[orcid=0000-0002-7188-155X]

[1]

[] [] [] [] [] []

[1]Corresponding author [1]Tool for Renewable Energy Potentials - Database
(doi: 10.5281/zenodo.6414018)

# Impact of Data Quality on Renewable Energy Potential Estimations

Stanley Risch<EMAIL_ADDRESS>Institute of Energy and Climate Research -
Techno-economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH,
Wilhelm-Johnen-Straße, 52428 Jülich, Germany  RWTH Aachen University, E.ON
Energy Research Center, Institute for Energy Efficient Buildings and Indoor
Climate, Mathieustraße 10, Aachen, 52074, Germany  TH Köln, Cologne Institute
for Renewable Energy (CIRE), Betzdorfer Straße 2, Köln, 50679, Germany  RWTH
Aachen University, c/o Institute of Energy and Climate Research - Techno-
economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-
Johnen-Straße, Jülich, 52428, Germany  Rachel Maier  Junsong Du  Noah
Pflugradt  Peter Stenzel  Leander Kotzur  Detlef Stolten

###### Abstract

Potential analyses identify possible locations for renewable energy
installations, such as as wind turbines and photovoltaic arrays. The results
of previous potential studies, however, are not consistent due to different
assumptions, methods, and datasets. In this study, we compare commonly used
land use data sources with regard to area and position. Using Corine Land
Cover leads to an overestimation of the potential areas in a typical wind
potential analysis by a factor of 4.6 and 5.2 in comparison to Basis-DLM and
Open Street Map, respectively. Furthermore, we develop scenarios for onshore
wind, offshore wind, and open-field photovoltaic potential estimations based
on land eligibility analyses and calculate rooftop photovoltaic potential
using 3D building data. The potential capacities and possible locations are
published for all administrative levels in Germany in the freely accessible
database trep-db,1 for example, to be incorporated into energy system models.
The investigations are validated using high-resolution regional potential
analyses and benchmarked against other studies in the literature. Findings
from the literature, which can be used by legislators to design regulation,
are rarely comparable and consistent due to differences in the datasets used.

###### keywords:

Solar-photovoltaics Wind Potential analysis Data quality 3D building models
Energy system modeling

##  1 Introduction

In 2015, the Paris Climate Agreement [1] was signed by 195 countries with the
aim of limiting global temperature increases to below
$2\text{\,}\mathrm{\SIUnitSymbolCelsius}\text{/}$ above pre-industrial levels.
Germany strengthened its ambitions to combat climate change in the Climate
Protection Act of 2021 [2] by setting the goal of climate-neutrality in 2045.
To achieve this target, the capacity of renewable energy technologies must be
greatly increased [3]. However, the specific land requirement of renewable
energy technologies as photovoltaic (PV) systems or wind turbines exceeds
those of conventional power plants, and therefore larger areas will be needed
for the future energy supply. The eligibility and availability of construction
areas constitute a limiting factor and determine the region-specific
potentials of renewable energy sources.

Energy system models and corresponding studies can support the planning
process of future energy systems with high shares of renewable energy.
However, the set potentials or renewable expansion goals significantly differ
in national energy system studies (see [3, 4, 5, 6, 7, 8, 9]) and often
information about methods, data sources, or assumptions is lacking. As energy
system analyses can have different regional scopes and levels, internally-
consistent potential data on different geographical levels is needed.

Regionalized potentials are estimated with potential analyses, which are
performed sequentially: First, eligible areas for the construction of
renewable power installations are determined. Then on the basis of these
areas, the potential capacity and energy generation can be estimated. The
definition of eligible areas therefore plays a crucial role in the estimation
of potentials. Depending on the technology, the calculation of eligible areas
can be performed by means of statistical formulas, with building models, or
with land eligibility analyses. For technologies requiring ’open space’, such
as open-field PV and wind, the latter methodology is utilized, which requires
geospatial datasets. The results of the eligible areas and the potential
capacity are therefore influenced by these datasets; however, the impact has
not yet been evaluated [10]. The comparability of studies using different
datasets is therefore unknown.

The following analyses the impact of commonly used datasets on land
eligibility analyses and presents scenarios for onshore, offshore wind, open-
field PV, and rooftop PV potentials in Germany. The structure of the paper is
twofold: In the first part (Section 2), the potential analyses on open spaces
for open-field PV and offshore and onshore wind are presented. These are based
on land eligibility analyses (Section 2.1) using geospatial datasets. We first
evaluate land use datasets in the context of potential analyses in Section
2.2, which closes a gap in the literature and also underlines the quality of
the following potential analyses. Then, we describe the state of the art and
methodology before presenting the results and discuss these for the following
technologies: onshore wind (Section 2.3), offshore wind (Section 2.4), and
open-field PV (Section 2.5). In the second part of the paper, we present the
potential analysis for rooftop PV in Section 3. Thereby, for the first time,
3D building data is used to estimate the potential for Germany. For the
considered technologies, we benchmark the results against other potential
studies to provide insights into the impact of data sources, exclusion
criteria and methodologies. The results of our potential analyses are
published in the open database trep-db, with utility, for instance, for energy
system modelers. The area and capacity potentials of high quality and
resolution are provided for several scenarios per technology, and for the
administrative levels spanning the municipality to national levels in Germany.

##  2 Renewable energy potentials on open spaces

The following chapter presents the potential analyses for renewable energy
technologies that require open space. First, the general methodology and
evaluation of the input data for land eligibility analyses is presented in
Section 2.1 and 2.2, respectively. Afterwards, potential analyses are
performed for onshore wind (Section 2.3), offshore wind (Section 2.4), and
open-field PV (Section 2.5).

###  2.1 Land eligibility analysis

The construction of renewable power generation sites requires eligible land.
Therefore, land eligibility analyses based on geospatial data are performed as
the first step of open space potential analyses. Geospatial data is used to
consider areas eligible, e.g., bare land, or to extract information to
identify ineligible areas due to criteria such as physical constraints, such
as steep slopes, or regulatory constraints, such as national parks. Aside from
considering areas ineligible, a setback around these areas may also not be
usable depending on the exclusion criteria. This can be achieved by adding a
buffer around identified land use categories. For instance, the construction
of wind turbines is not only ineligible for land use categories such as
settlements or streets but also within a certain distance around these areas.
To perform the land eligibility analysis, the open source tool GLAES [11] was
applied, which performs the necessary operations using geospatial datasets to
determine eligible areas for renewable energy sites. We chose a resolution of
$10\text{\,}\mathrm{m}\text{/}$$\times$$10\text{\,}\mathrm{m}\text{/}$ in the
land eligibility analyses to ensure the representation of detailed features
for the high-resolution potentials. However, as the chosen datasets have a
significant impact on the results of the land eligibility analysis, they are
analyzed in the following section.

###  2.2 Evaluation of land use datasets

To identify geographical features, land eligibility analyses require
geospatial datasets that describe land characteristics, e.g., land use,
elevation, and water depths. The quality and resolution of the feature
representation in the dataset therefore has an impact on the land eligibility
analysis. For instance, missing represented urban areas in a settlement
dataset can underestimate excluded areas within the potential analyses or a
coarse resolution can either under- or overestimate areas depending on its
classification. However, even though the datasets influence potential
analyses, McKenna et al. [10] state that the impact of employing different
data sources has not yet been evaluated.

In the present section, the first comparison and evaluation of the following
four land use datasets for potential analyses is performed for the regional
scope of Germany:

  * •

Basis-DLM [12]: Official German dataset with a high positional accuracy
(between $\pm$$3\text{\,}\text{/}$ and $\pm$$15\text{\,}\mathrm{m}\text{/}$
depending on the feature)

  * •

Corine Land Cover (CLC) [13]: Land cover raster dataset with
$100\text{\,}\mathrm{m}\text{/}$$\times$$100\text{\,}\mathrm{m}\text{/}$
resolution

  * •

Open Street Map (OSM) [14]: User based land cover vector dataset

  * •

World Database on Protected Areas (WDPA) [15]: Vector dataset with information
on protected areas

According to McKenna et al. [10], the datasets CLC, OSM, and WDPA are commonly
used global and continental ones for potential analyses.

Two parameters are used to compare the covered area using dataset $A(dataset)$
for different features. The first parameter is the Normalized Total Area (NTA)
(Equation 1): It describes the covered area by a dataset compared to the
maximum area covered by any other dataset capturing the feature:

$NTA(dataset_{i})=\frac{A(dataset_{i})}{\max\limits_{j}(A(dataset_{j})}$ (1)

The second parameter is the Intersection over Union (IoU) of two datasets
(Equation 2): It describes the area identified by both datasets divided by the
area identified when combining the two. An Intersection over Union of $100\%$
describes that identified areas of two datasets as equal:

$IoU(dataset_{(i,j)})=\frac{A(dataset_{i})\,\cap\,A(dataset_{j})}{A(dataset_{i})\,\cup\,A(dataset_{j})}$
(2)

The calculation of the parameters is performed with additional standard
setback distances for wind potential analyses (see the supplementary
material). Furthermore, a rasterized representation of the datasets with the
corresponding setback distance and a resolution of
$10\text{\,}\mathrm{m}\text{/}$$\times$$10\text{\,}\mathrm{m}\text{/}$ is
used.

Figure 1 shows the results for the Normalized Total Area and the Intersection
over Union for the analyzed land use categories.

Figure 1: Comparison of the Normalized Total Area (NTA) (upper plot) and
Intersection over Union (IoU) (lower plot) per category for the covered area
with additional setback distances of a wind potential analysis for different
data sources.

The definition of the filter used to identify the respective features in each
dataset can be found in the supplementary information.

Figure 2: Intersection over Union (bars) and Normalized Total Area (markers)
for the residential category for a variation of the setback distance.

It must be stated that the WDPA only provides information on protected areas,
in which its results are only comparable for these. Furthermore, the CLC does
not include all presented categories, such as features with narrow widths like
roads or power lines.

For the typical wind potential analysis, OSM and Basis-DLM identify the
largest area and share the highest Intersection over Union for most
categories. For categories of larger sizes, i.e., farmland, forests, mining,
and grassland, the CLC classifies a similar buffered area as Basis-DLM.
Nevertheless, the Intersection over Unions are only high (above $70\%$) for
the categories of farmland and forests, which means that the identified areas
vary largely in position for the categories of mining and grassland. In the
nature protection categories (’Nature Reserve’, ’National Park’), WDPA and
Basis-DLM identify almost identical areas, recognizable through Intersection
over Unions near $100\%$. The OSM-identified nature reserves, on the other
hand, show significant deviations from the Basis-DLM and WDPA, whereas
national parks cannot be identified with the OSM.

McKenna et al. [10] and Masurowski et al. [16] state that the land use
category settlements (often synonymously used with residential land use in
land eligibility analyses) highly influences the potential analysis in
Germany, which is why the category is further analyzed. For a typical setback
distance of $1000\text{\,}\mathrm{m}\text{/}$ in Figure 1, the OSM covers the
largest area, followed by the Basis-DLM. Figure 2 shows how the Normalized
Total Area and Intersection over Union of the residential category behaves
when varying the setback distance. Without any buffer, the OSM covers the
largest area, closely followed by the CLC. With increasing setback distances,
the Normalized Total Area of the CLC first drops rapidly, whereas the
Normalized Total Areas of the Basis-DLM and OSM converge. This indicates that
the Basis-DLM and OSM classify more, but smaller settlements in comparison to
the CLC. With increasing setback distances of above
$600\text{\,}\mathrm{m}\text{/}$, the Normalized Total Area of the CLC
increases again, as the buffered areas around smaller features identified by
the Basis-DLM and OSM exhibit increasing overlaps. The similarity of the
Basis-DLM and OSM is also shown by their Intersection over Union, which
increases over the buffer distance of up to $88\text{\,}\mathrm{\char
37\relax}\text{/}$. The described behavior of CLC in the residential category,
on the other hand, indicates that the representation of smaller detached
settlements is missing, whereas larger settlements are covered to a coarser
extent. This can be explained by the relatively coarse resolution of
$100\text{\,}\mathrm{m}\text{/}$$\times$$100\text{\,}\mathrm{m}\text{/}$ of
the dataset and the minimum mapping unit of $25\text{\,}\mathrm{ha}\text{/}$.

