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Towards the applicability of solar nowcasting: A practice on predictive PV power ramp-rate control

Author

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  • Chen, Xiaoyang
  • Du, Yang
  • Lim, Enggee
  • Fang, Lurui
  • Yan, Ke

Abstract

Solar forecasting has been widely adopted in modern power system operations to facilitate a reliable and continuous photovoltaic (PV) integration. Solar nowcasting, also known as intra-minute solar forecasting, is a new subdomain of solar forecasting. Nevertheless, despite the significant progress achieved in solar nowcasting over the last decade, one important aspect, that is, applicability—the value and operability of nowcasts in practical grid operations—is generally left out. To that end, this paper brings forth the applicability of solar nowcasting for the first time. Three time parameters involved in operational solar nowcasting are first identified, namely, forecast horizon, forecast resolution, and forecast model updating rate. Then paired with the state-of-the-art PV power ramp-rate control algorithm, i.e., predictive active power curtailment (PAPC), the effect of different time parameters is investigated, thus revealing the nowcasting applicability at large. Through four case studies and eight standardized deterministic and probabilistic solar nowcasting models, the applicability of solar nowcasting on PAPC is shown to be most characterized by the forecast horizon (up to a deviation of ramp smoothing rate around 12%, with smart persistence (SP) being the reference model), and least characterized by the forecast model updating rate (with a deviation of ramp smoothing rate less than 1% for SP). Moreover, the negatively-biased deterministic nowcasts and wider probabilistic nowcasts are found more applicable to PAPC. To promote solar nowcasting applicability on PAPC further, an outlook for future research is provided, from both a solar forecaster's and a system operator's viewpoints.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:195:y:2022:i:c:p:147-166
    DOI: 10.1016/j.renene.2022.05.166
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    as
    1. Brusaferri, Alessandro & Matteucci, Matteo & Portolani, Pietro & Vitali, Andrea, 2019. "Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices," Applied Energy, Elsevier, vol. 250(C), pages 1158-1175.
    2. Jinyue Yan & Ying Yang & Pietro Elia Campana & Jijiang He, 2019. "City-level analysis of subsidy-free solar photovoltaic electricity price, profits and grid parity in China," Nature Energy, Nature, vol. 4(8), pages 709-717, August.
    3. Yang, Dazhi & Yagli, Gokhan Mert & Srinivasan, Dipti, 2022. "Sub-minute probabilistic solar forecasting for real-time stochastic simulations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    4. Belhaouas, N. & Cheikh, M.-S. Ait & Agathoklis, P. & Oularbi, M.-R. & Amrouche, B. & Sedraoui, K. & Djilali, N., 2017. "PV array power output maximization under partial shading using new shifted PV array arrangements," Applied Energy, Elsevier, vol. 187(C), pages 326-337.
    5. Appino, Riccardo Remo & González Ordiano, Jorge Ángel & Mikut, Ralf & Faulwasser, Timm & Hagenmeyer, Veit, 2018. "On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages," Applied Energy, Elsevier, vol. 210(C), pages 1207-1218.
    6. Litjens, G.B.M.A. & Worrell, E. & van Sark, W.G.J.H.M., 2018. "Assessment of forecasting methods on performance of photovoltaic-battery systems," Applied Energy, Elsevier, vol. 221(C), pages 358-373.
    7. 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.
    8. Quan, Hao & Yang, Dazhi, 2020. "Probabilistic solar irradiance transposition models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 125(C).
    9. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Wen, Huiqing & Jiang, Lin, 2019. "Sensor network based PV power nowcasting with spatio-temporal preselection for grid-friendly control," Applied Energy, Elsevier, vol. 255(C).
    10. Cirés, E. & Marcos, J. & de la Parra, I. & García, M. & Marroyo, L., 2019. "The potential of forecasting in reducing the LCOE in PV plants under ramp-rate restrictions," Energy, Elsevier, vol. 188(C).
    11. van der Meer, Dennis & Wang, Guang Chao & Munkhammar, Joakim, 2021. "An alternative optimal strategy for stochastic model predictive control of a residential battery energy management system with solar photovoltaic," Applied Energy, Elsevier, vol. 283(C).
    12. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    13. Amaro e Silva, R. & Brito, M.C., 2019. "Spatio-temporal PV forecasting sensitivity to modules’ tilt and orientation," Applied Energy, Elsevier, vol. 255(C).
    14. André, Maïna & Dabo-Niang, Sophie & Soubdhan, Ted & Ould-Baba, Hanany, 2016. "Predictive spatio-temporal model for spatially sparse global solar radiation data," Energy, Elsevier, vol. 111(C), pages 599-608.
    15. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2017. "Forecasting with temporal hierarchies," European Journal of Operational Research, Elsevier, vol. 262(1), pages 60-74.
    16. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    17. Mashlakov, Aleksei & Kuronen, Toni & Lensu, Lasse & Kaarna, Arto & Honkapuro, Samuli, 2021. "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting," Applied Energy, Elsevier, vol. 285(C).
    18. Kong, Weicong & Jia, Youwei & Dong, Zhao Yang & Meng, Ke & Chai, Songjian, 2020. "Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting," Applied Energy, Elsevier, vol. 280(C).
    19. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    20. 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.
    21. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    22. Myles R. Allen & David A. Stainforth, 2002. "Towards objective probabalistic climate forecasting," Nature, Nature, vol. 419(6903), pages 228-228, September.
    23. Maharjan, Salish & Sampath Kumar, Dhivya & Khambadkone, Ashwin M., 2020. "Enhancing the voltage stability of distribution network during PV ramping conditions with variable speed drive loads," Applied Energy, Elsevier, vol. 264(C).
    24. Yang, Dazhi & Wu, Elynn & Kleissl, Jan, 2019. "Operational solar forecasting for the real-time market," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1499-1519.
    25. Barbieri, Florian & Rajakaruna, Sumedha & Ghosh, Arindam, 2017. "Very short-term photovoltaic power forecasting with cloud modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 242-263.
    26. 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.
    27. Elsinga, Boudewijn & van Sark, Wilfried G.J.H.M., 2017. "Short-term peer-to-peer solar forecasting in a network of photovoltaic systems," Applied Energy, Elsevier, vol. 206(C), pages 1464-1483.
    28. Lappalainen, Kari & Valkealahti, Seppo, 2017. "Output power variation of different PV array configurations during irradiance transitions caused by moving clouds," Applied Energy, Elsevier, vol. 190(C), pages 902-910.
    29. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Wen, Huiqing & Yan, Ke & Kirtley, James, 2020. "Power ramp-rates of utility-scale PV systems under passing clouds: Module-level emulation with cloud shadow modeling," Applied Energy, Elsevier, vol. 268(C).
    30. van Haaren, Rob & Morjaria, Mahesh & Fthenakis, Vasilis, 2015. "An energy storage algorithm for ramp rate control of utility scale PV (photovoltaics) plants," Energy, Elsevier, vol. 91(C), pages 894-902.
    31. Hou, Qingchun & Zhang, Ning & Du, Ershun & Miao, Miao & Peng, Fei & Kang, Chongqing, 2019. "Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China," Applied Energy, Elsevier, vol. 242(C), pages 205-215.
    32. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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