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© Fraunhofer ISI
Fraunhofer Institute for Systems and Innovation Research ISI
IAEE SESSION 7A: CLIMATE VI I
POTENTIAL AND COSTS FOR CO 2
MITIGATION FOR REFINERIES IN EU -28 FOR
2050
06.09.2017, Vienna, Austria
Solbin Kim, Fraunhofer ISI
Matthias Rehfeldt, Fraunhofer ISI
Andrea Herbst, Fraunhofer ISI
Source: EPA
© Fraunhofer ISI
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Mot iva t ion
The European Council’s reconfirmation of the EU objective of GHG mitigation
by 80-95% until 2050 compared to 1990
In EU emissions trading system (EU ETS), the refinery sector has 130
installations emit 130 Mt in 2015: 25% of emissions accounted for by
industrial activities in the EU ETS
Yet, comprehensive analysis on energy demand, CO2 emissions, CO2
mitigation potential and costs has not been studied for the European refinery
sector at the plant level.
European Commission. (2011). Communication from the commission to the European Parliament, the council, the European
economy and social committee and the committee of the regions: A roadmap for moving to a competitive low carbon economy in
2050 (COM (2011)). Brussels: European Union: European Commission
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Research Ques t ions
1. What is EU Refineries’ status-quo in terms of production, energy demand and
CO2 emission on site-level?
2. How could the energy demand and CO2 emission of the EU refineries
change, driven by the production projection until 2050 under various
scenarios?
3. What are the potential and costs for the EU refineries to reduce CO2
emissions by employing energy saving options under various diffusion
conditions?
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Methodo logy
Figure 1. Research methodology based on bottom-up approach.
Energy saving and CO2 mitigation potential
Energy saving option choice
Characterization Diffusion
Scenario development
Main drivers CorrelationsReference scenario
Policy scenario
Status-quo data
ThroughputEnergy demand
CO2
emissionProduction
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Methodo logy - S ta tus -Quo Data : Ca tegor iza t ion
Reinaud, J. (2005). The European refinery industry under the EU emissions trading scheme: Competitiveness, trade flows and
investment inplications. IEA information paper, Paris.
Figure 2. capacity share, process
configuration and product slates
of categories.
Complex
1Complex
2
Complex
3
Complex
4
Note: CDU- Crude Distillate Unit, RF- Reforming
unit, DSU- Desulfurization unit, FCCU- Fluid
catalytic cracking unit, HCU- Hydrocracking unit.
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Methodo logy
Figure 1. Research methodology based on bottom-up approach.
Energy saving and CO2 mitigation potential
Energy saving option choice
Characterization Diffusion
Scenario development
Main drivers CorrelationsReference scenario
Policy scenario
Status-quo data
ThroughputEnergy demand
CO2
emissionProduction
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Methodo logy - Ma in Dr ive rs
Main drivers
Transportation sector
Gasoline demand
Diesel demand
Kerosene demand
Inland and navigation
fuel demand
Substitution by LNG
Chemical sector
Gross value added
Elasticity
Heating & power sector
Energy demand
Fuel mix
Figure 3. Main driver definition by main uses of the refinery products.
Vita, A. de, Tasios, N., Evangelopoulou, S., Forsell, N., Fragiadakis, K., Fragkos, P.,. . . Zampara, M. (2016). EU reference
scenario 2016: Energy, transport and GHG emissions : trends to 2050. Luxembourg: Publications Office
Chan, Y., Kantamaneni, R., & Allington, M. (2015). Study on energy efficiency and energy saving potential in industry from
possible policy mechanisms. ICF Consulting Limited, London. Tratto il giorno, 6(08), 2016.
© Fraunhofer ISI
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Methodo logy
Figure 1. Research methodology based on bottom-up approach.