Using CLC to classify residential areas can lead to an underestimation of
excluded areas and therefore an overestimation of potential areas, especially
for typical setback distances for wind potential analyses of between
$600\text{\,}\text{/}$ and $1000\text{\,}\mathrm{m}\text{/}$. Furthermore, CLC
cannot represent categories with small size features or widths due to its
minimal mapping unit. Therefore, the usage of CLC for potential analyses is
not recommended.

The similarity of OSM and Basis-DLM can be seen for multiple categories, e.g.,
power lines, motorways, in which the Normalized Total Areas and Intersection
over Unions are near $100\%$. For these categories, the use of both datasets
is justified.

Figure 3 shows the results of a land eligibility analysis with the setback
distances of a typical wind potential analysis (see the supplementary
material) for Basis-DLM, OSM, and CLC. When all three of the datasets include
the feature, the corresponding dataset is used; otherwise, a default dataset
is applied for the exclusion. In particular, the use of the CLC for land
eligibility analyses leads to significantly different results. The resulting
total potential area using the CLC is roughly $80\%$ higher compared to the
OSM and Basis-DLM. However, the analyses using Basis-DLM and OSM, even though
resulting in similar total areas, have discrepancies locating the areas, which
is indicated by an Intersection over Union of $64\%$.

Figure 3: Normalized Total Area and Intersection over Union for the resulting
potential areas of a typical wind analysis using different datasets.

In summary, the presented analysis highlights the discrepancy of land use
declaration by different datasets, by which the results of potential analyses
using different datasets are hardly comparable. Furthermore, the analysis
reveals the large sensitivity of land eligibility results regarding the chosen
datasets and the importance of data with a high resolution and high positional
accuracy for potential analyses.

For the scenarios outlined in this paper and the enclosed database trep-db,
WDPA is used for the nature protection categories and Basis-DLM for most land
use categories in the land eligibility analyses. Further datasets are employed
for other individual categories (e.g., water protection areas), but are not
considered in this data analysis due to the lack of comparability to other
datasets. Basis-DLM was selected due to the exhibited characteristics and is
preferred over OSM due to its official character, high positional accuracy,
detailed documentation, and the categories covered. Furthermore, for the wind
potential analysis, the residential areas are split into inner and outer areas
(see Section 2.3.2), which is not possible by the sole use of OSM.

###  2.3 Onshore wind potential

####  2.3.1 Literature

Onshore wind analyses are generally performed with a greenfield approach (see,
e.g., [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 4]). Only Masurowski et al.
[16] proceed differently: First, initially available areas (pre-selected
areas), among others grassland and arable land, are selected and then further
exclusion criteria are applied. For the considered studies, apart from the
varying approach, the most influential differences correspond to the exclusion
definitions, i.e., the used criteria and setback distances, the used data
sources (cf. Section 2.2), and the capacity estimations.

All of the cited studies pertain to residential areas. Nevertheless, the
setback distances and the data sources vary significantly. Tröndle et al. [18]
use the European Settlement Map [27] to identify built-up areas and exclude
them without a buffer, whereas Peters et al. [20] and LANUV [21] differentiate
the setback distance between inner ($1000\text{\,}\mathrm{m}\text{/}$) and
outer areas ($750\text{\,}\text{/}$, $720\text{\,}\mathrm{m}\text{/}$) for
their analysis based on Basis-DLM, and Amme et al. [22] use OSM [14] to
identify residential areas and apply varying setback distances
($400\text{\,}\text{/}$ to $1000\text{\,}\mathrm{m}\text{/}$). Masurowski et
al. [16] specify $1000\text{\,}\mathrm{m}\text{/}$ for housing areas but vary
the setback distance in their scenarios, without stating a data source. Others
use CLC [13] to exclude settlements with $800\text{\,}\mathrm{m}\text{/}$ [23,
28] to $1000\text{\,}\mathrm{m}\text{/}$ [29]. Ryberg et al. [23] additionally
exclude urban areas from EuroStat Urban [30] with
$1200\text{\,}\mathrm{m}\text{/}$ setback. Lütkehus et al. [24] exclude
residential areas with $600\text{\,}\mathrm{m}\text{/}$ based on DLM250 [31].
Moreover, area-based residential, settlement, and urban exclusions of
individual residential buildings are specified in two regional potential
analyses [21, 25] and [4].

Another frequently discussed topic is the construction of wind turbines in
forests. The missing consensus in legislation of the federal states can also
be observed in the literature, i.e., Thuringia generally forbids the use of
forests for wind turbines, whereas in other federal states, construction of
these in forests is ongoing [32]. Ruiz et al. [17] exclude forests in the land
eligibility analysis, whereas others [18, 23] allow for construction in
forests. Other studies treat forests in a differentiated manner. Coniferous
forests in densely-wooded municipalities are cited in the LANUV report [21].
Amme et al. [22] and Peters et al. [20] consider scenarios that include and
exclude forests. Meanwhile, Ebner et al. [19] exclude forests in one scenario
and allow the construction in $10\%$ for another. Wiehe et al. [26]
successively reduce the usable parts of forests in their more restrictive
scenarios. Lütkehus et al. [24] exclude forests in federal states with less
than $15\%$ forest shares. Furthermore, selective exclusions are performed
based on the function of the forest.

Inconsistencies can also be seen for protected landscapes, which are a highly
influential exclusion ($28\%$ of Germany’s area [15]). Several studies [20,
22, 26, 24] regard protected landscapes differently between their scenarios,
whereas others [25, 23, 28] generally rule out the construction of wind
turbines in protected landscapes.

After determining the eligible areas, a few studies further reduce it, either
by a certain share [18, 19] or based on suitability factors [28, 29] per land
use category of CLC [13].

Based on the identified eligible areas, the capacity can be estimated. To this
end, two different methods are utilized: The majority of the analyzed studies
[21, 25, 19, 26, 4, 16, 24] distribute turbines to eligible areas using a
spacing distance specified by a multiple of the rotor diameter (D) in
prevailing and transverse wind directions between the individual turbines. The
turbine spacing ranges from $5\text{\,}\mathrm{D}$ to $9\text{\,}\mathrm{D}$
in the prevailing wind direction and $3\text{\,}\mathrm{D}$ to
$4\text{\,}\mathrm{D}$ in the transverse direction. In contrast, others use
fixed capacity densities, e.g.,
$5\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$ [17],
$8\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$ [18], and
$21\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$ [22]. Sensfuß et al. [29]
and McKenna et al. [28] vary the capacity density regionally.

####  2.3.2 Methodology

The available area for onshore wind was determined with a greenfield land
eligibility analysis (see Section 2.1). To this end, five different scenarios
are defined:

  * •

S1 Legislation: The exclusions are defined according to the laws of Germany’s
federal states based on [33] and own corrections

  * •

S2 Expansive: Wind expansion favoring exclusions including forests and
protected landscapes
S2a No Protected Landscapes: S2, excluding protected landscapes
S2b No Forests: S2, excluding forests

  * •

S3 Restrictive: Restrictive exclusions

In scenario 1, federal state-specific exclusions are applied. The exclusions
for each federal state are defined in accordance with Fachagentur Wind an Land
[33] with expert-based corrections and additions for keys that cannot be
directly retrieved from Fachagentur Wind an Land [33]. The definition of all
exclusions can be found in the supplementary data.

In scenarios 2, 2a, 2b, and 3 nation-wide exclusions are applied. Scenario S2
Expansive refers to typical buffers at the lower end of the federal state’s
legislation. Additionally, the construction of wind turbines in forests is
allowed. Scenarios 2a and 2b build upon scenario 2, but restrict construction
in protected landscapes and forests, respectively. Scenario 3 refers to
exclusions on the higher end of the setback distances in the legislation.
Table 1 displays the most relevant of these.

Inner areas are an influential exclusion, which refer to coherent built-up
areas in accordance with §34 BauGB [34] and are modeled as the AX_Ortslagen
features of Basis-DLM [12]. The residential buildings and other residential
areas are therefore treated as areas without a development plan (outer areas).
In the case of some the federal state laws, residential areas in outer areas
with a statute (Außenbereichssatzung) are protected identical manner to inner
areas. For scenario 1, we assume that all residential buildings are protected
by such a statute.

Following the land eligibility analysis, areas smaller than
$0.01\text{\,}{\mathrm{km}}^{2}\text{/}$ are excluded.

Table 1: Selected exclusion criteria for the scenarios of the wind onshore
potential analysis.

Criterion | Data Source | S11 | S22 | S2a2a | S2b2b | S33
---|---|---|---|---|---|---
Inner areas | Basis-DLM [12] | individual* | $1000\text{\,}\mathrm{m}\text{/}$ | $1000\text{\,}\mathrm{m}\text{/}$ | $1000\text{\,}\mathrm{m}\text{/}$ | $1000\text{\,}\mathrm{m}\text{/}$
Residential buildings, outer areas | Hausumringe [35] | individual* | $3\text{\,}\mathrm{H}$ | $3\text{\,}\mathrm{H}$ | $3\text{\,}\mathrm{H}$ | $1000\text{\,}\mathrm{m}\text{/}$
Forests | Basis-DLM [12] | individual* | not excluded | not excluded | $0\text{\,}\mathrm{m}\text{/}$ | $0\text{\,}\mathrm{m}\text{/}$
Protected Landscapes | WDPA [15] | individual* | not excluded | $0\text{\,}\mathrm{m}\text{/}$ | not excluded | $0\text{\,}\mathrm{m}\text{/}$

  * 1

1S1 Legislation;2S2 Expansive;2aS2a No Protected Landscapes;2bS2b No
Forests;3S3 Restrictive.

  * 2

*federal state-specific exclusions

To estimate the capacity potential, we classify turbines with GLAES [36] in
the eligible areas. To this end, a spacing distance of
$8\text{\,}\mathrm{D}$$\times$$4\text{\,}\mathrm{D}$ is used. A typical low
wind (wind class IEC IIIB) $4.7\text{\,}\mathrm{MW}\text{/}$ turbine with a
$155\text{\,}\mathrm{m}\text{/}$ rotor diameter and
$120\text{\,}\mathrm{m}\text{/}$ hub height is used as the reference turbine
model.

####  2.3.3 Results & Discussion

Table 2 shows the main results of the scenarios on the national level. In
scenario S2 Expansive, $7.25\%$ of Germany’s area is usable for wind turbines,
which leads to a capacity potential of $404\text{\,}\mathrm{GW}\text{/}$.
Scenario S3 Restrictive leads to only $90\text{\,}\mathrm{GW}\text{/}$ on
$1.1\%$ of Germany’s land. The capacity densities in the scenarios S1
Legislation, S2 Expansive, and S2a No Protected Landscapes are smaller than in
scenarios S2b No Forests S2b and S3 Restrictive due to larger contiguous areas
in the scenarios not excluding forests, i.e., S1, S2, and S2a. In the larger
areas, the distance between the turbines is more apparent as smaller areas
naturally promote spacing between them.

Table 2: Results for the scenarios of the onshore wind potential analysis at
the national level.

| S11 | S22 | S2a2a | S2b2b | S33
---|---|---|---|---|---
Area [${\mathrm{km}}^{2}\text{/}$] | 24663 | 25938 | 17613 | 10056 | 3923
Area Share [$\%$] | 6.89 | 7.25 | 4.92 | 2.81 | 1.10
Capacity [$\mathrm{GW}\text{/}$] | 385 | 403 | 287 | 241 | 90
Density [$\frac{\mathrm{MW}}{{\mathrm{km}}^{2}}$] | 15.6 | 15.5 | 16.3 | 23.9 | 22.8

  * 1

1S1 Legislation; 2S2 Expansive; 2aS2a No Protected Landscapes; 2bS2b No
Forests; 3S3 Restrictive

Two potential analyses on the federal state level [21, 25] are used to
validate the workflow. The area potential adds up to $103.3\%$ and $96\%$ in
relation to the study in Baden-Württemberg [25] and Northrhine-Westphalia
[21], respectively. When compared with the Landesanstalt für Umwelt Baden-
Württemberg [25], an Intersection over Union of $79.3\%$ is achieved.
Differences can be explained by the versions of Basis-DLM [12] and unclear
definitions of single exclusions.

