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