An empirical approach to parameterizing photovoltaic plants for power forecasting and simulation. (October 2015)

• Record Type: Journal Article
• Authors: Saint-Drenan, Y.M.; Bofinger, S.; Fritz, R.; Vogt, S.; Good, G.H.; Dobschinski, J.
• Is Part Of: Solar energy. Volume 120(2015)
• Page Start: 479
• Page End: 493
• Publication Date: 2015-10
• Subjects: Photovoltaic -- Simulation -- Forecast -- Characterization; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2015.07.024
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: We propose an approach to assess the most important parameters of a PV-plant. We propose a model compromising between simulation accuracy and complexity. We propose a robust parameter estimation. This approach is little sensitive to plant outage and measurement errors. The approach is helpful when plant description are missing or not accurate. Abstract: The aim of this work is to develop an algorithm that can utilize historical PV power measurements to establish the parameters of a physical model for power production. The chosen approach consists in evaluating the parameters of a PV model that maximize the likelihood that simulations match with power measurements. The proposed method offers advantages beyond the standard approaches used for the simulation or prediction of PV power production, as it makes maximum use of the information typically available on a PV plant (plant description and measurement history). Furthermore, an interpretation and control of the algorithm output is made possible. The performance of the proposed approach has been evaluated and analyzed using measurements from two PV plants. It is shown that the proposed approach may identify the orientation angles of a PV module to within an accuracy of less than 2° in optimal cases. Situations were also found with a difference between the estimated and actual angles of 5°, for which the estimated parameters lead to better simulation/forecast accuracy than the actual ones as they balance the systematic error of the chosen PV-model.