# Power sector effects of alternative options for de-fossilizing heavy-duty
vehicles: go electric, and charge smartly

Carlos Gaete-Morales  Julius Jöhrens  Florian Heining  Wolf-Peter Schill

###### Abstract

In the passenger car segment, battery-electric vehicles (BEV) have emerged as
the most promising option to de-fossilize transportation. For heavy-duty
vehicles (HDV), the technology space still appears to be more open. Aside from
BEV, electric road systems (ERS) for dynamic power transfer are discussed, as
well as indirect electrification with trucks that use hydrogen fuel cells or
e-fuels. Here we investigate the power sector implications of these
alternative options. We apply an open-source capacity expansion model to
future scenarios of Germany with high renewable energy shares, drawing on
detailed route-based truck traffic data. Results show that power sector costs
are lowest for flexibly charged BEV that also carry out vehicle-to-grid
operations, and highest for HDV using e-fuels. If BEV and ERS-BEV are not
charged in an optimized way, power sector costs increase, but are still
substantially lower than in scenarios with hydrogen or e-fuels. This is a
consequence of the relatively poor energy efficiency of indirect HDV
electrification, which outweighs its temporal flexibility benefits. We further
find a higher use of solar photovoltaic energy for BEV and ERS-BEV, while
hydrogen and e-fuel HDV lead to a higher use of wind power and fossil
electricity generation. Results are qualitatively robust in sensitivity
analyses without the European interconnection or with capacity limits for wind
power expansion.

###### keywords:

Heavy-duty vehicles , battery-electric vehicles , catenary , hydrogen , power
sector modeling

††journal: arXiv

[DIW]organization=German Institute for Economic Research (DIW Berlin),
addressline=Mohrenstraße 58, city=Berlin, postcode=10117, country=Germany

[ifeu]organization=ifeu - Institute for Energy and Environmental Research,
addressline=Wilckensstraße 3, city=Heidelberg, postcode=69120, country=Germany

##  1 Introduction

Making energy consumption climate neutral in all end-use sectors is of
paramount importance for mitigating climate change [10]. A key strategy for
achieving this is to substitute fossil fuels by renewable electricity,
facilitated by direct or indirect electrification of end uses in mobility,
heating, and industrial applications [41]. In the transportation sector,
battery-electric vehicles (BEV) have emerged as the most promising option for
de-fossilizing the passenger car segment. Already today, BEV can lead to
sizeable greenhouse gas emission reductions compared to internal combustion
engines [19], which will further increase when the electricity mix becomes
cleaner. In many countries, markets for electric passenger cars have been
soaring in the past years, and are expected to continue to grow strongly in
the near future [20]. For heavy-duty vehicles (HDV), however, the technology
space still appears to be more open. While the feasibility of pure battery-
electric HDV has been assessed to be increasing [30], they compete with other
options. This includes electric road systems (ERS), which allow for dynamic
power transfer to electric vehicles on the road [3, 42]; trucks with hydrogen
fuel cells; or conventional HDV with internal combustion engines that use
power-to-liquid (PtL), also referred to as e-fuels, which are produced mostly
with renewable electricity [17, 22, 34, 23].

These options of direct or indirect electrification of HDV have different
properties concerning, on the one hand, energy efficiency, and, on the other
hand, temporal flexibility of electricity use. For example, direct
electrification via BEV is more energy efficient compared to indirect
electrification via electrolysis-based hydrogen or e-fuels [46, 22]. Yet, the
temporal flexibility of BEV may be constrained by charging availability and
limited battery capacities, as vehicle batteries are costly and heavy. In
contrast, indirect electrification via hydrogen or e-fuels may entail large-
scale and low-cost storage options [44, 43], but the overall energy efficiency
of these supply chains is lower compared to BEV. Temporal power sector
flexibility becomes increasingly important with growing shares of renewables,
as the potential for firm renewable generation such as hydropower, bioenergy,
or geothermal power is limited in many countries. In contrast, wind and solar
power potentials are often abundant, but they have variable generation
profiles that depend on weather conditions and daily and seasonal cycles [26].
Integrating growing shares of variable renewable energy sources thus requires
an increasing use of temporal and spatial flexibility options in the power
sector. Flexibility may be provided on the supply side (e.g., dispatchable
generators) or the demand side (e.g., flexible loads), and via energy storage
or transmission [21, 40].

Against this background, we investigate the power sector implications of
different options for (in-)directly electrifying HDV, particularly focusing on
the trade-off between energy efficiency and temporal flexibility. To do so, we
apply an open-source capacity expansion model [47, 13] to 2030 scenarios of
the Central European power sector with high renewable energy shares. We focus
on the domestic traffic of HDV in Germany with a gross vehicle weight above 26
tonnes, drawing on a detailed data set of truck trips on inner-German routes.
We include stationary-charged BEV trucks as well as hybrid battery-catenary
trucks as a particular example of an electric road system technology (ERS-
BEV), fuel-cell hydrogen electric trucks (FCEV), and such with internal diesel
combustion engines powered by e-fuels (ICEV PtL), see Figure 1. We compare the
power sector costs of these options, as well as their repercussions on the
optimal power plant fleet and its use.

Figure 1: Overview of direct and indirect HDV electrification options covered
in this analysis. For direct electrification via BEV and ERS-BEV we
differentiate scenarios with flexible charging without and with V2G, as well
as scenarios with inflexible, i.e., non-optimized charging. Hydrogen and PtL
options are assumed to be always operated flexibly, constrained by hydrogen or
e-fuel storage capacities, and do not allow reconversion to electricity.

While there is a broad literature on the potential power sector impacts of
battery-electric passenger cars [36, 28, 27], according research for electric
HDV is sparse. A common finding of analyses focusing on passenger cars is that
controlled charging can help to avoid problematic load peaks, to integrate
renewable electricity generation, and to lower overall system costs [38, 16,
37]. In particular, feeding back electricity from car batteries to the system
(vehicle-to-grid, V2G) can help to balance the diurnal variability of solar
photovoltaic (PV) generation [7].

How electrified HDV interact with the power sector is hardly explored so far.
Some analyses investigate grid or peak load impacts without using power sector
models. For example, an analysis of the distribution grid impacts of BEV-HDV
with depot charging finds that many existing substations in the US would allow
for substantial HDV charging without grid upgrades [4]. Earlier studies
conclude that local grid impacts of electrified HDV near logistics centres and
along major roads may be substantial in Switzerland and Finland [24], or that
ERS-HDV could strongly increase the peak load in Norway [44]. Using capacity
expansion power sector models, other analyses come to somewhat deviating
conclusions on the impacts of electric HDV on the overall load profile and the
wider power sector; in a Scandinavian-German case study that includes ERS-HDV
in a subset of scenarios, effects are relatively substantial [45], while in a
European model study the impacts are more moderate [33]. Importantly, both
analyses assume exogenous, i.e., inflexible, charging profiles of HDV and do
not temporally optimize their (dis-)charging operations. Beyond power sector
effects, other studies focus on the impacts of alternative HDV options on
energy efficiency and its consequences for the total cost of ownership (TCO)
[29], or explore potential health and climate impacts of electric HDV [25].

We contribute to the literature with a dedicated analysis of the power sector
impacts of different HDV electrification options that co-optimizes their
charging and discharging operations (including V2G) with the capacity and
dispatch decisions in the power sector. We do so for a wide range of HDV
technologies, including dynamic power supply via electric road systems. We use
a power sector model that fully captures the hourly variability of load and
renewable generation over all hours of a full year, and apply it to a future
scenario with high shares of variable renewables. The model code and all input
data, including detailed hourly HDV mission profiles for domestic transport in
Germany, are provided open-source for transparency and reproducibility (see
4).

##  2 Results

In the main part of this paper, we show results for a setting where Germany is
interconnected with its neighbors, defined as the baseline model
specification. While this configuration can be considered to be a policy-
relevant scenario for 2030, where electric HDV in Germany may benefit from the
flexibility provided by the European interconnection, we also provide results
for a case where the German power sector is modeled in isolation in the
Supplemental Information (SI.4.1).111In the SI, we also show further results
for baseline assumptions, see section SI.3; another sensitivity analysis
explores the effects of an upper bound for wind power capacity, as the real-
world potential for wind capacity expansion by 2030 is uncertain, see section
SI.4. We compare nine HDV scenarios against a reference scenario without any
electrification of HDV (Ref). The first three scenarios cover pure BEV trucks
with varying degrees of flexibility: these are either charged as flexibly as
possible (BEV Flex), additionally have the option of feeding back electricity
to the grid (BEV Flex V2G), or are charged in a way that is not co-optimized
with the power sector (BEV Inflex). There are three respective scenarios for
ERS-BEV, which have smaller batteries than pure BEV, as they may also draw
electricity from an overhead line network. Three additional scenarios cover
indirectly electrified HDV powered by hydrogen or hydrogen-derived e-fuels. As
for fuel-cell trucks, we differentiate two domestic hydrogen supply chains,
either centralized electrolysis and transport via liquefied hydrogen, which
also comes with low-cost storage opportunities and thus higher temporal
flexibility (FCEV Centralized), or on-site electrolysis at filling stations,
which is temporally inflexible (FCEV On-Site). The final scenario represents
conventional trucks with internal combustion engines powered by e-fuels (ICEV
PtL). Hydrogen and e-fuels are assumed to be produced in Germany via
electrolysis and, in the case of PtL, using a non-specified but low-cost
carbon source. To separate effects, we assume that the whole domestic fleet of
HDV (318,700 vehicles > 26 t) in the year 2030 consists of the particular
vehicle type (compare 1).

