IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v309y2022ics0306261921016755.html
   My bibliography  Save this article

An unsupervised data-driven approach for behind-the-meter photovoltaic power generation disaggregation

Author

Listed:
  • Pan, Keda
  • Chen, Zhaohua
  • Lai, Chun Sing
  • Xie, Changhong
  • Wang, Dongxiao
  • Li, Xuecong
  • Zhao, Zhuoli
  • Tong, Ning
  • Lai, Loi Lei

Abstract

An increasing number of behind-the-meter (BtM) rooftop photovoltaic (PV) panels is being installed and maintained by site owners. However, invisible PV power generation (PVPG) will lead to the difficulty for system operators in power dispatch and affect the safety and stability of the power system. To better quantify BtM PVPG, a novel unsupervised data-driven disaggregation method freedom from PV system physical model assumption for BtM PVPG is proposed. After clustering the prosumers’ net load curves, a PVPG sensitivity estimation model is firstly built, based on the net load with approximate energy consumption (EC) and the corresponding irradiation data obtained from the pairing date. Then, an EC sensitivity model is developed according to the net load and temperature of the date with similar irradiation. Finally, a new net load disaggregation model is constructed by the PVPG sensitivity model with EC compensation. Case study based on Ausgrid data shows that the proposed method provides a better quality BtM PVPG disaggregation. The disaggregation accuracy improves by 5.06–5.87% as compared to the state-of-the-art methods.

Suggested Citation

  • Pan, Keda & Chen, Zhaohua & Lai, Chun Sing & Xie, Changhong & Wang, Dongxiao & Li, Xuecong & Zhao, Zhuoli & Tong, Ning & Lai, Loi Lei, 2022. "An unsupervised data-driven approach for behind-the-meter photovoltaic power generation disaggregation," Applied Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921016755
    DOI: 10.1016/j.apenergy.2021.118450
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921016755
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.118450?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. Li, Kangping & Wang, Fei & Mi, Zengqiang & Fotuhi-Firuzabad, Mahmoud & Duić, Neven & Wang, Tieqiang, 2019. "Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    3. Tang, Rui & Yildiz, Baran & Leong, Philip H.W. & Vassallo, Anthony & Dore, Jonathon, 2019. "Residential battery sizing model using net meter energy data clustering," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Gordon, Jeffrey M. & Fasquelle, Thomas & Nadal, Elie & Vossier, Alexis, 2021. "Providing large-scale electricity demand with photovoltaics and molten-salt storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Lai, Chun Sing & Jia, Youwei & Lai, Loi Lei & Xu, Zhao & McCulloch, Malcolm D. & Wong, Kit Po, 2017. "A comprehensive review on large-scale photovoltaic system with applications of electrical energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 439-451.
    6. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.
    7. Lai, Chun Sing & McCulloch, Malcolm D., 2017. "Levelized cost of electricity for solar photovoltaic and electrical energy storage," Applied Energy, Elsevier, vol. 190(C), pages 191-203.
    8. Chi, Fang'ai & Xu, Liming & Pan, Jiajie & Wang, Ruonan & Tao, Yekang & Guo, Yuang & Peng, Changhai, 2020. "Prediction of the total day-round thermal load for residential buildings at various scales based on weather forecast data," Applied Energy, Elsevier, vol. 280(C).
    9. Stainsby, Wendell & Zimmerle, Daniel & Duggan, Gerald P., 2020. "A method to estimate residential PV generation from net-metered load data and system install date," Applied Energy, Elsevier, vol. 267(C).
    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. Aydin Zaboli & Swetha Rani Kasimalla & Kuchan Park & Younggi Hong & Junho Hong, 2024. "A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies," Energies, MDPI, vol. 17(11), pages 1-27, May.
    2. Liu, Chao Charles & Chen, Hongkun & Shi, Jing & Chen, Lei, 2022. "Self-supervised learning method for consumer-level behind-the-meter PV estimation," Applied Energy, Elsevier, vol. 326(C).

