Economic Impacts of the Electric Road System Implementation on the Rotterdam–Antwerp Corridor

Abstract

Electric road systems (ERSs) are a group of technologies that allow powering adequately equipped road transport vehicles with electricity from the road infrastructure while in motorway traffic. They can be categorised into three technology groups: overhead catenary, ground conductive, and ground inductive, depending on the mode of power transfer used. The supplied energy is used for propulsion and for charging the vehicle batteries to be used once the vehicle leaves the electrified road section. Also, another energy source, e.g., diesel, natural gas, or hydrogen, can be used while away from the ERS. This research investigates the potential impacts of implementing the different ERS technologies on the Rotterdam–Antwerp motorway corridor that links the two largest ports in Europe. The aim is to identify which of the routes between the ports is best suited for the implementation of an ERS, whether there are substantial differences in the economic performance of the different ERS technologies, determine what ERS vehicle traffic volumes are required and potentially available for successful implementation, what investment is required to build the system and whether the ERS operator can be profitable, and whether transport operators could operate their trucks on ERS profitability in this corridor setting. The research shows that the route between Rotterdam and Antwerp that runs on motorway E19 is the best to be electrified from an economic standpoint. Our calculations show that the traffic on the Rotterdam–Antwerp corridor is sufficient for economically justifying ERS infrastructure rollout and operation. For transport operators who happen to have specific client bases, e.g., those who usually serve clients from one of the ports along the electrified route, the construction of an ERS on the route could prove to be very lucrative if they adopt the technology early.

1. Introduction

This paper investigates the impacts of implementing the Electric Road System (ERS) on the Rotterdam–Antwerp motorway corridor that links the two largest ports in Europe.

The topic of ERSs has gained significant attention from researchers and practitioners in recent years. The biggest progress has occurred in Germany, where three separate test sites with different conditions for testing of ERS vehicles have been established. This allowed investigation of the impacts and performance of the system in the German context [1,2,3,4,5,6]. Another frontrunner in the field has been Sweden, where test stretches of ERSs using different technologies have been built and operated on public roads [7,8], which allowed estimation of system performance [9,10] and assessment of the technology maturity [11]. In the United Kingdom setting, where a trial is under development, a white paper from the Centre for Sustainable Road Freight proposed business cases and a nationwide infrastructure rollout [12]. The adoption of catenary ERS on a specific route to assess its economic viability is also modelled in Canada [13].

Previous research on ERSs in Belgium and the Netherlands [14,15,16] investigated the socio-economic performance of ERSs and the corridors where such implementation could be interesting. The Dutch–Belgian road corridor between Rotterdam and Antwerp was shown to be one of the links where the traffic volumes and composition might justify the implementation of an ERS with substantial economic and environmental benefits.

Previous research, however, does not address the issue of corridor route selection and the economic performance of ERS for the actors involved in the Rotterdam–Antwerp cross-border corridor setting. This study answers several important questions which, due to the scale of previous studies, were not asked, or for which the answers of previous studies cannot be transposed. The aim is to identify which of the alternative routes is best suited for implementation of an ERS, whether there are substantial differences in the economic performance of the different types of ERS, see what the potential and sufficient ERS vehicle traffic volumes are for successful implementation, what investment is required to build the system and whether the ERS operator can be profitable, and whether transport operators could operate their trucks on ERS profitability in this corridor setting.

A road that is equipped with an ERS can supply electric power to the vehicles while in motion. This allows powering and charging adequately equipped road freight vehicles with electricity from the infrastructure while driving on ERS-equipped motorways, and using onboard batteries or another energy source for the final leg between the electrified portion of the road and the port terminal or customer. The benefits of such an approach include lower investment costs for vehicle purchase, lower vehicle operation costs, reduction in resource intensity of vehicle electrification by allowing the use of smaller batteries, improved logistics performance for ERS users, and more even time distribution of the electricity grid load.

In this research, we focus on four competing ERS technologies: overhead catenary (eHighway from Siemens), ground conductive (from Elonroad and Alstom), and ground inductive (from Electreon), which differ in the way infrastructure is built and the power is transferred to the vehicle.

Providing power to electric road freight transport vehicles from the infrastructure while in motion has a distinct advantage. The battery size of the vehicles can be significantly reduced, from a range of 800–1200 kWh of usable energy to 100–400 kWh, while allowing to perform the same transport operations and having the benefit of not having to allow time for battery fast charging, because the battery of the truck is recharged while driving in motorway traffic. Having a smaller battery size means lower investment in vehicle purchase and consequently improved return on investment. The flexibility of the operational pattern and suitability for different logistics uses of ERS vehicles that are used on the network approaches that of diesel vehicles.

In this article, Section 2 describes the research questions and the approach that is taken in this investigation. Section 3 details the modelling inputs. In particular, the characteristics of each of the four investigated ERS technologies, including their cost and energy performance assumptions, emissions performance and energy purchase cost to the infrastructure operator and price that road freight transport operators can buy it at. Also, this section describes the other inputs used and the alternative routes that are compared in the investigation. Section 4 presents the findings of the research concerning the level of required infrastructure investment for each technology, the profitability of the infrastructure operator for each of the alternative routes, the impacts on the road freight transport companies, and an assessment of the potential demand. Finally, Section 5 presents the conclusions of this research.