###  2.1 Lowest power sector costs and electricity prices for BEV with V2G

Compared to the reference case without electrified trucks, yearly power sector
costs222Power sector costs comprise fixed and variable costs of all
electricity generation and storage technologies over a full year. They do not
include the costs of HDV electrification infrastructure, e.g., overhead lines,
charging stations, or filling stations for hydrogen or PtL. increase in all
scenarios with electrified HDV (Figure 2, upper panel). That is, the cost of
the additional electricity demand induced by HDV always outweighs their
potential flexibility benefits. Cost effects, however, vary considerably
between different options. Flexible BEV with V2G incur the lowest additional
power sector costs of 1.8 bn Euros/year (around 5,600 Euros/year per vehicle),
followed by optimally charged BEV without V2G with 2.3 bn Euros/year (around
7,200 Euros/year per vehicle). If BEV charging is not optimized, system costs
are markedly higher, with 3.8 bn Euros/year (11,900 Euros/year per vehicle).
Results are qualitatively similar for ERS-BEV, but on a slightly higher cost
level. The differences between the three ERS-BEV cases are less pronounced
than for pure BEV, as their temporal flexibility potential is much smaller.
The battery capacity of an ERS-BEV is only around a quarter of that of a pure
BEV (655 kWh usable capacity per truck vs. 181 kWh), so ERS-BEV can make less
use of hours with low electricity prices (compare also Figure SI.9). In
contrast, power sector costs are substantially higher for FCEV (12.6 or 12.7
bn Euros/year, for decentralized or centralized hydrogen provision, i.e.
around 39,700 Euros/year per vehicle) and even more so for PtL (16.8 bn
Euros/year, or 52,700 Euros/year per vehicle). This is a direct consequence of
high conversion losses of hydrogen and PtL supply chains and vehicles drive
trains. Because of these losses, the two hydrogen supply chains increase the
electricity demand more than twice as much as the battery-electric options.
The electricity demand of PtL-HDV is nearly four times as high as in the case
of BEV. Notably, the cost differences between BEV and ERS-BEV scenarios are
much smaller than the differences between these direct-electric options and
indirect electrification via hydrogen or PtL.

Figure 2: Changes in yearly power sector costs and electricity demand induced
by different HDV options (upper panel), and average wholesale market prices of
charging electricity (lower panel).

Complementary to power sector costs, we also evaluate average yearly wholesale
electricity prices for HDV electricity (Figure 2, lower panel).333We interpret
the dual variables of the model’s energy balance as hourly wholesale prices,
which is common practice in energy modeling [6, 26]. This allows us to largely
separate the differences in overall electricity consumption of the various HDV
options from their ability to make use of low-cost electricity. Average prices
are calculated by multiplying hourly wholesale prices of electricity consumed
by the different HDV options or fed back to the grid with respective hourly
quantities, summing up over the whole year, and dividing by the overall
electricity consumption of the fleet. That is, the numbers also account for
revenues of electricity sold via V2G. That is, we assume that HDV operators
receive the respective hourly wholesale price whenever they feed electricity
back to the grid. BEV with V2G accordingly face the lowest average electricity
prices, as these also benefit from revenues of feeding back to the grid,
followed by BEV without V2G.

Average electricity prices paid by ERS-BEV are somewhat higher, as their
smaller batteries limit the ability to optimize their charging and V2G
decisions. In contrast, pure BEV can leverage their larger batteries to make
better use of hours with low electricity prices. In the case of inflexible
charging, results change: average electricity prices faced by inflexibly
charged BEV are high, and even slightly above those of inflexible ERS-BEV.
This is because inflexibly charged BEV benefit less from cheap electricity
prices around midday related to abundant PV feed-in than inflexible ERS-BEV,
which on average have a better (catenary) grid connection during these hours.

Prices for electricity used in hydrogen and PtL supply chains are in the same
range as those of ERS-BEV options but below those of inflexibly charged BEV or
ERS-BEV. Further, centralized hydrogen and PtL supply can make use of lower
prices than decentralized supply, because their low-cost storage options allow
for higher temporal flexibility. Electricity prices of centralized hydrogen
supply chains and PtL are also cheaper than those faced by inflexibly operated
BEV or ERS-BEV. In terms of overall costs, these temporal flexibility benefits
are, however, by far outweighed by the higher overall energy consumption of
the FCEV and PtL options (Figure 2, upper panel).

The average wholesale price in the reference scenario without electrified HDV
is 84.9 Euros/MWh. That is, inflexibly charged BEV and ERS-BEV, as well as
inflexible on-site electrolyzers, face slightly above-average prices. In
contrast, the more flexible HDV options can make use of below-average market
prices, and partly so to a very substantial extent.

###  2.2 Capacity and dispatch effects

Figure 3: Effects of different HDV scenarios on optimal generation capacity
(upper panel) and on yearly generation (lower panel) in Germany.

The upper panel of Figure 3 shows optimal generation capacities in the
reference (on the left) and the changes induced by HDV (on the right) in
Germany. In the reference, variable solar PV and onshore wind power dominate
the capacity mix.444In the Supplemental Information, we discuss the effects of
including an upper bound for wind power investments by the year 2030, see
section SI.4. These are complemented by smaller firm capacities of natural gas
and bioenergy. Optimal capacity additions related to the electrification of
HDV are predominantly a mix of solar PV and onshore wind power, as these are
the lowest-cost options that are available. Flexible BEV and ERS-BEV lead to
the highest PV shares in the capacity additions, especially if combined with
V2G. BEV trucks with V2G essentially serve as a short-duration electricity
storage option with a relatively low energy-to-power ratio and many yearly
cycles, which favors the expansion of solar PV. HDV options that are
temporally less flexible or that, overall, require more electricity favor
higher onshore wind power capacities. In this case, the wind power generation
profile fits better to HDV electricity demand than solar PV. Offshore wind
power is not added here because of relatively unfavorable cost assumptions
compared to onshore wind power.555Given the outcomes of current tenders for
offshore wind power capacity in Germany, it may play a larger role than
assumed here, while the real-world potentials to expand onshore wind power may
in fact be more limited. FCEV and PtL options have the highest capacity needs
because of substantial conversion losses. On-site electrolysis further
requires a substantial addition of long-duration electricity storage capacity
(power-to-gas-to-power) of 9.4 GW and 6.0 TWh (for more details, see section
SI.3.1). This is necessary to compensate for the temporal inflexibility of on-
site electrolyzers without large-scale hydrogen storage, which are forced to
operate with a similar time profile as hydrogen demand. No hard coal, lignite
or oil generation capacities are added, partly due to a CO2 price of 100
Euros/ton which discourages such investments, but some natural gas (OCGT)
generators. Especially in the scenarios with low temporal flexibility (BEV
Inflex, ERS-BEV Inflex and FCEV On-Site), the expansion of natural gas (CCGT)
capacity is at the assumed upper limit of 17.6 GW (compare section 4).

Yearly electricity generation in the reference, as well as the changes induced
by different HDV scenarios, are shown in the lower panel of Figure 3. The
graph also includes electricity storage output. Here, the picture is generally
similar to optimal capacity changes, but the share of wind power in additional
electricity generation tends to be higher because of higher full-load hours as
compared to solar PV. Flexible BEV, particularly if combined with V2G, cause
the highest increase in PV generation, which also drives up the overall
renewable share of the German power sector to above 81%. Indirect HDV
electrification options that use centralized hydrogen or PtL supply chains,
which require much more electricity than BEV and ERS-BEV, use their temporal
flexibility to also increase electricity imports and the use of the most
energy-efficient natural gas-fired power plants (CCGT). This is different in
the case of temporally inflexible hydrogen production at filling stations
(FCEV On-Site). As imports and CCGT are less available in the hours of
decentralized hydrogen production, more wind and solar generation in
combination with long-duration electricity storage are used, which also
increases the share of renewables in the overall system to nearly 83%. Higher
utilization of natural gas power plants also translates into increasing direct
carbon emissions, as discussed in section SI.3.4. The effects of BEV and ERS-
BEV on CO2 emissions are generally lower, and may even become negative when
combined with V2G. This is because V2G allows for an increase in solar
photovoltaic capacity and a higher renewable energy share, which can slightly
reduce overall carbon emissions compared to the reference case without
electrified HDV.

