The assessment of future options and pathways for sustainable energy systems requires considering multiple techno-economic, ecological and social issues. Multicriteria analysis methods, which are useful tools that aid decision processes involving various and even conflicting qualitative and quantitative criteria, could support such comprehensive analyses. With regard to energy policies, the key actors and stakeholders’ acceptance of emerging and innovative technologies for generating, converting and storing electricity, heat and fuels is crucial for their future implementation. The multiactor multicriteria (MAMCA) methodology was developed to involve stakeholders with vastly different views and objectives when addressing complex societal problems.
The transition from the current electricity system to a renewable electricity supply poses immense economic, technological and policy challenges [1][2]. Within this process towards a more sustainable energy system, energy scenarios can be a valuable instrument [3]. Energy scenarios are representations of possible development paths towards desired future energy system states, in order to provide guidance for decisions associated with the transition process [4][5].
Allowing citizens and companies to invest in renewable energy and thereby become independent power producers has not only advanced the population’s acceptance of renewable energy but has also accelerated the move towards a more decentralised and sustainable power supply [6][7]. Consequently, energy policies need to take divergent groups of key actors and stakeholders’ viewpoints into account. In turn, when transforming an energy system, policy-makers need to take multiple, often conflicting, criteria and stakeholder interests into account in order to identify, evaluate and, ultimately, implement possible development paths [8].
Multicriteria analysis’s (MCA) methods have been used to support decision processes involving energy scenarios and to explicitly allow for conflicting criteria (e.g., investments or emissions) [9][10][11]. In this context, these methods offer the possibility of evaluating several energy scenarios and considering path dependencies [3] but without explicitly taking different stakeholder objectives into account. See [8] for a recent overview of studies applying MCA to evaluate energy scenarios.
In this section, we present an application of the MAMCA method. The case study aims to illustrate the MAMCA method’s functionalities and to highlight this method’s distinctive contributions to decisions in the context of energy scenario planning. We do so by examining a bioenergy village in Lower Saxony, Germany [7][13]. The village’s goal is to transition to a self-sufficient power supply by expanding the capacity of its renewable energy technologies. The village has 1000 inhabitants and an electricity demand of 8021 MWh per year. The target is to satisfy at least 94% of the electricity requirement (7518 MWh/a) through renewable energy sources. The grid can provide the remaining 6% (503 MWh/a) required for peak loads. In the following, the MAMCA framework’s steps, are applied consecutively.
In this section, we present an application of the MAMCA method. The case study aims to illustrate the MAMCA method’s functionalities and to highlight this method’s distinctive contributions to decisions in the context of energy scenario planning. We do so by examining a bioenergy village in Lower Saxony, Germany [7,60]. The village’s goal is to transition to a self-sufficient power supply by expanding the capacity of its renewable energy technologies. The village has 1000 inhabitants and an electricity demand of 8021 MWh per year. The target is to satisfy at least 94% of the electricity requirement (7518 MWh/a) through renewable energy sources. The grid can provide the remaining 6% (503 MWh/a) required for peak loads. In the following, the MAMCA framework’s steps, as presented in Section 3, are applied consecutively.Unit |
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Preference Function | Preference Parameters |
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A2: |
Step 1:
There are three different renewable energy technologies available to this village. To achieve further decarbonisation and increase the village’s self-sufficiency, it can utilise solar energy, wind power and biomass fermentation [13].
There are three different renewable energy technologies available to this village. To achieve further decarbonisation and increase the village’s self-sufficiency, it can utilise solar energy, wind power and biomass fermentation [60].The photovoltaic (PV) systems can
either be built on rooftops or installed as ground-mounted systems. Based on this information, we define the decision problem’s alternatives as the different transition paths’ final states which the village should achieve within the 20-year planning horizon.Figure 1 illustrates the relevant configurations.
2 illustrates the relevant configurations.Figure 12.
Biomass and Photovoltaics |
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Step 4:
levelised costs of renewable technologies is based on the studies by Nestle and Wissel et al. [21][22]. The levelised costs of electricity drawn from the grid are calculated by using the values provided by the German Association of Energy and Water Industries [23] and supplemented by the information from the Federal Statistical Office of Germany [24].
of renewable technologies is based on the studies by Nestle and Wissel et al. [68,69]. The levelised costs of electricity drawn from the grid are calculated by using the values provided by the German Association of Energy and Water Industries [70] and supplemented by the information from the Federal Statistical Office of Germany [71]. Regarding theland use, we assume that photovoltaic-rooftop systems do not occupy any space. The other energy technologies’ specific land requirements are taken from [19]. The highest land use is required in scenario A1, in which the cultivation of crops for the biomass plant occupies larger surface area than in the other scenarios (494.95 ha/a). Consequently, the scenario that does not consider electricity from biomass (A4) occupies the least surface area.
