# Renewable Generation Data for European Energy System Analysis

Alexander Kies1, Bruno U. Schyska2, Mariia Bilousova1,3, Omar El Sayed1,3,
Jakub Jurasz4,5,6, Horst Stoecker1,3,7 1Frankfurt Institute for Advanced
Studies, Goethe-University Frankfurt, Ruth-Moufang-Str. 1, 60438 Frankfurt,
Germany
2German Aerospace Center (DLR), Institute of Networked Energy Systems, Carl-
von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
3Goethe-University, Frankfurt, Germany
4Faculty of Environmental Engineering, Wroclaw University of Science and
Technology, 50-370 Wroclaw, Poland
5Faculty of Management, AGH University, 30-059 Cracow, Poland
6School of Business, Society and Engineering, Mälardalens Högskola, 72113
Västerås, Sweden
7GSI Helmholtzzentrum für Schwerionenforschung, Planckstraße 1, 64291
Darmstadt, Germany

###### Abstract

In the process of decarbonization, the global energy mix is shifting from
fossil fuels to renewables. To study decarbonization pathways, large-scale
energy system models are utilized. These models require accurate data on
renewable generation to develop their full potential. Using different data can
lead to conflicting results and policy advice. In this work, we compare
several datasets that are commonly used to study the transition towards highly
renewable European power system. We find significant differences between these
datasets and cost-difference of about 10% result in the different energy mix.
We conclude that much more attention must be paid to the large uncertainties
of the input data.

###### keywords:

Energy System Analysis , Renewable Generation Data , Energy Meteorology ,
MERRA-2 , ERA5, renewables.ninja, EMHires

††journal: Journal Name

##  1 Introduction

Sustainable energy sources are a major solution for the imminent thread of
climate change [1, 2]. Increasing the number of installations of renewable
generation capacities and the electrification of energy sectors like heating
and transportation fosters decarbonization. As part of the Paris Agreement,
many countries around the world commited to reducing their greenhouse gas
emissions. In its turn, the EU set an aim to reduce emissions by at least 50%
by 2030, as compared to 1990 levels [3]. In this context, the sensitivity of
energy systems to weather and climate rises [4, 5]. Energy system models play
an important role in understanding and investigating energy systems of various
scales and scopes. For the renewable future, the use of adequate
meteorological data in the field of energy system analysis and modelling is
essential.

Common large-scale energy models for Europe cover the EU [6] and aggregate
quantities such as generation from renewable sources to country levels. There
are multiple datasets that provide data on hourly generation potentials from
renewable sources such as wind and solar PV. They are based on reanalysis
data, which is an assimilation of historical measurements and numerical models
into a consistent estimate of a state of the atmosphere. These datasets
provide relevant variables such as wind speed, irradiation or temperature used
to compute potential generation from renewables.

A comparison of two of these datasets was performed by Moraes et al. [7]. They
found that datasets diverge from each other even if they are based on the same
meteorological source. This is likely based on differences in technological
assumptions that are made to convert meteorological data to generation. These
include, for instance, assumptions on the wind turbines used, which can take
in historical data or projections into the future such as increasing hub
heights [8], as well as their placement.

In this paper, we compare seven datasets which provide time-resolved
aggregated generation data on the country level. Only five of them cover the
same time period (2003-2012), therefore we focus on them in particular.
Section 2 discusses these datasets and shows the major steps in converting
meteorological data to energy system model input. Section 3 analyses the
datasets with respect to different means such as annual capacity factors, ramp
rates and optimised mixes. Section 4 discusses the results and implications
and Section 5 concludes this paper.

This paper contains novelties relevant for the research community. It provides
the broadest comparison of renewable generation datasets to date, introduces
energy system-related measures to compare them and derives important
conclusions for research on the energy transition.

##  2 Data

Data on renewable generation potentials is essential for the modelling and
analysis of energy systems with significant shares of generation from
renewable sources. Among the most used datasets in the study of weather and
climate are reanalyses. A meteorological reanalysis is a method to create
long-term weather data using numerical weather prediction models and
assimilating historical data. Reanalyses are used to study climate variability
[9] as well as are commonly employed to study energy systems [10]. Wind speeds
for the datasets investigated in this work are mostly based on two reanalyses:
ERA5 and MERRA-2. MERRA [11] is provided by NASA. The official data production
was launched in 2008 with the use of the up-to-date GEOS-5 (Goddard Earth
Observing System Data Assimilation System Version 5) produced at NASA GMAO
(Global Modeling and Assimilation Office). ERA5 [12] is the newest global
reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF).
Wind speed data are provided for heights of 10 m as well as 100 m. The RE-
Europe is the only considered dataset that uses ECMWF forecasts (RE-Europe)
and the regional COSMO-REA6 reanalysis for the modelling of wind power [13].
For solar data, CM SAF SARAH is commonly used alongside the reanalyses [14].
It is a satellite-based climate data record of irradiance data and other
variables.

