We investigate the application of a solar-powered bus route to a
small-scale transportation system, as such of a university campus. In
particular, we explore the prospect of replacing conventional fossil fuel
buses by electric buses powered by solar energy and electricity provided by
the central grid. To this end, we employ GIS mapping technology to estimate
the solar radiation at the university campus and, accordingly, we
investigate three different scenarios for harnessing the available solar
power: (1) solar panels installed on the roof of bus stop shelters, (2) solar panels installed at an unused open space in the university, and (3) solar
roads, i.e. roads constructed by photovoltaic (PV) materials. For each of
the three scenarios, we investigate the optimal technical configuration, the
resulting energy generation, as well as the capital cost for application in
the case of NTUA campus in Athens (Greece). The preliminary feasibility
analysis showcases that all three scenarios contribute to satisfying
transportation demand, proportionately to their size, with scenario (2) presenting the lowest capital cost in relation to energy generation.
Therefore, we further explore this scenario by simulating its daily
operation including the actions of buying and selling energy to the central
grid, when there is energy deficit or surplus, respectively. A sensitivity
analysis is carried out in order to ascertain the optimal size of the solar
panel installation in relation to profit and reliability. Overall, results
indicate that, albeit the high capital costs, solar-powered
transportation schemes present a viable alternative for replacing
conventional buses at the studied location, especially considering
conventional PV panels. We note that present results heavily depend on the
choice of capacity factors of PV materials, which differ among technologies.
Yet, as capacity factors of PV panels are currently increasing, the studied
schemes might be more promising in the future.
Introduction
Renewable energy sources have been at the core of efforts towards
sustainability. In the past few years, a growing number of projects have
been implemented in order to exploit solar energy at large university
campuses for meeting building energy needs (Hasapis et al., 2017). Along the
same lines, electric buses have been deployed in a number of cases to reduce
the carbon footprint of on-campus transportation at large university
campuses (Jang, 2018). Evidently, solar energy utilization for small scale
transportation systems is yet at a preliminary stage. Even though several
patents (Durham, 2006; Brusaw and Brusaw, 2018) and studies on pilot
configurations (Duarte and Ferreira, 2016) can be found in literature,
analyses of implementation of such configurations are still scarce. The
exclusive use of solar panels to support the operation of campus bus routes
has not been thoroughly investigated, while the exploitation of solar energy
for this purpose, despite being promising, is still under consideration. In
their recent study, Chowdhury et al. (2018) considered a single
configuration for energy collection and storage, without penetrating into
the trade-offs between different energy storage and vehicle technology
systems.
(a) Flowchart of the methodology. (b) Vectorized data of
the study area (road network, bus stops, buildings, boundaries – Base Map
Layer: Bing Map v.7, Bing Satellite Base Map).
In this context, this study explores the prospect of replacing conventional
university campus buses powered by fossil fuels with electric ones powered
in priority by solar energy stored in batteries, but also connected to the
central electricity grid. On the basis of existing infrastructure and
facilities in the NTUA campus in Athens (Greece), three scenarios are
developed for the collection and use of solar energy for electric buses: (1) bus stations shelters covered with solar panels, (2) unused open spaces with
solar panels, and (3) solar roads, i.e. specially engineered panels that can
be installed on road surface. Since the availability of solar energy is
linked to sunshine levels, we explore the flexibility in the siting of
panels in each scenario, to select the locations with the highest solar
radiation. In the context of preliminary investigation, capital cost and
energy consumption of the selected vehicles, charging stations and solar
panels are investigated as well. A flowchart of the study is presented in
Fig. 1. Finally, the least intensive scenario in terms of capital cost,
i.e. solar panels in open space, is simulated using observed daily solar
radiation data from a station inside the campus to investigate the
performance of the system at the daily scale in terms of reliability and
profitability arising from the process of buying and selling energy to the
grid. We also evaluate the effect of the scale of the installation of the PV
panels to profit and reliability by repeating the analysis for various sizes
of the PV area.
