Energy Modelling in Rural Areas with Spatial and Temporal Data in

Germany and Czech Republic

Jane Wuth, Javier Valdes, Luis Ramirez Camargo and Wolfgang Dorner

Institute for Applied Informatics, Technology Campus Freyung, Technische Hochschule Deggendorf,

Grafenauer Straße 22, 94078 Freyung, Germany

Keywords: Geographical Information Systems, Spatiotemporal Data, Renewable Energies, Energy Transition and

Electric Vehicles.

Abstract: One of the major challenges for the energy transition is to reconcile variable renewable energy production

with stochastically changing energy demand including the pursued changes in e.g. transport like electro

mobility. This requires smart systems that should be designed to minimize balancing and transmission costs.

The design and modelling of such systems requires high resolution energy generation and demand data, which

usually either do not exist or is not available. Methodologies to address this lack of data populate scientific

literature but its replicability is limited by an inadequate level of detail in the description of the methodologies

and to a larger extent by the absence or low quality of basic data. This manuscript summarizes several years

of research in energy modelling using Geographical Information Systems as well as spatial and temporal data

of the rural areas in Bavaria (Germany) and the Czech Republic. Data requirements for energy demand and

energy supply including different types of users and technologies are addressed. Irreconcilable data gaps are

presented, examples to fill data gaps as well as recommendations for future necessary developments are

provided.

1 INTRODUCTION

When analysing energy demand and supply in large

areas, especially the exact localization is of major

importance. This is especially true when considering

renewable energies. They are less predictable in their

production outputs than fossils and thus may induce

great instability for the grid. (see, e.g., Passey et al.,

2011). Furthermore, user related changes, the

promotion of electro mobility, the increased use of

electricity as primary source of energy and migration

from the land induce an increased electric energy need

for a shrinking number of citizens when considering

rural regions (Valdes et al., 2019). The question raised

for every community and regional government facing

depopulation issues is therefore, how to tackle these

rising issues by simultaneously minimizing costs of

investments. The more detailed one could assess the

current demand and supply capabilities, the current

load of the electric grid as well as surrounding factors,

the better forecasts and alternative scenarios for a cost

efficient and sustainable development can be

implemented. The results of such models are

indispensable for regional planners, utilities and policy

makers in order to plan future energy supply in a cost

efficient and consumer friendly way.

High spatial resolution data of grid loads and

capacity measures as well as electric energy

consumption by different sectors are important factors

to be considered. Current trends need to be evaluated

by estimating its future impacts. One such major trend

of today, is the introduction of electro mobility which

is pursued by almost every country, and as a

consequence uncoordinated charging events of electric

vehicles might have negative impacts on the resilience

and economics of an electrical system (Mu et al.,

2014). Uncontrolled charging load of electric vehicles

can generate, among others, voltage drops that degrade

the power quality of electricity distribution systems

(Foley et al., 2013). Therefore, the placement, sizing

and supply of infrastructures such as electric vehicles

charging stations needs to be carefully assessed.

Throughout the past years, research questions

related to above mentioned topics were addressed

manifold and in an incremental way in international

scientific literature relying on geographical

information systems as well as spatiotemporal data and

modelling (Pagany et al., 2018; Ramirez Camargo and

220

Wuth, J., Valdes, J., Camargo, L. and Dorner, W.

Energy Modelling in Rural Areas with Spatial and Temporal Data in Germany and Czech Republic.

DOI: 10.5220/0007721002200227

In Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2019), pages 220-227

ISBN: 978-989-758-371-1

Stoeglehner, 2018). The main obstacle remaining, is

the acquisition of complete and high quality spatial and

statistical data, especially when modelling energy

infrastructures and related topics in rural regions. This

study summarizes several years of working in

geospatial energy system modelling. More precisely,

this review will include conducted energy use plans

and climate protection plans using a spatially explicit

approach in Bavaria (Germany) and the involvement in

ongoing projects analysing the developments of the

energy system in the cross-border region between

Germany (Bavaria) and Czech Republic.

The rest of this article is structured as follows:

Section 2 presents the type of research questions that

can be addressed. Section 3 describes the challenges of

spatial explicit energy modelling in the cross-border

region of Bavaria and Czech Republic and section 4

explains particular workarounds and the consequences

of deriving electricity demand and supply data from

other secondary sources. Finally, conclusions are

presented in the last chapter.

