Model-Based Techno-Economic Optimization of Grid Serving Electrolyzers on an Industrial Scale.

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MASTER THESIS
Nicolas Neubauer
Model–Based Techno–
Economic Optimization of
a Grid Serving Electrolyzer
on an Industrial Scale
FACULTY OF LIFE SCIENCES
Fakultät Life Sciences
DEPARTMENT OF PROCESS ENGINEERING
Department Verfahrenstechnik
HAMBURG UNIVERSITY
OF APPLIED SCIENCES
Hochschule für Angewandte
Wissenschaften Hamburg

The author would like to express his sincere and heartfelt gratitude to his parents who made his studies
possible that gave him so much pleasure. It is now perfect, in the literal sense of the word.
Thanks Mum, thanks Dad!

Master Thesis
Model–Based Techno–Economic Optimization of a
Grid Serving Electrolyzer on an Industrial Scale
Author: Nicolas Neubauer (B.Eng.)
Matriculation Number: 2570844
Submitted: January 4th, 2023 in Hamburg
University: Hamburg University of Applied Sciences (HAW)
Faculty: Life Sciences
Department: Process Engineering
Study Program: Process Engineering (M.Sc.)
Institute: Competence Center for Renewable Energies and
EnergyEfficiency (CC4E)
Research Program: Northern German Living Lab (NRL)
1. Supervisor: Professor Dr. Marc Hölling
2. Supervisor: Mike Blicker (Dipl.)
Tutor: Carsten Schütte (M.Sc.)

Contents
List of Figures IV
List of Tables VII
List of Abbreviations VIII
1 Introduction 1
2 Theoretical Background 3
2.1 Energymarkets............................................3
2.1.1 Balance of power generation and demand . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1.2 Excurse: Wholesale markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.3 Auxiliary service markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.4 Electricenergymarkets ...................................7
2.1.5 Emission–Trading–System..................................8
2.2 Elements of a PtH2unit.......................................9
2.2.1 Battery ............................................9
2.2.2 Electrolyzer......................................... 10
2.2.3 Pressurized H2storage................................... 13
2.3 Optimization of technical systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4 Economic and environmental assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.1 Levelized Costs of Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4.2 CO2–footprint of H2.................................... 20
3 Analysis of the Requirements 21
3.1 Researchquestion.......................................... 21
3.2 Optimizationtool.......................................... 22
3.3 Case study: Apply the tool at an Aurubis production site . . . . . . . . . . . . . . . . . . . 23
3.4 Summary of the derived research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4 Techno–Economic Analysis 24
4.1 Case study: Supplying Aurubis with hydrogen . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.1.1 Copper production at Aurubis in Hamburg . . . . . . . . . . . . . . . . . . . . . . . 25
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Master Thesis Nicolas Neubauer
4.1.2 Coalpowercontract .................................... 27
4.1.3 Determining the Hydrogen (H2)demand......................... 28
4.2 Power–to–Hydrogenunit...................................... 31
4.2.1 Flowchart.......................................... 31
4.2.2 Gridconnection ...................................... 32
4.2.3 Battery ........................................... 33
4.2.4 Electrolyzer......................................... 33
4.2.5 Compressor......................................... 37
4.2.6 Pressurizedstorage..................................... 37
4.2.7 Furtheraspects....................................... 39
4.3 Energymarkets........................................... 39
4.3.1 Electricityprices ...................................... 40
4.3.2 Auxiliaryserviceprices .................................. 42
4.3.3 System average emission factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3.4 Correlation ......................................... 45
4.4 Gridservingelectrolyzers ..................................... 47
4.5 Software............................................... 47
5 Design of the Optimization Tool 49
5.1 Structure of the optimization tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.2 Optimization model construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.2.1 Grid............................................. 51
5.2.2 Battery ........................................... 53
5.2.3 Electrolyzer......................................... 54
5.2.4 Compressor......................................... 57
