energies
ArticleTechno-Economic Analysis of Grid-Connected Hydrogen
Production via Water Electrolysis
Pengcheng Zhu * , Masahiro Mae and Ryuji Matsuhashi
Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan;
mae@enesys.t.u-tokyo.ac.jp (M.M.); matu@enesys.t.u-tokyo.ac.jp (R.M.)* Correspondence: zhu@enesys.t.u-tokyo.ac.jp

Abstract: As the global energy landscape transitions towards a more sustainable future, hydrogen has
emerged as a promising energy carrier due to its potential to decarbonize various sectors. However,
the economic competitiveness of hydrogen production by water electrolysis strongly depends onrenewable energy source (RES) availability. Thus, it is necessary to overcome the challenges related
to the intermittent nature of RESs. This paper presents a comprehensive techno-economic analysis
of complementing green hydrogen production with grid electricity. An evaluation model for thelevelized cost of hydrogen (LCOH) is proposed, considering both CO2 emissions and the influenceof RES fluctuations on electrolyzers. A minimum load restriction is required to avoid crossovergas. Moreover, a new operation strategy is developed for hydrogen production plants to determine
optimal bidding in the grid electricity market to minimize the LCOH. We evaluate the feasibilityof the proposed approach with a case study based on data from the Kyushu area in Japan. The
results show that the proposed method can reduce the LCOH by 11% to 33%, and increase hydrogenproductivity by 86% to 140%, without significantly increasing CO2 emission levels.
Keywords: hydrogen production; water electrolysis; renewable energy; operation strategy; CO2emission; levelized cost of hydrogen (LCOH)

1. Introduction
Citation: Zhu, P.; Mae, M.;
Matsuhashi, R. Techno-Economic
Analysis of Grid-Connected
Hydrogen Production via Water
Electrolysis. Energies 2024, 17, 1653.
https://doi.org/10.3390/en17071653
Academic Editor: Giovanni Esposito
Received: 5 March 2024
Revised: 24 March 2024
Accepted: 28 March 2024
Published: 29 March 2024

Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Nowadays, the high levels of greenhouse gas emissions resulting in global warming
are regarded as one of the major issues worldwide [1]. Green hydrogen, produced throughwater electrolysis using renewable energy sources (RESs), has gained significant attention
in recent years as a promising solution to address global energy and climate challenges.
Its potential applications span across various sectors, including transportation, power
generation, and industrial processes, making it a versatile and attractive energy carrier [2].
However, despite the considerable progress made in the development of green hydrogenproduction technologies, its large-scale deployment still faces several barriers, primarily due
to the intermittent nature of RESs and the high costs associated with electrolysis processes.
Among all RESs, the most used are photovoltaic (PV) and wind power generation,
which mainly depend on weather conditions [3]. Thus, the availability of electricity fromRESs is uncertain, affecting both the economy and the safety of water electrolysis. On the
one hand, a hydrogen production plant cannot work when the input power is unavailable,
which will reduce the operating hours of the plant and increase total production costs [4].
On the other hand, the direct use of fluctuating power from RESs will cause several
problems such as crossover gas and electrolyzer degradation, challenging the normal
operation of electrolysis [5]. Therefore, it is necessary to overcome these issues related tothe intermittent nature of RESs.
Water electrolysis is an electrochemical process that splits water into hydrogen andoxygen. Regarding water electrolysis technologies for hydrogen production, currently,
there are three main methods available or under development: alkaline electrolysis, proton

Energies 2024, 17, 1653. https://doi.org/10.3390/en17071653

https://www.mdpi.com/journal/energies

Energies 2024, 17, 1653

2 of 16exchange membrane (PEM) electrolysis, and solid oxide electrolysis cells (SOECs). In thispaper, we only focus on the first two as SOECs have not yet been commercialized, although
individual companies are now aiming to bring them to market [6]. Alkaline electrolysis
is the most mature and commercial technology with over 100 years of history. It is highly
durable, and already available at large scale, with relatively low capital cost [7]. However,this conventional technology is not designed to be operated with fluctuating power sources,
and it shows a very poor performance when the power supply is switched off [5]. PEM
electrolysis is relatively resistant to fluctuating power due to its fast response and flexible
operation, which allows it to provide a service with a larger load range than alkaline
electrolysis, especially in the low load range, and to take advantage of dynamic electricity
prices [8]. Moreover, PEM electrolysis shows less performance degradation when powerinterruption occurs [5]. Thus, it is beneficial in operations with intermittent power sources
like RESs. However, the high cost of precious metal electrode catalysts and the shorter
lifetime of PEM electrolyzers hinder the wide deployment of this technology [9].
Theoretically, the influence of RES power fluctuations on electrolyzers should be considered when modeling to avoid security risks. However, the existing literature usuallyignores this issue to simplify the model [7], or just advocates for PEM technology by highlighting the limits of alkaline technology without a specific techno-economic analysis [10],leading to misestimation of the actual benefits brought by alkaline electrolysis [11]. The
authors in [12] adopt the engineering model of an alkaline electrolyzer in a green hydrogenproduction system without any constraints for the uncertain input power. Few authors
explicitly model the impacts of fluctuating power supply. In [13], the limitation of alkalinetechnology is modeled as ramp-up constraints during the start-up period by setting theneeded time for electrolyzers to be operational. However, the influence of uncertain power
input on electrolyzers is ignored. In [14], the authors compared the cost of an off-gridhydrogen production plant when using alkaline and PEM electrolyzers. The differences
between alkaline and PEM technologies are considered but only in terms of economics,
ignoring environmental and safety factors. In [15], the low dynamic load range restriction is modeled for alkaline technology. The authors analyzed both the economic andenvironmental impacts of complementing green hydrogen production with grid electricity
in an average pricing scheme. Therefore, how to model the fluctuations’ influences onelectrolyzers is still a challenge.
Moreover, electricity pricing schemes are diverse according to the electricity market
and the employed model. In the wholesale market, electricity prices are available 24 h ahead(“day-ahead prices”) and 1 h ahead (“real-time prices”). Some papers omit dynamic prices
to facilitate calculation. For example, the authors in [15] use the average electricity price tocalculate hydrogen production cost, simplifying the optimization problem. Generally, ahydrogen production plant can reduce its operating cost by extending production capacity
during periods of cheap electricity. In [16], GAMS optimization is used based on day-ahead
electricity prices to compare the electricity cost with different operation times only for PEM
electrolysis. In [17], yearly historical data are utilized to determine the electricity priceat which the levelized cost becomes minimal. The authors in [18] consider both flat-rate
pricing schemes and real-time pricing schemes for grid-connected hydrogen production.
The levelized cost of hydrogen (LCOH) is usually used to analyze the economic
performance of hydrogen production. The LCOH is a measure of the average net present
cost of hydrogen production for a plant over its lifetime [15]. It is used to account for all
the capital and operating costs of producing hydrogen and to compare different methods
on a consistent basis.
This paper aims to analyze the techno-economic benefits of complementing greenhydrogen production with grid electricity. An evaluation model for LCOH is proposed,
including the CO2 penalty, under a wholesale electricity market. The proposed modelallows the hydrogen production plant to perform the following:••

Determine the bidding process in the grid electricity market to reduce the LCOH.
Improve hydrogen productivity without significantly increasing CO2 emission levels.

Energies 2024, 17, 1653

3 of 16•

Ensure the load of the electrolyzers meets the requirements of safe operation.