, we assume that photovoltaic-rooftop systems do not occupy any space. The other energy technologies’ specific land requirements are taken from [66]. The highest land use is required in scenario A1, in which the cultivation of crops for the biomass plant occupies larger surface area than in the other scenarios (494.95 ha/a). Consequently, the scenario that does not consider electricity from biomass (A4) occupies the least surface area. TheCO2-emissions due to the power drawn from the grid are derived from [25], while energy from renewable sources is considered carbon neutral. A linear regression was performed based on the power grid CO
due to the power drawn from the grid are derived from [72], while energy from renewable sources is considered carbon neutral. A linear regression was performed based on the power grid CO2 -emissions between 1990 and 2017 from [25] to estimate those during the entire transition period in the presented case study.
-emissions between 1990 and 2017 from [72] to estimate those during the entire transition period in the presented case study.Owing to solar and wind energy’s volatile nature, the village’s electricity demand is sometimes not matched or even surpassed. Using standard load profiles (SLP) for households and agricultural holdings [26], we extrapolated the village’s annual energy demand. The resulting hourly demand was compared to the amount of electricity from renewable sources fed into the local distribution network [27] and used to calculate the degree of
Owing to solar and wind energy’s volatile nature, the village’s electricity demand is sometimes not matched or even surpassed. Using standard load profiles (SLP) for households and agricultural holdings [73], we extrapolated the village’s annual energy demand. The resulting hourly demand was compared to the amount of electricity from renewable sources fed into the local distribution network [74] and used to calculate the degree ofself-sufficiency
. When demand cannot be fully met, additional electricity is purchased from the grid, but when there is an oversupply, the surplus electricity is fed back into the grid. Accordingly, self-sufficiency is highest in scenarios where a biomass plant is employed. Without a biomass plant, which is able to offset the variability of the more volatile energy provision from wind and photovoltaics, the lowest percentage of self-sufficiency is reached (13%), as seen in scenario A4. Regarding the qualitative criteriaimage
andlandscape aesthetics
, and given this case study’s illustrative purpose, we assigned exemplary scores. For the complete performance matrix and each of the actors’ scores, see .Table 1.
Stakeholder | Criteria | Unit | A1: Status quo |
A2: Biomass and Photovoltaics |
A3: Biomass and Wind Turbine |
A4: Wind Turbine and Photovoltaics |
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A3: | ||||||
Biomass and Wind Turbine | A4: | Wind Turbine and Photovoltaics | ||||
Inhabitants | Levelised costs of electricity | [Euro/kWh] | 0.1134 | 0.1185 | 0.1003 | 0.1117 |
0–29 | Land use | [ha/a] | 494.95 |
Status quo
alternative (A1):
Biomass and
photovoltaics (A2):
Biomass
and wind turbine (A3):
Wind turbine
and photovoltaics (A4):
Step 2:
Now that the alternatives have been defined, the stakeholders need to be identified and characterised. In our case study, we consider hypothetical village inhabitants and a group of experts and academics to be stakeholders. The village inhabitants are split into three demographic age groups, namely those who are 29 or younger, those between 30 and 50 years of age and those inhabitants older than 51 in order to testify the algorithm. We chose this group configuration, since we regard the inhabitants’ diverging goals and criteria as closely tied to their age. However, this does not necessarily imply that the relevant groups are fully homogeneous or that there are no intersecting opinions between them. We include the expert and academic group to foster mutual learning for all stakeholders by integrating the local inhabitants’ interests, values and beliefs with the technical experts’ knowledge through consulting and exchanging of information, as [14][15][16][17] advise.
Now that the alternatives have been defined, the stakeholders need to be identified and characterised. In our case study, we consider hypothetical village inhabitants and a group of experts and academics to be stakeholders. The village inhabitants are split into three demographic age groups, namely those who are 29 or younger, those between 30 and 50 years of age and those inhabitants older than 51 in order to testify the algorithm. We chose this group configuration, since we regard the inhabitants’ diverging goals and criteria as closely tied to their age. However, this does not necessarily imply that the relevant groups are fully homogeneous or that there are no intersecting opinions between them. We include the expert and academic group to foster mutual learning for all stakeholders by integrating the local inhabitants’ interests, values and beliefs with the technical experts’ knowledge through consulting and exchanging of information, as [61,62,63,64] advise.Step 3:
Figure 2 and reflect their slightly diverging interests. While some criteria are universal across all stakeholder groups, some of the criteria are either exclusive to a certain group or of different importance.
3 and reflect their slightly diverging interests. While some criteria are universal across all stakeholder groups, some of the criteria are either exclusive to a certain group or of different importance.Figure 23.
