Transparency in Energy Scenario Studies: Survey of Different

Approaches Combining Scenario Planning, Energy System Analysis,

and Multi-criteria Analysis

Tobias Witt

Chair of Production and Logistics, Georg-August-Universität Göttingen,

Platz der Göttinger Sieben 3, 37073 Göttingen, Germany

Keywords: Energy Scenarios, Transparency, External Uncertainty, Scenario Planning, Energy System Analysis,

Multi-criteria Analysis.

Abstract: The transition of today’s energy supply systems to renewable energy technologies requires planning processes

that are usually supported by energy scenario studies. If scenario planning, energy system analysis, and multi-

criteria analysis are combined in the design of such energy scenario studies, two possible method

combinations can be identified in the literature. In this paper, these method combinations are discussed with

regard to transparency and communication of uncertainties, which are basic requirements for energy scenarios.

Finally, a clear specification of the intended purpose and of the method commendation is recommended to

improve transparency in energy scenario studies and avoid over-interpretation by decision makers.

1 INTRODUCTION

The transition of today’s nuclear and fossil-fueled

energy supply systems to renewable energy

technologies poses a major challenge for the 21

century. For example, the European Commission

(2018) proposed a strategy to reach an economy with

net-zero GHG emissions until 2050, mainly based on

renewable energy technologies. To investigate how to

achieve the transition to a competitive, sustainable,

and secure energy supply, energy system analysis

helps to support decision-making with quantitative

data (Möst & Fichtner, 2009). The results of such

system analyses are usually published in energy

scenario studies. One key requirement for these

studies is that they are transparent, i.e., that all

necessary information which is needed to

comprehend and potentially replicate the study is

adequately published (Cao et al., 2016).

Given the long-term planning perspective, it is

important to consider uncertainties in planning

processes. Scenario planning has long been used to

support decision making under uncertainty

(Schoemaker, 1995; van der Heijden, 2009). While

not strictly required in (energy) system analysis,

https://orcid.org/0000-0002-0111-4216

scenario planning is often combined with system

analysis to support quantitative analyses with

qualitative stories. Usually, these stories are

conveyed more easily than quantitative analyses and

can be used to foster discussions among relevant

decision makers and stakeholders (Alcamo, 2008),

one objective being to support consensus among

stakeholders.

As the objective of energy scenario studies is to

identify suitable, sustainable energy supply systems,

evaluating the suitability of future options is a core

task in energy scenario studies. In quantitative

techno-economic analyses such as energy scenario

studies, sustainability is usually operationalized with

technical, economic, social, and environmental

criteria (Antunes & Henriques, 2016). The

performance of a particular alternative in terms of a

particular criterion is called performance score.

However, given possible alternative system

configurations, their performance scores can be at

least partially conflicting; criteria are usually

measured with incommensurable units; and different

stakeholders may weigh them differently.

Therefore, identifying the best transition pathway

towards a sustainable energy supply is challenging

and calls for integration of a problem structuring

114

Witt, T.

Transparency in Energy Scenario Studies: Survey of Different Approaches Combining Scenario Planning, Energy System Analysis, and Multi-criteria Analysis.

DOI: 10.5220/0009464601140121

In Proceedings of the 9th Inter national Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2020), pages 114-121

ISBN: 978-989-758-418-3

method (Antunes & Henriques, 2016; Grunwald et

al., 2016). Moreover, as strategic long-term decisions

with immense investments need to be made, decisions

should be well informed and transparent to increase

their acceptance (Dieckhoff et al., 2014). Methods

from multi-criteria analysis can be used to support

decisions given this complex background (Antunes &

Henriques, 2016).

Scenario planning and multi-criteria analysis can

complement energy system analysis in the

development and evaluation of energy scenarios

(Witt et al., 2020). The objective of the method

combination is to improve an energy scenario study’s

transparency regarding the consideration of

uncertainties during scenario construction and

evaluation. However, two possible method

combinations can be identified in the literature. In

particular, they differ in the consideration and

communication of external uncertainties, leading to

different levels of transparency. In this paper, these

approaches are described and discussed regarding

transparency.