MERRA-2 and ERA5 were compared by various researchers. Olauson [15] compared
ERA5 and MERRA-2 for the modelling of wind power on a country-level as well as
for individual turbines. His findings indicate that ERA5 performs considerably
better than MERRA-2 on both levels. Camargo et al. [16] used ERA5 data to
model multi-annual time series of solar PV generation. They performed a
validation with hourly data of PV plants in Chile and found a slightly
superior performance of ERA5 as compared to modelling based on MERRA-2. Gruber
et al. [17] compared MERRA-2 and ERA5 for wind power simulation bias-corrected
with the global wind atlas for the US, Brazil, New Zealand and South Africa
and found ERA5 to outperform MERRA-2. Urraca et al. [18] evaluated global
horizontal irradiance estimates from ERA5 and COSMO-REA6, a regional
reanalysis from the German Meteorological Service (DWD), and concluded that
both reanalyses reduce the quality gap between reanalysis and satellite data.
Jourdier [19] investigated ERA5, MERRA-2 and other datasets to examine wind
power production in France, and found that ERA5 is skilled, however, it
underestimates wind speeds, especially in mountainous areas. Piasecki et al.
[20] compared ERA5 data with measurements in several locations in Poland and
concluded that they are in good agreement for solar PV, with hourly
correlation coefficients above 0.9, while wind comparison showed a large
variability with differences in capacity factors of up to 15 percentage
points.

Dataset years wind PV capacity layout temporal resolution ref.
renewables.ninja 1980-2019 MERRA Sarah yes 1h [21] EMHires 1986-2015 MERRA
Sarah yes 1h [22] Restore 2003-2012 MERRA Sarah no 1h [23] UReading-E
1979-2018 ERA5 ERA5 yes 3h [24] UReading-M 1979-2018 MERRA MERRA yes 1h [25]
PyPSA-Eur 2013 ERA5 Sarah yes 1h [26] RE-Europe 2012-2014 diff. diff. no 1h
[27]

Table 1: Datasets providing data on renewable generation that were analysed
and compared in this work. Abbreviations used in this work are Ninja,
URead-E/M and RE-Eur-E/C.

Figure 1: Country coverage of the datasets. Purple-colored countries include
both solar PV and wind time series, yellow-colored ones include only wind.
Both UReading datasets exclude Estonia. The Restore dataset excludes Norway
and for countries Ireland, Denmark, Finland and Estonia provides only wind
time series.

Table 1 shows key features of each dataset. Figure 1 shows the countries
covered. To use a sufficiently long time period for every dataset, we set the
time frame of the following analyses to the period 2003-2012 and compare 5
datasets covering it.

For the sake of readability, we only briefly summarize the major steps of
modelling renewable energy generation for the datasets investigated in this
section, except for the Restore dataset, where we describe them in more
detail. In general, similar steps are taken for all datasets (e.g. Fig. 2). A
meteo model converts meteorological data to the needed input for a power
model, which yields space- and time-dependent power per installed capacity.
The output of this model is aggregated by a capacity model, which represents
how much capacity is installed at what location. The aggregated sum of this
model yields the generation time series for the renewable generation.

Figure 2: Schematic flowchart for PV/wind generation modelling for the RESTORE
dataset. Figure from Kies et al. [23].

###  2.1 Restore

The Restore dataset [23] was produced in the context of the RESTORE 2050
project. Besides wind and solar PV generation time series, it also contains
time series of other generation technologies, such as CSP, wave and hydro
inflow. To calculate wind power per grid cell in the Restore dataset, wind
speeds are interpolated to the grid of the COSMO-EU model [28], and
extrapolated to the turbine hub height using the wind speed log profile

$\displaystyle\frac{s(z_{1})}{s(z_{2})}$
$\displaystyle=\frac{\log(z_{1}/z_{0})}{\log(z_{2}/z_{0})},$ (1)

where $z_{1},z_{2}$ are the heights at which wind speeds are given and
desired, respectively and $z_{0}$ is the surface roughness length provided by
the dataset. Wind speeds are corrected using a linear regression according to
COSMO-EU wind speeds. The power curve of an Enercon E-126 turbine is then used
to convert wind speeds to wind power at 100m hub height. Additionally, the
dataset provides capacity factors generated with the same power curve for the
hub heights of 140m and 180m. In the following comparison, we use the dataset
based on 100m wind speeds. Any wind turbine power curve mapping wind speed $s$
to power output $g$ is approximately given by [29, 30]

$\displaystyle g=\begin{cases}0\text{ for }s<s_{0}\vee s>s_{\text{max}}\\\
\propto\left(s-s_{0}\right)^{3}\text{ for }s_{0}<s<s_{\text{nom}}\\\ =G\text{
for }s_{\text{nom}}<s<s_{\text{max}}.\end{cases}$ (2)