2 ELECTRIC SYSTEM AND

VEHICLE CHARGING INFRA-

STRUCTURE MODELLING

The UN Environment Assembly promotes global

standards for the use of renewable energies and the

increase in energy efficient usage. Moreover, at the

lower administrative levels, the transition to renewable

energy generation and to sustainable electric

transportation have become a priority (Cajot et al.,

2017). To develop and propose new models, policies

or energy saving measures, first of all the current state

of the art in energy usage and production needs to be

observed on a larger, detailed scale. For that, research

questions according to current data availability

standards are of major importance.

Urbanization, increased automation of production

processes and electrification of the energy matrix does

not only lead to higher energy requirements but

additionally puts pressure on the electric grid. This is

further intensified with an increasing share of

distributed renewable electricity generation.

Demographic changes and rural depopulation

decreases energy consumption in rural areas and lead

to sub-utilization of existing energy distribution

infrastructures. Moreover, technologies are advancing

in their energy efficiency and smart housing devices

help to eliminate unwanted energy consumption. This

simultaneous, interdependent and fast but partially

contrary development has to be analysed and studied

exhaustively because the decisions taken to maintain a

fully functioning energy infrastructure, will persist for

several decades.

Contemporary computational capacities and

models offer unprecedented capabilities to examine

future decision options about complex systems under a

wide range of assumptions. Current important research

topics include the integration of high shares of

renewable energy generation technologies, which

range from well-established ones, such as

photovoltaics and wind power, to technologies that

should solve the dispatch limitations, such as power to

gas and renewable fuels. Furthermore, decisions must

be taken about the mix of technologies to be adopted,

storage and backup systems as well as the integration

of new technologies that improve energy efficiency,

such as heat pumps. Technologies that are significant

hopes in the reduction of CO2 emissions of society like

electric vehicles and carbon storages, are further topics

that require thorough research and decision making.

Most of these technologies, as well as their impact on

the electric grid at the DSO and TSO levels, require not

only model approaches that can deal with future

uncertainties that grow with time (DeCarolis et al.,

2017) but also high quality data that provide a solid

basis. Otherwise, results and conclusions derived will

be misleading.

Especially in the present discussion of expanding

electro mobility, it is of high importance to know

where exactly energy is produced and consumed. It is

often mentioned that the stability and capacity of the

electric grid in its current state would not suffice for

larger penetration rates. Considering the suggestion for

better, more detailed data, would help in identifying

grid gaps. The identification of these gaps can provide

opportunities for electric vehicle charging stations to

stabilize the grid, similar to already existing models

like the “Building-to-Vehicle” approach

(Genikomsakis et al., 2016). Moreover, one of the most

interesting questions for a researcher in the area of

electro mobility is: what the role of the availability of

charging stations on the transition to an e-mobility

concept would be.

Modelling the electricity grid presents a huge

challenge for any researcher working in the area of

electro mobility and electricity system planning in

general. The main challenge is associated with the level

of resolution needed. In the case of future electric

vehicle charging processes, particularly with regards to

higher distance ranges of the vehicles, customers will

want to be able to charge their cars faster. Hereby fast

charging or hyper charging stations are needed.

E-mobility planning needs to forecast the future

scenarios with high degrees of electric vehicles

Energy Modelling in Rural Areas with Spatial and Temporal Data in Germany and Czech Republic

221

penetration rates. Thus the demand may require to

increase the grid supply capacity to several times ≥ 150

kW at central intersection points. Building numerous

charging points with capacities of 50 kW or higher on

main traffic arteries may have a huge impact on the

distribution and even the transmission grids (Nationale

Plattform Elektromobilität, 2015).

It is apparent that many topics concerning

electricity and infrastructure planning are of

importance not only to private companies but to

governments and the general public. A thorough

knowledge of the topic is therefore indispensable to

make valuable decisions for future electricity and

mobility reasons. For this, high quality, spatially and

temporally resolved data is necessary. Such data

however is either non-existent, or very expensive and

not affordable to research institutions, which leads to

establishing second best results with workarounds.