2. Methodology

This section lists the research questions for the research, and then the approach that is taken to answer those questions is described.

2.1. Research Questions

This research defines the following four groups of research questions:

• First, on the potential demand: what are the potential ERS traffic volumes that could use the Antwerp–Rotterdam corridor if it were developed? Are those sufficient to justify ERS implementation?
• Second, on the infrastructure investment and operation: what is the required investment, and can the break-even point for profitability be reached for this corridor in isolation, or is a wider electrified network required? Would this be a good investment at this scale?
• Third, on the best corridor route selection: with alternative road links available between Rotterdam and Antwerp, which route is best suited to be electrified from an economic perspective?
• Finally, from the point of view of the users—road freight transport operators: what do the economics look like, and could trucks be operated on ERS profitably in this corridor setting? Is this similar for all ERS technologies, or are there substantial differences due to which a specific ERS technology should be preferred?

2.2. Approach

To answer the research questions defined above the following steps are taken:

Step 1: Technology definition. There are several technology options available for developing ERS, each of those having a different technology readiness level. They can be categorised into three technology groups: overhead catenary, ground rail, and ground induction. In this research, we intend to remain unbiased towards the preference of any of those, with the different technologies reflected as different cost structure sets to simulate the differences in performance of the different infrastructures and vehicles.

Step 2: Route identification. This step identifies the alternative Rotterdam—Antwerp routes, as well as scenarios for different pre- and post-haulage differences defined based on the geographical locations of industry in the proximity of the selected routes.

Step 3: Scenario definition. This step prepares and validates the scenarios for cost-benefit analysis, taking into account the economic characteristics of the technology alternatives from Step 1, identified route scenarios from Step 2, and parameters that relate to other inputs and will allow us to perform sensitivity analysis, like energy prices.

Step 4: Cost analysis. The last step is the cost analysis. Its results allow us to answer research questions that relate to the required road freight demand for the profitable operation of the ERS system for system operator and users. The calculation of the alternative routing scenarios allows selecting the corridor route with the best economic performance, and determining the involved costs and benefits from investment and operation of the system for both the ERS operator and the system users.

For the analysis, we develop a model that allows quantifying the impacts of ERS implementation on the Rotterdam–Antwerp corridor to answer the above research questions.

First, as shown in Figure 1, the input data is collected. The input data, described in detail in Section 3, describes the geography of the investigated corridor and its roads, road freight traffic volumes on those roads, vehicle and technology performance characteristics, infrastructure cost assumptions, etc. Also, a number of technology adoption scenarios are developed to test the performance of each of the investigated ERS technologies and vehicle types separately and in a mix with other vehicle types.

Then, the segment-level calculation for the investigated corridor is performed. A road segment is a section of the road network which the vehicles cannot enter or leave in the middle. Entering or leaving a segment is possible only at the end of any segment. For every segment of the corridor user costs, ERS operator costs and revenues are calculated.

The calculations for each road segment and scenario in the model are performed based on several formulas, as listed further.

The total user energy cost C_user,_e,t,s for a technology on a specific segment in a scenario is calculated as the multiplication of the length of the segment l_s, adoption rate a_t,s of the specific technology in the assessed scenario, the volume of consumed energy v_energy,s and the cost of that energy c_user,en per km, as shown in Formula (1).

$$C_{\mathit{user},e,t,s}=l_s\ast a_{t,s}\ast v_{\mathit{energy},s}\ast c_{\mathit{user},en}$$

(1)

The total user operation and maintenance cost C_user,o&m,t,s with technology on a specific segment is calculated as a multiplication of the length of the segment l_s, adoption rate a_t,s of the specific technology in the assessed scenario, traffic volume v_traffic,s on that segment, and the cost of operation and maintenance c_o&m per km.

$$C_{\mathit{user},o\&m,t,s}=l_s\ast a_{t, s}\ast v_{\mathit{traffic},s}\ast c_{o\&m}$$

(2)

The total user investment cost C_user,I,t,s in technology on a specific segment in a scenario is calculated as a multiplication of the length of the segment l_s, adoption rate a_t,s of the specific technology, traffic volume v_traffic,s on that segment, and the investment cost c_i per km driven.

$$C_{\mathit{user},i,t,s}=l_s\ast a_{t, s}\ast v_{\mathit{traffic},s}\ast c_i$$

(3)

For the ERS operator, the variable energy costs C_operator,e,s per segment are calculated as a multiplication of energy purchase price per kWh on wholesale market c_w.en and volume of consumed energy v_energy,s on that segment, as shown in Formula (4). Similarly, the revenue of the system operator R_operator,s is calculated as a multiplication of energy selling price (same as the cost to the user) c_user,en by the energy consumption on v_energy,s, as shown in Formula (5).

$$C_{\mathit{operator},e,s}=c_{w.en}\ast v_{\mathit{energy},s}$$

(4)

$$R_{\mathit{operator},s}=c_{\mathit{user},en}\ast v_{\mathit{energy},s}$$

(5)

Next, along the second arrow in Figure 1, a summary of the segment-level results is performed. This allows translating the calculated local impacts into the results at the corridor level. Similarly, the impacts on the off-motorway traffic, e.g., part of the trip from the electrified motorway section to the port terminal or a client warehouse, are also calculated.