For low wind speeds, torque caused by the wind on blades of a turbine is too
low to generate angular momentum. Above a certain speed, referred to as cut-in
speed $s_{0}$, turbines start to rotate and produce power. The power curve now
resembles the characteristic $v^{3}$-dependency of the kinetic energy of wind
passing through a certain area. At the rated wind speed $s_{\text{nom}}$, the
rated power of the turbine is reached. This power is kept constant by the
turbine beyond this speed value, commonly by the adjustment of blade angles.
If speeds increase further, they can reach a critical level, referred to as
cut-out speed $s_{\text{max}}$, at which the rotor blades are turned out of
the wind to prevent a structural damage to the turbine.

For PV Power, global horizontal irradiance is converted to irradiation on
inclined surfaces based on the Klucher model [31]. For tilt angles, optimized
values per country are used.

The efficiency of the PV modules in dependency of incoming irradiation $I$ and
temperature $T$ is modelled via a parametric model for a standard temperature

$\displaystyle\eta(I,25^{\circ}C)=a1+a2\times I+a3\ln I,$ (3)

where $a1,a2,a3$ are device-specific parameters and the temperature dependency
of the efficiency is given via

$\displaystyle\eta(I,T)=\eta(I,25^{\circ}C)(1-0.004\Delta T).$ (4)

PV power output is then directly computable via

$\begin{split}g_{t}&=\eta_{t}I_{t}A.\end{split}$ (5)

Unlike the other datasets investigated in this work, the Restore dataset does
not take the locations of existing generation capacities into account.
Instead, country-wise capacities are distributed proportionally to the
underlying resource [23].

###  2.2 UReading

The UReading datasets [24, 25] were produced by researchers from the
University of Reading, UK. They contain hourly values of aggregated power
generation from wind and solar based on a representative distribution of wind
and solar farms as well as ERA5 (Reading-E) or MERRA-2 (Reading-M) reanalyses
data for 28 European countries. In addition, a daily time series of
electricity demand is provided. It is used in this paper to optimise
generation mixes.

###  2.3 Renewables.ninja

Renewables.ninja [21] is a web tool that provides potential generation of wind
and solar PV for single locations or countries globally. Pfenninger and
Staffell [32, 33] also found that significant correction factors are necessary
to model renewable feed-in from reanalyses in Europe. To model PV power, they
use the model by Huld et al. [34] with irradiation based on SARAH for
different azimuth/tilt combinations. For wind power, they use a virtual wind
farm model [35] and bias-correct it.

###  2.4 EMHires

European Meteorological derived HIgh resolution RES generation time series for
present and future scenarios (EMHires) is a dataset produced by the joint
research centre (JRC) with an aim to allow users to assess the impact of
meteorological and climate variability on renewable generation in Europe [22,
36, 37]. For wind power, this dataset combines MERRA-2 reanalysis data with
wind farms data from thewindpower.net [38]. Wind power time series are further
normalised to the reported ENTSO-E annual production statistics. Wind speeds
are statistically downscaled and interpolated to the desired hub height using
a wind profile power law. Wind speed data are then converted to wind power
using a specific power curve assigned to each wind farm considering the
characteristics of the wind farm, such as manufacturer. For PV generation, the
PVGIS model [39, 40, 41] is used to provide generation in dependency of
irradiation and solar module parameters. Together with assumptions such as
inclinations, PVGIS output is then aggregated to country levels.

###  2.5 PyPSA-Eur

PyPSA-EUR is a dataset for European generation and transmission expansion
planning studies from freely available data [26]. Besides time series of
renewable generation, it contains additional data to model a renewable
European energy system, such as alternating/direct current transmission lines,
substations, data on conventional generators and demand as well as renewable
capacity installation potentials. For renewable generation data, it uses data
from the ERA5 reanalysis with a logarithmic wind power profile to extrapolate
wind speeds to the desired hub height for wind power modelling. For PV
conversion, it also uses the PVGIS model [40].

###  2.6 RE-Europe

RE-Europe [27] is a dataset produced at the Technical University of Denmark
for the modelling of a highly renewable European power system. Analogously to
PyPSA-EUR, alongside renewable generation data it contains other data, for
instance data on transmission lines. To provide meteorological variables for
both, wind and solar PV, meteorological data from the ECMWF forecasts as well
as the COSMO-REA6 reanalysis are used. For the final conversion step, it uses
a smoothed power curve of a specific wind turbine (Siemens SWT 107) as well as
a specific solar panel (Scheuten P6–54 215 Multisol Integra Gold) to convert
meteorological variables to a potential generation. Generation capacities are
assigned to sub-national regions using two heuristic capacity layouts.

##  3 Results

In this section, we study different properties of the provided time series of
generation per unit $g_{n,s,t}^{i}$. Here $i$ denotes the dataset, $n$ is the
country, $s$ is the technology and $t$ is the timestep.

