IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v313y2022ics0306261922002574.html
   My bibliography  Save this article

Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

Author

Listed:
  • Li, Binghui
  • Feng, Cong
  • Siebenschuh, Carlo
  • Zhang, Rui
  • Spyrou, Evangelia
  • Krishnan, Venkat
  • Hobbs, Benjamin F.
  • Zhang, Jie

Abstract

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

Suggested Citation

  • Li, Binghui & Feng, Cong & Siebenschuh, Carlo & Zhang, Rui & Spyrou, Evangelia & Krishnan, Venkat & Hobbs, Benjamin F. & Zhang, Jie, 2022. "Sizing ramping reserve using probabilistic solar forecasts: A data-driven method," Applied Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:appene:v:313:y:2022:i:c:s0306261922002574
    DOI: 10.1016/j.apenergy.2022.118812
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922002574
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.118812?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. Zhang, Yao & Wang, Jianxue, 2016. "K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1074-1080.
    2. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    3. Kristof De Vos & Nicolas Stevens & Olivier Devolder & Anthony Papavasiliou & Bob Hebb & James Matthys-Donnadieu, 2019. "Dynamic dimensioning approach for operations reserves: proof of concept in Belgium," LIDAM Reprints CORE 2993, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    5. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    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. Yamujala, Sumanth & Kushwaha, Priyanka & Jain, Anjali & Bhakar, Rohit & Wu, Jianzhong & Mathur, Jyotirmay, 2021. "A stochastic multi-interval scheduling framework to quantify operational flexibility in low carbon power systems," Applied Energy, Elsevier, vol. 304(C).
    8. De Vos, K. & Stevens, N. & Devolder, O. & Papavasiliou, A. & Hebb, B. & Matthys-Donnadieu, J., 2019. "Dynamic dimensioning approach for operating reserves: Proof of concept in Belgium," Energy Policy, Elsevier, vol. 124(C), pages 272-285.
    9. Cui, Mingjian & Zhang, Jie, 2018. "Estimating ramping requirements with solar-friendly flexible ramping product in multi-timescale power system operations," Applied Energy, Elsevier, vol. 225(C), pages 27-41.
    10. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
    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. Wang, Qin & Tuohy, Aidan & Ortega-Vazquez, Miguel & Bello, Mobolaji & Ela, Erik & Kirk-Davidoff, Daniel & Hobbs, William B. & Ault, David J. & Philbrick, Russ, 2023. "Quantifying the value of probabilistic forecasting for power system operation planning," Applied Energy, Elsevier, vol. 343(C).
    2. Liu, Jingxuan & Zang, Haixiang & Ding, Tao & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2023. "Harvesting spatiotemporal correlation from sky image sequence to improve ultra-short-term solar irradiance forecasting," Renewable Energy, Elsevier, vol. 209(C), pages 619-631.

    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. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Fang, Lurui & Yan, Ke, 2022. "Towards the applicability of solar nowcasting: A practice on predictive PV power ramp-rate control," Renewable Energy, Elsevier, vol. 195(C), pages 147-166.
    2. 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).
    3. Rancilio, G. & Rossi, A. & Falabretti, D. & Galliani, A. & Merlo, M., 2022. "Ancillary services markets in europe: Evolution and regulatory trade-offs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    4. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    5. Ifaei, Pouya & Tayerani Charmchi, Amir Saman & Loy-Benitez, Jorge & Yang, Rebecca Jing & Yoo, ChangKyoo, 2022. "A data-driven analytical roadmap to a sustainable 2030 in South Korea based on optimal renewable microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    6. Prakash, Abhijith & Bruce, Anna & MacGill, Iain, 2022. "Insights on designing effective and efficient frequency control arrangements from the Australian National Electricity Market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    7. Hermans, Mathias & Bruninx, Kenneth & Van den Bergh, Kenneth & Poncelet, Kris & Delarue, Erik, 2021. "On the temporal granularity of joint energy-reserve markets in a high-RES system," Applied Energy, Elsevier, vol. 297(C).
    8. Silva-Rodriguez, Lina & Sanjab, Anibal & Fumagalli, Elena & Virag, Ana & Gibescu, Madeleine, 2022. "Short term wholesale electricity market designs: A review of identified challenges and promising solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    9. Felipe Nazaré & Luiz Barroso & Bernardo Bezerra, 2021. "A Probabilistic and Value-Based Planning Approach to Assess the Competitiveness between Gas-Fired and Renewables in Hydro-Dominated Systems: A Brazilian Case Study," Energies, MDPI, vol. 14(21), pages 1-21, November.
    10. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Probabilistic solar power forecasting based on weather scenario generation," Applied Energy, Elsevier, vol. 266(C).
    11. Lina Silva-Rodriguez & Anibal Sanjab & Elena Fumagalli & Ana Virag & Madeleine Gibescu, 2020. "Short Term Electricity Market Designs: Identified Challenges and Promising Solutions," Papers 2011.04587, arXiv.org.
    12. Bhavsar, S. & Pitchumani, R. & Ortega-Vazquez, M.A., 2021. "Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts," Applied Energy, Elsevier, vol. 293(C).
    13. Anthony Papavasiliou, 2021. "An Overview of Probabilistic Dimensioning of Frequency Restoration Reserves with a Focus on the Greek Electricity Market," Energies, MDPI, vol. 14(18), pages 1-19, September.
    14. Sreekumar, Sreenu & Yamujala, Sumanth & Sharma, Kailash Chand & Bhakar, Rohit & Simon, Sishaj P. & Rana, Ankur Singh, 2022. "Flexible Ramp Products: A solution to enhance power system flexibility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    15. Jihoon Moon & Yongsung Kim & Minjae Son & Eenjun Hwang, 2018. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron," Energies, MDPI, vol. 11(12), pages 1-20, November.
    16. Bojana Škrbić & Željko Đurišić, 2023. "Novel Planning Methodology for Spatially Optimized RES Development Which Minimizes Flexibility Requirements for Their Integration into the Power System," Energies, MDPI, vol. 16(7), pages 1-34, April.
    17. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    18. Reza Fachrizal & Joakim Munkhammar, 2020. "Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles," Energies, MDPI, vol. 13(5), pages 1-19, March.
    19. Zhang, Gaohang & Li, Fengting & Wang, Sen & Yin, Chunya, 2023. "Robust low-carbon energy and reserve scheduling considering operational risk and flexibility improvement," Energy, Elsevier, vol. 284(C).
    20. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.

    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:313:y:2022:i:c:s0306261922002574. 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.