Location and solar system parameter extraction from power measurement time series

Photovoltaic (PV) systems are considered an important pillar in the energy transition because they are usually located near the consumers. In order to provide accurate PV system models, e.g. for microgrid simulation or hybrid-physical forecast models, it is of high importance to know the underlying...

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Bibliographic Details
Published in:Energy Informatics 2021-09, Vol.4 (Suppl 3), p.1-20, Article 14
Main Authors: Danner, Philipp, de Meer, Hermann
Format: Article
Language:English
Subjects:
Albedo
Ambient temperature
Computer Science
Distributed generation
Information Systems and Communication Service
Mathematical models
Parameter estimation
Parameter sensitivity
Particle swarm optimization
Photovoltaic cells
Photovoltaics
Power measurement
PV model
Sensitivity analysis
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Summary:Photovoltaic (PV) systems are considered an important pillar in the energy transition because they are usually located near the consumers. In order to provide accurate PV system models, e.g. for microgrid simulation or hybrid-physical forecast models, it is of high importance to know the underlying PV system parameters, such as location, panel orientation and peak power. In most open PV generation databases, these parameters are missing or are inaccurate.In this paper, we present a framework based on particle swarm optimisation and the PVWatts model to estimate PV system parameters using only power feed-in measurements and satellite-based ERA5 climate reanalysis data. Our sensitivity analysis points out the most relevant PV system parameters, which are panel and inverter peak power, panel orientation, system location and a small but not negligible influence of ambient temperature and albedo. The detailed evaluation on one exemplary PV system shows an acceptable accuracy in panel azimuth and tilt for the use in microgrid PV system simulation. The extracted location has less than 25 km of positioning error in the best case, which is more than satisfying with respect to the underlying data resolution of the ERA5 dataset. Similar results are observed for 10 systems in Europe and the USA.
ISSN:2520-8942
2520-8942
DOI:10.1186/s42162-021-00176-2