Techno-economical modeling of a PtG plant for operational optimization in the context
of gas grid injection in France
Master thesis report
Submission Date: November 18, 2020
I would firstly like to thank FCLAB and my supervisors at the lab, Robin Roche and
Samir Jimei, for giving me the opportunity to work on the HYCAUNAIS project for my
master thesis; the project gave me great insight into the processes involved in power-to-gas
plants and where their opportunities and challenges lie in terms of technology as well as
economically. Secondly, I would like to thank my KTH supervisor Ann Cornell for her
added knowledge on technologies involved and overall thesis guidance. Last but not least
Andries Kr¨ uger, a PhD researcher at KTH, help further my understanding general but
fundamental theories which power-to-gas plants are built upon.
Climate change is the single largest challenge facing humanity in the 21st century.
To tackle this challenge, renewable energies are seeing a large increase in primary energy share globally. The natural variableness of solar and wind requires energy storage to be used in conjuction with them for an energy system transition. Power- to-Gas (PtG) technologies offer an attractive solution by allowing conversion of electrical energy to hydrogen or methane, enabling cross-energy-network and cross- sectoral integration. This thesis investigates profitability of a PtG plant with a primary application of producing synthetic methane (SNG) for natural gas (NG) grid injection. A techno-economical model was created to simulate plant operation over one year and extrapolate the results for the project lifespan. The model was designed based off of a pilot project being developed in France named HYCAUNAIS and used partner as well as literature data for processing. Due to limitations in local NG grid capacity, several scenarios were investigated that included adding additional investments that allow increased operational time and revenue streams, including: fixed electrical price or day-ahead (DA) market participation; mesh upgrade for increased NG grid capacity; and CH4
mobility. Electrolyser participation in the frequency containment reserve (FCR) was also considered for increased profitability. The results determined the standard case scenario (no additional investments) with participation in the DA electricity market was the most attractive in terms of three objectives investigated: net present value (NPV), payback period (PBP) and levelized cost of methane (LCOM). The operational hours of the standard case was found to be approximately 90% of the year; production was not hindered by limited grid capacity sufficiently to deem additional investments necessary. Further, participation in the DA market should be determined by a cut-off willingness to pay (WTP) for electricity as opposed to marginal profit (MP).
Using WTP as the determining factor allowed increased operational hours and lower LCOM. However, in all of the scenarios investigated, none were profitable; meaning that market conditions still need to greatly improve before PtG can gain momentum.
A sensitivity analysis was done on the standard case scenario to see which parameters influence profitability the most and should be the focus of further research and development. The SNG tariff was found to be the most influential on NPV, requiring a tariff of at least 188 e/MW h (120 e/MW h was used for modeling) to be profitable.
Electricity price was the second most influential and required an average market
price of 25 e/MW h to be profitable. As PtG technologies can provide several
external benefits that are not economically realized by investors, monetization of
them could provide a means of improving profitability. This includes, grid balancing
and flexibility, decarbonization, lower grid costs and improved energy security. In
conclusion, capital costs of equipment, electricity prices and fees associated to them,
and tariffs for green gases all need to improve dramatically for SNG production to
be an attractive solution for electricity curtailment and decarbonization.
Klimatf¨ or¨ andringar ¨ ar den enskilt st¨ orsta utmaningen som m¨ anskligheten st˚ ar inf¨ or under 2000-talet. F¨ or att ta itu med denna utmaning f¨ orutses f¨ ornybara energik¨ allor en stor ¨ okning av andelen prim¨ arenergi globalt. Den naturliga vari- abiliteten hos sol och vind kr¨ aver att energilagring anv¨ ands tillsammans med dem f¨ or en energisystem¨ overg˚ ang. Power-to-Gas (PtG) -teknologier erbjuder en attrak- tiv l¨ osning genom att m¨ ojligg¨ ora omvandling av elektrisk energi till v¨ atgas eller metan, vilket m¨ ojligg¨ or integration ¨ over n¨ atverk och sektor¨ overgripande integra- tion. Denna avhandling unders¨ oker l¨ onsamheten f¨ or en PtG-anl¨ aggning med en prim¨ ar applikation f¨ or att producera syntetisk metan (SNG) f¨ or injektion av natur- gas (NG). En teknik-ekonomisk modell skapades f¨ or att simulera anl¨ aggningens drift under ett ˚ ar och extrapolera resultaten f¨ or projektets livsl¨ angd. Modellen designades baserat p˚ a ett pilotprojekt som utvecklades i Frankrike med namnet HYCAUNAIS och har anv¨ ant partner-samt litteraturdata f¨ or bearbetning. P˚ a grund av begr¨ ansningar i den lokala NG-n¨ atkapaciteten unders¨ oktes flera scenarier som inkluderade att l¨ agga till ytterligare investeringar som m¨ ojligg¨ or ¨ okad driftstid och int¨ aktsstr¨ ommar, inklusive: fast elpris eller day-ahead (DA) marknadsdelt- agande; n¨ atuppgradering f¨ or ¨ okad NG-n¨ atkapacitet; och CH4
Elektrolys¨ orers deltagande i frekvensbegr¨ ansningsreserven (FCR) ans˚ ags ocks˚ a f¨ or
okad l¨ onsamhet. Resultaten visade att standardfallsscenariot (inga ytterligare investeringar) med deltagande p˚ a DA-elmarknaden var det mest attraktiva n¨ ar det g¨ aller tre unders¨ okta m˚ al: nettonuv¨ arde (NPV), ˚ aterbetalningsperiod (PBP) och niv˚ aniserad metankostnad (LCOM) . Driftstiden f¨ or standardfallet befanns vara cirka 90% av ˚ aret; produktionen hindrades inte av begr¨ ansad n¨ atkapacitet tillr¨ ackligt f¨ or att anse ytterligare investeringar n¨ odv¨ andiga. Vidare b¨ or deltagande p˚ a DA-marknaden best¨ ammas av en upph¨ ord betalningsvilja (WTP) f¨ or el i motsats till marginell vinst (MP). Att anv¨ anda WTP som avg¨ orande faktor till¨ at ¨ okade driftstimmar och l¨ agre LCOM. Men i alla unders¨ okta scenarier var inga l¨ onsamma;
vilket inneb¨ ar att marknadsf¨ orh˚ allandena fortfarande m˚ aste f¨ orb¨ attras kraftigt innan PtG kan f˚ a fart. En k¨ anslighetsanalys gjordes p˚ a standardfallsscenariot f¨ or att se vilka parametrar som p˚ averkar l¨ onsamheten mest och b¨ or vara i fokus f¨ or vidare forskning och utveckling. SNG-taxan visade sig vara den mest inflytelserika p˚ a NPV, vilket kr¨ avde att en tariff p˚ a minst 188 e/MW h (120 e/MW h anv¨andes f¨ or modellering) f¨ or att vara l¨ onsam. Elpriset var det n¨ ast mest inflytelserika och kr¨ avde ett genomsnittligt marknadspris p˚ a 25 e/MW h f¨or att vara l¨onsamt. Efter- som PtG-teknik kan ge flera externa f¨ ordelar som inte realiseras ekonomiskt av investerare, kan int¨ aktsgenerering av dem ge ett s¨ att att f¨ orb¨ attra l¨ onsamheten.
