Corey Duncan
email: corey.duncan@univ-fcomte.fr

Robin Roche
email: robin.roche@femto-st.fr

Samir Jemei
email: samir.jemei@femto-st.fr

Marie-Cecile Pera
email: marie-cecile.pera@femto-st.fr

Techno-economical modeling of a power-to-gas system for plant configuration evaluation in a local context

Keywords: power-to-gas, system modeling, techno-economical analysis, synthetic natural gas Nomenclature

published or not. The documents may come

Techno-economical modelling of a power-to-gas system for plant configuration evaluation in a local context

OP EX f ix total yearly fixed operational expenditure (A C) P elect,min /P elect,max electrolyser minimum/maximum power rating (MW)

p min,tank /p r,tank minimum/rated tank pressure (bar) P r,elect electrolyser rated power (MW)

Q H 2 ,

Introduction

The 2030 Climate and Energy Framework (CEF) for the European Union (EU) has translated Paris Agreement commitments to mandatory EU-wide targets, specifically: reducing greenhouse gas (GHG) emissions by 40% from 1990 levels and increasing the share of renewables in primary energy to 32.5% [START_REF]climate & energy framework[END_REF]. Achieving the CEF targets as well as meeting higher demands of electrical energy are fueling a large up-take of variable renewable energy (VRE), namely wind and solar. However, energy systems with high shares of VREs require flexibility due to the spatial and temporal imbalances of supply and demand, which can be provided by multiple approaches, each with unique economical and technical benefits [START_REF] Kondziella | Flexibility requirements of renewable energy based electricity systems -a review of research results and 54 methodologies[END_REF].

One such approach is using energy storage technologies as a means to absorb surplus electricity, shifting its time of delivery for periods of greater demand. Pumped hydro power is currently the primary energy storage technology used for electricity, with massive facilities worldwide. Several emerging storage solutions involve power-to-X (PtX) technologies, with over 150 demonstration projects in Europe either planned, operational or completed [START_REF] Thema | Power-to-Gas: Electrolysis and methanation status review[END_REF]. PtX can be defined as electrical power conversion to other energy applications, such as: transportation fuels, heating, chemical feedstock to produce liquid fuels or other chemicals or re-converted to power. A diagram of possible PtX pathways is shown in Figure 1. As is shown, PtX allows integration of gas and electricity networks, which provides several benefits such as: further improving flexibility of the system to integrate VREs, short-to long-term energy storage capabilities [START_REF] Boubenia | Techno economic assessment of hybrid renewable energy systems based on power to hydrogen and methane gas concepts[END_REF] and helping to decarbonize sectors other than electricity. [START_REF] Lewandowska-Bernat | Opportunities of power-to-gas technology in different energy systems architectures[END_REF]. When coupled with local VREs, it has been shown to decrease curtailment and avoid additional electrical infrastructure costs by using already available gas grid installations [START_REF] Qadrdan | Role of power-to-gas in an integrated gas and electricity system in great britain[END_REF]. The main concept of PtXproducing energy-rich gases such as hydrogen (H 2 ) and methane (CH 4 ) from renewable energy sources -is called power-to-gas (PtG).

The core conversion of PtG pathways is water electrolysis: electrical dissociation of water (H 2 O) to H 2 and oxygen (O 2 ). The H 2 gas can be used for transportation, providing significant sector carbon emission reduction po-tential when compared to fossil fuels [START_REF] Burkhardt | Hydrogen mobility from wind energy -a life cycle assessment focusing on the fuel supply[END_REF] as well as longer range and higher specific energy (when compressed to 350 or 700 bar) than batteries [START_REF]The Future of Hydrogen -Analysis[END_REF]. It can also be directly injected into the natural gas (NG) grid, with a volumetric capacity of 0.1%-12% depending upon the respective European country's standards [START_REF] Mckenna | The future role of power-to-gas in the energy transition: Regional and local techno-economic analyses in baden-württemberg[END_REF]. PtG also includes the possibility of further processing H 2 by combining it with carbon to produce methane (CH 4 ) via the Sabatier process in methanation reactors. The most common H 2 conversion process done in PtX projects [START_REF] Wulf | Review of Power-to-X Demonstration Projects in Europe[END_REF], CH 4 reactors can be classified as either biological or catalytic. Biological reactors produce CH 4 using organic matter, namely hydrogenotrophic methanogens, in a low pressure and temperature environment [START_REF] Lewandowska-Bernat | Opportunities of power-to-gas technology in different energy systems architectures[END_REF] whereas catalytic reactors use nickel-based catalyst using high pressure and temperature [START_REF] Götz | Renewable Power-to-Gas: A technological and economic review[END_REF]. The choice of proper technology depends on the system requirements, with biological reactors allowing higher levels of impurities and catalytic reactors achieving higher production rates [START_REF] Lewandowska-Bernat | Opportunities of power-to-gas technology in different energy systems architectures[END_REF]. Carbon dioxide (CO 2 ) is increasingly used as the carbon source for CH 4 production due to:

(1) high availability as a byproduct from industrial, waste treatment and biogas facilities; (2) the reduction of CO 2 in the atmosphere; and (3) its low cost of 0-100 A C/ton [START_REF] Götz | Renewable Power-to-Gas: A technological and economic review[END_REF]. The product CH 4 is also known as synthetic natural gas (SNG) as it can be directly injected into the NG grid due to its similar physical properties, allowing the over 1,000 TWh European NG network capacity to be used for transmission, distribution and storage of the product gas [START_REF]European Network of Transmission System Operators for Gas, System Development Map[END_REF]. In addition to the plethora of PtG production end-uses, it is also viewed as a technology capable of providing electricity grid ancillary services, ensuring grid stability in short (seconds) to long (months) durations [START_REF] Lund | Review of energy system flexibility measures to enable high levels of variable renewable electricity[END_REF].

Further development of the technologies involved in PtG as well as scaling

Figure 1: Overview of power-to-X pathways; source [START_REF] Wulf | Review of Power-to-Gas Projects in Europe[END_REF].

up are required to improve its economics. Götz et al. [START_REF] Götz | Renewable Power-to-Gas: A technological and economic review[END_REF] and van Leeuwen and Zauner [START_REF] Van Leeuwen | Report on the costs involved with PtG technologies and their potentials across the EU (D8.3)[END_REF] concluded that high investment and electricity costs are the primary reasons for poor economics, with high yearly operational hours (OH) needed to reduce their effect. The inevitable high cost of the equipment can be attributed to the novelty of the technologies, namely (other than alkaline) electrolysis and methanation [START_REF] Hissel | Hydrogen economy: myth or reality?[END_REF]. Nonetheless, PtG installed capacity has been exponentially increasing since the early 2000's as their costs continue to fall and business cases become realized [START_REF] Thema | Power-to-Gas: Electrolysis and methanation status review[END_REF]. De Bucy et al. [START_REF] De Bucy | The Potential of Power-to-Gas[END_REF] assessed the economical potential of PtG in Europe, concluding that mobility is the best current market with high OH and low electricity prices.

Further, they found that the levelized cost of H 2 and SNG for grid injec-tion were 5 and 8.5 times the current selling price of NG, respectively, and SNG costs 1.5-3 times higher than bio-NG, highlighting the need for regulatory incentives for commercial deployment. McDonagh et al. [START_REF] Mcdonagh | Modelling of a power-to-gas system to predict the levelised cost of energy of an advanced renewable gaseous transport fuel[END_REF] studied SNG production as a transport fuel, calculating a cost of 107-143 A C/MWh in 2020 when assuming an average electricity price of 35 A C/MWh and 6,500 OH. They also showed the impact of several incentives schemes that could help lower the cost below NG, such as selling produced O 2 and grid services. However, this study focuses on a national level (Ireland) and does not discuss cost reduction strategies for individual plants. Flexible plant operation has been found to be economically preferred -operating the system when electricity prices are favorable versus continuous operation -with more than one revenue stream harnessed. Breyer et al. [START_REF] Breyer | Power-to-gas as an emerging profitable business through creating an integrated value chain[END_REF] modeled a PtG plant used on-site of a pulp mill in Finland with frequency containment reserve (FCR) market participation and 4,000 OH and found the business case to be profitable, with grid services representing 40% of total income. Bio-diesel and SNG produced were either used on-site or sold for mobility purposes.

