An option for stranded renewables: electrolytic-hydrogen in future energy systems

Thomas Grube *a, Larissa Doré b, André Hoffrichter c, Laura Elisabeth Hombach b, Stephan Raths c, Martin Robinius a, Moritz Nobis c, Sebastian Schiebahn a, Vanessa Tietze a, Armin Schnettler c, Grit Walther b and Detlef Stolten ad
aElectrochemical Process Engineering (IEK-3), Forschungszentrum Jülich, 52425 Jülich, Germany. E-mail: th.grube@fz-juelich.de
bChair of Operations Management, RWTH Aachen University, 52072 Aachen, Germany
cInstitute for High Voltage Technology, RWTH Aachen University, 52056 Aachen, Germany
dChair for Fuel Cells, RWTH Aachen University, 52056 Aachen, Germany

Received 7th January 2018 , Accepted 2nd May 2018

First published on 21st May 2018


Future energy systems will likely be challenged by large quantities of stranded renewable electricity that cannot be used in the conventional electrical grid. Using surplus electricity for electrolysis and thereby producing hydrogen is seen as a valuable solution functioning as an energy storage and transport medium and providing other sectors, such as transport or industry, with required feedstocks at the same time. In this study, we suggest using a set of assessment tools to highlight the quantitative potential, cost and environmental performance of electrolytic hydrogen production, transmission and storage. Our approach employs power sector modeling for Germany with three sequential elements: (i) a market model, (ii) power flow modeling, and (iii) re-dispatch modeling. The results were then used to identify suitable locations for large scale electrolysis plants. Electrolysis, large-scale gas storage, a transmission pipeline and other system components were scaled-up and the total cost was calculated. In a final step, we looked at greenhouse gas emissions as one of the major aspects regarding the environmental performance of the hydrogen delivered. Based on our analysis, annual hydrogen production rates of up to 189 kilotons have been determined for the state of Schleswig-Holstein, which exhibits the largest potential for utilizing surplus power from renewables. The economic analysis reveals a hydrogen cost of 3.63–5.81€ kg−1, including installations, for large-scale storage and transmission. If surplus power from renewables is used for hydrogen production, the total greenhouse gas emissions of hydrogen provision were determined to be up to 435 gCO2-eq. kg−1. Using grid electricity, this value increased to some 17[thin space (1/6-em)]000 gCO2-eq. kg−1.


1 Introduction

A primary challenge facing climate-friendly future energy systems is how best to handle large quantities of fluctuating renewable power, low-cost energy storage an indispensable element of such systems. Mature technologies from this perspective are pumped hydro and compressed air energy storage (PHES and CAES); however, with respect to energy density and scale-up performance, future requirements can ideally be met by storing energy in chemical form.1–5 Related energy provision pathways build on hydrogen that is produced at high efficiency via water electrolysis (power-to-gas, PtG) and geologically stored in large quantities at low cost. Recent studies conclude that the utilization of hydrogen as a transport fuel could be cost-competitive to gasoline on a pre-tax basis6–8 and would enable a dramatic reduction in greenhouse gas (GHG) emissions of the transportation sector. Implementing hydrogen derived from renewable energy as a viable fuel for transportation or as a feedstock for industry would require the following:

■ The wide availability and utilization of renewable power;

■ Security of electricity supply, also taking into account grid extension measures and compensation for renewable power fluctuations;

■ Cost-effective hydrogen transport and storage on a large scale;

■ Economical electrolyzer operation considering investment, operational hours and electricity cost.

Simplified calculations indicate that primary cost drivers for electrolysis-derived hydrogen are the annual operating hours of electrolyzers and the electricity cost, as can be seen in Fig. 1. This cost is difficult to accurately specify for the medium- to long-term, as it depends on a variety of factors, such as the level of feed-in from renewable energy sources, the status of grid extension at a trans-regional level and the general condition of the electricity market. In addition, the potential for grid service-based compensations from transmission system operators (TSO) during redispatch measures in congested networks could mitigate the cost, and therefore needs to be quantified. Other issues of highest relevance relating to the market success of hydrogen include its obtainable quantities and the economic and environmental performance of the process chains in their entirety.


image file: c8se00008e-f1.tif
Fig. 1 Cost of hydrogen produced via electrolysis depending on electricity cost and annual full load hours (left). Share of electricity cost in total hydrogen production cost (right). Assumptions: specific electrolyzer cost is 500€ kWe−1 (annuity of 75€ per (kWe per annum)), operation and maintenance cost is 3% of investment, interest rate is 8%, depreciation period is 10 annum and electrolyzer efficiency is 70%.

In this context, the following highly relevant aspects of hydrogen provision will be addressed in this study: (i) the localization and quantification of surplus energy from renewable power generation to be used for PtG, (ii) the cost of hydrogen at the point of consumption, and (iii) the environmental performance – more specifically, the greenhouse gas emissions – of hydrogen provision. We apply our approach to Germany because the availability of required input data is high, particularly relating to time series for grid load and renewable power feed-in based on weather data. The methodology applied, however, is considered suitable for other countries or regions that offer significant potential for renewable power generation together with options for directly using hydrogen in transportation and industry or exporting it to other regions.

The following sections of this paper proceed with the documentation of our study as follows: after a literature review and the scientific task statement in Section 2, the methodology applied for grid and electricity market simulations is described in Section 3. Section 4 goes on to provide relevant information on case-specific parameters and boundary conditions related to the assessment of electrolyzer integration into the energy system. The hydrogen logistics analysis is presented in Section 5. Finally, Section 6 integrates data from grid and market simulation, hydrogen production and logistics analysis for the environmental assessment.

2 Literature review and assessment task

A comprehensive overview on power-to-gas projects is given in Gahleitner et al.9 Their assessment focused on the type of technology, plant configuration and operational experience of PtG plants until 2012. Typically, these plants were based on electrical solar or wind energy and conceived as stand-alone solutions without grid connectivity. Alkaline electrolysis was found to be the prevailing technology for hydrogen generation. PEM electrolyzer technology was, however, stated as a candidate technology for future large-scale projects in light of its plant footprint and efficiency. The produced hydrogen could either be used for re-conversion into electricity, fueling cars or subsequent hydrocarbon formation, predominantly methane. Based on the finding that PtG plants are challenging with respect to component scaling and complexity, as well as efficient and economic operation, centralized hydrogen production in large-scale plants was recommended. With a stronger focus on PtG concepts for providing methane, Götz et al.10 evaluated technical aspects such as reactor and process concepts for catalytic and biological methanation, as well as system analysis criteria, e.g., carbon sources, efficiency and plant economics. It was concluded that methane from PtG processes (substitute natural gas, SNG) would not be competitive to natural gas or biomethane. The role of electrolysis within the electricity market and as an option for providing fuels for the transport sector (electrofuels) was assessed by Ridjan et al.11 Firstly, options for providing balancing power for the electricity market by means of alkaline (AEL) or solid oxide (SOEC) electrolyzer technologies were analyzed. Here, it was found that only SOECs have the necessary dynamic capabilities to deliver balancing power. However, according to the study, there is a variety of alternative flexibility options with better performance and lower cost. In summation, electrolyzer applications should be shifted towards electrofuel production because their efficiency would be superior to biofuel generation. A detailed hydrogen infrastructure assessment has been presented by Reuß et al.12 providing data on the cost of renewable hydrogen for a variety of supply chain alternatives. The study concluded that for hydrogen demands above 50 t d−1 and transport distances above 100 km the combination of salt caverns and pipelines for seasonal storage and bulk transport, respectively, shows the lowest cost levels of all options considered.

An effort on specifying the role of hydrogen in a future energy environment has been made by Barton et al.13 for the case of the UK. GHG emission reduction of 80% by 2050 is assumed. The spreadsheet based model covers all energy sectors in the UK and – with regard to the electricity sector – assumes an unconstrained grid (“copperplate”) with an hourly resolution of time-dependent input data. An infrastructural assessment has not been carried out. Three cases are analyzed in the study: a high coal scenario employing carbon capture and storage to reduce GHG emissions, a high nuclear scenario and, finally, a high renewable energy scenario. The study concludes that the production of hydrogen from surplus power generation and its utilization in transport are key issues in achieving GHG emission targets.