Lastly, having the result summaries allows for the production of corridor-level outputs that describe the economic performance of ERS deployment on the investigated corridor— economic impacts for the trucking sector and the ERS network operator.

3. Modelling Inputs

This section provides a description of the modelling inputs that are used in this research. In the following subsections, we define the modelled technologies, describe the performance characteristics of the modelled vehicles and infrastructure, describe the modelled transport corridor and the alternative routes that could be electrified, and show how the technology adoption scenarios are defined.

3.1. Technology Definition

An Electric Road System (ERS) is a road that can supply electric power to vehicles in motion. This allows powering and charging adequately equipped road freight vehicles with electricity from the infrastructure while in motorway traffic, and using onboard batteries or another energy source (e.g., diesel, LNG, or hydrogen) for the final leg between the electrified portion of the road and the customer. The implementation of an ERS brings several benefits, including lower investment costs for vehicle purchase, reduction in resource intensity of vehicle electrification by allowing the use of smaller batteries, improved logistics performance for ERS users, and a more even time distribution of electricity grid load.

Using an ERS can provide a distinct advantage for battery electric trucks. The battery size can be significantly reduced, from a range of 800–1200 kWh of usable energy that would be required for a day’s work to 100–400 kWh, while allowing to perform the same transport operations. With an ERS, there is also no need to plan time for battery fast charging, because the battery of the truck is recharged while driving in motorway traffic. The smaller battery size allows for significantly reducing the required investment in vehicles and improves the return on investment while maintaining flexibility in operational patterns and suitability for different logistics business models. For fuel-propelled ERS vehicles, this benefit does not exist, because the ERS network fuel is used for propulsion.

There are several technology options available for developing ERSs. They can be categorised into three technology groups: overhead catenary [OC], ground conductive [GC], and ground inductive [GI], depending on the mode of power transfer used. In this subchapter, those three technology groups are described in detail and their characteristics are included in summary tables to be used in modelling. Additionally, to provide a baseline of comparison we are modelling some of the commonly used road freight vehicles, like diesel [D] and liquified natural gas [LNG], and those under consideration for wide adoption, like hydrogen fuel cell [FCEV] and battery electric [BEV] vehicles.

3.2. Vehicles

It is assumed that heavy goods transport can use diesel, LNG, hydrogen, electricity from batteries, and electricity from each of the three types of ERS for propulsion or charging. Also, selected hybrid combinations of all the fuels with ERS can be used. In this section, we describe our assumptions on vehicle purchase price, energy consumption, operation and maintenance costs, and also emissions.

3.2.1. Cost and Energy Performance

Several vehicle types are introduced in the model. There are vehicles that are powered by a single power source, like diesel [D], liquified natural gas [LNG], hydrogen [FCEV], or battery electric [BEV]. Also, hybrid vehicles that can use an ERS while on adequately equipped infrastructure and another power source while elsewhere have been added, e.g., eHighway catenary-diesel [CAT-D], Elonroad ground conductive-LNG [GCE-LNG], Alstom ground conductive-hydrogen [GCA-FCEV], and Electreon ground inductive battery electric vehicles [IND-B200]. The list included in the model is exhaustive and includes all possible powertrain combinations, even if those are not being produced or investigated as good implementation alternatives by the technology suppliers.

As summarised in

Table 1. Energy and economic performance of modelled vehicle types.