Figure 4: Comparison of onshore wind potentials for studies providing capacity
potential at the national level.

Figure 4 shows the range of $68\text{\,}\mathrm{GW}\text{/}$ to
$1188\text{\,}\mathrm{GW}\text{/}$ of onshore wind potential values in
literature. The results are influenced by the use of different datasets (see
Section 2.2), exclusion definitions, area correction factors, and capacity
estimation methods. Tröndle et al. [18], Ruiz et al. [17] and McKenna et al.
[28] utilize, amongst others, CLC for land use classification, which we show
to be problematic for potential analyses (Section 2.2). Furthermore, Tröndle
et al. [18], in technical-social-scenarios and McKenna et al. [28] correct the
estimated areas by a correction and suitability factor for land use
categories, respectively. These approaches are not comparable to our results,
which clearly indicate locations as being either eligible or ineligible. Amme
et al. [22] outlined a scenario excluding residential areas with a
$1000\text{\,}\mathrm{m}\text{/}$ setback distance as well as protected
landscapes but allowing forests, which is comparable to our scenario S2a No
Protected Landscapes. By comparing the results, Amme et al. [22] exceed the
presented eligible area and capacity by a factor of 2.47 and 3.18,
respectively. Lütkehus et al. [24] use comparably low setback distances, e.g.
$600\text{\,}\mathrm{m}\text{/}$ to inner areas, and neglect residential
buildings, leading to the highest capacity potential of the considered
studies. This emphasizes the impact of the chosen exclusion criteria and
setback distances. Furthermore, they use the land use dataset DLM250 [31] with
a positional accuracy of $\pm$$100\text{\,}\mathrm{m}\text{/}$.

There is no consensus on exclusions and setback distances in wind potential
analyses. One exclusion used by all of the cited studies is residential areas.
Nevertheless, individual residential buildings are often neglected due to data
availability issues. In our scenarios, neglecting residential buildings leads
to significantly higher capacity potentials ($27\%$, $9\%$, $10\%$, $12\%$,
and $34\%$). For regional or federal state analyses, this error is even more
apparent: For example, in Schleswig Holstein, the potential gets overestimated
by $56\%$, $25\%$, $25\%$, $27\%$, and $100\%$. To the knowledge of the
authors, no national potential analysis with published results has considered
buildings. In future work, a focus on the sensitivity regarding exclusion
definitions could help make such effects more transparent. Additionally, the
sensitivities of certain exclusion criteria, e.g., forests or buildings, could
help legislators quantify the impact of their decisions. Our restrictive
scenario results in a potential of $90\text{\,}\mathrm{GW}\text{/}$, which is
not sufficient to reach Germany’s climate goals according to different studies
[3, 4, 9, 5] and underlies the importance of liberal legislation towards wind
expansion.

Furthermore, the chosen turbine has a large impact on the capacity density, as
well as on the land eligibility analyses via height- or diameter-dependent
exclusions. We selected a typical wind turbine for low wind speeds (cf.
Section 2.3.2). When using a turbine for medium wind speeds (IEC II,
$5\text{\,}\mathrm{MW}\text{/}$ capacity, $145\text{\,}\mathrm{m}\text{/}$
diameter) with the same hub height, the capacity potential increases by
$16.2\%$, to $19.5\%$, in our scenarios. In future, site-specific turbine
selection could help improve the results in this regard. Nevertheless, with
respect to the chosen low wind speed turbine, the results are still robust.
Only $2\%$ and $29\%$ of the determined locations, respectively, reach an
average wind speed IEC II wind class in $100\text{\,}\mathrm{m}\text{/}$ and
$150\text{\,}\mathrm{m}\text{/}$ height [37].

###  2.4 Offshore wind potential

####  2.4.1 Literature

The following section provides an overview of offshore wind potential analyses
to be found in the literature. The regional coverage of the analyzed studies
varies between global [38, 39], European [19, 18, 17, 40] and national [4]. As
a first step in the potential analyses, eligible areas are determined. Most
studies [39, 19, 18, 41, 40, 17] use greenfield approaches, initially
considering the sea to be eligible and applying exclusion criteria. Sensfuß et
al. [42], however, follow a mixed approach of greenfield analysis and pre-
selected areas in their global analysis. For Germany, $80\text{\,}\%$ of the
declared offshore wind areas in 4C Offshore [43] are used as pre-selected
areas. Luderer et al. [4] consider the wind farm areas of the draft of the
Area Development Plan 2020 [44] and areas of existing plants [45] as pre-
selected ones.

The studies that follow the greenfield approach often use similar exclusion
criteria, but differ in terms of dataset, methods, and buffer distances.
Commonly used exclusion criteria include, amongst others, distance to shore,
sea depth, protected areas, shipping routes, and infrastructures. The minimal
distance to shore varies between $10\text{\,}\mathrm{km}\text{/}$ [39] and
$22.2\text{\,}\mathrm{km}\text{/}$ [17]. The exclusion of areas with large sea
depths differs between depths lower than $50\text{\,}\mathrm{m}\text{/}$ [18,
17, 40], $100\text{\,}\mathrm{m}\text{/}$ [17],
$1000\text{\,}\mathrm{m}\text{/}$ [39, 19, 41], or unlimited [40]. Shipping
routes are excluded by Ruiz et al. [17] and Caglayan et al. [41] based on the
dataset of Halpern et al. [46], which addressed the likelihood of ships.
Furthermore, Caglayan et al. [41] manually exclude known routes with a buffer
of $4\text{\,}\mathrm{km}\text{/}$. Ebner et al. [19] exclude shipping routes,
but without noting a methodology or data source. Additionally, certain studies
[41, 17, 39] exclude infrastructure, e.g., cables and pipelines with buffers
from $500\text{\,}\mathrm{m}\text{/}$ [41] to
$7.4\text{\,}\mathrm{km}\text{/}$ [17]. After applying the exclusion criteria,
Zappa and van den Broek [40] and Tröndle et al. [18], in their technical-
social, scenario reduce the resulting eligible areas to $20\text{\,}\%$ and
$10\text{\,}\%$, respectively. Zappa and van den Broek [40] explain the
reduction by a set factor due to missing exclusion categories.

The potential offshore wind capacity can be estimated for the eligible areas.
Most presented studies employ an aggregated method with capacity density
factors ranging from $3.14\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$ [39]
to $15\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$ [18]. Ebner et al. [19]
reduce $14\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$ to
$5\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$ due to a results deviation of
a factor of 3 to the German Bundesfachplan 2017 [47, 48]. Other studies [42,
4] use a variable capacity density per eligible area. Only Caglayan et al.
[41] employ a placing algorithm for wind turbines with a turbine spacing of
$10\text{\,}\mathrm{D}$$\times$$4\text{\,}\mathrm{D}$.

####  2.4.2 Methodology

The offshore wind potential analysis was performed for federal states in the
coastal sea and Exclusive Economic Zones (EEZ) in the Northern and Baltic sea
areas. Four different scenarios were considered:

  * •

S1 Expansive: Greenfield analyses with offshore wind expansion favoring
exclusions
S1a Military: S1, including usage of military areas

  * •

S2 Legislation: Current priority and reservation areas for offshore wind in
legislation

  * •

S3 Restrictive Legislation: Current priority areas for offshore wind in
legislation

The Low Exclusion Scenarios S1 and S1a are greenfield approaches that use
exclusion criteria with comparably low exclusion definitions. The following
describes the main exclusions; full information about these can be found in
the supplementary data. Areas within $15\text{\,}\mathrm{km}\text{/}$ of shore
and $500\text{\,}\mathrm{m}\text{/}$ to neighboring seas are considered
ineligible. To address shipping land use, the declared priority shipping areas
of the current legislation of the EEZ [49] and federal states [50, 51, 52] are
excluded with a buffer of $500\text{\,}\mathrm{m}\text{/}$. Furthermore,
infrastructure facilities, e.g., cables and platforms, are excluded by CONTIS
[53] with $500\text{\,}\mathrm{m}\text{/}$. The scenarios S1 Expansive and S1a
Military differ in the consideration of designated military areas, which cover
a significant share of the German coastlines. As military areas overlap with
designated wind areas, a complete exclusion is unrealistic. However, the
exclusion of individual sub-areas is not possible due to a lack of indications
of the suitability of mixed use. Only Scenario S1 Expansive further excludes
declared military areas according to CONTIS [54] and ROP [49].

The scenario S2 Legislation considers all designated areas for offshore wind
as pre-selected, whereas scenario S3 Restrictive Legislation only considers
the priority and conditional priority areas ones. The definition of these
areas is based on the current legislation of the EEZ [49], Lower Saxony [50],
and Mecklenburg-Western Pomerania [51]. Schleswig Holstein and Hamburg do not
designate areas for offshore wind [55, 56]. The Legislation scenarios S2 and
S3 further apply the infrastructure exclusions described in Low Exclusion
Scenarios S1 and S1a.

After employing the exclusions of the scenarios, areas smaller than
$0.1\text{\,}{\mathrm{km}}^{2}\text{/}$ are excluded.

The potential capacity of the scenarios is estimated with a similar approach
as that used for onshore wind (Section 2.3.2) by using the turbine placement
of GLAES [36] and a turbine spacing of
$8\text{\,}\mathrm{D}$$\times$$4\text{\,}\mathrm{D}$, respectively. A
reference turbine with a capacity of $8\text{\,}\mathrm{MW}\text{/}$ and a
rotor diameter of $167\text{\,}\mathrm{m}\text{/}$ is used.

####  2.4.3 Results & Discussion

The results of the offshore potential analysis on the national level are
presented in Table 3 and show a range of between
$34.1\text{\,}\mathrm{GW}\text{/}$ (S3 Restrictive Legislation) and
$99.6\text{\,}\mathrm{GW}\text{/}$ (S1a Military).

Table 3: Results for the scenarios of the offshore wind potential analysis on
a national level

| S11 | S1a1a | S22 | S33
---|---|---|---|---
Area [${\mathrm{km}}^{2}\text{/}$] | 7353 | 9275 | 5174 | 3182
Area Share [$\%$] | 13.07 | 16.48 | 9.19 | 5.65
Capacity [$\mathrm{GW}\text{/}$] | 79.1 | 99.6 | 55.8 | 34.1
Density [$\frac{\mathrm{MW}}{{\mathrm{km}}^{2}}$] | 10.75 | 10.74 | 10.79 | 10.71

  * 1

1S1 Expansive; 1aS1a Military; 2S2 Legislation; and 3S3 Restrictive
Legislation

It can be noted that there exists the possibility to increase the potential
from current legislation, i.e. S2 and S3, by limiting other declared land uses
such as shipping or military areas. However, the possible increase in capacity
potential highly differs between the Northern and Baltic seas. From scenario
S2 Legislation to S1 Expansive the potential is increased by
$21.6\text{\,}\mathrm{GW}\text{/}$ in the North Sea and
$1.6\text{\,}\mathrm{GW}\text{/}$ in Baltic. For the comparison of S2
Legislation to S1a Military, the capacity increase is higher, with
$39\text{\,}\mathrm{GW}\text{/}$ and $5\text{\,}\mathrm{GW}\text{/}$,
respectively. Thus, by redefining and limiting other declared land uses in
legislation as military or shipping areas, further areas could be designated
for offshore wind, especially in the North Sea. Future work could critically
revise these areas or consider combined usage with offshore wind projects.

