CHEMICAL ENGINEERING TRANSACTIONS

VOL. 81, 2020

A publication of

The Italian Association
of Chemical Engineering
Online at www.cetjournal.it

Guest Editors: Petar S. Varbanov, Qiuwang Wang, Min Zeng, Panos Seferlis, Ting Ma, Jiří J. Klemeš
Copyright © 2020, AIDIC Servizi S.r.l.

ISBN 978-88-95608-79-2; ISSN 2283-9216

Power-to-Gas Unit for Renewable Energy Storage

Nouaamane Kezibri, Chakib Bouallou*

MINES ParisTech, PSL Research University, Centre for Energy Efficiency of Systems, 60 bd Saint Michel, Paris, France

chakib.bouallou@mines-paristech.fr

In this work, dynamic modeling of Power-to-Gas work is presented. The created model was developed with

Aspen Plus DynamicsTM tool. Required control strategies and additional Balance of Plant components were

implemented to investigate Power-to-Gas transient behavior. Firstly, the developed model for Proton Exchange

Membrane electrolysis went through a validation phase to highlight its performance regarding its ability to

accurately describe the different phenomena observed in the real system. The created model was then scaled

up to meet Electrolysis, Methanation and Oxy-combustion unit requirements. Given its ability to represent the

entire system, its operation and its different dynamics, the developed model lends itself to different types of

applications. In particular, a case study of coupling with wind farm entirely dedicated to synthetic natural gas

production was analysed. The results of this integration have shown the ability of the developed concept to

absorb electrical source intermittency. The observed limitations of this kind of application were underlined

especially in the case of periods of low electricity production where a solution of buffer storage of excess

hydrogen should be considered.

1. Introduction

The EMO unit (Electrolysis, Methanation and Oxy-combustion) is a concrete technological solution to the

problem of intermittency of renewable energies and adapted to the evolution of energy needs in France in the

medium and long term (2020-2050). Figure 1 shows the overall design of the EMO unit. Electric power is first

converted to hydrogen in the proton exchange membrane (PEM) electrolyser. This hydrogen is reacted with

carbon dioxide to form synthetic methane at the methanation process outlet. Oxygen co-produced by electrolysis

as well as methane are stored in underground caverns. This is the Power-to-Gas phase of EMO unit. To recover

stored energy, an oxy-combustion process is used to produce electricity as well as CO2 which will be stored and

reused in methanation process during the next cycle.

Figure 1: Overall layout of the EMO unit

DOI: 10.3303/CET2081149

Paper Received: 13/04/2020; Revised: 05/05/2020; Accepted: 15/05/2020
Please cite this article as: Kezibri N., Bouallou C., 2020, Power-to-Gas Unit for Renewable Energy Storage, Chemical Engineering
Transactions, 81, 889-894 DOI:10.3303/CET2081149

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Compared to conventional Power-to-Gas (PtG) processes, this new concept has two remarkable advantages;

(i) it allows the use of oxygen co-produced by the electrolysis process and (ii) it ensures a continuous supply of

carbon dioxide for the methanation unit. Both at PEM stack scale and PEM electrolyser system, the majority of

models found in literature rarely include unsteady state operation. Some authors discussed the impact of fluid

dynamics on stack (Görgün, 2006) or system level (Nie et al., 2009). In the work of Grigoriev et al. (2010), the

conducted analysis deals with: (i) calculation of the output flow of the stack in order to establish mass balance

at each electrode; (ii) calculating the level in the separators; (iii) impact of the flow of water circulation on the

behavior of bubbles and their evacuation or (iv) CFD modelling of flow characteristics at bipolar plates.

More recently, Er-rbib et al. (2017) investigated one of the most emerging technologies of electricity conversion:

reversible solid oxide cells (RSOC) and Er-rbib et al. (2018) assessed the performance of a power-to-gas

process based on this technology. Oliver et al. (2017) were among the rare authors to develop a multiphysics

model for a complete PEM electrolysis system. The aim was to identify the dynamic behaviour of the technology

for Power-to-Gas applications (i.e. coupling with renewable energy sources). Their model uses the Bond Graph

formalism to represent the electrolyser stack as well as the rest of the auxiliary components conventionally found

in a commercial machine. The Bond Graph formalism, which follows a modular configuration, was found to be

flexible enough to allow structural or sensitivity analysis of system parameters. Dynamic model of PEM

electrolyser system

2. Dynamic model of PEM electrolyser system

2.1 Reference PEM electrolysis system

The steady state model of PEM electrolyser was used as a basis for the development of the dynamic model

using Aspen Plus DynamicsTM tool. The equations used to describe the electrochemical, thermal and kinetic

phenomena that occur within PEM stack have been modified to take into account unsteady state operation. This

includes material and energy balance as described by the equations Eq(1)-Eq(7):