The paper is structured as follows: In Section 2,

different interpretations of the term scenario within

the three methods are delineated. In Section 3, the two

approaches for combining the methods are identified,

based on the literature. In Section 4, advantages and

disadvantages of the methods with regard to the

different requirements for energy scenarios are

discussed. Finally, the paper is concluded with a short

summary and outlook.

2 THE MEANING OF

“SCENARIO” IN DIFFERENT

METHODS

In the following, different interpretations of the term

scenario are introduced in the contexts of scenario

planning, energy system analysis, and multi-criteria

analysis. Because these interpretations are intertwined

with the roles of decision makers and external

uncertainties, both are paid special attention to.

2.1 Scenario Planning

In scenario planning, scenarios are consistent

descriptions of future states and/or developments

(Grunwald et al., 2016; van der Heijden, 2009). Those

scenario planning techniques that have been

developed specifically for the application in corporate

planning, e.g., by Gausemeier et al. (1998) or van der

Heijden (2009), additionally include the perspective

of one or more decision makers. (For simplicity, the

singular of the word decision maker is used from now

on.)

In those approaches, both a decision and scenario

field need to be defined. In the decision field, a

decision maker has the authority over necessary

resources so that she or he can decide upon the future

developments, which is why Gausemeier et al. (1998)

call these developments influenceable. For example,

for a decision, which technologies should be used to

provide heat and power in a bioenergy village (Lerche

et al., 2017), the range of available technologies,

including biogas power plants, wind energy plants, or

PV systems, constitutes the decision field. In contrast,

the scenario field consists of all developments that are

investigated in a scenario. The scenario field can

include a decision field, but does necessarily have to.

Usually, non-influenceable developments, so-called

external uncertainties, are also included in scenarios.

Based on the delineation of decision and scenario

fields, three types of scenarios can be identified (see

Figure 1): internal scenario, external scenario, and

system scenario. Gausemeier et al. (1998) note that

system scenarios are “easy to create but different to

deal with”, because they are only influenceable in

parts and alternate between actions and side

conditions. The choice of the type of scenario

depends on the requirements and objectives of the

case-specific problem.

Figure 1: Scenario classification, based on Gausemeier et

al. (1998).

2.2 Energy System Analysis

In energy system analysis, a scenario is represented

by a set of assumptions (Grunwald et al., 2016; Möst

& Fichtner, 2009). “Calculating a scenario” means

that the model calculates results for the endogenous

variables, based on the input of a set of exogenous

Internal scenario System Scenario

No Scenario External Scenario

Yes

Is a decision

field included

in the scenario

field?

Are external uncertainties considered in the

scenario field?

Transparency in Energy Scenario Studies: Survey of Different Approaches Combining Scenario Planning, Energy System Analysis, and

Multi-criteria Analysis

115

variables – in other words, based on a scenario. From

a modelling perspective, the perspective of a decision

maker is irrelevant for this calculation, which is

explained in the following.

Endogenous variables are also called “decision

variables” of a model. This means that, e.g., an

optimization model yields values that these variables

need to assume to optimize the solution for a

particular objective function. These variables usually

correspond to factors that a decision maker can

influence, but do not necessarily have to. For

example, energy scenario studies usually have at least

national scope, i.e., the energy system of a whole

country is modeled (Witt et al., 2018). These studies

typically include bottom-up or top-down

optimization models minimizing system costs (Keles

et al., 2011). Due to the minimization of system costs,

the model determines optimal investment and unit

commitment decisions from a system perspective.

However, there is no single decision maker who can

implement all these investment and unit commitment

decisions, because, for example, the operation of

energy supply facilities (power plants, solar panels,

grid) is distributed across many different actors. Even

if all actors were to act according to the same rational

reasoning, information asymmetries and attempts to

maximize personal gains can lead to decisions of

individuals that diverge from the global optimum, i.e.,

system-optimal decisions.

From a mathematical point of view, it is therefore

irrelevant whether (endogenous or exogenous)

variables model developments, which are

influenceable by a particular decision maker or not.

Thus, energy system analysis can be used to model

internal scenarios, external scenarios, or system

scenarios. In the modeling process, the analyst needs

to pay special attention to the question, which

variables constitute a scenario, because this affects

the implications that can be drawn for a particular

decision maker.