Bennoua et al.14 (i) consider left-over energy from a nuclear power plant's (NPP) load following operation to produce hydrogen and (ii) look into hydrogen production to resolve grid issues. Large-scale hydrogen storage is assumed in underground salt caverns and in storage vessels similar to the ones of 200 bar tube trailers. The assessment is based on hourly resolved time-series with a claimed high accuracy of electricity generation patterns. The National Renewable Energy Laboratory's (NREL) optimization tool HOMER that aims at minimizing the cost of energy provision in microgrids is used to optimize energy generation cost. The study does not look into the future but rather at current prospects of hydrogen as a storage medium for grid electricity. The results range from 3.0 to 6.4€ kgH2−1 of hydrogen in the case of electrolyzer operation compensating for NPP operation that follows the grid load. A constant hydrogen demand is assumed. In the case of offering electrolyzer operation for downward as well as for upward and downward balancing, hydrogen costs are 9.70 and 1.38€ kgH2−1, respectively. The considerably higher value for the case of downward balancing is due to the low electric energy quantities available. If the electrolyzer was downscaled, operational hours would increase, resulting in significantly lower hydrogen cost. In summary, Bennoua et al. present results of a complex assessment targeting at low-GHG hydrogen production and show hydrogen cost levels that can be considered competitive related to hydrogen production based on natural gas reforming.

The discontinuous electrolyzer operation in the electricity market environment is the subject of the study presented in Mansilla et al.15 While focusing on the model-based electrolyzer operation effecting degradation, the assessment also brings up insights into the economic performance of hydrogen production when the electricity market price signal is used for a cost-optimized operation. Relevant to the context in the present study is the setting of the electrolyzer operation. Here, a threshold market price for electricity has been chosen below which the electrolyzer is operated at nominal load and above which the operation is set to the lower operational limit of 20% related to nominal power. This threshold is identified as 56€ MWhe−1 that results in a hydrogen production cost of 3.27€ kgH2−1. Hydrogen infrastructure cost is not considered in the study. However, a storage capacity has been scaled based on the assumption that a hydrogen consumer with a constant demand exists.

The optimal system design of integrated wind-hydrogen electricity networks and its operation are the aims of the study by Samsatli et al.16 Using a geographical information system (GIS), suitable sites of wind turbines have been localized and – together with spatially resolved energy demand – the infrastructural requirements have been derived. Referencing the base case, the total cost of the optimized network is 21[thin space (1/6-em)]820 million £ per annum. In four additional cases analyzed where (i) the already existing wind turbines were included in the analysis, (ii) underground storage was not permitted, (iii) hydrogen pipeline transmission was not allowed, and (vi) underground electricity transmission lines were allowed, the resulting network costs were 21[thin space (1/6-em)]473, 23[thin space (1/6-em)]020, 22[thin space (1/6-em)]325, and 23[thin space (1/6-em)]574 million £ per annum, respectively. Dividing these costs by the annual hydrogen demand of 0.996 million tH2 per annum, this would translate into an average hydrogen cost of 21.57–23.68£ kgH2−1, by far exceeding hydrogen costs from all other sources cited here. The reason for this deviation can be found within the cost assessment. Capital costs are annualized using a capital charge factor of three which means that annual capital costs are one third of the total investment. This is comparably high with respect to component-specific depreciation times and current interest rates. If for example an interest rate of 8% would be used in combination with depreciation periods of 10 years for electrolysis, 20 years for distribution networks and refueling stations and 40 years for transmission networks, the annual cost of the above mentioned base case would be 7743 million £ per annum. The according hydrogen cost would then decrease to 4.90£ kgH2−1 which resembles 6.12€ kgH2−1 at an exchange rate of 1.25€ per £.

Our own, Germany-focused, previous work according to Stolten et al.2 and Schiebahn et al.7 carried out for the case of an unconstrained electricity network (“copperplate”) resulted in a hydrogen cost of 19.6 ct kWhH2−1 (6.53€ kgH2−1) considering a drastic extension of renewable power feed-in with the time horizon until 2050. Infrastructure investments for hydrogen production, storage, transmission and distribution as well as car refueling are included in this cost assessment. The derived hydrogen quantity would suffice to cover 75% of Germany's passenger car-based transport.2,7 In a second approach, Robinius et al. also consider the spatial dimension of wind power generation and grid load. Assuming the same transportation volume to be covered by hydrogen-fueled fuel cell vehicles (FCVs) the study revealed a total hydrogen cost of 17.5 ct kWhH2−1 (5.83€ kgH2−1).17–19

The focus of this literature overview was aspects of system analysis, rather than the practical applications of PtG technology. With respect to the latter, a variety of projects have been reported in the literature.9,20,21 The overview given here shows that available system analysis studies are typically conducted for specific countries or regions because relevant parameters are highly specific for the analyzed region, e.g., time-series of renewable power feed-in, distances to be covered by energy transmission and distribution, and options for large-scale energy storage, particularly in underground caverns. Moreover, it is beneficial to have historical data on the time series of power supply and demand as well as on other parameters derived from energy-related national statistics at hand in order to provide a more reasonable background of the assessments. Moreover, national targets, such as GHG emission reduction, build the motivation of the assessments, so that every assessment follows its own philosophy, e.g., regarding energy-strategic or other targets to be achieved.

To conclude the literature review, the specific objective of this study was to examine the emerging role of hydrogen as an energy carrier in the context of energy systems with high installed capacities of renewable power generation. Going beyond the information in the literature, the importance and originality of this study is that it combines power sector simulation, detailed hydrogen infrastructure analysis and environmental performance assessments. In order to consider the fluctuating electricity feed-in from renewables, as well as the spatial and temporal variability of electricity demand, a spatially and temporally resolved approach was chosen for the power sector simulation. Moreover, grid restrictions and electricity exchanges to neighboring countries are considered. Within the hydrogen infrastructure analysis, the downstream components of hydrogen provision, including large-scale storage, are scaled in order to achieve more reliable investment estimates and to provide the required input data for environmental assessments. Based on these results and using comprehensible electricity cost scenarios, hydrogen provision cost is estimated and the GHG emission reduction potential is specified.

3 Methods

The starting point of this study is a combined trans-regional grid and electricity market simulation, which provides the required information on suitable grid nodes for the localization of electrolyzers and on electricity cost. The respective scenario targets the year 2035 considering the capacity extension of renewable power, grid extension and electricity demand. This was accomplished by employing two cases representing two different operational strategies for electrolyzers (see Fig. 2):
image file: c8se00008e-f2.tif
Fig. 2 Cases considered in this study. Case 1 is comprised of 3 sub cases regarding different levels of grid extension.

■ Case 1: a primarily grid-based approach assuming an exclusive utilization of negative residual power, also integrating the effects from different scenarios of grid extension into the analysis in the form of three subcases;

■ Case 2: a market-based approach assuming the utilization of grid electricity on the basis of the market simulation-based electricity price time series; grid constraints are neglected in this case.

On this basis, hydrogen quantities were derived for the different strategies and scenarios. Related to case 1, the amount of electricity that would have to be curtailed due to network congestions is taken as the potential for renewable-based hydrogen production. The electricity market simulation provides in case 2 the required time-series of electricity cost and power generation mix. Both cases deliver the required input values for the subsequent economic and environmental assessments. In this way, we analyze the coupling of the electricity sector assuming a significant extension of renewable power capacity and the gas sector focusing on hydrogen as an option for storing renewables and providing other sectors with a valuable energy resource. Highlights are the spatially and temporally resolved electricity market simulation as well as the detailed hydrogen infrastructure assessment. Hydrogen infrastructure is comprised of hydrogen production via electrolysis, storage in salt caverns and transport using pipelines.