5.2.5 Storage ........................................... 58
5.2.6 Process ........................................... 58
5.3 Objective .............................................. 59
5.3.1 Economicobjective..................................... 59
5.3.2 Environmentalobjective.................................. 61
5.3.3 Multi-objective optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6 Implementation and Tests 62
6.1 Evaluation of the single objective (LCoH2)............................ 62
6.1.1 Input ............................................ 62
6.1.2 Gridandbattery...................................... 63
6.1.3 Electrolyzer and compressor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6.1.4 Storageandprocess .................................... 65
6.1.5 Objective values and component sizes . . . . . . . . . . . . . . . . . . . . . . . . . . 66
6.2 Evaluation of the combined objective (LCoH2and FCO2,H 2).................. 67
6.3 Sensitivity of the resolution interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
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Nicolas Neubauer Master Thesis
7 Case Study: Results and Discussion 71
7.1 Resolutionsensitivity........................................ 71
7.2 Techno–economic parameter sensitivities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
7.2.1 Investment related sensitivities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
7.2.2 Technology related sensitivities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
7.3 Scenarioanalysis .......................................... 77
7.3.1 Energymarketsanalysis.................................. 78
7.3.2 Auxiliary services analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
7.4 Multi–objective optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
8 Conclusion 91
8.1 Summary .............................................. 91
8.2 Case Study: Recommendations for Aurubis . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
8.3 Consequences............................................ 94
8.4 Furtherdevelopmentoptions ................................... 94
Bibliography XI
Statutory Declaration XXIII
Acknowledgements XXIV
Appendix XXV
Contentsinappendix .........................................XXV
Listoffiguresinappendix...................................... XXVI
Listoftablesinappendix ..................................... XXVIII
A Gascomposition ........................................ XXIX
B Case Study: Standard H2demand ............................. XXXIV
C Powersystemdata .......................................XXXV
D Evaluation of the combined objective (LCoH2and FCO2,H2)...............XXXVII
E Sensitivities of the resolution interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XL
F Sensitivities of the economic parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . XLII
G Sensitivities of the technological parameters . . . . . . . . . . . . . . . . . . . . . . . . . XLIV
H Scenarioanalysis ...........................................L
H.1 Influence of the H2demand.................................L
H.2 Energymarketsanalysis.................................. LI
H.3 AuxSmarketsanalysis.................................. LIII
I MOOanalysis........................................... LIV
I.1 AEL based PtH2units.................................. LIV
I.2 PEMEL based PtH2units................................ LVI
J DigitalAppendix........................................ LVIII
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List of Figures
2.1 Comparison of the auxiliary services in place by their specific time of delivery. . . . . . . . . . 5
2.2 Schematic visualization of a wholesale market clearing mechanism and the merit–order. . . . 6
2.3 Comparison of the load–dependent PEMEL stack efficiency and the system efficiency. . . . 12
2.4 Visualization of an optimization problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5 Schematic visualization of the procedure to adjust investment costs and time–series
pricesforinflation. ......................................... 19
3.1 Graphical representation of the requirements to be fulfilled by this thesis and the
framework of the optimization tool to be programmed with its input and output parameters. 22
4.1 Graphical visualization of the processing steps applied for primary copper. . . . . . . . . . . 25
4.2 Monthly price series of a OTC coal contract based on NG and EUA price data. . . . . . . . 28
4.3 Visualization of the H2demand time–series determined for the case study and the
applied process–related filters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.4 Basic flowchart of the modeled PtH2unit. Dashed lines indicate a H2flow and straight