###  3.1 Annual Capacity Factors

The capacity factor of a generator is the ratio of the potential energy output
over a given period of time to the maximum possible energy output as given by
the rated capacity over that period [42],

cf $\displaystyle=\sum_{t}\frac{g^{+}_{n,s,t}}{G_{n,s}}.$ (6)

For renewable generators the capacity factor heavily depends on the resource
availability as well as on the technical parameters. Occasionally, it is
referred to as full load hours. Besides reductions due to resource
inavailability, one can, for instance, factor net congestions into effective
capacity factors [43].

Figure 3: Annual capacity factors for wind and solar PV for the chosen
countries. RE-Europe does not cover the UK. Solar PV capacity factors are the
highest for the RE-Europe dataset. Year-to-year changes are well caught by all
datasets for wind, implying the difference resulting from the conversion
process from meteorological data to generation. For solar PV, capacities in
some countries such as the UK and Italy were very small (well below 1 GW) at
the beginning of the investigated period. Strong changes in year-to-year
reported capacity factors are potentially just caused by rounding or reporting
errors. Also taking the large year-to-year changes in reported capacity
factors into account, it seems questionable, how valueable these values are in
many cases for a comparison with modelled data.

Figure 4: Average capacity factors for wind and solar PV in the years
2003-2012. The greyscale bar indicates the average capacity factor. The
colorbar plots per country show the average deviation in percentage points
from the mean of the ensembles (Restore, EMHires, URead-E, URead-M and Ninja).
Colors are: EMHires (red), URead-E (blue), URead-M (green), Restore (purple),
Ninja (orange) and reported (yellow). For solar PV, trends seem to be
consistent over all for Europe. For wind, there is more variation. For
instance, Restore produces comparably small capacity factors in South-East
Europe and Norway, while for other North Sea countries Restore capacity
factors are above average.

Figure 3 shows annual capacity factors of the different datasets. Annual
reported capacity factors were calculated by dividing reported generation by
reported installed capacities obtained from IRENA [44]. For wind, overall
capacity factors differ significantly between datasets, while relative year-
to-year changes are quite similar. Reading-M in general shows the highest
capacity factor in almost all cases for wind and solar PV, while EMHires shows
the lowest. Absolute capacity factor differences go up to around 20% (UK
wind), and relative capacity factors for some cases differ by a factor of
almost 3 (Italy wind). The UReading-M and UReading-E dataset have different
meteorological reanalyses as input but are based on the same assumptions. They
show a relatively good agreement for wind and are closest to each other in all
cases, except for the UK. For solar PV, UReading-M has relatively low
agreement with the other datasets. This potentially supports the observation
by Camargo et al. [16] that modelled PV power based on ERA5 has better
agreement with measurements than MERRA-2. For wind, differences when switching
between MERRA-2 and ERA5 seem to have a smaller effect than when choosing a
different methodological approach, as demonstrated by the proximity of
capacity factors for UReading-M and -E.

Another important point is the difference of the modelled values to the
reported capacity factors. In some cases, such as German wind, reported values
are well captured by Ninja and the EMHires dataset, while results for PV seem
to not meet the modelled values, in both absolute terms and year-to-year
variations. However, one should keep in mind that not all datasets have the
goal to reproduce historical values of capacity factors. Another likely
interpretation of the fact that year-to-year changes of reported values differ
from the modelled dataset is that modelled values are a subject to
meteorological year-to-year changes only, while reported values are also
influenced by changing capacity layouts or technological parameters. The
discrepancy between modelled and realized capacity factors with the latter
being considerably lower has already led to public debates [45]. There is also
a discrepancy between wind capacity factor estimates and realized values based
on oversimplifcations [46]. For PyPSA-EUR, the year 2013 and for RE-Europe the
years 2012-2014 were considered. Capacity factors in both datasets look
unremarkable with the exception of solar PV for RE-Europe; its capacity
factors are substantially higher in all considered countries based on both,
ECMWF forecasts as well as COSMO-REA6.

Figure 4 shows averaged capacity factors per country for five datasets and
reported values. Reported values for solar PV are for most countries are
significantly below the modelled data mean, except for Sweden and Finland.
U-Read-M reports highest solar PV capacity factors for almost all countries.
For solar PV, it can be concluded, that the methodology and the chosen
datasets are largely independent of the location. For wind, the picture looks
different. Restore, for instance, has mostly lower capacity factors in
Southern Europe, while in North- and Northwestern Europe they are close to or
above the ensemble mean. For EMHires, the picture looks the opposite, with
capacity factors below average in the North and Northwest and close or above
average in the South-East.

###  3.2 Correlation

Figure 5: Correlation of locally normalised three-hourly values from
2003-2012. Wind time series were normalised by computing the average and
standard deviation over a moving window of 30 days. For solar PV, this was
done for each hour of the day separately and a window size of 20 days.