3.1 Simulation of the power sector

In this study a fundamental market simulation approach is adopted to determine the market-driven hourly schedules of hydrothermal power plants within the European Network of Transmission System Operators for Electricity (ENTSO-E) for a selected scenario year. Subsequently, a power flow calculation provides the corresponding power line utilizations to identify congestions within the transmission network. To generate technically feasible power plant schedules, a redispatch calculation is performed. The redispatch results contain curtailments of renewable power to ensure system security, as well as spatial information about the affected transmission system nodes needed for placement and capacity planning of electrolyzers.
Electricity market simulation. The IFHT's European Electricity Market Simulation (EEMS) is a multi-stage, inter-temporal and multi-regional optimization model for the European power plant dispatch.22 The EEMS determines the most cost-efficient power plant and storage schedules for all European countries, taking into account all significant techno-economic properties of hydro and thermal generators and the restricted international transmission capacities. Hydrothermal scheduling for large-scale systems like those in Europe is a challenging task, both from a modeling and also from a computational perspective. Since unit commitment and economic dispatch for such large systems cannot be achieved in a single modeling step, the overall model has been separated into three levels that are solved in succession. Fig. 3 shows the overall model with its levels.
image file: c8se00008e-f3.tif
Fig. 3 European electricity market simulation.
Network analysis and redispatch simulation. The schedules of all conventional power plants yielded by the market simulation, the feed-in of renewable energy sources and the electrical demand, serve as input values for the analysis of the electrical grid. The power flow simulation delivers voltage magnitudes and angles at all high voltage substations and hence the utilizations of all branches, such as transformers, overhead lines or cables. To ensure the stable and reliable operation of the transmission system, the N-1 criterion is applied. The N-1 principle is one essential security standard defined by ENTSO-E and prescribes that no security limits are violated even in the case of a contingency, i.e., the outage of any technical equipment like lines, transformers or busbars.23

Pre- and post-outage line loadings that exceed the line limits given by the thermal current-carrying capacity violate the N-1 criterion and endanger the system's security. After taking into account grid-related remedial actions (e.g., switching, operation of flexible AC transmission systems, FACTS), TSOs are allowed to perform market-related redispatch measures to ensure the N-1 secure operation of the transmission system.24

Redispatch involves adapting the generation of both conventional and renewable feed-ins. According to Germany's present legal framework, the curtailment of renewable feed-ins is only permissible after the potential of conventional power plants has been exploited. Furthermore, cross-border redispatch is the ultimate intention. To identify the required amount of curtailed renewable energy and hence the potential grid nodes for the electrolyzer placement, a redispatch simulation must be performed. The redispatch simulation tool developed at the IFHT and applied in this study is an optimization with a mixed-integer programming structure (MIP).

The intended function is to minimize total redispatch cost (eqn (1)). This cost comprises the sum of adjusted redispatch power ΔPj(t) for every hour t and every unit j and coefficients that are chosen with respect to the legal framework. Consequently, conventional power plants, renewable feed-in and cross-border units are separated into three escalation levels. The technical restrictions of all thermal units are taken into account, along with the formulation of the unit commitment problem. This requires a binary state variable νj(t) to consider minimal and maximal power, as well as minimal up and down times (eqn (2)).25 To implement grid constraints, linear sensitivity factors are employed. Power Transfer Distribution Factors (PTDF) representing adjustments of the feed-ins to the power flows SlF in all branches l have a maximal transmission capacity SlF,max given by their thermal limits. By applying Line Outage Distribution Factors (LODF), N-1 simulations are included after an outage of another branch n (eqn (3)). The use of PTDF and LODF corresponds to a linearization of the power flow equations in the current operating point delivered by the power flow simulation and hence allows a linear formulation. To keep the power system in balance, the sum of all adjustments must be zero, i.e., every decrease of conventional or renewable inputs entails a unit whose power must be increased (eqn (4)).

 
image file: c8se00008e-t1.tif(1)
 
s.t. fUCPj(t),νj(t)) ≤ 0(2)
 
image file: c8se00008e-t2.tif(3)
 
image file: c8se00008e-t3.tif(4)

The scenario data used for this study are mainly based on the Scenario Outlook & Adequacy Forecast (SOAF) and Grid Development Plan (GDP), both renowned key publications with regard to the German and ENTSO-E-wide transmission grid development.26,27

The GDP provides unit-wise information about the expected future hydro-thermal power plant fleet in Germany for the targeted simulation year28 (Fig. 4). Apart from Germany, an expansion heuristic is applied to generate unit-wise power plant data from aggregated capacity assumptions by the SOAF.


image file: c8se00008e-f4.tif
Fig. 4 Installed conventional and renewable capacities according to GDP15/scenario B2035.28

The present electrical transmission system in Germany exclusively consists of high voltage AC (HVAC) substations and lines, while High Voltage DC (HVDC) links only connect non-synchronous electrical regions, such as Scandinavia, to the synchronous system of the Union for the Coordination of the Transmission of Electricity (UCTE). According to the GDP, the German transmission system operators (TSO) plan to implement various multi-terminal High Voltage DC (HVDC) links between the north and south of Germany in the HVAC system in order to increase trans-regional transport capacity and allow for the transmission of renewably generated electricity to the major centers of consumption.29 This is due to the fact that renewable power capacity extension is expected particularly in the North while, on the other hand, areas of high electricity consumption are located in the southern half of the country. The GDP defines five main connections, namely corridor A North, A South, B, C and D (see Table 1).

Table 1 Scenario definitions regarding the planned high-voltage DC lines for Germany according to the grid development plan6 (values in GW)a
Scenario Base BBP+ 2025 BBP Start + A2
a BBP: federal requirement plan, Start: start network.
A North 6 2 2 2
A South 2 2 2 2 2
B 4 2
C 6 4 4 4
D 4 2 4
Sum 22 12 12 8 2


While the expansion of renewable power generators is politically driven, limited public and social acceptance leads to delays of the expansion of the electrical transmission grid.30 Since the amount of curtailed renewable energy resulting from bottlenecks in the transmission system depends essentially on the grid expansion measures, multiple grid expansion scenarios are considered within this study. The base case contains all grid expansion measures defined in the GDP 2024 through 2035. Additionally, four more scenarios are considered taking the insecurity regarding the realization of planned grid expansions into account. Therefore, the scenarios are characterized by a delayed installation of the planned DC links. The base case contains 11 DC links – that are planned according to GDP – with a total transmission capacity of 22 GW, which are successively reduced in the further scenarios (see Table 1 and Fig. 5).


image file: c8se00008e-f5.tif
Fig. 5 German map with HVDC line connections considered in this study (cf.Table 1) and H2 pipelines connecting the grid node in the municipality of Heide (state of Schleswig Holstein) and an assumed point of H2 use in the municipality of Marl (state of North Rhine-Westphalia), see Section 3.3.

3.2 Integration of power-to-gas technology

As described above, two strategies for scaling and operating electrolyzers in Germany are analyzed in this paper: (i) in cases 1a–1c the surplus power generation in congested networks is used for three different grid extension levels; and (ii) case 2 is based on a time series for electricity market prices (Table 2). In total, four datasets – related to the 3 + 1 cases of the scenarios – are used for the subsequent hydrogen infrastructure assessment and GHG emission balance.
Table 2 Description of cases considered for assessments. Cases 1a–1c: German national network topologies for the year 2035 (“B2035”) according to the Grid Development Plan. For further details, see Table 1a
Cases considered Description
a BBP: federal requirement plan, Start: start network.
Case 1a Grid extension level: “Start + A2” (network corridor A south)
Case 1b Grid extension level: “BBP” (HVDC and BBP measures)
Case 1c Grid extension level: “Base” (HVDC target network 2025)
Case 2 Assessment based on electricity market price


While case 1 considers surplus power from renewables, the operational strategy according to case 2 was changed in a way that the electricity trading price signal is now guiding the electrolyzer power set point: a trading price threshold is defined, below which the electrolyzer is set in operation. During periods when the trading price is above the threshold, the electrolyzer is shut down. Technical constraints that may prevent the electrolyzer going into operation at any time, such as start-up procedures, are not considered. For better comparability, the hydrogen quantity to be produced is set to be equal to case 1a.

For the infrastructure assessment, the same electrolyzer location was assumed for all cases. The nominal electrolyzer power is determined on the basis of a defined curtailment that is set here with 5% of the available negative residual energy for cases 1a through 1c. In case 2, the electrolyzer power is set in such a way that the minimum hydrogen production costs are achieved while keeping the hydrogen production volume of case 1a for better comparability of the results. The procedure of how to scale the hydrogen storage is similar for all cases. Hydrogen consumption is assumed to be constant over time, while production follows the electrolyzer's operation that is either controlled by the availability of surplus power (cases 1a through 1c) or the defined electricity trading price threshold (case 2). The time series of balancing production and demand is used for scaling the salt cavern storage, also including a four week storage reserve on a consumption basis.

Related to the hydrogen cost analysis, relevant economic parameters are set with an 8% interest rate and depreciation times of 10 years for electrolyzers and 40 years for cavern storage and pipelines. The specific investment and operational costs of the electrolyzer have been assumed at 500€ kWe−1 and 3% of the investment, respectively.18 PEM technology was chosen, since it is expected to have a higher potential for increased efficiency and more dynamic operating capabilities.9 The efficiency is assumed at 70% relating to the lower heating value. The electricity cost was taken from the time series of electricity market prices according to the results from the power sector simulations. In a side calculation, we looked at the sensitivity of hydrogen by reducing the electricity cost by 50%. A case where electricity is considered at no cost has not been analyzed as it is not seen as realistic in the long-term and for large quantities of electricity.