Vehicle Type	Energy		Investment, EUR	Energy Consumption 1		Operation and Maintenance 1
	Long Distance	Regional		Long Distance, kWh/km	Regional, kWh/km	Long Distance, EUR/km	Regional, EUR/km
D	D	D	129,000 1	2.46 1	2.46 1	0.143	0.143
LNG	LNG	LNG	174,000 1,5,6	2.78 1	2.78 1	0.143	0.143
FCEV	H2	H2	324,000 3	2.25 1	2.25 1	0.137	0.137
BEV800	GEL	GEL	163,600 2	1.42 4	1.42 4	0.126	0.126
BEV1200	GEL	GEL	190,400 2	1.42 4	1.42 4	0.126	0.126
CAT-D	CAT	D	152,000 1,2	1.51 4	2.46 1	0.107	0.143
CAT-LNG	CAT	LNG	197,000 1,2,5&6	1.51 4	2.78 1	0.107	0.143
CAT-FCEV	CAT	H2	347,000 1,2,3	1.51 4	2.25 1	0.107	0.137
CAT-B400	CAT	CATEL	159,800 2	1.51 4	1.42 4	0.107	0.126
CAT-B200	CAT	CATEL	146,400 2	1.51 4	1.42 4	0.107	0.126
CAT-B100	CAT	CATEL	139,700 2	1.51 4	1.42 4	0.107	0.126
GCE-D	GCE	D	144,000 1,2	1.51 4	2.46 1	0.107	0.143
GCE-LNG	GCE	LNG	189,000 1,2,5&6	1.51 4	2.78 1	0.107	0.143
GCE-FCEV	GCE	H2	339,000 1,2,3	1.51 4	2.25 1	0.107	0.137
GCE-B400	GCE	GCEEL	151,800 2	1.51 4	1.42 4	0.107	0.126
GCE-B200	GCE	GCEEL	138,400 2	1.51 4	1.42 4	0.107	0.126
GCE-B100	GCE	GCEEL	131,700 2	1.51 4	1.42 4	0.107	0.126
GCA-D	GCA	D	150,000 1,2	1.51 4	2.46 1	0.107	0.143
GCA-LNG	GCA	LNG	195,000 1,2,5&6	1.51 4	2.78 1	0.107	0.143
GCA-FCEV	GCA	H2	345,000 1,2,3	1.51 4	2.25 1	0.107	0.137
GCA-B400	GCA	GCAEL	157,800 2	1.51 4	1.42 4	0.107	0.126
GCA-B200	GCA	GCAEL	144,400 2	1.51 4	1.42 4	0.107	0.126
GCA-B100	GCA	GCAEL	137,700 2	1.51 4	1.42 4	0.107	0.126
IND-D	IND	D	137,000 1,2	1.51 4	2.46 1	0.107	0.143
IND-LNG	IND	LNG	182,000 1,2,5&6	1.51 4	2.78 1	0.107	0.143
IND-FCEV	IND	H2	332,000 1,2,3	1.51 4	2.25 1	0.107	0.137
IND-B400	IND	INDEL	144,800 2	1.51 4	1.42 4	0.107	0.126
IND-B200	IND	INDEL	131,400 2	1.51 4	1.42 4	0.107	0.126
IND-B100	IND	INDEL	124,700 2	1.51 4	1.42 4	0.107	0.126

Sources: (¹) Based on [3], operation and maintenance costs exclude driver wage and administrative overheads, (²) estimation based on the following: (1) a hybrid truck without engine, battery, and ERS equipment costs EUR 110 thousand; (2) eHighway pantograph system costs EUR 23 thousand, Elonroad mechanism system costs EUR 15 thousand, Alstom mechanism system costs EUR 21 thousand, and Electreon coils and electronics cost EUR 8 thousand; (3) battery costs 67 EUR/kWh in 2030 based on manufacturer and expert forecasts as summarised in [17], (³) [3,18], (⁴) [15], (⁵) [19], (⁶) [20].

, we use several parameters that describe the energy and economic performance characteristics of each vehicle type.

Forecasting of vehicle technology developments that relate to energy efficiency improvements for the different technologies falls out of the scope of this research. Therefore, the future performance of technologies is not assumed, estimated, or modelled.

3.2.2. Energy Cost and Price

The energy cost and price that are used in the model are shown in

Table 2. Energy cost and price, EUR/kWh excluding VAT.

Energy	Cost	Price
D	-	0.149
LNG	-	0.082
H2	-	0.248
GEL	-	0.20
[ERS]	0.08	0.22
[ERS]EL	0.08	0.22

Sources: [21,22,24,25], unit conversion based on [26]. Energy type abbreviations: D—diesel, LNG—Liquified/compressed natural gas, H2—hydrogen, GEL—grid electricity, ERS—electricity supplied by ERS operator (any technology) for direct use on ERS network, [ERS]EL—electricity supplied by ERS operator (any technology) for use off the network.

. The cost column contains the electricity price that the ERS network operator would have to pay, estimated based on Eurostat data [21], but omitting the impacts of the Russia-Ukraine war energy price peak. The sale price of electricity by the network operator is set with 0.14 EUR/kWh markup, which was calculated to balance the requirement to cover the investment costs of the network operator with the economic sustainability of the catenary users, as the operator would do. The price of diesel is estimated based on Global Petrol Prices [22] for the Netherlands and Belgium, also taking into account the phase-out of the excise return system for professional diesel users in Belgium [23]. The hydrogen price was determined based on Carbu.com (accessed on 14 Septermber 2023) [24].

3.3. Infrastructure Cost

The model uses estimated infrastructure cost per kilometre of constructed infrastructure. In practice, this cost is project-specific and will vary a lot depending on the expected ERS vehicle flow, road layout, existing electricity infrastructure, and geographic features which the road crosses. To facilitate estimating the infrastructure costs, interviews were held with all ERS technology providers: Siemens, Alstom, Elonroad, and Electreon. All of them have some infrastructure construction experience, but none at a large scale.

ERS infrastructure costs can be split into two major categories. The costs that are similar for all ERS approaches relate to the generation and transmission of electricity. The technology-specific costs relate to the ERS itself with its supply and metering equipment and road equipment (see Figure 2). The former is included in the electricity purchase price as included in the model based on Eurostat [21], therefore for the infrastructure cost we focus on the latter.