The Pearson correlation coefficient measures linear correlation between two
variables. In the context of renewable generation, the Pearson correlation
coefficient is defined as

$\displaystyle\rho_{g^{\alpha},g^{\beta}}$
$\displaystyle=\frac{\text{cov}\left(g^{\alpha},g^{\beta}\right)}{\sigma_{g^{\alpha}}\sigma_{g^{\beta}}}.$
(7)

The Pearson correlation coefficient is commonly used to study whether
different renewable generation sources or renewable generation sources at
different locations are connected via transmission complement each other [47,
48, 49]. Here, we are interested in the correlations of the fast (in the order
of hours) variations of the wind and solar PV time series. The slow variations
caused by large-scale meteorological phenomena and the seasonal cycle of the
sun should be similar in all datasets. Therefore, we apply a local
normalisation as described in Schäfer and Guhr [50]: Wind time series are
normalised by computing the average and standard deviation over a moving
window of 30 days. Solar PV time series are normalised separately for each
hour of the day and with a window size of 20 days. This approach was used to
reduce the dependency of the time series on the diurnal cycle in case of solar
PV and large-scale synoptic conditions for wind. Figure 5 shows pairwise
correlation coefficients over the ten years from 2003-2012 for exemplary
countries. In general, correlations are very high between datasets despite
emphasising the differences by using the moving window approach, except for
solar Data from UReading-M, which has comparably low correlations in many
cases. However, generally high correlation factors indicate that aggregated
temporal patterns are well captured by every dataset. These patterns are
important to capture infrastructure requirements that couples different time
steps together, such as flexible backup power plant infrastructure or energy
storage.

###  3.3 Low Generation Events

Low generation events are times, when resources for both wind and solar PV are
not available. In Germany, these events are referred to as ”Dunkelflaute”,
which can be translated as dark doldrums. Low-wind-power events for Germany
were studied by Ohlendorf and Schill [51]. They found that long low-wind-power
events are rare. An average wind capacity factor below 10% for around five
consecutive days occurs on yearly basis and for a period of around eight days
every ten years. We characterise these events by the aggregated generation
from wind and solar PV of all countries together. First, normalised time
series are multiplied with installed capacities per country and aggregated

$\displaystyle G_{t}=\sum_{n,s}G_{n,s}g_{n,s,t}.$ (8)

ctry | wind | solar | ctry | wind | solar | ctry | wind | solar
---|---|---|---|---|---|---|---|---
AT | 2.1 | 0.8 | DE | 44.9 | 38.2 | PL | 3.8 | 0.0
BE | 2.0 | 3.1 | GR | 2.0 | 2.6 | PT | 4.9 | 0.4
BG | 0.7 | 1.0 | HU | 0.3 | 0.0 | RO | 3.0 | 1.3
CR | 0.3 | 0.0 | IE | 2.3 | 0.0 | SK | 0.0 | 0.6
CY | 0.2 | 0.0 | IT | 8.7 | 18.4 | SI | 0.0 | 0.3
CZ | 0.3 | 2.1 | LV | 0.0 | 0.0 | ES | 23.0 | 4.8
DK | 4.8 | 0.6 | LT | 0.3 | 0.1 | SE | 5.4 | 0.1
EE | 0.3 | 0.0 | LU | 0.1 | 0.1 | UK | 12.4 | 5.2
FI | 0.6 | 0.0 | MT | 0 | 0.1 |  |  |
FR | 9.3 | 5.6 | NL | 2.8 | 1.1 |  |  |
Table 2: Capacities per country in [GW] [52, 53] used to produce Fig. 6 and
Fig. 7.

Generation capacities by country from wind and solar PV for 2014 are given in
Table 2.

The resulting time series $\\{G_{1},G_{2},...G_{|t|}\\}$ are then split into
disjoint subsets $\\{G_{i},...,G_{j}\\}$ with
$G_{k}<\alpha\sum_{n,s}G_{n,s}\forall k\in[i,...,j]$ and
$G_{i-1}>=\alpha\sum_{n,s}G_{n,s}\text{ or }i=0$ and
$G_{j+1}>=\alpha\sum_{n,s}G_{n,s}\text{ or }j=|t|$. The number of events is
then given as the cardinality of the set of subsets
$\left|\\{\\{G_{i},...,G_{j}\\},...\\}\right|$ and the average length by the
average number of elements of this set. $\alpha$ is the threshold, below which
an event is considered to be a low generation event.

Figure 6: Number of EU28-wide low generation events (top) and their average
length (bottom). EMHires shows the highest number of events and length. It
should be noted that UReading-E provides only three-hourly data, which makes
the direct comparison difficult. Besides, the other ERA5-based datasets show
similar slopes for growing thresholds.

