Johannes Giehl, Arian Hohgräve, Melina Lohmann, Joachim
Müller-Kirchenbauer
Economic Analysis of Sector Coupling Business
Models: Application on Green Hydrogen Use
Cases
Open Access via institutional repository of Technische Universität Berlin
Document type
Preprint
Date of this version
4th October 2022
This version is available at
https://doi.org/10.14279/depositonce-16318
Citation details
Giehl, Johannes; Hohgräve, Arian; Lohmann, Melina; Müller-Kirchenbauer, Joachim (2022). Economic
Analysis of Sector Coupling Business Models: Application on Green Hydrogen Use Cases. Technische
Universität Berlin. https://doi.org/10.14279/depositonce-16318.
Terms of use
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International license:
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1
Economic Analysis of Sector Coupling Business Models:
Application on Green Hydrogen Use Cases
Johannes Giehl1,2, Arian Hohgräve1, Melina Lohmann1 and Joachim Müller-Kirchenbauer1
1Fachgebiet Energie- und Ressourcenmanagement, Technische Universität Berlin, Straße des 17.
Juni 135, 10623 Berlin
2correspoding author, g[email protected]erlin.de
Abstract:
Sector coupling will play a key role in the future energy system to realise greenhouse gas emission
reductions. A major factor will be green hydrogen based on renewable energies to defossilise
consumption sectors. Related business models of power to gas are not jet implemented on the market.
However, given the urgency of the change, this is essential.
This paper investigates hydrogen business models under current market conditions of high power
prices, no existing market for green hydrogen and the given regulatory framework with no levies for
green hydrogen production in the German market. For this purpose, an open-source business model
evaluation tool for sector coupling, which enables a simple and generic evaluation of sector coupling
business models including production and possible transportation infrastructure, is developed and
applied. Furthermore, the impact of changes of the input parameters like power prices and influence of
regulatory changes on profitability are assessed.
The results show that X-to-power business cases can be already profitable due to high power prices on
the wholesale market. However, power-to-X business models like hydrogen production still have
negative net present values and the net present value is worsened when infrastructure for hydrogen
transportation is considered. Key parameters for the negative result are investment costs and low
hydrogen prices. Nevertheless, it must be considered that higher hydrogen prices have a negative
impact on the X-to-power business model. To allow for profitable business cases, the market conditions
need to be adjusted to ensure sufficiently high prices for green hydrogen. Furthermore, subsidies on
investment or operational and maintenance cost can support the integration of power-to-X into the
market. Transportation infrastructure has a significant impact on profitability. Given these facts it is
necessary to create the required framework conditions to ensure the realisation of sector coupling.
Keywords: Sector coupling, Renewable energy, Hydrogen, Business model, Open source, Evaluation
tool
2
1. Introduction
To be in line with the Paris Climate Agreement and the goal of limiting the global temperature increase
well below 1.5°C [1], the EU and Germany aim to reach climate neutrality by 2050 [2] and 2045 [3]
respectively. The reduction of anthropogenic greenhouse gas emissions is of crucial importance for
achieving the target [4]. Emissions can be saved comparatively easily through technological
adjustments, efficiency gains or sufficiency in some sectors like power production of the economy. In
other sectors like heating, the reduction of greenhouse gases is much more complex [5,6]. These
challenges will require new links of different parts of the energy industry and sector coupling will be an
important element of the energy transition [7]. Power-to-Gas as one element of sector coupling enables
the transfer of climate-neutral, renewable energy (RE) from sources such as solar or wind energy to the
consumption sectors through the use of electrolysis [8,9].
Studies on greenhouse gas neutrality often show that hydrogen plays a crucial role in defossilisation of
many application areas [10]. In addition, hydrogen is a key element of the EU and German strategy to
achieve climate neutrality [2,11,12]. Therefore, the demand for hydrogen is expected to rise strongly
until the 2040s [13]. In addition to electrolysis as the core element of power-to-gas, storage technologies,
hydrogen turbines, fuel cells or refractory applications such as methanisation will be used. Applications
in the heat sector, process heat generation or in industrial processes up to mobility are possible fields
of use for hydrogen [8,14–16]. Most of the power-to-gas technologies have already been tested in pilot
projects and can demonstrate sufficient technological maturity for operation on an industrial scale [17–
20].
However, in addition to the technical feasibility, the economic aspects also must be considered. In the
current market environment, power-to-gas business models for hydrogen are not economically attractive
[21–23]. Thus, sector coupling business models of power-to-gas are not established on the market [24].
Cost (e.g., technology investment, electricity purchase), additional taxes and levies and possible
revenues from hydrogen production, have an impact on the profitability. In Germany, electricity tax, grid
charges and levies for combined heat and power (KWKG) and renewable energies (EEG) must be
considered. Recently, the EEG levy was higher than the wholesale electricity prices [25]. The levy was
first reduced to 3.72 ct / kWh in January and entirely cancelled in July 2022 [26]. Furthermore, different
studies show that power-to-gas can be economically feasible in the future and under certain market
conditions [23,25,27].
Sector coupling business models are investigated recently but the analyses and methods are often not
generically reusable. Examples are the analyses of battery storages in combination with power-to-heat
operation on the frequency containment reserve market by Draheim et al. [28] or renewable hydrogen
for rail transport by Guerra et al [29]. Balan et al. [30] derived a general approach for power-to-gas use
cases for the Rumanian energy market but only for hydrogen and synthetic methane. Further studies
like Agora Verkehrswende et al. [31] evaluate synthetic fuels in the transport and heating sector from
the perspective of the energy system. Economic assessments like Akhtaria and Baneshib [32] also focus
on the design of local energy system components but do not evaluate a specific business model.