Since the expected ERS vehicle flow is the characteristic that has the most impact on the infrastructure building cost due to the impact on the power requirement, it is included in the model as a determinant of the infrastructure build cost for specific road segments. Also, since the different technologies have different optimal capabilities to supply power, which means that in practice some technologies require lower kilometre coverage to supply the same power. For the purposes of comparability, the cost of building each kilometre is adjusted to reflect this.

Table 3. ERS infrastructure cost depending on freight traffic volume and ERS coverage.

Daily ERS Traffic Volume	Power per Electrified km, kW	ERS Cost per km in One Lane, Million EUR
		CAT	GCE	GCA	IND
<2000	500	0.96	0.67	0.70	1.01
<6000	1250	0.96	0.72	1.00	1.03
<12,000	2500	1.04	0.92	1.33	1.04
<20,000	4000	1.12	1.19	1.42	1.06
ERS coverage		80%	80%	40%	100%

CAT—eHighway catenary electric road system, GCE—Elonroad ground conductive, GCA—Alstom ground conductive, IND—Electreon ground inductive.

provides a summary of the cost estimations depending on daily ERS freight transport volume and recalculated to reflect the optimal ERS coverage. The cost figures are in million EUR and per km of ERS lane in a single direction.

3.4. Geography

The geographical layout of the road network that is used in this work is obtained from two sources. For Flanders, the Web Map Service (WMS) of Vlaams Verkeerscentrum which provides georeferenced maps and data over the internet is used. A technical description of the used WMS is available from Stuyts [27]. For the Netherlands, we use road shapefiles from Rijkswaterstaat [28]. In both datasets, each road in the data is divided into segments—stretches of road between two successive points where traffic can join or leave the road.

We also use the outline shapes of the Netherlands and Belgium from GADM [29], which provides maps and spatial data for countries and their sub-divisions.

All Dutch data is in the EPSG:28992 spatial coordinate reference, and Belgian data is in the EPSG:31370 system. Recalculation is performed to align those for working in the same reference system.

3.5. Traffic Data

The truck traffic data for Flemish roads are obtained from Vlaams Verkeerscentrum [30] for each road segment, and for Dutch roads from the Rijkswaterstaat’s INWEVA database [28]. Both datasets are at segment level and for an average day, but for modelling purposes, we convert those to yearly volume for each road segment.

The traffic data for Flanders is available by vehicle type, in which heavy goods vehicles are defined as those vehicles longer than 6.9 m, including all rigid or articulated vehicles. Similarly, for the Netherlands, the data covers medium and heavy goods traffic.

Recorded heavy goods vehicle traffic volumes are shown in Figure 3. Darker blue shows lower recorded traffic volumes, while yellow shows medium traffic volumes, and red shows high recorded traffic volumes. Motorways with fine dark grey lines have no recorded data available.

3.6. Identification of Alternative Routes

On the Rotterdam–Antwerp corridor, alternative road connections that could be equipped with ERS are identified for further assessment. Only the motorways that link both ports are selected, and junctions and complex intersections where building ERS might be challenging are excluded because ERS use on these sections would be limited due to the lane changes that truck traffic may have to do. Also, motorways within the ports themselves, like the section of the R2 between the A12 and E34 near Antwerp including the Liefkenshoektunnel, and the A15 from the A4 junction to Maasvlakte, are excluded due to their complex nature, and the fact that those distances can easily be covered by ERS vehicles without connection to the network.

Road segments to which double off- and on-ramps are joining are not excluded and we do not consider them complex. It has been demonstrated in the German ELISA project that a catenary can be built with supports four lanes across, which is linked with higher construction costs related to the overspan of the arms and higher construction poles. Also, it is assumed that road segments that are on bridges can be electrified, as this is especially easy on small overpasses.

With this approach, three alternative routes on this corridor are selected. The first route is on the west and goes from Antwerp on the A12 north through Bergen op Zoom to Rotterdam. The second, the middle route, starts from Antwerp on the A12, but near Bergen op Zoom turns eastwards on the A58/A17, passing Roosendaal, and near Moerdijk, joins the A16/E19 to Rotterdam. The third route, on the east, is the E19 between Antwerp and Rotterdam. The map in Figure 4 shows the selected routes in red and other motorways in dark grey. The western route consists of 104 segments with a total length of 174.2 km, the middle route has 149 segments with a length of 188.1 km, and the eastern route has 106 segments over 179.9 km. Some sections of the routes overlap, meaning that a segment could be part of two routes. Route lengths are measured per segment, and do not include sections where it is assumed an ERS cannot be built.

3.7. Scenario Definition

To test how the introduction of the different road freight vehicle propulsion technologies would impact the Rotterdam–Antwerp corridor, several adoption scenarios were constructed. A base scenario is developed based on vehicle stock data from Statistics Netherlands [31] and Statistics Belgium [32] to reflect the shares of adoption of various propulsion technologies in 2022. Each new scenario is then developed by substituting the share of diesel vehicles with the share of the technology that is tested in the scenario.