Figure 6 shows the statistics on low generation events for the datasets
investigated. Average observed lengths are a few hours and the numbers seems
to grow relatively linearly to the growing threshold until a 10% threshold of
total renewable generation. While all datasets show a similar shape, the
overall number of events differs quite a lot. It should be noted that for the
UReading-E dataset the number of events is influenced by its three-hourly
resolution. It is also interesting that the Ninja-dataset demonstrates a
stronger growth rate, while most datasets show a similar growth of the number
of events with increasing threshold. Really extreme low generation events are
very scarce in Ninja, however the number grows rapidly with the growing
threshold overtaking the UReading-M dataset for $\alpha=0.1$.

###  3.4 Ramp Rates

Another measure of importance for a power system are ramp rates. Ramp rates
describe changes in power output that can impact power system operation
disproportionately. Batteries are considered to be important components that
could deal with ramp rates of renewable generators [54, 55]. Ramp rates are
often considered in energy resource assessment studies [56, 57]. Ramp rates of
a power plant are calculated by taking the difference of the output over
passed time:

$\displaystyle RR_{t,\delta_{t}}=g_{t+\delta_{t}}-g_{t}.$ (9)

Figure 7 shows three-hourly europe-wide ramp rates of the different datasets
for wind and PV generation. While the distribution for wind is monotonous and
symmetric, the solar PV ramp rate distributions show minor peaks at around
$\pm 5$% of generation capacities. These are likely caused by the
deterministic diurnal pattern of PV generation resulting from the rotation of
the Earth. UReading-M ramp rates differ significantly from ramp rates
calculated based on other datasets if wind and solar PV are considered
separately. However, if wind and solar PV are added together, these effects
cancel out and the distribution becomes very similar, with the exception of
the relatively prominent second peaks at values $>\pm 0.1$.

Figure 7: EU28-wide three-hourly ramp rates relative to installed renewable
capacities. The small-shoulder peaks for solar PV are likely caused by the
deterministic diurnal pattern of the sun. Note that the URead-M data show
considerably higher ramp rates for wind than the other datasets. From the
system point of view, high ramp rates lead to additional need for balancing.

###  3.5 Optimised Generation Mixes

Generation data are commonly used to study highly renewable power systems.
These include a variety of technologies, such as conventional and renewable
generation technologies, storage, transmission technologies, etc. The purpose
of power system optimisation is to find the cost-optimal combination of these
technologies and their operation under given constraints, for instance carbon
dioxide reduction targets. Moreover, models often do not portray the
electricity sector alone, but also take heating and transportation into
account. This is mostly driven by the eminent need to decarbonize these
sectors and use the flexibility they offer to integrate renewables via
coupling of the sectors [58, 59].

For renewable generators, the capacity factor of a generator is a crucial
variable. One can compute levelized cost of electricity neglecting system
measures to balance variable renewable generation as the ratio of cost over
generation ($\propto$ capacity factor) [60, 61, 62]. To check the influence of
the different datasets on a cost-optimal power system, we present the results
of a simplified optimisation in this section. We optimise the mix of wind,
solar PV and open cycle gas turbines (OCGT) as flexible backup power to cover
the demand of the EU-28+CH+NO countries. The choice of gas was made because
gas is often considered to be a bridge technology towards clean energy that
buys time to foster the energy transition [63, 64].

The optimisation objective reads

$\displaystyle\min_{g,G,F}$ $\displaystyle\left(\sum_{n,s}c_{n,s}\cdot
G_{s}+\sum_{n,s,t}o_{s}\cdot g_{n,s,t}\right)\text{.}$ (10)

It consists of capital costs $c_{n,s}$ for installed capacity $G_{n,s}$ of a
carrier (wind/solar PV) $s$ at node $n$ and marginal costs of generation
$o_{s}$ for energy generation $g_{n,s,t}$ of carrier $s$ at node $n$ and time
$t$. It is furthermore subject to the following constraints.

$\displaystyle\sum_{s}g_{n,s,t}-d_{n,t}=0\quad\forall\quad n\text{,}t\text{.}$
(11)

Demand in space and time needs to be met by dispatched generation from the
various generating technologies and

$\displaystyle 0\cdot G_{n,s}\leq g_{n,s,t}\leq{g}^{+}_{n,s,t}\cdot
G_{n,s}\quad\forall\quad n\text{,}t\text{.},$ (12)

dispatched generation is limited by generation capacity times a weather-
dependent availability ${g}^{+}_{n,s,t}$ for renewables. This availability is
the hourly renewable capacity factor studied in the beginning of the Results
section.

Technology | Capital Cost | Marginal Cost | Emissions
---|---|---|---
| [USD/MW/a] | [USD/MWh] | CO2 [ton/MWh]
OCGT | 47,235 | 58.385 | 0.635
Wind | 136,428 | 0.005 | 0
Solar PV | 76,486 | 0.003 | 0
Table 3: Annualised cost assumptions for generation and storage technologies
derived from different sources [65, 66].