Different capital expenditures (CAPEX) and different operating expenses (OPEX) areevaluated in the proposed model. And the features of alkaline and PEM electrolysis are also
considered in our analysis. Moreover, an optimal operation strategy is developed to solve
this nonlinear evaluation model. It can calculate the optimal amount of grid electricity
required in each time step to cover the intermittency of RESs.
The main contributions of this paper compared with the existing literature are as follows:

•

•

•

An evaluation model for LCOH is proposed, including CO2 emission costs in a wholesale electricity market. It considers both the economic and environmental impacts ofgrid-connected systems and can take advantage of dynamic electricity prices.
The influences of RES fluctuations on alkaline and PEM electrolyzers are considered.
A minimum load restriction is required to avoid explosion due to high crossovergas concentrations.
A novel operation strategy for hydrogen production plants is developed to determine
optimal bidding in the grid electricity market to minimize the LCOH. It can simplify
the above optimization problem and solve it without any complex algorithms.

Some recent research also focuses on grid-connected hydrogen production systems.
In [19], the authors aimed to smooth the wind farms’ output fluctuation to produce hydrogen constantly with the grid-connected system, but they ignored the characteristics ofelectrolyzers. The evaluation model for the LCOH with CO2 emissions and a low load
range restriction is applied in [15] as well. However, it calculates the LCOH with theaverage electricity price, ignoring the advantages brought by dynamic electricity prices.
The wholesale electricity market is considered in [17,18]. However, in [17], it only focuseson transforming grid electricity into hydrogen, excluding RESs. The CO2 penalty is not discussed in [18], and it requires the prediction of hydrogen production demand and assumes
that hydrogen plants do not produce more than the forecasted demand. To the best of theauthors’ knowledge, an evaluation model for the LCOH in the wholesale electricity market,
considering CO2 emission costs and the influences of RES fluctuations on electrolyzers, has
not yet been discussed in any literature.
The remainder of this paper is organized as follows: Section 2 introduces the offgrid system and grid-connected system for producing hydrogen by electrolysis. Section 3
presents the LCOH model employed and the optimal operation strategy to minimize the
LCOH for hydrogen production plants. A case study in the Kyushu area is performed toillustrate hydrogen production with alkaline and PEM electrolyzers. The simulation results
are shown in Section 4 to evaluate the performance of the proposed LCOH model. Finally,
conclusions are drawn in Section 5.
2. Hydrogen Production by Water Electrolysis
Green hydrogen production by water electrolysis requires 100% RES electricity, which
can be achieved with an off-grid production plant, as shown in Figure 1. The power supply
is usually wind power or solar power. However, directly using RES electricity may causesome problems. For RES electricity, since the RESs are not always available, the plant
may work only during certain hours in a day, reducing the operation hours. In this case,
the CAPEX is relatively high, usually not at a competitive cost. Moreover, for alkaline
electrolyzers, the fluctuating power input may lead to crossover gas and performancedegradation [5]. Water electrolysis can produce not only H2 but also O2 . Crossover gas
is a mix of H2 and O2 , which can easily explode. From [20], we know that crossover gas
reaches relatively high concentrations in an alkaline electrolyzer when power generationis relatively low, sometimes even more than 1% in the experimental simulations, which isdangerous. Therefore, to keep the crossover gas concentration under control, it is necessary
to set a minimum load restriction for alkaline electrolyzers. As for PEM electrolyzers, since
they are able to operate with fluctuating power, there are no minimum load requirements
for PEM electrolyzers. The electrolyzer must stop operating when the power falls below

Energies 2024, 17, 1653

highconcentrations
an alkaline
electrolyzer
whenthat
power
generation
areaches
mix ofrelatively
H2 and O
2, which
can easilyin
explode.
From[20], we know
crossovergas
is relatively
low, sometimes
even morein
than
1% in theelectrolyzer
experimental
simulations,which isreaches
relatively
high concentrations
an alkaline
whenpower generation
dangerous.
Therefore,
to keep
the crossover
under control,
it isisrelatively low,sometimes
even morethan 1% ingas
theconcentration
experimental simulations,whichnecessary
tosetaminimumload
restrictionforalkaline
electrolyzers.
As
for
PEM
dangerous. Therefore, to keep the crossover gas concentration under control, it iselectrolyzers,
are ableload
to operatewith ﬂuctuating
power,
there are nonecessaryto since
set a theyminimum
restriction
for alkaline
electrolyzers.
As minimumfor 4PEMof 16
load requirements
electrolyzers.
stop
operatingwhen theelectrolyzers,
since for
theyPEMare ableto operateThewithelectrolyzer
ﬂuctuatingmust
power,
there
are no minimum
power
falls below therange. During
be utilized,
leadingload
requirements
foroperating
PEM electrolyzers.
The shutdown,
electrolyzerRESs
mustcannot
stop operating
when
the
to
a
reductioninoverallenergyeﬃciency.
power
falls below
theDuring
operatingrange. During
shutdown,
RESs cannot
be toutilized,leading
the operating
range.
shutdown,
RESs cannot
be utilized,
leadinga reduction
intoareductioninoverallenergyeﬃciency.
overall energy efficiency.

Figure 1. Oﬀ-grid hydrogen production system.
Figure 1. Off-grid hydrogen production system.
Figure 1. Oﬀ-grid hydrogen production system.

One potential solution is using grid electricity to complement the power supply to
One potential solution is using grid electricity to complement the power supply to thethe electrolyzer.
This
situationis presented
in Figure 2.complement
In this case, the powerelectrolyzer
willOne
potential
solutionusing
gridinelectricity
supplytoelectrolyzer.
This situation
isispresented
Figure 2. toIn this case, the theelectrolyzer
will use
use electrolyzer.
RES electricity
wheneverit is available
andgrid electricity
whennecessary.
AlthoughtheThissituation
presented
inFigure
2.
In
this
case,
the
electrolyzer
will
RES electricity whenever it is available and grid electricity when necessary. Although thethe RESelectricity
cost and emission
cost of andgridgrid
electricity
arewhenhigherthan thatof RESuse
electricity
it is available
electricity
necessary.
Althoughelectricity
cost andwheneveremission costof grid electricity
arehigher thanthatof RES electricity,
electricity,
thismethodcanextendtheoperating
hours
of
the
electrolyzer,
reaching
theandtheemission
costof gridelectricity
are reaching
higher thanthat capacityof RESa
this electricity
method cancost
extendoperatinghours
of theelectrolyzer,a higherhigher capacitymethodfactor andhydrogenproduction.
the electrolyzer
in thiselectricity,
can higher
extendthe operating
hours
ofThus,the electrolyzer,
reachingafactor and thishigher hydrogenproduction.
Thus, theelectrolyzer
in this system
is moresystemismoreproductive
thanthe
only-RES
case,
reducingthe
CAPEX.
Moreover,highercapacityfactorandhigherhydrogenproduction.
Thus,
the
electrolyzer
inthis
productive than the only-RES case, reducing the CAPEX. Moreover, this method can also
methodalsoproductive
avoid
gasonly-RES
and slowelectrolyzer
degradation.
system
iscan
more
than
the
case,
reducing
the CAPEX. Moreover, thisavoid
crossovergasandcrossover
slowelectrolyzer
degradation.
method can also avoid crossover gas and slow electrolyzer degradation.

Figure 2. Grid-connected hydrogen production system.
Figure 2. Grid-connected hydrogen production system.