Data collection
The design of a solar-powered transportation system requires topographic,
transportation as well as solar radiation data. First, the energy demands
for the routes are identified using an average figure for energy consumption
per distance unit, i.e. 1.35 kWh km-1 (Jang, 2018; Gao et al., 2017). Although
this is a crude estimate, it is reasonable, since, inside the campus, there
is no significant stop and go traffic neither large road segments with steep
slopes. Route operational characteristics are collected through the website
of the Athens Public Transport Operator (http://www.oasa.gr/, last access: 21 November 2019). The route is
circular and connects the metro line to the university comprising 19 bus
stops, 10 of which outside the campus (Fig. 1). Three buses run 239 d a
year and the number of bus routes operated daily is 66 covering a total of
495 km. Therefore, in terms of route energy requirements, the total amount
is approximated as 495 km d-1×1.35 kWh km-1= 668.25 kWh d-1.
Additionally, elevation data (Digital Elevation Model – DEM) are acquired
from Hellenic Cadastre at a spatial resolution of 5 m (Fig. 2). Last,
available solar panel and electric bus technologies are examined. The chosen
configurations for the electric buses in terms of battery and charger types
are based on the data reported from recent literature (Lajunen, 2018; Liu
and Song, 2017; Laizāns et al., 2016) and are analyzed in Sect. 4.
Area solar energy estimation and siting of PV panels
For the siting of the PV panels at the NTUA campus, solar radiation is
calculated at the spatial scale of the DEM using the solar radiation
analysis tool of ArcGIS, taking into account topographic effects (Mamassis
et al., 2012). The Area Solar Radiation tool of ArcGIS is based on methods
of the hemispherical viewshed algorithm developed by Fu (2000) and Fu and Rich (2002). The method consists of summing direct and diffuse radiation for
every location on the topographic surface, using a centroid at zenith angle
and azimuth angle, at the case of direct, and corrected by the gap fraction
and angle of incidence, at the case of diffuse radiation. The DEM of the
NTUA campus used in the analysis is expanded to include adjacent hills
that cause shading to the campus area at the morning hours. For the
preliminary stage of the feasibility analysis, the clearness index is
assumed constant disregarding its temporal variation (i.e. no diurnal,
seasonal or annual fluctuations) and with uniform spatial distribution over
the area of the campus. We consider the clearness index equal to 0.6 as
estimated by the expected hourly value of the clearness index during
daylight over the campus (Koudouris et al., 2017).
(a) Digital elevation model (DEM) of the campus. (b) The
total solar radiation at the campus area. (Base Map Layer: Bing Map v.7,
Bing Satellite Base Map).
The surface solar energy (kWh) is calculated as:
Ssur=SA
where S is the solar irradiance during the days of operation
(kWh m-2), i.e. 239 d yr-1, and A (m2) is
the direct area in which solar panels are installed in each case.
Photovoltaic energy generation is calculated as:
E=CFSsur
where the capacity factor (CF) of the conventional solar panels, which
ranges between 13 %–17 % (WEC, 2016) and for our study is assumed as 14 %
(following Jewel et al., 2006), while that of solar roads as 7 % due to
decreased performance from the use of thick protective glass, shading from
moving cars and the accumulation of dust, as explained in Sect. 4.3. The
resulting raster layer of solar radiation at the NTUA campus (Fig. 2) is
used to select the locations for installing solar panels in each scenario,
where possible, among the available areas (Table 1). Out of the three
scenarios examined, scenario 3, i.e. solar roads, albeit its lowest
efficiency in terms of harnessing solar radiation, allows the greater
flexibility in the selection of locations for the installation of solar
roads, due to the large availability of road surface at the campus. In
particular, the top 2400 m2 in terms of solar radiation are
selected out of the total road area of NTUA, which is 37 970 m2 (Fig. 4). In the other two scenarios, siting of solar
panels is restricted by existing locations of bus stops, in scenario 1, and
by the availability of large and unused open spaces, in scenario 2. In the
case of solar bus stops, a slightly higher average solar radiation is
achieved by selecting the 5 out of 9 existing campus bus stops that receive the maximum radiation.
