10.1007/s40095-023-00560-6

Development of a day-ahead solar power forecasting model chain for a 250 MW PV park in India

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  • Arindam Roy*1,
  • Aravindakshan Ramanan2,
  • Barun Kumar3,
  • Chris Alice Abraham3,
  • Annette Hammer1,
  • Elena Barykina4,
  • Detlev Heinemann5,
  • Naveen Kumar3,
  • Hans-Peter Waldl4,
  • Indradip Mitra2,
  • Prasun Kumar Das3,
  • R. Karthik3,
  • K. Boopathi3,
  • K. Balaraman3,
  1. German Aerospace Center (DLR) Institute of Networked Energy Systems, Oldenburg, 26129, DE
  2. Deutsche Gesellschaft Für Internationale Zusammenarbeit (GIZ) GmbH, New Delhi, 110029, IN
  3. Solar Radiation Resource Assessment, National Institute of Wind Energy, Chennai, 600100, IN
  4. Overspeed GmbH & Co. KG, Oldenburg, 26129, DE
  5. Institute of Physics, Carl von Ossietzky University of Oldenburg, Oldenburg, 26111, DE
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Published in Issue 2023-04-01

How to Cite

Roy, A., Ramanan, A., Kumar, B., Abraham, C. A., Hammer, A., Barykina, E., Heinemann, D., Kumar, N., Waldl, H.-P., Mitra, I., Das, P. K., Karthik, R., Boopathi, K., & Balaraman, K. (2023). Development of a day-ahead solar power forecasting model chain for a 250 MW PV park in India. International Journal of Energy and Environmental Engineering, 14(4 (December 2023). https://doi.org/10.1007/s40095-023-00560-6
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Abstract

Abstract Due to the steep rise in grid-connected solar Photovoltaic (PV) capacity and the intermittent nature of solar generation, accurate forecasts are becoming ever more essential for the secure and economic day-ahead scheduling of PV systems. The inherent uncertainty in Numerical Weather Prediction (NWP) forecasts and the limited availability of measured datasets for PV system modeling impacts the achievable day-ahead solar PV power forecast accuracy in regions like India. In this study, an operational day-ahead PV power forecast model chain is developed for a 250 MWp solar PV park located in Southern India using NWP-predicted Global Horizontal Irradiance (GHI) from the European Centre of Medium Range Weather Forecasts (ECMWF) and National Centre for Medium Range Weather Forecasting (NCMRWF) models. The performance of the Lorenz polynomial and a Neural Network (NN)-based bias correction method are benchmarked on a sliding window basis against ground-measured GHI for ten months. The usefulness of GHI transposition, even with uncertain monthly tilt values, is analyzed by comparing the Global Tilted Irradiance (GTI) and GHI forecasts with measured GTI for four months. A simple technique for back-calculating the virtual DC power is developed using the available aggregated AC power measurements and the inverter efficiency curve from a nearby plant with a similar rated inverter capacity. The AC power forecasts are validated against aggregated AC power measurements for six months. The ECMWF derived forecast outperforms the reference convex combination of climatology and persistence. The linear combination of ECMWF and NCMRWF derived AC forecasts showed the best result.

Keywords

  • Numerical Weather Prediction,
  • PV power forecast,
  • Model chain,
  • Combination of AC power forecasts,
  • Availability of limited design parameters,
  • Indian meteorological conditions
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References

