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Day-ahead resource forecasting for concentrated solar power integration

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  • Nonnenmacher, Lukas
  • Kaur, Amanpreet
  • Coimbra, Carlos F.M.

Abstract

In this work, we validate and enhance previously proposed singe-input direct normal irradiance (DNI) models based on numerical weather prediction (NWP) for intra-week forecasts with over 200,000 hours of ground measurements for 8 locations. Short latency re-forecasting methods to enhance the deterministic forecast accuracies are presented and discussed. The basic forecast is applied to 15 additional locations in North America with satellite-derived DNI data. The basic model outperforms the persistence model at all 23 locations with a skill between 12.4% and 38.2%. The RMSE of the basic forecast is in the range of 204.9 W m−2 to 309.9 W m−2. The implementation of stochastic learning re-forecasting methods yields further reduction in error from 204.9 W m−2 to 176.5 W m−2. To a great extent, the errors are caused by inaccuracies in the NWP cloud prediction. Improved assessment of atmospheric turbidity has limited impact on reducing forecast errors. Our results suggest that NWP-based DNI forecasts are very capable of reducing power and net-load uncertainty introduced by concentrated solar power plants at all locations in North America. Operating reserves to balance uncertainty in day-ahead schedules can be reduced on average by an estimated 28.6% through the application of the basic forecast.

Suggested Citation

  • Nonnenmacher, Lukas & Kaur, Amanpreet & Coimbra, Carlos F.M., 2016. "Day-ahead resource forecasting for concentrated solar power integration," Renewable Energy, Elsevier, vol. 86(C), pages 866-876.
    • Handle: RePEc:eee:renene:v:86:y:2016:i:c:p:866-876
    DOI: 10.1016/j.renene.2015.08.068
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    2. Andrea Salimbeni & Mario Porru & Luca Massidda & Alfonso Damiano, 2020. "A Forecasting-Based Control Algorithm for Improving Energy Managment in High Concentrator Photovoltaic Power Plant Integrated with Energy Storage Systems," Energies, MDPI, vol. 13(18), pages 1-20, September.
    3. Niu, Tong & Li, Jinkai & Wei, Wei & Yue, Hui, 2022. "A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting," Applied Energy, Elsevier, vol. 326(C).
    4. 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.
    5. Zang, Haixiang & Liu, Ling & Sun, Li & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2020. "Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations," Renewable Energy, Elsevier, vol. 160(C), pages 26-41.
    6. Luca Massidda & Marino Marrocu, 2018. "Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting," Energies, MDPI, vol. 11(7), pages 1-20, July.
    7. Shang, Chuanfu & Wei, Pengcheng, 2018. "Enhanced support vector regression based forecast engine to predict solar power output," Renewable Energy, Elsevier, vol. 127(C), pages 269-283.
    8. 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.
    9. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
    10. Jiawen Bai & Tao Ding & Zhe Wang & Jianhua Chen, 2019. "Day-Ahead Robust Economic Dispatch Considering Renewable Energy and Concentrated Solar Power Plants," Energies, MDPI, vol. 12(20), pages 1-17, October.
    11. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
    12. Kahvecioğlu, Gökçe & Morton, David P. & Wagner, Michael J., 2022. "Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices," Applied Energy, Elsevier, vol. 326(C).
    13. Zhao, Wei & Zhang, Haoran & Zheng, Jianqin & Dai, Yuanhao & Huang, Liqiao & Shang, Wenlong & Liang, Yongtu, 2021. "A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants," Energy, Elsevier, vol. 223(C).
    14. Polasek, Tomas & Čadík, Martin, 2023. "Predicting photovoltaic power production using high-uncertainty weather forecasts," Applied Energy, Elsevier, vol. 339(C).

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