In addition to the base scenario, two types of scenarios are introduced in the model (see

Table 4. Modelled technology adoption shares.

Scenarios	Technology Adoption Shares
	D	LNG	FCEV	BEV-	CAT-, GCE-, GCA-, IND-
				800, 1200	D, LNG, FCEV, B400, B200, B100
BASE	99.76%	0.22%	0%	0.03%	0%
Single-technology scenarios	100% for simulated technology
Introduction mix scenarios	99.76 − x%	0.22%	0%	0.03%	x = 5%, 10%, 15%, 20%, 30%, 40%, 50%

Abbreviations for heavy goods vehicle technologies: D—diesel, LNG—Liquefied/compressed natural gas, FCEV—fuel cell electric, BEV-xxxx—battery electric (index xxxx shows battery usable size in kWh), CAT-xxxx—eHighway catenary hybrid (xxxx shows the type of technology it is combined with), GCE-xxxx—Elonroad ground conductive hybrid, GCA-xxxx—Alstom ground conductive hybrid, IND-xxxx—Electreon ground inductive hybrid, index Bxxx for battery hybrid vehicles shows battery usable size in kWh.

). The first type is the “pure technology” scenario, which simulates an unrealistic situation where all vehicles on a road use one specific technology, e.g., LNG or inductive ERS. These scenarios are used for reference and assessing the performance of individual technology in an isolated environment. The second type of scenario simulates the introduction of a particular technology to replace currently dominant diesel powertrains at a certain level—5, 10, 15, 20, 30, 40, and 50 percent.

4. Findings

In this research, the construction and operation of different types of ERS on three alternative routes on the Rotterdam–Antwerp corridor was modelled. This section presents the findings that relate to the investment and operation of the infrastructure from the point of view of an infrastructure operator, its users, and society.

4.1. Infrastructure Investment

When considering the construction of an ERS, there are several factors that significantly influence the investment per route. First, the number and length of segments within the considered ERS route are important. A longer route with more segments requires a larger upfront investment than a shorter one. Also, the cost of electrification infrastructure itself, like overhead wires or conductive rail and the power supply equipment, will influence cost. Moreover, the coverage percentage of the route that is optimal for the specific technology is essential, according to discussions held with the technology providers. A lower coverage percentage implies that vehicles will be charging at a greater speed, while a higher coverage allows the charging speed to be lower for the same amount of electricity to be transferred for propulsion and charging the batteries.

Estimation of the investment cost is shown in

Table 5. Investment cost range per route, by ERS technology, mEUR.

Route		ERS Technology	Optimal Coverage	Investment Cost Range
Route 1		CAT	80%	167.2–167.8 mEUR
		GCE	80%	117.4–120.3 mEUR
		GCA	40%	122.3–133.4 mEUR
		IND	100%	176.3–177.0 mEUR
Route 2		CAT	80%	180.5–181.6 mEUR
		GCE	80%	126.8–131.0 mEUR
		GCA	40%	132.0–145.8 mEUR
		IND	100%	190.3–191.1 mEUR
Route 3		CAT	80%	172.7–174.8 mEUR
		GCE	80%	121.3–128.0 mEUR
		GCA	40%	126.3–143.7 mEUR
		IND	100%	182.0–183.0 mEUR

CAT—eHighway catenary electric road system, GCE—Elonroad ground conductive, GCA—Alstom ground conductive, IND—Electreon ground inductive.

. It is provided for each alternative route and separately for each ERS technology. For the calculation of the investment cost range, the actual traffic volumes on each specific motorway segment and modelled adoption rates were considered.

The cost estimations for the first western route range from 117.4 to 177.0 mEUR, for the middle route from 126.8 to 191.1 mEUR and for the eastern route from 121.3 to 183.0 mEUR. It must be mentioned that the inputs for this calculation are purely based on the inputs that were received during the interviews with technology providers. They currently do not yet have experience with constructing infrastructure at this scale, therefore some over- or underestimations are possible.

The investment cost comparison allows seeing approximate investment costs that one would have to account for when building ERS in this corridor. The purpose here is not to guide technology choice, because it cannot be claimed that lower investment costs make the technology better and should therefore be opted for. Other ERS infrastructure performance characteristics, like longevity, power transfer capability, related vehicle equipment cost and performance characteristics, and other operational characteristics and issues, should be considered when choosing the “best” technology for implementation. Evaluation of the full range of these criteria falls out of the scope of this research.

4.2. Profitability of Infrastructure Operator

As part of the analysis, we conducted ERS infrastructure operator profitability calculations by taking into account its revenues and costs under a wide range of scenarios and considering varying levels of technology adoption. This analysis allows insights to be gained into the financial viability of the infrastructure operator.

The calculations were performed for each route separately and compared. The third route, for which profitability heatmap is shown in Figure 5, provides the best return on investment for the ERS infrastructure operator. In the heatmap, each row represents the use of different ERS technology, and each column represents the adoption of that technology as a share of total traffic on the route. The table colours represent the level of profitability of the operator from red—unprofitable, to green—profitable. The values in the table show the profit or loss of the infrastructure operator for a specific technology adoption level. It can be seen that already at a modest level of adoption, the operator can be profitable.