linesanelectricenergyflow..................................... 32
4.5 Summary of the conducted specific El investment literature research and the assump-
tion made in this thesis indicated by a vertical dashed line. . . . . . . . . . . . . . . . . . . 34
4.6 Summary of the conducted El efficiency literature research and the assumption made
inthisthesis. ............................................ 36
4.7 Schematic visualization of the time–series data treatment process applied in this thesis. . . 40
4.8 The prices for electric energy traded on the spot markets (ID and DA) and the esti-
mated costs of a monthly altering coal PPA visualized in a monthly box–plot. . . . . . . . . 41
4.9 The prices for electric energy traded on the ID and DA markets visualized in a histogram. . 42
4.10 PriceCap per 15 min interval for the FCR and the aFRR. . . . . . . . . . . . . . . . . . . . . 43
4.11 Histogram describing the scattering of the aFRR operating prices. . . . . . . . . . . . . . . 44
4.12 The carbon footprint of the electricity mix is visualized as a box–plot. . . . . . . . . . . . . 45
4.13 Correlation between the carbon footprint of the electricity mix and the spot market
price based on data from FfE München and EPEX. . . . . . . . . . . . . . . . . . . . . . . . 46
5.1 Schematic block diagram visualizing the structure of the optimization tool. . . . . . . . . . 50
5.2 Graphical representation of the Gr unit and its variables. . . . . . . . . . . . . . . . . . . . 52
5.3 Graphical representation of the Ba unit and its variables. . . . . . . . . . . . . . . . . . . . 53
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Nicolas Neubauer Master Thesis
5.4 Graphical representation of the El unit and its variables. . . . . . . . . . . . . . . . . . . . . 54
5.5 Visualization of the AuxS implementation by a maximal capacity and the (El_in_Production-
_MWh_tint(i)) variable for the “Base”, “FCR” and “aFRR” cases. . . . . . . . . . . . . . 55
5.6 Graphical representation of the Co unit and its variables. . . . . . . . . . . . . . . . . . . . 57
5.7 Graphical representation of the St unit and its variables. . . . . . . . . . . . . . . . . . . . 58
5.8 Graphical representation of the Pr unit and its variables. . . . . . . . . . . . . . . . . . . . 59
6.1 Graphical definition of the input data including the electricity prices, the FCR prices
and the H2demand of three economic objectives (LCoH2) test cases. . . . . . . . . . . . . . 63
6.2 Graphical evaluation of the results concerning the Gr and the Ba dispatch for three
defined test cases with an economic objective (LCoH2). .................... 63
6.3 Graphical evaluation of the results concerning the El and the Co dispatch for three
defined test cases with an economic objective (LCoH2). .................... 64
6.4 Graphical evaluation of the results concerning the St and the Pr dispatch for three
defined test cases with an economic objective (LCoH2). .................... 66
6.5 Summary of the objective values and the PtH2component sizes computed by the
optimization tool for three defined test cases with an economic objective (LCoH2). . . . . . 66
6.6 The optimized dispatch of the El, the St SOC and the H2demand visualized as timelines
for three defined test cases with a combined objective (LCoH2and FCO2,H 2). ........ 67
6.7 Sensitivity of the temporal resolution related to the objective values LCoH2, FCO2and
the PtH2component sizes for the test cases. . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.8 Influence of the resolution tint on the optimized component sizes in respect to the
dispatch of the El, the St and the Pr units. . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
7.1 The sensitivity of the normalized objective values LCoH2and FCO2on the primary
y–axis and the corresponding computing time on the secondary y–axis related to the
resolution tint (left). ........................................ 72
7.2 Sensitivity of the optimization tool results in respect to an economic objective with
regard to individually varied specific investment costs of the PtH2elements. . . . . . . . . . 74
7.3 Sensitivity of the optimization tool results for an economic objective with regard to
the technical parameters of the El such as the Elmin, the ElGrad and the efficiency El∗
η
basedontheLHV. ......................................... 75
7.4 Sensitivity of the results incorporating an economic objective with regard to the tech-
nical parameters of the compression stage covering the input and output pressures of
the Co, its efficiency Coηand the output temperature of the El. . . . . . . . . . . . . . . . . 76
