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Impacts of scenario
definitions on CO2
mitigation cost
in energy system models
Lukasz Brodecki,
Annika Gillich
Source: [1]
• Introduction
• TIMES: Model, Scenarios, Results
• E2M2: Model, Scenarios, Results
• Discussion and Summary
• References
17-Nov-18IER University of Stuttgart 2
Agenda
BUT: different ways of modelling CO2-targets in ESM can lead to different results!
 How should those targets be modelled and which scenarios should be selected in order to derive sound
policy recommendations?
17-Nov-18IER University of Stuttgart 3
Ambitious greenhouse gas reduction goals defined at COP21 in Paris
Introduction
Source: [2]
Types of GHG targets differ across
countries, but a high share relies
on a maximum level of GHG
emissions in a target year!
Energy System Models (ESM):
used for planning on how to
achieve those targets and
assessment of progress
17-Nov-18IER University of Stuttgart 4
CO2-targets in Energy System Models: few model runs use budget
Literature Review
Total number of publications considered: 117
• Majority of publications consider a minimum share of renewables,
• One third considers a CO2-price or cap, only 2% use a CO2 budget
• Model foresight is often not mentioned explicitly, but relevant for interpretation of results
1) How does the selection
of CO2-constraint impact
model results?
2) Which CO2-constraint
should be used to assess
mitigation pathways with
energy system models?
17-Nov-18IER University of Stuttgart 5
Various CO2-constraints will be analysed in two case studies
Modelling Approach
Research questions Methodology
E2M2
TIMES-Local
BASE CAP BUDGETCAP-CPO CAP-AUT
Comparison of emission
reduction and mitigation cost
Comparison of emission
reduction and mitigation cost
Result comparison and
effect analysis
• Introduction
• TIMES: Model, Scenarios, Results
• E2M2: Model, Scenarios, Results
• Discussion and Summary
• References
17-Nov-18IER University of Stuttgart 6
Agenda
17-Nov-18IER University of Stuttgart 7
Model description TIMES Local
Source: [3-4]
17-Nov-18IER University of Stuttgart 8
Scenario description TIMES Local
General scenario framework:
• Linear optimizaton, bottom-up model
• Medium-sized municipality in Germany as
one region
• Focus on supply and demand processes
relevant for a city/district model, all sectors
• Starting point 2010, 5-year-steps until 2050
with perfect foresight
• Hourly time resolution with 5
representative seasons (original seasons
plus fall peak) adding up to 840 timeslices,
• Endogen investment and dispatch in
eletrical, thermal sevices and mobility
technologies
• No restrictions on CO2 (no upper bound, CO2-price = 0)
• Extrapolation of local development based on statistical data
BASE
• Limit of total CO2 emissions according to 2050 state targets
• Projection of targets until 2050 as yearly upper bound (UB)
 -90% vs. 1990 with linear interpolation for timesteps
between target years
CAP
• Sum of yearly upper bounds from scenario CAP as one single
UB over entire modelling period
• Additional UB only for 2050 in order to reach same CO2
reduction level (as in CAP and AUT)
BUDGET
• UB on CO2 according to scenario CAP
• Additional long term „energy-autarky (AUT) goal on local
level“ until 2050 – level of self-sufficiency in 2050 75%
• Linear interpolation for timesteps between years for AUT
CAP+AUT
17-Nov-18IER University of Stuttgart 9
System cost and average mitigation cost behave differently under CO2-constraints
Results TIMES Local
System cost:
• Definition of additional constraints increases overall system cost
• Slightly lower system cost in BUDGET compared to CAP due to
higher flexibility in selection of mitigation options
Average mitigation cost:
• BUDGET represents time-integral optimum for CAP reduction level
and therefore achieves lower system cost AND lower AMC!
• CAP+AUT leads to higher system cost but also to higher emission
reduction compared to CAP
• CAP+AUT results in lower AMC compared to CAP, although solution
space is smaller!