  1. International Energy Agency: Renewables 2021.
  2. https://www.iea.org/reports/renewables-2021
  3. . Accessed 31 Dec 2022
  4. Ministry of New and Renewable Energy: Physical Progress.
  5. https://mnre.gov.in/the-ministry/physical-progress
  6. . Accessed 31 Dec 2022
  7. Government of India: India’s Updated Nationally Determined Contribution.
  8. https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1847812
  9. . Accessed 31 Dec 2022
  10. Perez et al. (2013) Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe (pp. 305-326) https://doi.org/10.1016/j.solener.2013.05.005
  11. Inman et al. (2013) Solar forecasting methods for renewable energy integration (pp. 535-576) https://doi.org/10.1016/j.pecs.2013.06.002
  12. Wan et al. (2015) Photovoltaic and solar power forecasting for smart grid management 1(4) (pp. 38-46) https://doi.org/10.17775/CSEEJPES.2015.00046
  13. Mayer (2021) Influence of design data availability on the accuracy of physical photovoltaic power forecasts (pp. 532-540) https://doi.org/10.1016/j.solener.2021.09.044
  14. Leva et al. (2017) Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power (pp. 88-100) https://doi.org/10.1016/j.matcom.2015.05.010
  15. Theocharides, S., Makrides, G., Georghiou, G.E., Kyprianou, A.: Machine learning algorithms for photovoltaic system power output prediction. In: IEEE International Energy Conference (ENERGYCON), pp. 1–6 (2018).
  16. https://doi.org/10.1109/ENERGYCON.2018.8398737
  17. Wolff et al. (2016) Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data (pp. 197-208) https://doi.org/10.1016/j.solener.2016.05.051
  18. Cui et al. (2019) Evaluating combination models of solar irradiance on inclined surfaces and forecasting photovoltaic power generation (pp. 123-130) https://doi.org/10.1049/iet-stg.2018.0110
  19. Mayer and Gyula (2021) Extensive comparison of physical models for photovoltaic power forecasting https://doi.org/10.1016/j.apenergy.2020.116239
  20. Holland, N., Pang, X., Herzberg, W., Bor, J., Lorenz, E.: Combination of physics based simulation and machine learning for PV power forecasting of large power plants. In: EU PVSEC Programme Online, 6–10 September (2021)
  21. Huang and Thatcher (2017) Assessing the value of simulated regional weather variability in solar forecasting using numerical weather prediction (pp. 529-539) https://doi.org/10.1016/j.solener.2017.01.058
  22. Lorenz et al. (2009) Irradiance forecasting for the power prediction of grid-connected photovoltaic systems (pp. 2-10) https://doi.org/10.1109/JSTARS.2009.2020300
  23. Joshi et al. (2019) Evaluation of solar irradiance forecasting skills of the Australian Bureau of Meteorology’s ACCESS models (pp. 386-402) https://doi.org/10.1016/j.solener.2019.06.007
  24. Yagli, G.M., Monika, Yang, D., Srinivasan, D.: Using combinational methods for forecast improvement in PV power plants. In: IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), pp. 540–545 (2018).
  25. https://doi.org/10.1109/ISGT-Asia.2018.8467878
  26. Suksamosorn et al. (2021) Post-processing of NWP forecasts using kalman filtering with operational constraints for day-ahead solar power forecasting in Thailand (pp. 105409-105423) https://doi.org/10.1109/ACCESS.2021.3099481
  27. Yang (2019) On post-processing day-ahead NWP forecasts using Kalman filtering (pp. 179-181) https://doi.org/10.1016/j.solener.2019.02.044
  28. Lauret et al. (2014) A neural network post-processing approach to improving NWP solar radiation forecasts (pp. 1044-1052) https://doi.org/10.1016/j.egypro.2014.10.089
  29. Lauret et al. (2016) Solar forecasting in a challenging insular context https://doi.org/10.3390/atmos7020018