###  2.3 Time series reveal differences in flexibility characteristics

Figure 4: Electricity time series sample of five exemplary days (unit: GW):
“HDV electricity demand” illustrates electricity flowing to BEV or ERS-BEV
batteries, including amounts later charged back via V2G, or electricity needs
of hydrogen and PtL supply chains; “HDV discharging to the grid” are V2G power
flows from BEV or ERS-BEV to the grid; and “Residual load” is the net load of
the electricity sector after considering the feed-in potential of variable
renewables. The samples start on a Saturday. BEV Flex and ERS-BEV Flex without
V2G are omitted in the graph, as charging patterns are very similar to the
shown V2G cases. Additional time series are shown in the SI.

Flexibly operated BEV trucks (BEV Flex V2G) are able to charge their batteries
in hours of low residual load, as illustrated in Figure 4. Such periods
generally go along with low wholesale market prices (compare Figure SI.9).
This enables BEV to utilize renewable surplus energy (i.e., negative residual
load) to a substantial extent. They also make use of the V2G option and feed
back renewable surplus energy to the grid, whenever the battery capacity is
not needed for driving. In the exemplary illustration, this is visible
particularly in summer during weekends, when trucks are not driving, but
assumed to have a grid connection (top right panel in Figure 4)666Note that
the time series shown begin on a Saturday. For simplification, we assume that
both Saturday and Sunday are completely truck-free.. Here, BEV with V2G charge
their batteries with solar PV surplus energy on Saturday afternoon, and feed
it back to the grid in the night between Saturday and Sunday. This is
different in the following days, as HDV are used between Monday and Friday,
and much less battery capacity is available for V2G. If BEV charging is not
optimized, but follows an inflexible, pre-determined pattern (BEV Inflex),
charging profiles are less peaky and much more balanced. BEV are not able to
make particular use of cheap renewable surplus electricity in this case, but
carry out a substantial part of their charging in hours with positive residual
load.

ERS-BEV generally follow similar patterns as pure (non-catenary) BEV. Yet,
their smaller batteries make ERS-BEV temporally less flexible, so they can
make less use of renewable surplus energy and have to draw more electricity
from the grid during hours with positive residual load. For the same reason,
their potential for feeding electricity back to the grid is also much smaller
than in the case of pure BEV. ERS-BEV sometimes also charge their batteries
during driving (compare Figure SI.10).

Hydrogen and PtL supply chains show very different patterns of electricity
use. Centralized hydrogen supply chains (FCEV Centralized), which come with
large-scale hydrogen storage capacities, generally have a flat consumption
profile in many hours, as their high fixed costs make it optimal to use them
with relatively high full load hours. This limits their ability to make use of
renewable surplus energy. Yet, they can use the temporal flexibility provided
by large-scale hydrogen storage to reduce electricity consumption in hours of
high positive residual load, i.e., high prices. Decentralized hydrogen supply
chains (FCEV On-Site) have an electricity use profile that largely follows the
hydrogen demand profile, driven by limited on-site hydrogen storage capacity.
Accordingly, they are less able to avoid electricity consumption in hours of
high residual load. The PtL supply chain (ICEV PtL) also comes with large-
scale storage and thus has a relatively similar pattern as the centralized
hydrogen option. The load, however, is generally higher because of higher
overall energy consumption of PtL versus hydrogen. This goes along with larger
investments in electrolyzers, which are 24.4 GW in the ICEV PtL scenario, as
compared to 13.5 GW for FCEV Centralized (see section SI.3.2 for more
details).

##  3 Discussion

###  3.1 Limitations and avenues for future research

Our model-based analysis of the power sector effects of alternative options
for de-fossilizing heavy-duty vehicles inevitably has a range of limitations.
We briefly discuss how some limitations may qualitatively impact our results.

First, we deliberately focus on a detailed analysis of the power sector
implications of alternative HDV electrification options and do not aim to
compare the overall system costs of different options. The latter would also
require considering infrastructure costs related to catenary lines, chargers,
or filling stations, as well as differences in vehicle costs. Such future
costs are highly uncertain, so Monte Carlo approaches or other methods to deal
with parameter uncertainty would have to be used. Note that the differences in
the costs of vehicles and related charging infrastructure might in fact
outweigh some of the differences in power sector costs identified here,
especially the relatively small differences between pure BEV and ERS-BEV. Yet,
the very large power sector cost differences between (ERS-)BEV on the one
hand, and FCEV or PtL trucks on the other, would require the latter to be
substantially cheaper in terms of vehicle and infrastructure costs in order to
break even, which appears unlikely.

While focusing on the power sector, we further assume extensive charging and
overhead line infrastructure to be available for BEV and ERS-BEV, both in
depots and en route, and that vehicles are always connected to the grid when
idling. In reality, the availability of grid connections is likely to be lower
for a variety of reasons, which would decrease the flexibility potentials of
optimized charging. The flexibility benefits of realizing very high levels of
grid connection may not necessarily outweigh related infrastructure costs [5].

Another simplification of our numerical analysis is the assumption of perfect
foresight, which results in an upper limit of the flexibility benefits that
can be realized by optimized (dis-)charging of BEV and ERS-BEV. We also
abstract from including electric passenger vehicles or other future sector
coupling options, which may provide flexibility to the power sector and
compete with electric trucks for electricity use in times of high renewable
availability. In particular, power-to-heat technologies may be able to
efficiently exploit hours of high renewable generation and low energy prices,
as heat storage is relatively cheap compared to electricity storage [2].

To facilitate numerically efficient solutions and keep results tractable, we
further assume electricity generation capacities to be fixed in Germany’s
neighboring countries. At the same time, the modeled HDV fleets only cover
domestic truck transport in Germany. This may lead to an overestimation of the
power sector flexibility supplied by Germany’s neighbors for integrating
electric trucks in Germany.

Future research may address some of these limitations. While our analysis
focuses on the power sector, it also appears desirable to investigate
complementary cost measures. This includes detailed total cost of ownership
analyses from a HDV operator perspective, as well as investigations of overall
system cost effects, which would also consider cost differences of charging
and ERS infrastructures, as well as purchase cost differences of different
types of HDV. This requires a whole set of additional and detailed parameter
assumptions, which we leave for future research. Further, it would be
desirable to carry out similar analyses for other countries and world regions.
While we focus on Germany, we expect that general results should also hold for
comparable non-hydro electricity sectors in other countries in temperate
climates.

###  3.2 Differences in temporal flexibility and energy efficiency drive
effects

Our analysis focuses on the effects of alternative options for de-fossilizing
heavy-duty vehicles on power sector costs as well as optimal investment and
dispatch decisions of electricity generation and storage technologies. We find
that differences in temporal flexibility and energy efficiency are important
drivers of results.

Fleets of BEV or ERS-BEV trucks that are charged in an optimized way cause the
lowest increase in power sector costs. This is because they require much less
electricity than HDV powered by electrolysis-based hydrogen or e-fuels. At the
same time, they can offer temporal flexibility to the power sector, which
lowers the costs compared to non-optimized charging. This is increasingly
valuable with growing shares of variable renewable energy sources. Flexibility
benefits are particularly large if BEV also carry out vehicle-to-grid
operations, i.e., if the truck batteries can be used as a distributed grid
storage option to some extent. In contrast, indirect electrification of the
HDV fleet via FCEV or PtL incurs much higher power sector costs, even if
compared to inflexibly charged BEV or ERS-BEV. That is, the temporal
flexibility benefits of centralized hydrogen or PtL supply chains, which come
with low-cost storage options, do not outweigh their energy efficiency
drawbacks in terms of power sector costs.

Alternative options of (in-)directly electrifying HDV fleets further entail
different optimal generation capacity mixes. The additional electricity for
battery-electric HDV is largely supplied by solar PV in the lowest-cost
solution, particularly if V2G is also available. In contrast, centralized
hydrogen and PtL supply chains go along with higher optimal shares of wind
power, and also lead to a higher use of non-renewable electricity generators.
The latter translates into higher direct carbon emissions of these
technologies. However, emission outcomes depend on the assumed boundary
conditions, such as capacity bounds for non-renewable generators and CO2
prices.

Additional sensitivity analyses show that results are generally robust. For
instance, Germany is modeled as an electric island in a sensitivity analysis,
i.e., the flexibility benefits of the European interconnection are neglected
(section SI.4.1). In such a case, flexible BEV with V2G are even more
beneficial, and inflexible BEV operations lead to slightly higher cost
increases because an isolated power sector is more flexibility-constrained.
Further, results are robust against the assumption that the potential for wind
power expansion is limited, and solar PV accordingly plays a larger role
(section SI.4.2).