Investigations like Liu et al. [33] show a detailed approach of business model evaluation in the energy
3
industry but lack of sector coupling considerations. Most of the studies use the net present value (NPV)
for economic assessment.
The aim of this work is to analyse the potential of sector coupling technologies based on costs and
revenues and to identify which economic, technical, and regulatory factors are important for the success
of the business models. The focus lies on the currently high power prices and the regulatory influence
on profitability. These aspects are to be considered sensitively to show the effects of changes of input
parameters such as regulatory adjustments.
The further contribution of our work is the development of an open-source tool that covers the mentioned
aspects, and which is not existing in research yet. The tool is generically structured and can be easily
operated as an HTML application. This permits an easy application and transferability to different
countries and market situations, which often has been difficult to realise in previous studies. Thus, the
digital evaluation tool is an instrument that makes it possible to quickly assess the economic viability of
a business model and can also be used in practice. In this context, the integrated sensitivity analysis
allows the evaluation of uncertain developments when planning new business models.
The paper is structured in the following way. In section 2 the methodology of the tool is presented. This
includes the general design of the tool, the mathematics, and the data. This includes the description of
the three investigated business modes. The business models of hydrogen production with and without
necessary infrastructure for transportation, and a business model to produce electricity based on
hydrogen in the German market is selected. In section 3 the results of the tool are presented by providing
the NPV of the business models and the impact of current changes on the power price development
and regulatory framework (subsidies and levies) is shown. In section 5 the results are discussed, and
section 6 concludes the work.
2. The business model evaluation tool
In this section our model and the data basis of the analysis is presented. The term "business model" is
used to describe qualitatively the abstracted logic by which a company aims to make profits [34]. A
business case is the detailed plan including an analysis of financial aspects and the quantitative aspects
of a business model [35]. Thus, a business case is an explicit realisation of a business model. Therefore,
the open-source business model evaluation tool for sector coupling (OBMETSC) enables the assessment
of business models. In this paper, the NPV is used for the assessment, as a positive NPV is a key
parameter for the realisation of a business model [35,36].
OBMETSC is available on git [37] and consists of a web application based on Python Flask and HTML
for the user and several modules and functions in the python-based backend. The section includes the
overview over the basic design of OBMETSC (Section 2.1), the general mathematics of the tool (Section
2.2), the basic data for the generic part and information about the data a user must define (Section 2.3),
and the considered business models and used data for the final calculations (Section 2.4)
4
2.1 Approach of the business model evaluation tool
OBMETSC is structured into four blocks (figure 1). The basic concept is based on the modules: power
supply, power-to-X (PtX), X-to-power (XtP) and additional infrastructure. Interfaces are implemented
between the modules for the transfer of input and output values.
Figure 1: Modules of the open-source business model evaluation tool for sector coupling
The logic of OBMETSC is based on the distinction between business models that are based on a PtX
technology and XtP business models. Thus, it is possible to model single parts of the PtX-to-power chain
as individual and as integrated business models. The structure and components of the individual PtX
and XtP modules are therefore described below. Each module consists of different sub-modules, which
implement individual calculation functions and are connect via input and output parameters as
interfaces.
The first module of the assessment model is the power supply. Power suplly is the starting point for all
PtX business models. Electricity can be supplied either from the grid or directly from a RE source like
wind or solar power. The electricity consumption of the PtX module is included in the calculation of the
production profile and the economic feasibility of the entire plant.
The additional infrastructure module includes the possible design of a storage and transportation
element. The design of the two elements is mainly dependent on the temporal structure of the PtX
technology production profile. The dimensioning of the transportation element also depends on the
distance to the consumption location. The two elements are necessary because fluctuating RE may
require storage and, for example, in the case of hydrogen there are currently only a few cases where a
connection between production and consumption already exists.
Power Supply Power-to-XH2
X-to-Power
Additional
Infrastructure
Input Output
5
Figure 2: The Power-to-X Module of the open-source business model evaluation tool for sector coupling
The main input to the PtX module (figure 2) is the power supply. It builds the foundation for the
calculation of the production profile, the production cost, and possible revenues and thus, the economic
viability of the entire plant. Further inputs are cost parameters of the PtX technology, the operation
strategy, and technological parameters such as capacity and efficiency. Furthermore, information about
possible prices for the produced energy carrier (e.g., heat or hydrogen) are input of the Power-t-X
module. Output of the module are cost of power purchase, infrastructure, and production as well es
revenues for the produced energy carriers. All the information is used to compute the NPV of the
investigated business model.
