IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v126y2018icp865-875.html
   My bibliography  Save this article

Gaussian process regression based inertia emulation and reserve estimation for grid interfaced photovoltaic system

Author

Listed:
  • Kanwal, S.
  • Khan, B.
  • Ali, S.M.
  • Mehmood, C.A.

Abstract

Accurate power reserve estimation for a Photovoltaic Generator (PVG) is of paramount importance to combat frequency changes in a smart grid. Standalone PVG lacks inertia, or an internal power reserve due to power electronic converter grid-interface. Operating a PVG at deloaded percentage of its maximum power capacity mimics an internal power reserve, simulating the Automatic Generation Control (AGC) feature of synchronous machines. Thus, a deloaded PVG releases or absorbs the reserve according to the frequency variations for the grid stability. Moreover, an efficient switching between various reserves during grid operation is required. The common reserve estimation technique is to apply PVG manufacturer's specification based deterministic approach. In this work, we compare the deterministic modeling results with a statistical learning model of Gaussian Process Regression (GPR). The GPR model is trained by dataset of PVG maximum power values evaluated by load line analysis in a simulation, according to the irradiance and historical temperature of Abbottabad, Pakistan. The trained model performance is compared with the deterministic model in a simulation, where the PVG is saturated to turn on a synchronous generator. Time difference of turning on the backup generator between GPR model and deterministic modeling validates the importance of accurate reserve estimation.

Suggested Citation

  • Kanwal, S. & Khan, B. & Ali, S.M. & Mehmood, C.A., 2018. "Gaussian process regression based inertia emulation and reserve estimation for grid interfaced photovoltaic system," Renewable Energy, Elsevier, vol. 126(C), pages 865-875.
    • Handle: RePEc:eee:renene:v:126:y:2018:i:c:p:865-875
    DOI: 10.1016/j.renene.2018.04.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118304191
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.04.012?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. Wickramasinghe, Amila & Perera, Sarath & Agalgaonkar, Ashish P. & Meegahapola, Lasantha, 2016. "Synchronous mode operation of DFIG based wind turbines for improvement of power system inertia," Renewable Energy, Elsevier, vol. 95(C), pages 152-161.
    2. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    3. Seneviratne, Chinthaka & Ozansoy, C., 2016. "Frequency response due to a large generator loss with the increasing penetration of wind/PV generation – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 659-668.
    4. Kofinas, P. & Doltsinis, S. & Dounis, A.I. & Vouros, G.A., 2017. "A reinforcement learning approach for MPPT control method of photovoltaic sources," Renewable Energy, Elsevier, vol. 108(C), pages 461-473.
    5. Tielens, Pieter & Van Hertem, Dirk, 2016. "The relevance of inertia in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 999-1009.
    6. Dreidy, Mohammad & Mokhlis, H. & Mekhilef, Saad, 2017. "Inertia response and frequency control techniques for renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 144-155.
    7. Jiang, Yingni, 2009. "Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models," Energy, Elsevier, vol. 34(9), pages 1276-1283.
    8. Claudia Rahmann & Alfredo Castillo, 2014. "Fast Frequency Response Capability of Photovoltaic Power Plants: The Necessity of New Grid Requirements and Definitions," Energies, MDPI, vol. 7(10), pages 1-17, September.
    9. Rose, Amy & Stoner, Robert & Pérez-Arriaga, Ignacio, 2016. "Prospects for grid-connected solar PV in Kenya: A systems approach," Applied Energy, Elsevier, vol. 161(C), pages 583-590.
    10. Akarslan, Emre & Hocaoglu, Fatih Onur, 2016. "A novel adaptive approach for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 87(P1), pages 628-633.
    11. Mirhassani, SeyedMohsen & Ong, Hwai Chyuan & Chong, W.T. & Leong, K.Y., 2015. "Advances and challenges in grid tied photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 121-131.
    12. De Giorgi, M.G. & Malvoni, M. & Congedo, P.M., 2016. "Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine," Energy, Elsevier, vol. 107(C), pages 360-373.
    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. Lixiao Cao & Zheng Qian & Hamid Zareipour & David Wood & Ehsan Mollasalehi & Shuangshu Tian & Yan Pei, 2018. "Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions," Energies, MDPI, vol. 11(12), pages 1-20, November.

