HYDROGEN – THE FUTURE FUEL FOR CONSTRUCTION EQUIPMENT?
A well to tank analysis of hydrogen powered machine applications at Volvo CE
ANDREAS SJÖDIN ELIAS EKBERG
School of Business, Society and Engineering Supervisor MDH: Jakub Jurasz Course: Degree project in energy engineering Supervisor Volvo CE: Johan Ask Course code: ERA402 and ERA403 Examiner: Valentina Zaccaria Credits: 30 Customer: Volvo CE Eskilstuna Program: Master programs in engineering, Date: 2020-06-04 Energy systems and Industrial engineering Email: [email protected] [email protected]
ABSTRACT
As the world is moving towards a more sustainable energy perspective, construction equipment sees the requirement to change its current way of operation with fossil fuels to reduce its environmental impact. In order to pursue the electrification of construction equipment a dense power source is essential, where hydrogen powered fuel cells have the potential to be a sufficient energy source. This thesis work is carried out in order to find the least CO2 emissive pathway for hydrogen to various construction sites. This is done by collecting state of the art data for production, processing and storage technologies. With the assembled data an optimization model was developed using mixed integer linear programming.
The technologies found that showed promising adaptability for construction equipment in the state of art regarding production were steam methane reforming (SMR), proton exchange membrane electrolyser (PEMEC) and alkaline electrolyser. They showed promising characteristics due to their high level of maturity and possibility for reducing the environmental impact compared to the current operation. To investigate the hydrogen pathway and its possibilities, four scenarios were created for four types of construction sites. The scenarios have different settings for distance, grid connection and share of renewables, where the operations have various energy profiles that is to be satisfied.
The optimal hydrogen pathway to reduce the CO2 emissions according to the model, were either PEMEC on-site or gaseous delivery of SMR CCS produced hydrogen. The share of renewables in the energy mix showed to be an important factor to determine which of the hydrogen pathways that were chosen for the different scenarios. Moreover, in the long run PEMEC was considered to be a more sustainable solution due to SMR using natural gas as feedstock. It was therefore concluded that for a high share of renewables PEMEC was the optimal solution, where for a low share of renewables SMR CCS produced hydrogen was optimal as the energy mix would result in a more emissive operation when using PEMEC.
Keywords: Hydrogen, Well to tank, Hydrogen storage, MILP
PREFACE
This master thesis has been exciting and insightful work within the area of study. We would like to extend our gratitude to our supervisors Jakub Jurasz and Johan Ask, who both provided valuable advice, constructive criticism and helpful contributions which lead to entertaining and interesting meetings.
Andreas Sjödin and Elias Ekberg, June 2020
CONTENT
1 INTRODUCTION ...... 1
1.1 Background ...... 2 1.1.1 Hydrogen ...... 3 1.1.2 Well to tank ...... 3 1.1.3 Steam methane reforming ...... 4 1.1.4 Partial Oxidation Reforming ...... 5 1.1.5 Autothermal Reforming ...... 6 1.1.6 Gasification ...... 6 1.1.7 Alkaline electrolysis ...... 6 1.1.8 Proton exchange membrane electrolysis...... 7 1.1.9 Solid oxide electrolysis ...... 8 1.1.10 Fuel cells ...... 9 1.1.11 Compressed hydrogen gas ...... 10 1.1.12 Liquefied hydrogen ...... 11
1.2 Problem ...... 12
1.3 Purpose/Aim ...... 13
1.4 Research questions ...... 13
1.5 Delimitation ...... 14
2 METHOD ...... 14
2.1 Selection of method ...... 14
2.2 Literature assemblage ...... 15
2.3 Modelling...... 16
3 THEORETICAL FRAMEWORK ...... 17
3.1 Reforming methods ...... 17 3.1.1 Steam methane reforming ...... 18 3.1.2 Additional reforming technologies ...... 19 3.1.3 Gasification technologies ...... 20
3.2 Electrolysis ...... 22 3.2.1 Energy characteristics ...... 22 3.2.2 Economic characteristics ...... 22
3.3 Pressurized hydrogen storage ...... 23
3.4 Cryogenic liquefied hydrogen ...... 26
3.5 Fuelling interface ...... 26
3.1 Field study – hydrogen pathways ...... 27
3.2 Summary literature review ...... 27
4 CURRENT STUDY ...... 29
4.1 Scenario management ...... 29 4.1.1 Operations ...... 30
4.2 Input data ...... 32 4.2.1 Hydrogen demand ...... 33
4.3 Assumptions ...... 34
4.4 Optimization setup ...... 35 4.4.1 Cost equations ...... 37
5 RESULTS ...... 39
5.1 Mine evaluation scenario 1-4 ...... 40
5.2 Light evaluation scenario 1-4 ...... 41
5.3 Road evaluation scenario 1-4 ...... 42
5.4 Quarry evaluation scenario 1-4 ...... 43
5.5 CO2 & Cost evaluation ...... 45
5.1 Sensitivity analysis ...... 46
6 DISCUSSION...... 47
6.1 Site assessment ...... 47
6.2 Sensitivity analysis ...... 49
6.3 Method evaluation ...... 50
6.4 Assumptions discussion ...... 50
7 CONCLUSIONS ...... 51 8 SUGGESTIONS FOR FURTHER WORK ...... 52
9 REFERENCES ...... 54
APPENDIX 1 REFERENCES 3.6 SUMMARY LITERATURE REVIEW
APPENDIX 2 CODE
LIST OF FIGURES
Figure 1 Steam reforming process ...... 4 Figure 2 Schematic of alkaline electrolyser...... 7 Figure 3 Schematic of PEM electrolyser ...... 8 Figure 4 Schematic of solid oxide electrolyser ...... 9 Figure 5 Schematic PEM fuel cell ...... 9 Figure 6 Cryo-compressed hydrogen storage schematic ...... 11 Figure 7 Simplified schematic of liquefaction process ...... 12 Figure 8 Energy densities for various sources ...... 13 Figure 9 Model schematic ...... 15 Figure 10 Hydrogen production with natural gas as feedstock ...... 19 Figure 11 Presentation of type of pressure vessels ...... 24 Figure 12 well to tank analysis ...... 29 Figure 13 Flowsheet of energy profiling ...... 34 Figure 14 Colours representing different production options ...... 39 Figure 15 Total hydrogen demand for various sites ...... 39 Figure 16 Top left: SE, 10 km; Top right PL, 10 km; Bottom left, EU28, 10 km; Bottom right; EU28, 500 km ...... 40 Figure 17 Top left: SE, 10 km; Top right PL, 10 km; Bottom left, EU28, 10 km; Bottom right; EU28, 500 km ...... 41 Figure 18 Top left: SE, 10 km; Top right PL, 10 km; Bottom left, EU28, 10 km; Bottom right; EU28, 500 km ...... 42 Figure 19 Top left: SE, 10 km; Top right PL, 10 km; Bottom left, EU28, 10 km; Bottom right; EU28, 500 km ...... 43 Figure 20 scenario 3 without electrolyser ...... 44 Figure 21 Top/Bottom left: Energy mix 57g CO2/kWh; Top/Bottom right, Energy mix 58g CO2/kWh ...... 46
LIST OF TABLES
Table 1 Production characteristics ...... 28 Table 2 Machine nomenclature ...... 30 Table 3 Scenario matrix ...... 31 Table 4 Input data ...... 33 Table 5 Emitted CO2 ...... 45 Table 6 Total Costs ...... 45
NOMENCLATURE
Symbol Description Unit Efficiency % Bar Pressure N/m2 HHV Higher heating value MJ/kg LHV Lower heating value MJ/kg
m0 Initial mass of tank kg
mmax Maximal capacity of tank kg
mmin Minimal mass in tank kg P Power J/s Pa Pressure N/m2 Q Heat J W Work J
ABBREVIATIONS
Abbreviation Description AE Alkaline electrolyser AR Autothermal reforming BEV Battery electrical vehicle CAPEX Capital expenditures
CcH2 Cryo-compressed hydrogen CCS Carbon capture and storage
CH2 Compressed hydrogen FCEV Fuel cell electric vehicle GHG Greenhouse gas H2P Hydrogen density to power ratio HHV Higher heating value HSS Hydrogen storage system LCA Life Cycle Assessment MILP Mixed integer linear programming NG Natural gas NPV Net present value Abbreviation Description
LH2 Liquid hydrogen LHV Lower heating value OPEX Operational expenditures PEMEC Proton-exchange membrane/Polymer electrolyte membrane electrolyser cell PEM Proton-exchange membrane POX Partial oxidation reforming RES Renewable energy source SEC Specific energy consumption SMR Steam methane reforming SOC State of charge SOEC Solid oxide electrolyser cell SR Steam reforming WGS Water gas shift WTT Well to tank WTW Well to wheel
DEFINITIONS
Definition Description Endothermic A chemical reaction that absorbs energy from the surroundings Exothermic A chemical reaction that releases energy Well to tank Analysis of emissions for the entire chain from well to tank for a fuel 1 INTRODUCTION
With today’s reliance on fossil fuel powered applications and increased awareness of climate impact, the advancement of emission free alternatives has shown promising development. The push of electrical vehicles and integration of energy storage capabilities has expanded greatly, where production and installation of storage systems attempts to satisfy the increasing demand (Killer et al., 2020; Koohi-Fayegh & Rosen, 2020). Batteries have proven useful in several applications due to flexibility, cost and availability (Srinivasan, 2006, p.95). However, batteries struggle when it comes to high power intensive operations due to high discharging rates and charging need as it leads to a rapid reduction in overall lifetime (Araujo et al., 2017; Armenta-Deu et al., 2019), which provides possibilities for other technologies to emerge.
Hydrogen energy has matured during the latest years by seeing development in different areas such as transport, fuel cells and storage possibilities (Hirscher et al., 2019). As with other technologies there are some obstacles regarding storage, infrastructure and obtaining the hydrogen (Abdalla et al., 2018). Barriers for the hydrogen economy are its energy requirement for processing and costly materials, infrastructure readiness and sustainable production methods (Durbin & Malardier-Jugroot, 2013; Zheng et al., 2012)
This study will investigate the potential of a hydrogen powered construction equipment at various sites and its storage capabilities and ability to satisfy the construction equipment’s power demand. Construction equipment which consists of excavators, haulers, rollers, and loaders usually operates at peak power during processes where the need of a dense power source would be beneficial. The construction machines disadvantages are the difficulties regarding high emissions, low efficiency and high fuel consumption. Hence, the complete electrification with battery as storage equipment will provide obstacles in satisfying the energy intense operations of construction machinery in the aspects of charging, discharging and peak power potential (Durbin & Malardier-Jugroot, 2013; T. Li et al., 2018).
Currently Volvo CE has launched a campaign for its first zero-emissions battery based compact excavators and wheel loaders, which is to be commercially available from mid-2020. Considering the obstacles with battery storage, the charging time for these machines are currently 1-2 hours to reach an 80 % charge for a 20 – 39 kWh capacity (Volvo CE, 2019). When comparing the aspect of charging with hydrogen as an energy carrier, the time for re- fuelling of heavy-duty vehicles according to current standards are mere minutes (7.2 kg/min or app. 240 kWh/min). (SAE International, 2014; Wen et al., 2020).
When considering electrification of heavy-duty vehicles, the usual challenge is to overcome range and weight. There are several technologies that are under rapid development to face these challenges such as hybrid vehicles, battery electric vehicles and range-extender electric vehicles (Çabukoglu et al., 2018; Naumanen et al., 2019; Smallbone et al., 2020). However, as
1 previously mentioned the electrification of construction equipment faces difficulties regarding high power outtakes under short periods of time and strained intervals for re- fuelling which makes batteries a less suitable option for these kinds of operations.
With emission legislations becoming stricter it encourages manufacturers to further investigate electrification possibilities of their current business and determine its compatibility (IEA, 2019a). The construction equipment investigated in this study are from Volvo CE which has a large variety in their equipment’s size, demand and applications. The study will then look at the entire hydrogen supply chain and through optimization methods determine the least emission intensive pathway of hydrogen for various construction sites.
1.1 Background
This section will present the relevant technological background for this thesis work. The background will contain hydrogen production technologies, hydrogen applications and storage methods. The technologies principle and way of operation will be described and provide fundamental knowledge about the subject.
The procedure of producing hydrogen can be based on several techniques. Hydrogen generation can be produced by reforming hydrocarbons, biomass/coal gasification or electrolysis of water. Reforming methods are based on converting hydrocarbons into a hydrogen-rich gas where the hydrogen later is separated and purified. The three major reforming processes are steam reforming (SR), partial oxidation (POX) and autothermal reforming (AR) (O’Hayre et al., 2006, p.295; Srinivasan, 2006, p.384). Gasification of biomass/coal is based on heating a fuel in a sub-stoichiometric environment to produce a hydrogen-rich syngas. Three common types of gasification technologies are steam gasification, air gasification and oxygen gasification (Jeong et al., 2020; Srinivasan, 2006, p.383; Yaning Zhang et al., 2019). Production of hydrogen by electrolysis is done by applying a direct current to the cathode which enables the split of water molecules into hydrogen and oxygen. The most developed electrolysers are the proton exchange membrane (PEM), alkaline, and solid-oxide electrolysers (SOEC) (Srinivasan, 2006, p.402).
To utilize hydrogen in operations a proper storage of the fuel is necessary. Many different storage techniques have been investigated through the years which have resulted in many options with different key challenges, like boil off, low efficiency and high cost (Barthelemy et al., 2017; Moradi & Groth, 2019). The different storage systems can be divided in to two categories in how the hydrogen is stored, physical storage and material-based storage with additional subcategories of stationary and mobile storage. Physical storage includes compressed hydrogen gas and liquefied hydrogen. The material-based storage systems are based on storing hydrogen in metals and chemical compounds. The material based techniques are not likely to be in use for commercial purposes in a close future and will not be discussed in this report (Moradi & Groth, 2019).
2 1.1.1 Hydrogen Hydrogen is an abundant and well-known energy source which is considered both clean and renewable due to its emission only being water when combusted or used in fuel cell applications. (Abdalla et al., 2018). Hydrogen is widely used as an energy carrier or to store energy with potential of substituting the depleting fossil-fuelled infrastructure today’s energy applications are dependent on. Hydrogen is also beneficial due to it being a dense power source with a lower heating value of 120 MJ/kg and a higher heating value of 142 MJ/kg (Wester, 2015). The abundance of hydrogen is not found in its useful state in nature even though its vast quantity is stored in sea water, where largest potential of hydrogen attainability can be seen. Otherwise, hydrogen is usually found in hydrides (the anion of hydrogen, 퐻−), gases or other compounds (Abdalla et al., 2018; Stern, 2018).
The use of hydrogen applications has increased in popularity during the 21st century which can be seen in the first public instalment of a hydrogen fuel station in the UK 2011 and development of a hydrogen powered train in Germany 2018 (Din & Hillmansen, 2018). Furthermore, hydrogen is a hot topic and developments regarding material for storage, production, performance and storage techniques has seen promising development and presented key challenges for future research in the fields of transport, storage and infrastructure (Koohi-Fayegh & Rosen, 2020; Sharaf & Orhan, 2014; Xueping et al., 2019).
Hydrogen applications for heavy industry and construction equipment is a fairly unexplored area. Recently in March 2020, Hyundai construction equipment together with Hyundai motor group has begun development of hydrogen powered construction equipment (Fuel Cells Bulletin, 2020). According to the article there are limitations of lithium ion batteries regarding increase of battery capacity and structural issues for construction equipment. The development of hydrogen fuel cells however is showing promising technical aspects when examining the sizing of the total system capacity which makes them appealing for construction equipment applications.
1.1.2 Well to tank Well to wheel (WTW) analysis is a commonly used term in the automotive business which opts to analyse the entire energy flow for a fuel. The parameters that often are being analysed is greenhouse gas (GHG) emissions, energy efficiency and cost (Woo et al., 2017). A WTW analysis differs from a life cycle assessment (LCA) thus it only focuses on the emissions directly connected to the fuel, LCA is also taking construction of cars and end of lifetime in to consideration (European commission, 2016). A WTW chain differs between different fuels and applications. Fuels that are produced and used on site can have a shorter chain as no transportation is necessary. A typical WTW chain for hydrogen in electric vehicles is described in the following way (E. Yoo et al., 2018).
푈푝푠푡푟푒푎푚 → 퐻2 푝푟표푑푢푐푡𝑖표푛 → 퐶표푚푝푟푒푠푠𝑖표푛 & 푑𝑖푠푡푟𝑖푏푢푡𝑖표푛 → 푉푒ℎ𝑖푐푙푒 표푝푒푟푎푡𝑖표푛
Upstream refers to the processes that is done before the hydrogen production is performed. The processes vary between the different production techniques, e.g. if steam methane
3 reforming is used to produce hydrogen the extraction, treatment and transportation of natural gas must be considered.
Well to wheel can be divided in two parts, well to tank and tank to wheel (TTW). Well to tank is referring to the parts in the chain between the well and the final storage, i.e. the processes after the storage is excluded. TTW refers to the steps between the storage tank and the consumption in the vehicle (European commission, 2016)
1.1.3 Steam methane reforming Steam methane reforming (SMR) is a widely used method for large-scale hydrogen production (Sharma et al., 2019), meeting approximately 50 % of global demand (Dincer & Acar, 2015a). The concept of steam methane reforming is the reaction of steam and methane to eliminate impurities from the natural gas which usually is the feedstock. The catalysts in the steam-reforming process are sensible to sulphur compounds which presents the need for desulphurization. The natural gas is then mixed with the steam in a reformer which contains a catalyst typically consisting of nickel or another non-precious metal, where a typical effectiveness of the catalyst is 5 % (Abdalla et al., 2018). In the reformer an endothermic reaction takes place where heat is added through combustion of a fuel e.g. natural gases. Operating temperature and pressure usually lie within the interval of 750 °C to 850 °C and 3- 25 bar (Abdalla et al., 2018; Sharma et al., 2019; Srinivasan, 2006, p.384).