Figure 3: The X-to-Power Module of the open-source business model evaluation tool for sector coupling
Design of
Transportation
Capacity
Design of
Storage
Capacity
Cost of
Transportation
Infrastructure
Additional Infrastructure
Power Production
& Purchase
Calculation of
Production Profile
of Wind and PV
Power Plant
Cost of Power
Production
Cost of Power
Purchase from
Grid
Net
Present Value
of the
Business Model
Calculation of the Production Profile of the PtX-Plant
Cost of PtX-Production
Revenue from the PtX-
Production and / or Power
Production
Power-to-X
Technology of
Power Supply
Input Data
Installed Capacity
Power Production
Location
Installed Capacity
PtX-Production
Operation
Strategy
Cost Data
PtX-Production
Transport
Technology
Transport
Distance
Power
Price
Cost Data
Power Production
Price Data
PtX-Product
Cost of
Power Production
and
Power Purchase
Cost of Additional
Infrastructure
Cost of
PtX-Production
Revenue of
PtX-Production
Revenue of
Power Production
Output Data
Calculation of the Production Profile of the XtP-Plant
Cost of XtP-Production
Revenue from the XtP-
Production and / or Power
Production
X-to-Power
Net
Present Value
of the
Business Model
Design of
Transportation
Capacity
Design of
Storage
Capacity
Cost of
Transportation
Infrastructure
Additional Infrastructure
Installed Capacity
XtP-Production
Input Data
Operation
Strategy
Cost Data
XtP-Production
Price of Input
Energy Carrier
Power
Price
Heat
Price
Transport
Technology
Transport
Distance
Revenue of
Power Production
Cost of Additional
Infrastructure
Cost of
XtP-Production
Revenue of
XtP-Production
Output Data
6
The XtP module (figure 3) is necessary to model further conversion steps for energy carriers produced
by PtX business models. Thus, the input is either the result of the PtX module or individually set by the
user. Further input data refer to the used XtP technology (see technological parameters mentioned
above) and the business model defined by the user. Results are also information about cost and revenue
of the elements of the XtP module and the NPV.
2.2 Mathematics of the model
OBMETSC is based on various interrelationships and the resulting calculations. The calculations result
in the revenue and cost structure of the business model and the NPV.
The power supply module is mainly designed to calculate the electricity production from RE plants and
the associated costs. The module contains two elements, one for the load profile generation and the
second for the cost of the RE plant. Business models that draw electricity from the grid do not require
this module as power purchase cost are directly taken from the input data.
The production profile depends on the location, the installed capacity (PRE) and the production
technology (wind or photovoltaics (PV)). The temporal resolution of the model is hourly. Thus, for every
hour the capacity factor (QRE,t) of the RE source is computed (E.1) by using the provided energy of the
respective hour and a theoretical plant of 1 MW.
(
".1
)
&'!",$ =")*+,-!",$
./0/123-!"
Thus, the capacity factor (QRE,t) is a location-specific vector for the RE sources with a value for every
hour of the year. The realised electricity production (XRe,t) of a RE plant depends on the installed capacity
(Pinstalled,RE) defined by the user. The electricity production profile of the plant under consideration is the
result of multiplying the capacity factor with the installed capacity (E.2). The model also allows a
combination of different RE sources in a hybrid system (e.g., wind and PV).
(
".2
)
&5!",$ ='!",$ ∗&7%&'$())*+,!"
The specific operating costs have two components, one for annual fixed cost depending on the installed
capacity and the second on the produced power. The cost of the RE power (Cfixed,RE,a) is calculated
based on the installed capacity as no variable cost for RE input energy are assumed. Thus, the
investment costs (CapExRE), the specific fixed operating costs (OpExRE) and the lifetime (a) are relevant
input parameters (E.3). In case an own RE plant the second part is assumed to be zero.
(
".3
)
&.,%-*+,!",( =./7*9!",( ∗7%&'$())*+,!" +;0"9!*,( ∗7%&'$())*+,!"
In case of power supply by the wholesale market this factor corresponds to the wholesale electricity
price (PWS,t,a) and additional fees (e.g., grid usage fees and taxes). In case of power supply by the
wholesale market E.4 represents a cost factor. Furthermore, possible revenue by selling the power on
the wholesale market are considered (E.4). In this way the module considers the opportunity that an
alternative ways of power supply are possible and selling power could be more economically viable than
using the electricity for the PtX plant. The hourly net wholesale electricity price (PWS,t,a) is taken into
7
account and over the lifetime it is possible to assume a price development (PriceChangea) set by the
user (RRE,t,a).
(
".4
)
&=!",$,( =5!",$ ∗7./,$,( ∗PriceChange0
These values are the starting point for the calculations of the PtX module. It is possible to consider a
combined power purchase via the grid and a RE power plant. This is important as the user defines the
installed capacity of the components and the RE power plant might be too small or too big for the PtX
plant.
The first step is the calculation of the hourly production profile of the PtX plant (
5$,1$2)
. In addition to
plant-specific parameters such as the efficiency (ηPtX), information about the achievable revenue price
of the PtX product and the production costs are necessary. The module allows two different operation
modes: a maximisation of the production PtX plant and an optimisation of the revenues.
In the case of maximising production, the production profile depends on the availability of power. In
addition to the installed capacities, the produced quantity depends on the efficiency of the PtX plant
(E.5). In case of a grid connection the production profile is constant and equals the installed power
multiplied by the efficiency (E.6).
(
".5
)
&51$2,$ =IJK&(5!",$ ∗η345,P6784099:;,345 ∗η345)
(
".6
)
&51<2,$ =&P6784099:;,345 ∗η345
In the second case, the production depends on the hourly costs and revenues. In this operation mode,
production only takes place when marginal revenue is positive. When electricity prices are high, the
electricity is either not purchased or power produced by the RE plant can be sold on the wholesale
market. The PtX plant is operated when the sum of the variable costs (cvarc) and the quotient of power
costs (cpower) and efficiency (ηPtX) is smaller than the achievable revenues (RPtX) of the product (E.7).
The result of this condition is binary and is used to compute the production profile of the PtX plant
(
51$2,$
).
(
".7
)
1=>?*@,$,(
η345 +.A(@,1$2,$,( <=1$2,$,(
Furthermore, in case of RE plants for power supply the consumption of RE (consumptionRE,t) is
computed (E.8). Based on this result, the consumption of power from the grid (consumptionWS,t) is
calculated (E.9) in case of an undersupply or the amount of sold power on the wholesale market
(powersold,t) in case of an oversupply (E.10).