3 WHAT IS MISSING AND AN

IDEAL DATA WORLD

Every energy system modeller faces a double trade-off

when designing an energy model. They have to

consider both, the level of detail, as well as the scope

of the model. Due to the absence of data, a higher

number of hypotheses and generalizations are in place

most of the times and huge deviations from reality may

exist. Concerning the electric system the availability

and reliability of data deteriorates with decreasing level

of spatial and administrative aggregation. For instance,

at the national level in Germany it is possible to obtain

hourly data of electricity generation from fossil and

renewable sources as well as demand data (see, e.g.,

Fraunhofer, 2019). The same applies to the electricity

transmission system. Spatial and attribute data of the

high and highest voltage electricity grids as well as the

location and characteristics of the main fossil based

energy power plants are easily available. In the

German case, a list of all electricity generation

installations larger than 10 MW including location and

basic attribute data is provided by the State Grid

Agency (Deutsche Bundesnetzagentur, 2018).

However, at the state level there is no public data

available beyond yearly accumulated electricity

generation and demand of different economic sectors.

At the district and municipality level, this kind of data

is only available on request by local distribution system

operators (DSOs). Going further to a neighbourhood or

building level, such data might be available only if

some sort of specific data gathering project has been

conducted on-site. While data of renewable energy

generation installations covered by the renewable

energy law was systematized and made available to the

public until 2014, there is no reliable source about

these installations (when these are smaller than 10

MW) since then (Deutsche Gesellschaft für

Sonnenenergie e.V. (DGS), 2016) and even less is

known about shares and periods of self-consumption

of the electricity generated by these installations when

they are part of buildings. Similarly, data of the

medium and especially low voltage grids are rare

goods that are difficult to acquire even on request.

Ideally, electricity demand and generation data in a

high temporal resolution would be available at the

building level, while occupancy characteristics and

economic use of each building are also known. This

would include single family houses to industrial

production buildings in all federal states of a country.

The current data availability status is not only very far

away from this ideal, but the ideal itself would race

irresolvable data privacy and security issues.

Therefore, a compromise to keep private data safe is

necessary, when gathering data in high spatial and

temporal resolution. Specialized scientific literature on

data processing already offers attempts to develop

methodologies of data aggregation that gathers the

most valuable information for energy modellers by

simultaneously avoiding privacy issues. However,

even in the best of these cases very strong assumptions

have to be made. For instance, the best guess about the

electricity consumption profiles of industries in Europe

can only be made by comparing them to data from the

US (Voulis et al., 2018).

For Bavaria and the Czech Republic, key energy

demand figures divided by type of economic use and

generator are publicly available only at the state level

(NUTS 1). Moreover, temporally disaggregated data

about electricity consumption and generation is only

available at country level (NUTS 0) by Eurostat.

Additionally, in the Czech Republic, at least

standard load profiles of demand for different types of

users are provided for NUTS 1. The Czech Energy

Regulation Office (ERU) provides information about

the location of every energy plant of the country,

including for example photovoltaic panels installed by

private households. The data includes the owner and

installed capacity by type of primary energy but it does

not include any information about its use and in some

cases the street or cadastre reference is missing or

incomplete. As of December 2018 around a third of a

total of 1,746 generation installations selected in the

south-east of Czech Republic did not contain specific

information on its location or it was too vague to obtain

specific coordinates; the same problem appears in

Bavaria.

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Accessibility to geodata of the existing power grid

in Germany is heterogeneous and depends on the

region and voltage level. To produce realistic input

data for simulation studies, necessary data is not

publicly available or cannot be obtained with

reasonable effort in the context of scientific analyses.

For example, the datasets provided by the Bundesamt

für Kartographie und Geodäsie(AdV, 2019), provides

the spatial situation of overhead power lines but they

cannot be differentiated into voltage levels.

Furthermore, the lower and medium voltage levels are

missing completely.

Considering electro mobility planning, geo-

referenced data that would indicate where car drivers

stop mostly, occupancy rates of municipal parking

areas and frequency rates of streets that are not

typically large commuter routes are also missing. One

ideal data option is a widespread data pool that

indicates where people stop, for how long they stop and

how frequented those locations are but if such datasets

exist, it is mostly individually formatted and thus not

applicable for larger research areas.

Another data issue is the large difference in the

existence of information between urban and rural

regions (Pagany et al., 2018). The less frequented an

area is, the lower is the anticipated need for collecting

it. Utmost of rural regions are characterized by

countryside, national parks and rather small towns and

cities. This type of geographic regions is well at the end

of the “data food chain”. Not a lot of people live there

and the need for transportation system- and energy grid

system developments is lower compared to urban

regions

Besides these local factors, the ability of the grid is

important for this topic as well as roads and locations

that raise a refuelling demand. In Germany, data on

roads and its infrastructure is collected by the federal

offices. This data nevertheless is very expensive,

which is why many researchers replace it with

OpenStreetMap road data. All other datasets required

are not available in an openly collected way.