Figure2. Grid-connected
production
system. electrolyzer working in the above two
Figure3 describeshydrogenan example
of an alkaline

Figure3 describes
an example
of an alkaline
electrolyzer
in operates
the aboveintwo
systems.
Consideran off-grid
photovoltaic
hydrogenproductionworking
plant thatthesystems.
Consideranoﬀ-grid
photovoltaic
hydrogenproduction
plant
that
operatesinFigure3
describes
anexampleofanalkaline
electrolyzer
working
in
the
above
two
daytime when solar energy is available. Because of the minimum load restriction, part ofthedaytimewhensolarenergyisavailable.
Becauseoftheminimumload
restriction,partsystems.
Consideranoﬀ-grid
photovoltaic
hydrogenproduction
plant
that
operatesinthe RES electricity cannot be utilized. When connecting to the grid, it is possible to use theof theRES electricity
cannotbe isutilized.
connecting
to the grid,
it isrestriction,
possible
topartuse
thedaytimewhen
solar
available.
Becauseof theminimumload
RESelectricity
below
theenergy
operatingrangeWhenandincreasehydrogenproduction
by injecting
theRESelectricity
below
the
operatingrange
andincreasehydrogenproduction
byoftheelectricity,
RES electricity
be utilized.
When connecting
tothe
theCAPEX
grid, it per
is possible
to usegridwhich cannot
will significantly
influence
reduction ofunit (“unitary
injecting
gridelectricity,
which
will
signiﬁcantly
inﬂuence
reduction
of
the
CAPEX
per
the
RES electricity
the operating
range
production
CAPEX”).
However,below
the average
electricity
costandand increase
emissionhydrogencost will increase
due bytounitgrid(“unitary
CAPEX”).
However,theaveragebetween
electricity
cost
andCAPEX
emissioncostwill
injectinggrid electricity,whichwill signiﬁcantly
inﬂuenceof the and
CAPEX
per
the
electricity
used. Thereexistsa trade-off
thereduction
unitary
averageincrease
due
theemission
grid electricity
used.
There exists
a trade-oﬀ
betweenthecostunitary
unit(“unitary
CAPEX”).
However,
the average
electricity
cost andemissionwillelectricity
costtoandcost.
CAPEX and
electricity
cost andemissionincreasedueaverageto the gridelectricity
used.
There cost.
exists a trade-oﬀ between the unitary
CAPEX and average electricity cost and emission cost.

Energies 2024, 17, x FOR PEER REVIEW
Energies 2024, 17, 1653

5 of 16
5 of 16

Figure3.3.Example
Exampleofofan
analkaline
alkalineelectrolyzer
electrolyzerworking
workingconnectedconnectedtotogrid.
grid.The
Thebluebluearea
areaononthetheright
right
Figuremeans
thecomplementing
electricity
from
power
grid.
means the complementing electricity from power grid.

3. Model3. ModelIn this paper, we perform the optimization in a wholesale electricity market, which is
In this paper, we perform the optimization in a wholesale electricity market, whichcloser to the real situation in Japan. The proposed model is general, so it can be applicable
is closer to the real situation in Japan. The proposed model is general, so it can bein other wholesale markets globally. However, it is important to note that some countries
applicable in other wholesale markets globally. However, it is important to note that someset fixed charge rates for electricity, in which case our research may not be suitable.
countries set fixed charge rates for electricity, in which case our research may not besuitable.
3.1. Optimization Model of the LCOH
The objective function of the optimal LCOH can be defined as follows:
3.1. Optimization Model of the LCOH
minCLCOH
min(CelLCOH+ CCAPEX+ defined
COPEX +asCem)
The objective functionof the=optimal
can befollows:(1)

minCCLCOH= min(Cel + CCAPEX + COPEX + Cem )(1)where CLCOH is the LCOH,
el is the average electricity cost, CCAPEX stands for the unitary CAPEX,
COPEX
represents
excludingthe electricity
unit (“unitary
where
𝐶
is
the LCOH,
𝐶 theis OPEXthe average
electricity
cost, 𝐶cost perstandsfor theOPEX”),
and
C
means
the
averageemissioncost.
em
represents the OPEX excluding the electricity cost per unit
unitary CAPEX, 𝐶
For OPEX”),
the electricity
it includes
the basic
electricity(“unitary
and 𝐶cost,means
the average
emissioncost.cost, CRES and Cgrid , and thewheeling
charge
cost,
C
.
Here,
we
assume
that
only
grid
electricity
requireswh it includes the basic electricity cost,
For the electricity cost,𝐶
and 𝐶 a, wheelingand thecharge
since
the
green
hydrogenproduction
plantsareusually
in
the
same
place
as thewheeling charge cost, 𝐶 . Here, we assume that only grid electricity requires a wheeling
RES. Then,electricityproduction
cost Cel is asfollows:
chargesincethetheaveragegreen hydrogenplantsare usually in the same place as the

RES. Then, the average electricity cost (𝐶asCfollows:
CRESis +grid + Cwh )Cel = (C + C + C )
RES
grid
wh
PH2
Cel =
PH 2
where
t
where
CRES = ∑ ERES
· PricetRESttt E
C RES = 
RES ⋅ PriceRESt
Cgrid = ∑t Egrid· Pricetgridttt
C grid = E grid⋅ Pricegridtt
Cwh = ∑ Egrid · Pricewht
Cwh =  E grid
⋅ Pricewhttt
PH2 = η ∑ ( ERES
+ Egrid
)

(2)
(2)
(3)

(3)
(4)
(4)
(5)

t

(5)
(6)

t
t
PH 2 = η  ( ERES
+ Egrid)where CRES and Cgrid are the total electricity
costof
RESs and grid power, Cwh is (6)
the
t
total
wheeling
charge
cost,
and
P
means
hydrogenproduction.
When
the
time
periodH2electricity cost of RESs and grid power, 𝐶
where 𝐶and 𝐶
are the total
is the
t
t
T
=
1
year,
P
will
be
the
annualhydrogenproduction,
P
.
E
and
E
stand
H2
H2annual
RES
periodtotal wheeling charge cost, and 𝑃 means hydrogen production. When the timegrid

t

t
from
and
the grid production,in time-step t.𝑃 PricetRES. 𝐸
and Price
Tfor= 1electricity
year, 𝑃 usedwill betheRESsannualhydrogenand grid
𝐸 represent
standEnergies 2024, 17, 1653

6 of 16

RES and grid electricity prices in time-step t. Pricewh is the wheeling charge price. η is theefficiency of hydrogen production.
To ensure the total load within the operating range, the above equations are subject tothe following constraint:tt
Et = ( ERES
+ Egrid) ∈ [ Emin , Emax ](7)

where Et means the total electricity used in time-step t, and Emin and Emax represent
the minimum and maximum load for the electrolyzers. For an alkaline electrolyzer, theminimum load restriction here is set to be 10% of the capacity [6]. In the case of a PEM
electrolyzer, the minimum load is set as zero.
The unitary CAPEX can be calculated as the quotient between the annualized CAPEX,
annual and the annual production of hydrogen, P
CCAPEX
H2annual , as follows:
CCAPEX =annual
CCAPEX
PH2annual(8)

The unitary OPEX can be obtained as follows:
COPEX = OPEX f ixed + OPEXvar(9)

where OPEX f ixed is the fixed cost, such as the maintenance cost and employee salaries, and
OPEX var is the variable cost depending on total hydrogen production volume, for example,
the water consumption cost.
We assumed that there is no CO2 emission related to RES electricity. Thus, the average
emission cost can be calculated as follows:t
λ · µtax · ∑ Egrid

Cem =

t

PH2

(10)

where λ is the emission factor, which presents the CO2 emission related to the grid electricity
used, and µtax is the CO2 tax.
Finally, the LCOH can be obtained by adding the result of (2), (8), (9), and (10). Here,
we suppose that hydrogen production will use RES electricity whenever it is available, andgrid electricity when necessary. To minimize the LCOH means to find an optimal operationt . However, regarding the evaluation models, the P , which
strategy for the variable, Egrid
H2tt , is in the denominator of the equations. Thus, this optican be calculated by ERES
and Egridmization problem is nonlinear, which is hard to solve directly. Another limitation is that theelectricity spot price cannot be available one year in advance. One simplified but reasonablestrategy is to find the threshold price (TP) according to historical data to estimate whether
the current electricity price is relatively high or low. This strategy only uses historical datatand solves the nonlinear problem by determining Egrid
alone. Although it may not obtain
the global optimum of the original model due to the simplification, the solution still showsa high performance in a practical situation without any complex algorithms. The specificprocess of the operation strategy will be introduced in the next section.