Total area used, direct PV area (A), total solar irradiance (S), total surface solar radiation (Ssur) and total solar energy production (E) during days of operation per year for the three scenarios.
TotalPVSolarSurface solarEnergyareaarea Airradiance (S)radiationproductionScenario(m2)(m2)(kWh m-2)(Ssur) (MWh)E (MWh)1. Solar bus stops1501051109116162. PV area30002100107322523153. Solar roads2400168012322069145Scenario Analysis
Three scenarios are explored for the collection and use of solar energy: (1) solar panels installed on the top of bus station shelters (2) solar panels
installed at unused open spaces and (3) road segments constructed by
photovoltaic material, commonly referred to as solar roads. In all three
cases, it is assumed that a stationary charger at the terminal is required
for the vehicles to start the routes fully charged (Gao et al., 2017). In
the following sections, we analyze of each scenario in terms of its
technical configuration and energy produced.
Scenario 1: Solar bus stops
In this scenario, we explore the emerging concept of solar bus stops, i.e. bus stops collecting solar energy either from their shelter constructed from
PV material or from PV panels installed in close proximity to the bus stop
and transporting the energy to buses via induction (Franken and Meijer,
2014; Kawashima and Fujioka, 2008). This charging configuration is referred
to as opportunity charging, as buses charge during dwell times at stops
through high-power chargers (Iliopoulou et al., 2019; Lajunen, 2018). In
this case, we assume that candidate stop locations must be within the
campus, while the stops exposed to the highest amount of solar radiation
must be exploited. For this purpose, based on the resulting solar radiation
map (Fig. 1), we select the stops located at the areas exposed to the
highest amount of solar radiation. These are five stops namely those at Gate
Kokkkinopoulou, Gate Katechaki, School of Civil Engineering, School of Naval
Engineering and Student Housing. The selected stops are also associated with
high passenger loads (Asimomiti et al., 2019), thus they constitute
advantageous locations for charging during dwell times. The short route
length and thus, the corresponding low energy requirements, allow for the
deployment of low-capacity high-power batteries of 30 kWh (Jungmeier, 2017).
Further, we assume high-power charging of 200 kW and 60 s recharging
during dwell times, which are typical values for the concept of opportunity
charging (Jungmeier, 2017; Jang, 2018). Under these specifications, a
typical energy consumption and recharging scheme for two consecutive routes
is shown in Fig. 3, assuming 97 % charger efficiency (Gao et al., 2017).
Vehicles need to recharge approximately every hour, as the average cycle
time is 40 min with small daily fluctuations while as shown in Fig. 3
charging is required every other route. The solar roof of each bus stop is
assumed to be 30 m2 (10 m × 3 m), a value within the
literature range (Kawashima and Fujioka, 2008). Based on the average annual
solar radiation at the campus (1109 kWh m-2 yr-1), the solar
energy per year at the area of all bus stops is 166 MWh. Considering the
solar panel efficiency rate (14 %), the production of electricity during
the year amounts to 16 MWh in total, or approximately 16/239 = 0.067 MWh d-1
on average for each day of operation. These calculations are summarized in
Table 1. Obviously, the solar panels at the bus stops do not provide enough
energy to autonomously operate the buses and a total of 572 kWh d-1 of
supplementary electricity from the grid would be required on average.
(a) Energy consumption and recharging route profile. (b) The five selected bus stops receiving the highest solar radiation amount
(Base Map Layer: Bing Map v.7, Bing Satellite Base Map).