3.3 Analysis of hydrogen logistics components

This section introduces the procedure of how the required hydrogen logistic components, compressors, pipelines and salt caverns were scaled and the respective costs were estimated. The analysis is based on state equations corresponding to real gases via the thermodynamic potential of the free energies (see ref. 31). The methodology for modeling the relevant components of the process chain under consideration is described in the following.
Compressors. In the concept considered here, piston-type compressors are required for two different tasks. A pipeline compressor delivers a defined hydrogen mass flow directly from the electrolyzer into the 100 bar pipeline. A storage compressor is then used to deliver the hydrogen mass flow that exceeds the pipeline capacity into the salt cavern. The salt cavern's operational pressure depends on the cavern's filling level. In this assessment, the cavern pressure ranges from 60 to 180 bar. Depending on the electrolyzer's state of operation, three cases can be defined if the hydrogen mass flow in the pipeline is considered to be constant:

■ Hydrogen mass flow from the electrolyzer is equal to the constant mass flow of the pipeline; the storage compressor is not in operation.

■ Hydrogen mass flow is larger than the constant mass flow of the pipeline; the pipeline compressor delivers the defined mass flow into the pipeline. The storage compressor feeds the remainder into the cavern.

■ Hydrogen mass flow from the electrolyzer is smaller than the constant mass flow of the pipeline; the gap is filled by hydrogen delivered from the cavern. If the cavern pressure is below the pipeline pressure, then the storage compressor is operated for feeding the pipeline.

For the scaling of piston-type compressors considered in this study, an equal pressure ratio for each compression stage at nominal power is assumed. Pressure losses prior to and after each stage are accounted for. The calculations of the specific isentropic compression work are performed according to the first law for open systems derived directly from the specific enthalpies (see ref. 31). For calculating the actual specific compressor work, isentropic efficiency with a value of 92% was adopted. The temperature after compression was determined based on the respective specific enthalpy. According to ref. 32–34, the American Petroleum Institute mandates that the maximum permissible temperature of gaseous hydrogen is 135 °C. The required number of compressor stages is thus defined. Between the compressor stages, intercooling results in a maximum gas temperature of 40 °C. For the calculation of the compressor power uptake, hydrogen losses are neglected. Based on this compressor power, a clutch power is determined assuming a mechanical compressor efficiency of 98%. At 99% electrical efficiency, the electric power demand is calculated. According to Chen,32 the nominal motor power is set to 110% of this electrical power demand. The minimum suction pressure of 25 bar is selected here, assuming a small buffer storage between the 30 bar electrolyzer and compressor.

The investment cost of pipeline compressor stations depends on a variety of factors. Special attention must be paid to the required nominal power, type and number of compressors and requirements for buildings and land use, as well as country-specific conditions. In ACER,35 the investment cost of different European natural gas pipeline projects between 2005 and 2014 has been analyzed and statistically evaluated. The capacity-specific investments of newly built facilities based on electrically driven compressor stations with delivery pressures of 68 to 91 bar have been found to be 1.8–4.1 million €, averaging to 2.8 million €. Within the German Network Development Plan,36 standard planned cost figures for natural gas compressor stations have been published that include a standardized risk premium. For newly built stations with 1 + 1 machine sets in the power range of 8–11 MW and 12–15 MW, respectively, an investment of 72 million € and 82 million €, respectively, is provided.

Pipelines. Further assumptions related to the pipeline must be made regarding pipeline length. In our study, we set a value of 426 km representing the distance between the electrolyzer location in Heide in the state of Schleswig-Holstein (see Section 4.1) and an assumed point of hydrogen delivery in the municipality of Marl, which is located in the northern Ruhrgebiet in the state of North Rhine-Westphalia. This site was chosen because it offers different alternatives for large hydrogen consumption. First, there is a large chemical park that is, at present, connected to the Rhine-Ruhr hydrogen pipeline. Moreover, the Ruhrgebiet region is a densely populated region with some 5 million people offering the potential for large hydrogen quantities to be consumed as fuel for zero-emission passenger transport in the long term. Secondly, the gauge pressure at the pipeline inlet is set with 100 bar at maximum and with 95 bar as nominal operating pressure. At the pipeline exit 70 bar is assumed. Since we consider here a transmission pipeline with high pressure operation, only steel could be used for the pipeline's materials. Other hydrogen-tight materials, such as polyethylene, are not applicable because required wall thicknesses, in combination with high material costs, would cause problems with standard procedures for pipeline dimensioning and lead to substantially increased costs.

Under the assumption of a quasi-stationary isothermal and one-dimensional tube flow, the pressure loss calculation of high-pressure pipelines can be conducted using eqn (5), the Darcy–Weisbach equation (see ref. 37 and 38).

 
image file: c8se00008e-t4.tif(5)

The friction coefficient λ is determined according to Zanke,37 covering the full range of Reynolds numbers based on an interpolation approach. The average gas temperature is set to 20 °C. Since the pipeline dimensioning is based on commercial standard diameter classes and wall thicknesses, the exit pressure cannot be determined with precision. Assuming an inlet pressure of 95 bar and the required individual mass flow, the diameter class is selected such that the exit pressure during full-load operation is preferably low, but greater than 70 bar.

The investment cost of pipelines is influenced by various factors, such as diameter class, ground conditions, population density, operating pressure and country-specific conditions. Furthermore, the investment costs of European natural gas pipeline projects are assessed in ACER.35 In relation to pipelines with a 16′′ diameter (DN 400), specific investment was found to range from 200€ m−1 to 1300€ m−1. For the 16′′–27′′ diameter classes, the average investment was calculated to be 706€ m−1. The German Network Development Plan Gas 2015 issues standard planned cost related to natural gas transport pipelines, including a risk premium of 1040€ m−1 for DN 400 and a pressure level of 100 bar. For the 18′′ (DN 500) diameter class, no figures exist in ACER and NEP.35 It is assumed in this study that investment for DN 500 will be 5% higher than for DN 400. According to Chen,32 experts suggest a 10% higher investment in hydrogen pipelines than those for natural gas transport due to more stringent tests and inspections of the welds, as well as leak-free seals on isolations and valves. The values determined here are based on the average cost according to ACER and NEP,4 and include the premium of 10% for hydrogen pipelines, as mentioned above.

Salt caverns. The storage capacity of the salt cavern is determined on the basis of constant hydrogen delivery to the pipeline and variable hydrogen feed-in from the electrolyzer plant running on negative residual power or on the basis of the electricity market price signal. Electric power for hydrogen compression at the cavern storage site as well as for the pipeline is taken from the grid at a price of 15 ct kWhe−1.

The guiding principles of the calculation methods applied to the salt caverns are energy and mass conservation in accordance with the first law of thermodynamics. The resulting equations are described in detail in Tietze.31 In this work, however, the non-stationary heat conduction of salt rock is considered.

For all cases under consideration, salt caverns of the same dimensions are assumed. These caverns are cylindrically shaped and constitute a hollow geometric space of 0.75 million m3. The depth of the last cemented casing string is 1000 m. This results in minimum and maximum operational pressures of 58 bar and 175 bar, respectively. The total quantity of stored hydrogen amounts to 9090 t, with a cushion gas share of 35%.

The required number of caverns is determined in such a way that geological and technical constraints are overcome by dynamically charging and discharging hydrogen. Moreover, a working gas reserve of four weeks is assumed.

Detailed cost assessments relating to salt cavern storage facilities are scarce in the literature. Furthermore, a large variety of impact factors imply that such installations are to be considered unique. For a rough estimation, it is useful to divide the salt cavern storage system into three parts, namely: cavern construction, with the first filling including cushion gas and the above-ground facilities. The investment for the cavern construction, including the borehole, brining and completion is assumed here to be 40€ m−3 relating to the geometric volume. Scaling effects were also included. Apart from the procurement of the cushion gas for the first filling, human labor and energy cost must also be considered. Related cost is estimated here to be 0.4€ per normal cubic meter of cushion gas. The calculation of above-ground station investment follows the same scheme as for the pipeline compressors (see above). In order to take into account higher pressure levels, the more complex casing with longer distances to be covered and the additional gas clean-up systems, a premium of 30% is assumed.