Detta inkluderar n¨ atbalansering och flexibilitet, avkolning, l¨ agre n¨ atkostnader och
f¨ orb¨ attrad energis¨ akerhet. Sammanfattningsvis m˚ aste kapitalkostnaderna f¨ or utrust-
ning, elpriser och avgifter i samband med dessa samt taxor f¨ or gr¨ ona gaser f¨ orb¨ attras
dramatiskt f¨ or att SNG-produktionen ska vara en attraktiv l¨ osning f¨ or minskning
och avkolning av el.
List of Figures 7
List of Tables 9
Abbreviations and Acronyms 11
1 Introduction 12
1.1 Electrification for Decarbonization and Energy Storage . . . . 12
1.2 Power-to-Gas: An Energy Storage and Cross-Sectoral Solution . . . . 13
1.3 Carbon Emissions in France . . . . 14
2 Scope of Thesis 15 2.1 Thesis Objective . . . . 16
2.2 Significance of Model . . . . 16
2.3 Methodology . . . . 16
3 HYCAUNAIS Project 17 4 Theory and State-of-The-Art of PtG Equipment 19 4.1 Electrolysers . . . . 19
4.1.1 Basic Thermodynamics . . . . 19
4.1.2 Alkaline Electrolysers . . . . 21
4.1.3 Proton Exchange Membrane Electrolysers . . . . 22
4.1.4 Solid Oxide Electrolysers . . . . 24
4.1.5 Comparison of Electrolyser Technologies . . . . 25
4.2 Hydrogen Storage Technologies . . . . 26
4.2.1 Compressed Gas Tanks . . . . 26
4.2.2 Underground Caverns . . . . 26
4.2.3 Metal Hydride Storage . . . . 27
4.2.4 Liquid Organic Hydrogen Carriers . . . . 27
4.2.5 Cryo-Compressed Storage . . . . 27
4.2.6 Mobility Storage . . . . 27
4.2.7 Comparison of Storage Technologies . . . . 27
4.3 Gas Compressors . . . . 28
4.4 Methanation Reactors . . . . 30
4.4.1 Basic Thermodynamics . . . . 30
4.4.2 Catalytic Methanation . . . . 31
4.4.3 Biological Methanation . . . . 34
4.5 Methanation Reactor Comparison . . . . 36
4.6 Carbon Dioxide Separation Technologies . . . . 36
4.6.1 Pressure Swing Adsorption . . . . 37
4.6.2 Water Physical Scrubbing . . . . 38
4.6.3 Organic Physical Scrubbing . . . . 38
4.6.4 Chemical Scrubbing . . . . 38
4.6.5 Membrane Filtration . . . . 38
4.6.6 Cryogenic Distillation . . . . 39
4.6.7 Comparison of CO2
Separation Technologies . . . . 39
4.7 Gas Grid Injection . . . . 40
4.7.1 Hydrogen Injection . . . . 40
4.7.2 Synthetic Methane Injection . . . . 41
4.8 Mobility Stations . . . . 42
4.8.1 Filling Station . . . . 42
4.8.2 Refuelling Station . . . . 43
5 Power-to-Gas Business Models 44 5.1 Plant Costs . . . . 44
5.1.1 Water . . . . 44
5.1.2 Electricity . . . . 45
. . . . 47
5.1.4 Total Costs of PtG . . . . 48
5.2 Revenue Streams . . . . 49
5.2.1 Grid Services . . . . 49
5.2.2 Gas Grid Injection . . . . 51
5.2.3 Green Gas Prices . . . . 52
5.2.4 Mobility . . . . 54
5.2.5 Industry . . . . 54
5.2.6 Additional Revenue . . . . 55
5.3 Profitability of PtG Plants . . . . 56
5.4 Literature Review of PtG Economics . . . . 57
5.5 Comparison of Literature Results . . . . 59
5.6 Valorization of Positive Externalities from PtG . . . . 60
6 Model Design 62 6.1 Objectives . . . . 62
6.2 Operational Scenarios . . . . 63
6.3 Mobility Designs . . . . 63
6.4 Methodology and Constraints . . . . 64
6.5 Operational Assumptions . . . . 65
6.6 Top-Level Model . . . . 66
6.7 Input Data . . . . 67
6.7.1 Wind Power Profile . . . . 67
6.7.2 FCR Power Profile . . . . 67
6.7.3 Electricity Prices . . . . 68
6.7.4 Gas Grid Capacity . . . . 68
6.8 Defined Parameters . . . . 68
6.9 Plant Simulator . . . . 69
6.9.1 Power Controller . . . . 69
6.9.2 Gas Flow Controller . . . . 70
6.9.3 Determining Factor . . . . 71
6.9.4 Methane Controller . . . . 72
6.9.5 Electrolyser . . . . 72
6.9.6 Hydrogen Tank . . . . 74
Separation . . . . 75
6.9.8 Methanation Reactor . . . . 76
Mobility . . . . 77
Mobility . . . . 78
6.9.11 Grid Injection . . . . 79
6.10 Economics . . . . 79
6.11 Sensitivity Analysis . . . . 80
7 Results and Discussion 81 7.1 MP as Determining Factor . . . . 81
7.1.1 Operating Dynamics . . . . 81
7.1.2 Costs of Operation . . . . 86
7.1.3 Profitability . . . . 87
7.2 WTP as Determining Factor . . . . 90
7.3 Sensitivity Analysis . . . . 95
7.4 Conclusions and Future Work . . . . 97
A Appendix - Model Results 107
List of Figures
1.1 The Power-to-Gas process concept (Lehner et al., 2014) . . . . 14
Emissions in France by energy source in 2015 (data source: IEA). . 15
Emissions in France by sector in 2015 (data source: IEA). . . . . 16
2.1 Flowchart of methodology of thesis. . . . . 17
3.1 HYCAUNAIS project plant layout and process flow. Modification of original figure by author (Storengy, 2019). . . . . 18
4.1 Relationship between temperature, voltage and required energy for elec- trolysis in electrolyser thermodynamics (Lehner et al., 2014). . . . . 20
4.2 (a) Number of PtG projects with each electrolyser technology as of 2018; (b) number of PtG projects with each electrolyser technology and total PtG projects, including future planned (Wulf et al., 2018). . . . . 21
4.3 Design principle of AEL electrolysers (Lehner et al., 2014). . . . . 22
4.4 Design principle of PEM electrolysers (Lehner et al., 2014). . . . . 23
4.5 Design principle of SOEC electrolysers (Lehner et al., 2014). . . . . 24
4.6 Portion of current PtG projects implementing methanation, with the num- ber of project with each methanation technology (biological or catalytic) also shown (Wulf et al., 2018). . . . . 30
4.7 Conversion rate of H2
at stoichiometric ratio at 1 and 20 bar (G¨ otz et al., 2016). . . . . 31
4.8 Overview of different catalytic methanation routes. State of development: c - commercial, d - demonstration scale, r - research (R¨ onsch et al., 2016). 32 4.9 The three main biological reactor designs: (a) CSTR, (b) fixed-bed and (c) trickle-bed (Lecker et al., 2017). . . . . 35
4.10 General representations of the CO2
separation technologies: (a) absorption, (b) adsorption, (c) membrane and (d) cryogenic (Ghaib and Ben-Fares, 2018). 37 5.1 The frequency markets in France and their operation characteristics (RTE, 2020). . . . . 46
5.2 France SPOT DA daily average price for 2018 (Data source: RTE). . . . 47
5.3 France SPOT DA daily average price duration curve for 2018 (Data source: RTE). . . . . 48
5.4 Cost of CO2
capture in terms of its source (van Leeuwen and Zauner, 2018). 49 5.5 Electrolyser power in relation to grid frequency (Data source: RTE). . . . 50
5.6 Electrolyser power over a 2-hour period while participating in the FCR (Data source: RTE). . . . . 51
5.7 Hydrogen grid injection limits for some EU countries Tractebel and Hinicio (2017). . . . . 52
5.8 Natural gas DA market prices at TTF from 2015-2018 Bloomberg LP (2020). 52 5.9 Biomethane purchase tariff in France (GRDF et al., 2019). . . . . 53
5.10 Levelized cost of gas for grid injection from PtG plants at optimal load factors (de Bucy and Lacroix, 2016). . . . . 59
5.11 The four quadrant evaluation of key positive and negative effects of PtG (Jepma et al., 2019). . . . . 61
6.1 Top-level model operational flowchart. . . . . 67
6.2 Plant simulator blocks and operational flowchart. . . . . 69
6.3 Power controller flowchart. . . . . 70
6.4 Gas flow controller flowchart. . . . . 72 6.5 Determining factor logic flowchart. . . . . 73 6.6 Methane controller logic flowchart. . . . . 73 7.1 (a) Power to electrolyser over simulated year; (b) tank fullness over simu-