Figure 1 Steam reforming process inspired by (Sharma et al., 2019)
The endothermic reaction (1):
Equation 1
퐶퐻4(𝑔) + 퐻2푂(𝑔) + 퐻푒푎푡 ↔ 퐶푂(𝑔) + 3퐻2(𝑔)
Additionally, the following reactions are taking place in the reformer (2), (3) and (4):
Equation 2
퐶푂(𝑔) + 퐻2푂(𝑔) ↔ 퐶푂2(𝑔) + 퐻2(𝑔) + 퐻푒푎푡
Equation 3
퐶퐻4(𝑔) + 퐻푒푎푡 ↔ 퐶(푠) + 2퐻2(𝑔)
4 Equation 4
2퐶푂(𝑔) ↔ 퐶푂2(𝑔) + 퐶(푠) + 퐻푒푎푡
Reaction (3) and (4) results in coke solids on the catalyst which is reduced by excess steam in the reformer to further encourage reaction (1) and (2) and prevent coke deposition (Sharma et al., 2019). The outlet of the reformer is syngas which consists of 퐶푂, 퐶푂2, 퐻2, 퐻2푂 and 퐶퐻4 that has not been reformed. Furthermore, to increase the 퐻2 yield the 퐶푂 can be shifted by the exothermic reaction (2) which is a water-gas shift reaction. To further purify the hydrogen a pressure swing adsorption (PSA) unit is installed where the shifted hydrogen-rich gas is produced with a purity of 퐻2>99.99% by mole. (O’Hayre et al., 2006, p.306). Advantages of steam reforming is the highest methane yield but requirements of thermal management to provide heat for the reactions. Also, the process is sensible to the type of fuel used (O’Hayre et al., 2006, p.295).
1.1.4 Partial Oxidation Reforming Partial oxidation reforming (POX) is an exothermic reaction that undergoes the mixture of oxygen to partially oxidize/combust fuels containing hydrocarbons into 퐶푂 and 퐻2 (Srinivasan, 2006, p.389). In POX the fuel is combusted with less than the stoichiometric amount of 푂2 needed for complete combustion providing the desired product of 퐻2 and other gases like 퐶푂 as a by-product. In reactions (5), (6) and (7) examples of complete and incomplete combustion of propane are shown:
Equation 5
퐶3퐻8 + 5푂2 ↔ 3퐶푂2 + 4퐻2푂
The incomplete combustion that obtains 퐻2 and 퐶푂:
Equation 6
퐶3퐻8 + 푥푂2 ↔ 푦퐶푂 + 푧퐻2
Balancing according to the law of conservation of mass:
Equation 7
퐶3퐻8 + 1.5푂2 ↔ 3퐶푂 + 4퐻2
Comparing reaction (5) and (7) the stoichiometric need of 푂2 is 5 mol 푂2 / mol 퐶3퐻8, which in reaction (7) the quantity of 푂2 is less than the required amount for complete combustion. Benefits with POX is quick response time due to exothermic reactions and the disadvantage is a fairly low 퐻2 production and high emissions (O’Hayre et al., 2006, p.295). The 퐻2 yield could be increased with the same procedure as the SMR method, by the water-gas reaction shifting of the 퐶푂 according to reaction (2) (Srinivasan, 2006, p.389).
5 1.1.5 Autothermal Reforming Autothermal reforming (AR) is the combination of SMR reaction, POX reaction and water- gas shifting (WGS) in a single process. The process combines the reactions from AR such that they proceed in the same chemical reactor and the SMR and WGS required heat (endothermic) is provided by the exothermic POX reaction (O’Hayre et al., 2006, p.299; Srinivasan, 2006, p.390). The AR integrates the methods from SMR by using steam as a reactant and by using sub-stoichiometric amount of 푂2 it includes the POX methodology. (O’Hayre et al., 2006, p.299). The general reaction for autothermal reforming is:
Equation 8 1 1 퐶 퐻 + 푧퐻 푂 + (푥 − 푧) 푂 ↔ 푥퐶푂 + (푧 + 푦) 퐻 => 퐶푂, 퐶푂 , 퐻 , 퐻 푂 푥 푦 2 2 2 2 2 2 2 2 2
Reaction (8) should be energy neutral i.e. neither endothermic nor exothermic. Where the steam to carbon ratio should be chosen as z/x so that the reaction become energy neutral. Advantages of AR is simplification of thermal management by combining SMR’s endothermic reactions with POX’s exothermic reactions. Disadvantages is low hydrogen yield and careful balancing of exothermic and endothermic reactions.
1.1.6 Gasification Hydrogen can be produced by gasification processes which are based on different principles. The process takes biomass or other feedstock and converts it to a combustible gas. The gasification can be initiated by different methods such as oxygen gasification, air gasification and steam gasification (Yaning Zhang et al., 2019). The most efficient conversion method for biomass according to studies is steam gasification due to other agents provides a lower heating value due to dilution with nitrogen (air gasification) and consumption of combustible gases during the process (oxygen gasification) (Baratieri et al., 2008; Gao et al., 2008; Hosseini et al., 2012; Zhang et al., 2019).
To produce hydrogen through gasification the process can use biofuels, coals and coke. Biofuels has become a hot topic for producing hydrogen with the increased awareness of climate change, carbon neutral processes has become more attractive (Baratieri et al., 2008; Udomsirichakorn & Salam, 2014; Yao et al., 2017). The principle of gasification is to heat the fuel in a sub-stoichiometric environment to produce a combustible gas containing hydrogen. Depending on the method and agent (steam, oxygen or air) the produced gas composition will differ. Notably steam gasification seems to be favourable for a high hydrogen yield in the syngas composition (Fremaux et al., 2015; Jeong et al., 2020; Nipattummakul et al., 2010).
1.1.7 Alkaline electrolysis Electrolysis is a method which contains an anode, cathode, electricity supply and an electrolyte. Electrolysis is producing hydrogen by applying a direct current to sustain electron flow and electricity balance to the cathode where electrons are consumed by hydrogen ions to form hydrogen. By maintaining the electrical balance the hydroxide ions are transferred
6 through the electrolyte to the anode and thereby giving away their electrons (Zeng & Zhang, 2010). This procedure is a simple and well-known technology and also the reverse reaction of a fuel cell (Srinivasan, 2006, p.400). Electricity required to start this process can be e.g. be used by renewable energy sources e(RES) or other power production plants. The theoretical water requirement for water splitting via electrolysis can be derived from the chemical composition of water. The molar mass of oxygen is 16, and hydrogen 1 which yields a water requirement of 18 g water per gram hydrogen (Wester, 2015,p.295).
The production of hydrogen with alkaline electrolysis (AE) is the most commercialized and well-established technology introduced 1789 by Troostwijk and Diemann (Shiva Kumar & Himabindu, 2019; Srinivasan, 2006, p.402). The principle of the alkaline water electrolysis process is initially started with electrifying water at the cathode where the alkaline solution of − two molecules is reduced to one molecule of 퐻2 and additionally two hydroxyl ions 푂퐻 . The hydroxyl ions then transfer to the anode through a porous diaphragm and is discharged to ½
O2 and one molecule of H2O. Operating temperatures is within 30 – 80 ºC with an electrolyte concentration of 20 – 30 %. (Shiva Kumar & Himabindu, 2019). Reaction (9), (10) and (11) presents the alkaline electrolysis procedure:
Equation 9
− − Cathode: 2퐻2푂 + 2푒 → 퐻2 + 2푂퐻
Equation 10
1 Anode: 2푂퐻− → 퐻 푂 + 푂 + 2푒− 2 2 2
Equation 11
1 Overall cell reaction: 퐻 푂 → 퐻 + 푂 2 2 2 2
Figure 2 Schematic of alkaline electrolyser, own interpretation inspired by (Shiva Kumar & Himabindu, 2019)
The disadvantages with the alkaline electrolyser are the limited current densities 0.4 A/cm2, low operating pressures and a low efficiency. (Srinivasan, 2006, p.402; Zeng & Zhang, 2010).
1.1.8 Proton exchange membrane electrolysis Proton exchange membrane electrolyser cells or polymer electrolyte membrane electrolyser cells (PEMEC) are based on the principle of splitting the water molecule into hydrogen and oxygen. A PEMEC is using a solid proton exchanging membrane usually Nafion while the alkaline electrolyser is using an alkaline solution (Carmo et al., 2013). The solid membrane gives the PEMEC various good characteristics. Compared to the liquid electrolyte used in AE the membrane is much thinner, the membrane used in PEMEC usually has a thickness in the
7 range 20-300 µm (Shiva Kumar & Himabindu, 2019). This makes the proton transport in the electrolyte shorter which makes the PEMEC able to produce hydrogen with a lower power input and start-up time. The start-up time for AE is slower than PEMEC due to the inertia in the liquid electrolyte. This has shown great importance when electrolysis is used with intermittent RES (Clarke et al., 2009). The PEMEC can achieve current densities up to 2 A/cm2 compared to AE´s 0.4 A/cm2 which reduces the operational cost for a PEMEC (Carmo et al., 2013; Shiva Kumar & Himabindu, 2019).
The reactions in a PEMEC differs from the other electrolysers as the proton conducting membrane is used instead of an electrolyte. The reactions in a PEMEC is the following (Shiva Kumar & Himabindu, 2019):
Equation 12
1 Anode: 퐻 푂 → 2퐻+ + 푂 + 2푒− 2 2 2
Equation 13
+ − Cathode: 2퐻 + 2푒 → 퐻2
Equation 14
1 Overall cell reaction: 퐻 푂 → 퐻 + 푂 2 2 2 2
Figure 3 Schematic of PEM electrolyser, own interpretation inspired by (Shiva Kumar & Himabindu, 2019)
1.1.9 Solid oxide electrolysis Solid oxide electrolysis cell (SOEC) has shown promising development and interest due to its ultra-pure hydrogen production and high efficiency. One of the main differences with SOEC from PEMEC and AE is the operating temperature, a SOEC is usually operated at 500-850 ºC which contributes to high efficiency. The electrolyte used in a SOEC is a dense ionic conducting material, usually made of zirconia. (Shiva Kumar & Himabindu, 2019).
The overall reaction in a SOEC is the same as PEMEC and AE, although the anode and cathode reaction differs. An electric current is added to the cathode together with water and the water reacts with the cathode and produces hydrogen and oxygen ions. At the anode the oxygen ions react and oxygen is created (Shiva Kumar & Himabindu, 2019).
8
Equation 15
− 2− Cathode: 퐻2푂 + 2푒 → 퐻2 + 푂
Equation 16
1 Anode: 푂2− → 푂 + 2푒− 2 2
Equation 17
1 Overall cell reaction: 퐻 푂 → 퐻 + 푂 2 2 2 2
Figure 4 Schematic of solid oxide electrolyser, own interpretation inspired by (Shiva Kumar & Himabindu, 2019)
Although this technique is not yet in commercial use due to challenges with material stability as the operating temperature in a solid oxide electrolyse cell is high (Buttler & Spliethoff, 2018).
1.1.10 Fuel cells A fuel cell is a device that converts the chemical energy from a fuel in to electricity using an electrochemical process (Sharaf & Orhan, 2014). The electrochemical process is divided in two simultaneous reactions oxidation and reduction. Oxidation is the process that is occurring when the fuel is reacting with the anode and electrons leaves the fuels molecule. In a PEM fuel cell, which is one of the most commonly used fuel cells the reaction would be the following:
Equation 18
+ − 퐻2 => 2퐻 + 2푒
The other reaction is reduction which occurs at the cathode when electrons is received from the cathode to the oxidation medium, the oxidation medium in PEM fuel cells is air (Srinivasan, 2006).
Equation 19
1 + − 푂2 + 2퐻 + 2푒 => 퐻2푂 Figure 5 Schematic PEM fuel cell, own interpretation 2 inspired by (Sharaf & Orhan, 2014)
Between the anode and cathode, the electrolyte made of a proton conducting material is placed. Thus, the protons can pass through the electrolyte the electrons have to pass through
9 another material which gives an electric current that can be used. The total reaction in the fuel cell results in water, electric work and heat in the following way. (Sharaf & Orhan, 2014).
Equation 20 1 퐻 + 푂 => 퐻 푂 + 푊 + 푄 2 2 2 2 푒푙푒푐푡푟𝑖푐 ℎ푒푎푡
The applications for fuel cells have increased the past years due to the low emissions and they are widely used including propulsion systems, backup systems and portable systems. It exists various kinds of fuel cells, what varies between the fuel cells is mainly the different materials used in the cathode, anode and electrolyte. The efficiencies for the most common fuel cells are the following: PEM fuel cells is in the range 50 – 60 %, alkaline fuel cells ~60% and solid oxide fuel cells ~60 % (Sharaf & Orhan, 2014; US DOE, 2015).
1.1.11 Compressed hydrogen gas Gas phased storage is most commonly done in high pressure tanks at 350-700 bar at room temperature (Guney & Tepe, 2017; Sun et al., 2018). For stationary large-scale hydrogen storage, a unit can contain up 175 000 to 250 000 litres of hydrogen. According to literature this kind of storage is modular and thereby the economy linear (Srinivasan, 2006, p.421). With compressed hydrogen storage being the most commercially available and mature storage method, it is advantageous regarding costs, fast refuelling and simple methodology. The disadvantages is the low volumetric density as it doesn’t increase with the pressure, having high pressures also compromises safety (Yanxing et al., 2019).
Another pressurised storage possibility is cryo-compressed hydrogen. This technology is based on cryo-compressed hydrogen vessel with temperatures low as 20 K and high pressures around 250 up to 700 bar (Aceves et al., 2013; Moreno-Blanco et al., 2019; Yanxing et al., 2019). The main benefits are less evaporation losses and lower power consumption. Furthermore, the storage system is capable of thermal and pressure management by internal or external heat exchangers and self-cooling due to discharging by isentropic expansion. The disadvantage being thermal leakage (self-pressurization) which can be resolved with venting (Aceves et al., 2013; Stolten et al., 2016, p.163). Additionally, the volume of cryo-compressed gas is lower than ordinary compressed storage and some cost reduction in material can be obtained (Ahluwalia et al., 2018). In the figure below a schematic of a cryo-compressed hydrogen gas is presented.
10
Figure 6 Cryo-compressed hydrogen storage schematic – inspired by (Yanxing et al., 2019)
1.1.12 Liquefied hydrogen Hydrogen has a higher heating value (HHV) of 142 MJ/kg (Srinivasan, 2006, p.428) which is greater than the HHV of gasoline (47.3 MJ/kg). Although hydrogen is the lightest fuel with a density of 0.089 kg/m3 at normal conditions which yields a low energy content per volume unit 12.638 MJ/m3 compared to gasolines ~35 475 MJ/m3 (Di Profio et al., 2009; Srinivasan, 2006). Through liquefaction of hydrogen the energy content per volume unit can be greatly increased. Hydrogen at -253ºC has a mass density of 70.9 kg/m3 which gives an energy content per volume unit of 10 067.8 MJ/m3 (Srinivasan, 2006, p.428).
Liquid hydrogen is attractive due to the capability of transporting high amount of energy. Transport trucks for hydrogen are designed with a capacity greater than 60 000 litres (Barthelemy et al., 2017), to transport liquid hydrogen. The drawback of liquefied hydrogen is the work required to cool the hydrogen and the boil off (Hammad & Dincer, 2018) .
Hydrogen liquefaction can be classified into two major groups Linde-Hampson liquefaction and Claude liquefaction. The major difference between the two groups is how the gas is expanded, Linde-Hampson utilizes valves while Claude utilizes expanders (Sadaghiani & Mehrpooya, 2017). The process of liquefying hydrogen is usually divided in to three steps, the first step is compression, the second step is cooling through multiple nitrogen heat exchangers and the third is expansion through a valve which causes partial liquefaction. The gases that doesn’t change phase is cycled back to the compressor (Boudellal, 2018).
11
Figure 7 Simplified schematic of liquefaction process – inspired by (Boudellal, 2018)
1.2 Problem
Construction equipment is in front of a future electrification as emission legislations is becoming stricter and efficiency is increased. Due to the size of Volvos machines a complete electrification with batteries is hard to achieve due to the energy density, machine power need and lack of peak power availability that battery electric vehicle (BEV) would require for quick charging. Therefore, hydrogen fuel cells could have great potential with its higher energy density. Handling hydrogen is challenging and the creation and storage of it is important in order to industrialize the technology. Furthermore, the pathway of diesel fuelled construction equipment yields high quantities of CO2 and according to a WTT report by the European commission Edwards et al. ( 2014) the emission balance for diesel is 87 g
CO2eq/MJfinal fuel.
In the figure below the gravimetric energy density of various energy carriers are displayed, which puts further emphasis on the difficulties regarding already heavy equipment and the potential of other carriers. In the figure the gravimetric density is the energy content per kilogram of carrier, it is in relation to the volumetric energy density which is the energy content per litre of carrier. Worth noticing is the potential weight reduction and that an increase in tank storage on the equipment will be needed. However, with the machines large size and stationery move set a larger hydrogen tank compared to a battery would not be of significant issue.
Additionally, the operation time on various sites using construction equipment can be long without breaks which makes it necessary to be able to charge equipment fast. In this aspect hydrogen is preferable to batteries due to the low charging time for hydrogen while batteries lacks the possibility of rapid charging (Thomas, 2009).
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Figure 8 Energy densities for various sources - Retrieved from (Conor et al., 2017)
1.3 Purpose/Aim
The purpose with this thesis work is to determine the most suitable way to produce, store, distribute, process and transport hydrogen to construction equipment sites. This is done with regards to efficiency, CO2 emissions and economic viability. The proposed option should satisfy the energy demand of various types of construction sites which is characterized by availability of energy, existing distribution grid and energy profile of operation.
1.4 Research questions
This thesis work will be carried out in order to answer the following research questions.
• What is the most suitable pathway for production, processing and transportation of hydrogen at construction site, with regards to size, consumption and sustainability? • How can hydrogen be produced and distributed in an energy efficient way to
minimize the CO2 emissions and satisfy the energy demand of the construction equipment at site? • How will the environmental impact be compared to present operation of construction equipment and how will a future hydrogen pathway be resembled?
13 1.5 Delimitation
This work aims to present the overall efficiency of a construction site where the equipment is fuelled with hydrogen. As the operations varies at different sites and how the machines are used, the performance of the machine will not be evaluated because of design uncertainties of construction equipment. Therefore, a well to tank analysis is performed and the steps between the site tank and usage is neglected.
Regarding the upstream processes e.g. the extraction and processing of resources will not be evaluated as the scope of the work would be too substantial. The emissions connected to the production of electricity will be involved to give better credibility. The study will strive to be constrained within using technologies that are economically viable, efficient and sustainable.
2 METHOD
The current section is going to present the methodology of developing a model for evaluation of a well to tank analysis for hydrogen powered construction equipment at site. The upcoming section (2.1) will present the methodology of the literature study where the procedure of deciding on knowledge to obtain and technological aspects relevant for the WTT analysis is presented. The second section (2.2) will present the methodology for model development which is to represent the supply chain from hydrogen production to storage and distribution.