Combining this information with a comprehensive

dataset of grid capacities and occupancies, would

indicate the best places for recharging batteries without

inhibiting customers in their daily routines.

For many workarounds, Volunteered Geographic

Information (VGI) provides researchers with

alternative data sources. In view of OpenStreetMap

(OSM) data, some characteristics are apparent, that any

researcher would consider as ideal: it is free,

actualized constantly, detailed, rich and it is classified.

Nevertheless, OSM data belongs to the group of VGI

and thus contains common problems like its

incompleteness, the reduction to geographic

information only, and the absence of quality controls

and therefore infrequent wrong information (e.g.

outdated, closed sites). In an ideal world, the data

provided by OSM would contain even more

information than the geographic localization and rough

classification.

Newly, OSM also tries to cover grid related

information for energy modellers in the transmission

and distribution sectors. OSM data with the tag

“power” has been collected since the beginning of

2007 and includes not only high voltage transmission

lines but also information on medium voltage

transmission lines, transformers and other

infrastructure. New projects are exploring the

possibility to generate accurate distribution network

data based on this initiative (for an overview see e.g.,

https://wiki.openmod-initiative.org;Rivera et al.,

2017). OSM data thus provides a base of information

that is not available in this form anywhere else.

4 WORKAROUNDS AND THE

INEVITABLE

When considering such questions as addressed in

chapter 2, many of the required data sets are either too

expensive or just not existent in a format as needed.

The high accuracy and level of detail that is necessary

is difficult to access. Inevitable is that most of the data

is in hands of administrative complex institutions or

private sector organisations, that invest money to

gather data, which leads to the need of selling it at a

certain price, in many cases not affordable to research

institutions. Moreover, in the worst cases, data are in

private hands so that there is no way to access it or data

is not being gathered at all due to above mentioned

reasons.

4.1 Electricity Demand and Supply

Modelling

For municipalities in Germany, methodologies to

determine energy generation and demand in a spatially

explicit way using GIS related to the topics named in

section 2 can be found e.g. in (Bayerisches

Staatsministerium für Umwelt und Gesundheit et al.,

2011). Additionally, a review of methodologies for the

determination of spatiotemporal data for Germany and

Austria can be found in (Ramirez Camargo and

Stoeglehner, 2018). In the case of Bavaria and the

Czech Republic there are, however, thousands of

municipalities. The required computational and

monetary resources to model electricity demand and

Energy Modelling in Rural Areas with Spatial and Temporal Data in Germany and Czech Republic

223

generation in the level of detail applied to

municipalities would be immense. For instance, typical

data used to locate demand in space and to calculate

spatially potentials of renewable energy sources at the

municipal scale are data of individual buildings in a 3D

vector format with level of detail 1 (LOD1) or 2

(LOD2) from the Bavarian survey Agency (Bayerische

Vermessungsverwaltung). These data exist in Bavaria

but it is available only under request and monetary

charge per object. The cost for all existent (several

million) objects is prohibitive. The alternative that is

free of charge (for educational institutions conducting

research projects), are 2D vector data without attributes

apart from the location and the address. In Czech

Republic, the geodata infrastructure is expected to be

modernized until 2020 and 3D vector data of buildings

should then be available for the whole country

(Janečka and Souček, 2017). For the moment, 2D data

with multiple attributes is available to the public trough

e.g. the INSPIRE program. In order to enrich these 2D

Building datasets with attributes, such as size, use and

number of households (in the case of residential

buildings), further data, like high resolution digital

surface models (usually also not free of charge), land

use data and high spatial resolution population data

might be used. However, new issues emerge due to the

format, the spatial resolution and the creation and

reference dates of the data sets. Taking this and the

known limitations about the availability of time series

of electricity demand (section 3) into account, the best

spatial resolution that could be pursued for the

generation of spatiotemporal datasets of electricity

demand in Bavaria are municipalities and in the Czech

Republic federal states.

In order to produce data sets displaying the

electricity demand for each municipality as specific

and near to reality as possible, workarounds were

established. In Bavaria, due to a study on the share of

renewable energies in place (Energie-Atlas Bayern

,2018), the consumption of electricity per municipality

was calculated using the electric energy demand of

manufacturing industries on district level, dividing it

by the number of people working in those industries on

the same level and then multiplying it with the number

of inhabitants per municipality, working in the selected

field. Additionally, the same was done with households

and population sizes as well as employed people in

other industries as the above. This calculation does not

include electric energy needed by transportation

systems.