However, this model used an assumed fixed electricity price of 40 A C/MWh, neglecting actual dynamic day-ahead (DA) electricity market participation.

Limitations on local gas consumption were also not considered. pletely change the plant's operational objective and its feasibility by giving a more complete picture of it's unique conditions. This methodology is applied to a pilot project currently underway in the form of a case study. Although the specific model created for this case study cannot be used for other projects as they all will have unique local conditions, it is believed that the methodology presented can be easily applied to similar projects, providing a thorough analysis of project feasibility. Further, an innovative electrolyser design is investigated that allows maximum capacity to be 200% rated for short durations. This can allow participation in ancillary services while not sacrificing on lost production due to reserved power or over-sizing the electrolyser for such service. As previously mentioned, high investment and electricity costs will present a major challenge in finding an optimal operational configuration that is profitable. To overcome this challenge, an analysis of the most influential economical factors will be done, highlighting what needs to be done to make a similar project feasible.

The objectives of this paper are to:

• Present a techno-economical model methodology for PtG plant analysis using local limitations and opportunities as operating constraints and several metrics for thorough analysis.

• Present the pilot project that will be used as a case study of model methodology application.

• Develop operational scenarios based off of local conditions for the pilot project and perform a feasibility study.

• Perform a sensitivity analysis on the most favorable scenarios to determine most influential factors on the results.

Methodology

Standard Configuration of Pilot Plant

To develop the model, a current PtG project in which the authors are involved in named HYCAUNAIS is used. Located in Saint-Florentin, France the project is being led by Storengy and has several industrial and public partners, with secured funding from PIA ADEME, Bourgogne France-Comté region (FRI), FEDER and project partners [START_REF]Agence de la transition écologique[END_REF]. As the project is based in France, regulatory and market conditions there will be used henceforth. It must be emphasized that the work performed for this paper is researchedbased and not representative of actual project objectives of HYCAUNAISit is meant to analyse several possible scenarios which are based upon the project topology.

A diagram of the HYCAUNAIS plant layout and possible configurations is shown in Figure 2. The plant will produce low-carbon gas for NG grid injection and possibly mobility. Following the combined power production signal of two existing wind farms (1) provided by the wind farm operator and sourcing the electricity via a grid connection (2), a 1 MW el PEM electrolyser

(3) will produce hydrogen for a 50 Nm 3 /h biological methanation reactor (4), with intermediate H 2 storage (5) installed between the electrolyser and reactor. Currently, gas from the landfill (6) on-site is being upgraded by a WAGABOX ® unit [START_REF] Burkhardt | Hydrogen mobility from wind energy -a life cycle assessment focusing on the fuel supply[END_REF]. The technology, supplied by Waga Energy [START_REF] Energy | Technology: WAGABOX, an innovative landfill gas recovery solution[END_REF],

combines membrane filtration and cryogenic distillation to filter 98 vol% pure bio-CH 4 from landfill gas (CH 4 from a landfill is considered biological as per French regulation [START_REF] Grdf | Panorama du gaz renouvelable en[END_REF] and international agencies such as the IEA [START_REF]IEA, An introduction to biogas and biomethane -Outlook for biogas and biomethane: Prospects for organic growth -Analysis[END_REF]), recovering 90 vol% of the bio-CH 4 contained in it. This highly pure bio-CH 4 is being injected into the NG grid [START_REF]The Future of Hydrogen -Analysis[END_REF]. The CO 2 stream (including some impurities) normally vented during membrane distillation will be utilized as the carbon source for methanation, which must be purified [START_REF] Mckenna | The future role of power-to-gas in the energy transition: Regional and local techno-economic analyses in baden-württemberg[END_REF] prior to injection into the biological reactor. Intermediate storage of CO 2 (10) between the reactor and purifier is also used. Bio-NG captured during purification is combined with the produced SNG and injected into the NG grid at the same existing site used by the WAGABOX ® unit. The project boundary is input from the electrical grid and WAGABOX ® effluent to output to NG grid injection as shown in Figure 2. Normal cubic metres (Nm 3 ) are used throughout the article to represent gas volumes in a simple, comparative fashion. The configuration as described above is known as the standard configuration in this article. Mobility options and NG grid injection capacity improvement were also investigated and will be discussed in the next subsection. To improve economics of the plant, participation in electricity grid services by the electrolyser is considered in all scenarios simulated, namely the primary reserve or FCR. Electrolysers have been shown to be capable of operating dynamically at ramping rates faster than required for FCR participation [START_REF] Buttler | Current status of water electrolysis for energy storage, grid balancing and sector coupling via power-to-gas and power-to-liquids: A review[END_REF]. FCR consumer participation is compensated according to the reserve capacity in €/MW.30min in a market-based scheme, with participation given in 4-hour continuous blocks. More information on ancillary services are provided in [START_REF] Rte | Règles Services Système Fréquence[END_REF]. Guinot et al. [START_REF] Guinot | Profitability of an electrolysis based hydrogen production plant providing grid balancing services[END_REF] concluded that an electrolyser participating in the FCR in France was not economical with the technical and economical assumptions made and current compensation values. However, as stated earlier, this study will investigate the use of an innovative electrolyser stack that is capable of doubling its rated capacity for periods longer than required for maximum frequency disturbance (15 minutes). This will allow the operator to offer the rated capacity of the electrolyser to the FCR while still operating at rated capacity, avoiding loss of hydrogen production for dedicated capacity on reserve for FCR solicitation.

Additional Configurations

When injecting in the NG grid, knowledge of local capacity availability should be known as injection may not be possible all year round. If it is not, other end-use applications can be investigated to maximize the operational hours of the plant. NG grid expansion, increasing the local capacity, is also a possibility if the transmission systems operator (TSO) is interested in doing so. One specific scenario is called a "mesh upgrade" and includes installing a new pipeline between the local NG distribution grid and another grid in close proximity, essentially connecting two "island" distribution grids and consequently increasing both their capacities. This will be investigated as a possible plant "configuration": although the equipment will be the same as the standard configuration, the constraint of NG grid injection will be less stringent but still applicable, allowing for more injection throughout the year.

As seen in Figure 2, two mobility options are considered as additional end-use applications: H 2 and compressed natural gas (CNG). The mobility options must be designed for local transportation requirements to provide realistic approximation of their demand in the model simulation. Each mobility option will be evaluated separately as additional end-use applications to the standard configuration, increasing utilization of the plant.

Electricity Purchasing Contracts

Two electricity purchasing contracts were investigated: long-term fixedpriced contracts or short-term DA market purchasing. Long-term contracts allow for operation of the plant at a specified fixed electricity price whereas DA market purchasing allow plant operators to take advantage of lower prices, choosing to operate when electricity prices are satisfactory as per conditions set by them.

Equipment Description

Electrolyser

A 1 MW PEM electrolyser is used for the system and its modeling parameters are shown in Table 1. Values of parameters were either taken from sources listed or the HYCAUNAIS project directly. The investment cost or CAPEX listed only includes the cost of the electrolyser. Balance of plant (BOP) components (pumps, water purification, electronics, etc.) are included as a general BOP cost to the whole plant configuration of the scenario as shown in Equation 8. The electrical energy consumption of the electrolyser is represented as a range due to its variability in its operating range.