2.3 Multi-criteria Analysis

In multi-criteria analysis, a scenario consists of all

developments that cannot be influenced by a decision

maker (Stewart et al., 2013). Such a scenario is an

external scenario. The term for an option that a

decision maker can implement is alternative, which

corresponds to an internal scenario. External

scenarios can be used to investigate the effects of

uncertain developments on the performance scores of

given alternatives. To that end, during the problem-

structuring phase of multi-criteria analysis,

influenceable developments, leading to a selection of

alternatives, and non-influenceable developments,

leading to a selection of scenarios, are identified in an

iterative process (Belton & Stewart, 2003). To

quantify the performance scores, different methods of

consequence modeling can be applied, also including

(energy) system analysis (Witt et al., 2020).

In the sense of scenario planning, assumptions

regarding the future should be internally consistent

(Götze, 1993; Kosow, 2015), so that the assumptions

regarding alternatives and scenarios are non-

contradictory. Therefore, system scenarios seem to be

very suitable for developing scenarios and fitting

alternatives in a common process.

3 METHOD COMBINATIONS

WITH DIFFERENT SCENARIO

PURPOSES

In the literature, two approaches for combining the

abovementioned methods can be identified. These

differ regarding the objective of the corresponding

energy scenario studies: Scenarios providing general

orientation and scenarios for specific decisions.

3.1 Orientation Scenarios

In this approach, the methods are combined with the

objective to create and evaluate scenarios. The terms

alternative and scenario are used synonymously.

(This is contradictory to the approach described in

Section 2.3.) Therefore, influenceable and non-

influenceable developments are not separated during

scenario creation and evaluation. Thus, scenario

planning is applied to systematically identify possible

future states or developments that are to be evaluated.

For example, Madlener et al. (2007) apply scenario

planning to identify a set of possible scenarios

representing combinations of key factors. The

consequences of these scenarios are quantified with

energy system analysis and finally evaluated with

multi-criteria analysis. Studies that use this concept

include Bertsch & Fichtner (2016), Browne et al.

(2010), Diakoulaki & Karangelis (2007), Jovanović

et al. (2009), Kowalski et al. (2009), McDowall &

Eames (2007), McKenna et al. (2018), Oberschmidt

et al. (2010), Trutnevyte et al. (2011), and Volkart et

al. (2017). An excerpt from an exemplary decision

table in Bertsch & Fichtner (2016) with two criteria

and three alternatives/scenarios is shown in Table 1.

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116

Table 1: Exemplary decision table for orientation scenarios,

excerpt from Bertsch & Fichtner (2016).

Scenarios (S) /

Alternatives (A)

S/A1 S/A2 S/A3

Total expenses of

electricity supply

(in Billion EUR)

182 200 224

-emissions

(in Million t CO

/y)

204 201 158

On the one hand, evaluating such a decision table

can help to identify a desirable alternative/scenario.

Identifying an image of a desirable future corresponds

to one objective of scenario planning, namely shaping

the future (Götze, 1993). For example, such an

analysis can be used to investigate energy policy

targets related to the capacity expansion of renewable

energy technologies, so that a cost-minimal

expansion complying with GHG-reduction targets

can be found. Based on that analysis, energy policy

targets can be set accordingly.

On the other hand, this approach strongly suggests

that there actually is a choice between alternatives/

scenarios. This implies that the developed scenarios

are internal scenarios. Uncertain, external factors are

not considered. In the context of energy system

planning, a decision maker simply cannot stipulate all

future developments, due to the complexity of the

energy system, the long-term planning perspective,

and the limit to (geographical) system boundaries. To

exaggerate, a decision maker would always decide for

the best-case scenario, e.g., “successful energy

transition”, in which all performance scores develop

in the best possible way, resulting in the best

evaluation of said scenario. Thus, the authors of such

studies need to make clear that the developed

scenarios should not be interpreted as options for

choice, and thus avoid over-interpretation by a

decision maker. Rather, these scenarios can provide

orientation and loose guidelines, instead of options

for immediate implementation. Finally, eliciting crite-

ria weights for a multi-criteria analysis is complicated

in this approach, but can be supported with stake-

holder analysis (see, e.g., Steinhilber et al. (2016)).