###  3.3 Direct electrification and smart charging preferable

From a pure power sector perspective, direct electrification of the truck
fleet would thus be clearly preferable. Aside from the cost benefits
determined here, this appears to be even more important as the utilization of
renewable energy sources is unlikely to grow exponentially [18], and renewable
growth rates in fact have been smaller than what would be required in
$1.5^{\circ}C$-compatible scenarios [9]. A similar argument has been made for
the global scale-up of electrolysis [31]. All of this calls for energy-
efficient, direct electrification options of HDV wherever possible.

Moreover, smart charging of BEV or ERS-BEV is highly desirable, as it provides
temporal flexibility which leads to lower power sector costs and CO2
emissions, compared to non-optimized charging. This corroborates the findings
of Pickering et al., who also highlight the benefits of controlled vehicle
charging in decarbonized and largely renewable future European energy systems
[32]. If feeding back electricity to the grid via V2G is possible, this
further lowers the costs and increases the optimal shares of solar PV.

##  4 Experimental procedures

###  4.1 The power sector model DIETER

We use the open-source power sector model Dispatch and Investment Evaluation
Tool with Endogenous Renewables (DIETER). It is a linear program that
minimizes power sector costs by optimizing capacity and dispatch decisions for
a full year in an hourly resolution [47, 13]. Its objective function includes
fixed an variable costs of all electricity generation and storage
technologies, electrolysis and PtL plants, as well as hydrogen or e-fuels
transportation. It does not include the costs of charging or catenary
infrastructure, hydrogen filling stations, or PtL filling stations.
Accordingly, the power sector cost figures provided above do not include the
costs of HDV electrification infrastructure. We further do not consider the
option of hydrogen imports, as these are likely to be unavailable at scale by
2030. In general, the global scaling up of green hydrogen supply remains
uncertain [31].

Endogenous model variables include power sector costs, optimal generation and
electricity storage capacities (Germany) and their hourly use (all countries),
hourly decisions for HDV charging and discharging, as well as the capacity and
operational decisions of electrolysis and PtL generation and storage
infrastructure. In addition, we interpret the marginals of the hourly energy
balance as wholesale prices [6].

Exogenous model inputs include fixed and variable costs of all electricity
generation and storage technologies, efficiency parameters, as well as time-
series variable renewable energy availability profiles and electric load. In
the case of inflexible HDV charging (BEV Inflex, ERS-BEV Inflex), we assume
that the vehicles always start charging as soon as an opportunity arises, and
that vehicles batteries are fully charged by the time the next trip starts, if
possible. That is, the charging power is lower, the longer a vehicle is
connected to the grid. This resembles the “balanced” charging profile defined
in [14].

The geographic scope of the model version used here includes Germany and its
neighboring countries plus Italy. In order to reduce numerical complexity and
improve tractability, we allow for endogenous generation capacity investment
only in Germany, and fix the power plant portfolio for the other countries to
values derived from ENTSO-E’s Ten Year Network Development Plans [11, 12]. We
further assume upper limits for investments into fossil generation
technologies according to the main scenario for 2030 from the German Federal
Grid Development plan (NEP, [8]).

Different versions of the model have been used in various earlier studies to
investigate various aspects of renewable energy integration, electricity
storage, and sector coupling [e.g., 39, 40, 43, 15]. The DIETER model is
available open-source.777General DIETER repo: https://gitlab.com/diw-
evu/dieter_public. The particular model version used for the present study,
including input data as well as model results, are available under permissive
licenses in a dedicated repository: https://gitlab.com/diw-
evu/projects/my_eroads. A general model documentation is available here:
https://diw-evu.gitlab.io/dieter_public/dieterpy/.

###  4.2 Traffic data of heavy-duty vehicles

We generate synthetic truck usage patterns that are intended to approximate
the German fleet of HDV larger than 26 tons. The main data source for the
usage patterns is the traffic model PTV Validate [35], from which we extract a
database of daily truck trips in domestic German road freight transport. In
order to derive typical daily driving profiles, the following steps are
performed (for more detailed information, see SI):

  1. 1.

Calculation of annual mileage for transport relations. For this step, we build
on statistics of typical annual mileages for different distance classes and
derive a steady function.

  2. 2.

Calculation of daily idle time. This is calculated based on the number of
daily truck trips (and the number of stops in between) as well as typical
times for loading and unloading, depending on the goods type. In addition, a
mandatory driver’s break of 45 minutes is assumed for daily driving profiles >
4.5 hours.

  3. 3.

The daily operating time of a vehicle on a particular route is determined from
the above assumptions using the formula “operating time = journey time + idle
time + driver’s break”.

Repeating steps 1-3, synthetic daily travel profiles with a certain daily
operating time are obtained for all transport relations in the model. In order
to limit these to a manageable number of daily driving profiles for the power
sector model, all relations that have approximately the same daily operating
time are combined. We derive a number of 19 daily profiles which are
distributed over the course of the day such that the empirically observed
course of the daily mileage on the German road network is reproduced. For
these profiles, the time series of charging availability (in the depot, during
idle and driver’s resting times) and of the electricity demand of pure BEV-HDV
(500 km battery range) and ERS-HDV (150 km battery range) are calculated. The
resulting electricity demands and charging availabilities are used as inputs
for the DIETER model.

For FCEV and PtL trucks, only aggregate fuel demand time series are required.
These are based on the same empirical traffic data from German highways [1].
For FCEV, we assume a specific hydrogen demand of 6.8 kg/100km, and for ICEV-
PtL a specific Diesel demand of 27.1 l/100km.

## Acknowledgments

We thank Hinrich Helms and Michel Allekotte for their valuable comments on
earlier drafts. This work has benefited from research grants by the Federal
Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) and
by the Federal Ministry for Economic Affairs and Climate Action via the
projects “My eRoads” (Fkz 16EM4006-1) and “enERSyn” (Fkz 01MV22004B).

## Author contributions

Conceptualization: JJ, WPS; Methodology: CG, FH, JJ, WPS; Software: CG;
Investigation: CG, FH, JJ, WPS; Data curation: FH, WPS; Writing - original
draft: CG, WPS; writing - review & editing FH, JJ; Visualization: CG, FH;
Project administration: JJ; Funding acquisition: JJ, WPS.

## Resource availability

The DIETER model version used for the present study, including input data as
well as model results, are available under permissive licenses in a public
repository: https://gitlab.com/diw-evu/projects/my_eroads.