Furthermore, the reference turbine impacts the capacity potential. In the
future, site-specific turbine designs and a sensitivity analysis for turbine
selection could be applied.

Figure 5: Comparison of offshore wind potentials for studies providing
capacity potential on the national level

The results of the presented scenarios are compared to the literature in
Figure 5. The scenarios S1 Expansive and S1a Military follow a greenfield
approach, comparable to all considered studies except for that of Luderer et
al. [4]. Luderer et al. [4], who use the same designated wind farm area of BSH
leading to $56\text{\,}\mathrm{GW}\text{/}$, comparable to
$55.8\text{\,}\mathrm{GW}\text{/}$ in the present scenario, S2 Legislation.

For the greenfield analyses, none of the presented studies use official German
data, e.g., for shipping or military areas. Two scenarios of Ruiz et al. [17]
and the technical-social scenario of Tröndle et al. [18] even undercut the
capacity potential results of our scenarios S2 Legislation and S3 Restrictive
Legislation. Ruiz et al. [17] in their low exclusion scenario and Bosch et al.
[39] have comparable capacity potentials to S1a. However, as the capacity
densities of our study are around two and three times higher in comparison to
their studies, respectively, this indicates significantly fewer eligible areas
in our scenarios. The comparisons show the impact of data sources, exclusions,
and capacity densities on the land eligibility analyses and capacity
estimations.

###  2.5 Open-field photovoltaic potential

####  2.5.1 Literature

Although wind potential analyses mostly use greenfield approaches in land
eligibility analysis (see Section 2.3), open-field PV potential analyses often
first consider pre-selected areas as being eligible and then apply further
exclusion criteria [57, 17, 18, 19, 58, 59, 22, 4]. Only a few potential
analyses choose a greenfield exclusion approach [20, 60].

However, as the pre-selected areas highly differ accross the studies, several
examples are presented: Ruiz et al. [17] and Tröndle et al. [18] estimate the
potential for Europe and consider land use categories as pre-selected areas,
which leads to a high area potential for open-field PV. Ruiz et al. [17]
define amongst others, bare land, some cropland, and some eligible classes of
vegetation and use a combination of Global Land Cover [61] and Corine Land
Cover [13] to identify these. This leads to an initial eligible area of $44\%$
of Germany, from which a share of $3\%$ is considered eligible in another
scenario. Tröndle et al. [18] consider $10\%$ of bare and unused land defined
by GlobeCover2009 [62] as pre-selected areas in a technical-social potential
analysis. Lux et al. [57] define certain shares of categories of CLC [13]
eligible, e.g., $16\%$ of bare land and $2\%$ of bushland. Meanwhile, Ebner et
al. [19] consider agriculture and grazing land identified by CLC [13] in less
favored regions [63] eligible for technical potential. However, this is
further reduced to $7\%$ of the pre-selected area, which is $50\%$ of the area
share currently used for energy crops. Several studies with the scope of
Germany follow the legislation of the Renewable Energy Act (EEG) [64]. Amongst
other areas, the EEG considers less-favored areas and side strips of motorways
and railways to be eligible. The potential areas along side strips are either
defined by a $110\text{\,}\mathrm{m}\text{/}$ width in accordance with EEG
2017 [65] or a $200\text{\,}\mathrm{m}\text{/}$ width in accordance with EEG
2021 [64] minus a $15\text{\,}\mathrm{m}\text{/}$ animal migration buffer.
Luderer et al. [4] also use side strips, but consider
$185\text{\,}\mathrm{m}\text{/}$ around the line objects of OSM, therefore
neglecting the width of the road and the animal migration corridor.
Furthermore, they consider agricultural land with poor soil quality, which is
defined with the dataset Soil Quality Rating (SQR) [66] and a threshold of 40.
Amme et al. [22] similarly consider the less-favored areas, but also consider
side strips with a width of $500\text{\,}\mathrm{m}\text{/}$, which
corresponds to 2.7 times the current legislation. Agricultural land with
higher soil quality rating than 40 are excluded within the side strips. Other
EEG-favored areas are also neglected due to data availability and small
resulting areas. Several studies perform potential analyses on a federal state
level in Germany, e.g., Seidenstücker [59] for North Rhine-Westphalia and
Landesanstalt für Umwelt Baden-Württemberg [58] for Baden-Württemberg. Both
studies use input datasets with local coverage and high resolutions, e.g.,
Basis-DLM [12], and estimate the potential for the side strips of
$110\text{\,}\mathrm{m}\text{/}$ of rails and road ways in accordance with the
legislation of EEG 2017 [65].

After defining the pre-selected areas, further exclusion criteria can be
applied, resulting in eligible areas of various shapes and sizes. As it is not
economically-feasible to install open-field PV sites in small eligible areas,
several studies exclude areas smaller than a certain threshold. The threshold
can vary in the range from $500\text{\,}{\mathrm{m}}^{2}\text{/}$ [59] to
$100.000\text{\,}{\mathrm{m}}^{2}\text{/}$ [22].

As the second step of the potential analysis, the installable open-field PV
capacity on eligible areas is estimated. Open-field PV potential analysis uses
an empirical factor of the capacity density in
$\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$. However, the literature
reports a high range from $40\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$ in
[19] to $300\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$ [17]. Ebner et al.
[19] explain the factor of
$40\text{\,}\mathrm{M}\mathrm{W}\mathrm{/}\mathrm{k}\mathrm{m}\mathrm{{}^{2}}$
based on aerial photo evaluation of existing open-field PV systems. Tröndle et
al. [18] explain their value of
$80\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$ with a module efficiency of
$16\%$ and $50\%$ area reduction by row placements to prevent shadowing.
Seidenstücker [59] assume a module efficiency of $17\%$ and also an area
reduction of $50\%$ for slopes less than 20°, leading to
$85\text{\,}\mathrm{M}\mathrm{W}\mathrm{/}\mathrm{k}\mathrm{m}\mathrm{{}^{2}}$.
Wirth [67] reports a value of
$100\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$ based on row spacing and a
module efficiency of $20\%$.

####  2.5.2 Methodology

In the first step of the potential analysis for open-field PV pre-selected
areas are considered eligible followed by further exclusions. To this end
three scenarios are regarded:

  * •

S1 Side Strips: Side strips of motorways and railways in accordance with the
subsidy areas of the EEG 2021 [64]

  * •

S2 Poor Soil: Arable land with barren soil based on the Soil Quality Rating
(SQR) of [66]

  * •

S3 Combination: Side strips, for which arable land is restricted to SQR $<40$,
and S2

Scenario S1 Side Strips estimates the potential of the side strips of
motorways and railways. Their routes and widths are identified by Basis-DLM
[12]. Then, side strips of $200\text{\,}\mathrm{m}\text{/}$ are used, from
which $15\text{\,}\mathrm{m}\text{/}$ on the inside is subtracted for animal
migration. The remaining area represents the pre-selected area.

Scenario S2 Poor Soil pre-selects arable land with a bad soil quality based on
the SQR dataset [66]. The SQR threshold was chosen to be 30 based on a pre-
analysis. Figure 6 shows the area potential and share of arable land for SQR
limits of between 20 and 50. As is shown in Section 2.5.1, other potential
studies use a threshold of 40, which results in $6.28\%$ of arable land. We
assume this to be too ambitious due to land use conflicts with cultivation of
arable land and therefore choose the threshold of 30. All areas up to a
selected SQR value are intersected with the arable land in Germany from Basis-
DLM [12] due to the resolution of
$100\text{\,}\mathrm{m}\text{/}$$\times$$100\text{\,}\mathrm{m}\text{/}$ in
the BGR rating [66].

Figure 6: Sensitivity analysis of the Soil Quality Rating threshold for arable
land in the land eligibility analysis for open-field PV.

Scenario S3 Combination is a combination of a pre-selection using side strips
and arable land with barren soil. As side strips are considered less valuable
than other areas, arable land with SQR values of less than 40 are excluded. On
all other arable land, only SQR values of less than 30 are used as a pre-
selection. It should be noted that other land uses than agricultural inside
the side strips are not restricted by the SQR rating.

After identifying pre-selected eligible areas for each scenario, unsuitable
areas within these are deducted by further exclusion criteria. Table 4
displays the most relevant exclusions. The definition of all exclusions can be
found in the supplementary material. After the land eligibility analysis,
eligible areas smaller than a threshold of
$5000\text{\,}{\mathrm{m}}^{2}\text{/}$ were excluded for economic reasons,
which lies within the range of the presented literature (see Section 2.5.1).
However, this area threshold leads to a deviation of eligible areas at the
different regional levels. Areas may become ineligible if split by a border to
a size lower than the threshold, whereas on a higher regional level still
eligible. For the three scenarios, this leads to a capacity deviation from
$1.3\%$ to $1.5\%$ between the federal state and the municipality level.

Table 4: Selected exclusion criteria for the scenarios of the open-field PV potential analysis. Criterion | Data Source | S1 Side Strips | S2 Poor Soil | S3 Combination
---|---|---|---|---
Forests | Basis-DLM [12] | $10\text{\,}\mathrm{m}\text{/}$ | $10\text{\,}\mathrm{m}\text{/}$ | $10\text{\,}\mathrm{m}\text{/}$
All Buildings | Hausumringe [35] | $10\text{\,}\mathrm{m}\text{/}$ | $10\text{\,}\mathrm{m}\text{/}$ | $10\text{\,}\mathrm{m}\text{/}$
Arable land | Basis-DLM [12], SQR [66] | not excluded | SQR$\geq 30$ | SQR$\geq 30$, Sidestripes: SQR$\geq 40$
Motorways, Railways | Basis-DLM [12] | $15\text{\,}\mathrm{m}\text{/}$ | $200\text{\,}\mathrm{m}\text{/}$ | $15\text{\,}\mathrm{m}\text{/}$

As a next step in the potential analysis, the installable capacity on the
eligible land was estimated. Therefore, the eligible areas and their size
$A_{OFPV}$ are extracted, on which the modules are placed in a southerly
direction with an optimal tilt for the latitude, which is calculated using the
RESKit tool [11] in accordance with Ryberg [60]. This area can be converted by
taking a factor for row spacing $f_{RS}=0.5$, a factor for construction-
related obstructions, e.g., access roads or surrounding areas, $f_{c}=0.72$
[68], and the efficiency of the modules, which is assumed to be
$\eta_{PV}=0.22$ in line with current high-end modules [69, 70], into
consideration:

$\begin{split}P_{OFPV}&=\eta_{OFPV}\cdot f_{RS}\cdot f_{c}\cdot A_{OFPV}\\\
&=$79.2\text{\,}\mathrm{MW}\text{/}{\mathrm{km}}^{2}$\cdot
A_{OFPV}\end{split}$ (3)

####  2.5.3 Results & Discussion

The results at the national level for the potential area and the capacity of
the scenarios are shown in Table 5. The capacity potential varies between
$123.6\text{\,}\mathrm{G}\mathrm{W}_{\mathrm{p}}$ in S2 Poor Soil and
$456.1\text{\,}\mathrm{G}\mathrm{W}_{\mathrm{p}}$ in S1 Side Strips.

Table 5: Results for the scenarios of the open-field PV potential analysis at
the national level.

| S11 | S22 | S33
---|---|---|---
Area [${\mathrm{km}}^{2}\text{/}$] | 5723 | 1560 | 4373
Area Share [$\%$] | 1.60 | 0.44 | 1.22
Capacity [$\mathrm{G}\mathrm{W}_{\mathrm{p}}$] | 456.1 | 123.6 | 347.7
No. of Municipalities | 11003 | 11003 | 11003
…with pre-selected areas | 5667 | 1892 | 6446
…with potential | 5253 | 1711 | 5939

  * 1

1S1 Side Strips; 2S2 Poor Soil; and 3S3 Combination.