Energy saving and CO2 mitigation potential
Energy saving option choice
Characterization Diffusion
Scenario development
Main drivers CorrelationsReference scenario
Policy scenario
Status-quo data
ThroughputEnergy demand
CO2
emissionProduction
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Methodo logy -Cor re la t ions
Product Main use Main driver
LPGIndustry,
heating and power
Gross value added (GVA), elasticity,
Energy demand and fuel mix
Naphtha Industry GVA, elasticity
Gasoline Transportation Gasoline demand
Jet A1 Transportation Kerosene demand
Diesel Transportation Diesel demand
Heating Oil Heating & power Energy demand, fuel mix of residential sector
HFO (Low sulfur and
high sulfur)Heating & power Energy demand, fuel mix of residential sector
Bunker (Low sulfur
and high sulfur)Transportation
Energy demand from inland navigation, fuel
substitution by LNG
Bitumen, sulfur,
cokeIndustry Proportional production linked to gasoline
Table 2. Main driver correlations to the refinery products.
Vita, A. de, Tasios, N., Evangelopoulou, S., Forsell, N., Fragiadakis, K., Fragkos, P.,. . . Zampara, M. (2016). EU reference
scenario 2016: Energy, transport and GHG emissions : trends to 2050. Luxembourg: Publications Office
Chan, Y., Kantamaneni, R., & Allington, M. (2015). Study on energy efficiency and energy saving potential in industry from
possible policy mechanisms. ICF Consulting Limited, London. Tratto il giorno, 6(08), 2016.
© Fraunhofer ISI
Page 10
Methodo logy
Figure 1. Research methodology based on bottom-up approach.
Energy saving and CO2 mitigation potential
Energy saving option choice
Characterization Diffusion
Scenario development
Main drivers CorrelationsReference scenario
Policy scenario
Status-quo data
ThroughputEnergy demand
CO2
emissionProduction
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Methodo logy - Reference Scenar io
Pp,n,g,c,𝑦 = Pp,n,g,c,y−1 ∗ 1 + ∆𝐸𝐷p,n,c,𝑦
With:
-∆ED (%) as the annual change of the product demand.
-index: p as the specific product (jet fuel, gasoline, diesel and heavy fuel oil)
Er,c,𝑦 = Er,c,𝐵 ∗ Pt,n,g,c,𝑦
With:
-E (toe) as the energy demand
-P (2015=100) as the relative production
-Indices: r as the refinery, c as the country and y as the year, B as the base year, t as the total,
n as the complex, g as the geographical category.
er,c,𝑦 = er,c,𝐵 ∗ Pt,n,g,c,𝑦
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Methodo logy - Ma in Dr ive rs fo r Re fe rence
Scenar io
-10,00
-8,00
-6,00
-4,00
-2,00
0,00
2,00
4,00
6,00
8,00
10-'20 20-'30 30-'40 40-'50
An
nu
al
ch
an
ge i
n %
Periods
Residentialenergy demand
Chemicalindustry GVA
Gasolinedemand
Diesel demand
Kerosenedemand
Bunker fueldemand
Figure 4. Change of activities as main drivers for EU-28 from EU reference
scenario 2016
Vita, A. de, Tasios, N., Evangelopoulou, S., Forsell, N., Fragiadakis, K., Fragkos, P.,. . . Zampara, M. (2016). EU reference
scenario 2016: Energy, transport and GHG emissions : trends to 2050. Luxembourg: Publications Office
© Fraunhofer ISI
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Methodo logy
Figure 1. Research methodology based on bottom-up approach.
Energy saving and CO2 mitigation potential
Energy saving option choice
Characterization Diffusion
Scenario development
Main drivers CorrelationsReference scenario
Policy scenario
Status-quo data
ThroughputEnergy demand
CO2
emissionProduction
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Methodo logy - Po l i cy Scenar io
87%
13%
Reference scenario(EU Reference)
36%
25%
39%
Decarbonization scenario
(EU Roadmap)
Oil
Biofuel
Electricity
Figure 5. Fuel mix change comparison in road transportation in 2050.
Comparison of two scenarios:
1. Decarbonization scenario in EU Roadmap (European Commission, 2011)
2. Reference scenario in EU Reference scenario (Vita et al., 2016)
Inputs for the policy scenario:
• Fuel mix change in transportation sector and residential sector
• Efficiency increase in industry sector
Vita, A. de, Tasios, N., Evangelopoulou, S., Forsell, N., Fragiadakis, K., Fragkos, P.,. . . Zampara, M. (2016). EU reference scenario 2016:
Energy, transport and GHG emissions : trends to 2050. Luxembourg: Publications Office
European Commission. (2011). Communication from the commission to the European Parliament, the council, the European economy and
social committee and the committee of the regions: A roadmap for moving to a competitive low carbon economy in 2050 (COM (2011)).