3.2 Decision Scenarios

In this approach, the methods are combined with the

objective to create and evaluate alternatives under

different scenarios, in order to make robust decisions

(Schwarz et al., 2019; Witt et al., 2020). As a

precondition, the decision maker of a decision

problem needs to be known. Based on the decision

maker’s decision power, scenarios and alternatives

can be separated explicitly. (This corresponds to the

idea described in Section 2.3).

In the first step of this approach, system scenarios

are developed to ensure consistency of all

assumptions (Götze, 1993; Kosow, 2015). After

quantifying these assumptions, they can be used as

input for model calculations. To that end, the

assumptions are classified and separated. There are

(1) general parameters that are constant over all

scenarios and alternatives; (2) scenario-specific

parameters that vary for each scenario; (3)

alternative-specific parameters that vary for each

alternative. The parameters are specified successively

so that general parameters are quantified first. These

limit the possible range for scenario- and alternative-

specific parameters. After that, scenario-specific

parameters are quantified, further limiting the

possible range for alternatives. Finally, alternative-

specific parameters are quantified for each scenario.

The parameter classification also determines the

number of model runs required in an energy scenario

study, because each combination of scenario and alter-

native needs to be calculated with the energy system

model and evaluated with multi-criteria analysis (Witt

et al., 2019). For example, given two scenarios and

three alternatives, six model runs are needed to

quantify the effects of all scenarios on all alternatives.

An exemplary decision table with three criteria, three

alternatives and two scenarios is shown in Table 2.

Table 2: Exemplary decision table for decision scenarios, excerpt from Witt et al. (2020).

Scenarios (S)

Alterna-

tives (A)

Criteria

S1 S2

A1 A2 A3 A1 A2 A3

-emissions

(in kg CO

-eq/MWh)

90.88 84.80 84.56 65.83 65.34 66.60

ricultural Land Occupation

(in m

/MWh)

5.46 4.96 5.14 5.44 5.39 5.48

Costs of electricit

production and

rid expansion

(in €/MWh)

69.38 68.10 67.87 34.26 27.24 27.91

Transparency in Energy Scenario Studies: Survey of Different Approaches Combining Scenario Planning, Energy System Analysis, and

Multi-criteria Analysis

117

By evaluating this decision table, the effects of

external effects on the performance of alternatives

can be investigated. For example, a loss-minimizing

strategy would be to identify alternatives that perform

relatively well in all scenarios (Dieckhoff et al., 2014;

Götze, 1993; Porter, 1983). Notably, uncertain,

external developments are made explicit in this

approach. In combination with a multi-criteria

analysis, in which preferences of different actors can

be considered, implications and recommendations

can be derived in a transparent way.

4 DISCUSSION

According to Grunwald et al. (2016), energy scenario

studies need to meet three basic requirements:

scientific validity, transparency, and unbiasedness. In

this paper, I focus on transparency, because it is a

cornerstone to achieve the two other requirements.

Transparency can be viewed as a substitute for

involvement in scenario development and evaluation.

In the context of energy scenario studies,

transparency means that all necessary information

that is needed to comprehend and potentially replicate

the study is adequately published (Cao et al., 2016).

To that end, the recipient should be able to access the

used models, data, and further assumptions.

Grunwald et al. (2016) note that it is particularly

important to point out very clearly any uncertainties

in an analysis, as well as their consequences for the

results and conclusions. Furthermore, conclusions

should be drawn in a transparent way. Finally,

addressee-specific documentation can enhance the

transparency of energy scenario studies.

Some notes on transparency: First, the concept of

transparency is subjective. A documentation can be

transparent for some recipients, but intransparent for

others, because the technical expertise and skills of

the recipient are relevant for understanding complex,

(model-based) energy scenario studies (Baecker,

2010). Second, an excessive, confusing supply of

information is the opposite of transparency. A

recipient needs to select the relevant parts from all

available information. Therefore, supplying too much

information can also be counter-productive if one

wants to achieve a transparent documentation.

According to Grunwald et al. (2016), many

energy scenario studies lack transparency and

adequate communication of uncertainties. Two of

their suggestions to increase transparency are: (1)

development of methods to integrate diverging

interests and (2) integration and increased use of

methods for the systematic analysis of uncertainties.

Both method combinations (described in Sections

3.1 and 3.2) allow for the integration of diverging

interests in the evaluation of energy scenarios. For

example, different interests can be considered during

scenario creation in a scenario planning process. In

addition, stakeholders’ interests can be made explicit

in the weighting factors used in the multi-criteria

evaluation.