Anode

H2O
𝑑𝑛𝐻2𝑂,𝑎𝑛

𝑑𝑡
= �̇�𝐻2𝑂,𝑖𝑛,𝑎𝑛 − �̇�𝐻2𝑂,𝑜𝑢𝑡,𝑎𝑛 − �̇�𝐻2𝑂,𝑚 − �̇�𝐻2𝑂,𝑐𝑜𝑛𝑠

(1)

𝑑𝑛𝐻2,𝑎𝑛

𝑑𝑡
= �̇�𝐻2,𝑖𝑛,𝑎𝑛 − �̇�𝐻2,𝑜𝑢𝑡,𝑎𝑛 + �̇�𝐻2,𝑚

(2)

𝑑𝑛𝑂2,𝑎𝑛

𝑑𝑡
= �̇�𝑂2,𝑖𝑛,𝑎𝑛 − �̇�𝑂2,𝑜𝑢𝑡,𝑎𝑛 + �̇�𝑂2,𝑝𝑟𝑜𝑑 − �̇�𝑂2,𝑚

(3)

Cathode

H2O
𝑑𝑛𝐻2𝑂,𝑐𝑎

𝑑𝑡
= �̇�𝐻2𝑂,𝑖𝑛,𝑐𝑎 − �̇�𝐻2𝑂,𝑜𝑢𝑡,𝑐𝑎 + �̇�𝐻2𝑂,𝑚

(4)

𝑑𝑛𝐻2,𝑐𝑎

𝑑𝑡
= �̇�𝐻2,𝑖𝑛,𝑐𝑎 − �̇�𝐻2,𝑜𝑢𝑡,𝑐𝑎 + �̇�𝐻2,𝑝𝑟𝑜𝑑 − �̇�𝐻2,𝑚

(5)

𝑑𝑛𝑂2,𝑐𝑎

𝑑𝑡
= �̇�𝑂2,𝑖𝑛,𝑐𝑎 − �̇�𝑂2,𝑜𝑢𝑡,𝑐𝑎 + �̇�𝑂2,𝑚

(6)

Energy

Balance
𝐶𝑡ℎ

𝑑𝑇𝑖𝑛,𝑎𝑛
𝑑𝑡

= (𝑇𝑜𝑢𝑡,𝑎𝑛 − 𝑇𝑖𝑛,𝑎𝑛)𝛴𝐶𝑝𝑖�̇�𝑖,𝑜𝑢𝑡
𝑎𝑛 − �̇�𝑒𝑙 + �̇�𝐻2,𝑝𝑟𝑜𝑑∆𝐻𝑟

(7)

Where ṅ (mol.s-1.m-2), Ṅ (mol.s-1), T (K), W (W) correspond to molar flow rate density, species molar flow rate,

temperature and power and an, ca, el, prod correspond to the indexes relating to anode side cathode side,

electric and production.

During the transition from steady state to dynamic state models, several components have been added to ensure

system control under transient conditions. In particular, PID controllers were added to regulate separator tanks

pressures. On-off controllers were also used to regulate stack temperature and tanks levels. These regulators

parameters were chosen to adequately represent experimental data retrieved from the test rig (Kezibri, 2018).

A model validation step was then to compare the results of the dynamic model with the experimental data from

the test rig. This comparison was performed under rated power conditions.

2.2 EMO unit electrolysis module

Using the previously developed architecture for the 10 MWt electrolyser module (Kezibri and Bouallou, 2017), a

dynamic model was created for the scaled-up process to characterise its behaviour during different transient

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phases. Figure 2 shows the flowsheet of the developed model. Unlike steady state model, the system is

completed by the addition of a pure water tank (WT) as well as system required regulation for pressures,

temperatures and tank levels.