Scenario 2: Open space with solar panels
In this scenario, we consider the concept of overnight charging, where buses
charge in depots during night time, storing the energy in a high-capacity
battery of 700 kWh. In this configuration energy is produced from an open
parking area of 3000 m2 inside the campus (Fig. 4). The
area of a commercial solar panel is assumed to be 1.93 m2
(Jewel et al., 2016), while its capacity factor is assumed as 14 % (Jewel
et al., 2016). This area could allow for collection of 2252 MWh of solar
energy per year. In turn, the solar panels can generate 315 MWh annually or
approximately 1318 kWh d-1. This amount can adequately meet bus energy
requirements on average. The surplus energy will be sold to the grid.
Calculations are summarized in Table 1.
In the overnight charging method, solar energy collected in the daytime can
be converted to electric power and stored in batteries. Assuming total
energy requirements per vehicle of approximately 111 kWh d-1, electric buses
with battery capacity of 150–180 kWh may be deployed, considering the usable
capacity. With a medium-power 60 kW charger at the depot, an average
charging duration of 3 h would be required to power up a vehicle of 180 kWh (Gao et al., 2017). This charging approach ensures credibility in
running the routes while the large overnight charging durations are not an
issue in this case, as the buses operate during day-time. To meet charging
energy requirements for 3 vehicles, 1 charging station is required.
Scenario 3: Solar roads
The third scenario considers the installation of solar panels along segments
of the NTUA campus road network, in order to collect solar radiation and
dynamically power the buses as they move along them using induction
(Karataraki et al., 2019; Venugopal et al., 2018). To select these segments,
total annual solar radiation on the campus roads is mapped using GIS
software according to the process described in Sect. 3. Figure 4
illustrates the annual radiation for the PV area along the route.
(a) Solar radiation at the open space PV area (scenario 2) and (b) at the solar roads surface (scenario 3). (Base Map Layer: Bing Map
v.7, Bing Satellite Base Map).
The total road surface of the NTUA campus, shown in Fig. 4, is 37 970 m2. Based on the results shown in Fig. 4 on road solar
radiation, road segments of approximately 300 m in length and 8 m in width
(the total area is 2400 m2) are selected, using two
criteria: high incoming solar radiation and low shading from buildings and
trees. We also note that the selected areas have limited traffic and are not
used for short-term parking. Not considering effects from dust accumulation
and dynamic or static shading, the overall annual efficiency of similar
set-ups has been estimated as 8.6 % (Shekhar et al., 2015). In this work,
in order to account for shading from moving cars and increased concentration
of dust, the capacity factor is slightly reduced to 7 %. However, it has
to be noted that the capacity factor of such configurations has been
reported as low as 4 % (https://www.independent.co.uk/environment/solar-panel-roads-energy-bad-idea-renewable-climate, last access: 21 November 2019). The total number of panels installed in this area is thus 1238
(2400/1.937), while the solar energy per year at the area is 2069 MWh. So,
in one year 145 MWh can be produced (Table 1). For 239 d of operation,
the solar roads could on average generate 145/239 = 0.607 MWh d-1.
Preliminary capital cost analysis and scenario comparison
Indicative capital costs for each of the three scenarios are presented in
Table 2, using the PV cost figures found in IRENA (2012) and assuming the
costs for the electric buses and charging devices according to the analysis
of Lajunen (2018). Namely, for the scenario of solar bus stops, costs for
electric buses and dynamic charging devices are taken as EUR 350 000
and 250 000, respectively. Five chargers are required, plus one
at the terminal. Regarding battery costs, the value of EUR 100 kWh-1
(Liu and Song, 2017) is assumed for low-capacity batteries used for the
vehicles (3 batteries of 30 kWh) and for storing solar energy at the
specific bus stops (5 batteries of 10 kWh each). For the scenario of PV
panels in open space, one overnight charging device is required, with a unit
cost of EUR 20 000, and high-capacity batteries for the buses (180 kWh) and for storing the PV area energy (700 kWh) at a cost of EUR 600 kWh-1 (Laizāns et al., 2016). Finally, for the solar roads' scenario, we
assume four fast charging devices for the road segments plus one at the
terminal, and the same battery configuration as in the solar bus stop
scenario. Concerning the cost of PV panels per m2 (including
installation and panels), a value of EUR 4465 kW-1 is assumed for
conventional PV panels (IRENA, 2012) in open space, and twice as much (EUR 8930 kW-1) for PV panels in bus shelters. Finally, a value of EUR 1785 m-2 or EUR 11 905 kW-1 of installed capacity is used for
the cost of solar roads, based on the costs reported for the recent
large-scale solar road application in France (https://theconversation.com/solar-panels-replaced-tarmac-on-a-road-here-are-the-results, last access: 21 November 2019).