3.2. Operation Strategy in the Wholesale Market
In this section, an operation strategy is proposed to determine the electricity bidding process for a hydrogen production plant to minimize the LCOH in the wholesale
electricity market.
The details of the subsequent steps in determining the operation strategy of the waterelectrolysis plants are illustrated in Figure 4. In this paper, the time-step is set as 30 min,and the period T = 1 year. We can determine the threshold price by analyzing historical
data. Firstly, we calculate the LCOH at different initial threshold prices using last year’s
electricity prices. Then, we plot a graph of the LCOH against the threshold price, and theEnergies 2024, 17, 1653
Energies 2024, 17, x FOR PEER REVIEW

7 of 16
7 of 8threshold price is obtained by identifying the point on the figure with the lowest LCOH
thresholdis obtained
identifying
point onversion,
the ﬁgurewith the thelowestvalue.
The price
thresholdprice isby
determinedin thea one-year
considering
gridLCOH
price
value.
The
thresholdpriceisdeterminedinaone-yearversion,considering
thegrid pricefluctuations throughout the whole year. Next, by comparing the current grid electricity
ﬂuctuations
the whole
year. Next,
comparing
theiscurrent
grid electricity
pricewith thethroughout
threshold price,
the operation
state byof anelectrolyzer
determinedfrom twot
price withIfthe
thresholdprice,
theoperation
state
of
an
electrolyzer
isdeterminedfromscenarios.
Price
≤
TP,
the
electrolyzer
is
set
as
the
maximumload
toproduce
more
grid
𝑡𝑡
t
two
scenarios.
If
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃
≤
𝑇𝑇𝑇𝑇,
the
electrolyzer
is
set
as
the
maximumload
toproduce𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔hydrogen. Otherwise (Pricehydrogen production will return to the required
grid > TP),𝑡𝑡
more hydrogen.
( 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃), hydrogenproduction
will returnto thethe𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 > 𝑇𝑇𝑇𝑇
minimumload. In Otherwise
this case, gridelectricity
is only
used to makeup the differencefromtrequired minimum
case,
grid
only usedadditional
to make up
the diﬀerence
minimumload. So, ifload.
ERESIn≥thisEmin, thereiselectricity
no need toisconsume
powerfrom the𝑡𝑡tfromThus,
the minimum
So, if 𝐸𝐸𝑅𝑅𝑅𝑅𝑅𝑅
𝐸𝐸𝑚𝑚𝑚𝑚𝑚𝑚 , there is no need to consume additional power
grid.
Egrid
canload.
be calculated
as ≥follows:𝑡𝑡
from the grid. Thus, 𝐸𝐸𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔
can be calculated as follows:tt i f price t t ≤ TP
 E−maxERES− ERES
if pricegridgrid ≤ TP
 Emaxt − Et t i f pricet t > TP and Ett < E
Egrid=E t Emin(11)
grid > TP and ERES< E min
ERES if price(11)
 Emin RES
grid =−
grid
RES
min

0 
t
i f pricetgrid
t > TP and EtRES ≥ Eminif pricegrid > TP and ERES ≥ Emin
0
where
whereTP
TPmeansmeansthethethreshold
thresholdprice.
price.
t 𝑡𝑡 . The procedure is applied for every
Finally,
the
LCOH
can
be
calculated
Finally, the LCOH can be calculated with
with E𝐸𝐸grid
𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 . The procedure is applied for every
time-steptime-step within
within thethe period.
period. Moreover,Moreover, thethe proposed
proposed method
method isis capable
capable ofof estimating
estimating
negative
electricity
pricing
by
using
the
thresholdprice,
although
it
does
not
negative electricity pricing by using the threshold price, although it does notyet
yetexist
existininthecurrentJapanelectricity
market.
the current Japan electricity market.

Figure 4.4. Flowchart
Flowchart forfor determining
determining anan operation
operation strategy
strategy forfor waterwater electrolysis
electrolysis plants
plants inin thetheFigurewholesale electricity market.
wholesale electricity market.

This research allows variable H2 production rates, but in some cases, a ﬁxed H2
production rate for downstream applications is required. That is to say, the load of

Energies 2024, 17, 1653

8 of 16This research allows variable H2 production rates, but in some cases, a fixed H2 production rate for downstream applications is required. That is to say, the load of electrolyzers
should remain stable. In such situations, grid electricity can still complement the input
power when RES electricity is insufficient. Conversely, when RES power exceeds thedemand, the excess power can be integrated into the grid or stored in batteries.
4. Case Study
4.1. Data Source
The proposed model is general and can be applied to any area with significant RES
potential. To illustrate the proposed model, we choose the Kyushu area to perform the casestudy due to the relatively high penetration rate of RESs, especially PV installation. The
simulation data used in this paper come from previous literature and real data from theJapan electricity market. Tables 1 and 2 summarize the information on alkaline and PEM
electrolyzers [6,15,18]. Average values are used to calculate the optimal LCOH, and theranges presented are used to perform the sensitivity analysis.
Table 1. Hydrogen production with electrolyzers [6].

CAPEX
Efficiency
Load range

Alkaline

PEM

Units

55,000–154,000
63–70
10–110132,000–198,000
56–60
0–160

JPY/kWe%%

Table 2. Information on electrolyzers.
Parameter

Value

Units

Ref.

Lifetime
Other capital cost
Maintenance cost
Labor cost
Water consumption
Other OPEX

25
30.5%
3%
5%
10
10%

years
of total CAPEX
of total CAPEX
of total CAPEX
l/kg H2of total CAPEX

[15]
[15]
[15]
[18]
[15]
[15,18]The data of the grid were obtained from the Japan Electric Power eXchange (JEPX). In
particular, the spot price in the Kyushu area from April 2020 to March 2021 (data FY2020),
and from April 2021 to March 2022 (data FY2021). Moreover, the wheeling charge price inthe Kyushu area is 2.43 JPY/kWh. The CO2 tax considered is 289 JPY/t CO2 , which is thecurrent value in Japan.
The RES data were obtained from the Kyushu Electric Power Transmission and Distribution Company. In particular, the data consists of information on wind power and solar
power in the Kyushu area, which are shown in Figure 5. There is an assumption that thewhole of the Kyushu area is a hydrogen production plant. Since the proposed model isgeneral, there is no effect on the results.
The LCOH of hydrogen production adopting the proposed operation strategy issimulated in the following four cases:
Case 1: alkaline electrolyzer using solar power.
Case 2: alkaline electrolyzer using wind power.
Case 3: PEM electrolyzer using solar power.
Case 4: PEM electrolyzer using wind power.
There are three scenarios as follows:
Scenario 1: using only RES electricity.
Scenario 2: using both RES and grid electricity in a wholesale electricity market.
Scenario 3: using both RES and grid electricity under the average pricing scheme [15].

