- 1 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Modeling a Carbon Capture,
Transport and Storage
(CCTS) Infrastructure for
the Industrial Sector
IAEE International Conference, Stockholm, 22.06.2011
Technische Universität Berlin Fachgebiet Wirtschafts- und Infrastruktur Politik
Pao-Yu Oei ([email protected])
Andreas Tissen ([email protected])
Johannes Herold ([email protected])
- 2 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Agenda
1) Motivation
2) Description of the Model CCTSMOD
3) Implemented Data
4) Scenario Runs
a. BAU
b. Industry
c. Offshore
d. Storage
5) Conclusion and Further Research
- 3 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Motivation
Carbon Capture Transport and Storage – CCTS
• 70 % higher costs without CCTS (IEA, 2009)
• few running projects (esp. Industry)
Industry Sector
• Unavoidable CO2 emissions
• Solution for total decarbonization
• Higher CO2 concentration
Motivation CCTSMOD Data Scenarios Conclusion
Source: Clean Technica
- 4 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
CO2 Emissions from Heavy Industry Sources
Energy: 348 Mt Commerce: 89 Mt
Transport: 152 Mt Households: 128 Mt
Iron & Steel: 41 Mt
Refineries: 27 Mt
Cement: 26 Mt
Pulp & Paper: 1 Mt
CO2 Emissions Germany: 837 Mt
H2 & NH3: 6 MtCO2-free alternatives
CO2-free alternatives
Petrochemical: 19CO2-free alternatives
Industry: 94 MtIndustry: 94 Mt
Industry: 120 Mt
EPRTR, 2010
Motivation CCTSMOD Data Scenarios Conclusion
- 5 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Decision Tree of the CCTSMOD
• Omniscient planner designs cost-optimal CCTS infrastructure given costs
for infrastructure and CO2 Certificates
• CO2 Certificate price as initiator for CCTS development
• Time horizon 2010-2050
• Solved as an MIP with the CPLEX Solver in GAMS
Source: Own illustration
Motivation CCTSMOD Data Scenarios Conclusion
- 6 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Model: Cost Minimization Problem
, , , , , ,
___
1min _· _ _ · _
· · _ · _ _
1
· _
Pa Pa ija
ijda ija
a
Pa
Sa
P Pa
year start
Pa Painv x z
aPa a
x planinv f f y
ij i
Pinv
ja d ijdd
h c c ccs x cinv x inv x
E L c plan pla
ert
n c inv f i
z
v f
r
n
_ ·
_ _ · _
ija
Sa
i j
SaS
c
c f f
inv y inv y
Subject to:
• Amount of produced CO2 for every emitter
• Capacity constraint for each step of the CCTS chain
• Physical balance for infrastructure
Motivation CCTSMOD Data Scenarios Conclusion
- 7 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Mathematical Problem: Constraints
Motivation CCTSMOD Data Scenarios Conclusion
_ · _ · 0Sj Sija jia Pj Pai
ai P S
f f match P x match S y
2a a ap p pCx Oz
_Pa Pbb a
inv xx
_ · _ _ · _ijdb jidb
b aija d d
d db a
cap d inv f cap d if nv f
_Sab a
Sby yinv
_ _ ·ijda ijbd b a
inv f pmax ipe plan
_S aa
Sy cap stor
, , _, 0_ , ,Pa SPa Pa i aja Sax fx inv z y yinv
Balance:
CO2 – Balance:
Capture:
Flow:
Storage:
Planning:
Total Storage:
Non – Negativity:
- 8 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Data: Sources and Sinks in Germany
Motivation CCTSMOD Data Scenarios Conclusion
Source: Own illustriation
- 9 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Data: Capturing Costs
Source: Own calculations based on Tzimas (2009) and WI (2008)
Motivation CCTSMOD Data Scenarios Conclusion
Variable Costs
[€/tCO2]2010 2020 2030 2040 2050
Steam Coal 31,97 31,56 31,19 30,85 30,55
Gas 46,80 45,92 45,10 44,35 43,65
Lignite 29,35 29,06 28,81 28,58 28,37
Cement 16,89 16,89 16,89 16,89 16,89
Steel 16,39 16,39 16,39 16,39 16,39
Fixed Costs
[€/tCO2]2010 2020 2030 2040 2050
Steam Coal 150 150 139,29 119,39 93,80
Gas 275 275 255,36 218,88 171,98
Lignite 116 116 107,71 92,33 72,54
Cement 135 135 125,36 107,45 84,42
Steel 117 117 108,64 93,12 73,17
- 10 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Uncertainties influencing deployment of CCTS
Available storage potential
• Low resolution data
• Different estimation methods
Development of the CO2 Certificate price
• CO2 Certificate price is driving force
• Price is volatile to future climate policies
Accessibility of storage sites
• Growing public resistance
• Offshore storage involves less stakeholders
Motivation CCTSMOD Data Scenarios Conclusion
- 11 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Selected Scenarios