To achieve the objectives of the study, various scenarios were created with the customer and academy. These scenarios are based on two-dimensions considering the conditions of the construction site with variating kind of operation. The operating conditions of the construction equipment machines will be provided by Volvo CE and with the raw energy-data the study will evaluate the performance of a hydrogen energy system. The conditions of the scenarios are created to represent locations with different possibilities regarding grid- connection, length of transport and limitations of power supply.
2.1 Selection of method
With the current thesis work being a research project, the methodology can either be quantitative or qualitative. In a case research of the scientific method by Ketokivi & Choi (2014) they provide two definitions for the research method:
• Qualitative – “Research approach that examines concepts in terms of their meaning and interpretation in specific contexts of inquiry”. • Quantitative – “Research approach that examines concepts in terms of amount, intensity or frequency”.
14
The current project is being supplied with empirical data about energy usage, construction equipment and site location. The raw data of energy profiles and efficiencies is to be converted into meaningful data to provide evidence to prove the research questions. Hence, the research project is based on quantitative data analysis due to the model that is being developed is numerical based on empirical data (Dudovskiy, 2018).
2.2 Literature assemblage
To develop further knowledge in the area of hydrogen production, storage and distribution an in-depth literature study was performed. The basis of the study is founded in the flow- sheet below, explaining the concept of the system that is to be modelled. Hence, the study was conducted to satisfy the knowledge requirements regarding production, processing, transportation storage and distribution.
Figure 9 Model schematic
Regarding hydrogen production different technologies were evaluated for their performance,
CO2 emissions and production costs. The technologies reviewed was natural gas-based steam reforming, biomass gasification, autothermal reforming, partial oxidation and electrolysers as they all differ in the previously mentioned aspects. Operating conditions for the production technologies are also gathered to later be used for modelling inputs. Additionally, the production possibilities were investigated for centralized or decentralized hydrogen production to satisfy the power demand of the construction equipment. As the construction equipment at site vary, different scenarios will be considered and strive to improve overall efficiency of the total system for the WTT analysis.
Processing of the produced hydrogen is also taken into consideration where conventional compression storage and cryogenic liquefaction storage is evaluated. These technologies are a necessary step to either transport or store the hydrogen produced and evaluate the effect on the overall efficiency. The liquefaction of hydrogen is energy intensive and needs additional
15 work compared to compressed storage. However, the reason for considering liquefied hydrogen as a storage method is its energy density to volume and possibilities for long- distance transport.
Due to the different scenarios and constraints of construction sites, technology on both centralized and decentralized storage capabilities are obtained. Depending on the processing step this storage will either be based on pressurized or liquefied storage.
The literature used is primarily collected from databases and journals where the main research is peer-reviewed articles within the field. For this degree project articles earlier than 2010 are discarded or critically reviewed because of the rapid developments within different fields.
2.3 Modelling
The program used to construct the model was MATLAB R2018b, MS Excel was used in order to arrange and store data which later on was exported to MATLAB. The model is a numerical model based on input given by Volvo CE from existing construction sites, the parameter given is the energy profile of different construction equipment. Due to confidentiality the energy profile of each machine won’t be displayed but instead the collective average is used which is seen as a black box with an output of an energy demand that is to be satisfied. Other input data in the model is conditions for each site e.g. power supply, distribution settings and distance from power supply. The outputs of the model are the total power consumption, cost and CO2 emissions.
The first step of the modelling was to create grey box models of every component in the system i.e. models based on theoretical structure combined with empirical data. The models of each component are written as a function. The next step was to build a main script where all components from well to tank were included and a result could be acquired. When this step was done the main program was designed to be able to find the most suitable way of operation using optimization methods.
The nature of the modelling will be linear because of the absence of production profiles to provide variation in price, efficiency and production limits. The specific costs, emissions and production rates are then used most commonly in per kg produced hydrogen to determine
CO2 rates and costs. To optimize the hydrogen pathway, mixed integer linear programming (MILP) is chosen as the most suitable method for the system due to the constraints being linear and able to minimize either cost or CO2 depending on the site conditions.
16 3 THEORETICAL FRAMEWORK
In the following section the most relevant parameters and operating conditions for the techniques to be used in the model are presented. The presented data is based on books and scientific reports from journals within each area. All costs presented in the following chapters are presented in $ (USD) for the source year, if the cost in a report is presented in another currency the cost is calculated with exchange rate for the source’s year.
3.1 Reforming methods
The developments of hydrogen powered fuel cells and vehicle applications both stationary and mobile continuously increases the demand for hydrogen. When producing hydrogen several methods can be used, methane/natural gas reforming, electrolysers and gasification methods. According to Zhu et al. (2019) Steam methane reforming (SMR) is one of the main sources of industrial hydrogen production due to its accessibility, high hydrogen to carbon ratio and liquefaction possibilities. SMR or SR is mainly using natural gas as source for hydrogen production due to its abundancy (J. Yoo et al., 2017).
In the study by Bej et al. (2013) hydrogen production by reforming methane was done. The study investigated SR catalyst over a NiO-SiO2 supported by alumina which showed promising conversion of methane at 95.7 % at optimal conditions (700 ºC). In mole equivalents the hydrogen yield was approximately 3.8 moles of hydrogen per mole of methane reformed. Furthermore, in some studies by (Hufschmidt et al., 2010; Maluf & Assaf, 2009) nickel/aluminium catalyst variations has been investigated where it could be observed to have a good stability and reaching high steam/methane ratios.
Reforming methods also consist of partial oxidation (POX) and autothermal reforming (AR) technologies. The highest efficiency of the three is considered to be steam reforming with a commercial conversion efficiency of 70-85 %. Partial oxidation with a commercial efficiency of 60-75 % and autothermal efficiencies of 60-75 % but not completely commercialized yet. (Abdalla et al., 2018). Other sources also verifies that the order of most efficient processes are SR > AR > POX (Ersöz, 2008; Saeidi et al., 2017).
Regarding operational parameters for the steam reforming process, the rate of hydrogen production depends on the operating temperature and pressure. Because of the process being endothermic a high tube wall temperature should encourage hydrogen production. However, excessive temperature could lead to carbon formation on the catalysts, preventing further reforming reactions (Ersöz, 2008; Lao et al., 2016). The operating temperature differs where some literatures suggest a temperature range of 700 – 1000 ºC (O’Hayre et al., 2006; Srinivasan, 2006) and other studies suggest 700 - 850 ºC to ensure the thermal durability of commercial catalysts which usually operates < 800 ºC (Bej et al., 2013; Ersöz, 2008; Verbeeck et al., 2018).
Furthermore, the operating pressure is a wide range and according to literature and experiments the operating pressure lies within the interval of 2- 25 bar (Abdalla et al., 2018;
17 Srinivasan, 2006, p.384), where in some studies the operating pressure were set to 10 bar (Rosen, 1991; Simpson & Lutz, 2007) . In the study by Ersöz (2008) the aim was to investigate optimal operation parameters for hydrogen production, with the additional constraints of obtaining maximum hydrogen yield while decreasing CO and CH4 residues. In their simulation of different steam reforming techniques (SR, POX and AR) the hydrogen yield content of the product gases for SR resulted in 55.2 % mole fractions of the input gas. In literature the hydrogen mole fractions of the different techniques of hydrocarbon processing are SR 74 - 76%, POX 41 % and AR 47 – 50 %(O’Hayre et al., 2006, p.294; Srinivasan, 2006, p.384).
3.1.1 Steam methane reforming Hydrogen production through reforming can additionally be enhanced by some components. The most commonly used applications for hydrogen enrichment after the reforming steps are usually water-gas shift (WGS) reaction to further reduce CO or H2 perm-selective membrane reactor, which also contributes to a purer H2 production which is beneficial for fuel cell applications (X. Li, 2006). Most commonly a pressure swing adsorption (PSA) unit is installed after reforming and separation to produce 99.9% pure hydrogen (Nikolaidis & Poullikkas, 2017, p.306; O’Hayre et al., 2006; Saeidi et al., 2017).
Many authors determine that SMR or SR technologies are the most efficient, well developed and dominant production procedure of hydrogen both in capacity and cost (Abdalla et al., 2018; LeValley et al., 2014; Nikolaidis & Poullikkas, 2017). However, the disadvantages are its production of CO2 emissions and use of fossil fuels. Some reference values obtained from papers studying hydrogen reforming processes presented their carbon emissions with natural gas based production to be 7.7 kg CO2/kg H2 with no efficiency specified (Voldsund et al., 2016), a reference SMR unit with a conversion efficiency of 70.58 % was specified with the specific emission of 9.57 kg CO2/kg H2 (Mastropasqua et al., 2020a). Additionally, typical emissions produced from SMR plants in other reviews are approximately 9-11 kg CO2/kg H2 (Khojasteh Salkuyeh et al., 2017).
To reduce environmental impact of SMR plants the reduction could be done with carbon capture storage (CCS) systems. Nowadays, CCS systems are currently implemented in approximately 1 800 MW of today’s hydrogen production plants (less than 0.5 % from SMR production) (IEA, 2019b; Mastropasqua et al., 2020a). In an techno-economic evaluation by Collodi et al. (2017) a study was conducted to evaluate and demonstrate CCS from an SMR plant. In the study different cases where the base case of a modern SMR plant supplied with feedstock, reforming, WGS and a PSA unit conducted tests with and without CCS. The results presented a capture from 53 – 90 % depending on fuel composition and syngas production differences. The main findings were that CO2 could be captured from shifted syngas, PSA tail gas and SMR flue gas. Furthermore, the levelized cost of hydrogen (LCOH) comparing to the scenario without CCS and scenarios with CCS resulted in an increase by 2.37 to 5.87 c$/Nm3 (from 12.8 c$/Nm3). Another evaluation of hydrogen selling price with and without CCS for
SMR applications resulted in $1.11/kg H2 to $2.16/kg H2 (lowest selling price is conducted as the net present value (NPV) = 0 i.e. the production cost) (Khojasteh Salkuyeh et al., 2017).
18 Additional techno-economical reviews regarding hydrogen production presents different production costs of hydrogen depending on assumptions and year. In an evaluation of hydrogen production methods by Dincer and Acar (2015) the fossil fuel reforming (NG) of H2 was presented as $0.75/kg H2. Another review by Khojasteh Salkuyeh et al. (2017) also suggests that the fossil fuel reforming has the lowest production costs at $1 - $1.3 /kg H2 for large-scale production. Furthermore, in another assessment of SR economical aspects it is considered to be most beneficial to apply large-scale SMR production, where the study presented technical and input data of different scale of feedstock input and total costs for each type. The result from the study was that conventional large-scale production had a higher overall efficiency of approximately 10 % with the same annual load as the small-scale plant (Mueller-Langer et al., 2007). Additionally, in the study by Murthy Konda et al. (2011) an investigation of implementing large-scale SR compared to small and medium-scale. The study showed that the process efficiency increases with the scale, small-scale had an efficiency of 68 %, medium scale 72 % and large-scale 76 %.
According to IEA (2019a) in an outlook for hydrogen energy, it shows in the figure below the operational cost for producing hydrogen at different locations. The green bar represents the natural gas cost, dark blue is operational expenditures (OPEX) and light blue capital expenditures (CAPEX) in $/kg H2. The operational costs in the US is approximately $1 without CCS and $1.5 with CCS. The production costs is fairly low compared to other studies due to the cheap natural gas price.
Figure 10 Hydrogen production with natural gas as feedstock – Retrieved from (IEA, 2019a)
3.1.2 Additional reforming technologies
Depending on the upstream processes the cost of producing H2 varies. AR has seen some interest due to the CO2 recovery rate being higher because of the emissions being produced within the reactor. A study by Montenegro Camacho et al. (2017) built a autothermal
19 reforming plant based on biogas as feedstock and performed a techno-economic evaluation of green hydrogen production. The plant was built with CCS and a capacity of 8.9 kg H2/h. The production cost for that size resulted in 5.29 $/kg H2, which is within the range of other studies’ prices per kg of biogas feedstock for hydrogen production costs at 5.29 to 8.04 $/kg
H2 depending on pressure (Di Marcoberardino et al., 2019; Rau et al., 2019). Additional information on scaling were available from Montenegro Camacho et al. (2017) from 8.9 to
62.3 kg H2/h the production cost saw a reduction from 6.02 to 3.05 $/kg H2. For AR with natural gas the price is lower due to cheap feedstock which yields production costs with CCS to 1.59 – 2.1 $/kg H2 (Khojasteh Salkuyeh et al., 2017; Nikolaidis & Poullikkas, 2017), where
Nikolaidis presents the production cost without CCS as 1.47 $/kg H2. According to (Salkuyeh et al., 2018) techno economic assessment the AR production pathway has seen promising results regarding costs and CO2 emissions. However, it is limited in its use in todays’ industry due to the inexperience of the technology.
Partial oxidation is also commercially used and is mainly used for reforming hydrocarbons, fuel oil and coal with a commercial efficiency of 60-75 % with methane as fuel (IEA, 2019a; Kalamaras & Efstathiou, 2013). The production costs according to (IEA, 2014) technology system analysis programme was considered to be $1.09 – 2.34 /kg H2 for POX. In a life cycle assessment by (Hajjaji et al., 2013) the environmental load of CO2 emissions resulted in 8.69 kg CO2/kg H2 at a process efficiency of 71 .83 %. Additionally, in an evaluation of hydrogen production methods by Pilavachi et al. (2009) the emissions produced was evaluated to 12.35 kg CO2/kg H2.
3.1.3 Gasification technologies In an exergy analysis of hydrogen production through steam gasification by Zhang et al. (2019) different types of biomass feedstock were evaluated and gathered to compare them for their hydrogen yield potential. For this study’s purposes some biomass alternatives are not considered due to relevancy. Fremaux et al. (2015) studied wood residues which underwent the gasification process in three temperatures 700, 800 and 900 ºC at different retention times of 20 to 40 minutes with 5 minutes increasing interval. This provided a hydrogen yield of 25.78 mol/Kg (12.79 g/kg) at 40 minutes and 700 ºC, 25,68 mol/kg (12.74 g/kg) at 40 minutes and 800 ºC, 29.69 mol/kg (14.73 g/kg) at 40 minutes and 900 ºC. Additional feedstock were also investigated by Yang Zhang et al. (2014) which investigated demineralized tobacco which provided a hydrogen yield of 57.64 mol/kg (28.59 g/kg).
The main conclusions of the study by Zhang et al. (2019) were that the exergy energy is largely influenced by the hydrogen yield of the biomass. Furthermore, steam gasification is sensitive to change in parameters like temperature, flow rate and retention time which effects hydrogen yield. Further conclusions were that a higher gasification temperature was beneficial for exergy efficiency and thereby hydrogen production which other studies also confirm (Fremaux et al., 2015; Gao et al., 2008; Yang Zhang et al., 2014).
In a techno-economic evaluation by Yao et al.(2017) three technologies were simulated in
ASPEN plus for being CO2-neutral production methods able to supply a demand of 90 kg
H2/h. The methods simulated were biomass steam gasification in a dual fluidized bed, biogas
20 steam reforming and an alkaline electrolyser. It was concluded from the results that the steam gasification almost had double the conversion rate compared to the steam reforming of biogas. However, considering the cost of the system the three technologies had similar NPV at around 33 million Euro. The order of profitability was steam reforming, alkaline electrolyser and gasification but it was concluded that the three options showed similar economic feasibility. In a life cycle assessment by Salkuyeh et al. (2018) they compared biogas gasification processes to SMR to compare it to commercial hydrogen plants. The results indicate that fluidized bed steam and entrained flow gasification could play an important role on the market for commercial hydrogen production. However, at the moment the prices of natural gas are too low at $1.92 to $2.65 /GJ (EIA, 2020; IEA, 2019c) where a biomass price must be $60/tonne with CCS and $40/tonne without or even lower to compete with conventional SMR hydrogen production. Hydrogen costs would result in approximately
> $3.5 /kg H2 with CCS for steam gasification and below $2.2 /kg H2 for SMR with CCS. Additionally, the entrained flow gasification which can be used for coal and coke gasification, the process can either be air or oxygen based. Air gasification is not considered commercially viable due to dilation of the syngas produced. However, for gasification of biofuels with entrained flow would need high purity oxygen for partial combustion operating at high temperatures >1200 ºC (Qin et al., 2012; Salkuyeh et al., 2018). The production cost in the same evaluation by Salkuyeh et al. (2018) indicates that even with a higher efficiency than other gasification technologies, entrained flow had a minimum hydrogen production cost $0.07 to $0.33 higher than fluidized bed equipped with CCS. Hydrogen conversion efficiency for biomass gasification ranges between 30 - 50 % in reviews of hydrogen production techniques (El Shafie et al., 2019; IEA, 2014; Nikolaidis & Poullikkas, 2017).
Gasification of coal is an additional fossil-fuelled based conversion method for hydrogen. According to (IEA, 2014) in a technical system analysis program for hydrogen production by the conversion efficiency for gasification is 50-70%, which also seems to be the general range in other reviews (Biagini et al., 2008; Y. Li et al., 2010; Ramsden et al., 2013) .
In a techno-economic assessment of production pathways for hydrogen by (Ewan & Allen,
2005) the production cost resulted in $1.62/kg H2 without CCS and $3.1/kg H2 with CCS, where similar costs were modelled for underground gasification by Olateju & Kumar (2013) at $1.78 without and $2.16-2.77/kg H2 with CCS. In the previous mentioned report by (IEA, 2014) the production costs for non-implemented CCS was assumed to be the same as for
SMR at $0.78 – 1.56/kg H2, where numerous papers presented costs within that range (Lemus & Martínez Duart, 2010; Y. Li et al., 2010; Mason, 2007; Nikolaidis & Poullikkas, 2017; Ramsden et al., 2013).
In a life cycle assessment of hydrogen production by (Salkuyeh et al., 2018) they conducted a production of 11.3 kg CO2/kg H2 with coal gasification, where (Cetinkaya et al., 2012) had similar result in their life cycle assessment at 11.29 kg CO2/kg H2. Other studies have seen higher CO2 emissions of 18 kg CO2/kg H2 when investigating underground coal gasification
(Verma & Kumar, 2015) and 26.2 kg CO2/kg H2 due to low efficiency at 59 % (Ewan & Allen, 2005).
