PV Forecast for the Optimal Operation of the Medium Voltage Distribution Network: A Real-Life Implementation on a Large Scale Pilot

Aleksandar Dimovski, Matteo Moncecchi, Davide Falabretti and Marco Merlo
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Aleksandar Dimovski: Department of Energy, Politecnico di Milano, 20156 Milano, Italy
Matteo Moncecchi: Department of Energy, Politecnico di Milano, 20156 Milano, Italy
Davide Falabretti: Department of Energy, Politecnico di Milano, 20156 Milano, Italy
Marco Merlo: Department of Energy, Politecnico di Milano, 20156 Milano, Italy

Energies, 2020, vol. 13, issue 20, 1-21

Abstract: The goal of the paper is to develop an online forecasting procedure to be adopted within the H2020 InteGRIDy project, where the main objective is to use the photovoltaic (PV) forecast for optimizing the configuration of a distribution network (DN). Real-time measurements are obtained and saved for nine photovoltaic plants in a database, together with numerical weather predictions supplied from a commercial weather forecasting service. Adopting several error metrics as a performance index, as well as a historical data set for one of the plants on the DN, a preliminary analysis is performed investigating multiple statistical methods, with the objective of finding the most suitable one in terms of accuracy and computational effort. Hourly forecasts are performed each 6 h, for a horizon of 72 h. Having found the random forest method as the most suitable one, further hyper-parameter tuning of the algorithm was performed to improve performance. Optimal results with respect to normalized root mean square error (NRMSE) were found when training the algorithm using solar irradiation and a time vector, with a dataset consisting of 21 days. It was concluded that adding more features does not improve the accuracy when adopting relatively small training sets. Furthermore, the error was not significantly affected by the horizon of the forecast, where the 72-h horizon forecast showed an error increment of slightly above 2% when compared to the 6-h forecast. Thanks to the InteGRIDy project, the proposed algorithms were tested in a large scale real-life pilot, allowing the validation of the mathematical approach, but taking also into account both, problems related to faults in the telecommunication grids, as well as errors in the data exchange and storage procedures. Such an approach is capable of providing a proper quantification of the performances in a real-life scenario.

Keywords: PV forecast; online forecast; statistical methods; machine learning; random forests; real-life implementation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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