lated year. . . . . 82 7.2 SNG production rate over 10 hours, illustrating following the FCR (before
5-hour mark) and following the wind farm (after 5-hour mark) power profiles as well as the continuous flow to the reactor despite this fluctuation. . . . 83 7.3 ETT and TTR flow rates over the simulated year, noting the appearance
of direct following of the reactor to the electrolyser. . . . . 84 7.4 ETT, TTR and TTT flow rates over the simulated year. . . . . 84 7.5 SNG production and its final end-uses (grid injection or mobility) over the
simulated year. . . . . 85 7.6 ETT and TTR with the mesh upgrade over the simulated year. . . . . . 85 7.7 Electrolyser operating hours versus LCOM for each scenario, grouped by
configuration type. . . . . 86 7.8 Breakdown of LCOM for each scenario showing the respective cost by
equipment and feedstock; MP as DF. . . . . 87 7.9 MP of each scenario presented in a box and whisker chart. . . . . 89 7.10 WTP of electricity of each scenario presented in a box and whisker chart. 90 7.11 NPV versus PBP for each scenario, grouped by configuration type. . . . 90 7.12 Breakdown of LCOM for standard case scenarios showing the respective
cost by equipment and feedstock. . . . . 92 7.13 Breakdown of LCOM for H2
mobility scenarios showing the respective cost
by equipment and feedstock. . . . . 92 7.14 NPV versus PBP for each scenario with WTP and MP as DF, grouped by
configuration type. . . . . 93 7.15 Revenue per hour of electrolyser operation for each scenario and WTP and
MP as DF. . . . . 93 7.16 NPV, PBP and operational hours of the electrolyser for standard case
scenarios. . . . . 94 7.17 NPV, PBP and operational hours of the electrolyser for H2
scenarios. . . . . 94 7.18 Tornado chart of NPV sensitivity analysis of most influential parameters
in S7 with WTP = 65 e/MW h. . . . . 96 7.19 Tornado chart LCOM sensitivity analysis of most influential parameters in
S7 with WTP = 65 e/MW h. . . . . 97
List of Tables
1.1 Storage technologies and parameters critical for renewable energy-dominated
energy systems (Lehner et al., 2014). . . . . 13
4.1 Main characteristics of AEL, PEM, and SOEC electrolysers. . . . . 25
4.2 Main characteristics of hydrogen storage technologies. . . . . 28
4.3 Hydrogen compressor CAPEX model reference values and coefficients (Tractebel and Hinicio, 2017). . . . . 29
4.4 Main characteristics of methanation reactor technologies. . . . . 36
4.5 Main characteristics of CO2
separation technologies. . . . . 40
4.6 Natural gas grid quality standards in France (Petersson and Wellinger, 2009). 41 4.7 Hydrogen NG grid injection station and pipeline cost parameters. . . . . 41
4.8 Synthetic methane NG grid injection station and pipeline cost parameters (van Leeuwen and Zauner, 2018). . . . . 42
4.9 Hydrogen fill site CAPEX model reference value and coefficient (Tractebel and Hinicio, 2017). . . . . 43
4.10 CNG refuelling station equipment costs (Smith and Gonzales, 2014). . . . 43
4.11 Hydrogen refuelling station equipment costs (van Leeuwen and Zauner, 2018). 44 5.1 Acceptable selling prices of hydrogen in mobility applications (Tractebel and Hinicio, 2017). . . . . 54
5.2 Price range for industry in different applications (Tractebel and Hinicio, 2017). . . . . 55
5.3 Value of by-product oxygen based on current oxygen origin (Tractebel and Hinicio, 2017). . . . . 55
5.4 Summary of literature results in terms of PtG plant operation and profitability. 60 6.1 The 12 scenarios developed for model optimization, with the type of elec- tricity and configuration implemented in each scenario marked accordingly. 64 6.2 Electricity additional fees for a 1 and 10 M W electrolyser in France. . . . 68
6.3 Defined model parameters for the PEM electrolyser. . . . . 74
6.4 Defined model parameters for the steel hydrogen tank. . . . . 75
6.5 Defined model parameters for CO2
separation. . . . . 76
6.6 Defined model parameters for the biological methanation reactor. . . . . 77
6.7 Defined model parameters for H2
mobility. . . . . 78
6.8 Defined model parameters for CH4
mobility. . . . . 79
6.9 Defined model parameters for the grid injection. . . . . 79
6.10 Defined model parameters for economical values. . . . . 80
7.1 Equipment calculated CAPEX and OPEX values. . . . . 81
7.2 Scenario results using MP as DF. . . . . 82
7.3 Partial, over and total load hours of electrolyser and reactor in S1. . . . . 83
7.4 Revenue for each scenario by source. . . . . 88
7.5 Scenario results using WTP as DF, comparing them to fixed price and MP results. . . . . 91
7.6 Comparison of objective functions for fixed price, MP and WTP as DF scenarios with the fixed price scenarios as the base case. . . . . 95
7.7 The electricity price, SNG tariff and electrolyser CAPEX required to reach
NPV = 0 and their subsequent LCOM and electrolyser operational hours
for S7 with WTP as DF. . . . . 97
A.1 Cost of equipment for CO2
separation. . . . . 107
A.2 Cost of equipment for CH4
mobility. . . . . 107
A.3 Cost of equipment for H2
mobility. . . . . 107
A.4 Model results using MP as determining factor. . . . . 108
A.5 Model results using WTP as determining factor at various electricity prices
and also compared to fixed price and MP scenarios. . . . . 109
Abbreviations and Acronyms
AEL alkine electrolysis BOP balance of plant CAPEX capital expenditure
CEP average consumed electricity price
CNG compressed natural gas DSO distribution system operator FCEV fuel cell electric vehicle
FCR frequency containment reserve FIT feed-in tariff
FLH full-load hours GHG greenhouse gas
GHSV gas hourly space velocity HHV higher heating value LCOE levelized cost of energy LCOH levelized cost of hydrogen LCOM levelized cost of methane LHV lower heating value
M molar weight
MFR methane formation rate MP marginal profit
NG natural gas NPV net present value
OPEX operational expenditure PBP payback period
PEM proton exchange membrane (electrolyser)
Q volumetric flow rate R ideal gas constant SNG synthetic natural gas SOEC solid oxide electrolysis cell
TSO transmission system operator VRE variable renewable energy WTP willingness to pay
Z compressibility factor
Climate change is the single largest challenge facing humanity in the 21st century. Most importantly, the 2014 Synthesis Report from the International Panel on Climate Change (IPCC) stated with high certainty that the rise in global temperature is primarily driven by anthropogenic carbon emissions (IPCC, 2014). Additionally, the report also gives projections on future emissions to 2100 and the resulting predicted outcomes. The alarming results pushed the international community to come together and sign the historic Paris Agreement, aiming to limit global temperature rise to 1.5-2 °C above pre-industrial levels (UNFCC, 2015). In Europe, this agreement was translated into the 2030 Climate and Energy Framework (CEF) which set EU-wide targets for 2030, namely (European Commission, 2014):
• At least 40% cuts in greenhouse gas emissions (GHG) (from 1990 levels);
• At least 32% share for renewable energy; and
• At least 32.5% improvement in energy efficiency.