ScenarioCO2 Certificate
price in 2050
Public acceptance
Total Storage potential
BAU 75 Euro Onshore & Offshore 6.6 Gt
Industry 50 Euro Onshore & Offshore 6.6 Gt
Offshore 75 Euro Offshore only 1.2 Gt
Storage 75 Euro Onshore & Offshore 11.2 Gt
Motivation CCTSMOD Data Scenarios Conclusion
- 12 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Scenario Results: BAU (Business as Usual)
Motivation CCTSMOD Data Scenarios Conclusion
in 2025
Source: Own illustriation
Industry starts at 40 €/t
Coal starts at 55 €/t
2050: 80% captured (220Mt/a)
pipeline network of 3800 km
mostly onshore sinks
38 billion € Infrastructure costs
1.5 of 6.6 Gt storage left
in 2050
- 13 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
BAU: Costs for avoiding CO2 emissions
Motivation CCTSMOD Data Scenarios Conclusion
Source: Own illustriation
Industry Power Plants
- 14 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Scenario Results: Offshore only
Motivation CCTSMOD Data Scenarios Conclusion
Source: Own illustriation
in 2050 usage of all sinks
10 year delayed start
only Industry starts at 50€/t
2050: 20% captured: 54 Mt/a,
(57% of industrial emission)
Pipeline network of 2200 km
- 15 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Scenarios Results: Overview
ScenarioCO2 Price
in 2050
Remaining Storage
capacity in 2050 [Gt]
CO2 stored via CCTS in 2050 [Mt/a]
Industry share from 2010-2050
Pipeline length in
2050
CCTS Infrastructure Costs [bil €]
BAU 75 € 1.5 of 6.6 220 39 % 3800 km 38
Industry 50 € 5.3 of 6.6 61 100 % 1050 km 8,8
Offshore 75 € 0 of 1.2 54 100 % 2200 km 11,5
Storage 75 € 6.8 of 11.2 232 38 % 3000 km 39,7
Motivation CCTSMOD Data Scenarios Conclusion
- 16 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Conclusions I: Model Outcomes
• Industry first-mover, at CO2 prices of 50€/t in 2050
• Energy sector needs prices above 75€/t in 2050
• Up to 80% reduction possible for reasonable prices
• Not enough affordable storage capacities for all German emitters,
especially if only offshore storage possible
• CCTS especially for North Germany interesting
• Formation of regional clusters
• Interconnected pipeline network in the case of Offshore storage
Motivation CCTSMOD Data Scenarios Conclusion
- 17 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Conclusions II: General Outcomes
• High abatement potential for industrial emissions (first mover)
higher Network-Efficiency
• CCTS solution for unavoidable industrial CO2 emissions
• Combined usage of industrial and electrical sector leads to
storage scarcity (esp. in offshore case)
• Transport does matters (5% – 7% of all costs onshore, 17% offshore),
however only if the network is planned in an optimal way).
Europe-wide infrastructure planning
Motivation CCTSMOD Data Scenarios Conclusion
- 18 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Further Research
We are currently working on:
• Application of stochastic optimization
• Upscaling on European Level
• Integration of EOR
Thank you very much for your attention!
Are there any questions?
- 19 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
References
GeoCapacity (2009): Assessing European Capacity for Geological Storage of Carbon Dioxide – The EU GeoCapacity Project. Energy Procedia, Volume 1, Issue 1, February 2009, Pages 2663-2670.
IEA (2009): Technology Roadmap – Carbon Capture and Storage. OECD, Paris, France: International Energy Agency.
Kobos et al (2007): The 'String of Pearls': The Integrated Assessment Cost and Source-Sink Model [Conference] // 6th Annual Carbon Capture & Sequestration Conference, May 7-10 2007. - Pittsburgh, USA : Sandia National Laboratories.
Mendelevitch, R., Oei, P.Y., Tissen, A. and Herold, J. (2010): CO2- Highways – Modeling Aspects of a Future CO2 Transport Infrastructure, Centre for European Policy Studies (CEPS), Working Document No. 340; Brussels, Belgium.
Middleton R. and Bielicki J.(2009): A Comprehensive Carbon Capture and Storage Infrastructure Model [Conference] // Proceedings of the 9th International Conference on Greenhouse Gas Technologies.
Oei, P.Y., Mendelevitch, R. Herold, J., Tissen, A. von Hirschhausen, C. (2010): CO2-Autobahnen für Europa? Energiewirtschaftliche Tagesfragen (ET Magazin), Ausgabe 12/2010, ETV Energieverlag GmbH, Essen.