Energies 2024, 17, x FOR PEER REVIEW
Energies 2024, 17, x FOR PEER REVIEW
Energies 2024, 17, 1653

9 of 109 of 109 of 169000

9000

Solar electricity,
MWh
Solar electricity,
MWh

6000

6000

3000

3000

0
Apri

May

June

July

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Apri

May

June

July

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

400
0

400

Wind electricity,
MWhWind electricity,
MWh

300

300
200

200
100

100
0
Apri

May

June

July

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Apri

May

June

July

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

0

Figure 5. Solar and wind electricity in Kyushu over the period from April 2021 to March 2022.
Figure5.5.Solar
Solarandandwindwindelectricity
electricityininKyushu
Kyushuover
overthetheperiod
periodfrom
fromApril
April2021
2021totoMarch
March2022.
2022.
Figure4.2.
4.2.Optimization
OptimizationResults
Results
4.2. Optimization
Results
Based
on
data
Based on dataFY2020,
FY2020,we
wehavehaveobtained
obtainedthe
thethreshold
thresholdprices
pricesforfordiﬀerent
differentcases.
cases.The
The
Based
on
data
FY2020,wehave
obtainedthethresholdpricesfor
diﬀerentcases.
resultsarepresented
inFigure
6,
which
show
the
relationshipbetween
the
LCOH
and
results are presented in Figure 6, which show the relationship between the LCOHTheandresults
areprice.
presented
inﬁgure,Figure6,“withwhich
show
theusing
relationshipbetweentheandthreshold
this
“with
grid”
means
gridgrid
electricity
to supplement
thethresholdprice.InIn
thisfigure,
grid”
means
using
electricity
to LCOH
supplement
thresholdprice.
Inthis
ﬁgure,
“with
grid”
means
using
grid
electricity
tosupplement
power
supply,
while
“only
RES”
refers
to to
using
only
RESs
the power
supply,
while
“only
RES”
refersusing
only
RESstotoproduce
producehydrogen.
hydrogen.Itthe
Itisispower
supply,while
to using
only
RESs
to produce
It is
worth
the
LCOH
has
minimumvalue
when
connected
withtheworthnoting
notingthat
that
the“only
LCOHRES”
hasaarefers
minimumvalue
when
connected
withhydrogen.
thegrid,
grid,where
where
worth
noting thatthehasreferencea minimum
value
when
connected
withthethe
grid,
where
the
price
isisLCOH
set
for
Figure77shows
how
grid
price
thethreshold
threshold
price
setas
asthe
the
reference
for all
all cases.
cases.
Figure
shows
how
the
grid
price
the
threshold
price
is
set
as
the
referenceforallcases.
Figure
7
shows
how
the
grid
price
ﬂuctuatesinrelationtothethreshold
price
in
case
1.
The
threshold
price
can
avoid
using
fluctuates in relation to the threshold price in case 1. The threshold price can avoid using
ﬂuctuates
in electricity.
relationto the threshold price in case 1. The threshold price can avoid using
high-priced
high-priced
electricity.
high-priced electricity.

FigureFigure6.6.The
Therelationshiprelationshipbetween
betweenthe
theLCOHLCOHandandthreshold
thresholdpricepriceininFY2020.
FY2020.
Figure 6. The relationship between the LCOH and threshold price in FY2020.

Energies 2024, 17, x FOR PEER REVIEW

10 of 16

Energies 2024, 17, 1653

10 of 16

Figure 7. The electricity price and the threshold price in case 1.
Figure 7. The electricity price and the threshold price in case 1.

Based on data FY2021 and the threshold prices, we can obtain the optimization results
Based on data FY2021 and the threshold prices, we can obtain the optimizationof the LCOH in FY2021 for each case. The results are presented in Table 3 and Figure 8.
results of the LCOH in FY2021 for each case. The results are presented in Table 3 andAmong the three scenarios, the proposed method has the lowest LCOH, proving its effecFigure 8. Among the three scenarios, the proposed method has the lowest LCOH, proving
tiveness. In particular, when the electricity supply is supplemented with grid electricity
its ineﬀectiveness.
particular,
whenthetheLCOH
electricity
supplysupplemented
a wholesale In
electricity
market,decreasesby is11%
to 33% in thewith
fourgridcases,
electricity
inawholesale
electricity
market,the
LCOH
decreases
by
11%
to
33%
in
the fourwhich is much lower than the only-RES scenario. This is because the hydrogen production
cases,
which
is muchlower
theofonly-RES
This costs.
is because
the hydrogen
increase
impacts
morethanthanthe riseelectricityscenario.
and emission
However,
the average
production
increase
impacts
more
than
the
rise
of
electricity
andemissioncosts.
However,
price scenario shows poor performance in the alkaline cases, and the LCOH is even higher
thethanaverageprice scenario
shows
performancethealkaline
and thethe only-RES
scenario.
Onpoorthe otherhand, inin
thePEM
cases,cases,
althoughthe LCOH
LCOH isis evenhigherthan
theonly-RES
scenario.
On
the
other
hand,
in
the
PEM
cases,
althoughstill higher than scenario 2, it is lower than the only-RES scenario. This is because
using thetheaverage
LCOH electricity
is still higher
than
scenario
2, it electricity
is lower thanthecannot
only-RES
scenario.
This is of,
price
means
dynamic
pricesbe takenadvantage
becauseusing
theaverageelectricity
price
means
dynamic
electricity
pricescannotresulting in an increase in average electricity costs. For alkaline cases, the impact ofbe
this
taken
advantageof, resulting
in an increasein average
electricity
costs. For
alkaline forcases,
increase
is higher
than the impact
of increasedhydrogenproduction.
However,PEM
thecases,
impactofimpact
this increase
higherthan
impact hydrogenof increasedhydrogendue
production.
thisis still is
lower
than
thetheincreased
production
to the highHowever,for
PEM
cases,
this
impact
is
still
lower
than
the
increasedhydrogenproduction
CAPEX. For the same reason, the LCOH of the PEM electrolyzer is higher than that of thedue
to the high
CAPEX.inFor
same
reason,the LCOH
the PEM
electrolyzer
is higher
alkaline
electrolyzer
all thecases.
PEM
technology
mayofseem
economically
uncompetitive
than
that
of
the
alkaline
electrolyzer
inallcases.
PEM
technology
may
seem
economicallywhen compared to alkaline technology. However, it can offer other benefits that are helpful
uncompetitive
compared
to alkaline
can oﬀer
other benefits
for hydrogenwhen
production
processes.
PEMtechnology.
electrolysisHowever,allows foritflexible
operation
without
that
helpful forprocesses.
PEMCAPEX
electrolysis
allowsfor flexible
thearerestrictionof ahydrogen
minimum productionload. Additionally,
the highof PEM
technology
makes
operation
without
the restrictionof a minimum
load.operation
Additionally,highof
it profitable
to incorporate
grid electricity
to increase
hours.theThis
canCAPEX
be observed
PEM
technology
makes
profitable
to incorporate
grid larger
electricity
to increase
in Figure8, where
the it
LCOH
reductions
are relatively
in PEMcases. operation
hours. This can be observed in Figure 8, where the LCOH reductions are relatively larger
The LCOH for the four cases.
in Table
PEM 3.
cases.
JPY/kgH
Only RES
Table 3.Unit:
The LCOH
for2 the four cases.
Case 1
541.9
Unit: JPY/kgH
Only RES
Case 22535.8
CaseCase
1 3
541.9
894.4
Case
4
798.6
Case 2535.8