21 3.2 Electrolysis
The most commonly used electrolysers for commercial uses are proton exchange membrane electrolysers cell (PEMEC) and alkaline electrolysers (AE) which operates in similar way, the major difference between the techniques is the electrolyte. Other electrolysing techniques is emerging but many of them are still in the laboratory stage and has not yet been commercialized. One of the most emerging techniques is the solid oxide electrolyse cell (SOEC) which can operate with an efficiency up to 90% at voltage down to 1,07 V (Carmo et al., 2013).
3.2.1 Energy characteristics The efficiencies for different electrolysers are described in various articles and the values has differed between sources. In an article written by Lamy & Millet (2020) they discuss how efficiency in electrolysers are described. In a vast majority of literature, the efficiency is described from the theoretical work needed to split water and the actual electricity added. The theoretical work needed to split a kg of water into hydrogen is 39.5 kWh derived from the HHV of hydrogen 142 MJ/kg. Expressed in kWh/Nm3 this is 3.54 (Koponen et al., 2017). Discrepancy have been found in how efficiency is described in industrial and research purposes. Thus the discrepancy in efficiency much literature present the specific energy for produced hydrogen in energy used per normal cubic meter kWh/Nm3(Lamy & Millet, 2020).
The specific energy consumption (SEC) and efficiency for the two types of electrolysers is discussed in various articles although discrepancy between the sources is found. For the alkaline electrolyser efficiency, Chi & Yu (2018) writes that the efficiency is in the span 59- 70% and the SEC 4.5 - 5.5 kWh/Nm3 while other literature says 62-82% and 4.5-7 kWh/Nm3 (Carmo et al., 2013; Dincer & Acar, 2015a).
For PEMEC the efficiency is usually higher. The efficiency according to Carmo et al. (2013) is 67-82% and a SEC of 4.2 – 5.6 kWh/Nm3 which is strengthened by Clarke et al. (2010) who says the efficiency is approximately 80 %. In a study by Sarno & Ponticorvo (2019) new materials as catalyst in PEMEC was studied with molybdenum disulphide and ruthenium sulphide. The study showed great results as an efficiency of 93% was achieved, although this experiment was performed in small scale.
Regarding SOEC the efficiency is varying in different literature as the technique is not yet mature. According to Dawood et al. (2020) the efficiency of a SOEC is 75-90 % although he says that the technique is far from commercialized. Based on LHV this would give a SEC of 3.89 – 4.67 kWh/Nm3. Chi & Yu, (2018) strengthens this and says that the SOEC can have an efficiency greater than 90% due to the high operating temperature in the cell.
3.2.2 Economic characteristics According to a review from 2019 about the cost of electrolysis the investment cost for AE is 840-1 344 $/kW while the cost for PEMEC is 1 344-2 240 $/kW (Parra et al., 2019). In a report written for IRENA (2018) the authors strengthen this and says the investment cost is
22 840 $/kW for AE and 1 344 $/kW for PEMEC. The authors say in the same report that the operational cost is 2% of the capital cost each year. Regarding SOEC, IEA writes in the report “Future of hydrogen” (2019a) that the investment cost today is 2800 – 5600 $/kW, 500- 1400 $/kW for AE and 1100 – 1800 $/kW for PEMEC. Furthermore, they say that SOEC will not economically be competitive against PEMEC and AE in a foreseeable future. The cost of producing hydrogen with electrolysis is strongly influenced of the price of electricity which makes it hard to give a production cost.
In a report written at the Institute of Energy and Climate Research by Saba et al. (2018) the cost development of electrolysis from the past 30 years has been evaluated. The writers have also collected projection from various literature how the cost will develop in the coming years. In the later years the investment cost for electrolysis has decreased as the technique has been commercialized, mass produced and the production is more automated. The operational cost for hydrogen production has also reduced since the technique have matured and efficiencies increased (Dawood et al., 2020). In the report one of the key finding is that the PEMEC will reduce in price and approach the cost of an AE (Saba et al., 2018). Consensus prevails among researchers that the cost for producing hydrogen with electrolysis will reduce.
3.3 Pressurized hydrogen storage
In a paper by Barthelemy et al., (2017) an overview of materials for storage technologies were compared to what constraints these materials and methods have. In the paper the authors present four common types of pressure vessels for hydrogen storage presented as type 1 to type 4:
1. Fully metallic vessel – most conventional, cheap but the heaviest. Approximately 1.35 kg/L (Type 1). 2. Steel pressure vessel wrapped in fibre composite material – High tolerance to pressure and light weight, costs approx. 50 % more than the fully metallic vessel (Type 2). 3. Composite vessel with metal liner – Composite usually of carbon fibre with aluminium liner for sealing. Able to safely store for pressures up to 700 bar and are expensive. The weight is approximately 0.35 – 0.45 kg/L (Type 3). 4. Fully composite – Usually composed of a polymer with carbon fibre composites for structural load. It is the lightest storage vessel available, able to stand up to pressures of 1000 bar but expensive (Type 4).
23
Figure 11 Presentation of type of pressure vessels - Barthelamy et al., (2017)
In a study by Aceves et al. (2010) BMW investigated cryo-compressed storage system for automotive applications. The study concluded that hydrogen can be stored cryo-compressed with high density and with lower evaporation losses than liquid hydrogen storage. The hydrogen density is approximately two times denser than conventional compressed storage (Aceves et al., 2013; Ahluwalia et al., 2018).
A thermodynamic analysis of critical parameters for hydrogen storage was done by Yanxing et al. (2019) where different storage methods were evaluated. The study’s result was that the optimization of the compression stage is essential for an energy efficient system because of the energy intensity of the compressor. It is recommended to divide the compression into multiple stages (two or more) to lower the power consumption significantly. The power could approximately be reduced by 50 % compared to having five compressors instead of one. In the study the ratio between H2P = hydrogen density/power consumption was an important parameter when comparing compressed hydrogen to cryo-compressed storage. Optimal storage conditions for both technologies were suggested as 300 K at 700 bars for compressed 3 storage which yielded a H2P at 1.71 kg/m , kW. Cryo-compressed storage conditions resulted 3 in 110 K at 700 bars with a H2P at 2.3 kg/m , kW.
Another study by Ahluwalia et al. (2018) examined storage performance for cryo-compressed and compressed hydrogen storage for vehicles where operating conditions, gravimetric capacity and volumetric capacity were some of the key parameters. In the study the operating conditions which yielded the highest gravimetric and volumetric capacity for cryo- compressed storage on a fuel cell buss at 72 K at 500 bars, which yielded a gravimetric
24 capacity of 8.4 % and a volumetric capacity of 50.8 g/L. Regarding compressed storage the conditions were for the same vehicle at 288k at 350 bars which yielded a gravimetric capacity of 4.4 % and volumetric capacity at 18.5 g/L. The paper concludes that compared to baseline conventional hydrogen storage, improvement in gravimetric capacity can be up to 91 % and 175 % improved volumetric capacity. Additional savings can be obtained in the need of less composite material due to the volume being lower. Other studies has also confirmed that the gravimetric and volumetric capacity can be enhanced by cryo-compressed storage (Durbin & Malardier-Jugroot, 2013; Moreno-Blanco et al., 2019).
However, as many studies and authors suggest, cryo-compressed hydrogen storage compared to conventional gaseous storage are most suitable for mobile or large vehicles with long distance requirements (Aceves et al., 2013; Ahluwalia et al., 2018; Moreno-Blanco et al., 2019; Stolten et al., 2016; Yanxing et al., 2019). In a well to wheel analysis of cryo- compressed, liquefied hydrogen and compressed hydrogen storage for mobile applications by
(Paster et al., 2011), these technologies were compared in efficiency, CO2 emissions and volumetric efficiency. The cryo-compressed storage was considered to have the highest energy use, CO2 emissions and hydrogen cost. On the other hand, the analysis showed that it had the lowest system cost because of it being able to operate at lower pressures (<700 bar) and smaller tank due to its high volumetric efficiency. Di Profio et al. (2009) has gathered relevant parameters regarding energy requirements for gas compressed storage for the pressures 200, 350 and 700 bar. The energy requirements are respectively 2.86, 3.4 and 4.13 kWh/kg H2, where (Zheng et al., 2012) states that for 350 and 700 bar storage an approximate of 5 % to 20 % of LHV is appropriate for compression work (1.6 to 6.6 kWh/kg). Additional cooling requirements are needed for refuelling purposes where the gas should be cooled to -40 ºC before compression according to hydrogen gas companies, where precooling is considered to be an additional 0.18 kWh/kg H2 (DOE, 2009; Zheng et al., 2012). The insulated cryo-compressed storage tanks are flexible to the properties of the incoming hydrogen. The compatibility for gases and liquids are available at cryogenic liquid temperatures and ambient gaseous refuelling.
Gaseous hydrogen can be distributed and transported using pipelines. The technique is used today and the vast majority of the existing hydrogen pipelines are placed in USA and most of the pipelines are owned by hydrogen producers for chemical and refinery purposes (Shell, 2017). Pipelines are a good way to transport gas with low operational costs and emissions, the main drawbacks are the capital cost of the pipelines and the legislation. Even though the capital cost can be paid back in a few year pipelines is constrained following legislations regarding construction of pipelines (IEA, 2019a). The most commonly used way to transport gaseous hydrogen is by tube trailers. The amount of hydrogen to be transported is limited due to legal pressure constraints by DOT which constrains the trailers to carry approximately 300 kg with a maximum pressure of 250 bar (REDDI 2018). The cost to transport hydrogen by tube trailers according to IEA (2014) is 0.798 $/kg, 100 km. The cost associated with the compression to 250 bars is not included in the cost.
25 3.4 Cryogenic liquefied hydrogen
The power required to liquefy 1 kg of hydrogen is 12-15 kWh according to an article by Boudellal (2018). This is equal to 30-38% of the energy content in hydrogen. Aceves et al. (2013) strengthens this in a report where he says the SEC for liquefaction is 13.14 kWh/kg. According to Ohlig & Decker (2013) power consumption down to 10.8 – 12.7 kWh/kg can be achievable at the price of higher investment costs. According to Hammad & Dincer (2018) the efficiency of liquefaction plants tend to increase with the size. The constraints of the plants are usually economic rather than technical.
One of the challenges when handling liquid hydrogen is the boil off, i.e. the spontaneous evaporation of hydrogen following the conversion from ortho-to para-H2. Ortho and para is describing the nuclear spin in the hydrogen, at normal conditions the ratio between ortho and para is 1:4. During liquefaction the ortho hydrogen transforms into para hydrogen, this reaction releases energy which results in boil off, i.e. the hydrogen changes phase to gas. The boil-off gas has a lower density which will lead to an increase of pressure in the storage tank (Wijayanta et al., 2019). According to Wijayanta et al. (2019) the boil off is 0.2-0.3% of the mass per day in a storage tank and can be up to 3% in transportation trucks.
The sizing of liquefied hydrogen storage systems exists in various sizes, the biggest storage tank for liquid hydrogen is at Kennedy space centre in USA and has a volume of 3200 m3 and keeps 230 tons of liquid hydrogen (Godula-Jopek et al., 2012). For transportation purposes Moradi & Groth (2019) says that a cryogenic tanker usually can carry up to 5000 kg of hydrogen. IEA (2014) writes that the capacity for cryogenic tankers could reach 4000 kg and the cost to transport liquid hydrogen with tankers 100 km was 0.1729 $/kg.
3.5 Fuelling interface
One of the key features with petrol vehicles compared to BEV is the quick charging time (Björnsson & Karlsson, 2017). In order to make fuel cell electronic vehicles (FCEV) competitive regarding fuelling, standards have been made in order to be able charge a FCEV with 5-7 kg of hydrogen in 3-5 minutes. The standard SAEJ2601 made by Society of automotive engineers focuses on light duty vehicles with a maximum storage capacity of 10 kg (SAE International, 2016). An extension to the standard has later been done SAE J2601-2 which focuses on all heavy-duty vehicles with a storage capacity exceeding 10 kg (SAE International, 2014). The thermodynamic properties of hydrogen limit the fuelling as the hydrogen heats up during compression and exceeds the temperature suitable for the storage tanks (Reddi et al., 2017). According to J2601-2 the temperature limit of a storage tank operating with a pressure of 350 bar must be in the interval -40 to 85ºC, and the temperature at the fuelling nozzle must exceed -40ºC. While these conditions holds the maximum charging rate for heavy duty fuelling can reach 7.2 kg/min if special ISO certified equipment are used, the normal fuelling rate is approximately the half (SAE International, 2014).
26 3.1 Field study – hydrogen pathways
In a study by (Seo et al., 2020), the performance of a hydrogen supply chain was studied with a centralized storage model. The study investigated production from several different techniques such as SMR, electrolysers, coal and biomass gasification for on-site and off-site production to a centralized hydrogen fuel station. The study’s objective is to minimize the cost for the supply chain, where the optimization were formulated as a mixed-integer linear programming problem which seems common for a hydrogen pathway model (L. Li et al., 2019, 2020; Qadrdan et al., 2008). The results showed depending on market penetration, that the chosen hydrogen production technologies when the demand is adequate for centralized production plants was either large coal gasification plants or on-site SMR. The produced hydrogen was stored as liquid hydrogen and delivered by truck or pipeline, which was considered most cost effective compared to the low capacity and high unit operating cost for gaseous storage.
In a similar study by (L. Li et al., 2020) a hydrogen pathway was modelled as a mathematical optimization problem with mixed integer linear programming to minimize costs. The purpose of the study was to model a hydrogen supply chain network for future fuel station planning. The production technologies chosen to investigate were SMR, biomass gasification and electrolysis with production capacities ranging from 1000 kg/day to 5000 kg/day. The paper performed several simulations with varying conditions where on-site, off-site production was evaluated with and without feedstock transportation where a CCS system also was considered for some scenarios. The results showed that with a higher hydrogen demand the gap between liquid transportation and gaseous gets smaller. A liquid tanker for hydrogen delivery resulted in almost 23 times larger capacity than for gaseous tube trailer delivery. The liquefaction option was then considered to be a more attractive choice if the hydrogen demand were to increase. SMR plants are chosen as production path if the carbon pricing is low due to its low capital and production costs. However, for a medium and high demand a CCS system was adopted by the model and chose biogas gasification as production pathway when carbon pricing is high.
3.2 Summary literature review
The table below presents a summary of gathered information on production technologies regarding cost, emissions and efficiency. Some parameters are not relevant or non-attainable for every technique and is therefore left empty. The cost and CO2 emissions for the electrolysers is directly related to the origin of electricity and must therefore be calculated in the modelling stage. The electricity required per kg of hydrogen is only presented for the electrolysers as electricity is the sole source of emissions for electrolysers, while the emissions from the other production methods is related to the feedstock.
27 Table 1 Production characteristics
3 Production method Cost [$/kg H2] CAPEX [$/kW] kWh/Nm H2 kg CO2/kg H2 Efficiency % SMR natural gas 0.75 – 1.5 - - 7.7 – 11 70-85 SMR natural gas with 1.11 – 2.16 - - 3.6 – 5.17a 70-85 CCS 0.77 – 1.1b AR natural gas 1.48 - - - - AR natural gas with 1.59 – 2.1 - - - 60-75 CCS AR Biogas with CCS 2.7 – 7.44 - - - 60-75 POX 1.09 – 2.34 - - 8.7 – 12.3 60-75 POX with CCS 1.45 – 2.7c - - 4.09 – 5.78a - 0.87 – 1.23b Gasification coal 0.78 – 1.56 - - 11.3 – 26.2 50 – 70 Gasification coal with 2.16 – 3.1 - - 5.3 – 12.3a - CCS 1.13 – 2.62b Gasification Biomass >3.5 - - - 30-50 AE - 500 - 1400 4.5-5.7 - 62-82 PEMEC - 1100 - 1800 4.2 - 5.6 - 67-82 SOEC - 2800 - 5600 3.89 - 4.67 - 75-90
a: CCS capture rate of 53 %
b: CCS capture rate of 90 %
c: Additional CCS costs assumed to have an increase of 0.21 – 0.52 $/kg H2 where the mean is taken. The value was gathered from the theoretical
framework.
For the electrolysers SOEC shows the most promising results considering electricity required to produce one kilogram of hydrogen. Although, the CAPEX is much higher compared to the other technologies and the level of maturity for SOEC is low as the technique is not commercially available for large scale hydrogen production. The other electrolysers is commercially available and the CAPEX for AE is lower than PEMEC while the electricity required to produce 1 kg of hydrogen is lower for PEMEC, which will give a lower operational
cost. Regarding the reforming methods the CO2 emissions and production cost are the parameters that are relevant to compare. Common for all the reformers are the possibility to
use CCS equipment to mitigate the CO2 emissions which results in a higher cost. Considering
the CO2 emissions, CCS is the most efficient option for all techniques. The reformer with least emissions is CCS equipped SMR when the capture rate is assumed to be high. With a lower capture rate other reformer are competitive to SMR, AR with CCS has low emissions as well, although the maturity of AR is low. The emissions for POX with CCS is close to SMR with CCS, although the cost is higher which makes SMR preferable. Gasification has high emissions even with CCS and the cost is similar to the other reformers. Cost wise SMR is the best followed by gasification. The conclusions for the reformers is that SMR with CCS is the most preferable method considering cost, emissions and maturity.
28 4 CURRENT STUDY
In this section the case study will be presented for the well to tank analysis. The chapter will include scenario and modelling development which will consist of various input data and assumptions for the study’s model purposes. Furthermore, the objects and their energy demand will be presented within the upcoming sections with the corresponding equations and modelling procedure for processing, storage possibilities and fuel distribution.
Figure 12 well to tank analysis
In the above figure the origin for the analysis is presented. With upstream processes being the raw material extraction like natural gas, coal, biomass or water needs. The production represents the different conversion methods from the upstream processes to raw hydrogen of various purity and quality. Subsequently, the fuel will be processed with different techniques that will be evaluated in several aspects before storage, depending on the need and operation of the construction equipment site. The distance between production and operation might vary and will hence also be investigated and influence the characteristics of previous steps of the analysis with regards to efficiency, emissions and cost.