To validate the presented workflow, two potential analyses for the federal
state level [58, 59] were used due to their high spatial resolutions and usage
of local data. Similarly to Side Strips S1, the studies consider pre-selected
areas at the sides of roads and railways. Using similar exclusion criteria and
datasets as the corresponding studies, an Intersection over Union of $81.38\%$
for Landesanstalt für Umwelt Baden-Württemberg [58] and $87.88\%$ for
Seidenstücker [59] were achieved. The area potential of the presented workflow
in relation to that reported by [58] and [59] is $88\%$ and $103\%$,
respectively. Remaining differences between the results of the studies and
this paper arise due to different versions of land cover datasets and unclear
definitions of single exclusions.

As is shown in Figure 7, the literature provides a high range of
$90\text{\,}\mathrm{G}\mathrm{W}_{\mathrm{p}}$ [18] to
$1285\text{\,}\mathrm{G}\mathrm{W}_{\mathrm{p}}$ [17] as capacity potentials
for open-field PV, because the pre-selected areas, used datasets, area
reduction factors, and applied exclusion criteria highly differ between the
studies (cf. Section 2.5.1). Furthermore, the used capacity density factors
vary greatly between the studies, which leads to further deviations. Ruiz et
al. [17] and Tröndle et al. [18] use set shares, $3\%$ and $10\%$,
respectively, of large land use categories for the land eligibility analyses,
and are therefore not comparable to our scenarios.

Methodology-wise our presented scenarios are only comparable to Amme et al.
[22]. For the presented SQR scenario, the higher potential capacity of Amme et
al. [22] can be explained by its higher SQR threshold. For the side strips
scenario, Amme et al. [22] use side strips that are 2.7 times the width of
current legislation. Nevertheless, by additionally excluding areas with a SQR
higher than 40, the resulting capacity is lower than in our scenario S1 Side
Strips.

Figure 7: Comparison of open-field PV potentials for studies providing
capacity potential at the national level.

The presented workflow could be further extended by considering other subsidy-
areas of the Renewable Energy Act [64] like dumps and landfills as pre-
selected areas. Furthermore, categories beyond the scope of current
legislation could be chosen as pre-selected areas. However, there exists no
consensus in the literature about eligible land use categories and considering
entire categories eligible tend to lead to high potentials, as in Ruiz et al.
[17]. Such high area shares are critical due to the land use conflicts they
can induce: Whereas wind turbines only occupy a share of their designated
areas, open-field PV plants cover most of their appointed ones, which can lead
to conflicts with, for example, agricultural land use. Furthermore,
legislation has a significant impact on the distribution of potentials.
Especially in the open-field PV scenarios, many municipalities have no
potential (cf. Table 5), due to the limitation to municipalities with subsidy-
eligible areas. This can lead to unequally-distributed economic benefits among
municipalities, as well as higher resistance in society because of visual
impacts, for instance. As the results are also highly sensitive towards the
chosen capacity density factor, one avenue of further work could be to
incorporate future system designs. Additionally, the effect of placing the
modules flat or in an east-west orientation without any row-spacing could be
analyzed.

##  3 Rooftop photovoltaic potential

In the following chapter, the assessment of rooftop PV potential for Germany
is discussed. First, an overview of the literature is given (Section 3.1).
Then, the methodology to extract potential from 3D building data is described
(Section 3.2). The results are then presented and discussed (Section 3.3).

###  3.1 Literature

Rooftop PV potential analyses can be classified by their geographical extent
and the resolution of their results [71, 72].

The resolution of different approaches can be split into high, medium, and low
levels. High level approaches, which can assess the potential for individual
buildings include, for example, image recognition techniques [73, 74, 75, 76]
or workflows using 3D building models [77, 78, 79, 80, 81]. For instance,
Grothues and Seidenstücker [77] determine all roof geometries in North Rhine-
Westphalia on the basis of laser scan data at
$0.5\text{\,}\mathrm{m}\text{/}$$\times$$0.5\text{\,}\mathrm{m}\text{/}$
resolutions. Low- and medium-level approaches base their analysis mostly on
statistical data and are therefore limited to the assessment of potentials for
the regional scope of the data, e.g., to regions, countries, or raster
resolution [17, 18, 20, 72, 82, 83, 84, 19, 85]. Tröndle et al. [18], for
instance, use population data from the European Settlement Map [27] to
estimate roof areas and calibrate them with data from sonnendach.ch [86].
Meanwhile, Ruiz et al. [17] use CLC [13] data to identify residential and
industrial areas in which they estimate roof areas based on fixed factors.

The geographical extent of these analyses varies from single buildings to
continent-wide. Statistical approaches are mainly used to perform large-scale
analyses, such as at national [20, 72, 19, 85] or continental scales [17, 18,
84]. However, more recent studies were able to apply high-level methodologies
to the national scale. Walch et al. [79] use LoD2 3D data to extract rooftop
PV potential in Switzerland. To this end, they calculate the area and
orientations of 9.6 million rooftops directly from the data. Luderer et al.
[4] estimate the rooftop PV potential in Germany at the municipality level by
correlating it with building footprints in a region. Similarly, Wiehe et al.
[26] used building footprints in Germany and building use to estimate
generation potential. Eggers et al. [87] use LoD1 building models (without
information on rooftop geometry) to estimate German rooftop PV potential.

Approaches, that use data without information about superstructures, e.g.,
chimneys and windows, on roofs to estimate the available roof area, utilize
reduction factors to adapt the potentially usable areas. Mainzer et al. [72]
use a factor of $0.58$ to exclude obstacles on roofs and in areas with too
much shadowing. In turn, Fath et al. [80] differentiates between flat ($0.7$)
and tilted ($0.75$) roofs based on the work of Kaltschmitt [88]. The
International Energy Agency (2002) [89] determines a factor of 0.6 for
constructions, shading, and historical elements. Walch et al. [79] estimate
the factor from LoD4 data in Geneva (38,000 roofs) with a machine learning
approach for other LoD2 building models and estimate the factor to be between
0 and 0.8 depending on the roof size and tilt. Portmann et al. [86]
distinguish between flat ($0.7$) and tilted ($0.42-0.8$) roofs, for which a
differentiation between roof size and category is carried out. Eggers et al.
[87] use a factor of $0.486$ to account for obstructions, shading, and
inefficiencies when placing the modules. Wiehe et al. [26] assume that $60\%$
of the roof area on residential and $80\$$ of the roof area on industrial and
commercial structures are usable. Grothues and Seidenstücker [77] estimate the
impact of obstructions directly based on a highly resolved
($0.5\text{\,}\mathrm{m}\text{/}$$\times$$0.5\text{\,}\mathrm{m}\text{/}$)
digital elevation model.

In contrast to land eligibility analyses (see Section 2), in which a clear
workflow has been established, the methodologies between different rooftop PV
potential analyses vary greatly. Many high-resolution approaches have been
used to estimate potential on a smaller geographical scale. To the best of the
authors’ knowledge, no rooftop PV potential analysis has been performed using
3D building models with roof geometries for all of Germany.

###  3.2 Methodology

In the present study we used 3D building models based on LiDar in the CityGML
format with level of detail (LoD) 2 [90] of the Bundesamt für Kartographie und
Geodäsie (BKG) [91] (high resolution) to estimate rooftop PV potential in
Germany (high geographical extent). To this end, $93.1$ million roofs were
evaluated to estimate Germany’s rooftop PV potential. LoD2 corresponds to
simplified building geometries with standardized roof shapes [92]. Therefore,
the orientation of the roof, i.e., the tilt and azimuth, can be estimated.
However, information regarding superstructures reducing the usable area for
rooftop PV is missing.

In order to extract tilt and azimuth from roof geometries, the normal vector
on the plane of the geometry is determined. The north-based azimuth is defined
as the angle between the north vector and x-y part of the normal vector:

$azi=\arccos\left(\overrightarrow{n}\cdot[0,1,0]^{T}\right)$ (4)

The tilt can be determined by calculating the angle between the z-vector and
normal vector:

$tilt=\arccos\left(\overrightarrow{n}\cdot[0,0,1]^{T}\right)$ (5)

To further estimate the potential to install PV modules on rooftops, in a
first step the area of the roof geometries was directly retrieved from the
data. However, the usable area for rooftop PV is limited by shading or
obstacles like windows or chimneys, which is taken into account by
incorporating a factor. In this paper, the factor was set to $fac_{area}=0.6$,
which is in accordance with literature values. The capacity potential of
individual roofs is estimated by placing modules with an efficiency of
$\eta=0.22$ (see Section 2.5.2). Moreover, roofs with tilts lower than
$10\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$ are assumed to be flat.
Modules on flat roofs are placed in a southerly direction with an optimal tilt
angle in accordance with Ryberg [60]. For row spacing on flat roofs, an
additional factor of $fac_{RS}=0.5$ is employed. The capacity calculation for
single roofs can be summarized as follows:

$\displaystyle P_{PV,peak}=$ $\displaystyle 0.6\cdot\eta_{PV}\cdot A_{roof},$
for tilt $\geq$$10\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$ (6a)
$\displaystyle P_{PV,peak}=$ $\displaystyle 0.6\cdot 0.5\cdot\eta_{PV}\cdot
A_{roof},$ for tilt $<$$10\text{\,}\mathrm{\SIUnitSymbolDegree}\text{/}$ (6b)

Areas, that correspond to less than
$1\text{\,}\mathrm{k}\mathrm{W}_{\mathrm{p}}$ capacity are excluded from the
potential. This translates to an area threshold of
$7.6\text{\,}{\mathrm{m}}^{2}\text{/}$ for tilted roofs and
$15.2\text{\,}{\mathrm{m}}^{2}\text{/}$ for flat ones. The potentials in trep-
db are published in groups for each municipality, nuts3-region, and federal
state, but not individually for each building due to the large overhead
storage. To this end, a fixed grouping method is used: One flat group includes
all items up to a tilt angle of 20°. Eight azimuth groups (North (N), North-
West (NW), West (W), South-West (SW), South (S), South-East (SE), East (E),
and North-East (NE)) contain all elements from 20-90° in the respective
directions. For rooftop PV, no scenarios are considered, but the trep-db
includes two datasets, representing two scenarios:

  * •

All roofs

  * •

No northern roofs: Exclusion of north facing groups (N,NW,NE)

###  3.3 Results & Discussion

Based on the presented workflow, a potential of
$625\text{\,}\mathrm{G}\mathrm{W}_{\mathrm{p}}$ is estimated for all rooftops
in Germany. However, the potential decreases to
$492\text{\,}\mathrm{G}\mathrm{W}_{\mathrm{p}}$ if north-facing groups are
excluded.

Figure 8 and Figure 9 demonstrate the capacity-weighted potential distribution
of the azimuth angle and tilt angle for saddle roofs. The cardinal directions
show the highest capacity potential. A substantial capacity potential is
located on northern facing roofs.

Figure 8: Capacity weighted azimuth distribution of German roofs.

Figure 9 shows the distribution of the capacity weighted potential
distribution of the tilt angle. As only saddle roofs are considered for the
plots, only tilt angles from 10° upwards are present. The main capacity
potentials can be observed up to a tilt angle of 45° (see Figure 9).

Figure 9: Capacity-weighted tilt distribution of German roofs.

To validate the presented workflow, the results for the federal state North
Rhine-Westphalia are compared against Grothues and Seidenstücker [77]. When
aligning the workflows by lowering the efficiency to $17\%$, using
$f_{RS}=0.4$ and applying the same limit for areas
($7\text{\,}{\mathrm{m}}^{2}\text{/}$ for tilted roofs and
$17.5\text{\,}{\mathrm{m}}^{2}\text{/}$ for flat ones) the capacity of roofs
in North Rhine-Westphalia adds up to
$84.0\text{\,}\mathrm{G}\mathrm{W}_{\mathrm{p}}$, which is comparable to the
$81.4\text{\,}\mathrm{G}\mathrm{W}_{\mathrm{p}}$ estimated by Grothues and
Seidenstücker [77]. It should be noted that Grothues and Seidenstücker [77]
exclude rooftops with an irradiation lower than
$814\text{\,}{\mathrm{k}\mathrm{W}\mathrm{h}\mathrm{/}\mathrm{m}\mathrm{{}^{2}}}$,
which is not reproduced, and therefore a higher result is to be expected.
Furthermore, the potential reduction due to roof obstructions is not
comparable in the two studies.