1.1 Electrification for Decarbonization and Energy Storage
As can be seen in the CEF, one of the main tools used to reduce GHG emissions is a drastic increase in renewable energy. As variable renewable energies (VRE) such as wind and solar became increasingly cost competitive and nowadays more economically attractive than the cheapest fossil fuel options for electricity production (IRENA, 2018), large-scale rollout of these technologies are happening across the continent. However, as these technologies increase their share in the energy mix, the surrounding energy systems must also adapt due to their variability - the sun does not always shine as the wind does not always blow. When they do, it may not be at the time of day their energy production is needed, causing curtailment of VREs. A total of 4.65 TWh of wind energy was curtailed in Germany and Great Britain in 2016 alone, causing billions of euros of lost production and revenue (Joos and Staffell, 2018).
To capture this lost revenue, energy storage is becoming a hot topic in the energy sector.
Storage provides flexibility to VREs, a necessity for them to continue to grow (Kondziella and Bruckner, 2016). Storage of electrical energy can be done in many different ways, but must always be converted to another energy form (chemical, mechanical, thermal, etc.). Once stored, it can be converted back to electrical energy when demand is needed, or used in its converted form for other applications, allowing cross-sectoral integration.
Additionally, the action of storing and/or releasing electrical energy can allow participation in electrical grid energy markets, if the storage technology is capable to do so, increasing revenue from the system.
There are many types of energy storage technologies currently available (Mahlia et al.,
2014) with advantages and disadvantages to their application depending on many variables,
such as: storage capacity, storage duration, storage losses, investments costs, operational
costs, existing infrastructure, etc. When considering storage of large energy quantities
from surplus power generation, the following parameters are of high importance: storage
capacity, specific energy, decentralized application and long storage duration. Common
storage technologies in relation to these parameters are shown in 1.1. Pumped hydro can achieve high efficiencies, high capacity ratings and long storage duration, but their extremely low specific energy and necessity for existing geological conditions make it limited in its use. Compressed air has similar qualities to pumped hydro with a larger specific energy, but would require locally available geological storage (e.g. a depleted salt mine) to be economically feasible for large storage quantities. Battery technologies have high efficiencies and increasingly higher energy densities, but their lack of long storage durations makes them non-ideal candidates for this type of operation. Power-to-gas has potentially low efficiencies (depending on final energy use) but has the ability for high capacity ratings, storage durations from minutes to months, extremely high energy densities (especially if stored as methane) and has the flexibility to be installed wherever the application required is located.
Table 1.1: Storage technologies and parameters critical for renewable energy-dominated energy systems (Lehner et al., 2014).
Technology Efficiency Capacity Storage Specific Energy Decentralized?
(%) Rating (MW) Duration (kW hel
Pumped hydro 70-85 1-5,000 Hours- 0.23 No
months (∆H = 100m)
Compressed 70-75 50-300 Hours- 6.9 No
Lead acid 70-80 0.05-40 Minutes- 75 Yes
Sodium sulfur 75-85 0.05-34 Seconds- 150 Yes
Lithium-ion 80-90 0.1-50 Minutes- 270 Yes
Power-to-gas 30-75* 0.01-1,000 Minutes- 391 (H2
(@ 200 bar) months 1200 (CH4
*including re-conversion back to electricity. 50-75% without re-conversion.
1.2 Power-to-Gas: An Energy Storage and Cross-Sectoral Solu- tion
Power-to-Gas (PtG) is the concept of converting electrical energy chemically to energy-rich
gases hydrogen (H2
) or methane (CH4
). Specifically, the focus of PtG recently has been
converting surplus electricity – ideally from renewables such as solar or wind, thus making
them “green gases” – to H2
to be stored for minutes or months without significant
losses and finally used in multiple different applications. A breakdown of the process
from electrical generation, conversion and final use is shown in Figure 1.1. Using the
chemical process of splitting water using electrical power – electrolysis – hydrogen and
oxygen gas are produced. The oxygen is normally ejected into ambient air while the
energy-dense hydrogen is captured and compressed for storage. This hydrogen can be
directly injected into the natural gas grid (to a certain volumetric capacity depending
on local infrastructure and final use equipment), piped to a station for fuel cell electrical
vehicles (FCEVs), used as a raw material in chemical processing, or re-converted back to electricity for power.
Figure 1.1: The Power-to-Gas process concept (Lehner et al., 2014) .
Additionally, hydrogen can be further converted with carbon dioxide (CO2
) to form methane - a much higher specific energy gas (3 times more). The CO2
can be taken by processing the emissions from carbon-intensive industry, biogas plants, or by extraction directly from the air. Since there are rarely natural pure carbon sources to be harnessed (Prentice et al., 2001), the separation of CO2
from its source is required, although some applications allow for raw biogas to be used in methanation (G¨ otz et al., 2016). Methane is very advantageous to produce as it can be directly injected into massive existing natural gas infrastructure in Europe, capable of storing energy in the TWh scale and directly replacing fossil fuel without any system upgrades (Lehner et al., 2014). This ability for bi-directional energy flow between the power and gas grid gives great flexibility to overall system design and management. Large investments in costly capacity upgrades on the power grid can be avoided and little disturbance to current sector operation can be achieved. Conversion to gases has another added bonus: cross-sectoral applicability.