The electrolyser is assumed to be capable of responding instantaneously to changes in power consumption, so ramping is not considered. Further, as mentioned earlier, an electrolyser capable of operating up to 200% its nominal capacity is considered. All values provided by the HYCAUNAIS project and its partners are shown under Source as "project".

Methanation Reactor

A biological reactor capable of consuming the rated flow rate of the electrolyser is used -50 Nm 3 /h. The model parameters used are shown in Table 2. Reactor electrical consumption is due to the continuous mixing from its internal propeller [START_REF] Lecker | Biological hydrogen methanation -a review[END_REF] which is assumed to be fixed when operational. Unlike electrolysers, methanation reactors have not been shown to be capable of operating at sufficient ramping rates while still maintaining high gas quality [START_REF] Gorre | Cost benefits of optimizing hydrogen storage and methanation capacities for Power-to-Gas plants in dynamic operation[END_REF]. Thus, the model considers reactor ramping when changes in gas flow rates occur. Additionally, the reactor is assumed to be fed the methanation stoichiometric ratio of H 2 /CO 2 = 4 at all times and a fixed CO 2 conversion rate occurring inside the reactor of 98 vol% (which has been shown to be possible with transient operation [START_REF] Electrochaea | Power-to-Gas via Biological Catalysis (P2G-Biocat)[END_REF] and applied in other studies [START_REF] Gorre | Production costs for synthetic methane in 2030 and 2050 of an optimized power-to-gas plant with intermediate hydrogen storage[END_REF]). The fixed CO 2 conversion rate and H 2 /O 2 ratio is applied throughout the reactor operational range, eliminating the requirement of modelling reactor kinetics.

Carbon Dioxide Purification

The CO 2 purification technology used is chemical scrubbing via amines due to its low pressure requirement and market availability [START_REF] Bauer | Biogas upgrading -review of commercial technologies[END_REF]. However, it requires a heat source; it is considered to be harnessed from the reactor's exothermic heat dissipation. Model parameters used for chemical scrubbing are shown in Table 3. 4% of the biogas input is bio-NG -which can be captured and mixed with the produced SNG -and 88% is CO 2 . The chemical scrubbing system is sized such that it is capable of purifying the rated CO 2 capacity of the reactor.

Hydrogen Mobility

H 2 mobility was designed as a refilling site for tube-trailers as there is no hydrogen consumption anticipated on-site. These trailers are able to travel up to 400 km from the fill-site to refuelling stations in local regions. Some of these regions, such as Bourgogne-Franche-Comté, are planning to enlarge their fuel cell electric vehicle (FCEV) bus fleets in the coming years [START_REF] Pépinot | Transport: Les bus à hydrogène de l'Auxerrois mis en service le 1er janvier 2021 pour les voyageurs[END_REF]. The distributors in these regions are assumed to be the H 2 mobility consumers.

Two tube-trailers with a capacity of 400 kg each at 200 bar were used, with

an assumption that at least one will be available on-site to be filled at any time. The refilling station cost was calculated using a modeled developed by [START_REF] Tractebel | Study on early business cases for hydrogen in energy storage and more broadly power to hydrogen applications[END_REF]. H 2 mobility model parameters are shown in Table 4. The refilling site was sized to be capable of receiving the rated capacity of the electrolyser.

Compressed Natural Gas Mobility

A refuelling station was assumed to be installed on-site for CNG mobility which could be consumed by waste trucks used for landfill community pickup.

It is proposed that the fleet could be switched to operate on CNG, starting with two trucks and possibly increasing in later years. However, the model simulation will only assume two vehicles for the project lifespan. The trucks are assumed to be filled overnight. The station is sized so that it can accept the rated capacity of the reactor if NG grid injection is not possible. The resulting refuelling station costs are shown in Table 5. Waste trucks are not included in the cost of the station. Costs were taken from values and models given in [START_REF] Smith | Costs Associated With Compressed Natural Gas Vehicle Fueling Infrastructure[END_REF], which include a dispenser, time-fill post and gas dryer.

Compressor cost is also included in the station CAPEX.

Gas Storage

All tanks used in the system use the same CAPEX and OPEX parameters as listed in Table 6. The gas compressor parameters are also listed for production. The electrical consumption varies greatly due to the pressure and gas being compressed [START_REF] André | Time development of new hydrogen transmission pipeline networks for France[END_REF]. The CAPEX of a compressor also varies greatly depending on the gas and compressor type; values for H 2 [START_REF] Tractebel | Study on early business cases for hydrogen in energy storage and more broadly power to hydrogen applications[END_REF], CH 4

[38] and CO 2 [START_REF] Smith | Costs Associated With Compressed Natural Gas Vehicle Fueling Infrastructure[END_REF] were taken from their respective source. The compressor must be completely replaced at the end of their useful life, which is defined as 10 years of continuous operation [START_REF] Van Leeuwen | Report on the costs involved with PtG technologies and their potentials across the EU (D8.3)[END_REF].

Natural Gas Grid Injection

The costs associated to NG grid injection are shown in Table 7. The difference in CAPEX values is due to the increased cost for installation of the mesh pipeline to connect two distribution grids.

X X S2 X X S3 X X S4 X X S5 X X S6 X X S7 X X S8 X X
Table 9 shows the equipment considered for each scenario. As can be seen and was explained earlier, all scenarios consider SNG production and NG grid injection, with S2-S4 and S6-S8 also considering additional equipment to increase operational hours of the plant.

Equipment S1 S2 S3 S4 S5 S6 S7 S8 Electrolyser X X X X X X X X H 2 tank X X X X X X X X CO 2 purification X X X X X X X X CO 2 compressor X X X X X X X X CO 2 tank X X X X X X X X Reactor X X X X X X X X Grid injection X X X X X X X X Grid injection w/ mesh X X H 2 compressor X X H 2 tube-trailer X X CH 4 compressor X X CH 4 tank X X CH 4 fuelling station X X

Analysis Metrics

A multi-metric analysis was done to evaluate each operational scenario.

Using the analysis results, users can determine what is the preferred configuration and electricity contract for their plant. Five metrics were analysed: levelized cost of methane (LCOM), CAPEX, minimum selling price (MSP), electrolyser OH and tank size. These metrics can be divided into economical and operational metrics, as described in the following subsections.

Economical Metrics

LCOM is a modified version of the levelized cost of energy (LCOE) which calculates the production cost of each unit of energy produced in the project lifespan in terms of the reference year and is a common way to compare energy costs of different technologies [START_REF] Götz | Renewable Power-to-Gas: A technological and economic review[END_REF]. In this case, methane is being produced and the reference is the installation year (year 0). LCOM is found by using Equation 1:

LCOM = CAP EX + C rep + (OP EX + F ) • k a Q SN G • k a (1)
where: CAP EX is the capital expenditure of all equipment for the simulated scenario; C rep represents the total levelized cost of equipment replacement during the project lifespan; OP EX is the operational expenditure of all equipment for the simulated scenario in the first year of operation (year 1);

F represents the costs of plant feedstocks, namely electricity and water in year 1; Q SN G is the total amount of SNG production in year 1 used for both CNG mobility and NG grid injection; k a is the discount factor, used to extrapolate all year 1 values over the whole project lifespan. It is calculated as shown in Equation 2 below:

k a = (1 + r) n -1 r(1 + r) n ( 2
)
where r is the discount rate in % and n is the project lifespan in years. k a is used to extrapolate values from year 1 over the project lifespan, which is then used in economical analysis. The values of r and n used are shown in Table 10. OP EX can be broken down into two types of operational costs: fixed (OP EX f ix ) and variable (OP EX var ) and are summed as shown in Equation 3:

OP EX = m t=1 OP EX var,t + OP EX f ix (3)
where OP EX f ix represents the summed fixed operational costs of all equipment included in the simulated scenario, equal to a defined percentage of the equipment CAPEX. OP EX var,t represents the variable cost associated to operating the equipment at time step t in the set m, representing the total number of time steps to simulate the calendar year. Only the reactor variable costs are considered in the simulation which is associated to nutrient replacement and can be found by multiplying the quantity of SNG produced at time step t (Q SN G,t ) by a fixed cost of the nutrient (c var,rea ) as shown in Equation 4:

OP EX var,t = Q SN G,t • c var,rea (4)
It should be noted that variable costs associated to feedstocks are considered separately. Further, when considering feedstock costs F the electrical consumption of electrolysis, compressors, CO 2 separation and reactor mixing (continuously stirred tank reactor assumed to be used [START_REF] Lecker | Biological hydrogen methanation -a review[END_REF]) are included; electrolysis and CO 2 separation are considered for water consumption. For C rep , the total number of operational hours determine when equipment must be replaced, with the replacement cost equal to a determined percentage of the CAPEX.