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑚𝑖𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡 (𝐴𝑀𝐶) =
𝐶𝑂2
𝐵𝐴𝑆𝐸𝑇
𝑡=1 − 𝐶𝑂2
𝑆𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑇
𝑡=1
𝑆𝑦𝑠𝑡𝑒𝑚𝑐𝑜𝑠𝑡 𝑆𝑐𝑒𝑛𝑎𝑟𝑖𝑜 − 𝑆𝑦𝑠𝑡𝑒𝑚𝑐𝑜𝑠𝑡 𝐵𝐴𝑆𝐸
 BICO (BIased COst) effect 0
50
100
150
200
250
0
200
400
600
800
1000
BASE CAP CAP+AUT BUDGET
Averagemitigationcost[€/tCO2]
Reducedemissions
comparedtoBASE[kt]
Reduced emissions compared to BASE
Average CO2 mitigation cost
232 € 226 € 219 €
0
20
40
60
80
3800
3900
4000
4100
4200
BASE CAP CAP+AUT BUDGET
Totaldiscounted
systemcosts[M€2010]
+3.9% +4.5% +3.5%
≙ higher absolut cost, higher emission
reduction, BUT lower average mitigation
cost!
17-Nov-18IER University of Stuttgart 10
How do emission pathways develop over time?
Results TIMES Local
 Emission reduction through 2nd constraint approximates
BUDGET emission reduction path in the medium term
0
1.000
2.000
3.000
4.000
5.000
2010 2015 2020 2025 2030 2035 2040 2045 2050
CumulatedCO2Emissions[kt]
BASE CAP CAP+AUT BUDGET
2025 2026 2027 2028 2029 2030
BASE CAP
CAP+AUT BUDGET
• Profitable abatement
measures are already
drawn in BASE case
(degressive curve
character)
• 2nd constraint CAP+AUT
pushes emissions in 2050
below level of CAP and
BUDGET
• Introduction
• TIMES: Model, Scenarios, Results
• E2M2: Model, Scenarios, Results
• Discussion and Summary
• References
17-Nov-18IER University of Stuttgart 11
Agenda
17-Nov-18IER University of Stuttgart 12
Model description E2M2
 Power plants
 Dispatch
 Energy generation
Results
 System cost
 Electricity prices
 Market value
Input Model
 Linear programming
 Objective function
 Restrictions
European Electricity Market Model – E2M2
 Fundamental linear (mixed-integer) electricity market model for Europe
 Investment decisions for plants, storages, transmission capacity and other flexibility options and simultanous optimization of dipatch
 Provision of balancing energy and reserve capacity
 Myopic optimization on yearly basis with hourly time resolution
 Electricity prices for markets with perfect competition
Generation
 Production from RES
 Existing power plants
 Techn. + econ. parameter
Investment
 Power plants (therm. + RES)
 Flexibility options
Restrictions
 Satisfy demand
 Upper and lower bounds
RES: Renewable Energy Sources Source: [5-6]
17-Nov-18IER University of Stuttgart 13
Scenario description E2M2
General scenario framework:
• 5-year-steps until 2050, 2-hourly time
resolution
• Germany as one region
• Constant domestic electricity demand,
development from exporting country in
2020 to importing country in 2050
• Must-run for CHP-plants considered
• Endogen investment in thermal and
renewable power plants
• Base year for weather and demand data:
2006
• Perfect foresight over full period 2020-2050
• No restrictions on CO2 (no upper bound, CO2-price = 0)BASE
• Yearly upper bound (UB) on CO2 according to 2030 energy
sector targets (Klimaschutzplan 2050 [7])
• Projection of targets until 2050 ( 95,5% reduction vs. 1990)
• Linear interpolation for years between target years
CAP
• Sum of yearly upper bounds from scenario CAP as one single
UB over entire modelling period
• Additional UB only for 2050 in order to reach 95,5%
reduction level (as in CAP and CAP+CPO)
BUDGET
CAP+CPO
• CPO = Coal-phase out
• UB on CO2 acc. to scenario CAP
• Additional early phase-out of lignite and hard coal power
plants in Germany until 2045
17-Nov-18IER University of Stuttgart 14
BICO effect occurs also in power sector scenarios
Results E2M2
System cost:
• Coal phase-out as additional constraint results in higher
system cost than CAP due to limited solution space
• BUDGET shows lower system cost than CAP due to timely
flexibility of reduction
Average mitigation cost:
• CAP+CPO: induces higher emission reduction but slightly
lower average mitigation cost compared to BASE
scenario!
 BICO effect appears again
17-Nov-18IER University of Stuttgart 15
How do cost and emission pathways develop over time?
Results E2M2
BICO effect: 2nd constraint pushes emission reduction more towards BUDGET scenario (higher
emission reductions 2020 and 2025) an therefore towards a more cost-optimal solution!