Total costs are summarized in Table 2. At this stage lifecycle costs are not
taken into account although it is expected that the solar roads' technology
has the highest ones due to maintenance requirements, since the panels on
the road can be easily broken or ruined (Karataraki et al., 2019).
Overall, the open space scenario is the most advantageous, in terms of
capital cost per unit energy generation, with the primary drawback being the
sub-optimal exploitation of space since the area of installation cannot
support other operational uses as in the other two cases. Evidently, the
solar bus stop technology is overly capital intensive, at the scale
examined, as the cost for batteries, chargers and vehicles is prohibitive
for a system with such low energy generation. Furthermore, there are not
enough suitable stops in order to integrate this technology at a larger
scale. Solar road technology on the other hand, suffers from high
infrastructure cost and lower capacity factors, the combination of which
leads to high capital costs per energy unit produced, even though the scale
of the layout examined is comparable to the open space scenario. The total
capital costs are EUR 171 378 per MWh of energy generated for solar
bus stops, and this drops to EUR 10 891 for PV panels (scenario 2,
which is the most advantageous). It should be also noted that this
comparison refers to solar energy production and does not consider the power
transfer efficiency of each charging scheme. In this case, greater
discrepancies would arise between static and dynamic charging, as in the
former case efficiencies up to 97 % can be attained (Gao et al., 2017)
versus 80 for the latter (Jang, 2018).
Capital cost analysis.
BusStationTotal unitPV panelVehiclesChargersbatterybatteryenergy costScenario(EUR)(EUR)(EUR)(EUR)(EUR)(EUR MWh-1)1. Bus stops167 4211 050 0001 500 00072 0005000171 3782. PV area1 674 2061 050 00020 000270 000420 00010 8913. Solar roads4 284 0001 050 0001 250 00072 000500045 987Simulation of the daily operation of the open space PV system
With both preliminary capital cost investigation and energy generation
estimation converging to the prevalence of scenario 2, additional
investigation is carried out for PV placement in open space, in terms of the
relation of the scale of the system, i.e. the size of the PV surface, with
profit and reliability. For this, a model for the daily operation of the
system is developed based on the layout described in the corresponding
section. Namely, this consists of an area of 3000 m2, in which 375 kW of PV
panels are installed, with a 700 kWh battery for the charging station, used
for overnight charging of the buses. The total daily consumption of the electric buses is
668.25 kWh and the system is connected to the grid, buying for EUR 0.18 kWh-1 when in deficit and selling for EUR 0.08 kWh-1 when in
surplus
Actual values used have seasonal fluctuation and are
scalable. These are averages from price lists in Greece in 2018.
. To
simulate the operation of the system on the daily scale, we use historical
data of daily solar radiation from the meteorological station of NTUA
spanning from 17 Nov 1998 to 5 Sep 2009 (https://openmeteo.org/stations/1334/, last access: 21 November 2019). From the simulation, we identify the
occurrences where the energy demand was not met and thus energy is bought
from the grid, as well as the occurrences in which there was energy surplus
and therefore energy was sold to the grid. Accordingly, we estimate the
buy-sell ratio, and the estimated yearly profit from the operation, both
shown in Fig. 5. It is evident that the system becomes profitable only
when the installed PV surface exceeds 206 m2 while it becomes nearly
autonomous, i.e. achieving a 1 % buy-sell ratio, at 600 m2. Clearly,
for a given configuration, the degree of autonomy of the system is mainly
dependent on the natural variability of solar radiation, whereas its
profitability is additionally highly dependent on the variable cost of
buying and selling to the grid, which here is simplistically assumed to
remain constant over the years. Therefore, in order to reach autonomy, a
much smaller system could be employed, e.g. 600 m2, yet if
profitability is a target, both the life-cycle cost of the system and the
variability of the energy market should be considered.