Based on the calculations for all three routes, it can be concluded that the break-even of the investment and operation of ERS infrastructure depends on two factors: the electricity sale price and the technology adoption level. Those two determine the return that the initial investment is bringing. The operator has the incentive to keep the electricity sale price low because other alternatives to ERS exist. The adoption level can expedite reaching the break-even point. The investment cost of the technology, although important, has a lesser impact and is of secondary concern when choosing the technology for implementation.

From the economic perspective of the ERS operator, the eastern route, route 3, is clearly the best to be electrified. This is true even though concerning the investment cost, this is not the cheapest option. The profitability of the operator is the best because of the traffic volumes that allow wider use of the network and higher revenues for the operator.

By testing the sensitivity of infrastructure operator profitability to investment costs, it can be seen that the results are relatively insensitive (see

Table 6. Sensitivity to infrastructure investment cost of ERS operator profitability per adoption scenario, Route 3, mEUR.

Investment Cost	Profitability
According to technology providers
+25%
+50%

). Instead of 5–10% of ERS traffic that the operator requires for recovering investment cost (at infrastructure cost as indicated by technology providers), in the extreme case of a 50% infrastructure cost increase, the share of electrified traffic would only have to rise to 10–15% for profitable operation.

Other routes can also be operated profitably, although this would probably be considered an economically rational option only at higher levels of adoption of ERS technology and when the first-best option is already operational. This could, given certain policy goals, also be implemented as a suboptimal solution with government financial support, if for some reason it was considered that traffic should be shifted from route to route.

There seems to be potential for the ERS to achieve a flywheel effect, where, as the adoption of the technology grows, the infrastructure operator can reduce the electricity price charged to clients, leading to more road freight operators using the system. This has the potential to lead to a loop that reinforces itself.

We conclude that a wider electrified network is not a prerequisite for ensuring the viability of ERSs on the Rotterdam–Antwerp corridor. Picking a route with high enough traffic levels and therefore adoption potential, like the eastern route in this case, is sufficient. The existence of an interoperable ERS network in the vicinity, with which ERS traffic could connect with or without a gap would significantly strengthen the business case of the infrastructure operator, as shown in [14].

4.3. Economic Performance of Road Freight Transport Operators

The economic performance of ERSs for road freight transport is described in this section. By simulating the truck operation on each of the three routes the cost performance of each of the technologies, as compared to the diesel vehicle’s total cost of operation, is determined. The comparison is based on the cost per km. This simulation includes both the distances that trucks drive on the ERS network and also the distances that trucks drive off the network to reach terminals in the port or clients off the ERS network.

For route 3—the best one to be electrified—the total cost performance in percentage terms is shown in Figure 6.

Calculations for road freight transport operators show that from their cost perspective, route 3 is the best to equip with an ERS. This is because it allows the running of the vehicles with the lowest cost for the road freight transport operator. This is true for all technologies, except those that involve the use of LNG, because of the relatively much lower LNG price as input to the model. Also, from the perspective of serving clients, due to the higher density of industry along the third route, choosing to electrify this route seems the most rational, as it would allow a larger number of companies to reduce their transportation costs.

What would the use of an ERS mean for road freight transport operators from the perspective of profitability? Road freight transport operations occur in a highly competitive market, and there are no trade secrets or marketing actions that would allow a company to distinguish itself from the competition. The entry barriers in the market are low and operators are price takers.

A situation where a new technology, like ERS in this case, that allows changing the cost of providing the transport service appears is shown in Figure 7. Initially, two (or more) competing firms are using the same technology (currently dominant diesel trucks) and basically have similar total cost curves, ac₁0 and ac₂0. The firms produce transport services in quantities of q₁0 and q₂0. As the possibility to start using an ERS arises, firm 1 chooses to stick with the currently dominant technology, but firm 2 chooses to invest in a different technology and shift its average cost curve from ac₂0 to ac₂1. Having the opportunity to produce transport service at a lower cost, firm 2 as a rational actor produces and sells more transport service (q₂0 to q₂1) at a lower price than before p1 and gains additional profit which corresponds to the difference between its average cost curve and the sale price, marked with a green rectangle in the chart. The new price for the transport service has now become p1. At this price, the first firm, which did not adopt ERS, still has the previous cost curve ac₁0, while the price in the transport market is p1. The new price in the market is not sufficient to cover the first firm’s costs, therefore it starts making a loss, shown with a yellow rectangle in the graph, while still producing quantity q₁1 of transport service. If no action is taken, firm 1 eventually goes bankrupt. As the market participants are forced to make technological upgrades or face bankruptcy, the cost curves of all transport operators equalise and the first mover profit from the new technology disappears.

4.4. Potential Demand

The heavy goods vehicle traffic volumes on the Rotterdam—Antwerp corridor vary greatly depending on the road segment and its location. This variance is best shown in Figure 8, which shows a map with the traffic and the colour intensity bar for reference. The low traffic intensity is shown in blue, with medium in green and yellow, and high in red.