4.1 Scenario management
With construction equipment sites’ varying operations, several scenarios were developed with different machines and conditions. With every operation having a different energy profile regarding intensity, demand and size the scenarios were created to satisfy these differences. The four scenarios will be evaluated from the same features that is characterized as energy infrastructural barriers and possibilities for transport and storage. These four scenarios are then created according to the following conditions:
1. Close to production with electrical grid connection with high share of RES 2. Close to production with electrical grid connection with low share of RES 3. Close to production with constrained connection (1 MW) 4. Remote location without grid connection (500 km)
In the following sections different Volvo machines that are used in various operation will be mentioned. To get a better understanding the nomenclature for the machine will be presented in the table below. The last column describes the number of the machine e.g. an EC380 is a crawler excavator that weights 38 ton. All numbers are expressed as metric tons.
29 Table 2 Machine nomenclature
Model Type of machine Number refers to A Dump hauler Load capacity*0.907 EC Excavator crawler Weight of machine*0.1 EW Excavator wheel Weight of machine*0.1 L Loader Maximum tipping capacity*0.1
4.1.1 Operations Due to the various use of construction equipment, four different types of operations are chosen to simulate the most generic use. The frameworks chosen are road construction, mining, quarry and light duty operations. These sites are primarily chosen as they differ in intensity, demand and thereby size of machines. The demand will differ largely and so will the CO2 intensity of the operation which will provide different solutions for production, storage and cost.
• According to the customer road construction is characterized by continuously movement of the construction site which requires equipment and interface to be mobile. Equipment that is affected by the continuously movement can be fuelling interface, storage among others. The following machines are used on a generic road construction site according to Volvo, one EC380, one EC220, one L150 and four A30. The operation time for this framework is set to 08:00 – 16:00.
• Mining sites is characterized by high power outputs and equipment that operate at peak power most of the time. Mines is stationary which enables the use of stationary equipment at sites. According to Volvo the following machines are usually used at mining sites, two EC480, eight A40 and two L260. The operation time for this framework is set to 00:00 – 23:00.
• The operation of quarries is similar to mining sites with high power outputs and machines that operates at their peak. Although the operations are lighter which enables the use of smaller machines. Commonly used machines at quarries according to Volvo are the following: one EC220, two L120 and two A30. The operation time for this framework is set to 00:00 – 23:00.
• Light duty refers to operations where the power output is small, and the machines operate semi mobile i.e. waste handling and other utilities. The machines used in such operation are usually the following according to Volvo CE: two EW160, two L70 and one L120. The operation time for this framework is set to 08:00 – 16:00.
30 In the table below the characteristics of the model with respect to the scenarios is presented. From the table, the model development can be interpreted with regards to the constraint each scenario presents. The grid connection could provide further possibilities for hydrogen production with electrolysers which could contribute to a less CO2 intensive operation. 2.4 denotes scenario 2 for operation 4.
Table 3 Scenario matrix
Scenario Mining Light Quarry Road Close to production with grid connection high share of 1.1 1.2 1.3 1.4 RES Close to production with grid connection low share of 2.1 2.2 2.3 2.4 RES Close to production with constrained grid connection 3.1 3.2 3.3 3.4 EU28 electricity mix Remote location without grid connection (500 km) 4.1 4.2 4.3 4.4
Close to production with working infrastructure and stable grid connection settings is created due to simulate favourable conditions for various production possibilities, short transportation and a stable grid connection with a high-power supply. Production can be seen as buying the hydrogen according to the market price and thereby being responsible for the embodied CO2 if the production comes from e.g. SMR production facilities. The transportation of hydrogen should in this case be reasonably short. Hence, the construction site is considered to be 10 km from production and has the possibility to connect to the electrical grid without constraints on power outage for on-site production. This scenario will be divided into two scenarios where the source of electricity differs. One of the scenarios will simulate an electricity mix with high shares of renewables, and the other scenario will simulate an electricity mix with a low share of renewables. The electricity mixes used to simulate the high share of renewable and the low is Sweden’s and Poland’s.
The circumstances for the remote location setting are developed to show the impact of distance on cost and emissions. The production would with these circumstances be centralized and the processing of the hydrogen would need further consideration to preserve its energy density for the remote location site. The transportation length is therefore determined to be 500 km to the closest construction site. Additionally, the lack of grid connection will delimit the site for de-centralized production and put further challenges on transport and CO2 emission intensity.
Close to production without stable grid connection is created to simulate a site with favourable settings where certain constraints differs from the site close to production with an unconstrained power output. This setup represents big cities where the distance is short but the congestion on the grid constrains the output. The settings for this site is defined with a constrained power output of 1 MW. A source of hydrogen e.g. an SMR plant or electrolyser is positioned within 10 km of the construction site which enables transportation of hydrogen. The electricity mix used in this scenario is the mean value from all the EU members respective emissions from electricity generation.
31 4.2 Input data
In the table below the input data for the model is presented. The data is based on the theoretical framework from the previous chapter and delimited to the studies’ objectives. In the modelling some specific techniques from the literature study will be excluded as the technique is not commercially available and some techniques is excluded in order to improve the model. Regarding production methods SOEC and AR will be excluded due to the low level of maturity which would give a result were the production methods could not be used on a real site. To improve the performance of the model POX and gasification is excluded as the goal with the optimization is to minimize the CO2 emission. In this aspect SMR with CCS can produce the same amount of hydrogen with lower emissions and a lower cost. Therefore, POX and gasification would never give the optimal solution. SMR without CCS will not be included as the main objective is to minimize the CO2 which will make the CCS option preferable in all situations. The production methods that will be included is SMR with CCS as it is the most preferable largescale production considering emissions and cost, SMR with CCS will only be used off-site due to the complexity of the facilities. PEMEC and AE will be included in the model as on-site and off-site alternatives as the techniques have a potential to produce hydrogen with low emissions and cost at site if an electric supply is available. The delivery options in the model will be gaseous trailer and cryogenic liquid hydrogen tankers, i.e. cryo-compressed will not be considered as the level of maturity for large scale transport is too low.
32 Table 4 Input data
Parameter Value Unit Source CO2 emissions electricity EU28 295.8 g CO2/kWh European environment agency, 2018
CO2 emissions electricity Sweden 13.3 g CO2/kWh European environment agency, 2018
CO2 emissions electricity Poland 773.3 g CO2/kWh European environment agency, 2018
CO2 emissions SMR CCS 3.13 kg CO2/kg H2 Voldsund et al., 2016
Production cost SMR CCS 1.64 $/kg H2 IEA, 2019b Electricity price EU28 0.1387 $/kWh European commission, 2019 Electricity price Sweden 0.0818 $/kWh European commission, 2019 Electricity price Poland 0.111 $/kWh European commission, 2019
CGH2 Specific cost 350 bar storage 14 $/kWh Ahluwalia 2018 Energy demand Road construction 403.6 kWh/h Volvo CE Energy demand Mining 1151.3 kWh/h Volvo CE Energy demand Quarry 230.9 kWh/h Volvo CE Energy demand Light duty 137.03 kWh/h Volvo CE Diesel consumption transport truck 0.4 l/km Volvo CE SEC PEMEC 4.9 kWh/Nm3 Carmo et al., 2013 SEC AE 5 kWh/Nm3 Chi & Yu, 2018 SEC pressure 350 bar 1.6 kWh/kg Zheng et al., 2012 SEC liquefaction 13.14 kWh/kg Aceves et al., 2013
Boil off rate LH2 0.25 %/day Wijayanta et al., 2019 Liquid hydrogen tanker capacity 4500 kg/trailer Moradi & Groth, 2019 Gaseous hydrogen trailer capacity 300 kg/trailer IEA, 2014 Liquid hydrogen trailer cost 0.1729 $/kg, 100km IEA, 2014 Gaseous hydrogen trailer cost 0.798 $/kg, 100km IEA, 2014 Density hydrogen 1 bar 20 ºC 0.0898 kg/Nm3 Wester, 2015,p.147 CAPEX PEMEC 1450 $/kW IRENA, 2018 OPEX PEMEC 29 $/kW, year IRENA, 2018 CAPEX AE 950 $/kW IRENA, 2018 OPEX AE 19 $/kW, year IRENA, 2018 Efficiency PEM Fuel cell 55 % Sharaf & Orhan, 2014
CO2 emissions diesel 2.6 kg CO2/l Volvo Trucks, 2018
4.2.1 Hydrogen demand In the current sub-chapter, the way of managing the data from its raw-format to useful input will be described. The conversion of the current fuel demand to hydrogen fuelled applications are being re-calculated from its ordinary power demand to a hydrogen demand. This is done through population data provided from the costumer Volvo CE. The population data consists of the power profiles of certain machines at different construction sites, the population data varies depending on the machines’ location which contributes to a representative fuel consumption for that type of equipment. The average power demand is thereby obtained and re-calculated to a hydrogen demand for the number of different machines chosen for each scenario created in section 4.1.
33
Figure 13 Flowsheet of energy profiling
In the figure above, the black boxes represents which parts that are confidential and how the power profile of each machine is re-calculated to a hydrogen demand for the construction equipment. The type and number of machines in each scenario (e.g. Quarry) is provided by Volvo CE which is described in the above chapters and used to achieve the accumulated hydrogen demand for the different equipment used for each site.
The population data consists of detailed information on average power, fuel consumption and idle time. To achieve the average power profile for each machine the non-idle power was used. To get a representative power demand for hydrogen the time idling is removed from the non-idle power due to the machine being turned off during down-time, the idling time in this case will then be seen as dead-time where the hydrogen demand is zero.
Equation 21 푇표푡푎푙 푡𝑖푚푒 푃 = 푃 ∗ (1 − ) 퐶퐸푥 푛표푛−𝑖푑푙푒 퐼푑푙푒 푡𝑖푚푒
4.3 Assumptions
• Regarding the origin of electricity, no transnational transmission will be considered in the model, i.e. in the scenario for Poland polish electricity mix will be used. For the scenarios where no specific country is stated the EU28 electricity mix will be used. Electricity prices will be treated in the same way. • It is assumed that all of the sites will have a water supply that is enough to use the electrolysers, no further calculations regarding water will be performed. • The storage on the machinery is assumed to be able to satisfy the demand for eight hours, therefor the outtakes is limited to once per eight operating hours. • The maximum level of storage is dimensioned to be able to have five days of autonomy for every site. • Initial hydrogen level is set as 50 % of maximum storage capacity. • Minimum hydrogen level is set as 1 % of maximum storage capacity. • Processing technologies is assumed to be using sole electric power, where the emissions responsible is due to the energy mix used. • The production technologies depending on transport is assumed to deliver a 100 % truck/tanker capacity for liquid and compressed hydrogen. • OPEX for the electrolysers is expressed as $/kW, year in the literature, in the model this is expressed as $/kWh*8760 • All of the equipment used for production and processing on site is assumed to be rented, thus the capital cost for the equipment will not be included in the calculations.
34 4.4 Optimization setup
In the current section the equations, constraints and objective function will be described. The following equation describes how the work on site is calculated into a hydrogen demand.
The work output for each machine is written as Wnet and is multiplied with 3.6 to convert kWh into MJ, it is then divided by the heating value of hydrogen to get the work expressed as kg of hydrogen. At last the hydrogen demand is divided by the efficiency of the fuel cell to get the actual hydrogen needed. The demand for each machinery is then summed to get the hydrogen demand for the site.
Equation 22 푊 ∗ 3.6 푛푒푡,푟 = 퐻 휂 ∗ 퐿퐻푉 2,푑푒푚푎푛푑,푟
Equation 23
퐻2,푑푒푚푎푛푑,푠𝑖푡푒 = ∑ 퐻2,푑푒푚푎푛푑,푟
푟=1,2,3...푛푚푎푐ℎ𝑖푛푒푠
Liquid hydrogen will only be considered for mine operations due to the size of the liquid tankers. To calculate the boiloff the hydrogen was assumed to be stored for 60 hours as the time to consume the delivered hydrogen is approximately 60 hours. The boiloff was assumed to occur during the transport i.e. no loss in the tank. In the calculations 60 is divided by 24 as the boiloff is expressed as a daily loss. The following expression shows how the boiloff was calculated in detail. This was done in order to make the problem suitable for the chosen optimization method.
Equation 24
60 퐻2,푎푣푎𝑖푙푎푏푙푒 = 퐻2,푝푟표푑푢푐푒푑 ∗ (1 − 푏표𝑖푙표푓푓 푟푎푡푒)24
Sizing of the electrolysers was performed in order to reduce the irregular production of hydrogen which will reduce the errors related to start up and shut down and reduce the unnecessary capital cost . The size of the electrolyser was a function of operating time and demand and the capacity was set to zero if the desired capacity was greater than the maximal capacity. The capacity of the electrolyser was set to be slightly higher than the theoretical minimum to account for errors at start up and shut down, therefore the expression was divided by 20 instead of 24 to get the hourly capacity.
Equation 25
퐷푒푚푎푛푑ℎ표푢푟푙푦 ∗ (푆푡표푝표푝푒푟푎푡𝑖표푛 − 푆푡푎푟푡표푝푒푟푎푡𝑖표푛) 퐸푙푒푐푡푟표푙푦푠푒푟 = 퐶푎푝푎푐𝑖푡푦 20
35 The following equations will describe the optimizations constraints regarding production and state of charge (SOC). During the first hour of operation initial conditions has to be set to be able to determine the previous level of the tank. With these conditions set the optimization tool will be able to recognize the tank level and determine the amount of hydrogen needed to satisfy the demand.
Equation 26
푚1 = 푚0 + ∑ 퐻2,푝푟표푑푢푐푡𝑖표푛,1,푛 − 퐻2,푑푒푚푎푛푑,1 푛=1,2,3,4,5,6,7,8
Equation 27
푚𝑖 = 푚𝑖−1 + ∑ 퐻2,푝푟표푑푢푐푡𝑖표푛,𝑖,푛 − 퐻2,푑푒푚푎푛푑,𝑖 푛=1,2,3,4,5,6,7,8
Where n is the type of production method and i is the hour of operation. Furthermore, to endorse that a reasonable pattern of the storage optimization is obtained, additional constraints were implemented so by the end of the set time-span the state of charge should be equal or greater than the initial mass of the tank.
Equation 28
푚푒푛푑 ≥ 푚0
Additionally, the maximum and initial storage of the tank is defined as the following for every site. By having some initial hydrogen in the tank, a more realistic production profile will be provided. The amount of hydrogen is set to 50 % of the maximum tank storage.
Equation 29
푚푚푎푥 = 퐻2,푑푒푚푎푛푑,푛 ∗ (푠푡표푝푡𝑖푚푒 − 푠푡푎푟푡푡𝑖푚푒) ∗ 푎푢푡표푛표푚표푢푠
Equation 30
푚0 = 푚푚푎푥 ∗ 0.5
Equation 31
푚푚𝑖푛 = 푚푚푎푥 ∗ 0.01
The maximum storage is created to depend on the working hours of the specific site, where the principle of five days of autonomy is implemented. Hence, the site storage is dimensioned to be self-sufficient for at least five days.
To implement the option for hydrogen delivery to every construction site, the maximum storage capacity is dimensioned to at least be able to receive one delivery of hydrogen. The smallest delivery in the model is 300 kg of gaseous hydrogen at 350 bar and thereby the minimum maximum mass of tank capacity.
Equation 32
푚푚푎푥 ≥ 푚0 + 퐺푎푠 푑푒푙𝑖푣푒푟푦
36 The production variable for the off-site production is binary. Depending on production method chosen, the delivery is either used or not used and thereby responsible for a fully loaded truck or tanker with their respective emissions.
Equation 33
퐻2,푝푟표푑푢푐푡𝑖표푛,𝑖,푛 = 1, 퐻2,푝푟표푑푢푐푡𝑖표푛,𝑖,푛 = 0, 푛 = 1,2,3,4,6,7
Equation 34
퐶푂2,𝑖,푛 = (퐶푂2,푃푟표푑푢푐푡𝑖표푛,푛 + 퐶푂2,푃푟표푐푒푠푠𝑖푛𝑔) ∗ 퐷푒푙𝑖푣푒푟푦 퐶푎푝푎푐𝑖푡푦 + 푇푟푎푛푠푝 퐶푂2, 푛 = 1,2,3,4,6,7
However, for the on-site production possibility the emissions produced will not have a delivery aspect but instead produce the amount of hydrogen needed to satisfy the demand. The on-site production methods are thereby continuous and are constrained to the hydrogen yield of the electricity supply of the construction site.
Equation 35
0 ≤ 퐻2,푝푟표푑푢푐푡𝑖표푛,𝑖,푛 ≤ 퐻2,푚푎푥𝑖푚푢푚 푦𝑖푒푙푑, 푛 = 5,8
Equation 36
퐶푂2,𝑖,푛 = 퐶푂2,푃푟표푑푢푐푡𝑖표푛 + 퐶푂2,푃푟표푐푒푠푠𝑖푛𝑔, 푛 = 5,8
The following equation is implemented to accumulate the amount of CO2 for each production technology. Hence, the equation below is the objective function to be minimized by variating the values of the production variables:
Equation 37
푑푎푦푠∗24
푇표푡푎푙 퐶푂2 = ∑ ∑ 퐶푂2,𝑖,푛 ∗ 퐻2,푝푟표푑푢푐푡𝑖표푛,𝑖,푛 푛=1,2,3,4,5,6,7,8 𝑖=1
4.4.1 Cost equations
When the CO2 optimization had been performed the cost for the various operations was calculated. The variable cost per kg hydrogen for the electrolysers was calculated in the following way.
Equation 38 푂푃퐸푋 ∗ max (푃표푤푒푟 표푢푡푝푢푡) 퐶표푠푡 = 퐸푙푒푐푡푟𝑖푐𝑖푡푦 ∗ 푘푊ℎ + 푃푟표푑푢푐푡𝑖표푛,퐸푙푒푐푡푟표푙푦푠푒푟 푝푟𝑖푐푒 푘𝑔 8760
37 As the electrolysers is assumed to be rented a rental cost is payed if the electrolysers are used. The rental cost is dependent on the maximal power output of the electrolyser and calculated with the following equation. The rental cost is assumed to be paid every 30th day.
Equation 39
퐶표푠푡푟푒푛푡푎푙 = 0.03 ∗ 퐶퐴푃퐸푋 ∗ max(푃표푤푒푟 표푢푡푝푢푡)
Equation 40
퐶표푠푡𝑖,푛 = (퐶표푠푡푃푟표푑푢푐푡𝑖표푛 + 퐶표푠푡푃푟표푐푒푠푠𝑖푛𝑔) ∗ 푘푔퐻2 , 푛 = 5,8
The options that are used off site includes a transportation cost that is a function of distance and mass transported. The off-site options includes a marginal cost as well to give a better representation of the actual cost, this cost is a coefficient multiplied to the total costs.