  30. Pereira et al. (2019) Development of an ANN based corrective algorithm for the operational ECMWF global horizontal irradiation forecasts (pp. 387-405) https://doi.org/10.1016/j.solener.2019.04.070
  31. Watanabe et al. (2021) Post-processing correction method for surface solar irradiance forecast data from the numerical weather model using geostationary satellite observation data (pp. 202-216) https://doi.org/10.1016/j.solener.2021.05.055
  32. Tschopp et al. (2022) Measurement and modeling of diffuse irradiance masking on tilted planes for solar engineering applications (pp. 365-378) https://doi.org/10.1016/j.solener.2021.10.083
  33. Chandrasekaran and Kumar (1994) Hourly diffuse fraction correlation at a tropical location (pp. 505-510) https://doi.org/10.1016/0038-092X(94)90130-T
  34. Reindl et al. (1990) Diffuse fraction correlations 45(1) (pp. 1-7) https://doi.org/10.1016/0038-092X(90)90060-P
  35. Skartveit et al. (1998) An hourly diffuse fraction model with correction for variability and surface albedo 63(3) (pp. 173-183) https://doi.org/10.1016/S0038-092X(98)00067-X
  36. Paulescu and Blaga (2019) A simple and reliable empirical model with two predictors for estimating 1-minute diffuse fraction (pp. 75-84) https://doi.org/10.1016/j.solener.2019.01.029
  37. Boland et al. (2001) Modelling the diffuse fraction of global solar radiation on a horizontal surface (pp. 103-116) https://doi.org/10.1002/1099-095X(200103)12:2<103::AID-ENV447>3.0.CO;2-2
  38. Furlan et al. (2012) The role of clouds in improving the regression model for hourly values of diffuse solar radiation (pp. 240-254) https://doi.org/10.1016/j.apenergy.2011.10.032
  39. Ridley et al. (2010) Modelling of diffuse solar fraction with multiple predictors 35(2) (pp. 478-483) https://doi.org/10.1016/j.renene.2009.07.018
  40. Batzelis (2017) Simple PV performance equations theoretically well founded on the single-diode model (pp. 1400-1409) https://doi.org/10.1109/JPHOTOV.2017.2711431
  41. Yaqoob et al. (2021) Comparative study with practical validation of photovoltaic monocrystalline module for single and double diode models https://doi.org/10.1038/s41598-021-98593-6
  42. Beyer, H.G., Betcke, J., Drews, A., Heinemann, D., Lorenz, E., Heilscher, G., Bofinger, S.: Identification of a general model for the MPP performance of PV-modules for the application in a procedure for the performance check of grid connected systems. In: 19th European Photovoltaic Solar Energy Conference and Exhibition, p. 7, (2014)
  43. Deotti et al. (2021) Empirical models applied to distributed energy resources-an analysis in the light of regulatory aspects 14(2) https://doi.org/10.3390/en14020326
  44. Huld et al. (2011) A power-rating model for crystalline silicon PV modules (pp. 3359-3369) https://doi.org/10.1016/j.solmat.2011.07.026
  45. Soler-Castillo et al. (2021) Modelling of the efficiency of the photovoltaic modules: grid-connected plants to the Cuban national electrical system (pp. 150-157) https://doi.org/10.1016/j.solener.2021.05.052
  46. Padmavathi and Arul (2013) Performance analysis of a 3 MWp grid connected solar photovoltaic power plant in India (pp. 615-625) https://doi.org/10.1016/j.esd.2013.09.002
  47. Santiago et al. (2018) Modeling of photovoltaic cell temperature losses: a review and a practice case in South Spain (pp. 70-89) https://doi.org/10.1016/j.rser.2018.03.054
  48. Skoplaki et al. (2008) A simple correlation for the operating temperature of photovoltaic modules of arbitrary mounting (pp. 1393-1402) https://doi.org/10.1016/j.solmat.2008.05.016
  49. Roumpakias and Stamatelos (2019) Performance analysis of a grid-connected photovoltaic park after 6 years of operation (pp. 368-378) https://doi.org/10.1016/j.renene.2019.04.014
