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Multi-criteria evaluation of transition pathways
towards a sustainable energy system
Katharina Stahlecker, MSc
Prof. Dr. Jutta Geldermann
Chair of Production and Logistics
1. Problem Motivation: Research project
on sustainable electricity supply
2. Research question and research gap
3. Application of MCDA to evaluate
transition pathways with time-varying
criteria (Status quo)
4. Questions and challenges for further
research
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University of Göttingen
(Germany)
• Founded in 1734
• 13 Faculties with 27,500 students and
12,000 staff members (495 professors)
• 45 Nobel price laureates are linked to Göttingen via their CV
(2014: Stefan Hell (Chemistry) for "for the development
of super-resolved fluorescence microscopy”)
• Chair of Production and Logistics
(Faculty of Economic Sciences)
12 Research Associates /
Ph.D. students working on
current topics of sustainability and
energy efficiency,
using methods of Operations Research
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Transformation of the Energy Supply System
Large-scale power plants
380/220 kV
110 kV
20 kV
Industry
Industry/business units 0,4 kV
Households
Energy supply in former days:
0,4 kV
Flexible power plants
380/220 kV
110 kV
20 kV
Industry
Industry /business unit
Offshore-Windpower
Biogas plant
Wind power
plant
PV-parks
Co-generation
Onshore-Windfarm
Large-scale
storage
Storage
storage
E-mobility
Energy supply in future:
Households
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Energy mix pathway
Pathway 1 Pathway 2
Structure of the Primary Energy consumption in Germany for two different scenarios (BMU, 2012)
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Transition pathways towards a
sustainable electricity supply for Lower-Saxony
•16.11.2015
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OFFIS
Prof. Dr. Michael Sonnenschein
Prof. Dr. Dr. h.c. H.-Jürgen Appelrath †
Leibniz Universität Hannover
Prof. Dr.-Ing. habil. Lutz Hofmann
Jun.-Prof. Dr. Michael Hübler
TU Braunschweig
Prof. Dr. Frank Eggert
Prof. Dr.-Ing. Bernd Engel
Georg-August-Universität Göttingen
Prof. Dr. Jutta Geldermann
Universität Oldenburg
Jun.-Prof. Sebastian Lehnhoff
Prof. Dr. Dr. h.c. H.-Jürgen Appelrath †
apl. Prof. Dr. Niko Paech
Funded by Ministry
of Science and
Culture
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Transition pathways towards a
sustainable electricity supply for Lower-Saxony
Issue
• Targets:
– Reduction of GHG emissions by 80%-95% in 2050 compared to 1990
– Nuclear phase-out
• Transformation of the energy system with increased use of renewable energy
• Multiple conflicting objectives
Energy mix?
Grid expansion with more
fluctuating energy production?
Information technology
to flexibilize energy
demand? Electricity storage?
Security of supply Economic
competitiveness
Environmental
sustainability
Social acceptance
and wellbeing
Funded by Ministry
of Science and
Culture
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Research questions within NEDS
• Transition takes a long time
• Various stakeholder groups
• Conflicting criteria from economic,
ecological, technological and social
perspective
Bandwidth of the total electricity generation from renewable energies for different
scenarios with goal of 80% GHG emissions reduction in 2050 (BMU, 2012)
What are possible transition pathways towards a sustainable electricity
supply for Lower Saxony in 2050?
How can we evaluate the sustainability of the different transition pathways?