BUDGET and
CAP+CPO show
higher emission
reduction in early
years
• Introduction
• TIMES: Model, Scenarios, Results
• E2M2: Model, Scenarios, Results
• Discussion and Summary
• References
17-Nov-18IER University of Stuttgart 16
Agenda
17-Nov-18IER University of Stuttgart 17
Generic mitigation cost curve explains BICO effect
Effect Analysis
Simplifications compared to model runs:
• cost assumed constant over time
• interest rate=0%
• decommissioning of plants is possible
anytime at no cost (lifetime of new
plants = 1 year)
2020
2020
2025
reduced
t CO2
€ per reduced
t CO2
2020
2025
2030
mitigation
in BASE 2025 2030
2025
fuel switch
low emission investment replaces
high emission investment
2030
2030
2020
low emission investment
replaces existing plant
2025
a b
2025
2020
2020
2020
€ per reduced
t CO2
2020
2020
2030
2030
2025
2025
2025
2025
d e
2020
2025
2025
20
2030
2020
2020
€ per reduced
t CO2
2020
2020
reduced
t CO2
2030
2030
2025
2025
2025
2025
d e f
2020
2025
2025
2030
2030
2020
2020
€ per reduced
t CO2
2020
2020
reduced
t CO2
2030
2030
2025
2025
2025
2025
d e f
2020
2025
2025
2030
17-Nov-18IER University of Stuttgart 18
Generic mitigation cost curve explains BICO effect
Effect Analysis
BUDGET scenario sees all mitigation
options and has full flexibility of choice:
mitigation in BUDGET
CAP scenario sees all
mitigation options, but can
only choose options that are
effective to fulfill the yearly
restrictions!
d: emission reduction in CAP 2020
e: emission reduction in CAP 2025
f: emission reduction in CAP 2030
2020
2020
2025
reduced
t CO2
€ per reduced
t CO2
2020
2025
2030
mitigation
in BASE 2025 2030
2025
fuel switch
low emission investment replaces
high emission investment
2030
2030
2020
low emission investment
replaces existing plant
2025
a b
2025
2020
c
17-Nov-18IER University of Stuttgart 19
2nd constraint decreases average mitigation cost
Effect Analysis
Cause 1: Early use of low cost mitigation options
avg.
mitigation
cost 2030
d*
2030
2020
€ per reduced
t CO2
2020
2020
reduced
t CO2
2030
2030
2025
2025
2025
2025
d e f
2020
2025
2025
2030
avg.
mitigation
cost 2025
avg. mitigation
cost for additional
reduction through
coal phase-out
2020
2020
17-Nov-18IER University of Stuttgart 20
2nd constraint decreases average mitigation cost
Effect Analysis
Cause 2: Innovation of low emission technologies
avg.
mitigation
cost 2030
e*
2030
2020
€ per reduced
t CO2
2020
2020
reduced
t CO22030
2025
2025
d e f
2020
2025
2025
2030
avg.
mitigation
cost 2025
avg.
mitigation
cost 2020
2020
20302025
1) … the definition of model constraints plays a crucial role in energy system analysis and the evaluation of CO2
mitigation pathways, as costs differ significantly and distortion of AMC can appear!
2) … no general answer to when the BICO effect appears can be given, but it has been shown in two different
ESMs for two different research subjects.
3) … above explained two causes are catalyst for the effect, but whether it occurs, depends on model type, time
horizon and parameterization.
17-Nov-18IER University of Stuttgart 21
Our research has shown that…
Conclusion
Avoidance of BICO effect: compare CO2-cap and -price model runs with a BUDGET scenario!
Considering the following limtations…
17-Nov-18IER University of Stuttgart 22
Careful when using a BUDGET run as comparison
Discussion and OutlookQualitativeQuanti-
tative
 Upper bound of emissions in BUDGET shall equal resulting sum of emissions in CAP scenario.
 Additional upper bound in final year shall be set and be equal to the one in CAP to achieve same
reduction level.
 Compare resulting technology portfolio at the end of the modelling period (and therefore remaining
reduction potential of energysystem after final year).
 Consider salvage cost or use annuities in ESM with short/limited time horizon.
Further analyses should examine…
• Robustness of results regarding temporal resolution,
• Sensitivity of the models for technology parameterization,
• Impact of discount rate (highly relevant for results),
• Use of non-perfect-foresight models, e.g. myopic optimization, may increase the BICO effect.