Cost assumptions for generation capacities $c_{n,s}$ and marginal costs
$o_{s}$ as well as emission assumptions are given in Table 3.

Figure 8: Shares of installed renewable capacities and generation. Capacity
curves are less smooth (i.e. monotoneous) than generation curves. Besides,
there is a shift in capacity between wind and solar for datasets Restore and
Ninja at low CO2 prices indicating a flat optimisation maximum with respect to
capacity. The remaining shares comprise gas power plants.

Figure 8 shows shares of the generation and capacities for renewables in
dependency of the price for emitting carbon dioxide. This price is multiplied
with emissions per generation unit and added to the marginal cost of
generation. A growing CO2 price supports the cost-competitiveness of
renewables and increases their shares

Figure 9: Levelized cost of electricity at CO2 prices of 0 USD/ton (top) and
100 USD/ton (bottom) consisting of capital expenditures (CAPEX) and
operational expenditures (OPEX), which are neglible for renewables. Note that
overall differences are not significantly larger at a high CO2 price. Costs
are calculated without the imposed carbon emission price. At a low CO2 price,
some datasets (URead-E and URead-M) already render wind generation relatively
cost-competitive, while the other datasets do not.

Figure 10: Country-wise LCOE in dependency of the meteorological dataset used
for comparison in relation to the ensemble mean. Colors are: EMHires (red),
URead-E (blue), URead-M (green), Restore (purple), Ninja (orange) and reported
(yellow). These LCOE reflects the cost of autark energy supply per country
from renewables plus gas as a backup transition technology. LCOE strongly
depends on the datasets at high LCOE prices. Datasets that provide relatively
low capacity factors, like EMHires in case of Denmark, lead to a significantly
higher LCOE of more than 25%.

Figure 9 shows levelized cost of electricity and their composition for CO2
prices of 0 USD/ton and 100 USD/ton without the cost-contribution from
emitting carbon dioxide. Significant differences can be observed. Based on
EMHires, Restore or Ninja datasets, renewables are not cost-competitive at a
vanishing carbon price, where they contribute only 4-7%. For the two UReading
datasets, the story is different: even at a very low CO2 price, wind
contributes more than 10% to LCOE indicating relatively large shares of
installed capacities. At 100 USD per ton of carbon dioxide emissions,
renewables gain significant ground with respect to their cost-competitiveness.
Now, for all datasets, wind contributes around 30% to LCOE and solar PV around
10%.

Figure 10 shows levelized cost of electricity on the country level for the
different datasets and carbon emission prices of 0 and 100 USD/ton. At 0
USD/ton, most countries rely entirely on gas, because renewables are not cost-
competitive. However, for some countries, such as UK, Ireland, or Denmark,
where renewables successfully compete with gas, differences between the
datasets are significant. Using datasets such as URead-E and Uread-M that
provide relatively high capacity factors, significantly lowers LCOE, by about
10%. The difference in LCOE for countries that entirely rely on gas is based
on differences in the demand patterns, especially between winter and summer.
The effect of the difference in LCOE between datasets is more remarkable at
the CO2 price of 100 USD/ton. Cost differences are drastical in some countries
such as Denmark, where the results of different datasets for LCOE can differ
by more than 50%. The country-resolved plot shows that differences between
countries can be significant, if different meteorological datasets for power
system optimisation are considered. In case the whole continent is considered,
they level out to a large degree.

##  4 Discussion

Data on generation from renewable sources is commonly used in energy system
models to study their behaviour, transition pathways or create policy advice.
Different data potentially leads to conflicting policy advice and
misallocation of large amounts of money.

The integration of renewable energy is a challenging task due to their weather
dependency. To cope with non-dispatchable renewable energy sources, various
concepts can be applied, such as optimizing the mix of different generation
technologies, energy storage [67, 68], transmission, demand-side management
[69, 70] or sector-coupling [58, 59]. When studying multiple datasets that are
commonly used as input into large-scale energy system models, we observed
significant differences between the models. As the differences are also
observed in datasets based on the same meteorological database, they can
likely be attributed to different assumptions in the conversion process to
country-aggregated time series.

Low generation events are defined as periods of continent-wide low generation
from both, wind and solar PV. As these occur continent-wide, a transmission
grid does not help to tackle these. Instead, storage is a viable solution.
With an average length of a few hours, daily and synoptic storage options seem
to be a suitable choice to cope with these low generation events. Commonly,
lithium-ion batteries are proposed as a daily storage [71] and hydrogen
storage or pumped hydro storage (PHS) is a viable storage for the synoptic
scale [72, 59]. Energy system models suffer from uncertainties not only in
meteorological data, but also in assumptions made. Various approaches have
been studied to tackle the problem of uncertainty in energy system models [73,
74, 75, 76, 77].