7.5 Overview of the optimized scenarios on the energy markets and the AuxS markets.
Each analysis is performed for a PtH2unit based on an AEL and on a PEMEL respectively. 77
7.6 The resulting LCoH2distinguished in OpEx and CapEx of the scenario analysis fo-
cusing on the energy markets incorporating a coal–power contract and the DA and ID
markets. AEL and PEMEL are considered without the participation at the AuxS markets. . 78
7.7 The resulting LCoH2of the scenario analysis focusing on the energy markets incorpo-
rating a coal–power contract and the DA and ID markets. . . . . . . . . . . . . . . . . . . . 79
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Master Thesis Nicolas Neubauer
7.8 Levelized OpEx of the scenario analysis focusing on the energy markets incorporating
a coal–power contract, the DA and the ID market. . . . . . . . . . . . . . . . . . . . . . . . 79
7.9 Levelized CapEx of the scenario analysis focusing on the energy markets incorporating
a coal–power contract, the DA and the ID market. . . . . . . . . . . . . . . . . . . . . . . . 80
7.10 Resulting El dimensions and corresponding ElAF LH of the scenario analysis focusing
on the energy markets incorporating a coal–power contract, the DA and the ID market. . . 81
7.11 Resulting Ba and St dimensions of the scenario analysis focusing on the energy markets
incorporating a coal–power contract, the DA and the ID market. . . . . . . . . . . . . . . . 82
7.12 Comparison of three PEMEL based scenarios in 2021 with the specific electricity prices,
the dispatch of the El and the SOC of the H2St. ........................ 83
7.13 Optimized dispatch of the St represented by the St_SOC variable in the year 2021 for
the three energy–markets and both Electrolyzer (El) technologies. . . . . . . . . . . . . . . 84
7.14 Optimized dispatch of the El represented by the El_in_Production variable in the year
2021 for the three energy–markets and both El technologies. . . . . . . . . . . . . . . . . . . 85
7.15 Resulting ∆LCoH2of the AuxS scenarios in reference to the ID–Base scenario distin-
guished in an AEL and a PEMEL based PtH2setup....................... 87
7.16 Resulting ∆OpEx of the AuxS scenarios in reference to the ID–Base scenario distin-
guished in an AEL and a PEMEL based PtH2setup....................... 87
7.17 Resulting ∆CapEx of the AuxS scenarios in reference to the ID–Base scenario distin-
guished in an AEL and a PEMEL based PtH2setup....................... 88
7.18 Results of the MOO analysis of the PtH2unit with a PEMEL operating at the ID
marketintheyear2021....................................... 89
7.19 Visualization of the results for the four years of observation and a PEMEL and an
AEL respectively computed by the optimization tool for the MOO approach combining
LCoH2and FCO2,H 2inaPareto–chart. ............................. 90
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List of Tables
2.1 Overview of the AuxS regulation covering FCR and aFRR. . . . . . . . . . . . . . . . . . . . 7
2.2 Inflation rates based on the German Federal Statistical Office in change compared to
thepreviousyear........................................... 20
4.1 CO2,eq emissions of the primary metallurgic industry distinguished in ferrous, alu-
minum and others in relation to total emissions caused by the industrial sector in
Germanyin2021........................................... 26
4.2 Tabular overview of the number of NaN values within the coal OTC describing time–series. 27
4.3 Tabular overview of the number of NaN values within the case study NG measurements
time–series. ............................................. 30
4.4 Overview of the annual reducant demand in tReductant calculated for the study. . . . . . . . 30
4.5 Overview of the applied techno–economic parameters of the PtH2unit within this thesis. . 38
4.6 Tabular overview of the number of NaN values within the respective DA and ID related
time–series. ............................................. 40
4.7 Tabular overview of the number of NaN values within the AuxS related time–series. . . . . 43
4.8 Tabular overview of the number of NaN values within the FCO2related time–series. . . . . 45
5.1 Modelling properties of the variables defined within the Gr unit. . . . . . . . . . . . . . . . 52
5.2 Modelling properties of the variables defined during the construction of the Ba. . . . . . . . 53
5.3 Modelling properties of the variables defined during the construction of the El. . . . . . . . 54