• Introduction
• TIMES : Model, Scenarios, Results
• E2M2: Model, Scenarios, Results
• Discussion and Summary
• References
17-Nov-18IER University of Stuttgart 23
Agenda
[1] Agora Energiewende (2017): Die Energiewende im Stromsektor: „Stand der Dinge 2016. Rückblick auf die wesentlichen Entwicklungen sowie
Ausblick auf 2017.“
[2] CAIT Climate Data Explorer, CAIT Paris Contributions Map, (2016). https://www.climatewatchdata.org/ndcs-content, accessed 02.09.2018.
[3] R. Loulou, G. Goldstein, A. Kanudia, A. Lettila, U. Remme, Documentation for the TIMES Model - Part I, (2016) 1–78.
[4] L. Brodecki, M. Blesl, Modellgestützte Bewertung von Flexibilitätsoptionen und Versorgungsstrukturen eines Bilanzraums mit hohen
Eigenversorgungsgraden mit Energie, in: EnInnov, Graz, 2018: pp. 1–15.
[5] N. Sun, Modellgestützte Untersuchung des Elektrizitätsmarktes, University of Stuttgart, 2012.
[6] S. Bothor, M. Steurer, T. Eberl, H. Brand, A. Voß, Bedarf und Bedeutung von integrations- und Flexibilisierungsoptionen in
Elektrizitätssystemen mit steigendem Anteil erneuerbarer Energien, in: 9. Int. Energiewirtschaftstagung an Der TU Wien, IEWT 2015, 2015.
[7] „Klimaschutzplan 2050 – Klimaschutzpolitische Grundsätze und Ziele der Bundesregierung“, Bundesministerium für Umwelt, Bau und
Reaktorsicherheit (BMUB), (2016) 1–96. doi:10.1016/j.aqpro.2013.07.003.
17-Nov-18IER University of Stuttgart 24
References
e-mail
phone +49 (0) 711 685-
fax +49 (0) 711 685-
Universität Stuttgart
Thank you!
IER Institute for Energy Economics
and Rational Energy Use
Lukasz Brodecki, Annika Gillich
878 49
878 73
Institut für Energiewirtschaft und Rationelle Energieanwendungen (IER)
annika.gillich@ier.uni-stuttgart.de, lukasz.brodecki@ier.uni-stuttgart.de
Heßbrühlstraße 49a, 70565 Stuttgart

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Impacts of scenario definitions on CO2 mitigation cost in energy system models

  • 1. Click to edit Master subtitle style Impacts of scenario definitions on CO2 mitigation cost in energy system models Lukasz Brodecki, Annika Gillich Source: [1]
  • 2. • Introduction • TIMES: Model, Scenarios, Results • E2M2: Model, Scenarios, Results • Discussion and Summary • References 17-Nov-18IER University of Stuttgart 2 Agenda
  • 3. BUT: different ways of modelling CO2-targets in ESM can lead to different results!  How should those targets be modelled and which scenarios should be selected in order to derive sound policy recommendations? 17-Nov-18IER University of Stuttgart 3 Ambitious greenhouse gas reduction goals defined at COP21 in Paris Introduction Source: [2] Types of GHG targets differ across countries, but a high share relies on a maximum level of GHG emissions in a target year! Energy System Models (ESM): used for planning on how to achieve those targets and assessment of progress
  • 4. 17-Nov-18IER University of Stuttgart 4 CO2-targets in Energy System Models: few model runs use budget Literature Review Total number of publications considered: 117 • Majority of publications consider a minimum share of renewables, • One third considers a CO2-price or cap, only 2% use a CO2 budget • Model foresight is often not mentioned explicitly, but relevant for interpretation of results
  • 5. 1) How does the selection of CO2-constraint impact model results? 2) Which CO2-constraint should be used to assess mitigation pathways with energy system models? 17-Nov-18IER University of Stuttgart 5 Various CO2-constraints will be analysed in two case studies Modelling Approach Research questions Methodology E2M2 TIMES-Local BASE CAP BUDGETCAP-CPO CAP-AUT Comparison of emission reduction and mitigation cost Comparison of emission reduction and mitigation cost Result comparison and effect analysis
  • 6. • Introduction • TIMES: Model, Scenarios, Results • E2M2: Model, Scenarios, Results • Discussion and Summary • References 17-Nov-18IER University of Stuttgart 6 Agenda
  • 7. 17-Nov-18IER University of Stuttgart 7 Model description TIMES Local Source: [3-4]