MSP is a metric used to include the revenue of FCR participation and H 2 sold for mobility purposes as a reduction of the LCOM to determine what is the minimum selling price SNG can be sold (in A C/MWh) for either CNG mobility or gas grid injection applications. MSP can be found by using Equation 5:

M SP = (OP EX + F -R H 2 -R F CR )k a + CAP EX + C rep Q SN G • k a ( 5
)
where R H 2 and R F CR are the revenue from H 2 mobility and FCR, respectively, in year 1. It can be seen that besides the inclusion of these revenues the rest of the equation is identical to the LCOM equation. R H 2 is found by Equation 6:

R H 2 = m t=1 Q H 2 ,mob,t • v H 2 (6)
where Q H 2 ,mob,t is the quantity of H 2 used for mobility at time step t and v H 2 is the price value of hydrogen for mobility in A C/kg. R F CR,t is generated only if the electrolyser is participating in the FCR during the time step t as shown in Equation 7:

R F CR = m t=1 sig F CR,t • v F CR • P r,elect (7)
where sig F CR,t is a signal indicating if the electrolyser is active in the FCR at time step t (sig F CR,t = 1 if participating and sig F CR,t = 0 is not participating), v F CR is the price value of participating in the FCR in A C/MW/h and P r,elect is the rated power of the electrolyser in MW.

CAP EX is the total capital costs of all equipment in the simulated scenario multiplied by an additional balance of plant (BOP) cost -design, engineering, and other additional costs -as shown Equation 8:

CAP EX = CAP EX equip • (1 + c BOP ) (8)
where CAP EX equip is the total CAPEX of all equipment included in the simulated scenario and c BOP is the BOP cost. Although CAPEX is included in the calculation of the other economical metrics, it is valuable to evaluate independently to appreciate the initial cost required for plant construction.

Operational Metrics

OH of the electrolyser or OH elect is the total of partial, rated and overload operational hours of the electrolyser in the year. It is use as a metric gives insight into the effect OH has on the levelized cost and how much operation is required to obtain those costs.

Intermediate hydrogen tank size is determined independently from the main model for each scenario to give the lowest MSP. This will ensure that the tank is not oversized and increase the investment cost without any economical benefit. The resulting capacity will determine the duration of storage capable for the configuration: larger tanks can prolong production in times of unfavorable hydrogen production and allow continuous reactor operation; smaller tanks will require indeterminacy in reactor operation, more closely following electrolyser operation.

A scaled comparison of the key metrics in each scenario is helpful for analysis. Once scaled in a defined range [a, b], the ranking of each scenario's metric value can be compared. This can be done by normalizing each scenario metric using Equation 9:

x i,n = (b -a) x i -min(x i )) max(x i ) -min(x i ) + a (9)
where: x i is equal to metric i's value of each scenario; x i,n is the normalized value of metric i in each scenario and min(x i ) and max(x i ) are the minimum and maximum scenario value of metric i. This equation will rank values such that the highest value in each metric receives the highest rank. If the lowest value of the metric should be ranked the highest, the equation must be slightly modified as in Equation 10:

x i,n = (b -a) 1 - x i -min(x i )) max(x i ) -min(x i ) + a (10)

Model Constraints

Constraints of model variables must be defined for model operation.

Q SN G,t should not be higher than the current local NG grid consumption (cons grid,t ) for every iteration t in the simulation. If it is, there is no capacity available for grid injection. This is shown in Equation 11:

Q SN G,t ≤ cons grid,t (11)
A maximum yearly operational hours must be defined for the electrolyser (OH max,elect ) and reactor (OH max,rea ) in the simulation to allow for maintenance. These constraints are shown in Equations 12 and 13:

OH elect ≤ OH max,elect (12)
OH rea ≤ OH max,rea

where OH elect and OH rea are the total yearly operational hours of the electrolyser and reactor, respectively. The electrolyser and reactor must also operate within their allowable production limits. For the electrolyser this is defined by its electrical power consumption while the reactor is defined by its hydrogen consumption. This is shown in Equations 14 and 15:

P elect,min ≤ P elect,t ≤ P elect,max (14)
Q H 2 ,rea,min ≤ Q H 2 ,rea,t ≤ Q H 2 ,rea,max (15)
where P elect,min and P elect,max are the minimum and maximum operational power consumption of the electrolyser, respectively, and P elect,t is the operational power consumption at time step t. Q H 2 ,rea,min and Q H 2 ,rea,max are the minimum and maximum hydrogen consumption of the reactor, respectively, and Q H 2 ,rea,t is the hydrogen consumption of the reactor at time step t. The hydrogen tank pressure must be within its minimum and rated pressure, as shown in Equation 16:

p min,tank ≤ p tank,t ≤ p r,tank (16)
where: p tank,t is the hydrogen pressure in the tank at time step t; p r,tank is the rated pressure capacity and p min,tank is the minimum tank pressure.

Ramping for the electrolyser was assumed to be instantaneous as explained in 2.1; the reactor could not be assumed to do so. Two different ramping rates were used for the reactor depending upon the hydrogen flow rate to the reactor: above or below rated capacity. This is expressed in the conditional Equation 17:

rr rea =    rr rea,below , if Q H 2 ,rea,t ≤ Q r,H 2 ,rea rr rea,above , if Q H 2 ,rea,t ≥ Q r,H 2 ,rea (17)
where rr rea,below and rr above is the ramping rate below and above reactor rated capacity, respectively. The ramping rate values are sensitive to the project partner and thus cannot be listed. The electrolyser will ideally operate at rated power continuously for maximum production and reduced wear on the equipment. Further, as the profitability of the plant is the primary objective, participating in the FCR should be maximized. To be able to participate at 100% rated capacity, the electrolyser must be operating at rated capacity to offer a symmetrical reserve. Therefore, continuous operation of the electrolyser at rated capacity is desired, as long as other system limitations allow it.

