© 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

Page 2

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

© Fraunhofer ISI

Page 3

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?

© Fraunhofer ISI

Page 4

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

© Fraunhofer ISI

Page 5

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.

© Fraunhofer ISI

Page 6

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

© Fraunhofer ISI

Page 7

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

Page 8

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

© Fraunhofer ISI

Page 9

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

© Fraunhofer ISI

Page 11

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

© Fraunhofer ISI

Page 12

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

Page 13

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

© Fraunhofer ISI

Page 14

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

Page 15

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

© Fraunhofer ISI

Page 16

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

Page 17

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

© Fraunhofer ISI

Page 18

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%

© Fraunhofer ISI

Page 19

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.

© Fraunhofer ISI

Page 20

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.

© Fraunhofer ISI

Page 21

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.

© Fraunhofer ISI

Page 22

Q&A