  • 8. 17-Nov-18IER University of Stuttgart 8 Scenario description TIMES Local General scenario framework: • Linear optimizaton, bottom-up model • Medium-sized municipality in Germany as one region • Focus on supply and demand processes relevant for a city/district model, all sectors • Starting point 2010, 5-year-steps until 2050 with perfect foresight • Hourly time resolution with 5 representative seasons (original seasons plus fall peak) adding up to 840 timeslices, • Endogen investment and dispatch in eletrical, thermal sevices and mobility technologies • No restrictions on CO2 (no upper bound, CO2-price = 0) • Extrapolation of local development based on statistical data BASE • Limit of total CO2 emissions according to 2050 state targets • Projection of targets until 2050 as yearly upper bound (UB)  -90% vs. 1990 with linear interpolation for timesteps between target years CAP • Sum of yearly upper bounds from scenario CAP as one single UB over entire modelling period • Additional UB only for 2050 in order to reach same CO2 reduction level (as in CAP and AUT) BUDGET • UB on CO2 according to scenario CAP • Additional long term „energy-autarky (AUT) goal on local level“ until 2050 – level of self-sufficiency in 2050 75% • Linear interpolation for timesteps between years for AUT CAP+AUT
  • 9. 17-Nov-18IER University of Stuttgart 9 System cost and average mitigation cost behave differently under CO2-constraints Results TIMES Local System cost: • Definition of additional constraints increases overall system cost • Slightly lower system cost in BUDGET compared to CAP due to higher flexibility in selection of mitigation options Average mitigation cost: • BUDGET represents time-integral optimum for CAP reduction level and therefore achieves lower system cost AND lower AMC! • CAP+AUT leads to higher system cost but also to higher emission reduction compared to CAP • CAP+AUT results in lower AMC compared to CAP, although solution space is smaller! 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑚𝑖𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡 (𝐴𝑀𝐶) = 𝐶𝑂2 𝐵𝐴𝑆𝐸𝑇 𝑡=1 − 𝐶𝑂2 𝑆𝑐𝑒𝑛𝑎𝑟𝑖𝑜𝑇 𝑡=1 𝑆𝑦𝑠𝑡𝑒𝑚𝑐𝑜𝑠𝑡 𝑆𝑐𝑒𝑛𝑎𝑟𝑖𝑜 − 𝑆𝑦𝑠𝑡𝑒𝑚𝑐𝑜𝑠𝑡 𝐵𝐴𝑆𝐸  BICO (BIased COst) effect 0 50 100 150 200 250 0 200 400 600 800 1000 BASE CAP CAP+AUT BUDGET Averagemitigationcost[€/tCO2] Reducedemissions comparedtoBASE[kt] Reduced emissions compared to BASE Average CO2 mitigation cost 232 € 226 € 219 € 0 20 40 60 80 3800 3900 4000 4100 4200 BASE CAP CAP+AUT BUDGET Totaldiscounted systemcosts[M€2010] +3.9% +4.5% +3.5% ≙ higher absolut cost, higher emission reduction, BUT lower average mitigation cost!
  • 10. 17-Nov-18IER University of Stuttgart 10 How do emission pathways develop over time? Results TIMES Local  Emission reduction through 2nd constraint approximates BUDGET emission reduction path in the medium term 0 1.000 2.000 3.000 4.000 5.000 2010 2015 2020 2025 2030 2035 2040 2045 2050 CumulatedCO2Emissions[kt] BASE CAP CAP+AUT BUDGET 2025 2026 2027 2028 2029 2030 BASE CAP CAP+AUT BUDGET • Profitable abatement measures are already drawn in BASE case (degressive curve character) • 2nd constraint CAP+AUT pushes emissions in 2050 below level of CAP and BUDGET
  • 11. • Introduction • TIMES: Model, Scenarios, Results • E2M2: Model, Scenarios, Results • Discussion and Summary • References 17-Nov-18IER University of Stuttgart 11 Agenda
  • 12. 17-Nov-18IER University of Stuttgart 12 Model description E2M2  Power plants  Dispatch  Energy generation Results  System cost  Electricity prices  Market value Input Model  Linear programming  Objective function  Restrictions European Electricity Market Model – E2M2  Fundamental linear (mixed-integer) electricity market model for Europe  Investment decisions for plants, storages, transmission capacity and other flexibility options and simultanous optimization of dipatch  Provision of balancing energy and reserve capacity  Myopic optimization on yearly basis with hourly time resolution  Electricity prices for markets with perfect competition Generation  Production from RES  Existing power plants  Techn. + econ. parameter Investment  Power plants (therm. + RES)  Flexibility options Restrictions  Satisfy demand  Upper and lower bounds RES: Renewable Energy Sources Source: [5-6]