    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. Ratnam, Kamala Sarojini & Palanisamy, K. & Yang, Guangya, 2020. "Future low-inertia power systems: Requirements, issues, and solutions - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Cabrera-Tobar, Ana & Bullich-Massagué, Eduard & Aragüés-Peñalba, Mònica & Gomis-Bellmunt, Oriol, 2016. "Review of advanced grid requirements for the integration of large scale photovoltaic power plants in the transmission system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 971-987.
    3. Fernández-Guillamón, Ana & Gómez-Lázaro, Emilio & Muljadi, Eduard & Molina-García, Ángel, 2019. "Power systems with high renewable energy sources: A review of inertia and frequency control strategies over time," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    4. Andrey Rylov & Pavel Ilyushin & Aleksandr Kulikov & Konstantin Suslov, 2021. "Testing Photovoltaic Power Plants for Participation in General Primary Frequency Control under Various Topology and Operating Conditions," Energies, MDPI, vol. 14(16), pages 1-20, August.
    5. Pablo González-Inostroza & Claudia Rahmann & Ricardo Álvarez & Jannik Haas & Wolfgang Nowak & Christian Rehtanz, 2021. "The Role of Fast Frequency Response of Energy Storage Systems and Renewables for Ensuring Frequency Stability in Future Low-Inertia Power Systems," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
    6. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.
    7. Dario Garozzo & Giuseppe Marco Tina, 2020. "Evaluation of the Effective Active Power Reserve for Fast Frequency Response of PV with BESS Inverters Considering Reactive Power Control," Energies, MDPI, vol. 13(13), pages 1-16, July.
    8. Junfeng Qi & Fei Tang & Jiarui Xie & Xinang Li & Xiaoqing Wei & Zhuo Liu, 2022. "Research on Frequency Response Modeling and Frequency Modulation Parameters of the Power System Highly Penetrated by Wind Power," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    9. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    10. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
    11. Gürtürk, Mert, 2019. "Economic feasibility of solar power plants based on PV module with levelized cost analysis," Energy, Elsevier, vol. 171(C), pages 866-878.
    12. Ana Fernández-Guillamón & Guillermo Martínez-Lucas & Ángel Molina-García & Jose Ignacio Sarasua, 2020. "An Adaptive Control Scheme for Variable Speed Wind Turbines Providing Frequency Regulation in Isolated Power Systems with Thermal Generation," Energies, MDPI, vol. 13(13), pages 1-19, July.
    13. Hirase, Yuko & Abe, Kensho & Sugimoto, Kazushige & Sakimoto, Kenichi & Bevrani, Hassan & Ise, Toshifumi, 2018. "A novel control approach for virtual synchronous generators to suppress frequency and voltage fluctuations in microgrids," Applied Energy, Elsevier, vol. 210(C), pages 699-710.
    14. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    15. Bri‐Mathias S. Hodge & Himanshu Jain & Carlo Brancucci & Gab‐Su Seo & Magnus Korpås & Juha Kiviluoma & Hannele Holttinen & James Charles Smith & Antje Orths & Ana Estanqueiro & Lennart Söder & Damian , 2020. "Addressing technical challenges in 100% variable inverter‐based renewable energy power systems," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(5), September.
    16. Martínez – Lucas, Guillermo & Sarasua, José Ignacio & Fernández – Guillamón, Ana & Molina – García, Ángel, 2021. "Combined hydro-wind frequency control scheme: Modal analysis and isolated power system case example," Renewable Energy, Elsevier, vol. 180(C), pages 1056-1072.
    17. Nouha Mansouri & Abderezak Lashab & Dezso Sera & Josep M. Guerrero & Adnen Cherif, 2019. "Large Photovoltaic Power Plants Integration: A Review of Challenges and Solutions," Energies, MDPI, vol. 12(19), pages 1-16, October.
    18. Lasantha Meegahapola & Alfeu Sguarezi & Jack Stanley Bryant & Mingchen Gu & Eliomar R. Conde D. & Rafael B. A. Cunha, 2020. "Power System Stability with Power-Electronic Converter Interfaced Renewable Power Generation: Present Issues and Future Trends," Energies, MDPI, vol. 13(13), pages 1-35, July.
    19. Ana Fernández-Guillamón & Antonio Vigueras-Rodríguez & Emilio Gómez-Lázaro & Ángel Molina-García, 2018. "Fast Power Reserve Emulation Strategy for VSWT Supporting Frequency Control in Multi-Area Power Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    20. Hirase, Y. & Noro, O. & Nakagawa, H. & Yoshimura, E. & Katsura, S. & Abe, K. & Sugimoto, K. & Sakimoto, K., 2018. "Decentralised and interlink-less power interchange among residences in microgrids using virtual synchronous generator control," Applied Energy, Elsevier, vol. 228(C), pages 2437-2447.

    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:renene:v:126:y:2018:i:c:p:865-875. 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/renewable-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.