(
".8
)
&consumptionBC,4 =IJK(51$2,$
η345 ,5!",$)
(
".9
)
&consumptionDE,4 =51$2,$ −&&consumptionBC,4
(
".10
)
&power8F9;,4 =5!",$ −&&consumptionBC,4
8
The calculation of the fixed cost in every period follows the same structure as in case of the RE plant
(E.11). The difference is that the cost of the power production by the RE power plant (cRe,a) are
considered separately.
(
".11
)
&.,%-*+,1$2,( =./0"91$2,( ∗7%&'$())*+,1$2 +;0"91$2,( ∗7%&'$())*+,1$2 +&.!",(
The variable costs (Cvar,PtX,t,a) result from the costs for the power purchase and other variable costs
(E.12). At this part of the PtX module the impact of cost due to regulatory requirements is considered.
Taxes, levies, charges on the power purchase either by the RE plant (cReg,RE) or on the wholesale market
(cReg,WS) have an impact on the profitability. Costs like EEG or KWKG levy, grid usage charges or the
electricity tax are part of the specific cost parameter.
(
".12
)
&.A(@,1$2,$,(
=consumptionDE,4 ∗
\
1=>?*@,$,( +1!*G,./
]
+&
consumptionBC,4 ∗1!*G,!" +1>$H*@,A(@,$
∗51$2,$
Furthermore, it is possible to consider the impact of subsidies on the business model. The user can
investigate the impact either by setting subsidies direct on the fixed or the variable cost. In this case, the
cost components will be reduced before calculating the numbers.
The total revenues (
=<>$(),$,(
) of a PtX business model are generated by selling the produced PtX energy
carrier and the not used surplus electricity from the RE plant (E.13).
(
".13
)
&=<>$(),$,( =&=1$2,$,( +=!",$,( =51$2,$ ∗71$2,$,( ∗PriceChange0+&5!",$ ∗7./,$,( ∗PriceChange0
The NPV is formed from all costs and revenues, the lifetime, and the selected interest rate (i) by the
user (E.14).
(
".14
)
&K7^=
__
=<>$(),$,(
$
)IJ ∗
(
KIJ
(
1+3
)
LK)−
__
(.,%-*+,1$2,( +.A(@,1$2,$,()
$
)IJ ∗
(
KIJ
(
1+3
)
LK)
The XtP module is designed in a similar way. In the same way as for PtX, the production of another
energy carrier such as electricity or heat can also be realised either by maximising production or by
maximising revenues. The chosen operation mode defines the capacity factor in every hour (QXtP,t). In
addition, co-generation of different energy carriers is considered. For example, the use of waste heat in
electricity production can be used as a further revenue stream to generate additional income.
The production profile (E.15) of the specific energy carrier (XXtP,EC,t) is calculated by using the installed
capacity (PXtP) and efficiency (
ηCM,543
). The required amount of PtX energy (
51$2,$
) as input is calculated.
by using the production profile and the efficiency (E.16).
(
".15
)
&5N2$1,"O,$ =72$1 ∗ηCM,543
(
".16
)
&51$2,$ =52$1,"O,$
η543
9
The cost and revenue calculation are structured as described above and are composed by fixed and
variable cost. In the case of an integrated analysis, the variable price for the necessary amount of PtX
energy carrier (
11$2,$,(
) is result of the PtX module. In the case of an independent business model, the
price is set by the user.
In contrast to PtX business models, the economic effects of regulatory requirements are only considered
indirectly. In case of an integrated analysis, they are part of the PtX module. In case of an independent
business model, it is assumed that the supply with a PtX based energy carrier, such as hydrogen, is
realised via an independent infrastructure. Thus, costs for the supply are part of the costs set by the
user. Possible subsidies of fixed and variable costs are also possible, set analogously by the user and
applied directly to the cost parameters.
The revenues of XtP business models follow the logic in E.13. The component for produced electricity
is the same but in case of other carriers such as heat a specific price needs to be set by the user. The
final calculation of the NPV follows the logic in E.14.
For the economic evaluation of business models, the transport and storage infrastructure can be
considered as an additional element of the business model. For example, there is no publicly accessible
network for green hydrogen and additional infrastructure may be necessary as part of a business model.
For the business model assessment, it is assumed that no revenues can be generated through
infrastructure. Thus, it just has an impact on the costs. The module consists of two elements. The first
is for the infrastructure design and the second for the calculation of the cost (cinfrastrucutre).
The options assumed for the transportation are liquified gas trailer, gas trailer and pipeline. In addition
to the production profile, assumptions about the transport distance, the maximum permissible pressure
and the specific transport capacity of the individual technologies are necessary. For gas trailers and
liquefied gas trailers, the number of necessary transporters is calculated. For pipeline design, the
diameter is needed in addition to the distance for cost estimation.
For pipelines, the maximum output (
'
`
P(-
) is taken as input for dimensioning the diameter (E.17). For
the dimensioning, the flow rate in MWh is converted into a mass flow (E.18) by means of using the
energy density (
a
).
(
".17
)
&'
`
P(- =&Ib5
\
51$2,Q,…,51$2,RSTJ&
]
(
".18
)
&d
`
=&'
`
∗&a
The diameter of the pipeline (d) is calculated by on the volume flow (
^
`) and the velocity (v). Since
hydrogen shows almost ideal gas behaviour up to a pressure of 100 bar, the ideal gas equation can be
used to compute the volume flow (E.19) [38,39].