Brussels: European Union: European Commission.
© Fraunhofer ISI
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Methodo logy
Figure 1. Research methodology based on bottom-up approach.
Energy saving and CO2 mitigation potential
Energy saving option choice
Characterization Diffusion
Scenario development
Main drivers CorrelationsReference scenario
Policy scenario
Status-quo data
ThroughputEnergy demand
CO2
emissionProduction
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Methodo logy -Energy Sav ing Opt ions
Number Name Fuel savingElectricity
saving
Payback
period (year)
1 Installation of new internals 0-1.57 % 0.00-1.00 % 0.7
2 Flare gas recovery 1.09 % 0.67 % 2.0
3 Improvement of catalysts 0.20-1.17 % 0.01-0.13 % 1.0
4 Revamp heat integration 0.00-0.05 % 0.00-0.59 % 2.0
5 Installation of furnace air pre-heat 0.09-0.10 % 0.00 % 3.0
Table 3. Characterization of energy saving options.
From the literature Morrow III et al. (2013) Assessment of Energy Efficiency
Improvement in the United States Petroleum Refining Industry
Five energy saving options were chosen based upon penetration rate, total
fuel savings.
Note: Adopted from Morrow III et al. (2013).
Morrow III, W. R., Marano, J., Sathaye, J., Hasanbeigi, A., & Xu, T. (2013). Assessment of Energy Efficiency Improvement
in the United States Petroleum Refining Industry.
© Fraunhofer ISI
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Methodo logy
Figure 1. Research methodology based on bottom-up approach.
Energy saving and CO2 mitigation potential
Energy saving option choice
Characterization Diffusion
Scenario development
Main drivers CorrelationsReference scenario
Policy scenario
Status-quo data
ThroughputEnergy demand
CO2
emissionProduction
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Resu l t -S ta tus Quo
Figure 6. Status-quo of energy demand of refineries in EU-28 by the categories in
2015.
0,00
10,00
20,00
30,00
40,00
50,00
60,00
0,00
0,20
0,40
0,60
0,80
1,00
1,20
1,40
1,60
1,80
2,00
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105
Cum
ula
tive
en
erg
y d
em
an
d in M
toe
En
erg
y d
em
an
d in M
toe
Refinery number
Basic refinery Cat-conversion refinery Hydro-conversion refinery
Fleix-conversion refinery Cumulative energy demand
60%
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Resu l t - Produc t ion Pro jec t ion
20
40
60
80
100
2015 2020 2025 2030 2035 2040 2045 2050
Rela
tive p
roduction (
2015=
100)
Year
Reference Scenario
Figure 7. Production projection by complex in the reference scenario and policy
scenario.
20
40
60
80
100
2015 2020 2025 2030 2035 2040 2045 2050
Year
Policy Scenario
Complex 1
Complex 2
Complex 3
Complex 4
Kim, S. (2017). Potential Costs of CO2 Mitigations for Refineries in Europe for 2050 (Master Thesis).
Albert-Ludwigs-Universität Freiburg, Freiburg.
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Resu l t - Scenar ios Wi th Sav ing Poten t ia l
Figure 8. Energy demand projection in the reference and policy scenario with saving potential
Saving
potential:~9%
(energy efficiency)
Scenario difference: 53%
(activity)
Reference scenario: 16%
(activity)
Kim, S. (2017). Potential Costs of CO2 Mitigations for Refineries in Europe for 2050 (Master Thesis).
Albert-Ludwigs-Universität Freiburg, Freiburg.
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Conc lus ion
The study contributes to energy system research by;
• First, bottom-up approach to model the refinery sector on site-level.
• Second, projection methodology using production development.
• Finally, calculation of energy saving and CO2 mitigation potential of
applying ESOs that are not likely employed yet, under different diffusion
cases.
Further research:
• Elaboration of the correlations between demands and production
(considering international trades).
• Economic structure of refineries by type.
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Q&A
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