  • 13. 17-Nov-18IER University of Stuttgart 13 Scenario description E2M2 General scenario framework: • 5-year-steps until 2050, 2-hourly time resolution • Germany as one region • Constant domestic electricity demand, development from exporting country in 2020 to importing country in 2050 • Must-run for CHP-plants considered • Endogen investment in thermal and renewable power plants • Base year for weather and demand data: 2006 • Perfect foresight over full period 2020-2050 • No restrictions on CO2 (no upper bound, CO2-price = 0)BASE • Yearly upper bound (UB) on CO2 according to 2030 energy sector targets (Klimaschutzplan 2050 [7]) • Projection of targets until 2050 ( 95,5% reduction vs. 1990) • Linear interpolation for years between target years CAP • Sum of yearly upper bounds from scenario CAP as one single UB over entire modelling period • Additional UB only for 2050 in order to reach 95,5% reduction level (as in CAP and CAP+CPO) BUDGET CAP+CPO • CPO = Coal-phase out • UB on CO2 acc. to scenario CAP • Additional early phase-out of lignite and hard coal power plants in Germany until 2045
  • 14. 17-Nov-18IER University of Stuttgart 14 BICO effect occurs also in power sector scenarios Results E2M2 System cost: • Coal phase-out as additional constraint results in higher system cost than CAP due to limited solution space • BUDGET shows lower system cost than CAP due to timely flexibility of reduction Average mitigation cost: • CAP+CPO: induces higher emission reduction but slightly lower average mitigation cost compared to BASE scenario!  BICO effect appears again
  • 15. 17-Nov-18IER University of Stuttgart 15 How do cost and emission pathways develop over time? Results E2M2 BICO effect: 2nd constraint pushes emission reduction more towards BUDGET scenario (higher emission reductions 2020 and 2025) an therefore towards a more cost-optimal solution! BUDGET and CAP+CPO show higher emission reduction in early years
  • 16. • Introduction • TIMES: Model, Scenarios, Results • E2M2: Model, Scenarios, Results • Discussion and Summary • References 17-Nov-18IER University of Stuttgart 16 Agenda
  • 17. 17-Nov-18IER University of Stuttgart 17 Generic mitigation cost curve explains BICO effect Effect Analysis Simplifications compared to model runs: • cost assumed constant over time • interest rate=0% • decommissioning of plants is possible anytime at no cost (lifetime of new plants = 1 year) 2020 2020 2025 reduced t CO2 € per reduced t CO2 2020 2025 2030 mitigation in BASE 2025 2030 2025 fuel switch low emission investment replaces high emission investment 2030 2030 2020 low emission investment replaces existing plant 2025 a b 2025 2020
  • 18. 2020 2020 € per reduced t CO2 2020 2020 2030 2030 2025 2025 2025 2025 d e 2020 2025 2025 20 2030 2020 2020 € per reduced t CO2 2020 2020 reduced t CO2 2030 2030 2025 2025 2025 2025 d e f 2020 2025 2025 2030 2030 2020 2020 € per reduced t CO2 2020 2020 reduced t CO2 2030 2030 2025 2025 2025 2025 d e f 2020 2025 2025 2030 17-Nov-18IER University of Stuttgart 18 Generic mitigation cost curve explains BICO effect Effect Analysis BUDGET scenario sees all mitigation options and has full flexibility of choice: mitigation in BUDGET CAP scenario sees all mitigation options, but can only choose options that are effective to fulfill the yearly restrictions! d: emission reduction in CAP 2020 e: emission reduction in CAP 2025 f: emission reduction in CAP 2030 2020 2020 2025 reduced t CO2 € per reduced t CO2 2020 2025 2030 mitigation in BASE 2025 2030 2025 fuel switch low emission investment replaces high emission investment 2030 2030 2020 low emission investment replaces existing plant 2025 a b 2025 2020 c