Once a gas, electrical power generation from renewable sources can be chemically used in industrial processes, residential and commercial heating or transportation. Increasing the share of renewable energy in the primary energy mix no longer can be seen as an inevitable challenge but an advantage to decarbonize additional sectors.
1.3 Carbon Emissions in France
All EU countries were required to submit a 10-year (2021-2030) National energy and
Climate Plan (NECP) by the end of 2019 to address the CEF. In France, their NECP built
upon the Multiannual Energy Planning and National Low-Carbon Strategy documents which highlight the main objectives of decarbonizing the energy system and achieve carbon neutrality by 2050 (European Commission, 2020).
As of 2017, France was seventh in the EU for lowest GHG emission per capita at 7.2.
However, they were also the fourth highest carbon emitter at 481 million tonnes of carbon dioxide equivalent (Mt CO2
-eq) (European Environment Agency, 2020). Looking at their carbon emissions by source as shown in Figure 1.2 for 2015, oil is the main contributor at 60.7%. This was primarily used (around 50%) in transportation (IEA, 2020). Natural gas is the second highest emitter at 26.3% and was primarily used in industry (38%) and residential (27%). The carbon emissions can also be viewed by sector, as shown in Figure 1.3. Considering the sources of energy for transportation and residential, it is not surprising to see them as the highest sectors at 42.1% and 14%, respectively. Industry and electricity and heat producers come in close third and fourth at 13.4% and 13%, respectively.
Figure 1.2: CO2
Emissions in France by energy source in 2015 (data source: IEA).
2 Scope of Thesis
The thesis will focus on PtG technologies and how they can be used for harnessing surplus power generation from renewable energy in the context of a project currently being developed in France named HYCAUNAIS. Technologies outside the scope of this project will be presented in section 4 but only as a theoretical foundation for PtG technologies.
More information on the project will be given in section 3. The work done on this
thesis is on behalf of FCLAB, a joint research unit of five public laboratories in the
Bourgogne-Franche-Comt´ e region in the field of fuel cell systems and hydrogen energy
(FCLAB, 2020). The work done as part of this thesis will be used as a foundation for
more complex modeling to be done for the project and validation of the results to the
pilot plant actual operation. This will contribute to the main objective of FCLAB’s work
on the project: replicating the technological and commercial model for future plants.
Figure 1.3: CO2
Emissions in France by sector in 2015 (data source: IEA).
However, the foundation of the final model is the focus of this thesis while the additional work is out of the scope.
2.1 Thesis Objective
The objective of this thesis is to create a techno-economical model for optimization of the PtG system in the HYCAUNAIS project. Based upon the desired operational scenario, the model will calculate operational as well as economical parameters of the system. These results will then be used for further analysis of improvements that can be made either economically or operationally to achieve optimal operation of the plant.
2.2 Significance of Model
The model created for this project will be used for forecasting the operation of the plant as well as its profitability. These results will help project partners make decisions on investments, maintenance schedules, revenue stream evaluation and negotiation for gas tariffs. As this is one of the first projects in France to be injecting synthetic methane into the gas grid, there are currently no feed-in tariffs (FIT) available to producers, unlike biomethane. This tariff must be agreed upon with the French government and results from the model will help in these negotiations. As costs are currently very high for this technology, as will be shown later, innovative business models are needed that can combine several revenue streams that can properly capture the added value PtG can provide to local electricity grids as well as address environmental concerns. This is a large challenge that the investigation of this thesis and the model will attempt to address.
To create the model for the thesis, an in-depth understanding of state-of-the-art technology
for PtG plants is required, with a specific focus on the technologies being deployed in this
project. This includes the capital (CAPEX) and operational (OPEX) costs associated
to each technology, the current available capacities on the market, their operational performances and constraints as well as their technology readiness level (TRL). The findings from this technological research study will then be compared to data provided by project partners (as much as possible). Partner data will be used in the modeling, when possible, to provide realistic results on the project. The research will also include operational strategies currently being tested in other pilot projects and implemented at the industrial scale. How these projects design their business models to be profitable is especially important due to the high costs associated with the technologies involved.
Once a solid foundation of the technologies involved is completed, modeling of the plant will be done. All modeling will be written in Python ™ on the Spyder scientific development environment. Modules or “blocks” will be made for each system component and include the conversion efficiencies as well as the costs associated to the respective component.
Next, controller blocks will be designed based upon the required flow of the system and equipment operational constraints. These blocks are essential in determining how the overall system will operate in terms of component status (active or not active), what inputs will be used for each component, and what will be done with the outputs of each component.
The system blocks (equipment and controllers) will then be integrated into a ”plant simulator” module, which will simulate the plant operation over the defined year in a series of 10-minute steps. The top-level model will then call upon this simulator and run different scenarios to determine the ideal system operation and the limitations associated to them, calculating economical values to analyse them appropriately. These different scenarios will include: grid injection only, grid injection and mobility and varying system power source.
Finally, a sensitivity analysis of primary system parameters will be done for the scenario which is the most attractive in terms of cost and profitability. This will help determine what improvements to components are critical as well as policy or regulation changes that can influence the profitability of the system. Figure 2.1 shows the method described above, represented schematically.
Figure 2.1: Flowchart of methodology of thesis.
3 HYCAUNAIS Project
The HYCAUNAIS project is a PtG pilot plant located in Yonne, France. It is a private-
public partnership between six companies – Storengy, Areva H2Gen, Engie Green, Engie
Lab and Electrohchaea – and three public bodies – Syndicat D´ epartemental d’´ Energies de
l’Yonne (SDEY), Yonne ´ Energie and FCLAB. Figure 3.1 shows the general flow diagram
of the pilot plant with each step in the process labeled accordingly. Amendments were done to the original figure for detailed description of the plant and its operation. Receiving funding from l’Agence de l’Environnement et de la Maˆıtrise de l’´ Energie (ADEME), the plant will produce synthetic methane for gas grid injection as well as mobility. Following the power production up to two existing wind farms (1) virtually through a grid connection (2), a 1 MW PEM electrolyser (3) will produce hydrogen for a biological methanation reactor (4) for synthetic methane production. Intermediate H2
storage (5) will be installed between the ladder and the former to decouple operation. The CO2
utilized will be provided by purified (6) on-site landfill gas (7), also known as CO2
separation. The synthetic methane produced, as well as biomethane already captured, will then be mixed and injected into the local gas grid (8) or used for CH4
mobility (9). Alternatively, H2
mobility (10) will also be investigated as an operational scenario. The raw landfill gas is already being upgraded by a WAGABOX®
unit, separating and injecting about 90% of the available biomethane into the gas grid. The effluent from the WAGABOX®
unit – primarily CO2
– will be used by the HYCAUNAUS system. Additional purification of this carbon stream is required prior to input to the biological reactor. A grid injection station is already on-site due to the existing biomethane extraction and has additional available capacity. Therefore, the system boundaries of the project are power input from the electrical grid and WAGABOX®
effluent to the output from the methanation reactor.
If mobility is involved, required infrastructure and equipment will also be included in the system boundaries.