    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. Keda Pan & Changhong Xie & Chun Sing Lai & Dongxiao Wang & Loi Lei Lai, 2020. "Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems," Forecasting, MDPI, vol. 2(4), pages 1-18, November.
    2. Lai, Chun Sing & Locatelli, Giorgio, 2021. "Economic and financial appraisal of novel large-scale energy storage technologies," Energy, Elsevier, vol. 214(C).
    3. Liu, Chao Charles & Chen, Hongkun & Shi, Jing & Chen, Lei, 2022. "Self-supervised learning method for consumer-level behind-the-meter PV estimation," Applied Energy, Elsevier, vol. 326(C).
    4. Kies, Alexander & Schyska, Bruno U. & Bilousova, Mariia & El Sayed, Omar & Jurasz, Jakub & Stoecker, Horst, 2021. "Critical review of renewable generation datasets and their implications for European power system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    5. McTigue, Joshua D. & Castro, Jose & Mungas, Greg & Kramer, Nick & King, John & Turchi, Craig & Zhu, Guangdong, 2018. "Hybridizing a geothermal power plant with concentrating solar power and thermal storage to increase power generation and dispatchability," Applied Energy, Elsevier, vol. 228(C), pages 1837-1852.
    6. Lai, Chun Sing & Locatelli, Giorgio & Pimm, Andrew & Tao, Yingshan & Li, Xuecong & Lai, Loi Lei, 2019. "A financial model for lithium-ion storage in a photovoltaic and biogas energy system," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    7. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    8. Federica Cucchiella & Idiano D’Adamo & Massimo Gastaldi, 2017. "The Economic Feasibility of Residential Energy Storage Combined with PV Panels: The Role of Subsidies in Italy," Energies, MDPI, vol. 10(9), pages 1-18, September.
    9. Yuan-Kang Wu & Yi-Hui Lai & Cheng-Liang Huang & Nguyen Thi Bich Phuong & Wen-Shan Tan, 2022. "Artificial Intelligence Applications in Estimating Invisible Solar Power Generation," Energies, MDPI, vol. 15(4), pages 1-18, February.
    10. de Guibert, Paul & Shirizadeh, Behrang & Quirion, Philippe, 2020. "Variable time-step: A method for improving computational tractability for energy system models with long-term storage," Energy, Elsevier, vol. 213(C).
    11. Plain, N. & Hingray, B. & Mathy, S., 2019. "Accounting for low solar resource days to size 100% solar microgrids power systems in Africa," Renewable Energy, Elsevier, vol. 131(C), pages 448-458.
    12. Marko Hočevar & Lovrenc Novak & Primož Drešar & Gašper Rak, 2022. "The Status Quo and Future of Hydropower in Slovenia," Energies, MDPI, vol. 15(19), pages 1-13, September.
    13. Koecklin, Manuel Tong & Longoria, Genaro & Fitiwi, Desta Z. & DeCarolis, Joseph F. & Curtis, John, 2021. "Public acceptance of renewable electricity generation and transmission network developments: Insights from Ireland," Energy Policy, Elsevier, vol. 151(C).
    14. Lukas Kriechbaum & Philipp Gradl & Romeo Reichenhauser & Thomas Kienberger, 2020. "Modelling Grid Constraints in a Multi-Energy Municipal Energy System Using Cumulative Exergy Consumption Minimisation," Energies, MDPI, vol. 13(15), pages 1-23, July.
    15. Steinegger, Josef & Hammer, Andreas & Wallner, Stefan & Kienberger, Thomas, 2024. "Revolutionizing heat distribution: A method for harnessing industrial waste heat with supra-regional district heating networks," Applied Energy, Elsevier, vol. 372(C).
    16. Behrang Shirizadeh, Quentin Perrier, and Philippe Quirion, 2022. "How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    17. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    18. Nawaz Edoo & Robert T. F. Ah King, 2021. "Techno-Economic Analysis of Utility-Scale Solar Photovoltaic Plus Battery Power Plant," Energies, MDPI, vol. 14(23), pages 1-22, December.
    19. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Sun, Xinwu & Chen, Jianjun & Huang, Yuehui & Chen, Zhe & Blaabjerg, Frede, 2024. "Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method," Renewable Energy, Elsevier, vol. 225(C).
    20. Koh, Rachel & Kern, Jordan & Galelli, Stefano, 2022. "Hard-coupling water and power system models increases the complementarity of renewable energy sources," Applied Energy, Elsevier, vol. 321(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:309:y:2022:i:c:s0306261921016755. 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.