Figure 2: Dynamic Model of PEM electrolyser module (system control in dashed blue lines)

The analysis will focuses on the flexibility of the system and its ability to follow load changes through the

monitoring of its main characteristics. This also includes the measurement of load change impact system overall

efficiency and purity of produced gases.

In the studied process, the dimensioning of the auxiliary equipment is as necessary as reactor sizing to provide

an accurate dynamic response of the overall system. Particularly, this work has focused on the main

components of the heat recovery network (i.e. steam cycle). The heat exchangers are sized based on process

rated performance. Aspen Exchanger DesignTM tool was used to size all seven heat exchangers using a shell

and tube design. This sizing allows an accurate calculation for heat transfer performance as well as pressure

drop estimation.

In order to allow a more realistic behaviour of the steam cycle performance, performance curves for each of the

used turbines and circulation pumps are integrated. The model will re-evaluate the cycle’s performance with

each undergone change by means of specific work and isentropic efficiencies.

3. Results

This part is dedicated to a case study of a scenario that approaches a real operation of the installation. A

renewable electricity production from a wind farm dedicated entirely to energy storage is considered. The

quantities of hydrogen and SNG (Synthetic Natural Gas) that can be produced depending on load profiles

intermittency at Power-to-Gas system are analysed.

3.1 Wind energy production

To generate power production profiles of the considered wind farm, the open-source SAM (System Advisor

Model) tool developed by the US national renewable energy laboratory NREL is used. This simple tool allows

the prediction of wind farm annual production by means of real meteorological data. The model takes into

account the influence of several physical quantities measured for the simulated periods and usually provided

on hourly basis. These parameters include wind speed and direction as well as air temperature and pressure at

given altitude.

The wind farm was chosen to have a total installed power of 300 MW e which is slightly higher than overall

electrolysis process consumption of the EMO unit at rated power. In this case study, the wind farm consists of

100 Vestas V90 wind turbines of 3 MWe each. Based on these input data, SAM tool simulates hourly production

of the wind farm over one-year period. This result is used to select two representative periods of wind energy

production seasonality. February was considered for winter season with a total electricity production of 120 GWh

and August for summer period with a total electricity production of 65 GWh. Based on these assumptions,

summer period of the year is characterised by a strong discontinuity of electricity production (Figure 3). The

duration of production periods is relatively short, often below 24 h. The wind farm runs at rated capacity for only

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a few hours in this period. On the other hand, winter period is relatively more favourable to wind generation. It

is characterised by longer production durations and more frequent rated power operation of the wind farm.

Figure 3: Hourly generated wind power for (a) August and (b) February

3.2 Coupling with Power-to-Gas process

The PEM electrolysis process and CO2 methanation dynamic models were combined to simulate the production

of hydrogen and SNG based on available power at the wind farm output.

In this approach, an equivalence between each PEM electrolyser module of EMO unit was assumed. The power

available at system input is equitably distributed over the 20 modules available forming the electrolysis process.

With the results of dynamic analysis in mind, range operation of each module is restricted to 2 % of rated power.

When this limit is reached, the electrolyser system is switched to stand-by mode where operating current density

is set to zero.

Regarding methanation process, limitations defined through unsteady state analysis are taken into account. In

particular, a feed stream of reactants at stoichiometric proportion was assumed. This assumes continuous

availability of carbon dioxide from underground storage. The process operating range is set between 48 % and

100 % of rated power. This range, demonstrated in the previous subsection, firstly allows to maintain acceptable

temperatures for proper operation of fixed bed reactors (i.e. between 250 and 600 °C.). It also produces an SNG

with at least 95 % methane molar fraction. It guarantees steam quality at both turbines inlets in the cogeneration

cycle. By analysing the respective electrical production curves for each month, periods that are favourable for

SNG production given electrical power availability and process limitations have been identified. These periods

are summarised in Table 1.