Equation 41
퐶표푠푡𝑖,푛 = (퐶표푠푡푃푟표푑푢푐푡𝑖표푛 + 퐶표푠푡푃푟표푐푒푠푠𝑖푛𝑔 + (퐶표푠푡푇푟푎푛푠푝표푟푡 ∗ 푑𝑖푠푡푎푛푐푒)) ∗ 푘푔퐻2 ∗ 푀푎푟푔𝑖푛푎푙,
푛 = 1,2,3,4,6,7
The total cost for the operation is calculated in the following way:
Equation 42
퐶표푠푡푡표푡푎푙 = ∑ 퐶표푠푡𝑖,푛 + 퐶표푠푡푟푒푛푡푎푙
38 5 RESULTS
In this section the results from the modelling will be presented. The evaluation of each scenario will be assessed to the respective sites and have its profile compared. The following colours represents the different options available in the modelling and is used in the result section to show the production patterns. As previously mentioned, the only on-site applications are the electrolyser options which can be denoted as green and black in the figure below.
The evaluation of the various scenarios is done by comparing the production profiles which will consist of the SOC of the tank and production patterns for the optimal production method. The objective is to minimize the CO2 emitted with regards to the hydrogen demand of the site where the algorithm will choose the least CO2 intensive hydrogen pathway in the model. The simulations are performed for 30 days for every scenario and operation.
104 Total hydrogen demand 5 Figure 14 Colours representing different production options 4.5
4
3.5
3
g 2.5 k
2
1.5
1
0.5
0 Mine Light Road Quarry
Figure 15 Total hydrogen demand for various sites
In the figure above the total hydrogen demand is displayed. The figure represents the different demand from the four construction sites simulated in this thesis work. The demand presented is shown as the total demand for 30-day operation which all the investigated scenarios are based on. Furthermore, the demand for the mining site is approximately 45 tonnes of H2, 9 tonnes for quarry, 6 tonnes for road construction and 2 tonnes for light-duty operations. This figure is presented to emphasize the difference in magnitude of the different operations. The figures shown in the coming chapters will all have a different magnitude of charge in the simulated tank and will hence have different Y-axes.
39 5.1 Mine evaluation scenario 1-4
Below the results for the mining operation are presented, for the scenarios created in section 4.1.1. The top left figure is denoted as scenario 1 (SE energy mix, grid connection and short distance from hydrogen production). The top right figure is the results from scenario 2 (PL energy mix, grid connection and short distance from hydrogen production). The bottom left provides the results from scenario 3 (EU28 energy mix, constrained grid connection and short distance to hydrogen production). At last the bottom right figure presents the results from scenario 4 (EU28 energy mix, no grid connection and long distance to hydrogen production).
M SOC M SOC
M P M P
M SOC M SOC
M P M P
Figure 16 Top left: SE, 10 km; Top right PL, 10 km; Bottom left, EU28, 10 km; Bottom right; EU28, 500 km
From scenario 1 (top left) it can be seen that the least emissive option is to have a large PEM electrolyser which mainly produce hydrogen to satisfy the demand every hour. The increased production towards the end is due to the constraint of matching the tanks initial volume by the end of the simulation. Scenario 2, 3 and 4 are using SMR CCS gas delivery to satisfy the hydrogen demand with some distinct peaks in scenario 3 at 450-550 hours (bottom left).
40 5.2 Light evaluation scenario 1-4
The figures below present the results from the light-duty construction for the four developed scenarios. The scenarios are as previously mentioned 1, 2, 3 and 4 starting from top left to bottom right where the production rate of the different techniques and state of charge for the tank can be seen.
L SOC L SOC
L P L P
L SOC L SOC
L P L P
Figure 17 Top left: SE, 10 km; Top right PL, 10 km; Bottom left, EU28, 10 km; Bottom right; EU28, 500 km
In scenario 1 the least CO2 intensive way to satisfy the hydrogen demand is by using an
PEMEC on site. The electrolyser is approximately producing 3 kg H2/hour most of the time, the production is more intense towards the end of the simulation in order to satisfy the constraints of SOC. The SOC pattern for scenario 1 differs from the other as the production is more regular. Scenario 2, 3 and 4 is very similar to each other as the SOC profile are almost identical and the production method is gaseous SMR CCS for each scenario. The delivery of hydrogen occurs every 100th hour approximately and occurs when the SOC is too low to satisfy the demand.
41 5.3 Road evaluation scenario 1-4
Beneath the results for the road-construction site is presented. This section is also presenting the scenarios 1 – 4 from top left to bottom right where the production rate of the different techniques and state of charge for the tank can be seen.
R SOC R SOC
R P R P
R SOC R SOC
R P R P
Figure 18 Top left: SE, 10 km; Top right PL, 10 km; Bottom left, EU28, 10 km; Bottom right; EU28, 500 km
The solution for scenario 1 uses the PEMEC on-site. The production of hydrogen is done for both the operation time of the site and down-time, increasing the charge of the tank with a similar pattern to previous scenario 1 conditions. Additionally, scenarios 2, 3 and 4 have similar solutions and uses SMR CCS gas deliveries to satisfy the demand during the simulated days. Additionally, the optimal solution for scenario 2, 3 and 4 are similar due to the same production method. However, the differences in these scenarios are denoted of which suitable solution the optimization provides depending on the distance, mix and grid connection. This provides deliveries at different times and causes the difference in the SOC.
42 5.4 Quarry evaluation scenario 1-4
The following graphs presents production patterns and SOC for quarry for the different scenarios in the same order as the previous graphs.
SOC SOC
P P
SOC SOC
P P
Figure 19 Top left: SE, 10 km; Top right PL, 10 km; Bottom left, EU28, 10 km; Bottom right; EU28, 500 km
For scenario 1 it can be seen that the production pattern and SOC is very similar to scenario 1 for mine. The option that is used is on site PEMEC with a maximal capacity of approximately
15 kg H2/hour. The SOC and production is rather low in the beginning and ramps up after 400 hours to be able to satisfy the SOC constraints. The SOC profiles for the other scenarios are similar but not identical as the transports are done at different time steps. All the scenarios are using gaseous SMR CCS deliveries, noteworthy is that PEMEC is used in scenario 3 at time step ~450, the amount of hydrogen produced is very low and is not a realistic way to manage the production as rather big investments are required for the low amount of hydrogen produced.
43 SOC E E
P
Figure 20 scenario 3 without electrolyser
In the figure above scenario 3 is simulated without the option to use electrolyser, the acquired solution looks similar to the previous figure, except that no electrolyser is used. This represents a more realistic way of operation. This will result in a lower cost for the construction site and provide a similar solution to the previous simulation that included the option with an electrolyser.
44 5.5 CO2 & Cost evaluation
In the table below the total amount of CO2 emitted is presented. The scenarios 1, 2, 3 and 4 is
presented below and displaying the amount of CO2 in kg emitted. The parentheses are
presenting the kg CO2/ kg H2 for every operation.
Table 5 Emitted CO2
Scenario 1, 2, 3, 4 Mine kg CO2 Light kg CO2 Road kg CO2 Quarry kg CO2 Close to city/production with grid 34 180 (0.75) 1 435 (0.75) 4 509 (0.75) 7 053 (0.75) connection high share of RES (1) Close to city/production with grid 200 729 (4.43) 9 244 (4.85) 26 412 (4.41) 42 259 (4.51) connection low share of RES (2) Close to city/production with 165 890 (3.66) 7 640 (4.01) 21 828 (3.65) 34 838 (3.72) a constrained grid connection EU28 34 924 (3.73) electricity mix (3) Remote location without grid 243 350 (5.36) 11 207 (5.88) 32 020 (5.35) 51 231 (5.47) connection (500 km) (4) Discarded solution, high share of 145 279 (3.2) 6 690 (3.51) 19 116 (3.19) 30 585 (3.27) RES close to city
Hydrogen demand kg H2 45 360 1 904 5 984 9 360 Present operation Diesel 248 812 10 300 30 340 49 890 a Denotes the scenario when on-site electrolysers are excluded
For the mining operation there is a substantial difference between having the possibility to use a PEMEC with the SE energy mix, avoiding deliveries and the emissions connected to that alternative. This can also be seen for the other types of construction sites as the
emittance is lower due to the use of an electrolyser. The highest emittance of CO2 is seen in the remote location scenario due to the long transport for hydrogen delivery. The fifth row presents a non-optimal solution for scenario 1 where SMR CCS is manually chosen to
compare the amount of CO2 to the first row of the table (also scenario 1).
Table 6 Total Costs
Scenario 1, 2, 3, 4 Mine Cost $ Light Cost $ Road Cost $ Quarry Cost $ Close to city/production with grid 982 712 (21.66) 16 502 (8.67) 58 406 (9.76) 104 688 (11.18) connection high share of RES (1) Close to city/production with grid 103 826 (2.29) 4 781 (2.51) 13 661 (2.28) 21 858 (2.33) connection low share of RES (2) Close to city/production with 106 251 (2.34) 4 893 (2.57) 13 980 (2.33) 58 109 (6.21) constrained grid connection EU28 22 369a (2.39) electricity mix (3) Remote location without grid 320 217 (7.06) 14 747 (7.74) 42 134 (7.04) 67 414 (7.20) connection (500 km) (4) Discarded solution, high share of 101 127 (2.23) 4664 (2.45) 13 325 (2.23) 21 320 (2.28) RES close to city. a Denotes the scenario when on-site electrolysers are excluded
45 In the table above the total costs of each solution is presented. The costs for the mining construction are the highest due to the high demand. Most notably is the cost of having an electrolyser as production method as can be seen in scenario 1. The least expensive option for the construction site is scenario 2 due the exclusion of electrolysers and fairly low electrical costs. Additionally, the last row presents a non-optimal solution for scenario 1 where SMR CCS is manually chosen to compare the costs to the first row of the table (also scenario 1).
5.1 Sensitivity analysis
For scenario 1 and 2 the sole difference is the share of RES i.e. which electricity mix used in the modeling. In the production and SOC profiles big differences can be seen for these
scenarios regarding production methods used. In the table describing CO2 emissions it can be seen that the emissions for scenario 2 is much higher than for scenario 1. For scenario 1 the Swedish electricity mix is used, the emissions for that mix are 13.3 g/kWh, for scenario 2 where Polish mix is used the emissions are 773.3 g/kWh. To find the breaking point where the solver chose electrolyser instead of SMR CCS, different mixes have been tested.
Figure 21 Top/Bottom left: Energy mix 57g CO2/kWh; Top/Bottom right, Energy mix 58g CO2/kWh
46 In the underlaying graphs the SOC and production profiles for 57 g/kWh and 58 g/kWh are presented to visualize the breaking point. The left graphs show a mix with 57 g CO2/kWh and the right graphs show a mix with 58 g CO2/kWh. In the left graphs the only production method is PEMEC while SMR CCS is used in the right graphs. Note that the y-axis is logarithmic in the right upper corner in order to be able to visualize both the production methods.
6 DISCUSSION
In the current chapter the discussion of the results will be presented. Here the figures and tables will be discussed and further explained with corresponding analysis. Furthermore, it will be discussed how the results have been affected by the methods used and assumptions. Additionally, the research questions will be answered in this chapter to be further summarized in the next section conclusions.
6.1 Site assessment
In the figures 16 to 21, the results obtained from the four construction sites can be seen to have similar characteristics depending on the scenario implemented. In scenario 1 the results show that the electrolyser on-site option is the most suitable for minimizing CO2 due to the energy mix containing a high share of RES. The continuous use of an electrolyser to satisfy the demand is only CO2 viable for a clean electricity supply and is hence only chosen by the algorithm for the Swedish electricity mix. When considering the remaining scenarios that all have an energy mix that is consisting of considerably less RES. Consequently, all of these construction sites choses to use SMR CCS produced gas delivered hydrogen for all the remaining scenarios as it results in the least CO2 emissive operation. The only factor that changes is the intensity of deliveries at certain time-steps and depending on the solution found, the amount of deliveries for that specific construction site is the same. According to the literature review SMR produced hydrogen is the most used production method today and could in this case also be an attractive choice, if equipped with CCS which has showed a decent capture rate to a moderate cost. With today’s current availability of hydrogen, it would therefore seem to be most suitable production/delivery for the results from scenario 2 - 4.
Additionally, it can be noted that liquid delivery is never chosen as a suitable option since the processing and the losses when considering the boiloff of the delivered hydrogen are too great. The only site capable of using a liquid delivery would in this case be the mining construction site, which instead prefer the gas delivered option as it provides a lower CO2 yield. Comparing the gas and liquid processing, the amount of CO2 per kg is approximately 3 times larger for liquid than for gas. The distance for deliveries in the developed scenarios might not be far enough to consider liquid transport as a suitable solution, as the processing
47 emissions are too substantial and makes the option more appropriate for long distance deliveries.
When considering table 5 and 6, the amount of CO2 and cost are negatively correlated, which can especially be noted in the comparison between the optimal and non-optimal solutions. The PEMEC on-site is explicitly dependent on the input electricity mix and can with the right circumstances provide a low CO2 operating construction site. It is notable in table 5 that when the option for electrolyser is available the CO2 emittance is lower but also leads to the most expensive type of construction site due to the rental cost of the equipment. In the case of scenario 2 and 3 (Polish and EU28 electricity mix) the cost is fairly similar, with the cost for EU28 being slightly higher due to a higher electricity price but with similar production and charge profiles.
As the results obtained from the model presents the total CO2 emitted for 30 days of operation on various sites under differing conditions, it will furthermore be needed to put the results in perspective to today’s operation with diesel. As can be seen in table 5 the hydrogen- based operation is resulting in less CO2 except for the scenario with remote location transport and no electricity supply, where only the mining-site is resulting in a lower CO2 with hydrogen. However, the other operations are benefiting from having a hydrogen-based usage as it results in less CO2 compared to a diesel-based operation. When comparing the diesel and hydrogen fuel pathways they differ in the case of fuel usage. In this thesis work where the storage is modelled the emissions is nonexistence from the hydrogen being used as the by- product of fuel cells are H2O and thereby only providing emissions in the upstream, processing and delivery parts of the hydrogen chain. When considering the emissions from the diesel, the inspected fuel analysis is from production, processing, transport and combustion. By comparing the pathways in this approach, it will yield a more realistic picture of the emissions as the usage of diesel fuel will result in CO2 when consumed.
In the field study chapter, similar work related to hydrogen supply chains were investigated where the purpose of those papers was to minimize the cost of hydrogen pathways. The results from these works showed that liquid hydrogen was the optimal storage phase cost wise, when large quantities was evaluated. However, the quantities of hydrogen in the papers was considerably higher than the quantities in the current work. For liquid hydrogen to be preferable prior to gaseous hydrogen, improvements must occur cost wise and performance wise.
The emissions for the different pathways have been evaluated from production to storage on site, the usage on site is not evaluated nor the upstream processes. The usage on site does not affect the comparison of the various pathways as the same value would be acquired for all sites. Regarding the upstream processes the emissions and cost has been delimited as the scope of the work would be to substantial. With these emissions included in the model the
CO2 emissions would be higher for all scenarios and operations. If the emissions for some processes would be significantly higher than the other, another option could be used for the various scenarios.
48 6.2 Sensitivity analysis
In chapter 5.5 where the sensitivity analysis for the different electricity mixes was presented, it could be seen that SMR with CCS was the most suitable option when the CO2 emission for electricity exceeded 58 g/kWh for scenario 1 and 2. When the emissions was lower than 58 g/kWh PEMEC on site was chosen for all operations. The reason that SMR is preferable when the share of RES is low is because the option is less electricity intensive than the electrolysers, and thereby less dependent on the emission from the electricity. The emission for the different electricity mixes 2018 was the following, EU28 295.8 g/kWh, Poland 773.3 g /kWh and 13.3 g/kWh for Sweden. From these numbers it can be seen that Poland and EU in general must reduce their emission considerably for electrolysers to be favoured over SMR. Due to the magnitude of reduction in emissions necessary for EU28 to reach 58 g/kWh SMR most likely will be the most preferable alternative for the most countries in a foreseeable future. With projected improvements for PEMEC and SOEC the limit for electrolysers to be favourable can be raised. This would make electrolyser the most optimal option in additional cases.
As stated earlier SMR is the best option in terms of CO2 emissions for most of the scenarios. Although, the technique is dependent on fossil resources, as natural gas is the commonly used feedstock while electrolysers feedstock is water and electricity. In terms of sustainability it is hard to tell which of the options is the best as they are preferable in different aspects. It is considered that SMR with CCS will be a sustainable way to produce hydrogen in a short future; however, CCS equipped production by SMR is less than 0.5 % of the worlds installed capacity but demonstration plants have showed promising capture rates. By excluding CCS, the production of hydrogen by SMR would become directly unsustainable due to its high emission during reforming. Hence, to get some diversity in production methods from the modelling the CCS option was implemented as it is expected to have a larger penetration in the future. Furthermore, in a longer-term electrolyser will be preferable to reduce the dependency on fossil fuels. When comparing the CO2 emissions for the current operations to hydrogen-based sites this aspect must be considered as well.
The maximum storage is dimensioned according to the demand for the construction equipment’s operating hours and the amount of days of autonomy, see Equation 29. By adjusting the amount of days, the storage is able to be self-sufficient and will either increase or decrease the maximum storage capacity. The results are currently modelled with 5 days of autonomy and does in some cases result in an oversizing of the maximum capacity of the storage e.g. in scenario 1 for mine in section 5.1 and quarry in section 5.4. Having an oversized tank does not affect the amount of CO2 emitted but would instead lead to an unnecessary cost for that simulation as the tank capacity keeps increasing. Consequently, the state of charge will begin at a high capacity due to the initial volume being 50 % of the maximum capacity and thereafter supplying the demand down to a more reasonable max capacity. The production then intensifies to meet the constraint (Equation 28) of the mass being equal or greater to the initial mass by the end of the simulation, which can be seen in section 5.1 for mine and 5.4 for quarry. It can be considered, depending on the specification on the construction sites independence that the days of autonomy should be chosen to satisfy the possibilities for deliveries and on-site production to minimize CO2 and cost.
49 6.3 Method evaluation
The algorithm used in the modelling was MILP, the method was chosen as the objective function that was to be minimized could be expressed as a linear problem with few modifications. The method was also chosen as it has shown to be useful with satisfying results in other similar works. The on-site options were expressed as continuous variables while the options using transportation was expressed as binary integer variables. Due to the transportation variables being binary integers the acquired result has been affected. For the algorithm to satisfy the constraint of final SOC an increased intensity of deliveries can be seen in the results. The final delivery is overshooting in many of the cases, as the hydrogen is excessive, the associated CO2 emissions is suboptimal for the operation. Although, this error is very small in relation to the total CO2 emissions, which was considered not to have a large impact on the final results.