(
".19
)
&e=&
f
4
g∗d
`
∗=h∗i
j∗0
In case of the transportation via trailer, the ratio of the amount of necessary daily transport routes (TRtotal)
and the capacity of a trailer (captrailer) defines the necessary number of trailers (E.20). Further input is
10
the number of daily transport routes done by one trailer (TRtrailer, E.21). This factor depends on the
transport distance (s), the loading and unloading time (timeloading), transportation speed (vtrans) and daily
working time (WT). The necessary number of trailers (numbertrailer) is the quotient of necessary daily
transport routes and number of daily transport routes done by one trailer.
(
".20
)
&i=$>$() =
∑
51$2,$
365
./0$@(%)*@
(
".21
)
&i=$@(%)*@ =li
h
j$@(&' +23d*)>(+%&G
In case of the transportation via trailer a buffer storage is considered. The number of storages required
(E.22) depends on the maximum output (
'
`
P(-
), the daily working time (WT), the necessary daily
transport routes, and the storage capacity (capstorage).
(
".22
)
&Kmdn*+'$>@(G*' ='
`
P(- ∗li
i=$>$()
./0'$>@(G*
Specific investment and operational cost for gas trailers, liquified gas trailers, pipelines and storages are
used in the calculation of the cost for the additional infrastructure. In addition, costs for compression are
applied, which depend on the installed electrolysis capacity and the type of transport. The total cost
consist of investment elements (cinfrastrucutre,invest, E.23) and operation elements (cinfrastrucutre,operation, E.24).
In case existing infrastructure is used, the costs are input in the NPV calculation of the PtX module.
(
".23
)
.%&,@('$@UV$U@*,%&A*'$,(
=1oh2=%=*)%&*,+%(P*$*@,( ∗h+1oh2<@(%)*@,$W=*,( ∗Kmdn*+$@(%)*@',( +1oh2'>$@(G*',(
∗Kmdn*+'$>@(G*' +1oh2V>P=@*'%>&,$@(&'=>@$$W=*,( ∗71$2
(
".24
)
&.%&,@('$@UV$U@*,>=*@($%>&,$,(
=1oh2=%*)%&*,>=*@($%>&,$,( ∗h+1oh2$@(%)*@,>=*@($%>&,$,( ∗Kmdn*+$@(%)*@',(
+1oh2'>$@(G*',>=*@($%>&,$,( ∗Kmdn*+'$>@(G*' +1oh2V>P=@*'%>&,A(@,$,( ∗d
`
2.3 Data
In the next section, the data basis of the parameters used for the calculations of the four modules is
presented. The input parameters are divided into four categories: database parameters, input
parameters set by the user, opt-in parameters, which can be generated either by the user or from the
database, and output parameters of other modules and functions. OBMETSC provides a broad basis of
data for the calculation, which can be adapted to the specific case by the user. The approach allows the
user to freely quantify revenues from the sale of the energy carrier or costs of the selected technology
in the model. In contrast, energy industry parameters, such as the revenues or costs of electricity
purchase, can be estimated based on historical market information.
Parameters set completely by the database are wind and solar power generation profiles. Site-specific
production profiles for PV and wind plants are used for all federal states in Germany. The profiles were
11
calculated using the online calculation tool Renewables.ninja which is based on MERRA-2 weather data
[40–42].
The wholesale electricity prices are provided with the net exchange electricity prices of the day-ahead
market of the EPEX SPOT exchange (table 1). The time series for the German market zone is based
on the year 2021 [43,44]. Currently, the price for PtX energy sources cannot be determined based on a
reference market. Thus, provided information is based on existing references to existing submarkets or
conventional substitutes. When considering hydrogen, a distinction was made between three different
prices for green, blue and grey hydrogen [45]. This allows the consideration of different markets when
modelling a PtX business model. Furthermore, prices for ammonia and methane are part of the database
to provide the opportunity to model energy carriers based on hydrogen. The values can also be set by
the user based on own information. In the case of heat, the user must provide own assumptions for the
price.
Table 1: Cost parameters for energy carriers in the database of the OBMETSC
Parameter
Price [EUR / MWh]
Source
Green hydrogen (mean 2021)
278.00
[46]
Grey hydrogen (mean 2021)
89.91
[47]
Blue hydrogen (mean 2021)
95.20
[48]
Ammonia (mean 2021)
73,57
[49]
Methane (mean 2021)
55.89
[50,51]
Bio methane (mean 2021)
75.00
[52]
Power (mean 2021)
96.85
[53]
Further data (table 2) provided by the database describes the basic technological (e.g., thermal, and
electrical efficiency) and economic parameters of the sector coupling technologies. This data is either
set by the user or taken from the database. The database includes various electrolysis technologies and
the associated infrastructure, gas / hydrogen turbines for power and heat generation. In the absence of
cost data for pure hydrogen power plants, data for conventional gas and steam power plants are used.
In the case of conversion of existing power plants to hydrogen operation, the costs for operation with
hydrogen are approximately estimated at 50 % of the installation costs. The data basis for the
dimensioning of additional infrastructure is restricted to the transportation and storage of hydrogen. Data
for hydrogen transportation via pipelines includes pressure, flow velocity and diameters [54–56]. Data
for trailer transportation refers to transport pressure and the resulting transport capacity [57].