(a) PV surface vs expected yearly profit and (b) PV
surface vs. buy-sell ratio.
Discussion
Evidently, there is a lot of potential for the implementation of such
systems in places receiving high solar radiation. Yet, more research is
required in order to better account for the variability induced by the
stochastic nature of solar radiation. Here we have used observed historical
solar radiation data from a station inside the campus spanning approximately
10 years of daily data. We note that in general, the spatio-temporal
variability of the hourly clearness index may be significant since it
depends on local topographic characteristics (mostly affecting scenarios 1
and 3) and on the temporal variability, although we do not expect the
spatial variability to be significant at such a small scale. The temporal
variability is triggered by the short-term high autocorrelation as well as
by the long-term behaviour identified from the time series analysis
(Dimitriadis and Koutsoyiannis, 2015) of solar irradiation both over the
area of interest and globally (Koudouris et al., 2017, 2018). A proper
measure to express the strength of the long-term persistence of a stochastic
process is the Hurst parameter, (Hurst, 1951; Koutsoyiannis, 2010).
Interestingly, the Hurst parameter for the clearness index over the area of
interest is estimated around 0.8 (Koudouris et al., 2017) indicating
strong long-term persistence of the process and thus, a relevant model will
be developed in future research to assess the degree of uncertainty induced
from the variability of the clearness index based on synthetic solar
radiation timeseries longer than the historical data used here.
However, even the single simulation of the system based on historical data
is reflective of the effect of the scale of the system on both reliability,
pertaining to minimizing the energy required from the grid, and profit,
estimated from the process of buying and selling energy to the grid and
assuming the present energy prices. Additional considerations on the scale
of the system should take into account both potential conflicting uses of
the available spaces as well as aesthetic issues relating to the potential
degradation of the landscape (Sargentis et al., 2019).
Further research is required in order to quantify the whole life-cycle cost
of the project, taking into account specifications about the batteries
life-cycle as well as maintenance costs, which are expected to be
significant particularly for solar road technology. Level of transit service
provided by the different systems could also be investigated further
(Papantoniou et al., 2019). For example, in scenario 1, additional
stationary time in bus stops is required for charging in contrast to
scenario 2, in which overnight charging is utilized. Efficiency of service
was maintained in scenario 1 through the selection of stations with high
on-off load for charging, but as part of a large investment on the campus
transport system we believe that improving the efficiency of the system
should also be investigated as well as the trade-off between heavier
batteries for overnight charging and bus capacity (Vlahogianni et al., 2018).
Last but not least, it should be noted that the results are highly dependent
on the selected capacity factor, which is shown to often deviate from
theoretical values, especially in renewable energy technologies (Boccard,
2009; Ioannidis and Koutsoyiannis, 2019).
Conclusions
Solar energy is an important source of renewable energy presenting many
opportunities for replacing conventional fossil fuels for various
applications. In the light of emerging renewable energy technologies and
transportation innovations, we explore the possibility to harness solar
power to charge electric buses for a university campus bus route, utilizing
three different solar energy set-ups. All investigated schemes employ
electric buses that require charging equipment and infrastructure for their
operation but differ in the employed solar energy installations; solar
panels on top of bus station shelters (scenario 1), solar panels in
available open spaces (scenario 2), and solar roads (scenario 3). Results
demonstrate that differences are observed among scenarios both in the ratio
of capital cost to expected energy generation as well as in the degree of
siting flexibility, i.e. the availability of areas with high solar radiation
potential and reduced shading.