## References

  * [1] BASt (2023) Automatische Zählstellen 2018.  Note: https://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/Aktuell/zaehl_aktuell_node.html Cited by: §4.2.
  * [2] A. Bloess, W. Schill, and A. Zerrahn (2018) Power-to-heat for renewable energy integration: a review of technologies, modeling approaches, and flexibility potentials.  Applied Energy 212,  pp. 1611–1626.  External Links: Document Cited by: §3.1.
  * [3] M. Boltze, R. Linke, F. Schöpp, J. Wilke, O. Öztürk, and D. Wauri (2020-05) Insights into the operation of overhead line hybrid trucks on the elisa test track.  In ERSC2020 - 4th Electric Road Systems Conference,  pp. 1–6.  Cited by: §1.
  * [4] B. Borlaug, M. Muratori, M. Gilleran, D. Woody, W. Muston, T. Canada, A. Ingram, H. Gresham, and C. McQueen (2021) Heavy-duty truck electrification and the impacts of depot charging on electricity distribution systems.  Nature Energy 6 (6),  pp. 673–682.  External Links: Document Cited by: §1.
  * [5] N.B.G. Brinkel, W.L. Schram, T.A. AlSkaif, I. Lampropoulos, and W.G.J.H.M. van Sark (2020) Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits.  Applied Energy 276,  pp. 115285.  External Links: Document Cited by: §3.1.
  * [6] T. Brown and L. Reichenberg (2021) Decreasing market value of variable renewables can be avoided by policy action.  Energy Economics 100,  pp. 105354.  External Links: Document Cited by: §4.1, footnote 3.
  * [7] T. Brown, D. Schlachtberger, A. Kies, S. Schramm, and M. Greiner (2018) Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system.  Energy 160,  pp. 720–739.  External Links: Document Cited by: §1.
  * [8] Bundesnetzagentur (2018) Genehmigung des Szenariorahmens 2019-2030.  Note: Available at: https://www.netzentwicklungsplan.de/sites/default/files/paragraphs-files/Szenariorahmen_2019-2030_Genehmigung_0_0.pdf Cited by: §4.1.
  * [9] A. Cherp, V. Vinichenko, J. Tosun, J. A. Gordon, and J. Jewell (2021) National growth dynamics of wind and solar power compared to the growth required for global climate targets.  Nature Energy 6 (7),  pp. 742–754.  External Links: Document Cited by: §3.3.
  * [10] H. de Coninck, A. Revi, M. Babiker, P. Bertoldi, M. Buckeridge, A. Cartwright, W. Dong, J. Ford, S. Fuss, J.-C. Hourcade, D. Ley, R. Mechler, P. Newman, A. Revokatova, S. Schultz, L. Steg, and T. Sugiyama (2018) Strengthening and Implementing the Global Response.  In Global Warming of 1.5${}^{\,\circ}$C. An IPCC Special Report on the Impacts of Global Warming of 1.5${}^{\,\circ}$C Above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty, V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P. R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J. B. R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (Eds.),  pp. 313–444.  External Links: Document Cited by: §1.
  * [11] ENTSOE (2018) TYNDP 2018. Project Sheets.  Note: https://tyndp.entsoe.eu/tyndp2018/projects/projects Cited by: §4.1.
  * [12] ENTSOE (2020) TYNDP 2020 – Scenarios [online].  Note: https://www.entsos-tyndp2020-scenarios.eu/ Cited by: §4.1.
  * [13] C. Gaete-Morales, M. Kittel, A. Roth, and W. Schill (2021) DIETERpy: a Python framework for the Dispatch and Investment Evaluation Tool with Endogenous Renewables.  SoftwareX 15,  pp. 100784.  External Links: Document Cited by: §1, §4.1.
  * [14] C. Gaete-Morales, H. Kramer, W. Schill, and A. Zerrahn (2021) An open tool for creating battery-electric vehicle time series from empirical data, emobpy.  Scientific Data 8 (1),  pp. 152.  External Links: Document Cited by: §4.1.
  * [15] H. C. Gils, H. Gardian, M. Kittel, W. Schill, A. Murmann, J. Launer, F. Gaumnitz, J. van Ouwerkerk, J. Mikurda, and L. Torralba-Díaz (2022) Model-related outcome differences in power system models with sector coupling — quantification and drivers.  Renewable and Sustainable Energy Reviews 159,  pp. 112177.  External Links: Document Cited by: §4.1.
  * [16] T. Gnann, A. Klingler, and M. Kühnbach (2018) The load shift potential of plug-in electric vehicles with different amounts of charging infrastructure.  Journal of Power Sources 390,  pp. 20–29.  External Links: Document Cited by: §1.
  * [17] I. Hannula and D. M. Reiner (2019) Near-term potential of biofuels, electrofuels, and battery electric vehicles in decarbonizing road transport.  Joule 3 (10),  pp. 2390–2402.  External Links: Document Cited by: §1.
  * [18] J.P. Hansen, P.A. Narbel, and D.L. Aksnes (2017) Limits to growth in the renewable energy sector.  Renewable and Sustainable Energy Reviews 70,  pp. 769–774.  External Links: Document Cited by: §3.3.
  * [19] A. Hoekstra (2019) The underestimated potential of battery electric vehicles to reduce emissions.  Joule 3 (6),  pp. 1412–1414.  External Links: Document Cited by: §1.
  * [20] IEA (2022) Global EV outlook 2022.  Note: Parishttps://www.iea.org/reports/global-ev-outlook-2022 Cited by: §1.
  * [21] H. Kondziella and T. Bruckner (2016) Flexibility requirements of renewable energy based electricity systems - a review of research results and methodologies.  Renewable and Sustainable Energy Reviews 53,  pp. 10–22.  External Links: Document Cited by: §1.
  * [22] S. M. Lajevardi, J. Axsen, and C. Crawford (2022) Simulating competition among heavy-duty zero-emissions vehicles under different infrastructure conditions.  Transportation Research Part D: Transport and Environment 106,  pp. 103254.  External Links: Document Cited by: §1, §1.
  * [23] S. Li, N. Djilali, M. A. Rosen, C. Crawford, and P. Sui (2022) Transition of heavy-duty trucks from diesel to hydrogen fuel cells: opportunities, challenges, and recommendations.  International Journal of Energy Research 46 (9),  pp. 11718–11729.  Note: _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.8066 External Links: Link, Document Cited by: §1.
  * [24] H. Liimatainen, O. Vliet, and D. Aplyn (2019) The potential of electric trucks – an international commodity-level analysis.  Applied Energy 236 (15),  pp. 804–814.  External Links: Document Cited by: §1.
  * [25] Z. Lin (2021) Mostly positive implications of long-haul truck electrification.  Joule 5 (10),  pp. 2548–2550.  External Links: Document Cited by: §1.
  * [26] J. López-Prol and W. Schill (2021) The economics of variable renewable energy and electricity storage.  Annual Review of Resource Economics 13 (1),  pp. 443–467.  External Links: Document Cited by: §1, footnote 3.
  * [27] A. Mangipinto, F. Lombardi, F. D. Sanvito, M. Pavičević, S. Quoilin, and E. Colombo (2022-04-15) Impact of mass-scale deployment of electric vehicles and benefits of smart charging across all european countries.  Applied Energy 312,  pp. 118676.  External Links: ISSN 0306-2619, Link, Document Cited by: §1.
  * [28] M. Muratori and T. Mai (2020-12) The shape of electrified transportation.  Environmental Research Letters 16 (1),  pp. 011003 (en).  External Links: ISSN 1748-9326, Link, Document Cited by: §1.
  * [29] B. Noll, S. d. Val, T. S. Schmidt, and B. Steffen (2022) Analyzing the competitiveness of low-carbon drive-technologies in road-freight: A total cost of ownership analysis in Europe.  Applied Energy 306,  pp. 118079.  External Links: ISSN 0306-2619, Link, Document Cited by: §1.
  * [30] B. Nykvist and O. Olsson (2021) The feasibility of heavy battery electric trucks.  Joule 5 (4),  pp. 901–913.  External Links: Document Cited by: §1.
  * [31] A. Odenweller, F. Ueckerdt, G. F. Nemet, M. Jensterle, and G. Luderer (2022) Probabilistic feasibility space of scaling up green hydrogen supply.  Nature Energy 7 (9),  pp. 854–865.  External Links: Document Cited by: §3.3, §4.1.
  * [32] B. Pickering, F. Lombardi, and S. Pfenninger (2022) Diversity of options to eliminate fossil fuels and reach carbon neutrality across the entire european energy system.  Joule 6 (6),  pp. 1253–1276.  External Links: Document Cited by: §3.3.
  * [33] P. Plötz, T. Gnann, P. Jochem, H. U. Yilmaz, and T. Kaschub (2019) Impact of electric trucks powered by overhead lines on the European electricity system and CO2 emissions.  Energy Policy 130,  pp. 32–40.  External Links: Document Cited by: §1.
  * [34] P. Plötz (2022) Hydrogen technology is unlikely to play a major role in sustainable road transport.  Nature Electronics 5 (1),  pp. 8–10.  External Links: Document Cited by: §1.
  * [35] PTV GROUP (2023) PTV Validate: traffic model for the whole of germany.  Note: https://www.myptv.com/en/data/mobility-data Cited by: §4.2.
  * [36] D. B. Richardson (2013) Electric vehicles and the electric grid: a review of modeling approaches, impacts, and renewable energy integration.  Renewable and Sustainable Energy Reviews 19,  pp. 247–254.  External Links: ISSN 1364-0321, Document, Link Cited by: §1.
  * [37] O. Sadeghian, A. Oshnoei, B. Mohammadi-ivatloo, V. Vahidinasab, and A. Anvari-Moghaddam (2022) A comprehensive review on electric vehicles smart charging: solutions, strategies, technologies, and challenges.  Journal of Energy Storage 54,  pp. 105241.  External Links: Document Cited by: §1.
  * [38] W. Schill and C. Gerbaulet (2015) Power system impacts of electric vehicles in germany: charging with coal or renewables?.  Applied Energy 156,  pp. 185–196.  External Links: Document Cited by: §1.
  * [39] W. Schill and A. Zerrahn (2018) Long-run power storage requirements for high shares of renewables: results and sensitivities.  Renewable and Sustainable Energy Reviews 83,  pp. 156 – 171.  External Links: Document Cited by: §4.1.
  * [40] W. Schill (2020) Electricity storage and the renewable energy transition.  Joule 4 (10),  pp. 2059–2064.  External Links: Document Cited by: §1, §4.1.
  * [41] P.R. Shukla, J. Skea, R. Slade, A. A. Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, and J. Malley (Eds.) (2022) Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.  Cambridge University Press, Cambridge, UK and New York, NY, USA.  External Links: Document Cited by: §1.
  * [42] D. Speth and S. A. Funke (2021) Comparing options to electrify heavy-duty vehicles: findings of german pilot projects.  World Electric Vehicle Journal 12 (2),  pp. 67.  External Links: Document Cited by: §1.
  * [43] F. Stöckl, W. Schill, and A. Zerrahn (2021) Optimal supply chains and power sector benefits of green hydrogen.  Scientific Reports 11 (1),  pp. 14191.  External Links: Document Cited by: §1, §4.1.
  * [44] M. Taljegard, L. Göransson, M. Odenberger, and F. Johnsson (2017) Spacial and dynamic energy demand of the E39 highway – Implications on electrification options.  Applied Energy 195,  pp. 681–692.  External Links: Document Cited by: §1, §1.
  * [45] M. Taljegard, L. Göransson, M. Odenberger, and F. Johnsson (2019) Impacts of electric vehicles on the electricity generation portfolio - A Scandinavian-German case study.  Applied Energy 235,  pp. 1637–1650.  External Links: ISSN 0306-2619, Document Cited by: §1.
  * [46] F. Ueckerdt, C. Bauer, A. Dirnaichner, J. Everall, R. Sacchi, and G. Luderer (2021) Potential and risks of hydrogen-based e-fuels in climate change mitigation.  Nature Climate Change 11 (5),  pp. 384–393.  External Links: Document Cited by: §1.
  * [47] A. Zerrahn and W. Schill (2017) Long-run power storage requirements for high shares of renewables: review and a new model.  Renewable and Sustainable Energy Reviews 79,  pp. 1518–1534.  External Links: ISSN 1364-0321, Link, Document Cited by: §1, §4.1.