12
Table 2: Cost and technological parameters in the database of OBMETSC
Technology
Investment cost
Operational cost
[% of investment cost]
Efficiency
Sources
Polymer electrolyte
membrane
(PEM) electrolysis
1,610 [EUR / kwel]
4
0.71
[58,59]
Alkaline water
electrolysis
(AEL) electrolysis
878 [EUR / kwel]
4
0.68
[58,59]
Power-to-methane
2,275 [EUR / kwel]
4
0.56
[58,59]
Power-to-ammonia
2,653 [EUR / kwel]
4
0.45
[58,60]
Fuel cell
1,130 [EUR / kwel]
4
0.28th, 0.62el
[58,61,62]
Combined cycle gas
turbine
550 [EUR / kwel]
4
0.27th, 0.61el
[63,64]
Wind turbine
1,200 [EUR / kwel]
4
1
[42,58]
PV
650 [EUR / kwel]
2.5
1
[42,58]
Compression until
100 bars
3,000 [EUR / kWComp]
75
0.046 [kWComp/kWEl]
[57]
Compression until
500 bars
3,500 [EUR / kWComp]
75
0.157 [kWComp/kWEl]
[57]
Regasification
hydrogen
7,200 [EUR / kg h]
76
1
[57]
Gas trailer
150,000 [EUR / trailer]
75,000 [EUR / a]
1
[57]
Liquid gas trailer
650.000
[EUR / kWComp]
75,000 [EUR / a]
1
[57]
Pipeline DN 250
1,200,000 [EUR / km]
1,5
1
[58]
Pipeline DN 500
1,500,000 [EUR / km]
1,5
1
[58]
Pipeline DN 1100
2,800,000 [EUR / km]
1,5
1
[58]
Another aspect is the compressor to liquify hydrogen for trailer transportation. A constant compression
is assumed, which is only dependent on the throughput and thus, on the installed electrolysis power.
This ratio is described with a factor from the literature, which expresses the compressor costs as a
function of hydrogen production. For liquefied hydrogen, the dimensioning of a liquefaction plant
depends only on the throughput as liquified hydrogen is incompressible. The specific cost data is
selected from the literature and refers to the costs per liquefied unit of mass of hydrogen [57]. For
business models with the output methane or for electricity and heat, it is assumed that the necessary
transportation option already exists.
2.4 Business Cases
The presented OBMETSC will be applied and further presented by using three business models.
Business models of the hydrogen value chain are applied. In the first case, the operation of electrolysis
with electricity from own PV capacity is considered. The second case is an expansion of the first one
and includes the integration of additional infrastructure into the business model. This allows the
13
assessment of the impact of the infrastructure as in the current situation no hydrogen grid exists. In both
cases, revenues from selling hydrogen or power are possible. The third business case is the
consideration of a replacement of fossil gas turbines by hydrogen turbines. In this case, the revenues
are generated via selling power and heat.
The input parameters of the business model assessment are based on the information of the integrated
database presented in section 2.3. The further values to be set by the user are provided below.
The PEM electrolysis has an installed capacity of 10 MWel. The PV plant is dimensioned for 20 MWel.
As PV is used the plant is located in Bavaria in southern Germany with higher solar radiation. The grid
usage fees for industrial customers are 24 EUR / MWhel. The hydrogen power plant has a capacity of
30 MWel. The plants use the operation mode of maximising profits.
In all cases, a lifetime of 25 years and weighted average cost of capital (WACC) of 7 % is assumed. For
revenues and consumption from electricity on wholesale market, the 2019 timeseries is scaled to the
2021 mean and standard deviation to consider a more even price development over the year. Further
electricity price components are the grid usage fees of 24 €/MWh for industrial customers in Bavaria [65].
In the case of considering additional infrastructure, it is assumed that a customer 1 km away is supplied
at a price of 90 EUR / MWhH2. The hydrogen power plant is supplied with blue hydrogen at the same
cost of 90 EUR / MWhH2. Possible revenues from selling heat are assumed to be 20 EUR / MWh.
3. Results
The business models of PtX show in both cases, the green hydrogen production with and without
additional transportation infrastructure, a negative NPV. In contrast, the power production with a
hydrogen turbine has a positive NPV. Detailed results are discussed in the following sections.
3.1 Results of the business model assessment
The hydrogen production with PtX based on PV production is not economically viable under current
market conditions. The NVP of hydrogen production is -5.0 million EUR and it drops to -9.6 million EUR
when considering necessary transportation infrastructure.
Figure 4: Share of accumulated cost components of the PtX business models over the lifetime
without additional infrastructure
with additional infrastructure
O&M PEM
Investment PEM
O&M PV
Investment PV
O&M Infrastructure
Investment Infrastructure
Expenditure Analysis Power-to-X: Hydrogen Production
Total Expenditure:
67.5 Mio. EUR
Total Expenditure:
73.5 Mio. EUR
Regulatory Charges
Power Purchase
14
The PEM accounts for more than 23.9 % of the accumulated expenditure over lifetime of 67.5 million
EUR (figure 4). Investment and O&M expenditure contribute to the same extend to the total expenditure.
The expenditure of the PV plant is mainly driven by the investment expenditure (19,3 %). In case of the
additional transportation infrastructure via pipeline the cumulative expenditure increases to 73.5 million
EUR and is still mainly driven by the PEM (21.8 %). The contribution of the PV plant declines to 17.6 %.
The investment of the pipeline infrastructure accounts for 4.0 % and the O&M expenditure of the pipeline
for 4.5 % of the accumulated expenditure.
Figure 5: Share of accumulated revenue components of the PtX business models over the lifetime
The revenue structure of both business cases is the same, as the pipeline has no influence on the
revenue streams (figure 5). In both cases, most of the total revenue (90.2 million EUR) comes from the
sale of electricity generation on the wholesale market. Only 37.2 % of the revenue is generated by
hydrogen production.