With Grid

Average Price

480.5

563.8

With Grid
Average Price
373.8
555.0
480.5
563.8685.2
623.5
537.2
373.8
555.0675.1
Case 3
894.4
623.5
685.2
Case
4
798.6
537.2
675.1
Hydrogen productivity and emission cost are presented in Table 4 andFigure 9. Here,hydrogen productivity means the amount of hydrogen production per year and per unit
capacity of the electrolyzers. We can observe that, for all cases, hydrogen productivity
connecting to the grid increases by 86% to 140% compared with the only-RES electricity
scenario. It may be reasonable for a hydrogen plant to choose to inject grid power toincrease hydrogen production and gain more profits. However, the CO2 emissions related

Energies 2024, 17, 1653

11 of 16

to hydrogen production will increase. Figure 10 presents the specific hydrogen productivity
and emissions for scenario 2. When compared to scenario 1, the production from RES
electricity in alkaline cases increases, while that in PEM cases remains the same. This isbecause more RES electricity can be utilized by injecting grid power to meet the minimum
load restriction for alkaline electrolyzers. Figure 10 also shows the CO2 emission levels
for the four cases. The emission levels are affected by both the grid electricity proportionand electrolyzer efficiency. More CO2 emissions are produced when the grid electricity
proportion is high. For the same grid electricity proportion, high efficiency leads to morehydrogen production, reducing CO2 emission levels. For instance, the lowest emission
level is seen in case 2. This is because, compared to case 1, case 2 uses less grid electricity
at the same efficiency, producing less CO2 emissions. On the other hand, although thegrid electricity proportion in case 3 is lower than in case 2, the higher efficiency of case 2
reduces CO2 emission levels. The same reason applies to case 4. Moreover, compared todirectly using coal (20 kgCO2 /kgH2 ) or natural gas (8.5 kgCO2 /kgH2 ) [6], CO2 emissions
when either using alkaline or PEM electrolysis are always lower than those produced by
Energies 2024, 17, x FOR PEER REVIEWfossil-fueled hydrogen production. Therefore, complementing green hydrogen production
11 of 12with grid power can reduce the LCOH, increase hydrogen productivity, and keep CO2
emissions within an acceptable range.

Figure 8. The optimal LCOH for the four cases. The red numbers are the decreases between “onlyFigure 8. The optimal LCOH for the four cases. The red numbers are the decreases between “only
RES” scenario and “with grid” scenario.
RES” scenario and “with grid” scenario.
Table 4. Hydrogen productivity for the four cases.

Hydrogen productivity and emission cost are presented in Table 4 and Figure 9. Here,hydrogenproductivity means Only
the amountof hydrogenproduction
per year andper unit
Production
RES
With
Grid
Units
capacity oftheelectrolyzers.
We
canobserve
that,
for
all
cases,hydrogenproductivity
Case 1
21.5
51.6
kgH2 /year/kWeconnectingCaseto theelectricity
2 grid increases by
21.886% to 140% compared48.2 with the only-RES
kgH2 /year/kWe
scenario. Itmaychoose to inject
grid
power toCase
3 be reasonable for
19.7a hydrogen plant to
36.7
kgH
2 /year/kWe
Case 422.1
44.2
kgH2 /year/kWe
increase hydrogenproduction andgain more proﬁts. However,
the CO2 emissions
relatedto hydrogen production will increase. Figure 10 presents the speciﬁc hydrogenproductivity
andAnalyses
emissions for scenario 2. When compared to scenario 1, the production
4.3. Sensitivity
from RES
electricity
cases
while
thatthresholdin PEM cases
It is necessaryin
toalkaline
illustrate
the increases,
effectivenessof thepriceremains
used inthe
thissame.
paper,This
more
REShistoricalelectricity
canMeanwhile,
be utilized asbyelectrolysis
injecting grid
power tocontinues
meet theto
as itisisbecause
calculated
using
data.
technology
minimum
restrictionforalkaline electrolyzers.
Figure10 alsoshows
COcommercial
2 emission
develop,load
the CAPEX
andefficiency
are expected tochangein order
tothe
reach
levelsforthefourcases.
The
emissionlevelsareaﬀected
by
both
the
grid
electricity
maturity. In addition, the CO2 tax will be increased by the Japanese government
to further
proportion
andelectrolyzer
eﬃciency.
MoreCO2emissionsareproducedwhen
the gridemphasize the importance of carbon neutrality. Therefore, in order to analyze howtheseelectricity
proportion
high. For
the samegrid electricity
proportion,higheﬃciencychanges willaffect theisLCOH,
sensitivity
analyses
are applied
to the above
variations.
For
leads to more hydrogen production, reducing CO2 emission levels. For instance, thelowest emission level is seen in case 2. This is because, compared to case 1, case 2 uses lessgrid electricity at the same eﬃciency, producing less CO2 emissions. On the other hand,
although the grid electricity proportion in case 3 is lower than in case 2, the higher
eﬃciency of case 2 reduces CO2 emission levels. The same reason applies to case 4.
Moreover, compared to directly using coal (20 kgCO2/kgH2) or natural gas (8.5

Energies
2024,
1653PEER REVIEW
Energies
2024,
17,17,
x FOR

12 13
of 16
12 of

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simplicity, these sensitivity analyses are only presented for case 1, but similar qualitative
results can be obtained in the other cases.

Figure 9. Hydrogen productivity for the four cases.
Figure 9. Hydrogen productivity for the four cases.
Figure 9. Hydrogen productivity for the four cases.

FigureHydrogenproductivity
andemissionsscenario
Figure10.10.
Hydrogenproductivity
andemissions
forforscenario
2. 2.

