A model-based filtering strategy to reconstruct the maximum power generation of curtailed photovoltaic installations: Application to forecasting

E. Scolari , Fabrizio Sossan (1) , M. Paolone

1 PERSEE - Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques

In this paper, we propose a model-based filtering strategy to reconstruct the maximum power production of a PV power plant thanks to integrating measurements of the PV cell temperature, system DC voltage and DC current. The filter relies on a reversed physical model of the targeted PV system and enables to, first, determine analytically the irradiance incident to the panels, and, second, estimate the DC power production as if the plant was operating in maximum power point tracking (MPPT) mode. We present how the approach can be used to reconstruct the maximum power value starting from a generic operating point. As an application, we show that the proposed strategy can improve time series-based solar power forecasting techniques, in particular when the production of the PV system is curtailed and thus the measured power does not correspond to the maximum available.

Mots clés: Solar power estimation; maximum power fore- cast; Photovoltaic (PV) system; stochastic generation

Domaines: Energie électrique

E. Scolari, Fabrizio Sossan, M. Paolone. A model-based filtering strategy to reconstruct the maximum power generation of curtailed photovoltaic installations: Application to forecasting. 2017 IEEE Manchester PowerTech, Jun 2017, Manchester, United Kingdom. pp.1-6. ⟨hal-02108791⟩