RECCS+ (2010): RECCS plus – Regenerative Energien (RE) im Vergleich mit CO2-Abtrennung und Ablagerung (CCS) – Update und Erweiterung der RECCS-Studie. Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit (BMU).
Tzimas E. (2009): The Cost of Carbon Capture and Storage Demonstration Projects in Europe, European Commission, Joint Research Center, Institute of Energy.
- 20 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Industrial CCTS Projects around the world
Project Country Location Capture Rate [tCO2/day]
Prosint Methanol Production Plant
Brasil Rio de Janeiro 90
Kurosaki Chemical Plant Japan Kurosaki 283
Sumitomo Chemicals Plant Japan Ichihara 150
Nippon Steel CO2 Capture Project
Japan Kimitsu 170
Petronas Gas Processing Malaysia Kuala Lumpur 160
AES Shady Point USA Panama, Oklahoma 200
AES Warrior Run USA Cumberland, Maryland 123
American Electric Power - Mountaineer
USA New Haven 274
Bellingham Cogeneration Facility
USA Bellingham 335
Total Lacq (Oxyfuel) France Lacq 205
- 21 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Capture Costs in Industry
Investment costs (based on Ho et al., 2010):
195 €/tCO2 for iron and steel
225 €/tCO2 for cement
325 €/tCO2 for petrochemical
Variable costs (based on Ho et al., 2010):
17 €/tCO2 for iron and steel (60% HC)
24 €/tCO2 for cement (85% HC)
33 €/tCO2 for petrochemical (115% HC)
Pipeline investment costs:
Onshore: 800.000 €/km, Offshore: 960.000 €/km for a diameter of 18`` (Alstom, 2011)
From 150.000 €/km to 1.200.000 €/km, depending on diameter (Metz et al., 2005)
- 22 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Capture Costs in Industry
Storage investment costs (IEA, 2005):
12.5 – 16.9 M€/MtCO2 onshore
34.5 – 44 M€/MtCO2 offshore
Art der Speicherstätte Erschöpftes Gasfeld Saline Aquifere
Onshore Offshore Onshore Offshore
Bohrtiefe (vertikal + horizontal) [m] 3 000 4 000 3 000 4 000
Max. Injektionsrate gemäß IEA [Mt CO2/Jahr] 1,25 1,25 1 1
Max. Injektionsrate gemäß Gerling [Mt
CO2/Jahr]0,42 0,42 0,33 0,33
Kapitalkosten pro Bohrung [M€] 5,62 14,50 5,62 14,50
Betriebs-, Wartungs- und Monitoringkosten [%] 7 8 7 8
[1] Solch optimistische Einspeiseraten wie von der IEA angenommen treffen nur auf sehr wenige Speicherorte zu. Die durchschnittliche für Europa zu erwartende Injektionsrate liegt bei ungefähr 0,33 Millionen Tonnen pro Jahr. (vgl. Gerling, 2010) In Anlehnung an diese Aussage wird für das CCTSMOD mit einer dreimal geringeren als von der IEA (2005) angegebenen Einspeiserate gerechnet.
- 23 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Scenario Results: Industry only (on- & offshore)
Motivation CCTSMOD Data Scenarios Conclusion
Source: Own illustriation
- 24 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Costs for avoiding Industrial CO2 emissions
Motivation CCTSMOD Data Scenarios Conclusion
Source: Own illustriation
- 25 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
BAU: Costs for avoiding CO2 emissions
Motivation CCTSMOD Data Scenarios Conclusion
Source: Own illustriation
- 26 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Data: Costs for Reference and CCTS Power Plant
Source: Tzimas (2009), and Öko Institut (2011)
TechnologieInvestitionskosten
Demonstrationsprojekt in €08/kWEffizienz in %
Kohlestaubfeuerung 1478 46
Kohlestaubfeuerung mitPost-Combustion Abscheidung
2500 35
Integrated GasificationCombined Cycle (IGCC) mitCO2 Abscheidung
2700 35
Oxyfuel Carbon Capture 2900 35
Erdgas GUD Kraftwerk mit Post-Combustion Abscheidung
1300 46
Zementherstellung mit Post-Combustion Abscheidung
ca. 87% der Investitionskosten im Kraftwerksbereich
-
Kalkherstellung mit Post-Combustion Abscheidung
ca. 127 % der Investitionskosten im Kraftwerksbereich
-
Eisen- und Stahlherstellung mit Post-Combustion Abscheidung
ca. 61 % der Investitionskosten im Kraftwerksbereich
-
- 27 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
, , , , , ,
___
1min _· _ _ · _
· · _ · _ _
1
· _
Pa Pa ija
ijda ija
a
Pa
Sa
P Pa
year start
Pa Painv x z
aPa a
x planinv f f y
ij i
Pinv
ja d ijdd
h c c ccs x cinv x inv x
E L c plan pla
ert
n c inv f i
z
v f
r
n
_ ·
_ _ · _
ija
Sa
i j
SaS
c
c f f
inv y inv y
Mathematical Problem: Objective Function