To keep the carbon intensity of the plant minimized, the plant is said to be following the wind profile whenever total power output from the wind farms is above rated capacity of the electrolyser; if not, it will purchase from other sources on the grid. As France's power generation is over 87% low-carbon and 22% renewable [39] as of 2019, the gases could still be considered "green" depending on the definition, but is certainly low-carbon. This consideration for power source is shown in Equation 18:

P elect,t =    P wind,t , if P wind,t ≥ P r,elect P grid,t , else (18)
where P wind,t is the the electrical power of the wind farm at time step t and P grid,t is electrical power sourced from other grid sources. When considering DA market electricity prices (S5-S8), it is beneficial to operate only when electricity prices favor production. This can be defined as the willingness to pay (WTP) for electricity: if the electricity price is above a certain value, the operator of the plant will choose not to run production; if the price is below that value the plant will operate. In the short-term, this value would be determined by the marginal profit of the plant at each hour whereas in the long-term, total costs and revenues must also be considered [START_REF] Van Leeuwen | Power-to-gas in electricity markets dominated by renewables[END_REF]. For the purposes of the model, a range of electricity prices will be used to find the optimal WTP for plant operation in each scenario: if the variable cost of electricity at time step t (c el,t ) is lower than or equal to the pre-determined WTP (W T P det ), the electrolyser will operate at the determined power level;

if W T P det is larger than c el,t , the electrolyser will sit idle. This is expressed in Equation 19:

P elect,t =    P elect,t , if c el,t ≤ W T P det 0, else (19)
As electrolyser and reactor operation are decoupled due to intermediate hydrogen storage, the reactor may still be producing if the electrolyser is sitting idle: reactor operation depends on hydrogen availability in the storage tank and not hydrogen production. When participating in the FCR, the electrolyser power has a further constraint such that it must follow the frequency of the grid: when the frequency is below 50 Hz, less power must be consumed to bring it back to balance; when the frequency is above 50 Hz, more power must be consumed. This relationship can be expressed in Equations 20 and 21:

P F CR,t = sig F CR,t • K(f t -f n ) ( 20
)
P elect,tot,t = P elect,t + P F CR,t

where P F CR,t power adjustment of the electrolyser at time step t in MW; K is the FCR gain as defined by the TSO (RTE) for the consumption site in MW/Hz [START_REF] Rte | Frequency Ancillary Services Terms and Conditions[END_REF]; f t is the measured grid frequency at time step t in Hz; f n is the nominal grid frequency in Hz and P elect,tot,t is the total electrolyser power consumption at time step t. Note that P F CR,t can be negative or positive, depending on the grid frequency measured at that time step.

Operational Strategy

Operational strategies when mobility is included as an extra end-use application is required. For H 2 mobility configurations, a refilling station with tube-trailers is used and consumption is only considered when SNG production is not possible. For CNG mobility configurations, a waste truck refuelling station on-site is considered which will have a continuous flow to fill the trucks daily, plus can also accept SNG production surplus when grid injection is not possible.

A continuous flow rate from the reactor to the refuelling station is assumed that is equal to the estimated yearly mileage and fuel efficiency of a CNG waste truck as proposed by [START_REF] Burke | Fuel Economy Analysis of Medium/Heavy-duty Trucks -2015-2050[END_REF] and shown in Equation 22:

Q CN G,r = n trucks (35N m 3 /100km)(20, 000km/year) 8760h ( 22
)
where Q CN G,r is the hourly SNG production to the refuelling station and n trucks is the number of CNG waste trucks.

The constraints and assumptions presented above provide the framework for operation simulation. The model begins operation with variable input data and defined parameters, performing a loop of one year in defined time steps. Logic controllers determine the flow of model execution at each iteration, inputting the required data to modules representing the plant equipment. These equipment modules compute their production costs and resultant gas flow rates, inputting them into the next controller or to the final module for economical analysis.

The time step used for modeling is 10-minutes to equal the resolution of data given for the wind farm power profile for an entire year, equaling 52,560 time steps performed by the simulation. Further, a maximum operational time of 95% of the year was chosen for the electrolyser and reactor (OH max,elect = OH max,rea = 8322), allowing some hours during the year for operational maintenance.

Economical Parameters

The economical parameters used for modeling are shown in Table 10. It was assumed that a fixed electricity contract of 65 A C/MWh could be attained for the duration of the project. A local fixed water price is used for the model [START_REF] Tariffs | Ville de Strasbourg Water Dept[END_REF]. Enumeration for electrolysers in the FCR is based upon the DA market reserve price, with payment given to each MW for the bid duration.

Historical data is provided by [START_REF] Rte | Portail Clients -Réserve contractualisée d'ajustement[END_REF], given in A C/MW/30min. Between 2016-2019, hourly prices have fluctuated between 4-20 A C/MW/h, with the average in 2019 being 9 A C/MW/h. The price of H 2 sold to distributors (refuelling stations) is taken from [START_REF] Tractebel | Study on early business cases for hydrogen in energy storage and more broadly power to hydrogen applications[END_REF]. A discount rate of 7% and project lifespan of 20 years are used.

Electricity Price Analysis

As electricity is the most costly part of power-to-gas operation, an analysis of DA market SPOT electricity prices over the year can give important operational insights. Figure 3a shows the average daily price variance over 2017 and 2018. A price duration curve -a curve showing the distribution of electricity prices by its frequency in the year -for 2017 and 2018 are shown in Figure 3b and include the taxes, fees, levies and wholesale price. The fixed price used is also represented as a horizontal line for reference. We can see the the average price of 2018 is roughly equal to the fixed price (64.8

A C/MWh) while 2017 is lower at 59.57 A C/MWh, suggesting lower operational costs are possible in the DA market but this can vary by year.

To determine how the variability of price will effect operational costs, 6

WTP det values were tested: 55, 65, 75, 85, 95 and 105 A C/MWh. Based upon these "cut-off" prices, the maximum amount of possible operational hours for the year (shown as the duration of the year) and the average electricity price for those available hours can be found. They are shown in Table 11.

It can be seen that at 55-65 A C/MWh, the duration of the year possible for operation varies greatly by year, but then converge at around 85 A C/MWh. This suggests great variability by year in operation (27-71%) if lower WTP det values are used, greatly influencing the production costs of the plant. Further, it can be seen that the average price is never higher than the fixed price used, meaning lower overall electricity prices when participating in the DA market. To determine the ideal WTP det , the scenarios were simulated with each WTP det value to see their respective LCOM. The result for S5 in 2017 is shown in Figure 4 as well as the relationship to OH. As can be seen,

Gas Consumption Analysis

A study performed by the grid operator for the project revealed that SNG production could not be injected into the local grid year-round: at certain times of the year, namely summer months, there was not enough (close to zero) NG consumption to allow for grid capacity to be available for SNG injection. Local NG distribution network consumption determines the availability of gas grid capacity for SNG injection: as long as consumption is greater than SNG production, injection can take place. If not, the SNG must be used elsewhere, stored or not produced at all. This condition is very site-specific as consumption depends on many factors, such as: number of consumers, types of consumers, capacity of network, etc. If the duration curves are plotted for each year with and without the mesh and compared to the reactor rated capacity, as shown normalized in Figure 5, the amount of time in the year injection can take place is clearly seen: 86-88% without mesh and 94-98% with mesh. When a mesh upgrade is not installed, there will be many hours throughout the year where injection cannot take place, mainly in the summer months. This suggests additional end-use applications could be favorable to increase plant utilization, as long as their costs outweigh their benefits. It would be difficult to apply the same logic when the mesh upgrade is installed as there are very little hours in the year, if any, left to justify the additional investment.

Wind Power Analysis

The wind farm power profiles are to be followed virtually by the electrolyser, via a connection to the grid. Data for two wind farms for a total capacity of 24 MW was provided by a project partner. The maximum amount of wind power utilization is desired to produce green hydrogen. Analysing the wind power profiles produced from the two wind farms in each year will give insights into how much of H 2 production can be produced from virtually with lower production in the warmer months. Fortunately, the total rated capacity of the wind farms is significantly higher than the electrolyser rated capacity, allowing for majority of gas production to be done with renewable electricity. Indeed, the wind power exceeds electrolyser rated capacity for roughly 75% of the year in 2017 and 2018 as can be seen in normalized Figure 6b.

Results of Key Metrics

The key metric results are shown in Table 12 for each scenario in 2017 and 2018. Each metric will be discussed independently in the following subsections. price scenarios (S5, S6 and S8). This is caused by two factors: the higher average electricity price and gas grid availability for the year.

The highest LCOM is always when H 2 mobility is considered (S3 and S7).