5.4 Modelling properties of the variables defined during the construction of the Co. . . . . . . . 57
5.5 Modelling properties of the variables defined during the construction of the St. . . . . . . . 58
5.6 Modelling properties of the variables defined in the Pr construction. . . . . . . . . . . . . . 59
7.1 Comparison of the averaged LCoH2computed by the optimization tool based on the
energy markets’ scenarios with the currently used NG. . . . . . . . . . . . . . . . . . . . . . 81
7.2 Comparison of the averaged FCO2computed by the optimization tool based on the
coal–power and the spot market scenarios with the currently used NG. . . . . . . . . . . . . 86
Page VII

List of Abbrevations
AEL Alkaline Electrolyzer
AF Anode Furnaces
AFLH Annual Full Load Hours
aFRR Automatic Frequency
Restoration Reserve
ArAnnuity factor
AuxS Auxiliary Service
AuxSint Auxiliary Service Interval
Ba Battery
BCM Balancing Capacity Market
BEM Balancing Energy Market
BNetzA “Bundesnetzagentur”
CapEx Capital Expenditures
CCfD Carbon–Contracts–for–
Difference
CH4Methane
Ch/int Hours per Interval
ci Gas component
Co Compressor
CO2Carbon Dioxide
CO2,eq Carbon Dioxide Equivalents
DA Day–Ahead
DSO Distribution System Operator
EEX Europan Energy Exchange AG
El Electrolyzer
ElAF LH El AFLH
ElGrad El load gradient
Elmin El minimal load
Entso–E European Network of
Transmission System Operators
for Electricity
EP Electric Power
EPC Electricity–Price–Compensation
EPEX Epex Spot SE
ηEfficiency
ETS Emission–Trading–System
EUA European–Union–Allowances
EXAA Energy Exchange Austria AG
FCO2,H 2CO2–Footprint of H2
FCO2CO2–Footprint
FCR Frequency Containment Reserve
fF CO2Environmental weighting factor
fweighting weighting factor
FfE München “Forschungsstelle für
Energiewirtschaft
München e. V.”
fLCoH 2Economic weighting factor
Fraunhofer ISE “Fraunhofer–Institut für Solare
Energiesysteme”
fred Reducing factor
GoO Gurantees of Origin
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Nicolas Neubauer Master Thesis
Gr Grid
HEnthalpy
HHV Higher Heating Value
H2Hydrogen
H2OWater
ID Intraday
IDX Index
Inf Inflation
Inv Investment
Invre Reinvestment
InvResidual Residual Investment
IRENA International Renewable Energy
Agency
LCoE Levelized Costs of Energy
LCoH2Levelized Costs of H2
LHV Lower Heating Value
Li–Ion Ba Lithium-Ion Battery
MMaintenance
mFRR Manual Frequency Restoration
Reserve
MpY Minutes per Year
MOO Multi–Objective Optimization
NaN Not–a–Number
NG Natural Gas
NNI Non–Negative–Integers
NNR Non–Negative–Reals
NRL Northern German Living Lab
N2Nitrogen
O2Oxygen
OpEx Operating Expenditures
OTC Over–the–Counter
PEMEL Proton Exchange Membrane
Electrolyzer
PPA Power Purchase Agreement
PriceCap Capacity Price
PriceOp Operating Price
Pkg(H2)/MW specific H2production
Pr Process
PtH2Power–to–Hydrogen
PYOMO Python Open–source Modelling
and Optimization
QHeat
rint Interest rate
RLI Reiner–Lemoine–Institut
SOC State–of–Charge
SOEL Solid Oxide Electrolyzer
SRU “Sachverständigenrat für
Umweltfragen”
St Storage
StDrel Relative Standard Deviation
tComp Component lifetime
tint Interval
tP roject Project lifetime
TRL Technology Readiness Level
TS Time–Stamp
TSO Transmission System Operator
UTC Coordinated Universal Time
wreal real work
wrev reversible work
xVolumetric concentration
ρDensity
Page IX

Page X

Chapter 1
Introduction
Embedded in the decarbonization ambitions of industrial actors and the transition to Hydrogen (H2) as
a feedstock for reducing processes, the installed capacity of Power–to–Hydrogen (PtH2) units based on
electrolysis is growing [41], [77]. These PtH2units come with two positive effects. They can, on the one
hand, convert electrical energy into chemical energy, which is useful for all kinds of industrial processes and
mobility applications. This allows the increasing amount of renewably sourced electricity to be coupled
with other non–electric sectors. On the other hand, PtH2units offer the option of reacting flexibly to
external factors such as the availability of renewable electricity generation or the demand for electric
energy by market participants. This is possible primarily because the production of H2is decoupled from
the consumer by means of storage units. This flexibility can be used to operate “grid serving” by reflecting
the balance of supply and demand (e.g. indicated by the electricity price, discussed later) [22], [75]. These
additional degrees of freedom induce a component sizing and dispatching issue for PtH2units.