TheHydrogenthresholdprice sensitivity
is shown
Figure2.11. Here, “CAPEX”, “other OPEX”,
Figure10.
productivity
and emissions
forin
scenario
4.3.
Sensitivity
Analyses“electricity cost”, and “emission cost” are computed as expressed in (8), (9), (2), and (10),
It is necessary
to illustrate
the eﬀectivenessof theused inaffected
this paper,
respectively.
We canobserve thateach componentof thresholdthe LCOHpriceis strongly
by the
4.3.
Sensitivity
Analysesas threshold
it is calculatedusing
historicaldata.
Meanwhile,aselectrolysis
technology
continues
toprice.
Whenthethresholdprice
isrelatively
low,
there
is
a
decrease
in
the
LCOH
It is necessary to illustrate the eﬀectiveness of the threshold price used in this paper,
develop,
the
CAPEX
and
eﬃciencyareexpected
tochange
in
order
to
reach
commercial
shown.
This using
situation
occursdata.
becausethe reductionsin the CAPEX
and other
OPEXtoare
asas
it is
calculated
historical
Meanwhile,
as electrolysis
technology
continues
maturity.
In addition,
the COin
2 tax will be increased by the Japanese government to further
higherthantheincreases
electricity
costandemission
cost
with
the
cheap
electricity
develop, the CAPEX and eﬃciency are expected to change in order to reach commercial
emphasize
thetheimportanceof carbon
neutrality.
Therefore,
inrelatively
order to high,analyzetheseprices. In
On
other
when
thresholdpricethehow
increasematurity.
addition,thehand,
CO2 tax
willthebe increasedby
the isJapanesegovernment
tofurther inchanges
willcost
aﬀect
LCOH,
sensitivity
analyses areto theabove
variations.
Forelectricity
is themuchhigherthan thereductionsin applied
the CAPEX
andOPEX,
emphasize
the importanceof carbon
neutrality.
Therefore,
in ordertootheranalyze
howresulting
these
simplicity,
these insensitivity
analyses
are onlypresented
for case 1,pricebut similar
qualitative
inanincrease
the
LCOH.
In
addition,thereference
threshold
based
on
last
year’schanges will aﬀect the LCOH, sensitivity analyses are applied to the above variations. For
results
bethresholdobtained in
the usedotherincases.
data canis theprice
previous analyses. It can be observed that the LCOH
simplicity, these sensitivity analyses are only presented for case 1, but similar qualitativeThe
thresholdpricesensitivity
isFigure 11. Here,“other
OPEX”,
related to the reference threshold shown
price isinapproachingthe “CAPEX”,
lowest pointof the
LCOH.
results can be obtained in the other cases.
“electricity
cost”,
and“emissioncost”
arecomputedasexpressed
in
(8),
(9),
(2),
and
(10),theThat is to say, we can decrease the LCOH to close to the minimum value. Therefore,
The threshold price sensitivity is shown in Figure 11. Here, “CAPEX”, “other OPEX”,
respectively.
We can observethat
component
LCOH is strongly
aﬀectedtheoperation strategy
we usedcaneachactuallyobtainofanthe
approximate
optimalresult, by
proving“electricity cost”, and “emission cost” are computed as expressed in (8), (9), (2), and (10),thresholdprice. Whenthe thresholdprice
is relatively
low,between
there is athedecreasein the
LCOH
its effectiveness.
However,
there still
existsa little gap
calculated
LCOH
andrespectively. We can observe that each component of the LCOH is strongly aﬀected by theas theshown.
This situation
becausetheprecise
reductionsin theprice,
CAPEXother OPEX
areminimum
LCOH. occurs
To obtaina morethresholdit isandnecessaryto analyze
threshold price. When the threshold price is relatively low, there is a decrease in the LCOH
higherthan theincreasestrend
in electricity
cost andpriceemissioncost withcheap priceelectricity
the annual
fluctuatingof the thresholdand predict
the theelectricity
for theas shown. This situation occurs because the reductions in the CAPEX and other OPEX are
prices.
Onyear.
the otherhand, when
the thresholdprice
is relatively
increaseusing
incomingBy forecasting
electricity
prices, thethresholdprice high,can bethecalculated
higher than the increases in electricity cost and emission cost with the cheap electricity
electricity
muchdata,
higherthan inthea lower
reductionsin Thisthe CAPEX
OPEX, inboth pastcost
andisfuture
resulting
LCOH.
approachandwillotherbe included
prices. On the other hand, when the threshold price is relatively high, the increase in
our futurework.
resultingin anincrease in the LCOH. In addition, the reference threshold price based onelectricity cost is much higher than the reductions in the CAPEX and other OPEX,
last year’s data is the threshold price used in previous analyses. It can be observed that
resulting in an increase in the LCOH. In addition, the reference threshold price based onlast year’s data is the threshold price used in previous analyses. It can be observed that

Figure 11. Sensitivity analysis for the threshold price: alkaline technology using solar power.
Figure11.
11.Sensitivity
Sensitivityanalysis
analysisfor
forthethethreshold
thresholdprice:
price:alkaline
alkalinetechnology
technologyusing
usingsolar
solarpower.
power.
FigureThe result of the sensitivity analysis for the CAPEX is shown in Figure 12. It can be
Thethatresultof thesensitivity
analysisfor thealso
CAPEX
is shown
in Figure 12.
It can beobserved
as theCAPEX
increases,
the LCOH
increases
proportionally.
However,
The resultofthesensitivity
analysisthe
forLCOH
the CAPEX
is shownproportionally.
in Figure 12. ItHowever,can beobserved
that
as
the
CAPEX
increases,
also
increases
when the CAPEX is relatively high, the impact on the LCOH becomes less significant. Thisobserved
that
as
the
CAPEX
increases,
the
LCOH
also
increases
proportionally.
However,thetheCAPEX
is relatively
the impact
on thesignificant.
iswhendue tofact thatcheap gridhigh,electricity
can oﬀset
theLCOHeﬀectsbecomesof a highless
CAPEX
to a
when
the
CAPEX
is
relatively
high,
the
impacton
the
LCOH
becomeslesssigniﬁcant.
This
This isextent.
due toBasedthe fact
grid electricity
canoffset the effects
of apresented
high CAPEX
certainonthatthe cheap
sensitivity
analysis forelectrolyzer
eﬃciencyinistodueto the extent.
fact that
cheap
grid
electricity canoﬀsetfortheelectrolyzer
eﬀects of aefficiency
high CAPEX to aa certain
Based
the sensitivity
analysis
Figure13, the LCOH
will ondecrease
when theelectrolyzer
eﬃciency increases.presented
This iscertainextent.
Basedon theanalysiselectrolyzer
eﬃciencypresented
inis
in Figure
the
LCOH
willsensitivity
decrease
when
theforelectrolyzer
efficiency
increases.
Thisbecausethe13,
highereﬃciency
means morehydrogenproduction
with thesame energy
Figure
13,
the
LCOH
will
decreasewhentheelectrolyzer
eﬃciency
increases.
Thisisbecausethe higher
efficiency
means
more hydrogenproduction
same energy
input,
input,
leading
to a lower
LCOH.
However,
it is necessary
to notewith
thatthediminishingreturns
becausethehighereﬃciencymeans
morehydrogenproduction
with
the
same
energyleading
to athelower
LCOH.
it is necessary
to bothnote thethatCAPEX
diminishingreturns
occur
occur
when
eﬃciency
is However,relatively high.
In general,and theeﬃciency
input,
leading
to a lower
LCOH. However,
it is necessary
toCAPEXnote thatdiminishingreturnswhen
theefficiency
isrelatively
high.
In
general,
both
the
and
the
efficiencyoftheof the electrolyzer have a significant impact on the LCOH. To reduce hydrogen production
occur
when theeﬃciency
is relatively
high.
In LCOH.
general,To
both
the CAPEX
andthe eﬃciency
electrolyzer
haveasignificant
impactonthereducehydrogenproduction
costs
costs in the future, it may be more eﬀective to concentrate on improving electrolyzer
ofinthehave
amore
signiﬁcant
impacton the LCOH.
To reducehydrogen production
theelectrolyzer
future,suchit mayeffective
concentrateon improving
electrolyzer
technologies,
technologies,
asbe
reducing
capitalto
costs
and enhancing
electrolysis
eﬃciency.
costs
in
the
future,
it
may
be
more
eﬀective
toconcentrateon
improving
electrolyzer
such as reducing capital costs and enhancing electrolysis efficiency.
technologies, such as reducing capital costs and enhancing electrolysis eﬃciency.
600
600

550

500

2

)

LCOH (JPY/kgH 2 )550

LCOH (JPY/kgH

Energies 2024, 17, 1653

result, proving its eﬀectiveness. However, there still exists a little gap between thenecessary to analyze the annual fluctuating trend of the threshold price and predict thecalculated LCOH and the minimum LCOH. To obtain a more precise threshold price, it iselectricity price for the coming year. By forecasting electricity prices, the threshold pricenecessary to analyze the annual ﬂuctuating trend of the threshold price and predict thecan be calculated using both past and future data, resulting in a lower LCOH. This
electricity price for the coming year. By forecasting electricity prices, the threshold priceapproach will be included in our future work.
can be calculated using both past and future data, resulting in a lower LCOH. This
13 of 16
approach will be included in our future work.

