IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v372y2024ics030626192401153x.html
   My bibliography  Save this article

Enhancing PV hosting capacity and mitigating congestion in distribution networks with deep learning based PV forecasting and battery management

Author

Listed:
  • Shabbir, Noman
  • Kütt, Lauri
  • Astapov, Victor
  • Daniel, Kamran
  • Jawad, Muhammad
  • Husev, Oleksandr
  • Rosin, Argo
  • Martins, João

Abstract

The extensive deployment of domestic photovoltaic (PV) systems may result in exceeding the limits of the network's PV hosting capacity (HC), which leads to energy delivery congestion and overvoltage problems in low voltage (LV) networks. These problems amplify at times when PV energy generation is near its peak and the domestic load is at its lowest. The convenient solution is to upgrade powerlines or utilize external mitigation devices, both are costly and reconstructing all the networks is a challenging task. Therefore, in this study, a machine learning-based control (MLC) solution is proposed to relieve the overloading of the lines and overvoltage problems caused by the high penetration of PV installations without costly line replacements or additional devices. This MLC technique employs a long short-term memory (LSTM) algorithm for a day ahead PV forecast incorporated to decide the charging/discharging of the residential battery energy storage systems (BESS). The study is conducted based on a real-life LV distribution system with 15 households in the network segment by considering the measured data of actual residential loads and installed PV energy generation for the entire year. Four different rated PV system installation scenarios are investigated through power flow analysis. Moreover, three different BESS utilization techniques are compared along with traditional peak power curtailment (PPC) and reactive power control (RPC) techniques. The MLC approach shows a 30% and 4% lower number of overvoltage hours compared to analytics-driven control (ADC) and trivial battery control (BTC) techniques, respectively for excessive PV installations (case 1). The MLC methods address the HC in a more dedicated manner, eliminating congestion problems in PV installation according to the peak load (Case 2 & 3) and net zero energy (Case 4). The techno-economic analysis indicates that the MLC technique with local grid-supporting capabilities increases the HC and reduces overloading and overvoltage problems in the LV network in all cases. The customers receive 8% to 22% more in savings as compared to TBC control technique and could also get further lower energy costs from the utilities for grid service.