Figure 3.1: HYCAUNAIS project plant layout and process flow. Modification of original figure by author (Storengy, 2019).
The main objective of the HYCAUNAIS project is to demonstrate the technical and economical feasibility and replicability of a PtG plant in France and in a local context.
Additional revenue from market participation in the frequency market is proposed, with
particular focus on the frequency containment reserve (FCR). The electrolyser is proposed
to be able to increased its power input by 200% (2 M W ) intermittently to allow for full
power participation in the FCR, maximising revenues. To be able to virtually follow the
wind farm power profile, a partner will design a controller to predict wind speed and
thus the power profile for real-time electrolyser operation adaptation. This controller
will also be used to predict future wind speeds for decisions on plant operations and
4 Theory and State-of-The-Art of PtG Equipment
The theory covered in this section will discuss the state-of-the-art of technologies used in a PtG plant, with a larger focus on the technologies being implemented in the HY- CAUNAIS project. The equipment covered will be electrolysers, storage tanks, gas compressors, methanation reactors, CO2
separation technologies, gas grid injection and mobility stations.
4.1.1 Basic Thermodynamics
Electrolysis – the decomposition process of water into its constituent elements hydrogen and oxygen using electrical energy – is the foundational reaction that power-to-gas plants are based upon. This dissociation of a water molecule is shown in Equation 4.1.
O(l) =⇒ H2
(g) + 1
The reaction occurs inside the cells of the electrolyser, which are connected together to make electrolyser stacks. For this reaction to take place both electrical energy - the reversible Gibbs free energy (∆G) - and thermal energy - the irreversible entropy ((∆S) - are required. This total energy - the enthalpy (∆H) - at standard conditions (298 K and 1 atm) is shown in Equation 4.2.
+ T ∆S0
= 237.22 + 48.62 = 285.84kJ/mol (4.2) To find the minimum required voltage to apply to electrolyser cell for hydrogen production, also known as the reversible cell potential or Vrev
can be divided by the number of electrons (n) multiplied by the Faraday constant (F). This is shown in Equation 4.3.
nF = 237.22
2 ∗ 96.487 = 1.23V (4.3)
However, this voltage will only begin the process of splitting: the entropy or thermal energy is required to complete the reaction. This required voltage can be expressed as the thermoneutral voltage (Vth
) as shown in Equation 4.4.
nF = 285.84
2 ∗ 96.487 = 1.48V (4.4)
The total energy required for electrolysis and its constituents greatly depend upon pressure
as well as temperature and the state of the water being used for the reaction. If the water
is already vaporized prior to input to the electrolyser the amount of required energy, and
, greatly diminishes. This relationship can be seen in Figure 4.1. If the voltage
supplied to the cell (Ecell
) is equal to Vth
, the electrolyser will operate isometrically as the heat produced within the cell equals the heat consumption of the endothermic reaction.
is greater than Vth
, surplus heat is created and must be removed from the system to reduce degradation (Lehner et al., 2014).
Figure 4.1: Relationship between temperature, voltage and required energy for electrolysis in electrolyser thermodynamics (Lehner et al., 2014).
In reality, all electrolysers today produce excess heat during operation, signifying high cell voltages. Ecell
can be expressed as the accumulation of potential losses inside the cell, as shown in Equation 4.5.
+ Edif f
(V ) (4.5)
is known as the activation potential and represents losses from electro-chemical kinetics in the cells from proton exchanges between the cathode and anode electrodes and the rate of reaction at the electrodes themselves (Lebbal and Lecœuche, 2009). Edif f
is the diffusion potential where are losses occurring at the electrodes because of oxygen and hydrogen production: the gases form as bubbles on the electrodes, impeding further reactions while they move away from the reaction area. Eele
represents losses that are caused by the electrolyte inside the cells. More detailed theory on the chemical and electrical processes inside an electrolyser can be seen in supporting literature (Lebbal and Lecœuche, 2009; Dincer and Zamfirescu, 2016).
The electrolyser efficiency can be expressed in several different ways, depending on the
energy being represented as consumption (stack energy or system energy) or required
(Gibbs energy or enthalpy, which can also be represented as the lower and higher heating
value of hydrogen, respectively). In most cases, water is supplied to the system as liquid,
so evaporation energy must be considered. Further, to compare different electrolysers
easily, the system energy consumption is considered (the system being the electrolyser
stack plus balance of plant or BOP). As electrical energy is usually the only energy input
to the system, the electricity input per cubic meter of hydrogen produced, also known
as the specific energy (eel
). The Faradic efficiency, also known as the current efficiency,
represents the amount of crossover of hydrogen to the opposite side of the cell, reducing
the actual hydrogen outputted from the system. However, for the systems considered it is not very prominent and can be neglected. Thus, the efficiency of the electrolyser can be expressed as shown in Equation 4.6.
Depending on the technology used, this efficiency can vary drastically. There are three main electrolyser technologies that are currently being implemented and/or developed for PtG applications with different levels of TRL: alkaline (AEL), proton exchange membrane (PEM) and solid oxide (SOEC).
4.1.2 Alkaline Electrolysers
Alkaline electrolysis is the most mature and widely used technology currently deployed due to its high TRL and low-cost, although this has been changing in recent years in favour of PEM as seen in Figure 4.2 (Gahleitner, 2013). As the name suggests, the electrodes are immersed in a liquid alkaline electrolyte with a microporous diaphragm separating them.
The electrodes, separator and containment materials are normally made from nickel and stainless steel, keeping the cost low. When electricity is applied to the cell, water injected on the cathode side decomposes to hydrogen and hydroxide-ions. Hydrogen ions join to form hydrogen gas and bubble to the surface while the hydroxide-ions travel through the separator to the anode where oxygen gas is produced. Several cells are electrically connected to form stacks; capacities of 100 kW or more can have multiple stacks in series to obtain the desired capacity. This type of built structure is the same for all electrolysers.
Figure 4.3 shows a schematic and design principle of an AEL.
Figure 4.2: (a) Number of PtG projects with each electrolyser technology as of 2018; (b) number of PtG projects with each electrolyser technology and total PtG projects,
including future planned (Wulf et al., 2018).
In use since the 1920’s, it is readily available in the tens of MW with some projects in the
early 2020’s planning to install in the hundreds of MW (Thema et al., 2019). The specific
energy of AEL can have a wide range, depending on the capacity and manufacturer, but
is generally higher than other electrolyser technologies (G¨ otz et al., 2016). The hydrogen
purity is at least 99.5%, which is sufficient for grid injection or methanation, but may be
Figure 4.3: Design principle of AEL electrolysers (Lehner et al., 2014).
an issue for mobility as quality standards require 99.995% (Fraile Montoro et al., 2015).
When already in hot standby (no production but at operating temperature and pressure), AELs can fluctuate their output production within minutes and can span from 15-150% of rated capacity, which could be problematic in intermittent systems such as PtG (Matute et al., 2019; Lehner et al., 2014; Tractebel and Hinicio, 2017). Output pressure of hydrogen is normally atmospheric, but can be up to 15 bar from some manufacturers (Matute et al., 2019). The durability of AELs allows for longer stack lifetimes, around 80,000 operating hours, which have lower replacement costs than PEM stacks (van Leeuwen and Zauner, 2018; Matute et al., 2019; Tractebel and Hinicio, 2017). Their CAPEX ranges depending on the capacity, but is lower than PEMs due to its maturity and inexpensive materials (van Leeuwen and Zauner, 2018).