This is due to its very high capital costs, namely the refilling site and tubetrailers. However, this can be slightly misleading as the LCOM is considering the levelized costs per unit energy of methane, which would be produced less in this configuration as the H 2 is used for mobility instead (when producing SNG is not possible). The revenue gained from H 2 mobility also needs to be considered to see if the additional investment is justified.

Minimum Selling Price

As shown in Equation 5, MSP subtracts H 2 mobility and FCR participation revenue from LCOM, providing the minimum price produced SNG would need to be sold at to break-even on the project. As seen in Table 12, the most favorable scenarios from LCOM analysis are also the same for MSP.

It is interesting to investigate the influence the additional revenue streams have on reducing the MSP of SNG which is done by first taking the difference between LCOM and MSP and computing each revenues' portion of this LCOM reduction. This difference is shown graphically in Figure 8 with each revenue type highlighted. As one can see, the reduction in LCOM by FCR revenue is roughly the same for every scenario and year (29.73-34.20 A C/MWh); this is due to electrolyser operational hours of the plant being very similar in every scenario (discussed later in section 3.4.3). As the electrolyser is participating in the FCR whenever it is operational, revenue is directly correlated to electrolyser OH (see Equation 7). When H This can be seen especially if looking at the scenarios with the lowest OH:

NG injection only (S1 and S5). OH elect and OH rea are shown in Table 13 and a count of the hours maximum tank capacity (tank cap ) stopped electrolyser production and grid injection capacity (grid cap ) prohibited reactor operation.

As hydrogen storage is considered in the plant, operation of the electrolyser and reactor are decoupled and thus these restrictions only limit the directly affected component. As electricity price is not a constraint in S1, the summation of OH elect and tank cap equals the total hours in a year (8760) as does OH rea and grid cap . However, in S5, the additional constraint of WTP det = 95 A C/MWh reduces OH elect by about 300-500 hours per year. This reduction has a ripple effect on reactor operation due to the small tank sizes. A general trend of lower OH can be seen in scenarios using the DA market prices, attributed to the more flexible operation to take advantage of lower electricity prices. One final point is the amount of OH elect which virtually followed the wind power profile, as shown in Table 13: 75-81% of the presented scenarios electrolyser consumed power. The other scenarios fall in this same range, showing a high majority of renewable power used for H 2 production.

Tank Size

Tank sizes were optimized for each scenario in terms of minimizing LCOM and are shown in Table 12. The same sizes were used in both years to compare its effect in each year and simplify modeling. The largest size is found in S5 (NG injection only) which is equal about 1.5 hours of electrolyser operation, meaning longer storage of days or seasonally is not economically attractive for this project. This is largely based upon the high grid injection availability year-round. Further, the mobility scenarios have minimal storage due to their independent higher pressure storage on-site of their respective stations.

CAPEX

The CAPEX of all scenarios for both years are the same as the equipment does not change. As stated previously, S2-S4 and S6-S8 have equipment which is added to S1 and S5, respectively, to increase SNG production, meaning a higher CAPEX. As would be expected, the standard configuration (S1)

has the lowest CAPEX due to its limited amount of equipment compared to other scenarios. The H 2 mobility scenarios (S3 and S7) have the highest CAPEX due to the current high capital cost of equipment required, namely the tube-trailers, refilling site and compressor.

Key Metric Spider Chart Comparison

A scaled visual comparison of the key metrics can be done using Equations It should be noted that CNG mobility being one of the best configura-sent to the station, which is assumed to be consumed in some fashion by increased truck consumption. This volatility in consumption, portrayed very favorably in modeling, does not provide guaranteed revenue like grid injection would in real-world applications. In addition, CNG mobility stations are normally connected to the gas grid instead of directly to production sites as modeled here; this presents uncertainty in tariffs being applied to SNG sold for mobility in this configuration.

The attractiveness of hydrogen mobility could be seen in the results presented, as other sources have also confirmed as currently the best market for power-to-gas [START_REF] Tractebel | Study on early business cases for hydrogen in energy storage and more broadly power to hydrogen applications[END_REF][START_REF] De Bucy | The Potential of Power-to-Gas[END_REF][START_REF] Breyer | Power-to-gas as an emerging profitable business through creating an integrated value chain[END_REF]. However, the plant studied used mobility as a secondary application to SNG grid injection, which did not provide enough hydrogen production to be sold to mobility distributors for sufficient returns on the high equipment cost. An alternative plant configuration would be to have H 2 mobility as the primary application, with grid injection -preferably pure H 2 if the local grid distribution network allows it -as the secondary application. Indeed, this type of configuration was studied by [START_REF] Tractebel | Study on early business cases for hydrogen in energy storage and more broadly power to hydrogen applications[END_REF] which also considered it an attractive topology.

Sensitivity Analysis

A sensitivity analysis of the most influential factors for S5 and S8 is done to see their effect on MSP. The electricity price, gas grid availability, electrolyser efficiency and electrolyser and reactor CAPEX are modified by ±20%. The data of year 2017 is used due to its better performance in the original analysis. The results are shown in Figure 10.

The most influential factor is electrolyser efficiency, which is able to reduce the MSP by 21-24 A C/MWh for a minimum of 149.01 A C/MWh, due to the reduced electricity consumption and thus electricity cost. In contrast, it is also capable of greatly increasing the MSP if the efficiency were to decrease.

Electricity price is a close second in its impact on MSP, giving the lowest results in S5 at 153.80 A C/MWh and an overall 18-22 A C/MWh reduction. The electrolyser and reactor CAPEX are the next most influential, respectively, with their impact less than 50% of that done by electricity price. Referencing Figure 7a showing the total CAPEX portion on scenario LCOM, this relative impact is to be expected. Grid capacity has extremely little impact on S5

and no impact at all on S8. For S5, this negligible impact is mainly due to the already high availability of grid injection and variability in times of no availability: there is almost never long durations in limited grid availability that would greatly effect production. S5 has 6.5 hours of intermediate H 2

storage that seems to be able to allow production to continue with reduced grid consumption as per the sensitivity analysis. No impact on S8 is due to the plant's ability to direct the SNG production to CNG mobility when there is no grid availability.

The sensitivity analysis clearly shows the impact of improved electrolyser efficiency on a PtG plant's profitability. The electrolyser efficiency used is approximately 71% 5kW h/N m 3

3.54kW h/N m 3 HHV = 0.71 , meaning efficiency would need to increase to 90% to get similar results as shown. Studies show efficiencies of 83% are projected by mid-decade and possibly up to 90% by 2030, suggesting these results could be possible when commercial plants are being deployed [START_REF] Bertuccioli | Study on development of water electrolysis in the EU[END_REF]. The presented CAPEX reduction is projected to be significantly surpassed with projections as low as 250 A C/kW el by 2030 for PEM electrolysers [START_REF] Bertuccioli | Study on development of water electrolysis in the EU[END_REF] and 500 A C/kW el for biological reactors [START_REF] Thema | Power-to-Gas: Electrolysis and methanation status review[END_REF]. In general, electricity prices are projected to increase as the share of renewables increases in power production, with lower variability in the year due to lower marginal costs [START_REF] Dnv Gl | Imperial College London, NERA Economic Consulting, Integration of Renewable Energy in Europe[END_REF]. This will mean government support for PtG plants in forms of tax exemptions or other schemes is needed to reduce the primary production cost until CAPEX's or electrolyser efficiency improve.

Conclusion and Future Work

This paper has described a modeling methodology for analysing PtG plants with a special focus on unique local conditions, limitations and opportunities for the purpose of performing a feasibility analysis for projects.