When MILP is used the algorithm looks for an optimal solution that satisfies the given constraints and the decision variables are not necessary integers. When the optimal solution is found the algorithm tries to find an integer solution which matches the optimal solution. Due to this the acquired results from the model can differentiate from the optimal solution, however the error is relatively small in relation to the values acquired, as the acquired results has the magnitude of thousands of kilograms. Therefore, the algorithm is used despite the small errors in the given solutions.
The quantitative data analysis performed provided satisfying results. The concepts of examining frequency and intensity of the results provided by the empirical data given by the costumer could with minimal altering be sufficient for developing the model. The combination of the gathered input parameters according to Figure 9 Model schematic proved useful for supplying input data to the model. However, there is with some uncertainty the data provides the whole picture. Gathered data is often subject to the authors own assumptions and implications to produce values. The measures taken has been to stay critical of collected values and with a broad perspective find numerous sources that emphasises the final value used as input for the model. The logged vehicle data provided from the customer was subject to secrecy. The way the data was handled had no significant impact on the modelling where the confidential data was treated as black boxes described in section 4.2.1, where measures was taken so the power profile of a specific machine could not be calculated.
6.4 Assumptions discussion
In this subsection it will be discussed how the assumptions made in the model will affect the results and if the assumptions are realistic.
Regarding the assumption saying all work input in the processing was electrical, the effects varied for the techniques. For the compressed hydrogen, the assumption was realistic as the process is a compression which usually only uses electricity as an input. Regarding the liquefaction the assumption most likely had bigger effects as the liquefaction process is more
50 complex than compression. This assumption can possibly be a reason why liquefaction was not chosen for any scenario, it may have been chosen if the actual emissions would be lower.
Assuming sufficient water supply for all the sites that are able to use an electrolyser can be unrealistic for many locations as some areas have scarce water supply. Although, the assumption was done as none of the scenarios is done for a specific place and an in-depth analysis of different locations would have to be done, in order to set constraints on water supply. This assumption has affected the results, as no costs or emissions have been included regarding equipment and operations necessary to provide a water supply to the sites. The error related to this assumption varies in size depending if water has to be transported to site with tankers or if the site has a sufficient water supply.
Regarding the cost for the electrolysers used on site the equipment is assumed to be rented. According to the customer such equipment is usually rented at sites using construction equipment, this is why the assumption is made. The rental cost is assumed to be 3 percent of the CAPEX each month, this value can vary much between different sites as the cost is a deal between two companies. The specific value for rental cost will affect the total cost for the scenarios much as the rental cost is the bigger share of the total cost.
Some of the operations in the scenarios have different time spans, which makes renting of equipment preferable for some cases while the option to buy the equipment can be better for other operations. Road and light construction sites is usually operated for a limited time while mines and quarries can be active for many years. For the operations with a short operation time renting is a good way to lower the investment costs while it would be more beneficial for the long-term operations to buy the equipment.
7 CONCLUSIONS
The thesis work has established that a hydrogen pathway could be developed to satisfy the demand of hydrogen powered construction equipment. It can be determined that for some of the created scenarios, a hydrogen powered construction site could prove beneficial in the aspects of minimizing or reduce CO2. Consequently, when comparing the resulting emissions between hydrogen and diesel it could be concluded that for most of the developed scenarios except for the long-distance case that hydrogen fuelled construction equipment would result in a lower CO2 yield. To succeed in reducing the environmental impact of construction equipment at site, electrolysers shows promising results if the electricity supply has a high share of RES. If the energy mix has a low share of RES, emphasis would lie on the transportation of hydrogen to the corresponding construction site. Feasible locations for electrolysis-based hydrogen production would be a country with an energy mix similar to Sweden’s, which could provide solutions for PEMEC on-site production. The only solution found depending exclusively on PEMEC production was the Swedish developed scenario,
51 where EU28 and Poland would need a substantial improvement in their energy mix to see similar solutions.
By reviewing the technologies chosen by the model, it can be seen that for most cases SMR CCS delivered hydrogen in gaseous form is the most used production pathway. However, depending on future incentives and the penetration of renewables the most common production pathways for hydrogen could change. Hence, it could be considered that every hydrogen pathway needs to be adapted to the specification of the chosen construction site with regards to demand, transport distance and energy mix.
The environmental impact compared to present day operation of construction equipment was improved for most of the scenarios. An average of 32.7 % reduction in CO2 emissions for all simulated scenarios could be achieved. The only scenario not benefiting from hydrogen as fuel was the remote location case. This is due to the long transport needed which contributes to an increased amount of CO2 as the emissions for the transport option is expressed as emissions per kilometre transported hydrogen. The remote location case is also constrained since the scenario was developed without a grid connection, which eliminates the possibility for an electrolyser on-site. Otherwise, it was concluded that all the other scenarios consisting of various energy mixes, distances and hydrogen demand saw an improvement compared to present usage of diesel. It can hence be considered that compared to present day operation it is possible to see improvements in sustainability, environmental impact and potential storage possibilities on-site, with hydrogen as a substitute fuel under the right circumstances.
Furthermore, when reviewing the economic aspects of the hydrogen pathway it showed that the least emissive operations had higher costs and the high emissive operations had lower costs. The most expensive operations were the options using electrolyser on site as the rental cost for the machines was included in the scenarios. Whether renting of equipment is the best way to finance the operation is dependent of the characteristics of the site, where operation time of the site is the most important factor.
8 SUGGESTIONS FOR FURTHER WORK
The purpose with this thesis work is to find the most suitable pathway to use hydrogen on construction equipment sites with currently available technologies. The collected data is state of the art parameters for the used technologies. Throughout this work many emerging technologies with a low level of maturity have been discarded either in the literature study or in the modelling.
To get a good estimation of how the pathways for future operation would be, it would be interesting to make a similar work were the emerging techniques are included. Production methods that would be interesting to investigate could be SOEC or biogas reforming. Techniques that could be interesting regarding storage and transportation could be cryo- compressed and material-based storage.
52 In this work a well to tank analysis is performed i.e. the steps between storage and usage are excluded. This was done in order to limit the scope of the work and due to uncertainties on machine storage. To complete this work a tank to wheel analysis could be done to simulate the distribution on site, charging and an in-depth study of storage and fuel consumption on specific machines.
Furthermore, it would be interesting to evaluate the possibility to power a hydrogen construction equipment site using sole renewable energy sources on a remote location. This could be done with the purpose to find a pathway for hydrogen which can be applied to various areas and have no direct emissions from the production. To perform this work, it would be interesting to use wind- and sun profiles to predict the potential hydrogen yield. Although, this would most likely only be adaptable for smaller sites.
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64 APPENDIX 1: REFERENCES 3.6 SUMMARY LITERATURE REVIEW
3 Technology Cost [$/kg H2] CAPEX [$/kW] kWh/Nm H2 kg CO2/kg H2 Efficiency % SMR natural (Dincer & Acar, 2015b; - - (Khojasteh Salkuyeh et al., 2017; (Ersöz, 2008; Saeidi et al., gas IEA, 2019a; Khojasteh Mastropasqua et al., 2020b; Voldsund et 2017) Salkuyeh et al., 2017) al., 2016) SMR natural (Khojasteh Salkuyeh et - - (Khojasteh Salkuyeh et al., 2017; (Ersöz, 2008; Saeidi et al., gas with CCS al., 2017) Mastropasqua et al., 2020b; Voldsund et 2017) al., 2016) AR natural gas - - - (Abdalla et al., 2018)
AR natural gas (Khojasteh Salkuyeh et - - - (Abdalla et al., 2018) with CCS al., 2017; Nikolaidis & Poullikkas, 2017) AR Biogas with (Montenegro Camacho - - - (Abdalla et al., 2018) CCS et al. 2017) POX (IEA, 2014) - - (Hajjaji et al., 2013; Pilavachi et al., 2009) POX with CCS (IEA, 2014) - - (Abdalla et al., 2018)
Gasification (Lemus & Martínez - - (Cetinkaya et al., 2012; Ewan & Allen, (El Shafie et al., 2019; IEA, coal Duart, 2010; Y. Li et al., 2005; Salkuyeh et al., 2018; Verma & 2014; Nikolaidis & 2010; Mason, 2007; Kumar, 2015) Poullikkas, 2017). Nikolaidis & Poullikkas, 2017; Ramsden et al., 2013). Gasification Olateju & Kumar - - (Cetinkaya et al., 2012; Ewan & Allen, - coal with CCS (2013) 2005; Salkuyeh et al., 2018; Verma & Kumar, 2015)
1 Gasification Olateju & Kumar - - - - Biomass (2013) AE - (IEA, 2019a) (Carmo et al., 2013; Chi - (Carmo et al., 2013; Chi & & Yu, 2018; Dincer & Yu, 2018; Dincer & Acar, Acar, 2015a) 2015a) PEMEC - (IEA, 2019a) (Carmo et al., 2013; - (Carmo et al., 2013; Clarke Clarke et al., 2010) et al., 2010) SOEC - (IEA, 2019a) (Chi & Yu, 2018; Dawood - (Chi & Yu, 2018; Dawood et et al., 2020) al., 2020)
2 APPENDIX 2: CODE
MAIN days = 30; CO2Pricing = 0; Elecsupply = 10000; Distance = 10; %km mix = 3; % 1 = SE, 2 = EU28, 3 = PL
%Mine settings site = 1; [start_mine,stop_mine,Vmax_mine,V0_mine,Vmin_mine,D_H2_mine,CO2_CGH2_350_X, CO2_LH2_X,CO2_AE_X,CO2_PEMEC_X,H2_COST_PEMEC_X,H2_COST_SMR_CCS,Cost_tube,Co st_tanker,ELECTROLYSER_mine,EM_CO2_SMR_CCS,TranspCO2,Capgas,Capliq,COST_LH2 _X,COST_CGH2_350_X,H2_COST_AE_X,ELECTROLYSER_AE,Rent_PEMEC_m,Rent_AE_m,M] = Settings(site,mix,Elecsupply,Distance); [sol_mine,TotCost_mine,exitflag_mine,Dem_H2_m,TotCO2_mine] = optimization(V0_mine,Vmax_mine,Vmin_mine,days,start_mine,stop_mine,CO2_CGH2 _350_X,CO2_LH2_X,CO2_AE_X,CO2_PEMEC_X,D_H2_mine,Capgas,Capliq,CO2Pricing,H2 _COST_SMR_CCS,H2_COST_PEMEC_X,Cost_tube,Cost_tanker,ELECTROLYSER_mine,EM_CO 2_SMR_CCS,TranspCO2,COST_LH2_X,COST_CGH2_350_X,H2_COST_AE_X,ELECTROLYSER_AE ,Rent_PEMEC_m,Rent_AE_m,M);
%Light Duty settings site = 2; [start_light,stop_light,Vmax_light,V0_light,Vmin_light,D_H2_light,CO2_CGH2_ 350_X,CO2_LH2_X,CO2_AE_X,CO2_PEMEC_X,H2_COST_PEMEC_X,H2_COST_SMR_CCS,Cost_t ube,Cost_tanker,ELECTROLYSER_light,EM_CO2_SMR_CCS,TranspCO2,Capgas,Capliq,C OST_LH2_X,COST_CGH2_350_X,H2_COST_AE_X,ELECTROLYSER_AE,Rent_PEMEC_l,Rent_AE _l,M] = Settings(site,mix,Elecsupply,Distance); [sol_light,TotCost_light,exitflag_light,Dem_H2_l,TotCO2_light] = optimization(V0_light,Vmax_light,Vmin_light,days,start_light,stop_light,CO2 _CGH2_350_X,CO2_LH2_X,CO2_AE_X,CO2_PEMEC_X,D_H2_light,Capgas,Capliq,CO2Pric ing,H2_COST_SMR_CCS,H2_COST_PEMEC_X,Cost_tube,Cost_tanker,ELECTROLYSER_ligh t,EM_CO2_SMR_CCS,TranspCO2,COST_LH2_X,COST_CGH2_350_X,H2_COST_AE_X,ELECTROL YSER_AE,Rent_PEMEC_l,Rent_AE_l,M);
%Quarry settings site = 3; [start_quarry,stop_quarry,Vmax_quarry,V0_quarry,Vmin_quarry,D_H2_quarry,CO2 _CGH2_350_X,CO2_LH2_X,CO2_AE_X,CO2_PEMEC_X,H2_COST_PEMEC_X,H2_COST_SMR_CCS, Cost_tube,Cost_tanker,ELECTROLYSER_quarry,EM_CO2_SMR_CCS,TranspCO2,Capgas,C apliq,COST_LH2_X,COST_CGH2_350_X,H2_COST_AE_X,ELECTROLYSER_AE,Rent_PEMEC_q, Rent_AE_q,M] = Settings(site,mix,Elecsupply,Distance); [sol_quarry,TotCost_quarry,exitflag_quarry,Dem_H2_q,TotCO2_quarry] = optimization(V0_quarry,Vmax_quarry,Vmin_quarry,days,start_quarry,stop_quarr y,CO2_CGH2_350_X,CO2_LH2_X,CO2_AE_X,CO2_PEMEC_X,D_H2_quarry,Capgas,Capliq,C O2Pricing,H2_COST_SMR_CCS,H2_COST_PEMEC_X,Cost_tube,Cost_tanker,ELECTROLYSE R_quarry,EM_CO2_SMR_CCS,TranspCO2,COST_LH2_X,COST_CGH2_350_X,H2_COST_AE_X,E LECTROLYSER_AE,Rent_PEMEC_q,Rent_AE_q,M);
%Road Construction settings site = 4; [start_road,stop_road,Vmax_road,V0_road,Vmin_road,D_H2_road,CO2_CGH2_350_X, CO2_LH2_X,CO2_AE_X,CO2_PEMEC_X,H2_COST_PEMEC_X,H2_COST_SMR_CCS,Cost_tube,Co st_tanker,ELECTROLYSER_road,EM_CO2_SMR_CCS,TranspCO2,Capgas,Capliq,COST_LH2 _X,COST_CGH2_350_X,H2_COST_AE_X,ELECTROLYSER_AE,Rent_PEMEC_r,Rent_AE_r,M] = Settings(site,mix,Elecsupply,Distance);