While current demands of electricity are relatively

easy to derive with calculations as the above

mentioned, they are also only approximations how

reality might seem, commuters are for example left

aside. Forecasting the development of electricity

demand however is even more difficult. Several

aspects need to be taken into account, like future

technological advancements, new ways of living

(smart homes, heat through electricity, electro

mobility, etc.) as well as demographic changes or rural

depopulation and urbanization.

To develop a calculation for the future demands of

electricity per municipality, the current electricity

demand was distributed into the three sectors industry,

agriculture and households. From there, LUISA data is

used to match the current numbers with future

estimations of land use and demographic

developments. The LUISA Territorial Platform

supplies data for the future estimation of land use and

further indicators per km2, provided by the European

Council (Barbosa et al., 2015). By combining the

current electricity demands per municipality,

differentiated into the three different sectors with the

percent changes of rising or shrinking numbers of

selected Land Use and demographic data, future

electricity demands per municipality can be estimated.

By introducing further factors, like technological

trends, electro mobility or policy recommendations’

induced developments, the estimations can be adapted.

In the case of existing renewable energy

installations, apart from the data that has not been

gathered at all (mainly installations smaller than

10MW since 2015), the major issue is the estimation of

the output in a high temporal resolution. Usually, for

municipalities, data from several installations can be

generalized to all installations in the study area.

Alternatively, data from a close weather station

together with physical models of PV and wind turbines

are used to estimate the output of installed capacities.

In order to develop such an estimation for Bavaria and

the Czech Republic without having to deal with

incomplete data sets from hundreds of sources, we opt

to use high resolution reanalysis data. The COSMO-

REA6 regional reanalysis (Hans-Ertel-Zentrum für

Wetterforschung, 2018) offer, among many others, the

U and V components of wind speed at different

altitudes, downward diffuse and direct shortwave

radiation flux at surface as well as temperature data for

the period 1995-2015 in hourly temporal resolution

and 6 km x 6 km spatial resolution. These are not only

complete data sets (full spatial coverage and time series

without missing values) but they also have been

validated and are in many cases more accurate than

satellite imagery derived data (Ramirez Camargo et al.,

2018). These data together with the models of PV and

wind power serve to estimate the output of existing

installations. However, it is impossible to correct cases

in which the location or size of the installations are not

GISTAM 2019 - 5th International Conference on Geographical Information Systems Theory, Applications and Management

224

available at all. To provide an accurate estimation of

the output when the installations are integrated in

buildings and the self-consumption rates are unknown

would be further points to improve.

Figure 1: Example of one-time step (2010-05-19 at 00:00)

wind power generation of a small wind turbine with 10.5

kW calculated using COSMO-REA6 reanalysis data for

Germany and the Czech Republic [kWh].

In order to produce data sets of spatiotemporal

potentials of renewable energy generation, a similar

approach to the case of existing installations can be

followed. COSMO-REA6 data can be used to easily

generate long and high resolution time series of

potential electricity generation for entire countries as

presented in Figure 1. Therefore, the only requirement

to create spatiotemporal datasets of potentials, is the

identification of areas fulfilling suitability criteria for

the deployment of certain technologies (e.g. small,

medium and large wind turbines or free standing PV).

These areas are defined using 2D building data, road

and river data from OSM, official and free data of

Nature 2000 areas, as well as water bodies and forest

data from official sources and free data of land use.

Apart from incomplete or inaccurate data, errors that

are difficult to find and correct in this case are related

to the reference date of the spatial data and changes that

might have occurred until the day of calculation of the

potential. For instance, buildings that do no longer

exist may increase the supposed suitable areas or new

buildings or infrastructure may transform suitable

areas in now unsuitable ones.

4.2 Electro Mobility Charging Station

Placement

Most research studies that focus on the modelling of

charging infrastructure propose charging station

locations dependent on different factors but all in

urban, very densely populated areas. The methods with

a greater resolution or accuracy are based on the

collection of GPS data, as e.g. (Dong et al., 2014). Such

methods imply the collection of data in a long time

span and for different driver profiles, which requires a

large quantity of resources the larger the area observed

and the lower the density of traffic.