Use of Multi-criteria
decision analysis
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Sustainability of energy systems: Related Research
Energy scenarios (Germany) MCDA & Energy scenarios Time-dependent MCDA
Objective
Forecasting/Backcasting
of scenarios for 2050
Explorative and normative
scenarios
Energy system modelling
Consideration of
sustainaibility through
cost and CO2
MCDA for decision between
different energy scenarios
Different MCDA methods
applied
Consideration of technical,
social, ecological and
economic criteria
MCDA for long-term, multi-
period decisions
Uncertainty in evaluation is
tackled e.g. through
scenario analysis,
probabilities, fuzzy numbers
Alternatives are static
One multi-period approach
Selected
papers
Prognos (2010)
Nitsch et al. (2012)
Faulstich et al. (2016)
Repenning et al.(2015)
Keles et al.(2011)
Kronenberg et al. (2011)
Diakoulaki & Karangelis (2007)
Kowalski et al. (2009)
Ribeiro et al. (2013)
Santoyo-Castelazo & Azapagic
(2014)
Zhang et al. (2015)
Bertsch & Fichtner (2015)
Wang et al. (2009)
Oberschmidt (2010)
Frini & BenAmor (2014)
Goumas & Lygerou (2000)
Heinrich (2007)
Durbach & Stewart (2012)
Stewart et al. (2013)
Research
Gap
Modeling different
sustainability criteria besides
CO2 emissions and cost
Study for a federal state (Lower-
Saxony)
Connection of different modeling
tools
Consideration of pathways with
time- varying criteria values
(“multi-period MCDA”)
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Alternative 1: Grid
expansion
Alternative 2:
Information technology
Grid simulation of alternatives (Hoffmann & Blaufuß)
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Methodological framework
Multi-criteria
decision
analysis
Determination of sustainability criteria
Technical model Economic model
Ecological model Social model
CO O
Future scenarios
(framework
conditions)
Identification of
relevant
parameters for
alternative
energy supply
Stakeholder
participation
Illustration based on Hoffmann (2015)
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Multi-criteria decision analysis – General process
Identification of the
problem / issue
Developing an
action plan
Problem
structuring Values
Stakeholders
Goals
Alternative
Uncertainties
Key issues
External
Environment
Constraints
Using the model to
inform and
challenge thinking
Model
Building Specifying
alternatives
Eliciting
values Defining
criteria Synthesis
of information
Challenging
intuition
Creating new
alternatives
Sensitivity
analysis
Robustness
analysis
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Belton and Stewart (2002)
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Goal Subgoals Subgoals Level 2 Subgoals Level 3 Criteria Attribute
Security of supply Availability of energy Import quota Min %
technical
Average Outage Min h/year
Reliability of supply
…
Cost of electricity Min €/kWh
economic
…
Sustainable electricity
supply
Noise exposure Min Qualitative scale
Quality of life
…
social
Income inequality Min Qualitative scale
Social justice
…
GHG emissions Minkg CO2-
Equivalents
Climate and air quality
…
ecological
Land use Min m²
Ressource protection
…
Sustainability criteria
• Criteria have been developed by research team based on the results
from public symposium and literature
• In total 33 criteria
• Challenge: Redundancy and independence of criteria with participation
Sustainable
electricity supply
Ressource
protection
ecological
social
Security of
supply
Reliability of
supply
technical
economic
Climate and air
quality
Quality of life
Social justice
Land use
…
Availability of
energy
Average Outage
…
Cost of electricity
Import quota
…
…
Noise exposure
…
Income inequality
…
GHG emissions Min
Min
%
h/year
€/kWh
Qualitative
scale
Qualitative
scale
kg CO2-
Equivalents
m²
Min
Min
Min
Min
Min
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Formal decision problem for MCDA
X
x x x x a
x x x x a
x x x x a
x x x x a
c c c c
mn mj 2 m 1 m m
in ij 2 i 1 i i
n 2 j 2 22 21 2
n 1 j 1 12 11 1
n j 2 1
=
L L
M O M O M M M
L L
M O M O M M M
L L
L L
L L
Decision matrix
Technical modelEconomic model
Ecological modelSocial model
CO O
