E3S Web Conf.
Volume 152, 20202019 International Conference on Power, Energy and Electrical Engineering (PEEE 2019)
|Number of page(s)||4|
|Section||Photovoltaic Power Generation System and Technology|
|Published online||14 February 2020|
Net demand short-term forecasting in a distribution substation with PV power generation
Department of Electrical Engineering, University of La Rioja, Logroño, Spain.
* Corresponding author: firstname.lastname@example.org
The integration of renewable energies, specifically solar energy, in electric distribution systems is increasingly common. For an optimal operation, it is very important to forecast the final net demand of the power distribution network, considering the variability of solar energy combined with the variability of the electric energy consumption habits of population. This paper presents the methodology followed to forecast the net demand in a power distribution substation. Two approaches are considered, the net demand direct prediction, and the indirect prediction with the forecasts of PV power generation and load demand. Artificial Neural Network (ANN) based models and autoregressive models with exogenous variables (ARX) are used to predict the net demand, directly and indirectly, for the 24 hours of the day-ahead. The methodology is applied to a medium voltage distribution substation and the direct and indirect forecasts are compared.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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