Abstract
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.
Highlights
Due to the transition of the power grid towards clean energy, an increasing penetration of distributed renewable energy sources, mainly Photovoltaic (PV) systems at rooftops, have been observed
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
PV system data and pre-processing The exemplary PV system DC peak power is rated with 11.55 kW and the panels are connected to two inverters with each 4.6 kW nominal AC output (5.06 kW maximum)
Summary
Due to the transition of the power grid towards clean energy, an increasing penetration of distributed renewable energy sources, mainly Photovoltaic (PV) systems at rooftops, have been observed. PV generation models typically require essential system parameters, such as the panel azimuth, panel tilt, installation location, as well as PV cell and inverter electrical behaviour (e.g., influence of ambient temperature, efficiency and rated power limits). Some of those PV system parameters are available in power plant databases such as PVOutput.org or national registration databases. The generated power output of a PV system is usually measured for remuneration purpose using smart meters or is monitored via values from the inverters This data could be used to automatically determine or validate given PV system parameters and gain a more accurate PV model for microgrid simulation or forecasting. Locating energy data might cause privacy issues as shown by Chen et al (2016); Chen and Irwin (2017a)
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