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Generation Data of Synthetic High Frequency Solar Irradiance for Data-Driven Decision-Making in Electrical Distribution Grids

Author

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  • Mohammad Rayati

    (Institut d’Energie et Systèmes Electriques (IESE), Haute École d’Ingénierie et de Gestion du Canton de Vaud (HEIG-VD), Haute École Spécialisée de Suisse Occidentale (HES-SO), 1041 Yverdon-les-Bains, Switzerland)

  • Pasquale De Falco

    (Department of Engineering, University of Naples Parthenope, 80133 Naples, Italy)

  • Daniela Proto

    (Department of Electrical Engineering, Università Federico II of Napoli, 80138 Naples, Italy)

  • Mokhtar Bozorg

    (Institut d’Energie et Systèmes Electriques (IESE), Haute École d’Ingénierie et de Gestion du Canton de Vaud (HEIG-VD), Haute École Spécialisée de Suisse Occidentale (HES-SO), 1041 Yverdon-les-Bains, Switzerland)

  • Mauro Carpita

    (Institut d’Energie et Systèmes Electriques (IESE), Haute École d’Ingénierie et de Gestion du Canton de Vaud (HEIG-VD), Haute École Spécialisée de Suisse Occidentale (HES-SO), 1041 Yverdon-les-Bains, Switzerland)

Abstract

In this paper, we introduce a model representing the key characteristics of high frequency variations of solar irradiance and photovoltaic (PV) power production based on Clear Sky Index (CSI) data. The model is suitable for data-driven decision-making in electrical distribution grids, e.g., descriptive/predictive analyses, optimization, and numerical simulation. We concentrate on solar irradiance data since the power production of a PV system strongly correlates with solar irradiance at the site location. The solar irradiance is not constant due to the Earth’s orbit and irradiance absorption/scattering from the clouds. To simulate the operation of a PV system with one-minute resolution for a specific coordinate, we have to use a model based on the CSI of the solar irradiance data, capturing the uncertainties caused by cloud movements. The proposed model is based on clustering the days of each year into groups of days, e.g., (i) cloudy, (ii) intermittent cloudy, and (iii) clear sky. The CSI data of each group are divided into bins of magnitudes and the transition probabilities among the bins are identified to deliver a Markov Chain (MC) model to track the intraday weather condition variations. The proposed model is tested on the measurements of two PV systems located at two different climatic regions: (a) Yverdon-les-Bains, Switzerland; and (b) Oahu, Hawaii, USA. The model is compared with a previously published N -state MC model and the performance of the proposed model is elaborated.

Suggested Citation

  • Mohammad Rayati & Pasquale De Falco & Daniela Proto & Mokhtar Bozorg & Mauro Carpita, 2021. "Generation Data of Synthetic High Frequency Solar Irradiance for Data-Driven Decision-Making in Electrical Distribution Grids," Energies, MDPI, vol. 14(16), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4734-:d:608282
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    References listed on IDEAS

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    1. Prusty, B Rajanarayan & Jena, Debashisha, 2017. "A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1286-1302.
    2. Bracale, Antonio & Carpinelli, Guido & De Falco, Pasquale, 2017. "A new finite mixture distribution and its expectation-maximization procedure for extreme wind speed characterization," Renewable Energy, Elsevier, vol. 113(C), pages 1366-1377.
    3. Kabir, M.N. & Mishra, Y. & Bansal, R.C., 2016. "Probabilistic load flow for distribution systems with uncertain PV generation," Applied Energy, Elsevier, vol. 163(C), pages 343-351.
    4. Jain, P.K. & Lungu, E.M., 2002. "Stochastic models for sunshine duration and solar irradiation," Renewable Energy, Elsevier, vol. 27(2), pages 197-209.
    5. Youcef Ettoumi, F. & Mefti, A. & Adane, A. & Bouroubi, M.Y., 2002. "Statistical analysis of solar measurements in Algeria using beta distributions," Renewable Energy, Elsevier, vol. 26(1), pages 47-67.
    6. Mohamed Lotfi & Mohammad Javadi & Gerardo J. Osório & Cláudio Monteiro & João P. S. Catalão, 2020. "A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation," Energies, MDPI, vol. 13(1), pages 1-19, January.
    7. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2011. "Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation," Energy, Elsevier, vol. 36(1), pages 348-359.
    8. Cervone, A. & Carbone, G. & Santini, E. & Teodori, S., 2016. "Optimization of the battery size for PV systems under regulatory rules using a Markov-Chains approach," Renewable Energy, Elsevier, vol. 85(C), pages 657-665.
    9. Le Gal La Salle, Josselin & Badosa, Jordi & David, Mathieu & Pinson, Pierre & Lauret, Philippe, 2020. "Added-value of ensemble prediction system on the quality of solar irradiance probabilistic forecasts," Renewable Energy, Elsevier, vol. 162(C), pages 1321-1339.
    10. Sulaiman, M.Yusof & Hlaing Oo, W.M & Abd Wahab, Mahdi & Zakaria, Azmi, 1999. "Application of beta distribution model to Malaysian sunshine data," Renewable Energy, Elsevier, vol. 18(4), pages 573-579.
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