IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v253y2019ic42.html
   My bibliography  Save this article

Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation

Author

Listed:
  • Li, Kangping
  • Wang, Fei
  • Mi, Zengqiang
  • Fotuhi-Firuzabad, Mahmoud
  • Duić, Neven
  • Wang, Tieqiang

Abstract

Accurate customer baseline load (CBL) estimation is critical for implementing incentive-based demand response (DR) programs. The increasing penetration of grid-tied distributed photovoltaic systems (DPVS) complicates customers’ load patterns, making the CBL estimation more difficult because the volatile actual load and the intermittent PV output power are coupled together. A PV-load decoupling framework is proposed in this paper to address the above issue. The basic idea is to decouple the actual load power and the PV output power, then estimate them separately. To this end, historical PV output power data of each individual DPVS is required. However, pure historical PV output power data is usually unavailable for small-scale DPVSs, since they are normally located behind the meter, thus only the net load (i.e. actual load power minus PV output power) data is metered. Therefore, this paper proposes a machine learning approach to disaggregate the output power of each individual DPVS from net load data. The proposed approach includes two stages: DPVS capacity estimation and PV output power estimation. The first stage consists of two steps. First, a net load curve optimal pairing-based feature extraction method is proposed to extract features from the discrepancy between two different net load curves of the customers under heterogeneous weather conditions. Second, a multiple support vector regression-based ensemble model with the input features extracted in the first step is established to estimate the DPVS capacity. In the second stage, the output power of each DPVS is estimated by its capacity multiplied by the output power of a standard DPVS. Case studies using a real dataset from Sydney indicate that the proposed approach shows a promising performance on PV output power estimation and can significantly improve the CBL estimation accuracy for customers with DPVSs.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:42
    DOI: 10.1016/j.apenergy.2019.113595
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919312693
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113595?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. Heydarian-Forushani, E. & Golshan, M.E.H. & Siano, Pierluigi, 2017. "Evaluating the benefits of coordinated emerging flexible resources in electricity markets," Applied Energy, Elsevier, vol. 199(C), pages 142-154.
    2. Fei Wang & Kangping Li & Xinkang Wang & Lihui Jiang & Jianguo Ren & Zengqiang Mi & Miadreza Shafie-khah & João P. S. Catalão, 2018. "A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features," Energies, MDPI, vol. 11(7), pages 1-19, July.
    3. Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
    4. Fei Wang & Zengqiang Mi & Shi Su & Hongshan Zhao, 2012. "Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters," Energies, MDPI, vol. 5(5), pages 1-16, May.
    5. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    6. Malof, Jordan M. & Bradbury, Kyle & Collins, Leslie M. & Newell, Richard G., 2016. "Automatic detection of solar photovoltaic arrays in high resolution aerial imagery," Applied Energy, Elsevier, vol. 183(C), pages 229-240.
    7. Wang, Fei & Xu, Hanchen & Xu, Ti & Li, Kangping & Shafie-khah, Miadreza & Catalão, João. P.S., 2017. "The values of market-based demand response on improving power system reliability under extreme circumstances," Applied Energy, Elsevier, vol. 193(C), pages 220-231.
    8. Komušanac, Ivan & Ćosić, Boris & Duić, Neven, 2016. "Impact of high penetration of wind and solar PV generation on the country power system load: The case study of Croatia," Applied Energy, Elsevier, vol. 184(C), pages 1470-1482.
    9. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    10. Motalleb, Mahdi & Siano, Pierluigi & Ghorbani, Reza, 2019. "Networked Stackelberg Competition in a Demand Response Market," Applied Energy, Elsevier, vol. 239(C), pages 680-691.
    11. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
    12. Wang, Lan & Lee, Eric W.M. & Yuen, Richard K.K., 2018. "Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach," Applied Energy, Elsevier, vol. 228(C), pages 1740-1753.
    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. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    2. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
    3. Fei Wang & Kangping Li & Xinkang Wang & Lihui Jiang & Jianguo Ren & Zengqiang Mi & Miadreza Shafie-khah & João P. S. Catalão, 2018. "A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features," Energies, MDPI, vol. 11(7), pages 1-19, July.
    4. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    5. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    6. Fei Wang & Yili Yu & Xinkang Wang & Hui Ren & Miadreza Shafie-Khah & João P. S. Catalão, 2018. "Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression," Energies, MDPI, vol. 11(5), pages 1-26, May.
    7. Hui, Hongxun & Ding, Yi & Song, Yonghua & Rahman, Saifur, 2019. "Modeling and control of flexible loads for frequency regulation services considering compensation of communication latency and detection error," Applied Energy, Elsevier, vol. 250(C), pages 161-174.
    8. Ng, Rong Wang & Begam, Kasim Mumtaj & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2021. "An improved self-organizing incremental neural network model for short-term time-series load prediction," Applied Energy, Elsevier, vol. 292(C).
    9. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    10. Jinling Lu & Bo Wang & Hui Ren & Daqian Zhao & Fei Wang & Miadreza Shafie-khah & João P. S. Catalão, 2017. "Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
    11. Thomas Steens & Jan-Simon Telle & Benedikt Hanke & Karsten von Maydell & Carsten Agert & Gian-Luca Di Modica & Bernd Engel & Matthias Grottke, 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV," Energies, MDPI, vol. 14(12), pages 1-25, June.
    12. Sen Wang & Yonghui Sun & Yan Zhou & Rabea Jamil Mahfoud & Dongchen Hou, 2019. "A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM," Energies, MDPI, vol. 13(1), pages 1-17, December.
    13. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Large-scale demand response and its implications for spot prices, load and policies: Insights from the German-Austrian electricity market," Applied Energy, Elsevier, vol. 210(C), pages 1290-1298.
    14. Chan-Uk Yeom & Keun-Chang Kwak, 2017. "Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation," Energies, MDPI, vol. 10(10), pages 1-18, October.
    15. Hui, Hongxun & Chen, Yulin & Yang, Shaohua & Zhang, Hongcai & Jiang, Tao, 2022. "Coordination control of distributed generators and load resources for frequency restoration in isolated urban microgrids," Applied Energy, Elsevier, vol. 327(C).
    16. Li, Yiyan & Zhang, Si & Hu, Rongxing & Lu, Ning, 2021. "A meta-learning based distribution system load forecasting model selection framework," Applied Energy, Elsevier, vol. 294(C).
    17. Wu, Jinran & Cui, Zhesen & Chen, Yanyan & Kong, Demeng & Wang, You-Gan, 2019. "A new hybrid model to predict the electrical load in five states of Australia," Energy, Elsevier, vol. 166(C), pages 598-609.
    18. Sun, Jian & Liu, Gang & Sun, Boyang & Xiao, Gang, 2021. "Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection," Applied Energy, Elsevier, vol. 303(C).
    19. Khoshrou, Abdolrahman & Pauwels, Eric J., 2019. "Short-term scenario-based probabilistic load forecasting: A data-driven approach," Applied Energy, Elsevier, vol. 238(C), pages 1258-1268.
    20. Fei Wang & Zhao Zhen & Chun Liu & Zengqiang Mi & Miadreza Shafie-khah & João P. S. Catalão, 2018. "Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization," Energies, MDPI, vol. 11(1), pages 1-17, January.

    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:253:y:2019:i:c:42. 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.