Figure 6: Share of accumulated cost components of the XtP business model over the lifetime
In contrast to the PtX business cases, the XtP case with the gas turbine has a positive NPV of 1.4 million
EUR. The expenditure of the XtP business case (figure 6) of the hydrogen turbine is mostly characterised
by expenditure for the hydrogen procurement (feedstock). The expenditure of the investment and O&M
contribute with 7.4 % each. Total expenditure over lifetime is 224.2 million EUR.
without additional infrastructure
with additional infrastructure
Revenue Hydrogen Production Revenue Power Production
Revenue Analysis Power-to-X: Hydrogen Production
Total Revenue:
90.2 Mio. EUR
Total Revenue:
90.2 Mio. EUR
Expenditure Investment Turbine Expenditure O&M Turbine
Expenditure Analysis X-to-Power: Power Production
Expenditure Feedstock
Hydrogen Turbine
Total Expenditure:
224.2 Mio. EUR
15
Figure 7: Share of accumulated revenue components of the XtP business model over the lifetime
The total revenue of the hydrogen driven gas turbine is 246.2 million EUR over the lifetime. The majority
is accounted to the sale of power on the wholesale market (figure 7). The share of power sales is 97.2 %
compared to only 2.8 % of the revenues generated by the sale of heat.
3.2 Impact of input prices on the profitability
The results show that the production of green hydrogen based on own renewable electricity generation
does not enable an economic business case under current framework conditions. Furthermore, the
revenue is mainly generated from the sale of PV power on the wholesale market. This is valid for both,
the PtX case with and without the consideration of additional infrastructure.
Figure 8: Sensitivity results for the NPV of the PtX business models
In case of the PtX case without additional infrastructure the investment costs of electrolysis, the WACC
and the hydrogen price offer a possibility to realise a positive NPV (figure 8). Low investment cost or a
low WACC show the highest potential to enable positive business cases. If the WACC is below 70 % of
the assumed 7 %, a positive NPV can be achieved. The influence of the WACC is important, as it is
usually mainly influenced by the debt capital provision. A positive NPV is also possible at 70 % of the
assumed investment costs of 1610 EUR / kW of the PEM. If the hydrogen price rises to 150 % and thus
Revenue Analysis X-to-Power: Power Production
Hydrogen Turbine
Revenue Power Production Revenue Heat Production
Total Revenue:
246.2 Mio. EUR
Efficiency
Power Price Change
Sensitivity Analysis Power-to-X: Hydrogen Production
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
110%
120%
130%
140%
150%
160%
170%
180%
190%
200%
without additional infrastructure
Regulatory Expenditure
Investment PEM
WACC
Hydrogen Price
30
Million EUR
-40
20
0
-30
-20
-10
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
110%
120%
130%
140%
150%
160%
170%
180%
190%
200%
with additional infrastructure
10
16
to over 135 EUR / MWh, then a positive NPV can also be achieved by using the electrolysis. This applies
similarly to the change of efficiency. In contrast, even with lower power prices a positive business case
is ensured, as it favours the hydrogen production but cannot compensate for the expenditure of the PV
plant in all cases. A decrease of regulatory expenditure cannot enable a positive NPV.
In case of PtX with additional infrastructure, the level of NPV is mainly shifted downwards. Thus, low
WACC and investment cost enables profitable business cases. For a positive NPV, the investment costs
must fall to 50 % and the WACC to 40 % of the input values. High power prices also lead to a positive
NPV, but only due to power production. Compared to the analysis without infrastructure, a positive
business case is possible by further increasing the hydrogen price to 170 %. Similarly, an increase in
regulatory levies worsens the business case.
Figure 9: The impact of subsidies on the NPV of PtX business models
The impact of public subsidies also shows that investment costs are an important factor in realising
positive NPVs (figure 9). The direct support of the investment ensures profitable business cases in both
PtX cases. The slightly higher values when the NPV becomes positive compared to the sensitivity
analysis above can be explained by the fact that although part of the investment cost is covered by
another source, the actual amount of the investment is not lower. Supporting annual O&M costs can
also contribute to profitability. However, in this case, a positive NPV is only given in the PtX case without
additional infrastructure and for this over 70 % of the O&M costs need to be covered. In case of
additional infrastructure, a positive NPV cannot be realised by subsidising the O&M cost.
Subsidy on Investment Cost Subsidy on O&M Cost
Subsidies on Power-to-X: Hydrogen Production
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
without additional infrastructure
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
with additional infrastructure
Million EUR
8
-12
- 8
- 4
0
4
12
17
Figure 10: Sensitivity of the NPV (left) and the impact of subsidies on the NPV (right) of the XtP business model
The positive NPV of the hydrogen turbine business case is depressed by higher costs but remains
positive in most cases (figure 10). The NPV becomes negative in case of 50 % lower heat price. An
increase to 120 % of the investment costs also creates a negative NPV. The parameters hydrogen price,
electricity price and efficiency have a higher impact on the NPV. Due to high share of feedstock cost,
an increase of the hydrogen price by 10 % and a reduction pf the power price by 10 % can push the
NPV into negative values. Due to the already positive NPV, subsidies further increase profitability.
4. Discussion
The results show that electrolysis in combination with own renewable electricity production cannot be
operated economically under current market and political conditions. In the case of further and
necessary infrastructure for hydrogen transportation, the economic situation continues to decline.