Even though the technology is very mature, there is research being done into further improving the technology. The gap between the electrodes and separator are directly correlated to the ohmic resistance in cell: the larger the gap the higher the resistance. Zero- gap systems are being developed with several solutions investigated, such as electrolyte absorbing layers and gas diffusive electrodes (Lehner et al., 2014). Improving the reaction time of AELs is also ongoing research as it is a critical characteristic in PtG systems. Anion exchange membranes (AEM) are also a technology being tested in labs which are a cross between AEL and PEM, aiming to extract the advantages of both while eliminating their weaknesses. This and other technological advances plus increased production capacities project to bring costs down to around 500 e/kW by 2050 ( Thema et al., 2019).
4.1.3 Proton Exchange Membrane Electrolysers
PEM electrolysis is widely becoming the electrolyser choice for PtG applications as the
capacities increase and costs plummet as seen in Figure 4.2. Unlike AELs, solid polymer
membranes which conduct protons are used as an electrolyte. This membrane is normally
made from Nafion®
, an expensive material based on perfluorosulfonic acid (Ito et al.,
2011). An electro-catalytic layer is placed on each side of the membrane to accelerate this
exchange. The catalyst is usually a base material coated in a platinum group metal due
to their excellent electrochemical properties and corrosion resistance, but are scarce and
costly (Lehner et al., 2014). Water is injected on the anode side of the electrolyser which
makes contact with an electrically connected porous current collector. Oxygen is stripped from the water molecule and bubbles to the top as oxygen gas. The hydrogen-ions diffuse across the membrane to the cathode side where it combines and becomes hydrogen gas.
This process is displayed in Figure 4.4.
Figure 4.4: Design principle of PEM electrolysers (Lehner et al., 2014).
PEM is a much newer technology than AEL, in use since 2007, with several different and opportunistic characteristics because of its solid, more compact design. Capacities go from only a few kW’s up to multiple MWs, with the largest electrolyser currently being built at 20 MW consisting of 2MW stacks (Thomas, 2019). The specific energy of PEMs is similar to AELs, with promise of higher densities in the future due to fewer losses (G¨ otz et al., 2016; Lehner et al., 2014). A great advantage is the ability to quickly change production rates with fluctuating power input as well as operating in a wide partial load range (down to 5%) and the ability for short intervals of overload (up to 200%). Due to the material used for the membrane, temperatures are limited to 80 °C, however, it does allow for high pressures (up to 200 bar) and current densities (up to 2 A/cm2
) (Matute et al., 2019;
Lehner et al., 2014; Carmo et al., 2013). Due to the highly corrosive environment from the acidic membrane, stack lifetimes are currently around 40,000-60,000 operating hours (van Leeuwen and Zauner, 2018; Matute et al., 2019). Due to the expensive materials used, PEM electrolysers currently have a CAPEX around 1,600-1,900 e/kW and stack replacements costs around 470-525 e/kW ( van Leeuwen and Zauner, 2018; Matute et al., 2019; Tractebel and Hinicio, 2017).
Extensive research is being done into advancing PEM electrolysis to further bring down costs and improve the overall efficiency. Other membrane materials than Nafion®
that can operate above 80 °C and with other less volatile ionic liquids than water have been extensively investigated (such as polyether-ether ketones, polyether-sulfones and sulfonated polyphenyl quinoxaline) but so far suffer from lower current densities and durability (Carmo et al., 2013; Lehner et al., 2014). To lower the amount of the catalyst used, dispersal of catalyst nano-particles onto support materials may be possible (Lehner et al., 2014).
Other catalyst materials are also being researched that are more abundant and cheaper,
however oxidation on the anode side has been a problem as well as overall lower cell
efficiency (Lehner et al., 2014). Nickel-based materials with corrosion-resistant coatings
are seeing interest in this regard. Other factors that will greatly lower the cost is scaling up of production, automation of assembly and increased capacities.
4.1.4 Solid Oxide Electrolysers
SOEC electrolysers are the least mature technology being presented, with few projects currently past demonstration phase, as seen in Figure 4.2 (Schmidt et al., 2017). Similar to PEM electrolysis, SOEC cells have a solid ion-conducting membrane that acts as an electrolyte. However, this dense solid oxide membrane conducts oxygen-ions instead of hydrogen and is made from much cheaper ceramics with Yttria stabilised Zirconia (YSZ) as the most commonly used (Lehner et al., 2014). Electrodes also made from ceramics – as well as a small amount of nickel as a catalyst – are sandwiched on either end of the membrane with porous current collectors. Water vapor is normally fed on the cathode side, meaning the cells operate at temperatures above 100 °C, and usually well above that.
Water entering as vapor reduces the voltage required for electrolysis. as mentioned in Section 4.1.1. This high operation temperature also allows SOEC electrolysers to be run in reverse, as a fuel cell, with little or no modifications. Oxygen-ions migrate through the membrane to the anode side to become oxygen gas. SOEC cells are also capable of reducing CO2
to CO, with some applications using the technology to produce syngas (CO and H2
) from water (H2
O) and CO2
. Figure 4.5 shows the working principle of
Figure 4.5: Design principle of SOEC electrolysers (Lehner et al., 2014).
Demonstration projects have been taking place since 2014 for SOEC, although it is gaining interest because of its unique properties. The largest capacity active in projects is 150 kW thus far, which is expected to increase in the next decade (Wulf et al., 2018; Schmidt et al., 2017). Very low volumetric energy densities have been presented in some cases, with voltages as low as 1.07 V and temperatures upt to 700-100 °C ( Laguna-Bercero, 2012;
Lehner et al., 2014). Additionally, an external heat source, which historically has lower
prices than electricity, could be used to reduce system costs. These high temperatures,
however, negatively impact stack life as they quickly degrade the materials used. SOEC
systems have been shown to have similar reaction times and current densities to AEL,
which can be seen as negative characteristics in compact intermittent applications (Lehner et al., 2014; Schmidt et al., 2017). Costs of the system are not currently reliable due to its low TRL, but studies have estimated at least 2,000 e/kW for the CAPEX alone.
Heavy research is currently being done to improve SOEC technology. Cell degradation from high temperatures is a critical area of research and has prompted investigation into lower operational temperatures (500-700 °C), new materials to withstand the conditions, or coatings of existing materials (Lehner et al., 2014; Laguna-Bercero, 2012). Working at elevated pressures is known to reduce the internal cell losses and has been investigated on solid oxide fuel cells, with pressures up to 25 bar being tested in labs.