Figure 10 presents the Germany-wide capacity potential of rooftop PV in this
paper and other studies. The comparison shows a range of
$43\text{\,}\mathrm{G}\mathrm{W}_{\mathrm{p}}$ [17] to
$746\text{\,}\mathrm{G}\mathrm{W}_{\mathrm{p}}$ [18]. However, comparability
between the studies is limited. The studies significantly differ in their
methodologies for rooftop area estimations and assumptions as the reduction
factor for superstructures and efficiency in the capacity estimation.
Furthermore, some studies only consider the rooftop PV potential for
individual building categories, e.g., residential in Mainzer et al. [73]. The
only capacity potential, which has been estimated by a high resolution
approach (see Section 3.1) assumes $504\text{\,}\mathrm{GW}\text{/}$ [87],
which is of similar magnitude to the potentials estimated in our approach.

Figure 10: Comparison of rooftop PV potentials for studies providing capacity
potential at the national level.

In future, the estimation of unusable areas on roofs can be improved when
LoD3/LoD4-data becomes available for Germany. Alternatively, as proposed by
Walch et al. [79], the LoD4-Dataset from Geneva could be used to estimate the
share of unusable roof areas by means of a machine learning approach.

##  4 Discussion

The capacity estimations of the reviewed literature for different technologies
in Germany are shown in Figure 11.

Figure 11: Potential capacities of different potential studies on the national
level for onshore wind, offshore wind, open-field photovoltaic, and rooftop
photovoltaic.

The potentials exhibit large variations between the studies and scenarios. Our
data analysis (Section 2.2) shows one reason for the present deviations: the
used input datasets make the findings of different studies using different
datasets hardly comparable. Another reason for the significantly deviating
potentials is the chosen exclusion criteria and the corresponding buffers.
Neglecting residential buildings in wind potential analyses, for example, can
lead to considerable over-estimations. In future work, a sensitivity analysis
regarding the criteria could help quantify the effects in a more in-depth
manner. Nevertheless, without applying high-quality datasets, the conclusions
would not be meaningful. In other regions of the world, OSM may not be as
comprehensive and a dataset with official characteristic such as Basis-DLM may
not be available. In future work, world-wide high-resolution datasets should
be used to reevaluate global renewable potentials.

Figure 12 shows the spatial distribution of the capacity potential in
Germany’s municipalities for one scenario of onshore wind, open-field PV, and
rooftop PV, which visualizes the spatial heterogeneity of the results.
Additionally, Figure 13 shows the cumulative share of Germany’s municipalities
over all scenarios for the regarded technologies and the impact of boundary
conditions, e.g., pre-selected areas or exclusions, on regional potentials in
municipalities.

Figure 12: Potential capacity density in Germany’s Municipalities for
different technologies.

For the wind scenarios, the share of municipalities without potential highly
differs between $28\%$ and $70\%$, showing the high impact of the exclusion
criteria on the regional level. For all open-field PV scenarios, a large
number of municipalities are without potentials, whereas rooftop PV potentials
are more equally distributed. As is shown on the right-hand side of the
figure, the maximum capacities found in the municipalities highly vary for the
technologies and scenarios between $634\text{\,}\mathrm{MW}\text{/}$ (On.
Wind, S3 Restrictive) and $9.3\text{\,}\mathrm{GW}\text{/}$ (Rooftop PV, All
Roofs). For the use case of energy system models, the unequal distribution of
potentials in municipalities emphasizes the importance of detailed and
qualitative potentials for regional analyses.

Figure 13: Cumulative municipalities over capacity potential for the scenarios
of open-field, rooftop PV and wind onshore.

##  5 Conclusions

The first time evaluation of land use datasets and the proposed renewable
potential scenarios reveal significant biases in the datasets that are
commonly applied by the energy systems community: The use of Corine Land Cover
[13] leads to a significant overestimation of the potentially usable area for
renewable energy technologies by a factor of 4.6 to 5.2 in comparison to
Basis-DLM [12] and Open Street Map [14], respectively. High-quality datasets
are needed to supply reliable input for policy-makers or energy system models.
In the case of Germany, Basis-DLM [12] and Open Street Map [14] provide
similar information for several categories, especially line-like features,
such as power lines or railways. For others, Basis-DLM and Open Street Map
show significant differences, e.g., industry/commercial, lakes, or rivers.
Furthermore, the impact of several exclusion criteria is apparent in the
presented scenarios and variations. For example, the disregard of residential
buildings in onshore wind analyses leads to an overestimation of the capacity
by up to $34\%$ for our scenarios.

The presented scenarios for onshore wind, offshore wind, open-field, and
rooftop photovoltaic are published in the open access database trep-db. For
rooftop photovoltaic, the potentials are estimated by CityGML Level of Detail
2 data for the first time, which leads to a
$492\text{\,}\mathrm{G}\mathrm{W}_{\mathrm{p}}$ potential capacity when
northerly-facing roofs are excluded.

This work should motivate improvement in the internationally-available land
use datasets, as their quality has a significant impact on the correct design
of regulations for renewable resource emplacements.

##  6 Acknowledgements

This work was supported by the BMWi (German Federal Ministry of Economic
Affairs and Energy) [promotional reference 3EE5031D]; and the Helmholtz
Association under the program “Energy System Design.”