  50. Driesse, A., Jain, P., Harrison, S.: Beyond the curves: modeling the electrical efficiency of photovoltaic inverters. In: 33rd IEEE Photovoltaic Specialists Conference, pp. 1–6 (2008).
  51. https://doi.org/10.1109/PVSC.2008.4922827
  52. King et al. (2004) Photovoltaic array performance model https://doi.org/10.2172/919131
  53. King, D.L., Gonzalez, S., Galbraith, G.M., Boyson, W.E.: Performance model for grid-connected photovoltaic inverters. Sandia National Laboratories SAND2007-5036 (2007)
  54. Lave, M., Ellis, A., Stein, J.S.: Simulating solar power plant variability: a review of current methods. Sandia National Laboratories: Alburquerque, NM, USA (2013)
  55. Schmidt and Sauer (1996) Wechselrichter-Wirkungsgrade (pp. 43-47)
  56. Yang and Meer (2021) Post-processing in solar forecasting: ten overarching thinking tools https://doi.org/10.1016/j.rser.2021.110735
  57. Böök and Lindfors (2020) Site-specific adjustment of a NWP-based photovoltaic production forecast 211(15) (pp. 779-788) https://doi.org/10.1016/j.solener.2020.10.024
  58. Filipe, J.M., Bessa, R.J., Sumaili, J., Tomé R., Sousa, J.N.: A hybrid short-term solar power forecasting tool. In: 18th International Conference on Intelligent System Application to Power Systems (ISAP), pp. 1–6 (2015). doi:
  59. https://doi.org/10.1109/ISAP.2015.7325543
  60. Yang (2019) Standard of reference in operational day-ahead deterministic solar forecasting https://doi.org/10.1063/1.5114985
  61. Yang (2019) Making reference solar forecasts with climatology, persistence, and their optimal convex combination (pp. 981-985) https://doi.org/10.1016/j.solener.2019.10.006
  62. Kumar et al. (2014) Field experiences with the operation of solar radiation resource assessment stations in India https://doi.org/10.1016/j.egypro.2014.03.249
  63. Schwandt et al. (2014) Quality check procedures and statistics for the Indian SRRA solar radiation measurement network (pp. 1227-1236) https://doi.org/10.1016/j.egypro.2014.10.112
  64. Yang et al. (2020) Verification of deterministic solar forecasts (pp. 20-37) https://doi.org/10.1016/j.solener.2020.04.019
  65. Ineichen and Perez (2002) A new airmass independent formulation for the linke turbidity coefficient (pp. 151-157) https://doi.org/10.1016/S0038-092X(02)00045-2
  66. Rincón et al. (2018) Bias correction of global irradiance modelled with weather and research forecasting model over Paraguay (pp. 201-211) https://doi.org/10.1016/j.solener.2018.05.061
  67. Liu and Jordan (1963) The long-term average performance of flat-plate solar-energy collectors: with design data for the U.S., it’s outlying possessions and Canada 7(2) (pp. 53-74) https://doi.org/10.1016/0038-092X(63)90006-9
  68. Temps and Coulson (1977) Solar radiation incident upon slopes of different orientations 19(2) (pp. 179-184) https://doi.org/10.1016/0038-092X(77)90056-1
  69. Faiman (2008) Assessing the outdoor operating temperature of photovoltaic modules (pp. 307-315) https://doi.org/10.1002/pip.813
  70. Ross, R.G.: Interface design considerations for terrestrial solar cell modules. In: 12th Photovoltaic Specialists Conference, pp. 801–806 (1976).
  71. https://doi.org/10.1109/ENERGYCON.2018.8398737
  72. .
  73. https://ui.adsabs.harvard.edu/abs/1976pvsp.conf..801R
  74. Lorenz, E., Remund, J., Müller, S. C., Traunmüller, W., Steinmaurer, G., Pozo, D., Ruiz-Arias J.A., Fanego, V.L., Ramirez, L., Romeo, M.G., Kurz, C., Pomares, L.M., Guerrero, C. G.: Benchmarking of different approaches to forecast solar irradiance. In: 24th European Photovoltaic Solar Energy Conference, pp. 21–25 (2009). Hamburg, Germany
  75. Notton et al. (2010) Optimal sizing of a grid-connected PV system for various PV module technologies and inclinations, inverter efficiency characteristics and locations 35(2) (pp. 541-554) https://doi.org/10.1016/j.renene.2009.07.013