- 28 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Mathematical Problem: Constraints
Minimization Problem
, , , ,, , ,
__ _
· 5 _ · _ _ · _
1min
1
_ ·
5 _ _ _ · _ 5
·
Pa Pa ija
ijda i
a
Pa
Sa Saja
Pa Pa
ij ija d ijd ij
x fin
year start
x inv zainv y
P Pa
v f pl
a
ai j d
a
PP
n y
ac ccs x c inv x inv x cert
E c f f c inv f inv f c plan pla
z
hr
n
_ _ · _ Sa SaS
c inv y inv y
(1)
Subject to
2a a ap p pCx Oz (2)
_Pa Pbb a
inv xx
(3)
_ · _ _ · _ijdb jidbb a
ija d dd db a
cap d inv f cap d if nv f
(4)
_ _ ·ijda ijbd b a
inv f pmax ipe plan
(5)
5· _S aa
Sy cap stor (6)
_Sab a
Sby yinv
(7)
_ · _ · 0Sj Sija jia Pj Pai
ai P S
f f match P x match S y (8)
, , _, 0_ , ,Pa SPa Pa i aja Sax fx inv z y yinv (9)
0,1ijaplan (10)
0_ ijdainv f N (11)
Subject to:
- 29 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Mathematical Problem: Indices, Parameters and Variables
Variables
h – net present value of total CO2 abatement costs
over the whole model time frame [€]
Pax – quantity of CO2 captured by producer P
in period a [t CO2/a]
_ Painv x – investment in additional CO2 capture capacity for
producer P in period a [1]
Paz – quantity of CO2 emitted into atmosphere by
producer P in period a [t CO2/a]
ijaf – CO2 flow from node i to j in period a [t CO2/a]
_ ijdainv f – investment in additional pipeline capacity with
diameter d connecting nodes i and j in period a [1]
ijaplan – pipeline planning and development between nodes
i and j in period a [1]
Say – quantity of CO2 stored per year in sink S in period a [t CO2/a]
Indices
a , b – model period
P – individual CO2 producer
S – individual CO2 storage site
i , j – node
d – pipeline diameter [m]
Parameters
r – rate of interest [%]
ayear – starting year of a model period a
start – starting year of the model
end – ending year of the model
_ Pac ccs – variable costs of carbon capture for producer P in period a [€/t CO2]
_ _ Pc inv x – investment costs of carbon capture for producer P [€/kw]
2ap
CO – total quantity of CO2 produced by producer P in period a [t CO2]
acert – CO2 Certificate price in period a [€/ t CO2]
_c f – CO2 flow costs [t CO2]
_ _ dc inv f – pipeline investment costs [€/km·m (diameter)]
_c plan – pipeline planning and development costs [€/km]
_ dcap d – capacity of a pipeline with diameter d [t CO2/a]
_max pipe – max. number of pipelines built along planned route [1]
_ _ Sainv yc – investment costs for storage in sink S in period a [€/t CO2]
_ Scap stor – storage capacity of sink S [t CO2]
_ Pjmatch P – mapping of producer P to node j
_ Sjmatch S – mapping of Sink S to node j
ijE – adjacent matrix of possible connections between nodes i and j
- 30 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Data: CO2 Sources and Potential Storage Sites
• Point sources are often
agglomerated
• Large point sources are
mainly power plants
• Main storage potential in
Northern Germany and North
Sea Region
• Potential storage sites and
CO2 sources are spatially
divided in most cases
Source: Own illustration according to data from EEA and GeoCapacity
Power Plant
Industrial Facility
Offshore Saline Aquifere
Onshore Saline Aquifere
Depleted Gasfield
Motivation CCTSMOD Data Scenarios Conclusion
- 31 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
BAU for Europe in the year 2050
• CCTS only an option for countries with a regional proximity between CO2 intensive regions and storage sites.
• Domination of point to point connections – no interconnected transnational network
• Industry is first mover
Source: Own illustration based on model results
Power plant
Industrial facility
CO2 storage site
Pipeline capacity
CO2 flow
- 32 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011
Off 120 for Europe in the year 2050
• Interconnected European CCTS infrastructure.
• Central and Eastern Europe is not connected to the network
• Immense infrastructure investments only lucrative for integration of CO2 capture from power generation.
Source: Own illustration based on model results
Power plant
Industrial facility
CO2 storage site
Pipeline capacity
CO2 flow
- 33 -Technische Universität BerlinFachgebiet Wirtschafts- und InfrastrukturPolitik (WIP)
22. June 2011