##  SI Supplemental Information

###  SI.1 Generation of BEV and ERS-BEV profiles

Vehicle load profiles are key input parameters to this analysis. In case of
non-optimized, inflexible charging, respective time series are provided to the
power sector model as exogenous parameters. In case of optimized charging, the
vehicles’ charging and, if V2G is available, discharging profiles are
determined as endogenous variables. In this case, mission-profiles become
important model inputs. Data sets for truck trips are available for Germany
and also Europe [7]. However, these trips are not connected to vehicle mission
profiles, e.g. information on daily starting and charging times, in these data
sets. Therefore, some studies choose to simply refer to the observed traffic
volume on typical roads in order to approximate the energy demand profile of
the vehicle fleet [9]. However, it is impossible to determine flexibility
potentials in the load profile when using this approach.

This study is based on synthetic truck usage patterns that are intended to
roughly approximate the German HDV fleet (> 26 t) in 2030, when a broad
electrification of heavy-duty road transport is assumed. The main data source
for the usage patterns is the traffic model PTV Validate [3] which in turn is
based on the official German governmental forecast for goods flow matrices for
the year 2030 [5]. Within PTV Validate, the freight flows are allocated to
vehicle trips between approximately 10,000 origin and destination districts in
Germany, i.e., to individual transport relations. For the present study, we
use aggregated source and destination districts at county level (approx. 400
counties in Germany). PTV Validate also determines the vehicle class of trucks
used for the transport of a certain goods type on a given transport relation.
Only the journeys of trucks in the size class > 26 t are considered here, as
these account for a large proportion of the energy consumption of the truck
fleet and also represent the primarily intended field of application for
electricity consumption by overhead lines.

The result is a database of daily trips by HDV in domestic German traffic.
International traffic is not considered here to reduce complexity. Note that
the purpose of this analysis is mainly to illustrate the general power sector
implications of different HDV technologies, rather than forecasting their
absolute impacts.

In order to derive typical daily driving profiles from the aforementioned trip
database, the following steps are performed:

  1. 1.

Calculation of annual mileage for transport relations: The distance of a
transport relation is assumed as characteristic for the radius of operation
that a vehicle performing transports on the given transport relation will
typically have. This allows us to utilize official statistics from the German
Federal Road Administration which give typical annual mileages per vehicle
class for the operating ranges 0-50 km, 50-150 km and > 150 km. Using a
regression approach, a dependency between the distance of each transport
relation and the typical annual mileage of vehicles operating there is
obtained.

  2. 2.

Calculation of daily idle time: First, the number of daily trips made by a
truck is estimated. For example, trucks on short shuttle routes often make
multiple trips a day, whereas trucks on long-distance routes often only make
one trip a day. The number of trips entails a corresponding number of stops,
which can in principle be used to recharge the traction battery if the stop is
long enough. Second, depending on the type of goods transported, typical
durations for loading and unloading were estimated. These determine the
duration of the assumed idle times in the daily driving profile. In addition,
a mandatory driver’s break of 45 minutes is assumed for daily driving profiles
of > 4.5 hours.

  3. 3.

Calculation of daily operating time: The daily operation time of a vehicle on
a particular route is therefore defined as

operating time = journey time + idle time + driver’s break. (1)

For step 1, we use the regression function

$M=a\cdot R^{b}+c$ (2)

where M is the annual mileage, R is the distance of a relation and a, b and c
optimization coefficients. The objective function is constructed such that the
average annual mileages of each distance class from Table SI.1 are reproduced
when iterating over all transport relations.

The regression result for the parameters is a = 21,180, b = 0.2979 and c = 0.
The resulting dependency is shown in Figure SI.1.

Table SI.1: Annual mileage of HDV > 26 t per distance class. Source: [1] Distance class |  | Average annual mileage
---
[km]
<50 km | 45,684
51-150 km | 78,190
>150 km | 117,121
All classes | 94,950

For step 2, we use the ratio of the daily mileage888Annual mileage (cf. step
1) divided by 240 working days. and the distance of the transport relation to
obtain the number of trips per day and thus the number of stops on a daily
mission. The idle time per stop is determined based on an expert guess for
each goods class. By multiplying the number of stops with the idle time per
stop, we obtain the total daily idle time for the vehicle operation on a given
transport relation, considering the type of transported goods.

Figure SI.1: Annual mileage as a function of distance class.

Repeating steps 1-3, synthetic daily travel profiles with a certain daily
operating time are obtained for all transport relations in the model. In order
to limit these to a manageable number of daily driving profiles for the energy
system modelling, all relations with a similar daily operating time are
combined. For each daily operating time, averaged values for the idle times
are used. The essential parameters for the operational profiles are summarized
in Table SI.2.

Table SI.2: Key data of daily driving profiles

| Daily operating
---
time [h]
| Average distance of
---
relation [km]
| Average daily
---
mileage [km]
| Average daily
---
idle time [h]
| Number of
---
vehicles
| Total annual
---
mileage [bn. km]
3 | 23 | 196 | 0.11 | 21,073 | 0.99
4 | 74 | 281 | 0.06 | 29,007 | 1.96
5 | 136 | 354 | 0.04 | 7,069 | 0.6
6 | 175 | 383 | 0.78 | 34,772 | 3.2
7 | 237 | 415 | 1.43 | 43,544 | 4.34
8 | 219 | 399 | 2.43 | 86,826 | 8.31
9 | 204 | 351 | 3.97 | 77,117 | 6.49
10 | 448 | 527 | 2.89 | 19,166 | 2.42

The assumptions regarding the vehicles and their infrastructure are made
independently of the daily operating time for the sake of simplicity and are
summarized in Table SI.3. The design of the vehicles is oriented towards the
needs of long-haul transport with a range of 500 km for BEV. For profiles in
local and regional transport (with mostly lower daily mileage), smaller
batteries could generally also be sufficient, so the battery capacity tends to
be overestimated in this analysis.

Table SI.3: Assumptions on BEV and ERS-BEV HDV and infrastructure

| BEV | ERS-BEV
---|---|---
Vehicle size class | >26 t gross vehicle weight
Battery range | 500 km | 150 km
Effective battery capacity | 655 kWh | 181 kWh
Average speed | 79 km/h
Energy consumption | 1.31 kWh/km |  | Overhead line connected: 1.42 kWh/km
---
Overhead line disconnected: 1.25 kWh/km
| Intermediate charging during
---
(un-)loading stop
| 200 kW nominal
---
(166 kW effective )
-
| Intermediate charging during
---
driving time break
| 500 kW
---
(415 kW effective)
-
Power supply from overhead line | - | 400 kW

The structure of the synthetic daily driving profiles is illustrated below
using the example of a profile with an operating time of 8 hours (Figure SI.2,
panel a). The profile starts with a fully charged battery in the depot. For
the case of ERS trucks, the share of electrified roads is assumed according to
the motorway share of the transport relations on which the profile is based
(assuming that all motorways are equipped with overhead lines). At
loading/unloading stops, a charging opportunity with a power of 200 kW is
assumed for battery trucks, and a high-power charging option with 500 kW is
assumed to be available during the mandatory driver’s break. For ERS trucks,
on the other hand, a stationary charging opportunity is assumed only at the
depot.

The number of idle stops during loading/unloading is in the range between 2
and 3 for average daily mileages above 300 km, which makes up the bulk of the
total vehicle fleet. Only for short transport relations, it is higher. For the
sake of simplicity, we assume 2 idling stops for all synthetic driving
profiles. However, the calculation of absolute idle time per day (which is
more relevant for the energy system modelling) is based on the respective
number of idle stops for all contributing transport relations.

(a)

(a)

(b)

(c)

(b)

(d)

(e)

(c)

(f)

Figure SI.2: Exemplary daily driving profile with an operating time of 8 hours

The resulting time series of hourly electricity demand and charging
availability are shown in Figure SI.2 (for BEV in panel b and ERS-BEV in panel
c).