Besides uncertainty from the generation data source itself and the assumptions
made in creating it, additional uncertainty arises from what period to choose.
Renewable generation resources tend to vary on decadal scales and models
predict that climate change has also profound effects on renewable generation:
Schlott et al. [78] investigated the effects of climate change on a future
European energy system and found an increasing competitiveness of solar PV due
to changing correlation patterns. Wohland et al. [79] found an increasing need
of backup energy due to climate change, Weber et al. [80] concluded the same
and found besides the increasing need for backup energy an increasing need for
storage due to climate change. Kozarcanin et al. [81] studied various metrics
such as variability of renewable generation or short term dispatchable
capacity under climate change but concluded that there is no discernible
effect on these measures. Bloomfield et al. [82] saw significant uncertainty
in power system design due to climate change and pledged for better
understanding of this climate uncertainty. Besides uncertainty arising from
meteorological data, other uncertainties affect power systems, such as
uncertainty in cost assumptions as well as technological developments [83,
84]. The relevance of these effects should be compared to the relevance of
uncertainty arising from meteorological data.

Considering LCOE that were studied in subsection 3.5 it is interesting to note
that differences in the results did not appear to get larger as renewable
shares grew. This seems contradictory to the naive estimate that the choice of
the renewable generation database should increase the dependency on the choice
of meteorological data. However, in a fully detailed energy system model,
complex interdependencies might exist that render smaller or larger effect of
different datasets. One should also note that capacity factors are not the
sole factor in determining cost-efficiency of renewables, because non-
dispatchable renewables might start cannibalising themselves at high
penetration levels reducing their market value [85, 86]. Brown and Reichenberg
[87] have argued that this is the result of policy. A possible way to deal
with reductions in market value are system-friendly renewable generator
designs [88, 89] that aim at producing more at times of higher prices, for
instance PV modules with different azimuths/tilts [90].

###  4.1 Critical Appraisal

A number of systematic differences increase the difficulty to perform the
analysis of the scenarios compared in this paper: datasets are given with
different temporal resolution. While one-hourly data are considered sufficient
to model renewable country-spanning energy systems with sufficient robustness
[91], for three-hourly values this is less evident. However, due to the fact
that computational limitations and linear optimisation problems are in the
complexity class P-complete, we had to find a sweet spot between model
details, temporal and spatial resolution [92, 93] and computational
feasibility. At last, we decided to focus on onshore wind. Offshore wind
energy was treated differently in the different datasets. However, this effect
is rather small, as the installed offshore capacities contribute only a small
percentage of the overall wind capacities as of 2014. Nevertheless, its shares
are rising quickly, due to technical advances, cost reductions and limited
onshore wind potentials [94, 95, 96, 97], although installation potentials
vary significantly in the literature and some researchers suggests far higher
numbers [98].

##  5 Summary and Conclusions

This paper, compares several distinct datasets, which provide renewable
generation time series for both sources, wind and solar PV, on the country
level. Different measures are used, such as ramp rates, correlation
coefficients, annual capacity factors and optimized mixes of generation, from
wind/solar/gas. From the presented results, the following conclusions can be
drawn:

  * 1.

Differences between model statistics are significant, even if they are based
on the same meteorological database. These differences are likely due to
different assumptions about the conversion from weather to energy. There is a
significant need for more research on the effects and interactions of choices
made in the modelling chain weather-to-energy at every step, to achieve better
understanding.

  * 2.

These significant differences have severe consequences for the optimisation
and further studies of power systems. Differences in capacity factors directly
affect both, CAPEX and OPEX, of renewable energy generation, transport and
storage technologies. Hence their optimized shares in power system expansion
models are quite sensible to these uncertainties.

  * 3.

Emphasis in renewable energy systems research must be put on the use of
adequate generation data and discussion of its properties, before we can offer
reliable research results and robust policy advice.

Future research must focus on the study of these critical issues using a
reliable power system model, which allows to capture complex dependencies
between both, spatial and temporal effects as well as different types of
technologies. The methods proposed by Schyska and Kies [75], Nacken et al.
[73] and Neumann and Brown [74] shall be considered to improve the study of
the effects of the differences in the input weather data on the resulting
output of the large-scale energy system models.

## Acknowledgements

Research is funded by the Federal Ministry of Economic Affairs and Energy
(BMWi) under grant nr. FKZ03EI1028A (EnergiesysAI) and the Federal Ministry of
Research and Education (BMBF) under grant nr. FKZ 03EK3055C (CoNDyNet II). HS
acknowledges the Judah Eisenberg Professor Laureatus of the Fachbereich Physik
and the Walter Greiner Gesellschaft.

## Data Availability

The datasets analysed in this work are available in a harmonised form under
https://github.com/alexfias/compare_met_data/.

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