500

450
450

400
400

350
55,00071,50088,00055,000104,500

132,000

137,500

154,000

137,500

154,000CAPEX (JPY/kWe)

350
71,50088,000104,500

132,000

Figure 12. Sensitivity analysis for the CAPEX: alkaline technology using solar power.
CAPEX (JPY/kWe)In Figure 14a, the results of the sensitivity analysis for the CO2 tax are presented. It
can be observed that an increase in the CO2 tax will slightly raise the LCOH. This is because
the tax penalty imposed on CO2 emissions is currently low in Japan, at only 289 JPY/t CO2 .
The low CO2 tax also has a minimal effect on determining the threshold price, making therelationship between the CO2 tax and LCOH almost linear. The Japanese government isplanning to increase the CO2 tax, which means that in the future, the cost of CO2 emissions
will have a more significant impact on the LCOH. To further analyze the influence of CO2
emission costs, we chose the CO2 reduction credit offered by the J-credit Institution, whichequals 1500 JPY/t CO2 , for the sensitivity analysis. The results are illustrated in Figure 14b.
After the CO2 price increases compared to the current CO2 tax, we observe a larger impact

Energies 2024, 17, 1653

14 of 16

Energies 2024, 17, x FOR PEER REVIEW

14 of 16

on the LCOH. In other words, environmental factors will have a more significant impact on
the economy with higher CO prices. Therefore, the increase in CO2 pricecan emphasize
Figure 12. Sensitivity analysis for 2the CAPEX: alkaline technology using solar
power.
the importance of the environment.
Figure 13. Sensitivity analysis for electrolyzer efficiency: alkaline technology using solar power.
600

LCOH (JPY/kgH 2 )In Figure 14a, the results of the sensitivity analysis for the CO2 tax are presented. It
canbeobserved that an increase in the CO2 tax will slightly raise the LCOH. This is550
because the tax penalty imposed on CO2 emissions is currently low in Japan, at only 289
JPY/t CO2. The low CO2 tax also has a minimal effect on determining the threshold price,500
makingthe relationship between the CO2 tax and LCOH almost linear. The Japanese
government is planning to increase the CO2 tax, which means that in the future, the costof450
CO2 emissions will have a more significant impact on the LCOH. To further analyze theinfluence of CO2 emission costs, we chose the CO2 reduction credit offered by the J-credit
Institution,
which equals 1500 JPY/t CO2, for the sensitivity analysis. The results are
400
illustrated in Figure 14b. After the CO2 price increases compared to the current CO2 tax,
we observe a larger impact on the LCOH. In other words, environmental factors will have350
63% significant
64%
65% impact
66%
67%
68%
69% with
70%higher CO2 prices. Therefore, the increasea moreonthe economy
Electrolyzer Efficiency
in CO2 price can emphasize
the importance of the environment.

FigureFigure13.
13.Sensitivity
Sensitivityanalysis
analysisfor
forelectrolyzer
electrolyzereﬃciency:
efficiency:alkaline
alkalinetechnology
technologyusing
usingsolar
solarpower.
power.

In Figure 14a, the results of the sensitivity analysis for the CO2 tax are presented. It
can be observed that an increase in the CO2 tax will slightly raise the LCOH. This isbecause the tax penalty imposed on CO2 emissions is currently low in Japan, at only 289
JPY/t CO2. The low CO2 tax also has a minimal eﬀect on determining the threshold price,
making the relationship between the CO2 tax and LCOH almost linear. The Japanese
government is planning to increase the CO2 tax, which means that in the future, the costof CO2 emissions will have a more significant impact on the LCOH. To further analyze theinfluence of CO2 emission costs, we chose the CO2 reduction credit oﬀered by the J-credit
Institution, which equals 1500 JPY/t CO2, for the sensitivity analysis. The results areillustrated in Figure 14b. After the CO2 price increases compared to the current CO2 tax,
we observe a larger impact on the LCOH. In other words, environmental factors will havea more significant impact on the economy with higher CO2 prices. Therefore, the increase
in CO2 price can emphasize the importance of the environment.
(a)
480.9

FigureSensitivityanalysis
analysisfortheCOtax:
2 tax: (a) current CO2 tax; (b) CO2 price in J-credit.
Figure14.14.Sensitivity
for
the
CO
(a) current CO2 tax; (b) CO2 price in J-credit.
496 2

480.8

5. Conclusions

480.7
480.6
480.5
480.4
480.3
480.2
480.1
− 30%

(b)
498

− 20%

494

The objective of this paper is492
to analyze the techno-economic benefits of complementing green hydrogen production 490
with grid electricity. A model is proposed to evaluate
the LCOH, including CO2 emissions,
to minimize the LCOH in the wholesale electricity
488
market. The proposed model considers
the influence of power fluctuations from RESs on
486
water electrolysis, especially the minimum load restriction of alkaline electrolyzers to avoid
484
crossover gas. Moreover, an optimal operation strategy is developed to solve the above
nonlinear optimization problem. 482
By injecting grid electricity, the LCOH decreases by 11%
current CO2 tax
480
to 33%, compared to using an only-RES
supply. Hydrogen productivity
increases by 86%
J-credit
to 140%, while CO2 emission levels
478are maintained within an acceptable range. In addition,
− 10%
0%
10%
20%
30%
100
500
1000
1500
2000
2500
3000
electrolyzer
efficiency
and
the
CAPEX
have larger impact
magnitudes on the LCOH, while
CO2 tax (0%: 289 JPY/t CO 2 )CO2 tax (JPY/t CO2)
the impact of the CO2 tax is relatively small compared to other factors.
(a) Regarding future work, our main objective is to enhance(b)
the algorithm and achieve
betterresults.
The
threshold
price
used
in
this
paper
is
based
on
data. Although
Figure 14. Sensitivity analysis for the CO2 tax: (a) current CO2 tax; (b) COhistorical
2 price in J-credit.
it is a simple approach, it may not lead to the lowest LCOH. Therefore, to estimate theprice more accurately, it is necessary to include the prediction of electricity prices.
5.threshold
Conclusions
By forecasting
electricity
theisthresholdpricethecan techno-economic
be calculated using
recent andThe objectiveof thisprices,paper
to analyze
benefitsofcomplementing green hydrogen production with grid electricity. A model is proposed toEnergies 2024, 17, 1653

15 of 16

future data, resulting in a more precise outcome. In addition, in the off-grid system scenario,
akaline electrolyzers are not suitable for real conditions, as they cannot frequently startand stop, leading to safety issues. Therefore, the inclusion of a battery may be necessary inthis case. Moreover, to study the entire hydrogen supply chain, it may be useful to include
some elements such as the compression, storage, and distribution of hydrogen to demand
locations. The inclusion of these elements may alter some of the results obtained here.
Author Contributions: Conceptualization, P.Z. and R.M.; methodology, P.Z.; software, P.Z.; validation,
P.Z.; formal analysis, P.Z.; investigation, P.Z.; resources, P.Z.; data curation, P.Z.; writing—originaldraft preparation, P.Z.; writing—review and editing, P.Z., M.M. and R.M.; visualization, P.Z.; supervision, M.M. and R.M.; project administration, R.M.; funding acquisition, M.M. and R.M. All authors
have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The data presented in this study are available on request from thecorresponding author (due to privacy).
Conflicts of Interest: The authors declare no conflicts of interest.

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