It has shown that these parameters can greatly influence production costs and minimum selling price of product gases, namely synthetic natural gas for the pilot project presented as a case study. Most importantly, the analysis showed that the cost of production is still too high for synthetic natural gas to compete with not only natural gas but biomethane. The current support tive survey of a project's local surroundings. What has been seen in literature to-date are generic analyses of either national power-to-gas potential or plant installation without a full consideration of local limitations or opportunities, such as: natural gas grid availability, all possible gas markets or electricity prices. Individually, these parameters can have profound implications on operational strategy; together they can completely change the plant's operational objective and its feasibility. Meeting climate targets for emission reductions and renewable energy cannot be realized without the help of technologies such as power-to-gas and these technologies cannot be implemented without complex, holistic feasibility analyses.

  PtX power-to-X SNG synthetic natural gas TSO transmission system operator VRE variable renewable energy WTP willingness to pay Parameters c BOP cost of BOP (% of CAPEX) c var,rea cost of reactor nutrient (A C/Nm 3 ) CAP EX total capital expenditure in year 0, including BOP (A C) CAP EX equip total capital expenditure in year 0 (A C) cons grid,t local NG grid consumption at time step t (Nm 3 /10min) f n nominal electrical grid frequency (Hz) f t electrical grid frequency at time step t (Hz) K FCR gain (MW/Hz) k a discount factor n project lifespan (years) OH x,max maximum yearly operational hours for equipment x (hours) OP EX total yearly operational expenditure (A C)

Figure 2 :

 2 Figure 2: HYCAUNAIS project plant schematic and process flow.

  will have a continuous flow to fill the trucks daily, plus can also accept SNG production surplus when grid injection is not possible. Each configuration is tested independently to discover their individual attributes. Fixed electricity contract (S1-S4) and DA market participation (S5-S8) are also investigated for each configuration type to find what is the preferred electricity contract.

  (a) Average DA market SPOT price over the year. (b) SPOT price duration curve.

Figure 3 :

 3 Figure 3: DA market SPOT price for 2017 and 2018 represented by its yearly distribution and duration of the year; the fixed price is also shown for reference.

Figure 4 :

 4 Figure 4: WTP det versus LCOM and electrolyser OH for S5 in 2017.

  (a) LCOM grouped by cost type. (b) LCOM grouped by equipment or electricity type.

Figure 7 :

 7 Figure 7: Stacked column charts of the LCOM for each scenario in 2017 grouped by cost type and equipment or electricity type.

9 and 10 .

 10 For the operational metrics -OH elect and H 2 tank size -the largest value was ranked the highest whereas the economical metrics -LCOM, MSP and CAPEX -the lowest value was ranked the highest. Using a range = [0, 5], the spider charts shown in Figure9were generated showing the scaled values of the metrics for each scenario and year.Using this figure and the previous sections, the most interesting scenarios for this project are S5 and S8 -using variable DA market electricity prices with either the standard configuration or including a CNG mobility station on-site. It should be noted that the difference in input data for each year caused considerable changes in each scenario's LCOM and MSP -the most important metrics to consider for project feasibility studies.

  (a) S5 -standard configuration with DA electricity. (b) S8 -CNG mobility with DA electricity.

Figure 10 :

 10 Figure 10: Tornado charts of S5 and S8 for 2017 showing a ±20% sensitivity analysis on MSP.

  rea,min /q H 2 ,rea,max reactor minimum/maximum H 2 consumption (Nm 3 /10min)

	Variables
	C rep total levelized cost of equipment replacement during the project lifes-
		pan (A C)
	F	total yearly cost of feedstocks
	OP EX var total yearly variable operational expenditure (A C)
	P elect,tot,t total electrolyser power at time step t, including FCR participation
		(MW)

Q H 2 ,rea,t reactor H 2 consumption at time step t (Nm 3 ) r discount rate (%) rr rea reactor ramping rate (Nm 3 /10min) t current time step of simulation ∀t ∈ m v F CR price value of FCR participation (A C/MW/h) v H 2 price value of H 2 for mobility (A C/kg) W T P det pre-determined willingness to pay (A C/MWh)

Table 1 :

 1 PEM electrolyser model parameters.

	Parameter	Unit	Value	Source
	Rated power	MW el	1	project
	Operating pressure	bar	30	project
	Electrical energy consumption	kWh/Nm 3 H 2	4.6-5.1	[11]
	Operational range	% rated power	10-200	project
	Stack life	hours	60,000	[15]
	Water consumption	L H 2 O/Nm 3	2	[29]
		H 2
	CAPEX	A C	1,400,000	[15]
	Fixed OPEX	% CAPEX/a	2	[15]
	Stack replacement cost	% CAPEX	25	[15]

Table 2 :

 2 Biological methanation reactor model parameters.

	Parameter	Unit	Value	Source
	Rated SNG capacity	Nm 3 /h	50	project
	Inlet pressure	bar	16	project
	Electrical energy	kWh/Nm 3
			1	[11]
	consumption	SNG
	CAPEX	A C	817,500	[15]
	Fixed OPEX	% CAPEX/a	5	[15]

Table 3 :

 3 CO 2 purification model parameters.

	Parameter	Unit	Value	Source
	Electrical energy	kWh/Nm 3 biogas	0.15	[34]
	consumption
	Water consumption	L H 2 O/Nm 3	0.032	[33]
		biogas
	CAPEX	A C	91,200	[33]
	Fixed OPEX	% CAPEX/a	3	[33]

Table 4 :

 4 H 2 mobility model parameters.

	Parameter	Unit	Value	Source
	Tube-trailer CAPEX	A C/kg	500	[20]
	Tube-trailer fixed OPEX	% CAPEX/a	2	[20]
	Site CAPEX	A C	232,709	[20]
	Site fixed OPEX	% CAPEX/a	3	[20]

Table 5 :

 5 CNG mobility model parameters.

	Parameter	Unit	Value	Source
	Station CAPEX	A C	232,417	[36]
	Station fixed OPEX	% CAPEX/a	3	[36]

in this table. Intermediate H 2 storage is done at the equivalent pressure of electrolyser output, 30 bar, eliminating the need of a compressor. However, a compressor is required if H 2 mobility is considered for the tube-trailers, as described below. Intermediate CO 2 is pressurized to 16 bar to meet reactor input requirements while the CH 4 storage is at 200 bar for CNG mobility requirements. All tanks are assumed to have their complete capacity available

Table 6 :

 6 Gas tank and compressor model parameters.

	Parameter	Unit		Value		Source
			H 2	CO 2	CH 4
	Tank capacity	Nm 3	see	50	600	project
			3.4.4
	Tank rated pressure	bar	30	16	200	project
	Tank CAPEX	A C/Nm 3	100	100	100	[15]
	Tank fixed OPEX	% CAPEX/a	2	2	2	[15]
	Compressor electrical
		kWh/kg	1.68	0.09	0.20	[37]
	consumption
	Compressor lifespan	hours	87,600 87,600 87,600	[15]
	Compressor CAPEX	A C	200,000 234,636 101,398 [20, 38, 36]
	Compressor fixed OPEX	% CAPEX/a	3	3	3	[15]
	Compressor replacement cost	% CAPEX	100	100	100	[15]

Table 7 :

 7 NG grid injection model parameters.

	Parameter	Unit	Value	Source
	Grid injection CAPEX (no mesh) Grid injection CAPEX (mesh)	A C	20,500 252,100	project project
	Grid injection fixed OPEX	% CAPEX/a	8	[15]
	2.5. Operational Scenarios
	Eight scenarios were developed to evaluate the different plant configura-
	tions and electricity contracts. They are listed in Table 8, with the con-
	figuration and electricity purchasing option used marked accordingly. A

Table 8 :

 8 Eight scenarios developed for project evaluation, with the type of electricity contract and configuration implemented in each scenario marked accordingly.