## References

  * United Nations [2015] United Nations, Paris Agreement, Technical Report, United Nations, Paris, 2015\. URL: https://unfccc.int/sites/default/files/english_paris_agreement.pdf.
  * noa [2021] Bundes-Klimaschutzgesetz (KSG), 2021. URL: https://www.gesetze-im-internet.de/ksg/KSG.pdf.
  * Stolten et al. [2021] D. Stolten, P. Markewitz, T. Schöb, L. Kotzur, Strategien für eine treibhausgasneutrale Energieversorgung bis zum Jahr 2045. (Kurzfassung), Technical Report, Forschungszentrum Jülich GmbH, Jülich, 2021. URL: https://www.fz-juelich.de/iek/iek-3/DE/_Documents/Downloads/transformationStrategies2045ShortStudy.pdf?__blob=publicationFile.
  * Luderer et al. [2021] G. Luderer, C. Kost, D. Sörgel, Deutschland auf dem Weg zur Klimaneutralität 2045 - Szenarien und Pfade im Modellvergleich, Technical Report, 2021. URL: https://publications.pik-potsdam.de/pubman/item/item_26056, artwork Size: 359 pages Publisher: Potsdam Institute for Climate Impact Research.
  * Brandes et al. [2021] J. Brandes, M. Haun, D. Wrede, P. Jürgens, C. Kost, H.-M. Henning, H.-M. Henning, H.-M. Henning, Wege zu einem klimaneutralen Energiesystem - Die deutsche Energiewende im Kontext gesellschaftlicher Verhaltensweisen - Update November 2021: Klimaneutralität 2045, Technical Report, Fraunhofer-Institut für Solare Energiesysteme IS, Freiburg, 2021.
  * Kendziorski et al. [2021] M. Kendziorski, L. Göke, C. Kemfert, von Hirschhausen, E. Zozmann, 100% erneuerbare Energie für Deutschland unter besonderer Berücksichtigung von Dezentralität und räumlicher Verbrauchsnähe – Potenziale, Szenarien und Auswirkungen auf Netzinfrastrukturen, Technical Report, Deutsches Institut für Wirtschaftsforschung, Berlin, 2021.
  * Prognos et al. [2021] Prognos, Öko-Insitut, Wuppertal-Institut, Klimaneutrales Deutschland 2045 Wie Deutschland seine Klimaziele schon vor 2050 erreichen kann, Technical Report, Agora Energiewende und Agora Verkehrswende, 2021. URL: https://static.agora-energiewende.de/fileadmin/Projekte/2021/2021_04_KNDE45/A-EW_209_KNDE2045_Zusammenfassung_DE_WEB.pdf.
  * Fette et al. [2020] M. Fette, C. Brandstätt, H. C. Gils, H. Gardian, T. Pregger, J. Schaffert, E. Tali, N. Brücken, Multi-Sektor-Kopplung - Modellbasierte Analyse der Integration erneuerbarer Stromerzeugung durch die Kopplung der Stromversorgung mit dem Wärme-, Gas- und Verkehrssektor, Technical Report, Fraunhofer-Institut für Fertigungstechnik und Angewandte Materialforschung IFAM,Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Gas- und Wärme-Institut Essen e.V., 2020. URL: https://elib.dlr.de/135971/1/MuSeKo-Endbericht-2020-08-31.pdf.
  * Göke et al. [2021] L. Göke, C. Kemfert, M. Kendziorski, C. V. Hirschhausen, 100 Prozent erneuerbare Energien für Deutschland: Koordinierte Ausbauplanung notwendig, Technical Report, 2021\. URL: http://www.diw.de/sixcms/detail.php?id=diw_01.c.821878.de, publisher: DIW - Deutsches Institut für Wirtschaftsforschung Version Number: 2.0.
  * McKenna et al. [2021] R. McKenna, S. Pfenninger, H. Heinrichs, J. Schmidt, I. Staffell, C. Bauer, K. Gruber, A. N. Hahmann, M. Jansen, M. Klingler, N. Landwehr, X. G. Larsén, J. Lilliestam, B. Pickering, M. Robinius, T. Tröndle, O. Turkovska, S. Wehrle, J. M. Weinand, J. Wohland,  High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs,  Renewable Energy (2021) S0960148121014841. URL: https://linkinghub.elsevier.com/retrieve/pii/S0960148121014841. doi:10.1016/j.renene.2021.10.027.
  * Ryberg et al. [2018] D. Ryberg, M. Robinius, D. Stolten,  Evaluating Land Eligibility Constraints of Renewable Energy Sources in Europe,  Energies 11 (2018) 1246. URL: http://www.mdpi.com/1996-1073/11/5/1246. doi:10.3390/en11051246.
  * Geobasisdaten: © GeoBasis-DE / BKG (2021) [2021] [dataset] Geobasisdaten: © GeoBasis-DE / BKG (2021),  Digitales Basis-Landschaftsmodell (Ebenen) (Basis-DLM) (2021).
  * Copernicus Programme [2018] [dataset] Copernicus Programme,  CORINE Land Cover (CLC) (2018). URL: https://land.copernicus.eu/pan-european/corine-land-cover.
  * OpenStreetMap contributors [2021] [dataset] OpenStreetMap contributors,  Open Street Map (2021). URL: https://www.openstreetmap.org/.
  * UNEP-WCMC, IUCN [2016] [dataset] UNEP-WCMC, IUCN,  The world database on protected areas (2016). URL: https://www.protectedplanet.net/23.
  * Masurowski et al. [2016] F. Masurowski, M. Drechsler, K. Frank,  A spatially explicit assessment of the wind energy potential in response to an increased distance between wind turbines and settlements in Germany,  Energy Policy 97 (2016) 343–350. URL: https://linkinghub.elsevier.com/retrieve/pii/S0301421516303718. doi:10.1016/j.enpol.2016.07.021.
  * Ruiz et al. [2019] P. Ruiz, W. Nijs, D. Tarvydas, A. Sgobbi, A. Zucker, R. Pilli, R. Jonsson, A. Camia, C. Thiel, C. Hoyer-Klick, F. Dalla Longa, T. Kober, J. Badger, P. Volker, B. Elbersen, A. Brosowski, D. Thrän,  ENSPRESO - an open, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials,  Energy Strategy Reviews 26 (2019) 100379. URL: https://linkinghub.elsevier.com/retrieve/pii/S2211467X19300720. doi:10.1016/j.esr.2019.100379.
  * Tröndle et al. [2019] T. Tröndle, S. Pfenninger, J. Lilliestam,  Home-made or imported: On the possibility for renewable electricity autarky on all scales in Europe,  Energy Strategy Reviews 26 (2019) 100388. URL: https://linkinghub.elsevier.com/retrieve/pii/S2211467X19300811. doi:10.1016/j.esr.2019.100388.
  * Ebner et al. [2019] M. Ebner, C. Fiedler, F. Jetter, T. Schmid,  Regionalized Potential Assessment of Variable Renewable Energy Sources in Europe,  in: 2019 16th International Conference on the European Energy Market (EEM), IEEE, Ljubljana, Slovenia, 2019, pp. 1–5. URL: https://ieeexplore.ieee.org/document/8916317/. doi:10.1109/EEM.2019.8916317.
  * Peters et al. [2015] D. W. Peters, S. Schicketanz, D. M. Hanusch, A. Rohr, M. Kothe, P. Kinast, Räumlich differenzierte Flächenpotentiale für erneuerbare Energien in Deutschland, Technical Report, Bundesministerium für Verkehr und digitale Infrastruktur (BM, Berlin, 2015. URL: https://www.bbsr.bund.de/BBSR/DE/veroeffentlichungen/ministerien/bmvi/bmvionline/%****␣main.bbl␣Line␣225␣****2015/BMVI_Online_08_15.html.
  * Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen (2021) [LANUV] Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen (LANUV), Potenzialstudie Erneuerbare Energien NRW – Windenergie, 2021. URL: https://www.lanuv.nrw.de/fileadmin/lanuvpubl/1_infoblaetter/Handout_Potenzialstudie_Windenergie_Druck.pdf.
  * Amme et al. [2021] J. Amme, E. Kötter, F. Janiak, B. Lancien,  Der Photovoltaik- und Windflächenrechner - Methoden und Daten (2021). URL: https://zenodo.org/record/4731921. doi:10.5281/ZENODO.4731921, publisher: Zenodo Version Number: v1.0.
  * Ryberg et al. [2020] D. S. Ryberg, Z. Tulemat, D. Stolten, M. Robinius,  Uniformly constrained land eligibility for onshore European wind power,  Renewable Energy 146 (2020) 921–931. URL: https://linkinghub.elsevier.com/retrieve/pii/S0960148119309619. doi:10.1016/j.renene.2019.06.127.
  * Lütkehus et al. [2013] I. Lütkehus, H. Salecker, K. Adlunger, Potenzial der Windenergie an Land, Technical Report, Umweltbundesamt, Dessau-Roßlau, 2013.
  * Landesanstalt für Umwelt Baden-Württemberg [2019] Landesanstalt für Umwelt Baden-Württemberg, Potenzialanalyse der Windenergie an Land, 2019\. URL: https://www.energieatlas-bw.de/wind/potenzialanalyse/uberblick.
  * Wiehe et al. [2021] J. Wiehe, J. Thiele, A. Walter, A. Hashemifarzad, J. Hingst, C. Haaren,  Nothing to regret: Reconciling renewable energies with human wellbeing and nature in the German Energy Transition,  International Journal of Energy Research 45 (2021) 745–758. URL: https://onlinelibrary.wiley.com/doi/10.1002/er.5870. doi:10.1002/er.5870.
  * European Commission. Joint Research Centre. [2016] [dataset] European Commission. Joint Research Centre.,  The European settlement map 2017 release: methodology and output of the European settlement map (ESM2p5m). (2016). URL: https://data.europa.eu/doi/10.2760/780799. doi:10.2760/780799.
  * McKenna et al. [2014] R. McKenna, S. Hollnaicher, W. Fichtner,  Cost-potential curves for onshore wind energy: A high-resolution analysis for Germany,  Applied Energy 115 (2014) 103–115. URL: https://linkinghub.elsevier.com/retrieve/pii/S0306261913008507. doi:10.1016/j.apenergy.2013.10.030.
  * Sensfuß et al. [2021] F. Sensfuß, K. Franke, D. C. Kleinschmitt, Langfristszenarien 3 - Potentiale Windenergie an Land - Datensatz 174, Technical Report, Bundesministerium für Wirtschaft und Energie (BMWi), Karlsruhe, 2021. URL: https://www.langfristszenarien.de/enertile-explorer-wAssets/docs/Potentiale-Windenergie-an-Land-Datensatz-174-final.pdf.
  * Eurostat [2011] [dataset] Eurostat,  EuroStat Urban 2011 (2011). URL: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/clusters.
  * [31] [dataset] G. ©. G.-D. . B. (2012),  Digitales Landschaftsmodell 1:250 000 (Ebenen) (DLM250) (2012).
  * Fachagentur Wind an Land [2021a] Fachagentur Wind an Land, Entwicklung der Windenergie im Wald, 2021a.
  * Fachagentur Wind an Land [2021b] Fachagentur Wind an Land, Überblick Abstandsempfehlungen und Vorgaben zur Ausweisung von Windenergiegebieten in den Bundesländern, 2021b.
  * noa [2017] Baugesetzbuch in der Fassung der Bekanntmachung vom 3. November 2017 (BGBl. I S. 3634), das zuletzt durch Artikel 9 des Gesetzes vom 10. September 2021 (BGBl. I S. 4147) geändert worden ist, 2017.
  * Geobasisdaten: © GeoBasis-DE / BKG (2021) [2021] [dataset] Geobasisdaten: © GeoBasis-DE / BKG (2021),  Amtliche Hausumringe Deutschland (HU-DE) (2021).
  * Ryberg et al. [2018] D. S. Ryberg, D. G. Caglayan, S. Schmitt, J. Linßen, D. Stolten,  The Future of European Onshore Wind Energy Potential: Detailed Distribution and Simulation of Advanced Turbine Designs (2018) 22.
  * Technical University of Denmark [2021] [dataset] Technical University of Denmark,  Global Wind Atlas (GWA) 3.0 (2021). URL: https://globalwindatlas.info/download/gis-files.
  * Sensfuß et al. [2021] F. Sensfuß, B. Lux, B. Bernath, Christiane, C. Kiefer, B. Pfluger, C. Kleinschmitt, K. Franke, G. Deac, Langfristszenarien für die Transformation des Energiesystems in Deutschland, Technical Report, Bundesministerium für Wirtschaft und Energie (BMWi), Berlin, 2021. URL: https://www.langfristszenarien.de/enertile-explorer-wAssets/docs/LFS_Kurzbericht_final_v5.pdf.
  * Bosch et al. [2018] J. Bosch, I. Staffell, A. D. Hawkes,  Temporally explicit and spatially resolved global offshore wind energy potentials,  Energy 163 (2018) 766–781. URL: https://linkinghub.elsevier.com/retrieve/pii/S036054421831689X. doi:10.1016/j.energy.2018.08.153.
  * Zappa and van den Broek [2018] W. Zappa, M. van den Broek,  Analysing the potential of integrating wind and solar power in Europe using spatial optimisation under various scenarios,  Renewable and Sustainable Energy Reviews 94 (2018) 1192–1216. URL: https://linkinghub.elsevier.com/retrieve/pii/S1364032118304362. doi:10.1016/j.rser.2018.05.071.
  * Caglayan et al. [2019] D. G. Caglayan, D. S. Ryberg, H. Heinrichs, J. Linßen, D. Stolten, M. Robinius,  The techno-economic potential of offshore wind energy with optimized future turbine designs in Europe,  Applied Energy 255 (2019) 113794. URL: https://linkinghub.elsevier.com/retrieve/pii/S0306261919314813. doi:10.1016/j.apenergy.2019.113794.
  * Sensfuß et al. [2021] D. F. Sensfuß, K. Franke, D. C. Kleinschmitt, Langfristszenarien 3 - Potentiale Windenergie auf See - Datensatz 127, Technical Report, Bundesministerium für Wirtschaft und Energie (BMWi), Karlsruhe, 2021. URL: https://www.langfristszenarien.de/enertile-explorer-wAssets/docs/Potentiale-der-Windenergie-auf-See-Datensatz-127.pdf.
  * 4C Offshore [2021] [dataset] 4C Offshore,  Global Offshore Renewable Map (2021). URL: https://map.4coffshore.com/offshorewind/.
  * Bundesamt für Seeschifffahrt und Hydrographie [2020] Bundesamt für Seeschifffahrt und Hydrographie, Entwurf Flächenentwicklungsplan 2020 für die deutsche Nord- und Ostsee, 2020. URL: https://www.bsh.de/DE/THEMEN/Offshore/Meeresfachplanung/Fortschreibung/_Anlagen/Downloads/Entwurf_FEP_2020.pdf;jsessionid=4E1EA7F377C9600D693C60CD059E972B.live11311?__blob=publicationFile&v=6.