To understand the transport flows between the regions of Rotterdam and Antwerp, we use transport flow data [33] that provide road freight traffic flows between NUTS 3 regions (the port of Rotterdam is in NUTS 3 region NL339 Groot-Rijnmond, and the port of Antwerp is in regions BE211 Antwerpen and B236 Sint-Niklaas) and assign them to the E-road network. The data shows that 2.3% of the heavy goods vehicles that leave the port of Rotterdam region are heading to the port of Antwerp region. In the opposite direction, 2.1% of the heavy goods vehicles from the port of Antwerp region are heading to the port of Rotterdam region. When looking at the total motorway truck traffic, the majority of the traffic that passes the Rotterdam–Antwerp corridor is either local or passing in transit, and only approximately 0.5% of the trucks that appear on the Rotterdam–Antwerp link are moving between the areas of the two ports.

Given the calculation results of the infrastructure operator, it is clear that there is no way the road freight traffic between the ports of Rotterdam and Antwerp alone can be sufficient to justify the construction of an ERS on the road link, unless financial support is provided from the government for infrastructure investment. This could, of course, change if trade patterns were to change substantially, which seems unlikely.

The other traffic on the corridor, however, can be served in an economically sustainable manner that ensures the profitability of the ERS. Depending on the specific technology used, the profitability threshold of the infrastructure operator starts at approximately 5% of the traffic flow. There the majority of traffic could be local freight traffic serving the ports and industrial locations along the electrified road section, and the traffic between ports could contribute up to a maximum of 0.5 percentage points.

The technology transition and, as shown in Figure 6, related cost reduction by 4.6 to 10.2% for ERS “-B200” and “-B100” trucks, which would most probably be the ones most often chosen, allows shifting the transport operator cost curve substantially, as shown in Figure 7. For road freight transport where the profitability of the transport operators is often reported in the range of 0.8 to 3.5% from sales, a cost reduction of several players on the scale that ERSs enable would ensure price reductions in the market and undermine the non-adopters, as the price would fall under their average cost curve. This leads to the belief that an ERS, once proven by a few early adopters, would experience a rapid adoption by road freight operators with trucks driving sufficiently on the electrified stretches of the motorway, e.g., those that have important clients served by the electrified road.

5. Conclusions

This paper investigated the impacts that the implementation of an ERS would have on the Rotterdam–Antwerp motorway corridor. This is a part of the road network that links two of the largest ports in Europe, located in the Netherlands and Belgium.

In the investigation, we noticed that the advantages of ERS that have been demonstrated in previous research [14] remain in the corridor setting. For road freight operators the use of smaller batteries is still feasible and allows for a substantial reduction in investment costs, which translates into overall cost savings per driven kilometre. There is also an external benefit of reduced resource intensity associated with vehicle electrification. The flexibility of the operational pattern of the vehicles, which is an important advantage of the currently dominant diesel vehicles, can to a degree be retained even in the corridor setting.

From modelling the alternative ERS routes, we conclude that the third route, on the east, which mostly runs on the E19 between Rotterdam and Antwerp, with modelled 106 ERS segments over 179.9 km, has the best potential for implementation from the point of view of both the road freight operators and the ERS operator. For the operators, it ensures a larger number of warehouses, industrial locations and terminals to serve along the motorway and a quicker ammortisation of vehicle investment cost. For the infrastructure operator, this is the route that ensures the highest potential usage of the network, and therefore higher return on investment and lower adoption rates to reach the break-even point and profitability.

For route 3, the best one to electrify, the investment cost for the 179.9 km of the electrifyable motorway depending on the technology and adoption rates ranges from 121.3 to 183 million EUR. The cheapest route to electrify, route 1, is counterintuitively not the best for doing that, because of lower traffic levels, fewer use cases, and the highest adoption rates required to reach infrastructure operator profitability.

We also notice that the traffic between Rotterdam and Antwerp port regions alone is not sufficient for justifying an ERS for that purpose, even if all that traffic were to be forced to use it. The other traffic, however, seems sufficient to justify the investment, especially on route 3, where starting from approximately 5% of the traffic using an ERS ensures operator profitability.

Implementation of ERSs at a small scale is sub-optimal because this means that it can serve only a small part of clients using that road—operators that use specifically that road stretch for a substantial part of their operations. Other market participants would be excluded, because for them the potential savings would not outweigh the additional cost of equipping their vehicles. The infrastructure operator will understand that building additional ERSs on adjacent road sections improves the economic performance of existing electrified road sections. There is an optimum network size for an operator, as highlighted by Aronietis and Vanelslander [14], but at a much larger scale than the Rotterdam–Antwerp corridor.

For some transport operators who happen to have specific client bases, for instance those who usually serve clients from one of the ports along the electrified route, the construction of an ERS on the route could prove to be very lucrative for early adopters. As the road freight operator would charge the market rate to his clients, its cost curve with the use of the ERS would shift substantially lower, allowing for above-average profit. This would only apply to early adopters, as market rates would adapt as more transport companies adopt the technology.