The aim of this thesis is to compute optimal component sizes and dispatch based on different scenarios
for such PtH2units including an electrolyzer, a battery and a pressurized H2storage with a compressor.
Thereby, the thesis incorporates the local circumstances to source H2at the production facility of Aurubis
AG (in the following Aurubis) in Hamburg as a case study. The optimum is defined by the case study
to represent minimized Levelized Costs of H2(LCoH2) in a first economic step and the CO2–Footprint
of H2(FCO2,H 2) based on the FC O2of the converted electricity in a second environmental step. This
thesis, therefore, develops a software–based optimization tool to compute optimal dimensions for flexible
operating industrial–scale PtH2units. Moreover, this work assesses the option for flexible operating PtH2
units to provide Auxiliary Service (AuxS). The optimization tool is based on deterministic mathematical
optimization and makes optimized component sizes together with an optimized dispatch for the PtH2
elements available. It is tested with generic data for validation. This is followed by applying the opti-
mization tool to the case study based on an economic objective represented by the LCoH2. First, the
sensitivity of the obtained results is analyzed for the case study with respect to technical and economic
input parameters. Second, the case study is analyzed with respect to different scenarios which are worked
out in cooperation with the industry partner Aurubis. In addition, the resulting FCO 2,H2is analyzed and
targeted in a subsequent analysis within a combined objective approach.
Thereby this research is based on the findings of Schütte et al. (2022) and Röben et al. (2021) who
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Master Thesis Nicolas Neubauer
assessed the implementation of a PtH2unit at the production facility of Aurubis qualitatively and simula-
tively in a techno–economic manner [111], [117]. This thesis aims to extend these findings by collaborating
closely with the industry partner to replicate the case study precisely. Further, this consideration includes
a grid–serving operating strategy of the PtH2unit by reflecting the balance in the electricity grid based
on the spot market prices. Thereby, the thesis extends a scheduling approach of Wagner et al. (2022)
who focused on optimized energy procurement and showed that a flexible operating strategy based on a
H2storage results in financial benefits [143]. Moreover, this work analyzes the possibility of providing
AuxS and identifies the effects on the LCoH2together with effects on the optimized PtH2dimensions.
The applied objective function is subsequently extended to include an environmental assessment based on
the CO2–Footprint (FCO2) of the converted electric energy.
This thesis is worked out under the framework of the Northern German Living Lab (NRL) which
is coordinated by the Competence Center for Renewable Energies and EnergyEfficiency (CC4E) at the
University of Applied Sciences (UAS, German: HAW) in Hamburg. The research project NRL is a
joint research program with industrial and scientific partners to develop solutions for the transition to a
renewable–based energy system with a focus on industrial H2applications and sector coupling. The NRL
is part of the funding initiative “Reallabore der Energiewende” and is funded by the Federal Ministry
of Economics and Climate Protection (BMWK). This thesis is involved in a subordinated project with
the industry partner Aurubis, which runs a primary copper smelter in Hamburg. The project aims to
decarbonize a processing unit through the replacement of fossil natural gas by renewable–sourced H2. The
unit process takes place in the Anode Furnaces (AF), which includes the last pyrometallurgical refinement
step of copper. In order to advance the energy transition, literature recommends extensive electrification
to enable sector coupling as renewable energy is primarily available as electric energy [3], [30], [114].