In order to make assumptions about user behaviour

and charging events, Triebke et al., (2016) collected a

large dataset. More than 15.000 charging events were

accumulated and analysed in both rural and urban

areas. In urban areas, charging behaviour is rather

predictable and assessable, however in rural areas not.

The occupancy rates are very low and random

compared to urban areas. As highlighted before, in

Bavaria and Czech Republic, there are hundreds of

municipalities and the resources to model Electric

Vehicle Charging infrastructures based on GPS data

would have to be immense. With the purpose to

provide these municipalities with a tool to design and

develop new transport infrastructures Zink et al.,

(2017) and Valdes et al., (2019) established a model to

define demand for electro mobility charging stations

by help of designating a certain demand for energy to

each Point of Interest in the project area based on OSM

data. The project area is the border region between

Bavaria and Czech Republic, which is a very rural

region including a large national park. The presented

workaround is based on Zink et al.,(2017), and

extended by energy generation measures.

The charging demand calculation is based on a

spatial statistical methodology considering demo-

graphic parameters, the distance of charging stations to

chosen Points of Interest (POI’s) (extracted from

OSM) and time spent at the POI’s according to

different age groups.

In order to include OSM data in the model, it has

to be first validated. Based on Valdes et al., (2019), the

data is first “pre-validated”, to concentrate on the

relevant data sets and then processed in order to

identify the relevant POIs. The validation process is

customized to the South Bohemian and Bavarian

regions as well as the model design defined in Zink et

al., (2017). By processing data in the in Valdes et al.,

(2019) presented way, the electric vehicle charging

model is filled with improved data, which allows to

reduce a bias associated to particularities of OSM data.

Energy Modelling in Rural Areas with Spatial and Temporal Data in Germany and Czech Republic

225

The demand model furthermore contains the

average time use per day, dwelling time at one POI, the

penetration rate of electric vehicles and selected POI’s.

It allows to allocate an electricity demand to each POI

and aggregate it at the municipal level (see figure 2).

The models input data partly consist of national

surveys without geographical differentiation which

may be a potential source for biases as long as

behaviour in rural and urban areas is significantly

different, for example due to the availability and

distance to amenities. Moreover, there is no common

method of data collection and the classification of

activities and time resolution is not the same in the

German and Czech surveys.

The walking distance determines the optimal

charging station placement within a region with high

energy demand by minimizing the average distance to

the surrounding POIs.

Figure 2: Example of Electric Vehicle Charging Demand

and Walking Distances from POIs in Freyung, Germany.

The available electricity generation sites in the

project region are estimated in their average energy

supply capabilities is assessed and geographically

localized using the methodology and data described in

the section above. The geographical range to supply a

charging station directly with the sites provided energy

is enclosed. After both sides, the energy supply and -

demand, are calculated and geographically displayed,

areas with high demand and possible renewable energy

supply indicate the best spots for charging stations.

With this model, possible charging station

locations are not only bound to Points of Interest but

also existing and potential generation sites that provide

the charging stations with enough endogenous

renewable energy to relax a possibly congested grid,

not only in urban but also rural areas.

5 CONCLUSIONS

The more detailed and correct data is provided,

independent of its content, the better and precise

models can be used to estimate certain phenomena.

Here, the focus was put on electricity contexts

considering demand and supply potentials as well as

electro mobility charging station placements. The

future is known to be unsure, however for that, models

are developed to establish a better forecast and to help

policy makers and regional developers explore

possibilities and make plans in order to supply the

society with the right choices and capabilities. In order

for these models to be as precise and near to reality as

possible, the availability of detailed and geographically

precise data is of major importance.

It is therefore recommendable to make use of the

possibility to produce open source software and data

sets. This would support every researcher to gain

knowledge and help others to make the right choices

on energy related topics.

The presented workarounds may help to assume

future electricity demands and supply as well as electro

mobility charging station capabilities, and the placing

of such. It nevertheless stays to be a (valid)

workaround. With the above explained need for data to

achieve an ideal data world, one would be able to more

precisely model and calculate all of the above

mentioned topics and provide better data, results and

policy recommendations to interested parties.

ACKNOWLEDGEMENTS

The presented study results were conducted within the

framework of the projects “CrossEnergy: energy

infrastructure – future perspectives for a region in

change” (EU project number: 036) and “e-Road Písek-

Deggendorf” (EU Project number: 93), funded by the

European Regional Development Fund and in the

frame of the INTERREG V programme between the

Federal State of Bavaria (Germany) and the Czech

Republic.

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