3.4 Environmental assessment

The environmental performance of hydrogen provision (electrolysis and hydrogen logistics including storage and pipeline transport) is evaluated applying a Life Cycle Assessment (LCA) approach to the four different scenarios (Table 2). A LCA is a method for analyzing the environmental impacts inherent to products. Herein, a product's entire lifecycle (raw material extraction, production, use, waste treatment, recycling and final disposal) is taken into account. LCA is standardized within DIN EN ISO 1404039 and DIN EN ISO 14044.40

In this study, the assessment is done in terms of GHG emissions – measured in CO2 equivalents – and the evaluation is conducted on the basis of the functional unit of “1 kg of H2”. Data regarding the efficiency of the electrolyzer and compressor (for hydrogen logistics) are drawn from this study and are used for estimating the relevant Life Cycle Inventory (LCI) data. The amount of water needed for electrolysis is taken from the literature and amounts to 10.11 kgH2O kgH2−1.41 The evaluation of the electricity sources in terms of GHG emissions is done based on data from the GEMIS (version 4.95) database, as this database integrates increases in efficiency in electricity generation in the future. The relevant LCI data are summarized in the ESI (refer to Table 10).

The four cases 1a–1c and 2 differ in terms of electricity sources used for electrolysis and hydrogen logistics. For cases 1a–1c, the surplus power from offshore and onshore wind as well as photovoltaic (PV) electricity from the electrical network node Heide (refer to Section 4.1) in North-Germany is assumed to be used for electrolysis, whereby the grid electricity is assumed to be used for hydrogen logistics. For case 2, grid electricity is assumed to be used for both electrolysis as well as hydrogen logistics. The simulation of the German electricity market in 2035§ carried out in this study provides the information needed on the future grid electricity mix with regard to used energy carriers.

Herein, it should be noted that the grid electricity mix is slightly different for the three grid extension levels considered for the electrolyzer based on the electricity market price (case 2). However, analyses show that the GHG emission results vary only slightly by maximum 2% for different grid extension levels (refer to Table 11 in the ESI). Therefore, the impact of the grid extension level in case 2 on the overall GHG emissions can be considered low. Thus, the grid extension level “Start + A2” is applied for the environmental assessment without further variations.

As electrolyzers and compressors are not operated continuously, the share of electricity sources in the grid electricity mix as well as the utilization of the electrolyzer and compressor vary every hour. Thus, the hourly grid electricity mix weighted by the utilization of the electrolyzer and compressor respectively is used for the environmental evaluation rather than the annual average grid electricity mix.

4 Results and discussion

As a result of the application of the employed methods according to Section 3, the results of our analysis will be presented in this section. The power sector-related simulations provide values regarding redispatch volumes and curtailed electricity from renewable production. The latter form the basis for localizing and scaling the electrolyzer for hydrogen production and, subsequently, for scaling the hydrogen logistical components. The results are ultimately used for determining the total cost and environmental performance of hydrogen provision.

4.1 Redispatch volumes and curtailed energy

The model illustrated in Section 3.1 is applied to the defined grid expansion scenarios using the market simulation and power flow results. The redispatch simulation identifies the power of all units that must be adjusted to ensure an N-1 secure system operation. Fig. 6 (left) shows the resulting positive and negative redispatch volumes that vary between 11.5 TWh in the base case and 62 TWh in the scenario “Start + A2”, in the year 2035. The redispatch measures are broken down into conventional power plants, the curtailment of renewable energies and cross-border redispatch. The integration of DC links reinforces the transmission grid and reduces the redispatch volumes by decreasing the utilization of critical lines and relieving the AC grid. Fig. 6 (right) illustrates the curtailment of onshore wind, offshore wind and photovoltaic feed-ins of the five grid expansion scenarios presented in Fig. 6 (left). The curtailed renewable energy may then be used to feed electrolyzers, which requires a detailed analysis of renewable curtailment and its localization based on the simulation results. Section 4 describes the procedure and results of the localization of large-scale electrolyzers and presents electrolyzer capacities and full-load hours as the basis for the calculations related to hydrogen yield, its production cost and, consequently, the GHG emission level. The depicted redispatch volumes and the curtailed renewable energy refer to the projected renewable feed-in in Germany and domestic grid expansion measures. Due to pan-European efforts to expand renewable capacities, other countries could face these issues as well. A transfer of this approach to other market areas in future publications is therefore vital for holistic statements regarding the European single market.
image file: c8se00008e-f6.tif
Fig. 6 Redispatch volumes for the five grid expansion scenarios in 2035 (a) and curtailed onshore, offshore and PV energy of the five grid expansion scenarios in 2035 (b).

4.2 Power-to-gas infrastructure and hydrogen provision cost

As the first step in our power-to-gas infrastructure analysis, the location of the electrolyzer was determined. From the grid simulations, a total of 10 grid nodes have been identified that show large annual curtailed energies totaling 11, 8.8 and 6.9 TWhe for the cases 1a–1c, respectively. Fig. 7 (left) shows the surplus power duration curves for the 10 nodes related to case 1a. For further calculations, it is assumed that a more economical way of making use of the available energy is to place the electrolyzer at a geographically central point. For this reason, all nodes in the state of Schleswig-Holstein are clustered together and the municipality of Heide serves as the location for the electrolyzer. Here, also, a suitable geological salt formation exists that is already being used for underground energy storage comprising nine caverns with geometric volumes of up to 270[thin space (1/6-em)]000 m3.43
image file: c8se00008e-f7.tif
Fig. 7 Electrolyzer power at all 10 grid nodes (a) considered and the clustering of nodes (b). Values for case 1a (grid extension “Start + A2”); electrolyzer scaled according to the 5% curtailment criterion (see text); SH: state of Schleswig-Holstein (most northerly state in Germany); MV: state of Mecklenburg-Vorpommern (northeast Germany); NI: state of Lower Saxony (northwest Germany).

The resulting surplus power duration curve of all clustered nodes is shown in Fig. 7 for the case 1a. It becomes clear that with the cluster “All SH nodes”, the major share of the available energy can be made available for hydrogen production. Integrating the other grid nodes would require substantially higher infrastructural resources with little benefit for the recovered energy quantities.

Secondly, the nominal electrolyzer power must be specified. As the general criterion of case 1, 5% of curtailed energy is selected in order to ensure high operational hours. The correlation between power curtailment and relevant parameters in this study is displayed in Fig. 8. The figure shows that even for low curtailments of as little as 1.8%, neither the hydrogen cost nor yield is seriously affected. With less than 1.8% of curtailment, installed electrolyzer capacity and hydrogen cost increase more strongly. With respect to the utilization of the required storage and transportation assets that may suffer more severely from lower hydrogen quantities, a curtailment of 5% was chosen. A more detailed economic analysis would be a desirable objective in future work.


image file: c8se00008e-f8.tif
Fig. 8 Correlation between curtailment of negative residual electricity and electrolyzer capacity, annual full load hours, H2 yield and H2 cost before storage and transport. Figure related to case 1a; economic parameters according to Fig. 1; electricity cost based on market prices. EL: electrolyzer.

The results regarding installed electrolyzer capacity, full-load hours, hydrogen yield, required storage size and hydrogen cost are listed in Table 3. As mentioned in the Methods section, we looked at the sensitivity of the hydrogen production cost against the electricity cost. The results are also shown in the table using: (i) the market price; (ii) 50% of that market price; and, for comparison (iii) the levelized cost of electricity (LCOE) according to Robinius et al.44 (column 5). For subsequent hydrogen cost assessments, electricity market prices are assumed for electrolysis operation.

Table 3 Results for installed electrolyzer capacity and full-load hours, hydrogen yield, hydrogen production cost and required storage size in relation to the three selected grid extension scenarios of case 1. Clustered grid nodes (“all SH nodes”); electrolyzers scaled according to the 5% curtailment criterion
Cases Installed EL capacity [MWe] EL full-load hours [h per annum] H2 yield [ktH2 per annum] H2 production costa [€ kgH2−1] Required storage size [tH2]
a Hydrogen cost, excluding storage, depending on the electricity cost assumption: market price (left), half market price (center), LCOE accounting for Robinius et al.44 (right). EL: electrolyzer.
Case 1a 3307 2721 189 3.87|3.05|4.98 38[thin space (1/6-em)]547
Case 1b 3767 1881 149 4.51|3.72|5.68 53[thin space (1/6-em)]223
Case 1c 3623 1624 124 4.85|4.06|6.04 48[thin space (1/6-em)]233


The results of our calculations related to case 1 show that amongst the scenarios considered, case 1a offers the largest potential for utilizing surplus electricity in order to produce hydrogen. Moreover, the electrolyzer capacity is lowest and full-load hours are highest. This translates into a hydrogen cost of 3.87€ kgH2−1 based on electricity market prices – the lowest of the considered scenarios. Finally, the required storage size that allows for seasonal balancing and four weeks of reserve on a consumption basis is smallest.

