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Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution

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  • Visser, Lennard
  • AlSkaif, Tarek
  • van Sark, Wilfried

Abstract

Accurate forecasts of the power production of distributed photovoltaic (PV) systems are essential to support grid operation and enable a high PV penetration rate in the electricity grid. In this study, we analyse the performance of 12 different models that forecast the day-ahead power production in agreement with market conditions. These models include regression, support vector regression, ensemble learning, deep learning and physical based techniques. In addition, we examine the effect of aggregating multiple PV systems with a varying inter-system distance on the forecast model performance. The models are evaluated both on their technical and economic performance. From a technical perspective, the results show a positive effect from both an increasing inter-system distance and a larger sized PV fleet on the model performance, which was not the case for the economic assessment. Furthermore, the ensemble and deep learning models perform better than the alternatives from a technical point of view. For the economic assessment, the results indicate the superiority of the physical based model, followed by the deep learning models. Lastly, our findings show the importance of considering the user's objective when assessing solar power forecast models.

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  • Visser, Lennard & AlSkaif, Tarek & van Sark, Wilfried, 2022. "Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution," Renewable Energy, Elsevier, vol. 183(C), pages 267-282.
    • Handle: RePEc:eee:renene:v:183:y:2022:i:c:p:267-282
    DOI: 10.1016/j.renene.2021.10.102
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    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Eseye, Abinet Tesfaye & Zhang, Jianhua & Zheng, Dehua, 2018. "Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information," Renewable Energy, Elsevier, vol. 118(C), pages 357-367.
    3. Temitayo O. Olowu & Aditya Sundararajan & Masood Moghaddami & Arif I. Sarwat, 2018. "Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey," Energies, MDPI, vol. 11(7), pages 1-32, July.
    4. Gao, Mingming & Li, Jianjing & Hong, Feng & Long, Dongteng, 2019. "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, Elsevier, vol. 187(C).
    5. Dewangan, Chaman Lal & Singh, S.N. & Chakrabarti, S., 2020. "Combining forecasts of day-ahead solar power," Energy, Elsevier, vol. 202(C).
    6. 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.
    7. Clò, Stefano & Fumagalli, Elena, 2019. "The effect of price regulation on energy imbalances: A Difference in Differences design," Energy Economics, Elsevier, vol. 81(C), pages 754-764.
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