  • 19. 17-Nov-18IER University of Stuttgart 19 2nd constraint decreases average mitigation cost Effect Analysis Cause 1: Early use of low cost mitigation options avg. mitigation cost 2030 d* 2030 2020 € per reduced t CO2 2020 2020 reduced t CO2 2030 2030 2025 2025 2025 2025 d e f 2020 2025 2025 2030 avg. mitigation cost 2025 avg. mitigation cost for additional reduction through coal phase-out 2020 2020
  • 20. 17-Nov-18IER University of Stuttgart 20 2nd constraint decreases average mitigation cost Effect Analysis Cause 2: Innovation of low emission technologies avg. mitigation cost 2030 e* 2030 2020 € per reduced t CO2 2020 2020 reduced t CO22030 2025 2025 d e f 2020 2025 2025 2030 avg. mitigation cost 2025 avg. mitigation cost 2020 2020 20302025
  • 21. 1) … the definition of model constraints plays a crucial role in energy system analysis and the evaluation of CO2 mitigation pathways, as costs differ significantly and distortion of AMC can appear! 2) … no general answer to when the BICO effect appears can be given, but it has been shown in two different ESMs for two different research subjects. 3) … above explained two causes are catalyst for the effect, but whether it occurs, depends on model type, time horizon and parameterization. 17-Nov-18IER University of Stuttgart 21 Our research has shown that… Conclusion Avoidance of BICO effect: compare CO2-cap and -price model runs with a BUDGET scenario! Considering the following limtations…
  • 22. 17-Nov-18IER University of Stuttgart 22 Careful when using a BUDGET run as comparison Discussion and OutlookQualitativeQuanti- tative  Upper bound of emissions in BUDGET shall equal resulting sum of emissions in CAP scenario.  Additional upper bound in final year shall be set and be equal to the one in CAP to achieve same reduction level.  Compare resulting technology portfolio at the end of the modelling period (and therefore remaining reduction potential of energysystem after final year).  Consider salvage cost or use annuities in ESM with short/limited time horizon. Further analyses should examine… • Robustness of results regarding temporal resolution, • Sensitivity of the models for technology parameterization, • Impact of discount rate (highly relevant for results), • Use of non-perfect-foresight models, e.g. myopic optimization, may increase the BICO effect.
  • 23. • Introduction • TIMES : Model, Scenarios, Results • E2M2: Model, Scenarios, Results • Discussion and Summary • References 17-Nov-18IER University of Stuttgart 23 Agenda
  • 24. [1] Agora Energiewende (2017): Die Energiewende im Stromsektor: „Stand der Dinge 2016. Rückblick auf die wesentlichen Entwicklungen sowie Ausblick auf 2017.“ [2] CAIT Climate Data Explorer, CAIT Paris Contributions Map, (2016). https://www.climatewatchdata.org/ndcs-content, accessed 02.09.2018. [3] R. Loulou, G. Goldstein, A. Kanudia, A. Lettila, U. Remme, Documentation for the TIMES Model - Part I, (2016) 1–78. [4] L. Brodecki, M. Blesl, Modellgestützte Bewertung von Flexibilitätsoptionen und Versorgungsstrukturen eines Bilanzraums mit hohen Eigenversorgungsgraden mit Energie, in: EnInnov, Graz, 2018: pp. 1–15. [5] N. Sun, Modellgestützte Untersuchung des Elektrizitätsmarktes, University of Stuttgart, 2012. [6] S. Bothor, M. Steurer, T. Eberl, H. Brand, A. Voß, Bedarf und Bedeutung von integrations- und Flexibilisierungsoptionen in Elektrizitätssystemen mit steigendem Anteil erneuerbarer Energien, in: 9. Int. Energiewirtschaftstagung an Der TU Wien, IEWT 2015, 2015. [7] „Klimaschutzplan 2050 – Klimaschutzpolitische Grundsätze und Ziele der Bundesregierung“, Bundesministerium für Umwelt, Bau und Reaktorsicherheit (BMUB), (2016) 1–96. doi:10.1016/j.aqpro.2013.07.003. 17-Nov-18IER University of Stuttgart 24 References
  • 25. e-mail phone +49 (0) 711 685- fax +49 (0) 711 685- Universität Stuttgart Thank you! IER Institute for Energy Economics and Rational Energy Use Lukasz Brodecki, Annika Gillich 878 49 878 73 Institut für Energiewirtschaft und Rationelle Energieanwendungen (IER) annika.gillich@ier.uni-stuttgart.de, lukasz.brodecki@ier.uni-stuttgart.de Heßbrühlstraße 49a, 70565 Stuttgart