However, regarding the communication of

uncertainties, I argue that the presented approaches

differ considerably. The approach based on

orientation scenarios is suitable if no particular

decision makers are involved, i.e., if no specific

decision is to be supported. The objective is to

identify desirable future states that are relevant for a

problem and foster discussion about them. For

example, the potential effects of certain energy policy

measures (represented by alternatives/scenarios) can

be determined and desirable or non-desirable

developments can be identified (Dieckhoff et al.,

2014). In general, this approach is less suitable for

supporting specific decisions, because uncertainties

that are relevant for specific decision makers are not

identified and their effects are not modeled explicitly.

This approach leaves untapped a powerful potential

of scenario planning, namely sensitizing decision

makers to effects of external uncertainties (Stewart et

al., 2013). Special care is required by decision makers

when they interpret the results.

The approach based on decision scenarios is

suitable if the perspectives of specific decision

makers need to be included. It allows including and

analyzing the effects of external uncertainties on the

performance scores of decision makers’ alternatives.

Thereby, an alternative can be recommended with a

transparent procedure that also considers different

developments of external factors (Dieckhoff et al.,

2014). This approach focuses on problem structuring,

so that decision makers and analysts are forced to

consider, which different alternatives and

uncertainties are relevant for and should be

quantitatively modeled in the decision problem.

Thereby, underlying assumptions that would

otherwise be unspoken can be discussed, which

allows decision makers and analysts to achieve a

deeper understanding of the decision problem. This

deeper understanding is presumed to lead to better

decision-making (Götze, 1993), in addition to the

quantitative results of a multi-criteria analysis of

alternatives.

To improve the transparency, authors of energy

scenario studies combining scenario planning, energy

system analysis, and multi-criteria analysis, should

therefore make very clear, which purpose their

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118

method combination fulfills: providing general

orientation or providing decision support for a

specific decision with known decision makers. I

argue that this can limit the unintended effects of

over-interpretation of energy scenario studies. While

an approach based on orientation scenarios can

support a discussion of possible futures and their

desirability, an approach based on decision scenarios

can support specific decision makers’ choices

between options for immediate implementation in

uncertain environments.

However, an approach with decision scenarios

requires more effort for decision support, because, in

general, more parameter quantifications and energy

system model runs are needed (Witt et al., 2019).

Finally, a multi-criteria evaluation of alternatives

under different scenarios proves to be challenging to

be interpreted by decision makers (Durbach &

Stewart, 2020; Marttunen et al., 2017). This also

stresses the need to communicate clearly, which

implications can or cannot be drawn from a multi-

criteria analysis, based on an analysis of system

scenarios and specific decision makers’ preferences.

5 CONCLUSION

In this paper, two different approaches for combining

scenario planning, energy system analysis, and multi-

criteria analysis have been investigated, one based on

orientation scenarios, the other based on decision

scenarios. Their impact on the transparency and

communication of uncertainties in energy scenario

studies has been investigated. I argue that authors of

energy scenario studies should make very clear the

purpose of their method combination. This should

increase not only the transparency of energy scenario

studies that are based on these methods, but also

increase acceptance of the implications and

recommendations drawn from them by the relevant

stakeholders. Increased acceptance may make it

easier to implement measures to reach energy policy

goals and thereby foster the transition to sustainable

energy supply systems.

Commissioning institutions of energy scenarios

need to clarify energy scenario studies’ objectives,

decision makers, stakeholders, the consideration of

uncertainties and other desired features of the

methodology in their tenders (Grunwald et al., 2016).

Additionally, a short summary of a study’s features

would be helpful for transparent documentation. For

example, the morphological analysis provided in Witt

et al. (2018) could be extended by different methods

(energy system analysis, scenario planning, multi-

criteria analysis) and their corresponding scenario

purposes to provide an overview of studies’ key

features.

ACKNOWLEDGEMENTS

Discussions with my colleagues on the research

project NEDS – Nachhaltige Energieversorgung

Niedersachsen (www.neds-niedersachsen.de),

especially Marcel Dumeier and Jutta Geldermann, are

gratefully acknowledged. The views expressed in this

paper are entirely those of the author, however.

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