Relating to case 2, the analysis was modified in such a way that an electricity market price threshold is set above which the electrolyzer is set in operation. For better comparability of the results, the electrolyzer's location was assumed to be the same as for case 1. Considering the fact that RESs are predominantly available in the north of Germany, larger negative residual energy quantities can be expected there, which favors electrolyzers in this region. However, concrete investment decisions on electrolyzers are more complex and might, in reality, result in a different selection of suitable locations.

As a first step in determining the optimal electricity trading price threshold, a treading price was identified as 26€ MWhe−1 (point (1) in Fig. 9), achieving the desired hydrogen production volume of 189 ktH2 per annum similarly to case 1a. Subsequently, the electrolyzer's capacity has been decreased while increasing the electricity price limit in order to maintain the desired production volume until the minimum hydrogen costs were reached (point (2) in Fig. 9). The minimum hydrogen cost corresponds to an electrolyzer capacity of 1530 MWe and an electricity market price threshold of 54€ MWhe−1.


image file: c8se00008e-f9.tif
Fig. 9 Optimization of H2 cost under variation of the electricity price limit and electrolyzer capacity. (1) Electricity price limit at which desired H2 quantity can be produced; (2) electricity price limit, at which – under variation of electrolyzer capacity – minimum H2 costs are achieved.

In the final step of our hydrogen production cost assessment related to case 2, we analyzed in a simplified way the impact on the electricity market price that a large consumer such as the electrolyzer considered here would have. We assumed that a correlation exists between the availability of negative residual power and the electricity market price. The data points from the electricity market simulation are shown as a dot cloud in Fig. 10 (left). As a curve fit we derived a 5th order polynomial equation that describes the desired functional correlation. Given the electrolyzer power of 1530 MWe which is required in order to achieve minimum hydrogen cost, the new electricity market price curve could be derived which is shown in Fig. 10 (right). In order to achieve this, the absolute value of the available residual power is decreased by the nominal electrolyzer power. The result is the new price curve (“with electrolyzer”, Fig. 10) that has been taken for determining the electricity cost in case 2. The resulting hydrogen production cost is 94€ MWhH2−1 or 3.15€ kgH2−1.


image file: c8se00008e-f10.tif
Fig. 10 Electricity market assessment: derivation of a functional correlation between residual load and electricity market price (left) and impact of electrolyzer operation according to this study on the price curve (right). Values are taken from combined electricity grid and market simulations (see Section 2).

The results for case 2 are shown in Table 4. Due to the significantly higher annual operational hours (column 3), the electrolyzer capacity can be significantly decreased at the pre-defined hydrogen production volume of 189 ktH2 per annum according to case 1a. Also, as expected, the hydrogen production cost is lower compared to the results from cases 1a–1c. Moreover, the storage size is less than half of the value for case 1a at the same hydrogen production volume. Electrolysis operation is more evenly distributed over the year as it is not so stringently coupled to periods of negative residual power generation in the state of Schleswig-Holstein.

Table 4 Results for installed electrolyzer capacity and full-load hours, hydrogen yield, hydrogen production cost and required storage size in relation to the three cases considered. Clustered grid nodes (“all SH nodes”); economic parameters according to Fig. 1; electrolyzers scaled according to the 5% curtailment criterion; hydrogen cost, excluding storage. EL: electrolyzer
Installed EL capacity [MWe] EL full-load hours [h per annum] H2 yield [ktH2 per annum] H2 production cost [€ kgH2−1] Required storage size [tH2]
Case 2 1530 5880 189 3.15 17[thin space (1/6-em)]510


Subsequent to the scaling of the electrolyzer and storage the costs of hydrogen logistics have been determined. Firstly, Table 5 summarizes all investments required at the salt cavern sites providing information on the number of required caverns, investment for the cushion gas and the station including the storage compressor.

Table 5 Summary of required investments at salt cavern sites
Case Number of caverns [—] Total geometric volume [106 m3] Cavern invest [106€] Invest of cushion gas [106€] Station cost incl. compressor [106€] Total invest [106€]
Case 1a 11 8.25 330 157 217 704
Case 1b 15 11.25 450 214 250 914
Case 1c 14 10.5 420 199 250 869
Case 2 9 6.75 270 128 86 484


The total annual cost and specific hydrogen cost related to hydrogen logistics are summarized in Table 6. Here, all further investments and all annual operational costs, also considering the electricity cost for operating the compressors, are included. The values show that cases 1b and 1c exhibit the highest annual capital and operational cost. Annual capital cost dominates the total cost of hydrogen logistics with shares of 88–94%. High capital costs in cases 1b and 1c are caused by the highest storage requirements according to Tables 3 and 4.

Table 6 Cost of hydrogen logistics. Economic parameters according to Fig. 1, except for depreciation time which was taken with 40 years for pipelines and caverns. Capital cost as annuities of investment according to Table 5
Case Annual capital cost [106€ per annum] Operational cost [106€ per annum] Electricity cost [106€ per annum] Total annual cost [106€ per annum] Specific H2 logistics cost [€ kgH2−1]
Case 1a 102 3.1 4.2 109 0.58
Case 1b 117 3.5 3.3 124 0.83
Case 1c 113 3.4 2.7 119 0.96
Case 2 84 2.5 4.4 91 0.48


The total hydrogen provision cost has been determined to be 3.63–5.81€ kgH2−1 considering all scenarios that have been analyzed in this study (Table 7). Again, the cases 1b and 1c show the higher values while the lowest cost is found for case 2. In general the hydrogen production cost dominates the overall provision cost by 83–88%. However, if hydrogen utilization subsequent to long distance transport would require additional distributional efforts, the cost share of hydrogen production would decrease.

Table 7 Total cost of H2 provision in the 4 scenarios analyzed, summarizing values in Tables 3, 4 and 6
Case Specific cost of H2 production [€ kgH2−1] Specific cost of H2 logistics [€ kgH2−1] Total H2 provision cost [€ kgH2−1]
Case 1a 3.87 0.58 4.45
Case 1b 4.51 0.83 5.34
Case 1c 4.85 0.96 5.81
Case 2 3.15 0.48 3.63


4.3 Environmental performance

Table 8 summarizes the GHG emission results for the four cases considered. As can be seen, the utilization of the electrolyzer based on the electricity market price (case 2) leads to much higher GHG emissions (about 40 times as high) than the application of surplus power (cases 1a–1c). This difference is mainly caused by emissions resulting from electricity generation for electrolysis. Offshore and onshore wind as well as PV electricity (with GHG emissions of 0 gCO2-eq. MWhe−1; see Table 10 in the ESI) is assumed to be used for electrolysis within cases 1a–1c. Thus, GHG emissions related to electrolysis in cases 1a–1c are caused by the provision of water only. In case 2 however, fossil electricity sources are used for electrolysis (see Table 8) resulting in high GHG emissions. Results for hydrogen logistics differ only slightly between cases 1a–1c and 2.
Table 8 Results of the environmental impact assessmenta
Case GHG emissions [gCO2-eq. kgH2−1] Electricity mix relating to electrolyzer operation – average share of:
Total H2 production H2 logistics Fossil sources Renewable sources
a GHG: greenhouse gases.
Case 1a 413 10 403 0% 100%
Case 1b 435 10 425 0% 100%
Case 1c 412 10 402 0% 100%
Case 2 17[thin space (1/6-em)]106 16[thin space (1/6-em)]669 437 47.0% 53.0%
H2 produced by steam reforming45 n.a. 10[thin space (1/6-em)]920–14[thin space (1/6-em)]280 n.a. n.a. n.a.


Comparing cases 1a–1c, it can be seen that case 1c leads to the lowest GHG emissions followed by cases 1a and 1b. However, differences can be considered small.

Comparing the results of the environmental evaluation of this study with the GHG emissions of hydrogen produced by steam reforming (values are taken from the literature) shows that applying surplus power (cases 1a–1c) leads to lower GHG emissions, whereby the application of grid electricity based on the electricity market price (case 2) is associated with a higher emission than hydrogen produced by steam reforming. GHG emissions are up to 54% higher for case 2 compared to hydrogen produced by steam reforming. Performing a sensitivity analysis, Fig. 11 shows the influence of an increasing share of renewable electricity sources in the electricity mix that is used for electrolysis on the GHG emissions. As can be seen, a share of 60% renewable electricity sources in the grid electricity mix would be needed to achieve the GHG emission level that is comparable to the level of hydrogen produced by steam reforming based on natural gas. Respectively, 69% renewable energy sources would be needed to achieve the same GHG emission level as hydrogen production based on crude oil steam reforming.


image file: c8se00008e-f11.tif
Fig. 11 Influence of an increasing share of renewable energy sources within the electricity mix used for electrolysis on the GHG emissions of H2 production by electrolysis. Excluding compression for storage and transport; given the assumption that the proportion of the shares of the fossil energy sources in the electricity mix stays the same; values from the literature for H2 production by steam reforming of natural gas45 and crude oil.45

Summarizing, the environmental performance of hydrogen provision depends strongly on the electricity sources used for electrolysis and especially on the share of renewable electricity sources. A sufficient percentage of renewables within the electricity mix used for electrolysis is needed in order to provide hydrogen that has lower GHG emissions than hydrogen produced by steam reforming.