	Electricity Purchasing	Configurations
	Scenario
	Fixed Contract DA Market Standard Mesh H 2 Mobility CH 4 Mobility
	S1

Table 9 :

 9 The equipment considered for each scenarios configuration.

Table 11 :

 11 The duration of year and average electricity price for each WTP det .

	WTP det	2017		2018
	(A C/MWh) Duration of year (%) Avg price (A C/MWh) Duration of year (%) Avg price (A C/MWh)
	55	50.64	45.37	27.58	43.67
	65	70.61	49.39	51.54	51.4
	75	81.47	52.06	73.40	56.86
	85	89.55	54.56	88.88	60.87
	95	94.60	56.42	96.46	63.01
	105	96.94	57.47	98.50	63.84

Table 12 :

 12 Key metric results for each scenario by year. The minimum values for LCOM, MSP and CAPEX are highlighted green and maximum highlighted red, as a lower value is desired. The maximum values in OH elect and tank size are highlighted green and minimum highlighted red, as a higher value is desired. , with S7 having the largest difference at 17 A C/MWh. This relationship between the years is attributed to the difference in DA electricity prices, showing its impact on production costs. Figure7shows a breakdown of the LCOM by cost type and also by equipment or electricity type, with the top three contributors in each breakdown value shown. Fuel costs includes water and electricity as CO 2 is free in the project. As the water cost is almost negligible, fuel costs essentially represents the cost of electricity, which is 46-55% of the LCOM, depending on the scenario. Electricity cost is less in S5-S8, which is due to the lower average electricity price as described in section 3.1. The reason for such a high cost proportion for electricity is water electrolysis to produce H 2 , which accounts for 43-51% of LCOM, depending on the scenario. The next highest costs by equipment are the electrolyser and reactor, respectively, due to their high CAPEX.

	Scenario	LCOM (A C/MWh)	MSP (A C/MWh)	OH elect (hours)	H 2 Tank Size (Nm 3 )	CAPEX (A C)
			2017
	S1	213.19	183.24	8,031	215	3,411,242
	S2	218.07	188.28	8,322	130	3,723,582
	S3	262.25	204.73	8,322	50	4,553,934
	S4	216.67	186.91	8,322	50	3,721,956
	S5	205.60	175.79	7,587	325	3,426,642
	S6	204.38	174.60	8,264	175	3,729,882
	S7	247.15	189.47	8,275	120	4,563,734
	S8	202.67	172.91	8,287	120	3,731,756
			2018
	S1	213.33	183.36	8,035	215	3,411,242
	S2	218.95	189.06	8,322	130	3,723,582
	S3	268.16	204.62	8,322	50	4,553,934
	S4	216.62	186.88	8,322	50	3,721,956
	S5	213.24	183.35	7,767	325	3,426,642
	S6	218.79	188.97	8,052	175	3,729,882
	S7	264.87	201.31	8,322	120	4,563,734
	S8	214.27	184.48	8,303	120	3,731,756

year

Table 13 :

 13 Operational hours of electrolyser and reactor for the NG injection scenarios (S1 and S5) in each year, showing their limitations and portion of wind power.

	Scenario Year	OH elect	Tank cap	OH rea	Grid cap	OH elect,wind
	2017	8,031	728	7,831	929	6065
	S1
	2018	8,035	725	7,655	1,105	6,110
	2017	7,587	699	7,384	929	5,790
	S5
	2018	7,767	683	7,425	1,105	6,267

Funding

This research was supported by the HYCAUNAIS project (agreement number 1882C0096) with financial support provided by the Agency of Ecological Transition in France (ADEME) under the "Investissements d'Avenir de l'Etat" aid programme, the EIPHI Graduate School (contract number ANR-17-EURE-0002) and the Region Bourgogne Franche-Comté and Bpifrance under "fond régional d'investissement" (FRI).

Parameter Unit Value Source

Fixed electricity price A C/MWh 65 project

Water cost A C/m 3 1.60 [START_REF] Tariffs | Ville de Strasbourg Water Dept[END_REF] BOP (c BOP ) % CAPEX 40 [START_REF] Van Leeuwen | Report on the costs involved with PtG technologies and their potentials across the EU (D8.3)[END_REF] FCR participation price A C/MW/h 9 [START_REF] Rte | Portail Clients -Réserve contractualisée d'ajustement[END_REF] H 2 selling price (distributor) A C/kg 7 [START_REF] Tractebel | Study on early business cases for hydrogen in energy storage and more broadly power to hydrogen applications[END_REF] Discount rate (r) % 7 project

Project lifespan (n) years 20 project

Sensitivity Analysis

A sensitivity analysis is performed to visualize the influence of various input values. The values chosen to be investigated are: electricity price, gas grid availability, electrolyser efficiency and electrolyser and reactor CAPEX.

The sensitivity analysis is chosen to be done on the most favourable scenarios as chosen during the analysis of the key metrics.

Results and Discussion

An analysis of model inputs is done first to understand what limitations and opportunities they provide. This includes: electricity price, local gas consumption and wind power. The key parameters results for each scenario in 2017 and 2018 will then be analysed, determining the advantages and disadvantages of the configurations and where improvements are needed to give more favorable results. with and without the mesh upgrade; reactor rated capacity is also shown as a constant production.

following the wind power profile over the year, determining the "greenness" of the gas. Figure 6a shows the normalized average daily total wind power for 2017 and 2018. The high variability of wind power can easily be seen.

Levelized Cost of Methane

The LCOM for fixed electricity price scenarios (S1-S4) for 2017 and 2018 are very similar, while DA pricing scenarios (S5-S8) can vary significantly by (S3 and S7) are still higher than all other scenarios despite this doubling in LCOM reduction. This can be attributed to the high CAPEX associated to H 2 mobility (discussed in section 3.4.5). A C/MWh [START_REF] Eex | EEX: Natural gas markets[END_REF]. Bio-NG is currently sold and injected into the French NG grid via fixed tariffs between the producer and gas supplier for a fixed term at a price between 45-139 A C/MWh depending upon the biogas source and production capacity [START_REF] Grdf | Panorama du gaz renouvelable en[END_REF]. However, as per the multiannual energy programming (PPE), these tariffs are to be reduced to a target price of 75 A C/MWh by 2023 and 60 A C/MWh by 2028 [START_REF] Lemonde | What is the State of Biogas and Biomethane Market in France?, Biogas World[END_REF]. SNG is currently not given any government support as it is a relatively new product which has been implemented in only 3 projects in France at the time of writing with power ratings no greater than tions should be taken with caution. For modeling purposes, a continuous flow rate of SNG is sent to the station equal to two waste trucks' yearly consumption. Further, extra SNG production not accepted by the grid is structure for biomethane in France allows operators to receive a fixed price for production, giving security to their investment. Similar regulation must be put in place for synthetic natural gas, as well as hydrogen grid injection, if

France hopes to build up the renewable gas market domestically. These regulations can highlight that power-to-gas plants are not only producing lower emission gases but can also reduce variable renewable energy penetration, if built in strategic locations. In terms of HYCAUNAIS, carbon emissions from the landfill is being reduced as much as possible while maximising methane production, which seeks to benefit all involved.

Another positive from power-to-gas facilities is the multiple end-use applications of the product gases. As seen in this analysis, mobility and gas grid injection could both be applied to the plant, provided the additional operational hours and cost don't result in higher levelized costs. Although hydrogen mobility is known to have higher capital costs, this could be outweighed by the premium price paid at the pump. Primary applications of the plant must be holistically considered to ensure profit maximisation occurs, meaning the main end-use application of the plant should be considered with all local conditions and market values known. A market which was shown to help improve the economics was electricity ancillary services. This added revenue stream could be done without sacrificing production because of an innovative electrolyser design allowing 200% maximum capacity for short durations. If these types of electrolysers were deployed and accepted by regulators for market participation, they can greatly improve power-to-gas business cases.

In conclusion, what the analysis has shown is the importance of a qualita-