Regarding the solar energy technologies examined, we note the low capital
cost of conventional PV panels in relation to the alternative technologies.
Evidently, scenario 2 is the most cost-effective, due to low capital cost of
panels, low installation costs and lower cumulative cost of chargers and
batteries for the electric buses. In fact, the requirement of expensive
charging equipment in scenarios 1 and 3 and the high infrastructure cost of
solar road technology are the most decisive factors for this discrepancy.
Overall, we conclude that the utilization of solar energy in its most widely
used form, photovoltaic panels in open spaces (scenario 2), is at the
moment, the most cost-efficient way to introduce solar energy in small-scale
transportation systems. This layout is advantageous because of lower capital
and installation costs for PV panels as well as lower cumulative costs for
battery and chargers for the electrical buses. Meanwhile, advantages related
to siting flexibility for the optimal exploitation of incoming solar
radiation, are case-dependent and might render technologies like solar roads
and solar bus stops – or the combination thereof, necessary, in case
open spaces for the installation of solar PV panels are unavailable.
In general, it is evident that in terms of reliability, even a small-scale
open PV surface system, e.g. 300–600 m2 depending on the desired degree
of autonomy, storing the daily solar energy at batteries and charging the
buses overnight, is a viable option for the given location, which generally
receives considerable amounts of solar radiation yearlong. Additionally, the
overnight charging is expected to be the most advantageous charging scheme
as in contrast to charging at hourly intervals, it is not impacted by the
sub-daily intermittency of solar radiation. Regarding profits, a simple
analysis of the operation of buying and selling to the grid shows that the
system is profitable even at a small scale (>200 m2), while
profits generally rise with the increase of the scale. In any case, capacity
factors are increasing as available technologies are becoming more
efficient, shaping a future technological environment, in which these
schemes will present an economically viable transportation scheme, with the
environmental benefit of reducing dependency on fossil fuels. In this
respect, small-scale closed systems, such as those located inside university
campuses, present an ideal environment for testing the merits, as well as
the technical and the economic viability of emerging renewable energy
technologies.
Data availability
Historical solar radiation data for the NTUA campus can be accessed at https://openmeteo.org/api/stations/1334/timeseries/231/data/?fmt=csv (last access: 21 November 2019; National Technical University of Athens, 2019).
Elevation data (Digital Elevation Model – DEM) can be accessed upon request from the Hellenic Cadastre at http://www.ktimatologio.gr/sites/en/aboutus/Pages/qoQhyNCvtozm6ajS_EN.aspx (last access: 21 November 2019; Hellenic Cadastre and Mapping, 2019). The data are only provided for free to the Greek Public Sector.
Author contributions
Study conception: TI, PD, GK, RI; data analysis: RI,
TI, CI, LK; data collection: LK, AP, MEM, MEA, NP; manuscript preparation: RI,
TI, CI, LK, NM, PD; study supervision: DK, NM, KK, EIV, CP. All authors read
and edited the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “European Geosciences Union General Assembly 2019, EGU Division Energy, Resources & Environment (ERE)”. It is a result of the EGU General Assembly 2019, Vienna, Austria, 7–12 April 2019.
Acknowledgements
An initial version of this work, prepared by the graduating
students and their advising tutors in terms of their projects, was presented
in posters in the EGU 2019 Assembly. All students wish to acknowledge the
School of Civil Engineering of NTUA for giving the opportunity for active
participation into the research process. Authors gratefully appreciate the
constructive comments by Dylan Ryan and an anonymous reviewer which helped improve the paper. We thank Attiko Metro S.A for providing financial support to students for their participation at the EGU General Assembly 2019.
Financial support
This research has been supported by the Eugenides Foundation (Scholarship for doctoral studies in NTUA grant).
Review statement
This paper was edited by Sonja Martens and reviewed by Dylan Ryan and one anonymous referee.
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