Revenues are mainly generated from the sale of power due to high power prices on the one hand and
low hydrogen prices on the other side. Without a sufficient guarantee of demand at sufficiently high
hydrogen prices, electrolysis cannot be operated profitably. This could be created by implementing an
own market for green hydrogen in combination with obligatory deployment quotas in demand sectors as
fossil-based hydrogen is significantly cheaper.
Furthermore, the impact of political measures by changing the framework conditions became evident.
Changes to the levies have already been realised but even without additional fees the PtX cases are
still unprofitable. Thus, further support by subsidies could help to achieve economic viability. As power
for electrolysis is produced by own capacities, the direct support of the investment cost is the most
promising approach. It should be mentioned here, however, that with a PEM, a comparatively expensive
technology was selected. In case of an AEL, the profitability could be already possible at current
investment costs provided in table 2.
If required, the infrastructure has a negative impact on the profitability. Even a doubling of hydrogen
prices does not lead to profitability under the given conditions if infrastructure is considered. This shows
that for a large-scale hydrogen economy and profitable business cases, the transport issue is crucial.
Currently, only local hydrogen networks exist. New projects must also consider connections between
Sensitivity Analysis and Subsidies on X-to-Power: Power Production
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
110%
120%
130%
140%
150%
160%
170%
180%
190%
200%
Hydrogen Turbine
Heat Price
Investment Turbine
WACC
Hydrogen Price
250
Million EUR
-50
200
150
0
50
100
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Hydrogen Turbine
20
Million EUR
0
16
4
8
12
Subsidy on O&M Cost
Subsidy on Investment Cost
300
350
Efficiency
Power Price Change
18
production and consumption. A limitation of the model is, that the investment is completely covered by
the business model. The investment could be shared between hydrogen producer and customer. Further
factors, such as the planning for public hydrogen transportation networks, must also be considered.
Initial ideas for hydrogen transportation grids exist already and could diversify the investment risk,
involving grid customers only in the form of grid fees. [66].
XtP business cases could be realised economically at current market conditions. Hydrogen turbines
benefit from the high electricity prices in relation to the prices for fossil hydrogen. However, as only
green hydrogen ensures no negative impact on greenhouse gas emissions, this conflicts with
profitability. High electricity prices support the XtP business model but jeopardise the PtX business
model for green hydrogen and vice versa. Higher hydrogen prices would enable profitable business
cases of PtX but then affect XtP in a negative way. This conflict cannot be easily resolved. In the case
of the hydrogen turbine, other markets such as a balancing market could be considered, or an adapted
market design with e.g., capacity markets could enable revenues beyond the whole sale market. This is
relevant as hydrogen will play a crucial role in the future electricity system [67] and viable business
cases for reconversion are necessary. In this respect, the subsidies are not necessary at current market
prices. But as soon as more expensive green hydrogen is used, it appears to be useful to help the
business cases that will be necessary in the future to become viable.
It should be noted that only measures with direct impact on the business models are considered by the
OBMETSC. Indirect measures, such as the rising price of CO2, are not included. Also, regulatory options
such as guaranteed sales quotas are only effective if the linked price generates a sufficient return. The
level of detail is reduced due to the generic applicability along the sector coupling value chain. Thus, the
technology under consideration is limited in terms of the number of required input values and aspects
such as start-up times and partial load ranges are not integrated. However, this does not stand in the
way of an adequate quantification of PtX-to-power business models depending on regulatory, technical,
and economic framework conditions.
5. Conclusions
The paper investigates green hydrogen production and use under the current political framework in
Germany with currently high electricity prices. For this purpose, the OBMETSC is presented as a
framework to assess sector coupling business models. An application of the tool beyond Germany is
pending but possible by customising the input data including RE generation data and market data.
The results conclude that PtX business cases are currently not economically viable while XtP business
models are. Further research is required to find a way of exploiting the potential of all parts of the value
chain as their success is mutually dependent: Firstly, profit from the low levelised cost of electricity of
RE secondly, thereby supporting profitable PtX business cases and thirdly, all these by providing low-
cost green hydrogen for XtP business cases. Measures such as an adaptation of the market design are
plausible in order to raise the potential of hydrogen business models of sector coupling and to foster the
energy transition.
19
Author Contributions
This paper is based on results of analyses by J.G. with contributions by A.H. The methodological
approach of the paper was developed by J.G. and A.H. Together with J.M.-K., they developed the
concept and approach of the paper. The tool OMBETSC was mainly developed by A.H. and further
improved by M.L and J.G. The data used in OMBETSC was mainly surveyed by A.H. and supported by
J.G. The main parts are written, and visualisations created by J.G. and A.H. The writing and editing
process was further supported by M.L. The research was initiated by J.G. and J.M.-K and J.G. managed
the editing process. All authors have read and agreed to the published version of the manuscript.
Funding
The paper was not funded by a specific project. Thanks to J.M.-K for providing the conditions to work
independently on the development of the tool OMBETSC based on the general funding of the chair of
energy and resource management from the budget of the Technische Universität Berlin.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that
could have appeared to influence the work reported in this paper.
Acknowledgments
Previous works of this research have been presented at the YEES 2022 in Copenhagen. We, therefore,
thank the conference and workshop audiences as well as the anonymous reviewers of this publication
for useful discussions and suggestions, the usual disclaimer applies. We like to thank Friederike Dobler
for feedback during the modelling and writing process. We also like to thank Benjamin Grosse, Flora
von Mikulicz-Radecki and Johannes Kochems for the discussions and impulses during the development
of OMBETSC.
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