5 Summary

In the following, the results from our different steps of our assessment are merged. At first, the grid simulation revealed that, in this study, the curtailed energy is highest for the cases where grid extension is limited. Relating to the 2035 target network (“Base” scenario), the requirement for redispatch measures (±10 TWh), the curtailed energy (1 TWh) and therefore the power-to-gas potential is lowest. In contrast, considering the “Start + A2” scenario, the redispatch volume increases to ±60 TWh and curtailed energy increases to 22 TWh bearing the highest potential for PtG application. However, it remains to be analyzed if grid extension is the most economic measure to deal with large energy quantities from renewables in the case of a trans-regional and network-wide dramatic increase of renewable power feed-in.

Table 9 displays the main results of our study. In the field of node-based electrolyzer operation (cases 1a–1c) the lowest hydrogen production cost as well as the total hydrogen cost have been found for case 1a to be 3.87€ kgH2−1 and 4.21€ kgH2−1, respectively, if electricity market prices are assumed. This case is characterized by the highest operational hours of the electrolyzer and lowest storage size requirements. In general we found that full-load hours and storage size are reversely correlated given the specific assumptions of this study. The total hydrogen yield ranges from 124 to 189 ktH2 per annum. Based on passenger car statistics for Germany with an annual mileage of 14[thin space (1/6-em)]000 km per annum and assuming the hydrogen consumption of the FCV Toyota Mirai to be 0.76 kgH2 (100 km)−1, the produced hydrogen would cover the fuel demand of 1.2–1.8 million FCVs.

Table 9 Results from our assessmenta
Case EL capacity and full-load hours [MW]|[h per annum] H2 production volume [ktH2 per annum] Cost of H2 prod.|logistics [€ kgH2−1] Total H2 cost [€ kgH2−1] Total GHG emissions [gCO2-eq. kgH2−1]
a EL: electrolyzer; GHG: greenhouse gases; prod.: production.
Case 1a 3307 189 3.87|0.58 4.45 413
Case 1b 3767 149 4.51|0.83 5.34 435
Case 1c 3623 124 4.85|0.96 5.81 412
Case 2 1530 189 3.15|0.48 3.63 17[thin space (1/6-em)]106


Relating to the trading price-driven operation of the electrolyzer (case 2) we found that up to a trading price of 24€ MWhe−1 the desired hydrogen volume – that was set to be 189 ktH2 per annum in accordance with case 1a – cannot be achieved. The lowest hydrogen costs have been found for a trading price of 54€ MWhe−1 at an electrolyzer capacity of 1530 MWe. This result is consistent with data obtained in Mansilla et al.15 who derived 56€ MWhe−1. Also, hydrogen cost which has been determined in the present study to be 3.15€ kgH2−1 is in agreement with Mansilla et al. who found 3.27€ kgH2−1. Total hydrogen cost related to case 2 of our study is determined to be 3.59€ kgH2−1.

Related to the environmental performance of the cases considered in our study we found the lowest GHG emissions for case 1c with 412 gCO2-eq. kgH2−1. While differences between the GHG levels are small comparing cases 1a–1c, case 2 shows the highest emission level of 17[thin space (1/6-em)]106 gCO2-eq. kgH2−1 because electricity for operating the electrolyzers is assumed to have the actual GHG levels of the German electricity mix. Expectedly, the share of renewable power in the electricity mix is decisive for the overall GHG balance. In our analysis the GHG emission level is for case 2 even higher as compared to the conventional hydrogen production from natural gas or crude oil. The small deviations of GHG levels of cases 1a–1c result almost exclusively from storage and transport-related emissions.

These results are consistent with findings from previous studies. According to the literature, hydrogen produced from electrolysis with renewable energy sources (wind and PV) is associated with GHG emissions of 600 gCO2-eq. kgH2−1 (ref. 45) up to 2500 gCO2-eq. kgH2−1.46–48 However, using grid electricity as an energy source for electrolysis leads to considerably higher GHG emissions of hydrogen production. Studies report emissions ranging from 8000 gCO2-eq. kgH2−1 (ref. 49) to 27[thin space (1/6-em)]600 gCO2-eq. kgH2−1.45 Thus, GHG emission results from our study are of the same order of magnitude as findings from previous studies. In particular, GHG emission results in the literature also depend strongly on the assumed electricity sources for electrolysis. Differences between our findings and previous studies may be caused by different assumptions concerning the system boundary, e.g., the year under consideration, the mix of power generation technologies or the incorporation of construction and maintenance of infrastructure. Reiter et al.,45 for example, relate their GHG emissions in the case of purely renewable-based production exclusively to wind power utilization and assume an electrolyzer efficiency of 57%, which is significantly lower compared to the value in our study of 70%.45 Other studies provide processed data from the literature as a review with a broad spectrum of assumptions.

6 Conclusions

Future energy systems may face severe challenges in handling large quantities of renewable electricity that may not be usable within the traditional electric network. Moreover, the utilization of hydrogen as a storage medium for such energy offers a promising opportunity to reduce greenhouse gas emissions and to mitigate the dependence on imported feedstocks.

In this paper, we presented results from our Germany-based study focusing on electrolysis-based hydrogen production analysis integrated with a thorough electric network and market simulation, a hydrogen infrastructure assessment and an environmental performance analysis related to GHG emission levels. Assuming the utilization of otherwise curtailed electricity in the node-based assessment case 1 of our study, we showed that with ongoing electric network extension the obtainable hydrogen quantities decrease. Given our assumptions based on plans for the German network extension through 2035, the maximum production level was determined to be 189 kilotons of annual hydrogen for the state of Schleswig-Holstein, which provides the largest potential of surplus power for the least extended network. This would be enough hydrogen to cover the fuel demand of a large passenger car fleet. When considering hydrogen production purely from renewable energy, GHG emissions result from operating hydrogen logistics components, particularly from using grid-based electricity for hydrogen compression. GHG emissions ranging from 412–435 gCO2-eq. kgH2−1 and specific hydrogen provision costs of 4.45–5.81€ kg−1 were found for case 1. We compared the cost and GHG emission results with a second scenario, namely case 2, where the electrolyzer is operated according to market prices depending on the merit order of power production. The lowest hydrogen cost is found to be 3.63€ kg−1 for this second case. With some 17[thin space (1/6-em)]000 gCO2-eq. kgH2−1 GHG emissions, however, are significantly higher because the emission factor of the German electricity generation mix would apply for electricity that is utilized for electrolysis in this case. GHG emissions are then even higher as compared to producing hydrogen from steam reforming of natural gas or crude oil.

Based on our study we found that future work should focus on the potential for hydrogen production for the case of a network-wide massive, i.e., Europe-wide, increase of renewable power generation. Additionally, a further research topic should focus on avoided the cost of grid extension if otherwise curtailed renewable power would be utilized for producing hydrogen. On the downside an increased demand for redispatch measures must be considered.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This work was performed as part of the project “Technology-based Energy Systems Analysis” of JARA ENERGY which is funded by the Excellence Initiative of the German federal and state governments to promote science and research at German universities.