The traffic model data does not include information on the starting time of a
driving profile. Therefore, in the next step, the profiles are distributed
over the day according to the empirically observed course of the daily mileage
[2], taking into account their relative frequency (i.e. the mileage
represented by them). For each daily operating period, two different starting
times are defined. The operating durations of eight and nine hours, which are
particularly strongly represented, are given three and four different start
times respectively. The resulting temporal distribution of traffic volumes is
shown in Figure SI.3. The contribution of the example profile shown above
(eight hours operating time, with three different starting times) corresponds
to the dark blue coloured areas in Figure SI.3. The resulting electricity
demands and charging availabilities of the profiles are fed into the DIETER
model, and are available in the public repository https://gitlab.com/diw-
evu/projects/my_eroads.

Figure SI.3: Distribution of daily driving patterns over the course of the day

With the procedure described, we have generated an ensemble of operational
profiles for domestic German heavy road freight transport that reflect well
both the macroscopic parameters of the German transport sector (mileage,
distribution of daily driving distances, daily pattern of traffic volume) and
operational boundary conditions (loading / unloading times, mandatory driver’s
breaks).

###  SI.2 Fuel demand profiles of FCEV and Ptl trucks

For FCEV and PtL trucks, no differentiated driving and charging availability
time series are needed; instead, only aggregate fuel demand time series are
required. These are based on the same empirical traffic data from German
highways [2], which are also used for deriving the (ERS-)BEV profiles. For
FCEV, we assume a specific hydrogen demand of 6.8 kg/100km, and for ICEV-PtL a
specific Diesel demand of 27.1 l/100km. Assuming the overall HDV fleet to be
either FCEV or ICEV-PtL, this corresponds to a yearly hydrogen demand of
around 68 TWh, or a yearly e-fuel demand of 80 TWh, respectively. The
resulting fuel demand time series are available together with all other input
parameters in the public repository https://gitlab.com/diw-
evu/projects/my_eroads.

###  SI.3 Additional results for the baseline model specification

In the following, we provide additional results for the baseline model
specification discussed in the main part of the paper. This includes the
effects of electrified HDV on capacity and yearly use of electricity storage,
electrolysis capacity, the shares of renewable energy sources in overall
demand, and carbon emissions, as well as additional time series results.

####  SI.3.1 Electricity storage capacity and use

Figure SI.4 shows optimal electricity storage energy capacities in the
reference and the changes induced by HDV (upper panel), as well as optimal
storage output power rating in the reference, and corresponding effects of HDV
(lower panel). Effects are differentiated by technology, i.e., for lithium-ion
batteries and power-to-gas-to-power storage. Batteries come with relatively
high energy-specific investment costs and relatively low power-specific costs,
which makes them a typical short-duration storage technology with a low
energy-to-power ratio and many yearly cycles; power-to-gas-to-power,
conversely, comes with very low energy-specific costs and higher power-
specific costs, and is thus optimally used as a long-duration storage
technology [6]. Pumped hydro storage is assumed to be fixed to TYNDP scenario
assumptions because of limited expansion opportunities in Germany.

As for storage energy capacity, long-duration electricity storage clearly
dominates because of much lower energy-specific investment costs. We further
find the largest impact of HDV on electricity storage in the case of on-site,
i.e., inflexible, hydrogen provision. Here, the installed energy capacity is
nearly four times as high as in the reference. As hydrogen has to be generated
largely at the time of demand, and the potential to expand fossil generators
is limited, this means that long-duration storage is used to balance variable
renewable electricity generation with electricity demand for hydrogen
production. For inflexible BEV or ERS-BEV, as well as centralized hydrogen
supply, long-duration electricity storage capacity is expanded to a much
smaller extent; and it is nearly zero for centralized PtL despite the highest
additional electricity demand, as liquid e-fuels come with even cheaper bulk
storage options.

When it comes to optimal storage output power, (in-)directly electrified HDV
have a noticeable effect on both batteries and long-duration storage
technologies (lower panel of Figure SI.4). Overall, BEV or ERS-BEV with V2G
substantially reduce the required storage power, as truck batteries substitute
stationary batteries, which would otherwise be needed to balance diurnal
fluctuations of solar PV. Inflexible charging of BEV or ERS-BEV conversely
increases optimal storage power. For long-duration storage, the impacts of HDV
on power output capacity are largely similar to those for energy capacity,
with by far the largest effect for inflexible on-site hydrogen generation.

Figure SI.4: Effects of different HDV options on optimal electricity storage
energy capacity (top panel), and on storage output power capacity (lower
panel)

####  SI.3.2 Electrolysis capacity

Optimal investments into electrolyzers are shown in Figure SI.5. Only
investments into proton exchange membrane (PEM) electrolyzers are made, which
have lower conversion losses, but higher specific investment costs than
alkaline electrolyzers (ALK). This is because renewable energy is relatively
scarce and costly under baseline model assumptions, and higher energy
efficiency of electrolyzers accordingly matters more than lower investment
costs. This changes if Germany is modeled in isolation, as this leads to
larger renewable surplus generation, which in turn makes lower-cost ALK
technology a part of the optimal electrolysis technology mix (compare also
[8]).

The electrolysis capacity required to supply the German HDV fleet is highest
in the PtL scenario with 24.4 GW, as e-fuels require more hydrogen than the
two FCEV cases. Among the hydrogen scenarios, inflexible on-site generation
further leads to a substantially higher optimal electrolysis capacity of 19.1
GW compared to 13.5 GW for centralized electrolysis. This is, again, because
on-site electrolyzers have to produce hydrogen with a similar time profile as
the hydrogen demand at filling stations. In contrast, centralized
electrolyzers can use low-cost production-site hydrogen storage to balance
fluctuations of renewable supply and hydrogen demand.

Figure SI.5: Optimal electrolysis capacity

####  SI.3.3 Renewable shares of the German power sector

Figure SI.6 shows the shares of renewable electricity generation in
electricity demand for Germany for alternative HDV electrification scenarios.
Flexible BEV and ERS-BEV, especially if combined with V2G, increase the
renewable share by almost up to four percentage points in the BEV Flex V2G
scenario. This is because HDV batteries can foster the integration of
additional solar PV generation, even beyond the electricity demand related to
the vehicles themselves. This is, in contrast, not the case for inflexibly
charged BEV or ERS-BEV.

Centralized hydrogen and PtL supply chains hardly increase the optimal
renewable share, despite relatively high flexibility potentials related to
their low-cost centralized storage options. This is because these flexibility
benefits are outweighed by the sheer amount of additional renewable
electricity needed for HDV, which is substantially higher than in BEV or ERS-
BEV cases (compare Figure 2) and thus causes increasing renewable integration
efforts. On-site electrolysis, in turn, leads to a higher increase in the
renewable share because its temporal inflexibility induces higher long-
duration electricity storage investments that allow for better integration of
variable renewables.

Figure SI.6: Share of renewable electricity generation in total electricity
demand for Germany

####  SI.3.4 Direct carbon emissions

Among all scenarios, CO2 emissions increase the most if the HDV fleet uses
e-fuels or hydrogen (Figure SI.7). In these scenarios, this is a direct
consequence of additional electricity generation from natural gas, and to a
smaller extent from lignite and hard coal in neighboring countries. Among the
two hydrogen cases, on-site electrolysis at filling stations leads to lower
emission impacts, as its temporal flexibility restrictions in combination with
the assumed transmission capacities limit the possibility of increasing
imports or generation from natural gas plants; instead, additional renewable
energy, combined with long-duration electricity storage, is used (compare
section 2.2). Emission effects are smaller for BEV and ERS-BEV, and even
negative for flexibly charged BEV, especially if combined with V2G. The latter
is driven by an additional expansion of solar PV facilitated by V2G. For
neighboring countries, relative emission effects are smaller, as by
assumption, they have lower renewable energy shares and, in turn, higher
emissions, as well as no electrified truck fleets.

Figure SI.7: Direct CO2 emissions from electricity generation. The chart
displays emissions from Germany, its Neighbors and the two together. Every
group contains two subplots; the subplot on the left shows the overall
emissions of the reference without electrified HDV, and the right-hand side
subplot shows differences between HDV scenarios and the reference.

####  SI.3.5 Time series results

Figure SI.8 shows additional electricity time series for the BEV Flex and ERS-
BEV Flex scenarios without V2G, which are omitted in Figure 4 because of space
restrictions. The Figure also differentiates the grid electricity demand of
HDV for electricity directly used for driving in ERS-BEV (Grid2Wheel) and
battery charging (Grid2Bat). Flexible BEV, similar to BEV Flex V2G, use
renewable surplus energy (negative residual load) for charging. Flexible ERS-
BEV effectively use overhead line electrification while driving in combination
with optimal charging of batteries at charging stations.

Figure SI.8: Additional time series of electricity demand, complementary to
Figure 4 (unit: GW). The chart shows a sample of five exemplary days starting
with Saturday.

Figure SI.9 is complementary to Figure 4 as it does not show residual load,
but wholesale electricity market prices. These largely follow the residual
load; prices are generally lower when the residual load is lower.