  * Directorate-General for Maritime Affairs and Fisheries [2021] Directorate-General for Maritime Affairs and Fisheries, European Marine Observation and Data Network (EMODnet), 2021. URL: https://emodnet.ec.europa.eu/en.
  * Halpern et al. [2015] [dataset] B. Halpern, M. Frazier, J. Potapenko, K. Casey, K. Koenig, C. Longo, J. Lowndes, C. Rockwood, E. Selig, K. Selkoe, S. Walbridge,  Cumulative human impacts: raw stressor data (2008 and 2013) (2015). URL: https://knb.ecoinformatics.org/view/doi:10.5063/F1S180FS. doi:10.5063/F1S180FS.
  * Bundesamt für Seeschifffahrt und Hydrographie [2017a] Bundesamt für Seeschifffahrt und Hydrographie, Bundesfachplan Offshore für die deutsche ausschließliche Wirtschaftszone der Nordsee 2016/2017 und Umweltbericht, 2017a. URL: https://www.bsh.de/DE/PUBLIKATIONEN/_Anlagen/Downloads/Offshore/Bundesfachplan-Nordsee/Bundesfachplan-Offshore-Nordsee-2016-2017.pdf;jsessionid=4C07B4A5A082CF03C0C83DAB77F4CC16.live21304?__blob=publicationFile&v=15.
  * Bundesamt für Seeschifffahrt und Hydrographie [2017b] Bundesamt für Seeschifffahrt und Hydrographie, Bundesfachplan Offshore für die deutsche ausschließliche Wirtschaftszone der Ostsee 2016 /2017 und Umweltbericht, 2017b.
  * Bundesamt für Seeschifffahrt und Hydrographie [2021] [dataset] Bundesamt für Seeschifffahrt und Hydrographie,  Raumordnungsplan AWZ – WMS (2021). URL: https://www.geoseaportal.de/mapapps/resources/apps/meeresnutzung/index.html?lang=de.
  * Niedersächsisches Ministerium für Ernährung, Landwirtschaft und Vebraucherschutz [2017] [dataset] Niedersächsisches Ministerium für Ernährung, Landwirtschaft und Vebraucherschutz,  Neubekanntmachung der LROP-Verordnung 2017 (2017). URL: https://www.ml.niedersachsen.de/startseite/themen/raumordnung_landesplanung/landesraumordnungsprogramm/datenabgabe_lrop_2017/neubekanntmachung-der-lrop-verordnung-2017-158625.html.
  * Ministerium für Energie, Infrastruktur und Digitalisierung [2016] [dataset] Ministerium für Energie, Infrastruktur und Digitalisierung,  Landesraumentwicklungsprogramm Mecklenburg-Vorpommern 2016 (LEP M-V 2016) (2016). URL: https://www.regierung-mv.de/Landesregierung/em/Raumordnung/Landesraumentwicklungsprogramm/aktuelles-Programm/.
  * Landesplanung Schleswig-Holstein/MILIG [2021] [dataset] Landesplanung Schleswig-Holstein/MILIG,  Fortschreibung des Landesentwicklungsplans Schleswig-Holstein 2010 (2. Entwurf 2020) (2021). URL: https://www.bolapla-sh.de/verfahren/bf4796a7-f729-11ea-a85e-0050569710bc/public/detail#procedureDetailsDocumentlist.
  * Bundesamt für Seeschifffahrt und Hydrographie [2020] [dataset] Bundesamt für Seeschifffahrt und Hydrographie,  CONTIS Facilities (2020). URL: https://gdiwiki.bsh.de/wiki/index.php/CONTIS_Facilities.
  * Bundesamt für Seeschifffahrt und Hydrographie [2019] [dataset] Bundesamt für Seeschifffahrt und Hydrographie,  CONTIS Administration - WMS (2019). URL: https://www.geoseaportal.de/wss/service/CONTIS_Administration/guest?SERVICE=WMS&REQUEST=GetCapabilities&VERSION=1.3.0.
  * Bundesamt für Seeschifffahrt und Hydrographie [2020] Bundesamt für Seeschifffahrt und Hydrographie, Flächenentwicklungsplan 2020 für die deutsche Nord- und Ostsee, 2020.
  * Landesbetrieb Geoinformation und Vermessung Hamburg(2021) [LGV] [dataset] Landesbetrieb Geoinformation und Vermessung (LGV) Hamburg,  Flächennutzungsplan Hamburg (2021).
  * Lux et al. [2021] B. Lux, D. F. Sensfuß, D. G. Deac, D. C. Kiefer, C. Bernath, D. J. Fragoso-Garcia, D. B. Pfluger, Langfristszenarien für die Transformation des Energiesystems in Deutschland - Angebotsseite Treibhausgasneutrale Szenarien, Technical Report, Bundesministerium für Wirtschaft und Energie (BMWi), 2021\. URL: https://www.langfristszenarien.de/enertile-explorer-wAssets/docs/LFS_Webinar_Angebot_final.pdf.
  * Landesanstalt für Umwelt Baden-Württemberg [2018] Landesanstalt für Umwelt Baden-Württemberg, Potenzialanalyse der Freiflächen-Photovoltaik, 2018\. URL: https://www.energieatlas-bw.de/sonne/freiflachen/potenzialanalyse.
  * Seidenstücker [2020] C. Seidenstücker, Solarkataster NRW - Neues Tool für die Planung von Freiflächen Photovoltaik, 2020\.
  * Ryberg [2019] D. S. Ryberg, Generation Lulls from the Future Potential of Wind and Solar Energy in Europe, Ph.D. thesis, RWTH Aachen University, 2019.
  * European Environment Agency [2016] [dataset] European Environment Agency,  Global land cover - 250m (2016). URL: https://www.eea.europa.eu/data-and-maps/data/global-land-cover-250m.
  * ESA [2009] [dataset] ESA,  GlobCover 2009 (2009). URL: http://due.esrin.esa.int/page_globcover.php.
  * European Environment Agency [2012] [dataset] European Environment Agency,  Less favoured areas (2012). URL: https://www.eea.europa.eu/data-and-maps/figures/less-favoured-areas.
  * noa [2021] Gesetz für den Ausbau erneuerbarer Energien (Erneuerbare-Energien-Gesetz - EEG 2021), 2021. URL: https://www.gesetze-im-internet.de/eeg_2014/EEG_2021.pdf.
  * noa [2017] Gesetz für den Ausbau erneuerbarer Energien (Erneuerbare-Energien-Gesetz - EEG 2017), 2017. URL: https://www.gesetze-im-internet.de/eeg_2014/EEG_2017.pdf.
  * bun [2013] [dataset] Ackerbauliches Ertragspotenzial der Böden in Deutschland 1:1.000.000. Datenquelle: SQR1000 V1.0 (2013).
  * Wirth [2021] D. H. Wirth, Recent Facts about Photovoltaics in Germany, 2021. URL: https://www.ise.fraunhofer.de/en/publications/studies/recent-facts-about-pv-in-germany.html.
  * Ong et al. [2013] S. Ong, C. Campbell, P. Denholm, R. Margolis, G. Heath, Land-Use Requirements for Solar Power Plants in the United States, Technical Report NREL/TP-6A20-56290, 1086349, 2013. URL: http://www.osti.gov/servlets/purl/1086349/. doi:10.2172/1086349.
  * SUNTECH [2021] SUNTECH, Ultra X Plus - 132 HALF-CELL MONOFACIAL MODULE, 2021. URL: https://www.suntech-power.com/wp-content/uploads/download/product-specification/EN_Ultra_X_Plus_STP670S_D66_Wmh.pdf.
  * LG [2021] LG, LG NEON R - LG370 Q1C-A5, 2021\. URL: https://static1.squarespace.com/static/5354537ce4b0e65f5c20d562/t/5b56f6a18a922dbc850387a9/1532425900596/LG+Solar+Datasheet+2018+NeONR+370.pdf.
  * Castellanos et al. [2017] S. Castellanos, D. A. Sunter, D. M. Kammen,  Rooftop solar photovoltaic potential in cities: how scalable are assessment approaches?,  Environmental Research Letters 12 (2017) 125005. URL: https://iopscience.iop.org/article/10.1088/1748-9326/aa7857. doi:10.1088/1748-9326/aa7857.
  * Mainzer et al. [2014] K. Mainzer, K. Fath, R. McKenna, J. Stengel, W. Fichtner, F. Schultmann,  A high-resolution determination of the technical potential for residential-roof-mounted photovoltaic systems in Germany,  Solar Energy 105 (2014) 715–731. URL: https://linkinghub.elsevier.com/retrieve/pii/S0038092X14002114. doi:10.1016/j.solener.2014.04.015.
  * Mainzer et al. [2017] K. Mainzer, S. Killinger, R. McKenna, W. Fichtner,  Assessment of rooftop photovoltaic potentials at the urban level using publicly available geodata and image recognition techniques,  Solar Energy 155 (2017) 561–573. URL: https://linkinghub.elsevier.com/retrieve/pii/S0038092X17305686. doi:10.1016/j.solener.2017.06.065.
  * Song et al. [2018] X. Song, Y. Huang, C. Zhao, Y. Liu, Y. Lu, Y. Chang, J. Yang,  An Approach for Estimating Solar Photovoltaic Potential Based on Rooftop Retrieval from Remote Sensing Images,  Energies 11 (2018) 3172. URL: http://www.mdpi.com/1996-1073/11/11/3172. doi:10.3390/en11113172.
  * Sampath et al. [2019] A. Sampath, P. Bijapur, A. Karanam, V. Umadevi, M. Parathodiyil,  Estimation of rooftop solar energy generation using Satellite Image Segmentation,  in: 2019 IEEE 9th International Conference on Advanced Computing (IACC), IEEE, Tiruchirappalli, India, 2019, pp. 38–44. URL: https://ieeexplore.ieee.org/document/8971578/. doi:10.1109/IACC48062.2019.8971578.
  * Singh and Banerjee [2013] R. Singh, R. Banerjee,  Estimation of roof-top photovoltaic potential using satellite imagery and GIS,  in: 2013 IEEE 39th Photovoltaic Specialists Conference (PVSC), IEEE, Tampa, FL, USA, 2013, pp. 2343–2347. URL: http://ieeexplore.ieee.org/document/6744945/. doi:10.1109/PVSC.2013.6744945.
  * Grothues and Seidenstücker [2018] E. Grothues, C. Seidenstücker, Das landesweite Solarkataster Nordrhein-Westfalen, Technical Report 43, Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen (LANUV), 2018\.
  * Strzalka et al. [2012] A. Strzalka, N. Alam, E. Duminil, V. Coors, U. Eicker,  Large scale integration of photovoltaics in cities,  Applied Energy 93 (2012) 413–421. URL: https://linkinghub.elsevier.com/retrieve/pii/S0306261911008294. doi:10.1016/j.apenergy.2011.12.033.
  * Walch et al. [2020] A. Walch, R. Castello, N. Mohajeri, J.-L. Scartezzini,  Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty,  Applied Energy 262 (2020) 114404. URL: https://linkinghub.elsevier.com/retrieve/pii/S0306261919320914. doi:10.1016/j.apenergy.2019.114404.
  * Fath et al. [2015] K. Fath, J. Stengel, W. Sprenger, H. R. Wilson, F. Schultmann, T. E. Kuhn,  A method for predicting the economic potential of (building-integrated) photovoltaics in urban areas based on hourly Radiance simulations,  Solar Energy 116 (2015) 357–370. URL: https://linkinghub.elsevier.com/retrieve/pii/S0038092X15001413. doi:10.1016/j.solener.2015.03.023.
  * Jakubiec and Reinhart [2013] J. A. Jakubiec, C. F. Reinhart,  A method for predicting city-wide electricity gains from photovoltaic panels based on LiDAR and GIS data combined with hourly Daysim simulations,  Solar Energy 93 (2013) 127–143. URL: https://linkinghub.elsevier.com/retrieve/pii/S0038092X13001291. doi:10.1016/j.solener.2013.03.022.
  * Kurdgelashvili et al. [2016] L. Kurdgelashvili, J. Li, C.-H. Shih, B. Attia,  Estimating technical potential for rooftop photovoltaics in California, Arizona and New Jersey,  Renewable Energy 95 (2016) 286–302. URL: https://linkinghub.elsevier.com/retrieve/pii/S0960148116302890. doi:10.1016/j.renene.2016.03.105.
  * Assouline et al. [2017] D. Assouline, N. Mohajeri, J.-L. Scartezzini,  Quantifying rooftop photovoltaic solar energy potential: A machine learning approach,  Solar Energy 141 (2017) 278–296. URL: https://linkinghub.elsevier.com/retrieve/pii/S0038092X16305850. doi:10.1016/j.solener.2016.11.045.
  * Bódis [2019] K. Bódis,  A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union,  Renewable and Sustainable Energy Reviews (2019) 13.
  * Schmid et al. [2021] T. Schmid, F. Jetter, T. Limmer, Regionalisierung des Ausbaus der Erneuerbaren Energien - Begleitdokument zum Netzentwicklungsplan Strom 2035 (Version 2021), 2021.
  * Portmann et al. [2019] M. Portmann, D. Galvagno-Erny, P. Lorenz, D. Schacher, R. Heinrich, Sonnendach.ch und Sonnenfassade.ch: Berechnung von Potenzialen in Gemeinden, 2019\.
  * Eggers et al. [2020] J.-B. Eggers, M. Behnisch, J. Eisenlohr, H. Poglitsch, W.-F. Phung, C. Ferrara, T. E. Kuhn,  PV-Ausbauerfordernisse versus Gebäudepotenzial: Ergebnis einer gebäudescharfen Analyse für ganz Deutschland,  2020, p. 21.
  * Kaltschmitt [1992] M. Kaltschmitt, Potentiale und Kosten regenerativer Energieträger in Baden-Württemberg, 1992\. URL: HTTP://zbsfx.zb.kfa-juelich.de/sfx_local?sid=google&auinit=M&aulast=Kaltschmitt&title=Potentiale%20und%20Kosten%20regenerativer%20Energietr%C3%A4ger%20in%20Baden-W%C3%BCrttemberg&genre=book&date=1992.
  * International Energy Agency (2002) [IEA] International Energy Agency (IEA), Potential for Building Integrated Photovoltaics, 2002\.
  * Biljecki et al. [2016] F. Biljecki, H. Ledoux, J. Stoter,  An improved LOD specification for 3D building models,  Computers, Environment and Urban Systems 59 (2016) 25–37. URL: https://linkinghub.elsevier.com/retrieve/pii/S0198971516300436. doi:10.1016/j.compenvurbsys.2016.04.005.
  * Geobasisdaten: © GeoBasis-DE / BKG (2021) [2021] [dataset] Geobasisdaten: © GeoBasis-DE / BKG (2021),  3D-Gebäudemodelle LoD2 Deutschland (LoD2-DE) (2021).
  * Bezirksregierung Köln [2021] Bezirksregierung Köln, Nutzerinformationen zur 3D-Gebäudemodelle-übersicht für NRW, 2021. URL: https://www.bezreg-koeln.nrw.de/brk_internet/geobasis/3d_gebaeudemodelle/nutzer_info_3d-gm-uebersicht.pdf.