References

  1. N. Armaroli and V. Balzani, Towards an electricity-powered world, Energy Environ. Sci., 2011, 4, 3193–3222 Search PubMed .
  2. D. Stolten, et al., Hydrogen as an Enabler for Renewable Energies, Transition to Renewable Energy Systems, Wiley-VCH Verlag GmbH & Co. KGaA, 2013, pp. 195–216, ISBN 978-3-527-33239-7 Search PubMed .
  3. S. H. Jensen, et al., Large-scale electricity storage utilizing reversible solid oxide cells combined with underground storage of CO2 and CH4, Energy Environ. Sci., 2015, 8, 2471–2479 Search PubMed .
  4. M. A. Pellow, et al., Hydrogen or batteries for grid storage? A net energy analysis, Energy Environ. Sci., 2015, 8, 1938–1952 Search PubMed .
  5. H. Safaei and D. W. Keith, How much bulk energy storage is needed to decarbonize electricity?, Energy Environ. Sci., 2015, 8, 3409–3417 Search PubMed .
  6. B. A. Pinaud, et al., Technical and economic feasibility of centralized facilities for solar hydrogen production via photocatalysis and photoelectrochemistry, Energy Environ. Sci., 2013, 6, 1983–2002 Search PubMed .
  7. S. Schiebahn, et al., Power to gas: Technological overview, systems analysis and economic assessment for a case study in Germany, Int. J. Hydrogen Energy, 2015, 40, 4285–4294 CrossRef .
  8. J. d. Bucy, et al., Non-individual transport – Paving the way for renewable power-to-gas (RE-P2G) - Sector Analysis and Policy Recommendation, IEA-RETD, Utrecht, 2016 Search PubMed .
  9. G. Gahleitner, Hydrogen from renewable electricity: An international review of power-to-gas pilot plants for stationary applications, Int. J. Hydrogen Energy, 2013, 38, 2039–2061 CrossRef .
  10. M. Götz, et al., Renewable Power-to-Gas: A technological and economic review, Renewable Energy, 2016, 85, 1371–1390 CrossRef .
  11. I. Ridjan, et al., The Role of Electrolysers in Energy Systems: Energy Markets, Grid Stabilisation and Transport Fuels, Aalborg University, Department of Development and Planning, Aalborg, 2016 Search PubMed .
  12. M. Reuß, et al., Seasonal storage and alternative carriers: A flexible hydrogen supply chain model, Appl. Energy, 2017, 200, 290–302 CrossRef .
  13. J. Barton and R. Gammon, The production of hydrogen fuel from renewable sources and its role in grid operations, J. Power Sources, 2010, 195, 8222–8235 CrossRef .
  14. S. Bennoua, et al., Role of hydrogen in resolving electricity grid issues, Int. J. Hydrogen Energy, 2015, 40, 7231–7245 CrossRef .
  15. C. Mansilla, et al., Reducing the hydrogen production cost by operating alkaline electrolysis as a discontinuous process in the French market context, Int. J. Hydrogen Energy, 2011, 36, 6407–6413 CrossRef .
  16. S. Samsatli, et al., Optimal design and operation of integrated wind-hydrogen-electricity networks for decarbonising the domestic transport sector in Great Britain, Int. J. Hydrogen Energy, 2016, 41, 447–475 CrossRef .
  17. M. Robinius, et al., Linking the Power and Transport Sectors—Part 1: The Principle of Sector Coupling, Energies, 2017, 10, 956 CrossRef .
  18. M. Robinius, et al., Linking the Power and Transport Sectors—Part 2: Modelling a Sector Coupling Scenario for Germany, Energies, 2017, 10, 957 CrossRef .
  19. M. Robinius, et al., A Top-Down Spatially Resolved Electrical Load Model, Energies, 2017, 10, 361 CrossRef .
  20. M. Bailera, et al., Power to Gas projects review: Lab, pilot and demo plants for storing renewable energy and CO2, Renewable Sustainable Energy Rev., 2017, 69, 292–312 CrossRef .
  21. M. Kopp, et al., Energiepark Mainz: Technical and economic analysis of the worldwide largest Power-to-Gas plant with PEM electrolysis, Int. J. Hydrogen Energy, 2017, 42, 13311–13320 CrossRef .
  22. S. Raths: Multi Level European Electricity Market Simulation using Network Flow Algorithm and Lagrangian Relaxation. In proceedings: Operations Research 2013 (OR2013), Rotterdam, Netherlands, September 03-06, 2013 Search PubMed .
  23. Operation Handbook – Policy 3: Operational Security, 2013 Search PubMed.
  24. Gesetz über die Elektrizitäts- und Gasversorgung (Energiewirtschaftsgesetz - EnWG), 2015.
  25. M. Carrion and J. M. Arroyo, A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem, IEEE Trans. Power Syst., 2006, 21, 1371–1378 CrossRef .
  26. Scenario Outlook & Adequacy Forecast (SO&AF), 2015.
  27. Szenariorahmen für die Netzentwicklungspläne Strom - Entwurf der Übertragungsnetzbetrieber, 2015.
  28. Netzentwicklungsplan Strom - Begleitdokumente (Grid Development Plan - Accompanying Documents), https://www.netzentwicklungsplan.de/mediathek/downloads/299, Aktualisierungsdatum: 28.02.2017.
  29. Netzentwicklungsplan Strom 2014-Zweiter Entwurf der Übertragungsnetzbetreiber, 50Hertz Transmission GmbH, Amprion GmbH, TenneT TSO GmbH, TransnetBW GmbH, Berlin, 2014.
  30. Monitoringbericht 2015, Bundesnetzagentur | Bundeskartellamt, Bonn, 2016 Search PubMed .
  31. V. Tietze and D. Stolten, Thermodynamics of Pressurized Gas Storage, in Hydrogen Science and Engineering, ed. D. Stolten and E. Bernd, Wiley-VCH, Weinheim, 2016 Search PubMed .
  32. T.-P. Chen, Hydrogen Delivery Infrastructure Options Analysis, Nexant Inc., 2010 Search PubMed .
  33. W. Eifler, et al., Küttner Kolbenmaschinen: Kolbenpumpen, Kolbenverdichter, Brennkraftmaschinen, Vieweg+Teubner Verlag, 2008, ISBN 9783835100626 Search PubMed .
  34. D. B. Myers, et al., Cost and Performance of Stationary Hydrogen Fueling Appliances, 2002 Search PubMed .
  35. Report on Unit Investment Cost Indicators and Corresponding Reference Values for Electricity and Gas Infrastructure, Ljubljana, 2015 Search PubMed .
  36. Netzentwicklungsplan Gas, 2015 Search PubMed .
  37. J. Mischner, et al., gas2energy.net: Systemplanung in der Gasversorgung - gaswirtschaftliche Grundlagen, Oldenbourg Industrieverlag, 2011, ISBN 9783835632363 Search PubMed .
  38. G. Cerbe: Grundlagen der Gastechnik: Gasbeschaffung- Gasverteilung- Gasverwendung, Hanser Fachbuchverlag, 2008, ISBN 9783446413528 Search PubMed .
  39. DIN EN ISO 14040, Environmental management – Life cycle assessment – Principles and framework (ISO 14040:2006), July 2006 Search PubMed .
  40. DIN EN ISO 14044, Environmental management – Life cycle assessment – Requirements and guidelines (ISO 14044:2006), July 2006 Search PubMed .
  41. J. C. Koj, et al., Life Cycle Assessment of Improved High Pressure Alkaline Electrolysis, Energy Procedia, 2015, 75, 2871–2877 CrossRef .
  42. U. R. Fritsche and H.-W. Greß, Der nichterneuerbare kumulierte Energieverbrauch und THG-Emissionen des deutschen Strommix im Jahr 2015 sowie Ausblicke auf 2020 und 2030, Internationales Institut für Nachhaltigkeitsanalysen und -strategien GmbH, Darmstadt, 2016 Search PubMed .
  43. Standort Heide, Aktualisierungsdatum: 06.03.2017, Nord-West Kavernengesellschaft mbH Search PubMed.
  44. M. Robinius, et al., An Energy Concept for Supplying the Transport Sector with Hydrogen from Renewable Energy Sources. In proceedings: 21st World Hydrogen Energy Conference, Zaragoza, June 13-17, 2016 Search PubMed .
  45. G. Reiter and J. Lindorfer, Global warming potential of hydrogen and methane production from renewable electricity via power-to-gas technology, Int. J. Life Cycle Assess., 2015, 20, 477–489 CrossRef .
  46. R. Bhandari, et al., Life cycle assessment of hydrogen production via electrolysis – a review, J. Cleaner Prod., 2014, 85, 151–163 CrossRef .
  47. E. Cetinkaya, et al., Life cycle assessment of various hydrogen production methods, Int. J. Hydrogen Energy, 2012, 37, 2071–2080 CrossRef .
  48. A. Ozbilen, et al., Comparative environmental impact and efficiency assessment of selected hydrogen production methods, Environ. Impact Assess. Rev., 2013, 42, 1–9 CrossRef .
  49. I. Dincer and C. Acar, Review and evaluation of hydrogen production methods for better sustainability, Int. J. Hydrogen Energy, 2015, 40, 11094–11111 CrossRef .

Footnotes

Electronic supplementary information (ESI) available. See DOI: 10.1039/c8se00008e
IFHT: Institute for High Voltage Technology, RWTH Aachen University.
§ Shares are determined based on the simulation of the gross electricity production in Germany in 2035 excluding imports and exports with neighboring countries. This approach is chosen analogous to Fritsche & Greß.42

This journal is © The Royal Society of Chemistry 2018