However, the energy supply of the AF cannot be directly electrified as they consume energy carriers as
feedstock to chemically reduce the intermediate copper product (blister copper). H2can work as a link
between renewable energies and their application as a reducing agent. This fuel switch taking place in
the AF is analyzed from a metallurgical point of view by Edens et al. (2022) at the production facility of
Aurubis and in other projects [35], [46].
The structure of this thesis is as follows. The theoretical background is provided in chapter 2 right
after this introduction. The fundamentals are distinguished in the sections energy markets, elements of
the PtH2unit, optimization and the economic and environmental assessment. This section is followed
by an analysis of the requirements for this thesis in chapter 3. After the requirements are analyzed,
the technical and economic circumstances relevant to this thesis are analyzed in chaper 4. It is divided
up into sections covering the energy markets, the PtH2unit itself, the case study, and the definition of
grid services and a brief outlook on the used software. Thereafter, the design of the optimization tool is
presented by addressing each element of the PtH2unit individually. The design chapter is followed by
the implementation of the optimization tool, which demonstrates the operating procedure with generic
test data. The optimization tool is then applied to the case study in chapter 7. This chapter includes a
sensitivity study, a scenario analysis based on the LCoH2objective and a combined objective approach
which factors in the FCO2,H 2. To conclude, the aspects concerning the case study are summarized and
consequences are drawn in chapter 8.
Page 2

Chapter 2
Theoretical Background
This section provides fundamental interrelationships upon which the further chapters build. Referring
to energy and in particular chemical and electric energy, the word “convert” is used to address the
conversion of one form of energy to another. Following the laws of thermodynamics, real conversions are
affected by losses. These losses are considered within this thesis if not stated differently. This chapter on
the theoretical background of this thesis addresses the aspects of the energy markets in the first place.
This includes the function of the spot and Over–the–Counter (OTC) markets. In the second place, the
fundamentals of a Power–to–Hydrogen (PtH2) unit with its technical elements are described. Thereby, a
special focus is on the power to hydrogen conversion by electrolyzers. The procedure of optimization and
the environmental and economic assessment are discussed in the third and fourth place.
2.1 Energy markets
The electric power system connects electricity–generating units with electricity–consuming customers of
all kinds. The correct function of such a system is constrained by physical means in the first place and
by regulative means in the second place. The key physical challenge is to maintain the exact balance of
generation and demand, as this balance determines the frequency of the alternating current. The key
regulative and economic challenge is to reduce the costs related to the consumption and transmission of
electric energy. The physical property of the electric power system is discussed in the first subsection.
The regulating bodies, on the European and the national level, reflect this physical challenge in their
regulation of the energy markets. The markets aim to allocate predicted energy generation to predicted
energy consumption before the energy itself is flowing. The operation of these energy markets is described
in the second subsection. To maintain the balance between generation and consumption for all points in
time, additional measures are required which react to imbalances in the power system. These measures,
summarized with the term Auxiliary Service (AuxS), are explained in the third subsection.
2.1.1 Balance of power generation and demand
The electric power system consists of three main elements which are the power–generating units, the
power–transmission system and the power–consuming units. Power can be generated and consumed by a
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Master Thesis Nicolas Neubauer
variety of different participants from an industrial, commercial or private background. The power system
in Germany itself is operated by four Distribution System Operators (DSO) on the upper level and on a
smaller level by the Transmission System Operators (TSO). This system is defined in Germany by the
federal law ”Energiewirschaftsgesetz” [13]. The law issues the system operators to maintain a frequency
of 50 Hz [15], [98].
Power generation The power generated by the generating units in the power system can be distin-
guished into two categories. On the one hand, there are conventional