "Coordinated expansion planning of coupled power and transportation networks considering dynamic network equilibrium," Applied Energy, Elsevier, vol. 360(C).
    18. Park, Sung-Won & Son, Sung-Yong, 2023. "Techno-economic analysis for the electric vehicle battery aging management of charge point operator," Energy, Elsevier, vol. 280(C).
    19. Schauf, Andrew & Oh, Poong, 2021. "Myopic reallocation of extraction improves collective outcomes in networked common-pool resource games," SocArXiv w2cxp, Center for Open Science.
    20. Xiong, Kang & Hu, Weihao & Cao, Di & Li, Sichen & Zhang, Guozhou & Liu, Wen & Huang, Qi & Chen, Zhe, 2023. "Coordinated energy management strategy for multi-energy hub with thermo-electrochemical effect based power-to-ammonia: A multi-agent deep reinforcement learning enabled approach," Renewable Energy, Elsevier, vol. 214(C), pages 216-232.

    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:s0306261923017907. 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.