1 [sol_road,TotCost_road,exitflag_road,Dem_H2_r,TotCO2_road] = optimization(V0_road,Vmax_road,Vmin_road,days,start_road,stop_road,CO2_CGH2 _350_X,CO2_LH2_X,CO2_AE_X,CO2_PEMEC_X,D_H2_road,Capgas,Capliq,CO2Pricing,H2 _COST_SMR_CCS,H2_COST_PEMEC_X,Cost_tube,Cost_tanker,ELECTROLYSER_road,EM_CO 2_SMR_CCS,TranspCO2,COST_LH2_X,COST_CGH2_350_X,H2_COST_AE_X,ELECTROLYSER_AE ,Rent_PEMEC_r,Rent_AE_r,M);
% ------Plots ------%
Mine([sol_mine.V],[sol_mine.produce1*Capgas, sol_mine.produce2*Capliq, sol_mine.produce3*Capgas, sol_mine.produce4*Capliq, sol_mine.produce5, sol_mine.produce6*Capgas, sol_mine.produce7*Capliq, sol_mine.produce8],days); % print -dsvg mine_x % close all Light([sol_light.V],[sol_light.produce1*Capgas, sol_light.produce2*Capliq, sol_light.produce3*Capgas,sol_light.produce4*Capliq,sol_light.produce5,sol_ light.produce6*Capgas, sol_light.produce7*Capliq, sol_light.produce8],days); % print -dsvg light_x % close all Quarry([sol_quarry.V], [sol_quarry.produce1*Capgas, sol_quarry.produce2*Capliq, sol_quarry.produce3*Capgas,sol_quarry.produce4*Capliq,sol_quarry.produce5, sol_quarry.produce6*Capgas, sol_quarry.produce7*Capliq, sol_quarry.produce8],days) % print -dsvg quarry_x % close all Road([sol_road.V], [sol_road.produce1*Capgas, sol_road.produce2*Capliq, sol_road.produce3*Capgas,sol_road.produce4*Capliq,sol_road.produce5, sol_road.produce6*Capgas, sol_road.produce7*Capliq, sol_road.produce8],days) % print -dsvg road_x % close all movegui(1,'northwest') movegui(2,'northeast') movegui(3,'southwest') movegui(4,'southeast')
INPUT function [CcGH2_COST_700,CcGH2_COST_350,EM_CO2_EU28,EM_CO2_SE,EM_CO2_PL,EM_CO2_SMR,E M_CO2_SMR_CCS,EM_CO2_AR_CCS,H2_COST_SMR,H2_COST_SMR_CCS,H2_COST_AR_CCS,EL_C OST_EU28,EL_COST_SE,EL_COST_PL,CGH2_COST_350,CGH2_COST_700,LH2_COST,SEC_PEM EC,SEC_AE,SEC_CGH2_350,SEC_CGH2_700,SEC_CcGH2_700,SEC_LH2,BOILOFF_LH2,H2_CO ST_AR,OPEX_AE,OPEX_PEMEC,Capgas,Capliq,CAPEX_PEMEC,CAPEX_AE] = Input() %Input modelling data
Capgas = 300; %Capacity for gas tube trailer Capliq = 4500; %Capacity for liquid tanker
EM_CO2_EU28 = 0.2958; %kg CO2/kWh EM_CO2_SE = 0.0133; %kg CO2/kWh EM_CO2_PL = 0.7733; %kg CO2/kWh 0.7733 limit 0.057 - 0.058 pure electrolyser -> mixed production
EM_CO2_SMR = 9.35; %kg CO2/kg H2 EM_CO2_SMR_CCS = 3.13; %kg CO2/kg H2
2 EM_CO2_AR_CCS = 1.3; %kg CO2/kg H2
H2_COST_SMR = 1.13; H2_COST_SMR_CCS = 1.64; H2_COST_AR = 1.48; H2_COST_AR_CCS = 1.85;
EL_COST_EU28 = 0.1387; %$/kWh Electrical cost EL_COST_SE = 0.0818; %$/kWh Electrical cost EL_COST_PL = 0.111; %$/kWh Electrical cost
CGH2_COST_350 = 14; %$/kWh Storage cost CGH2_COST_700 = 20; %$/kWh Storage cost LH2_COST = 9; %$/kWh Storage cost CcGH2_COST_350 = 10; %$/kWh Storage cost CcGH2_COST_700 = 13; %$/kWh Storage cost
SEC_PEMEC = 4.9; %kWh/Nm^3 Specific energy consumption SEC_AE = 5; %kWh/Nm^3 SEC_CGH2_350 = 1.6; %kWh/kg Energy consumption storage SEC_CGH2_700 = 3.2; %kWh/kg Energy consumption storage SEC_CcGH2_700 = 8; %kWh/kg Energy consumption storage SEC_LH2 = 13.14; %kWh/kg Energy consumption storage BOILOFF_LH2 = 2.5; % %/day TRAILER_LH2 = 5000; % kg/trailer Capacity TRAILER_CGH2 = 300; % kg/trailer Capacity
OPEX_AE = 19; OPEX_PEMEC = 29; %$/kw,year* CAPEX_AE = 950; CAPEX_PEMEC = 1450; end
SETTINGS
function [start,stop,Vmax,V0,Vmin,Dem,CO2_CGH2_350_X,CO2_LH2_X,CO2_AE_X,CO2_PEMEC_X, H2_COST_PEMEC_X,H2_COST_SMR_CCS,Cost_tube,Cost_tanker,ELECTROLYSER_PEMEC,EM _CO2_SMR_CCS,TranspCO2,Capgas,Capliq,COST_LH2_X,COST_CGH2_350_X,H2_COST_AE_ X,ELECTROLYSER_AE,Rent_PEMEC,Rent_AE,M] = Settings(site,mix,Elecsupply,DISTANCE) % ------Input ------%
Cost_tube = 0.00798*DISTANCE; %$/kg,km Cost_tanker = 0.001729*DISTANCE; %$/kg,km CO2_diesel = 2.6; %kg/L Diesel_consumption = 0.4; TranspCO2 = DISTANCE*CO2_diesel*Diesel_consumption; [~,~,EM_CO2_EU28,EM_CO2_SE,EM_CO2_PL,~,EM_CO2_SMR_CCS,~,~,H2_COST_SMR_CCS,~ ,EL_COST_EU28,EL_COST_SE,EL_COST_PL,~,~,~,SEC_PEMEC,SEC_AE,SEC_CGH2_350,SEC _CGH2_700,~,SEC_LH2,~,~,OPEX_AE,OPEX_PEMEC,Capgas,Capliq,CAPEX_PEMEC,CAPEX_ AE] = Input(); [COST_PEMEC_EU28,COST_PEMEC_SE,COST_PEMEC_PL,CO2_PEMEC_EU28,CO2_PEMEC_SE,CO 2_PEMEC_PL,H2_PEMEC_yield,kWh_PEMEC] = Electrolyser(Elecsupply,EL_COST_EU28,EM_CO2_EU28,EL_COST_SE,EM_CO2_SE,EL_CO ST_PL,EM_CO2_PL,SEC_PEMEC); [COST_AE_EU28,COST_AE_SE,COST_AE_PL,CO2_AE_EU28,CO2_AE_SE,CO2_AE_PL,H2_AE_y ield,kWh_AE] =
3 Electrolyser(Elecsupply,EL_COST_EU28,EM_CO2_EU28,EL_COST_SE,EM_CO2_SE,EL_CO ST_PL,EM_CO2_PL,SEC_AE); [CO2_LH2_PL,CO2_CGH2_350_PL,CO2_CGH2_350_SE,CO2_LH2_SE,CO2_LH2_EU28,CO2_CGH 2_350_EU28,COST_CGH2_350_EU28,COST_LH2_EU28,COST_CGH2_350_SE,COST_LH2_SE,CO ST_CGH2_350_PL,COST_LH2_PL] = Processing(SEC_CGH2_350,SEC_CGH2_700,SEC_LH2,EL_COST_EU28,EL_COST_SE,EL_COS T_PL,EM_CO2_SE,EM_CO2_PL,EM_CO2_EU28); autonomous = 5; R = 0.03; M = 1.2; mine_start = 0; mine_stop = 23; road_start = 8; road_stop = 16; quarry_start = 0; quarry_stop = 23; light_start = 8; light_stop = 16;
E_road = 403; E_mine = 1151; E_quarry = 231; E_light =137;
LHV_H2 = 119.96; %[MJ/kg] n_PEMFC = 0.55; %[Efficiency]
% ------H2 Demand ------%
D_H2_road = round(E_road*(3.6/LHV_H2)/n_PEMFC); %[kg H2/h] D_H2_mine = round(E_mine*(3.6/LHV_H2)/n_PEMFC); %[kg H2/h] D_H2_quarry = round(E_quarry*(3.6/LHV_H2)/n_PEMFC); %[kg H2/h] D_H2_light = round(E_light*(3.6/LHV_H2)/n_PEMFC); %[kg H2/h]
% ------1 = SE, 2 = EU28, 3 = PL ------% if site == 1 start = mine_start; stop = mine_stop; Dem = D_H2_mine; elseif site == 2 start = light_start; stop = light_stop; Dem = D_H2_light; elseif site == 3 start = quarry_start; stop = quarry_stop; Dem = D_H2_quarry; elseif site == 4 start = road_start; stop = road_stop; Dem = D_H2_road; end if mix == 1 CO2_CGH2_350_X = CO2_CGH2_350_SE; CO2_LH2_X = CO2_LH2_SE;
4 CO2_AE_X = CO2_AE_SE; CO2_PEMEC_X = CO2_PEMEC_SE; H2_COST_PEMEC_X = COST_PEMEC_SE; H2_COST_AE_X = COST_AE_SE; COST_LH2_X = COST_LH2_SE; COST_CGH2_350_X = COST_CGH2_350_SE; elseif mix == 2 CO2_CGH2_350_X = CO2_CGH2_350_EU28; CO2_LH2_X = CO2_LH2_EU28; CO2_AE_X = CO2_AE_EU28; CO2_PEMEC_X = CO2_PEMEC_EU28; H2_COST_PEMEC_X = COST_PEMEC_EU28; H2_COST_AE_X = COST_AE_EU28; COST_LH2_X = COST_LH2_EU28; COST_CGH2_350_X = COST_CGH2_350_EU28; elseif mix == 3 CO2_CGH2_350_X = CO2_CGH2_350_PL; CO2_LH2_X = CO2_LH2_PL; CO2_AE_X = CO2_AE_PL; CO2_PEMEC_X = CO2_PEMEC_PL; H2_COST_PEMEC_X = COST_PEMEC_PL; H2_COST_AE_X = COST_AE_PL; COST_LH2_X = COST_LH2_PL; COST_CGH2_350_X = COST_CGH2_350_PL; end Vmax = Dem * (stop-start)*autonomous; V0 = Vmax*0.5; Vmin = Vmax*0.01; if (Dem * (stop-start))/24 <= H2_PEMEC_yield ELECTROLYSER_PEMEC = (Dem * (stop-start))/20; else ELECTROLYSER_PEMEC = 0; end if (Dem * (stop-start))/24 <= H2_AE_yield ELECTROLYSER_AE = (Dem * (stop-start))/20; else ELECTROLYSER_AE = 0; end if Vmax <= 300 Vmax = V0+Capgas; end kW_PEMEC = kWh_PEMEC * ELECTROLYSER_PEMEC; kW_AE = kWh_AE * ELECTROLYSER_AE;
Rent_PEMEC = kW_PEMEC*CAPEX_PEMEC*R; Rent_AE = kW_AE*CAPEX_AE*R;
H2_COST_PEMEC_X = H2_COST_PEMEC_X + (OPEX_PEMEC*kW_PEMEC/8760); H2_COST_AE_X = H2_COST_AE_X + (OPEX_AE*kW_AE/8760); End
PROCESSING function [CO2_LH2_PL,CO2_CGH2_350_PL,CO2_CGH2_350_SE,CO2_LH2_SE,CO2_LH2_EU28,CO2_CGH 2_350_EU28,COST_CGH2_350_EU28,COST_LH2_EU28,COST_CGH2_350_SE,COST_LH2_SE,CO ST_CGH2_350_PL,COST_LH2_PL] = Processing(SEC_CGH2_350,SEC_CGH2_700,SEC_LH2,EL_COST_EU28,EL_COST_SE,EL_COS T_PL,EM_CO2_SE,EM_CO2_PL,EM_CO2_EU28)
5 % ------Processing costs ------COST_CGH2_350_EU28 = EL_COST_EU28*SEC_CGH2_350; %$/kg COST_LH2_EU28 = EL_COST_EU28*SEC_LH2; %$/kg CO2_CGH2_350_EU28 = EM_CO2_EU28*SEC_CGH2_350; %kg Co2/ kg H2 CO2_LH2_EU28 = EM_CO2_EU28*SEC_LH2; %kg Co2/ kg H2
COST_CGH2_350_SE = EL_COST_SE*SEC_CGH2_350; %$/kg COST_LH2_SE = EL_COST_SE*SEC_LH2; %$/kg CO2_CGH2_350_SE = EM_CO2_SE*SEC_CGH2_350; %kg Co2/ kg H2 CO2_LH2_SE = EM_CO2_SE*SEC_LH2; %kg Co2/ kg H2
COST_CGH2_350_PL= EL_COST_PL*SEC_CGH2_350; %$/kg COST_LH2_PL = EL_COST_PL*SEC_LH2; %$/kg CO2_CGH2_350_PL = EM_CO2_PL*SEC_CGH2_350; %kg Co2/ kg H2 CO2_LH2_PL = EM_CO2_PL*SEC_LH2; %kg Co2/ kg H2 End
TRANSPORT function [Delivered_X,CO2_X,COST_X] = Transport(SIZE,DISTANCE) CO2_diesel = 2.6; %kg/L Diesel_consumption = 0.4; %l/km BOILOFF_LH2 = 0.25; % [% of mass per day] CO2_X = Diesel_consumption * DISTANCE * CO2_diesel; %kg CO2 independent of size Cost_tube = 0.00798; %$/kg,km Cost_tanker = 0.001729; %$/kg,km if SIZE == 5000 Delivered_X =(1-(BOILOFF_LH2)*0.01); %[kg delivered/kg produced] COST_X = Cost_tanker * DISTANCE; % [dollar / kg] else Delivered_X = 1; %[kg delivered/kg produced] COST_X = Cost_tube * DISTANCE; % [dollar / kg] end end
ELECTROLYSER function [COST_EU28,COST_SE,COST_PL,CO2_EU28,CO2_SE,CO2_PL,H2yield,kWhperkg] = Electrolyser(Elecsupply,EL_COST_EU28,EM_CO2_EU28,EL_COST_SE,EM_CO2_SE,EL_CO ST_PL,EM_CO2_PL,SEC) density = 0.089; % kg/m3 kWhperkg= SEC/density; % kWh per kg H2 H2yield = Elecsupply/kWhperkg; % Maximum hydrogen yield COST_EU28 = EL_COST_EU28 * kWhperkg; %[$/h COST_PL = EL_COST_PL * kWhperkg; %[$/h] COST_SE = EL_COST_SE* kWhperkg; %[$/h] CO2_EU28 = EM_CO2_EU28*kWhperkg; %[kg CO2/h] CO2_PL = EM_CO2_PL*kWhperkg; %[kg CO2/h] CO2_SE = EM_CO2_SE*kWhperkg; %[kg CO2/h] end OPTIMIZATION function [sol,TOTCOST,exitflag,Dem_H2,TotCO2] = optimization(V0_X,Vmax_X,Vmin_X,days,start_t,end_t,CO2_CGH2_350_X,CO2_LH2_X ,CO2_AE_X,CO2_PEMEC_X,D_H2_X,Capgas,Capliq,CO2Pricing,H2_COST_SMR_CCS,H2_CO ST_PEMEC_X,Cost_tube,Cost_tanker,H2_PEMEC_yield,EM_CO2_SMR_CCS,TranspCO2,CO ST_LH2_X,COST_CGH2_350_X,H2_COST_AE_X,H2_AE_yield,Rent_PEMEC,Rent_AE,M)
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dag = 0; hours = days*24; %Observed hours OP = -1; Capliq_BO = Capliq*(1-0.025)^(60/24); % Liquid capacity with boiloff for n=1:hours if mod(n,24) == 0 dag = dag +1; end if n >= start_t+24*dag && n <= end_t+24*dag OP = OP+1; end
if mod(OP,8) == 0 && n >= start_t+24*dag && n <= end_t+24*dag Dem_H2(n) = D_H2_X*8; else Dem_H2(n) = 0; end end
% ------Production ------% produce1 = optimvar('produce1',hours,1,'Type','integer','LowerBound',0,'UpperBound',1) ; produce2 = optimvar('produce2',hours,1,'Type','integer','LowerBound',0,'UpperBound',1) ; produce3 = optimvar('produce3',hours,1,'Type','integer','LowerBound',0,'UpperBound',1) ; produce4 = optimvar('produce4',hours,1,'Type','integer','LowerBound',0,'UpperBound',1) ; produce5 = optimvar('produce5',hours,1,'LowerBound',0,'UpperBound',H2_PEMEC_yield); produce6 = optimvar('produce6',hours,1,'Type','integer','LowerBound',0,'UpperBound',1) ; produce7 = optimvar('produce7',hours,1,'Type','integer','LowerBound',0,'UpperBound',1) ; produce8 = optimvar('produce8',hours,1,'LowerBound',0,'UpperBound',H2_AE_yield);
% ------Volume in tank ------%
V = optimvar('V',hours,1,'LowerBound',Vmin_X,'UpperBound',Vmax_X); Vol = optimconstr(hours); Vol2 = optimconstr(hours); Vol(1) = V(1) == V0_X + produce1(1)*Capgas+produce2(1)*Capliq_BO+produce3(1)*Capgas+produce4(1)*Cap liq_BO+produce5(1)+produce6(1)*Capgas+produce7(1)*Capliq_BO+produce8(1)- Dem_H2(1); Vol(2:hours) = V(2:hours) == V(1:hours- 1)+produce1(2:hours)*Capgas+produce2(2:hours)*Capliq_BO+produce3(2:hours)*C apgas+produce4(2:hours)*Capliq_BO+produce5(2:hours)+produce6(2:hours)*Capga s+produce7(2:hours)*Capliq_BO+produce8(2:hours)-Dem_H2(2:hours)';
7 Vol2(hours) = V(hours) >= V0_X;
% % ------CO2 calculations ------%
COST1(1:hours) = (H2_COST_SMR_CCS+Cost_tube+COST_CGH2_350_X)*M*Capgas; COST2(1:hours) = (H2_COST_SMR_CCS+Cost_tanker+COST_LH2_X)*M*Capliq; COST3(1:hours) = (H2_COST_PEMEC_X+Cost_tube+COST_CGH2_350_X)*M*Capgas; COST4(1:hours) = (H2_COST_PEMEC_X+Cost_tanker+COST_LH2_X)*M*Capliq; COST5(1:hours) = (H2_COST_PEMEC_X+COST_CGH2_350_X); COST6(1:hours) = (H2_COST_AE_X+Cost_tube+COST_CGH2_350_X)*M*Capgas; COST7(1:hours) = (H2_COST_AE_X+Cost_tanker+COST_LH2_X)*M*Capliq; COST8(1:hours) = (H2_COST_AE_X+COST_CGH2_350_X);
CO1(1:hours) = (EM_CO2_SMR_CCS+CO2_CGH2_350_X)*Capgas+TranspCO2; CO2(1:hours) = (EM_CO2_SMR_CCS+CO2_LH2_X)*Capliq+TranspCO2; CO3(1:hours) = (CO2_PEMEC_X+CO2_CGH2_350_X)*Capgas+TranspCO2; CO4(1:hours) = (CO2_PEMEC_X+CO2_LH2_X)*Capliq+TranspCO2; CO5(1:hours) = (CO2_PEMEC_X+CO2_CGH2_350_X); CO6(1:hours) = (CO2_AE_X+CO2_CGH2_350_X)*Capgas+TranspCO2; CO7(1:hours) = (CO2_AE_X+CO2_LH2_X)*Capliq+TranspCO2; CO8(1:hours) = CO2_AE_X+CO2_CGH2_350_X;
CO_1 = sum(CO1(1:hours)*produce1); CO_2 = sum(CO2(1:hours)*produce2); CO_3 = sum(CO3(1:hours)*produce3); CO_4 = sum(CO4(1:hours)*produce4); CO_5 = sum(CO5(1:hours)*produce5); CO_6 = sum(CO6(1:hours)*produce6); CO_7 = sum(CO7(1:hours)*produce7); CO_8 = sum(CO8(1:hours)*produce8);
TotCO2 = CO_1+CO_2+CO_3+CO_4+CO_5+CO_6+CO_7+CO_8;
% ------Solution ------% Optilol = optimproblem('ObjectiveSense','minimize'); options = optimoptions('intlinprog', 'MaxNodes',10000,'BranchRule','maxpscost','Heuristics','advanced','IntegerT olerance',1e-6,'ConstraintTolerance',1e- 9,'CutGeneration','advanced','HeuristicsMaxNodes',1000); Optilol.Constraints.Vol = Vol; Optilol.Constraints.Vol2 = Vol2; Optilol.Objective = TotCO2; [sol,TotCO2,exitflag] = solve(Optilol, 'Solver', 'intlinprog', 'Options', options); %For older MATLAB versions -> replace 'Options' with 'Intlinprog'
COST_1 = sum(COST1(1:hours)*sol.produce1); COST_2 = sum(COST2(1:hours)*sol.produce2); COST_3 = sum(COST3(1:hours)*sol.produce3); COST_4 = sum(COST4(1:hours)*sol.produce4); COST_5 = sum(COST5(1:hours)*sol.produce5); COST_6 = sum(COST6(1:hours)*sol.produce6); COST_7 = sum(COST7(1:hours)*sol.produce7); COST_8 = sum(COST8(1:hours)*sol.produce8); d = 1; for k=1:days if mod(k,31)== 0 d=d+1; end end if sum(sol.produce5)>0 COST_9 = Rent_PEMEC*d;
8 else COST_9 = 0; end if sum(sol.produce8)>0 COST_10 = Rent_AE*d; else COST_10 = 0; end COST = COST_1+COST_2+COST_3+COST_4+COST_5+COST_6+COST_7+COST_8+COST_9+COST_10; TOTCOST = TotCO2*CO2Pricing+COST; end
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