Table 1: Results of SNG production for chosen periods

Period Start End Duration

[h]

Produced

SNG

[GWh]

Consumed

H2 [GWh]

Consumed

CO2 [t]

Backup H2

[Nm3]

130 159 29 2.77 3.61 499 36,275

245 292 47 6.58 8.46 1,169 0

August 322 385 63 7.81 10.1 1,396 3,385

726 744 18 2.24 2.87 397 0

Total – – 157 19.4 25.0 3,461 39,660

0 122 122 17.4 22.6 3,737 5,426

212 235 23 3.39 34.0 4,699 0

February 336 524 188 26.3 7.13 985 0

575 616 41 5.53 4.39 607 0

Total – – 374 52.6 68.1 10,028 5,426

Potential periods of operation requiring high hydrogen backup were excluded. These periods would require

massive hydrogen storage capacities which is not intended for the EMO unit primary design. For the summer

period (August), the production of 19.4 GWh of synthetic methane requires more than 3,461 t CO2, which

corresponds to around 21 operational hours of the oxy-combustion cycle at rated power. The winter period

(a)

(b)

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(February) consumes up to 10,028 t CO2 to produce 52.6 GWh of synthetic methane, equivalent to running the

oxy-combustion cycle for about 57 hours.

Figure 4: Simulation of (a) hydrogen and (b) SNG production from wind power for chosen periods

The simulation results of wind farm coupling with Power-to-Gas process indicate (Figure 4) that a maximal

production of 46.1 GWh of hydrogen is possible in summer period, compared to 84.0 GWh in winter. Given the

operational limitations of methanation process, only 54.2 % of this hydrogen is consumed by the reactors to

produce 19.4 GWh of SNG in summer, compared to 81.0 % in winter, to produce 52.6 GWh of SNG. In this

specific case, it would be more efficient for periods with low renewable electricity generation, to consider

underground storage for excess hydrogen production.

The simulation results are used to analyse energy efficiency of Power-to-Gas process for the two considered

periods. This efficiency combines the performances of both PEM electrolysis and carbon dioxide methanation

processes. Overall, process efficiency stays close to the rated value of 57.4 %HHV. During winter period, this

value appears in a more frequent and consistent manner compared to summer period. This fluctuation is also

linked to the initial assumption which considers a uniform distribution of inlet electric power to all PEM

electrolysis modules. However, by optimising electricity distribution across these modules, a lower efficiency

operation that can occur during limited electric power availability can be avoided.

To conclude this case study, the results obtained for hydrogen and SNG production in August and February are

applied to the rest of the year. Firstly, a summer production period starting from May to September which will

follow the same production performance observed for August has been defined. The second winter period, from

October to April, will follow the typical production performance of February. The basic assumption here is to

consider that each month of summer period can convert 71.0 % of available wind power into hydrogen and

29.9 % of that same power into SNG. Similarly, each month of winter period can convert 70.3% of renewable

electricity into hydrogen and 43.9 % into SNG. These ratios are retrieved from the characteristic performance

of each respective period shown in Sankey diagrams (Figure 5).

Figure 5: Sankey diagrams of Power-to-Gas performance during (a) summer and (b) winter periods

(a) (b)

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The annual production of hydrogen and SNG based on specific performance of each characteristic period can

be estimated. Figure 6 shows the result of this analysis applied to one year of wind farm production. From 1,219

GWh of renewable electricity produced by the 300 MW wind farm, the PEM electrolysis process is able to

produce up to 859 GWh of hydrogen, which represents 70.4 % of total input energy. On the other hand, the

Power-to-Gas process is able to produce up to 488 GWh per year of SNG, representing 40.0 % of the annual

electrical energy supplied by the wind farm. This production of synthetic methane will require about 91,752 t

carbon dioxide, equivalent to an annual operating time of 560 h for the oxy-combustion process.

Figure 6: Prediction of monthly production of hydrogen and SNG from wind energy

4. Conclusion

In this work, dynamic modelling of Power-to-Gas work was presented. The created model was developed with

Aspen Plus DynamicsTM tool. Required control strategies and additional Balance of Plant components were

implemented to investigate Power-to-Gas transient behaviour. Firstly, the developed model for PEM electrolysis

went through a validation phase to highlight its performance regarding its ability to accurately describe the

different phenomena observed in the real system. The created model was scaled up to meet EMO unit

requirements. Given its ability to represent the entire system, its operation and its different dynamics, the

developed model lends itself to different types of applications. In particular, a case study of coupling with wind

farm entirely dedicated to SNG production was analysed. The results of this integration has shown the ability of

the developed concept to absorb electrical source intermittency. Observed limitations of this kind of application

were underlined. Especially in the case of periods of low electricity production where a solution of buffer storage

of excess hydrogen should be considered.

Acknowledgements

This work was partly supported by the French National Research Agency (ANR) trough FluidStory Project ref.

Projet-ANR-15-CE06-0015

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