Comparison of long-term wind and photovoltaic power capacity factor datasets with open-license

L. Moraes Jr., C. Bussara, P. Stoecker, Kevin Jacquéa, Mokhi Changa, D.U. Sauer; Applied Energy 225 (2018), 209–220

Investigation of pathways toward decarbonisation of energy supply systems strongly relies on integration of
electricity generation from wind and photovoltaics (PV). Energy system model authors are typically not experts
in creation of representative weather datasets, which are fundamental for an unbiased representation of volatile
power generation within the models. The aim of this work is therefore to benchmark data quality and verify
against feed-in records for datasets published from two projects: EMHIRES and Renewables.ninja; feed-in records
taken from Transmission System Operators (TSO). Both projects used meteorological reanalysis data from NASA
(National Aeronautics and Space Administration) and Meteosat-based datasets from CM-SAF (Satellite
Application Facility on Climate Monitoring) to generate long-term hourly PV and wind power capacity factor
time series. Although datasets were based on the same raw data sources, they present significant differences due
to modelling of energy conversion technologies, correction and validation methods. Comparison of duration
curves, full load hours, plots of hourly PV capacity factors as well as correlation analysis between datasets reveal
that for PV generation EMHIRES is more similar to TSO’s data, while the Ninja dataset revealed more similarity
when comparing wind datasets. Results showed that even based on the same data sources, time series were
strongly dependent on methods applied subsequently. Application of the datasets within energy system models
therefore could present a form of hidden exogenous bias to results. System modelers, who need weather based
open license data to perform energy simulations, may be aware of differences in open license datasets available.

Link: https://www.sciencedirect.com/science/article/pii/S0306261918306767