Suggested Citation

  • Shabbir, Noman & Kütt, Lauri & Astapov, Victor & Daniel, Kamran & Jawad, Muhammad & Husev, Oleksandr & Rosin, Argo & Martins, João, 2024. "Enhancing PV hosting capacity and mitigating congestion in distribution networks with deep learning based PV forecasting and battery management," Applied Energy, Elsevier, vol. 372(C).
    • Handle: RePEc:eee:appene:v:372:y:2024:i:c:s030626192401153x
    DOI: 10.1016/j.apenergy.2024.123770
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192401153X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.123770?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. Noman Shabbir & Lauri Kütt & Kamran Daniel & Victor Astapov & Hadi Ashraf Raja & Muhammad Naveed Iqbal & Oleksandr Husev, 2022. "Feasibility Investigation for Residential Battery Sizing Considering EV Charging Demand," Sustainability, MDPI, vol. 14(3), pages 1-13, January.
    2. Hennig, Roman J. & de Vries, Laurens J. & Tindemans, Simon H., 2023. "Congestion management in electricity distribution networks: Smart tariffs, local markets and direct control," Utilities Policy, Elsevier, vol. 85(C).
    3. Alam, Mollah Rezaul & Alam, M.J.E. & Somani, Abhishek & Melton, Ronald B. & Tushar, Wayes & Bai, Feifei & Yan, Ruifeng & Saha, Tapan K., 2021. "Evaluating the feasibility of transactive approach for voltage management using inverters of a PV plant," Applied Energy, Elsevier, vol. 291(C).
    4. Chathurangi, D. & Jayatunga, U. & Perera, S., 2022. "Recent investigations on the evaluation of solar PV hosting capacity in LV distribution networks constrained by voltage rise," Renewable Energy, Elsevier, vol. 199(C), pages 11-20.
    5. Azaioud, Hakim & Farnam, Arash & Knockaert, Jos & Vandevelde, Lieven & Desmet, Jan, 2024. "Efficiency optimisation and converterless PV integration by applying a dynamic voltage on an LVDC backbone," Applied Energy, Elsevier, vol. 356(C).
    6. Attar, Mehdi & Repo, Sami & Mann, Pierre, 2022. "Congestion management market design- Approach for the Nordics and Central Europe," Applied Energy, Elsevier, vol. 313(C).
    7. Samar Fatima & Verner Püvi & Matti Lehtonen, 2021. "Comparison of Different References When Assessing PV HC in Distribution Networks," Clean Technol., MDPI, vol. 3(1), pages 1-15, February.
    8. Zhen, Hao & Niu, Dongxiao & Wang, Keke & Shi, Yucheng & Ji, Zhengsen & Xu, Xiaomin, 2021. "Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information," Energy, Elsevier, vol. 231(C).
    9. 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).
    10. Samar Fatima & Verner Püvi & Matti Lehtonen, 2020. "Review on the PV Hosting Capacity in Distribution Networks," Energies, MDPI, vol. 13(18), pages 1-34, September.
    11. Karmaker, Ashish Kumar & Prakash, Krishneel & Siddique, Md Nazrul Islam & Hossain, Md Alamgir & Pota, Hemanshu, 2024. "Electric vehicle hosting capacity analysis: Challenges and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    12. Shabbir, Noman & Kütt, Lauri & Raja, Hadi A. & Jawad, Muhammad & Allik, Alo & Husev, Oleksandr, 2022. "Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia," Energy, Elsevier, vol. 253(C).
    13. Gupta, Rahul & Sossan, Fabrizio, 2023. "Optimal sizing and siting of energy storage systems considering curtailable photovoltaic generation in power distribution networks," Applied Energy, Elsevier, vol. 339(C).
    14. Zare Oskouei, Morteza & Gharehpetian, Gevork B., 2024. "Flexibility enhancement of multi-district DISCOs considering a trade-off between congestion and extractable reserve capacity from virtual energy storage systems," Applied Energy, Elsevier, vol. 353(PB).
    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. Hennig, Roman J. & de Vries, Laurens J. & Tindemans, Simon H., 2024. "Risk vs. restriction—An investigation of capacity-limitation based congestion management in electric distribution grids," Energy Policy, Elsevier, vol. 186(C).
    2. Shabbir, Noman & Kütt, Lauri & Raja, Hadi A. & Jawad, Muhammad & Allik, Alo & Husev, Oleksandr, 2022. "Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia," Energy, Elsevier, vol. 253(C).
    3. Xinghua Wang & Fucheng Zhong & Yilin Xu & Xixian Liu & Zezhong Li & Jianan Liu & Zhuoli Zhao, 2023. "Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics," Energies, MDPI, vol. 16(18), pages 1-19, September.
    4. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    5. Liao, Qi & Tu, Renfu & Zhang, Wan & Wang, Bohong & Liang, Yongtu & Zhang, Haoran, 2023. "Auction design for capacity allocation in the petroleum pipeline under fair opening," Energy, Elsevier, vol. 264(C).
    6. Luyao Zhang & Fan Zhang, 2023. "Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary Perspective," Papers 2305.02552, arXiv.org.
    7. M. Usman Saleem & Mustafa Shakir & M. Rehan Usman & M. Hamza Tahir Bajwa & Noman Shabbir & Payam Shams Ghahfarokhi & Kamran Daniel, 2023. "Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids," Energies, MDPI, vol. 16(12), pages 1-21, June.
    8. Shariatio, O. & Coker, P.J. & Smith, S.T. & Potter, B. & Holderbaum, W., 2024. "An integrated techno-economic approach for design and energy management of heavy goods electric vehicle charging station with energy storage systems," Applied Energy, Elsevier, vol. 369(C).
    9. Silvestri, Luca & De Santis, Michele, 2024. "Renewable-based load shifting system for demand response to enhance energy-economic-environmental performance of industrial enterprises," Applied Energy, Elsevier, vol. 358(C).
    10. Yifei Chen & Zhihan Fu, 2023. "Multi-Step Ahead Forecasting of the Energy Consumed by the Residential and Commercial Sectors in the United States Based on a Hybrid CNN-BiLSTM Model," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    11. Ramitha Dissanayake & Akila Wijethunge & Janaka Wijayakulasooriya & Janaka Ekanayake, 2022. "Optimizing PV-Hosting Capacity with the Integrated Employment of Dynamic Line Rating and Voltage Regulation," Energies, MDPI, vol. 15(22), pages 1-19, November.
    12. Alizadeh, Ali & Kamwa, Innocent & Moeini, Ali & Mohseni-Bonab, Seyed Masoud, 2023. "Energy management in microgrids using transactive energy control concept under high penetration of Renewables; A survey and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    13. Yang, Shuxia & Wang, Xiongfei & Xu, Jiayu & Tang, Mingrun & Chen, Guang, 2023. "Distribution network adaptability assessment considering distributed PV “reverse power flow” behavior - a case study in Beijing," Energy, Elsevier, vol. 275(C).
    14. Lai, Wenzhe & Zhen, Zhao & Wang, Fei & Fu, Wenjie & Wang, Junlong & Zhang, Xudong & Ren, Hui, 2024. "Sub-region division based short-term regional distributed PV power forecasting method considering spatio-temporal correlations," Energy, Elsevier, vol. 288(C).
    15. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    16. Paweł Kelm & Rozmysław Mieński & Irena Wasiak, 2024. "Modular PV System for Applications in Prosumer Installations with Uncontrolled, Unbalanced and Non-Linear Loads," Energies, MDPI, vol. 17(7), pages 1-13, March.
    17. Zhipeng Jing & Lipo Gao & Yu Mu & Dong Liang, 2024. "Flexibility-Constrained Energy Storage System Placement for Flexible Interconnected Distribution Networks," Sustainability, MDPI, vol. 16(20), pages 1-17, October.
    18. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    19. Wu, Thomas & Hu, Ruifeng & Zhu, Hongyu & Jiang, Meihui & Lv, Kunye & Dong, Yunxuan & Zhang, Dongdong, 2024. "Combined IXGBoost-KELM short-term photovoltaic power prediction model based on multidimensional similar day clustering and dual decomposition," Energy, Elsevier, vol. 288(C).
    20. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe, 2024. "Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning," Energy, Elsevier, vol. 302(C).

    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:372:y:2024:i:c:s030626192401153x. 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.