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Evaluation of transition pathways: Time-varying alternatives
2015 2020 2030 2040 2050
Ecological
criteria
Technical
criteria
Social
criteria
Economic
criteria
System state 2015
Ecological
criteria
Technical
criteria
Social
criteria
Economic
criteria
Target state 2050 Ecological
criteria
Technical
criteria
Social
criteria
Economic
criteria
System state 20XX
Ecological
criteria
Technical
criteria
Social
criteria
Economic
criteria
System state 20XX
Ecological
criteria
Technical
criteria
Social
criteria
Economic
criteria
System state 20XX
Ecological
criteria
Technical
criteria
Social
criteria
Economic
criteria
System state 20XX
Ecological
criteria
Technical
criteria
Social
criteria
Economic
criteria
System state 20XX
Ecological
criteria
Technical
criteria
Social
criteria
Economic
criteria
System state 20XX
Ecological
criteria
Technical
criteria
Social
criteria
Economic
criteria
System state 20XX
Ecological
criteria
Technical
criteria
Social
criteria
Economic
criteria
System state 20XX
Ecological
criteria
Technical
criteria
Social
criteria
Economic
criteria
System state 20XX
MCDA with time-dependent criteria values
MCDA per time step:
One ranking per time step
t=1 t=2 t=3 t=4
Criteria values for one criterion over time
Alternative 1 Alternative 2 Alternative 3
t=1 t=2 t=3 t=4
MCDA Evaluation at discrete time steps
Alternative 1 Alternative 2 Alternative 3
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Why is it important to account for the time component in MCDA?
Distributional aspects
• Distribution of costs and benefits over time
• Intergenerational justice
Uncertainties in long-term decision making
• Technological change
• Values and behaviour changes over time
• Political and and economic framework conditions
• Interdependencies
Behavioural research (Psychology/ Economics)
• Intertemporal Preferences*
• Behavioural bias
*Berns et al. (2007), Loewenstein et. al (2002)
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PROMETHEE Preference Ranking Organisation Method for Enrichment Evaluations*
b1 a
p(b1,a)
p(a, b1)
a
b c
d e
a
d e
b c
Partial ranking
Complete ranking
*Brans et al. (1986)
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Possible ways to handle time-varying data for PROMETHEE
*Mareschal (2014), **Banamar & De Smet (2015)
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Questions and challenges for further research
Implementation of research methodology:
How to link MCDA to the results of other modeling tools? Is this
possible/reasonable?
Participation in complex decision problems:
How to deal with different stakeholder groups where nobody is the
actual decision maker?
How much participation is constructive?
Long-term decision making and time-component in MCDA:
How to deal with decisions affecting a long time horizon?
Which modification of the method is appropriate and well-founded to
account for the time-varying criteria values?
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Literature (1/3)
Banamar, I. & De Smet, Y. (2015). Extension of PROMETHEE method to temporal evaluations.
http://cs.ulb.ac.be/conferences/imw2015/files/slides/Banamar.pdf
Belton, V., & Stewart, T. (2002). Multiple criteria decision analysis: an integrated approach. Springer Science
& Business Media.
Berns, G. S., Laibson, D., & Loewenstein, G. (2007). Intertemporal choice–toward an integrative
framework. Trends in cognitive sciences, 11(11), 482-488.
Bertsch, V., & Fichtner, W. (2015). A participatory multi-criteria approach for power generation and
transmission planning. Annals of Operations Research, 1-31.
Brans, J. P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE
method. European journal of operational research, 24(2), 228-238.
Diakoulaki, D., & Karangelis, F. (2007). Multi-criteria decision analysis and cost–benefit analysis of
alternative scenarios for the power generation sector in Greece. Renewable and Sustainable Energy
Reviews, 11(4), 716-727.
Durbach, I. N., & Stewart, T. J. (2012). Modeling uncertainty in multi-criteria decision analysis. European
Journal of Operational Research, 223(1), 1-14.