Figure SI.9: Sample of electricity time series five exemplary days: “HDV
electricity demand” illustrates electricity flowing to BEV or ERS-BEV
batteries, including energy later discharged back via V2G, as well as
electricity needs of hydrogen and PtL supply chains; “HDV discharging to the
grid” are V2G power flows from BEV or ERS-BEV to the grid; and “Electricity
price” is the wholesale electricity market price in the respective hour. The
samples start on a Saturday.

Figures SI.10 and SI.11 show additional exemplary time series not for the
aggregate HDV fleet, but for two specific vehicle profiles. Vehicle profile 5
represents a truck with a driving pattern in the morning between 4 am and 11
am, and profile 16 is driving between 12 noon and 9 pm with one hour stop.
Both vehicles have a charging station available before and after every trip.
These profiles are “peakier” than aggregate fleet profiles. The sequence of
Grid2Bat and V2G operations is more clearly visible than in the aggregate
fleet time series. It can also be seen that ERS-BEV are partly charging their
batteries while connected to the overhead lines, which is relevant for the V2G
configuration, especially when the residual load is negative.

Figure SI.10: Time series of electricity demand for vehicle profile nr. 05
(unit: GW) Figure SI.11: Time series of electricity demand for vehicle profile
nr. 16 (unit: GW)

###  SI.4 Sensitivity analyses

####  SI.4.1 Effects of the European interconnection

In the main part of the paper, we show results for a central European
interconnection where the integration of variable renewables and electrified
HDV in Germany benefits from the flexibility provided by geographical
balancing. It has been shown that the flexibility provided by the European
interconnection substantially reduces electricity storage needs, as it allows
smoothing complementary wind power profiles in different countries [4]. In the
following, we compare results for alternative model runs where the German
power sector is modeled in isolation. This setting is less realistic, but
helps to separate the effects of the European interconnection on results. It
also gives a qualitative indication of how results are potentially distorted
in model analyses that focus on single countries.

Results are qualitatively similar for a case where the German power sector is
modeled in isolation from its neighboring countries, but slightly more
pronounced (top right panel of Figure SI.12). Here, flexible BEV with V2G are
even more beneficial than in the setting where Germany is interconnected with
its neighboring countries (1.6 bn Euros vs. 1.8 bn Euros additional costs).
This is because in such a flexibility-constrained setting, electricity storage
provided by HDV batteries becomes even more beneficial for integrating
variable renewables. On the contrary, inflexible BEV operations lead to a
slightly higher cost increase (3.9 bn Euros vs. 3.8 bn Euros) than in a case
with interconnection.

If Germany is modeled as an electric island, average electricity prices for
HDV options that are particularly flexible (BEV Flex, BEV Flex V2G, ICEV PtL)
are lower than in the interconnected case (lower right panel of Figure SI.12).
The reason is that flexible HDV can make use of more pronounced renewable
surplus generation events which go along with lower prices. In contrast,
temporally less flexible options are forced to charge their batteries in hours
with higher average prices.

Figure SI.12: Changes in yearly power sector costs and electricity demand
induced by different HDV options (top panels) and average prices of charging
electricity (lower panels), evaluating the effects of the European
interconnection

When Germany’s electricity sector is considered in isolation, the missing
European interconnection no longer allows for import capacity, so both overall
and firm capacity needs to increase (top right panels of Figure SI.13)).
Further, HDV-induced effects on optimal generation capacities are generally
smaller than if the interconnection with neighboring countries is considered.
This is because the reference (Ref) of an isolated setting already has a
larger power plant fleet, with more under-utilized capacity available. This
particularly benefits the temporally flexible BEV and ERS-BEV options
(especially with V2G), as these allow making use of parts of the renewable
surplus energy present in the reference. In the hydrogen and PtL cases, this
effect vanishes or even reverses, as the additional load induced by HDV is
much higher than the renewable surplus energy available in the reference.

Figure SI.13: Effects of different HDV options on optimal electricity
generation capacity (top panels), and on yearly electricity generation (lower
panels), evaluating the effects of the European interconnection

The resulting renewable share of the German power sector tends to be lower if
Germany is modeled as an electric island (Figure SI.14). This is again because
the missing flexibility of the European interconnection makes it harder to
integrate variable renewables. The ICEV PtL case is an exception. Here, low-
cost e-fuel storage enables higher use of solar PV and wind power, and hard
coal is no longer used for electricity generation.

Figure SI.14: Renewable share of the German power sector considering scenarios
with neighboring countries (left panel), and with Germany modeled in isolation
(right panel)

####  SI.4.2 Effects of a capacity constraint for wind power

In the central part of the analysis, we assume that solar PV and wind power
investments are possible without any limits. We consider this to be a
meaningful setting for highlighting general effects, which should also be
generally applicable for other geographic settings. Yet in the specific German
case, the medium-term capacity expansion of onshore and offshore wind power
may, in fact, be constrained. Considering long lead times related to planning
and admission processes, we specify a less optimistic scenario where we assume
that onshore and offshore wind power expansion may not exceed 100 GW and 30 GW
by 2030, respectively.

Figure SI.15 again shows cost differences of various HDV options against a
reference without HDV (top panels). As expected, the case with wind capacity
constraints on the right-hand side leads to slightly higher additional costs.
The lower expansion of wind capacity is compensated by increasing solar PV
investments, as shown in Figure SI.16 (top right panels), which are slightly
more expensive in Germany. Only in the case of flexible BEV with V2G, costs
and average charging electricity prices do not increase compared to baseline
assumptions. This is because the short-duration electricity storage
characteristics of V2G fits particularly well to solar PV generation profiles,
which also helps avoiding electricity generation from natural gas and
substituting firm capacity to some extent.

Yearly electricity generation results (Figure SI.16, lower panels) shows that
the German power sector relies more on imports and natural gas when wind
capacity is constrained. This is particularly significant for FCEV and
hydrogen-based PtL. That is, wind power is only partly replaced by solar PV,
and more fossil-fueled power generation is used in almost all HDV
configurations. Accordingly, the renewable share of a power sector with
constrained wind power expansion is also lower than under baseline assumptions
(Figure SI.17).

Figure SI.15: Changes in yearly power sector costs and electricity demand
induced by different HDV options (top panels), and average prices of charging
electricity (lower panels), evaluating the effects of capacity constraints for
wind power Figure SI.16: Effects of different HDV options on optimal
electricity generation capacity (top panels), and on yearly electricity
generation (lower panels), evaluating the effects of capacity constraints for
wind power Figure SI.17: Renewable share of the German power sector
considering scenarios without (left panel) and with wind capacity constraints
(right panel)

## References

  * [1] BASt (2017) Fahrleistungserhebung 2014.  Note: https://www.bast.de/DE/Verkehrssicherheit/Fachthemen/U2-fahrleistung-2014/u2-fahrleistung-2014.html Cited by: Table SI.1.
  * [2] BASt (2023) Automatische Zählstellen 2018.  Note: https://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/Aktuell/zaehl_aktuell_node.html Cited by: §SI.1, §SI.2.
  * [3] PTV GROUP (2023) PTV Validate: traffic model for the whole of germany.  Note: https://www.myptv.com/en/data/mobility-data Cited by: §SI.1.
  * [4] A. Roth and W. Schill (2023) Geographical balancing of wind power decreases storage needs in a 100% renewable European power sector.  iScience 26 (7).  External Links: Document Cited by: §SI.4.1.
  * [5] M. Schubert, T. Kluth, G. Nebauer, R. Ratzenberger, S. Kotzagiorgis, B. Butz, W. Schneider, and M. Leible (2014) Verkehrsverflechtungsprognose 2030 - Schlussbericht.  Technical report Technical Report Forschungsbericht FE-Nr.: 96.0981/2011, BVU, ITP, IVV, Planco.  Cited by: §SI.1.
  * [6] N. A. Sepulveda, J. D. Jenkins, A. Edington, D. S. Mallapragada, and R. K. Lester (2021) The design space for long-duration energy storage in decarbonized power systems.  Nature Energy 6 (5),  pp. 506–516.  External Links: Document Cited by: §SI.3.1.
  * [7] D. Speth, V. Sauter, P. Plötz, and T. Signer (2022) Synthetic european road freight transport flow data.  Data in Brief 40,  pp. 107786.  External Links: Document Cited by: §SI.1.
  * [8] F. Stöckl, W. Schill, and A. Zerrahn (2021) Optimal supply chains and power sector benefits of green hydrogen.  Scientific Reports 11 (1),  pp. 14191.  External Links: Document Cited by: §SI.3.2.
  * [9] M. Taljegard, L. Göransson, M. Odenberger, and F. Johnsson (2019) Impacts of electric vehicles on the electricity generation portfolio - A Scandinavian-German case study.  Applied Energy 235,  pp. 1637–1650.  External Links: ISSN 0306-2619, Document Cited by: §SI.1.