4.1.5 Comparison of Electrolyser Technologies
Table 4.1 compares the three electrolysis technologies discussed in previous sections with the best technology in each category highlighted. As can be seen, each technology has advantages, which highlights the necessity to understand the required application and its characteristics to choose the ideal technology. For the HYCAUNAIS project, hydrogen will be produced by following a wind profile as well as participation in the electrical grid frequency regulation market. Both these applications require a technology with a fast response time within seconds and large load ranges. The intermittency of hydrogen production will ideally be smoothed out with buffer storage and having elevated output pressures will lower the energy required for further compression, if required at all. Additionally, methanation reactors usually operate at elevated pressures, reinforcing the desire for higher electrolyser pressure. For the purposes of hydrogen mobility, high purity is required. Additional equipment for purification will mean higher project costs.
For all projects, costs of the system want to be as low as possible with high efficiency of production.
Table 4.1: Main characteristics of AEL, PEM, and SOEC electrolysers.
Parameter Unit AEL PEM SOEC
Capacity kW 1.8-100,0001,2
Energy density kW h/N m3
purity % 99.8-99.99981
Hot standby time Minutes4,6
Operating °C 40-905
Output pressure bar 1-156
Stack lifetime k − hours 80-902,6
Current density A/cm2
Load range %f ull load 15-1505,8
CAPEX e/kW 1,100-1,4002
≥ 2, 0003
OPEX % CAP EX 2-52
Stack replacement e/kW 380-4206,8
1(Bertuccioli et al.,2014);2(van Leeuwen and Zauner,2018);3(Schmidt et al.,2017);4(G¨otz et al.,2016);5(Lehner et al.,2014);
6(Matute et al.,2019);7(Carmo et al.,2013);8(Tractebel and Hinicio,2017)
4.2 Hydrogen Storage Technologies
Storage of gases used in PtG systems – hydrogen, carbon dioxide and methane – is usually necessary to capture the most value from intermittent operation (Gorre et al., 2020). For the purposes of this report only hydrogen storage will be discussed, but many of the same technologies can be used for other gases. CO2
storage that may be required will assumed to be included in the CO2
separation technology. As synthetic methane will be injected directly into the natural gas grid, on-site storage is considered not applicable.
However, in a methane mobility application storage will be needed. In this scenario, costs of methane storage are assumed to be the same as hydrogen. In the case of synthetic methane production from methanation reactors, storage of hydrogen between electrolysis and methanation allows the two systems to be decoupled, operating independent of each other. This type of buffer storage allows operators to take advantage of market prices and availability in terms of arbitrage. Longer term seasonal storage may be required in applications of supply-shifting (eg. consume summer solar energy in the winter). Large tanks or underground reservoirs will be needed in these applications. Due to the low energy density of hydrogen, it is compressed to reduce the volume it occupies. The stored pressure depends upon the application and physical constraints on-site.
4.2.1 Compressed Gas Tanks
Currently, gas storage is the most widely used technology for hydrogen in PtG projects at almost 90% (Gorre et al., 2020; Gahleitner, 2013). Gas is compressed to pressures between 4-500 bar, depending on the application, and stored in tanks. Four types of tanks are used for gas storage, named type I-IV, respectively: fully metallic, steel with glass fiber composite overwrap, full composite wrap with metal liner and fully composite (Moradi and Groth, 2019). A fifth type – full composite and linerless – is currently in development. Most PtG projects in operation use metallic tanks due to their high TRL, low costs, simplicity and ability for high pressure applications (up to 500 bar) and are also projected to be the main technology used in PtG in the future (van Leeuwen and Zauner, 2018; Gahleitner, 2013). However, their costs can vary greatly depending on the application. For smaller buffer storage applications, medium-pressure tanks (30-50 bar) are usually sufficient and have low costs (470 e/kg) ( Tractebel and Hinicio, 2017).
4.2.2 Underground Caverns
Underground caverns are widely used to store natural gas and could also be used for
hydrogen. Depleted gas and oil reservoirs, aquifers and salt caverns are the most commonly
used geological formations (van Leeuwen and Zauner, 2018). Salt caverns are seen as the
most suitable for hydrogen due to their ability to hold hydrogen under pressure (around
90 bar) without leaking and being inert. In such large storage applications (average of
500,000 N m3
for salt caverns), there is a requirement for a “cushion gas” to keep an
adequate pressure, which can be up to 30% of total volume and 50% of total investment
costs. However, due to the massive size, the investment costs per unit volume is extremely
low. Unfortunately, underground storage is very dependent on site location and thus
cannot be used in most applications.
4.2.3 Metal Hydride Storage
Another way to store hydrogen is through chemical sorption, where the hydrogen molecules are split into atoms and integrated into the chemical structure of a material (Moradi and Groth, 2019). The most common type of chemical sorption are metal hydrides, with at least five PtG systems using them currently (Gahleitner, 2013). However, the technology is still in the developmental phase. Improvements to the charge/discharge cycles, weight and overall cost need to lower before larger projects can be implemented.
4.2.4 Liquid Organic Hydrogen Carriers
An interesting technology being researched are liquid organic hydrogen carriers (LOHC) which also store hydrogen through chemical sorption, like metal hydrides. However, they are liquids that can be stored and discharged at ambient conditions (Moradi and Groth, 2019). Hydrogen is released through catalytic dehydrogenation which does not consume the carrier, allowing repetitive use. They are also very stable and stored at low pressures and could be very suitable for long term storage or transportation applications (Gahleitner, 2013). However, they currently have very low hydrogen storage capacity (7.2% wt) and need further development for pilot testing.
4.2.5 Cryo-Compressed Storage
A new technology introduced in 2010, it is the process of cryo-compressing hydrogen to a super critical cryogenic gas (Moradi and Groth, 2019). It is cooled to -233 °C but does not become a liquid. It has shown promise to have about 12% more storage density than cryogenic storage and is being investigated for mobility purposes. However, it still needs further development and infrastructure deployed to become useful.
4.2.6 Mobility Storage
Hydrogen mobility storage can be considered in two scenarios: storing to sell to refuelling stations or storing to sell to end-users at an on-site refuelling station. Only the former will be considered in this report. When selling to distributors, bulk storage in large metallic tanks or bundles at pressures up to 200 bar is typically done for stationary (on-site) storage (Tractebel and Hinicio, 2017). More expensive composites may be considered when limited space or high pressures are needed. Mobile storage systems may also be considered where metallic bundles or tube-trailers up to 200 bar are filled from smaller bulk storage on-site and travel to clients sites for off-loading. Type IV composite tube-trailers with a pressure of 500 bar and 1000 kg of hydrogen capacity are being developed but are currently not market-ready.
4.2.7 Comparison of Storage Technologies
Table 4.2 shows the main characteristics of the storage technologies that are currently
being readily used in PtG commercial applications. Obviously, salt caverns would be the
ideal choice because of their cost but must be geologically available near the project site
to be feasible. Additionally, they require large volumes, which are currently not required
for PtG plant capacities. All tanks have lifetimes longer than 20 years, so replacements before project completion is not an issue.
Table 4.2: Main characteristics of hydrogen storage technologies.
Parameter Unit Metallic Tanks Underground Bundles/
Salt Caverns tube-trailers*
Volume N m3
Pressure bar 1-5001
Lifetime year >201,2
( e/kg) (278-2,357) (1.2) (470-830)
OPEX % CAP EX 0.5-2.51
*for mobility applications 1(van Leeuwen and Zauner,2018);2(Tractebel and Hinicio,2017)