Faulstich, M. , Beck, H.-P., Von Haaren, C., Kuck, J. &…&Yilmaz, C. (2016). Szenarien zur
Energieversorgung in Niedersachsen im Jahr 2050. Gutachten im Auftrag des Niedersächsischen
Ministeriums für Umwelt, Energie und Klimaschutz. Hannover.
Frederick, S., Loewenstein, G., & O'donoghue, T. (2002). Time discounting and time preference: A critical
review. Journal of economic literature, 40(2), 351-401.
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Literature (2/3)
Frini, A., & BenAmor, S. (2015). A TOPSIS multi-criteria multi-period approach for selecting projects in
sustainable development context. In Industrial Engineering and Operations Management (IEOM), 2015
International Conference on (pp. 1-9). IEEE.
Goumas, M., & Lygerou, V. (2000). An extension of the PROMETHEE method for decision making in fuzzy
environment: Ranking of alternative energy exploitation projects. European Journal of Operational Research,
123(3), 606-613.
Heinrich, G., Basson, L., Cohen, B., Howells, M., & Petrie, J. (2007). Ranking and selection of power
expansion alternatives for multiple objectives under uncertainty. Energy, 32(12), 2350-2369.
Keles, D., Möst, D., & Fichtner, W. (2011). The development of the German energy market until 2030—a
critical survey of selected scenarios. Energy Policy, 39(2), 812-825.
Kowalski, K., Stagl, S., Madlener, R., & Omann, I. (2009). Sustainable energy futures: Methodological
challenges in combining scenarios and participatory multi-criteria analysis. European Journal of Operational
Research, 197(3), 1063-1074.
Kronenberg, T., Martinsen, D., Pesch, T., Sander, M., Fischer, W., Hake, J. F., ... & Markewitz, P. (2011).
Energieszenarien für Deutschland: Stand der Literatur und methodische Auswertung. Energiewende-Aspekte,
Optionen, Herausforderungen, 132-166.
Mareschal, B. (2014). Dynamic MCDA with PROMETHEE and GAIA. http://www.promethee-
gaia.net/assets/dynmcda2014.pdf
Nitsch, J., Pregger, T., Naegler, T., Heide, D., de Tena, D. L., Trieb, F., ... & Trost, T. (2012).
Langfristszenarien und Strategien für den Ausbau der erneuerbaren Energien in Deutschland bei
Berücksichtigung der Entwicklung in Europa und global. Schlussbericht BMU–FKZ 03MAP146. Deutsches
Zentrum für Luft-und Raumfahrt
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Literature (3/3)
Oberschmidt, J. (2010). Multikriterielle Bewertung von Technologien zur Bereitstellung von Strom und
Wärme. Fraunhofer Verlag.
Repenning, J., Matthes, F., Blanck, R., Emele, L., Döring, U., Förster, H., ... & Jörß, W. (2015).
Klimaschutzszenario 2050. 2. Endbericht. Studie im Auftrag des BMUB. Technical report, Öko-Institut,
Fraunhofer ISI.
Ribeiro, F., Ferreira, P., & Araújo, M. (2013). Evaluating future scenarios for the power generation sector
using a multi-criteria decision analysis (MCDA) tool: the Portuguese case. Energy, 52, 126-136.
Santoyo-Castelazo, E., & Azapagic, A. (2014). Sustainability assessment of energy systems: integrating
environmental, economic and social aspects. Journal of Cleaner Production, 80, 119-138.
Prognos, EWI, GWS (2010). Energieszenarien für ein Energiekonzept der Bundesregierung. Basel, Köln,
Osnabrück.
Stewart, T. J., French, S., & Rios, J. (2013). Integrating multicriteria decision analysis and scenario
planning—Review and extension. Omega, 41(4), 679-688.
Wang, J. J., Jing, Y. Y., Zhang, C. F., & Zhao, J. H. (2009). Review on multi-criteria decision analysis aid in
sustainable energy decision-making. Renewable and Sustainable Energy Reviews, 13(9), 2263-2278.