The overall set of criteria assesses the alternatives with regard to their environmental, economic, social and technical aspects, finding that they are adequate [18]. The criteria are defined and measured as follows:
The overall set of criteria assesses the alternatives with regard to their environmental, economic, social and technical aspects, finding that they are adequate [65]. The criteria are defined and measured as follows:Levelised costs of electricity | Min | ||||||||||||
Inhabitants 0–29 | ϕ+1(aj | [Euro/kWh] | ) | 0.3532Type III: Linear | 0.1005pil = 0.0812 | ||||||||
0.3379 | 0.3225 | 377.63 | 384.11 | 23.12 | |||||||||
Land use | Min | [ha/a] | |||||||||||
Type III: Linear | ϕ−1(aj) | p | i | 0.3055l = 471.83 | |||||||||
0.3489 | 0.1848 | 0.2708 | CO2-emissions | [t/a] | 1638.83 | 1952.40 | 1952.40 | 2074.78 | |||||
CO2-emissions | Min | [t/a] | Type III: Linear | pil = 435.95 | |||||||||
ϕnet1(aj) | 0.04767 | −0.2483 | 0.1489 | 0.05170 | Image | [points] | Self-sufficiency | Max2.00 | 5.00 | 4.00 | 8.00 | ||
Inhabitants 30–50 | ϕ+2(a | [%] | j | Type III: Linear | ) | p | 0.43328il = 6 | ||||||
0.2783 | 0.4065 | 0.1306 | Inhabitants | Levelised costs of electricity | [Euro/kWh] | 0.1134 | 0.1185 | 0.1003 | 0.1117 | ||||
Image | Max | [points] | Type II: Quasi | ||||||||||
ϕ−2(aj) | p | i | l | 0.13077 = 6, | 0.2431qil = 1.2 | ||||||||
0.3008 | 0.5740 | 30–50 | Land use | [ha/a] | 494.95 | 377.63 | 384.11 | 23.12 | |||||
Landscape aesthetics | Max | [points] | Type II: Quasi | ||||||||||
ϕnet2(aj) | p | i | l = 6, qil = 1.2 | Landscape aesthetics | [points] | 7.00 | 8.00 | 4.00 | 1.00 | ||||
0.3025 | 0.0353 | 0.1057 | −0.4435 | ||||||||||
Inhabitants 51 or older | ϕ+3(aj) | 0.2500 | 0.1154 | 0.3010 | 0.4086 | CO2-emissions | [t/a] | 1638.83 | 1952.40 | 1952.40 | 2074.78 | ||
ϕ−3(aj) | 0.2767 | 0.2969 | 0.1575 | 0.3438 | Self-sufficiency | [%] | 19 | ||||||
17 | 18 | 13 | |||||||||||
ϕ | net3(aj) | −0.0268 | −0.1816 | 0.1435 | 0.0648 | Inhabitants 51 | Levelised costs of electricity | [Euro/kWh] | |||||
Experts and academics | ϕ+4 | 0.1134 | (aj) | 0.3516 | 0.10080.1185 | 0.1003 | 0.1117 | ||||||
0.3218 | 0.2486 | or older | Land use | [ha/a] | |||||||||
494.95 | ϕ−4(aj) | 377.63 | 384.11 | 23.12 | |||||||||
0.1914 | 0.3020 | 0.1387 | 0.3907 | CO2-emissions | [t/a] | 1638.83 | 1952.40 | 1952.40 | |||||
ϕnet | 2074.78 | ||||||||||||
4 | (aj) | 0.1602 | −0.2012 | Self-sufficiency | [%] | 19 | 17 | 18 | 13 | ||||
0.1831 | −0.1420 | Experts and | Levelised costs of electricity | [Euro/kWh] | 0.1134 | 0.1185 | 0.1003 | 0.1117 | |||||
academics | Land use | [ha/a] | 494.95 | 377.63 | 384.11 | 23.12 | |||||||
CO2-emissions | [t/a] | 1638.83 | 1952.40 | 1952.40 | 2074.78 | ||||||||
Self-sufficiency | [%] | 18.67 | 16.79 | 17.57 | 13.08 |
Step 5:
We chose PROMETHEE for this case study to aggregate the scores and determine the alternatives’ ranking within the different stakeholder groups.
We chose PROMETHEE for this case study to aggregate the scores and determine the alternatives’ ranking within the different stakeholder groups, as described in Section 3. To model the stakeholders’ preferences according to PROMETHEE, we assigned, as shown inTable 2, one of the six generic types of preference functions that PROMETHEE provides [28] to each criterion. With respect to the quantitative criteria, we chose the Type III linear preference function, in which the preference increases linearly until the deviation in the performance scores between two alternatives equals the strict preference threshold. The preferences regarding the qualitative criteria
, one of the six generic types of preference functions that PROMETHEE provides [75] to each criterion. With respect to the quantitative criteria, we chose the Type III linear preference function, in which the preference increases linearly until the deviation in the performance scores between two alternatives equals the strict preference threshold. The preferences regarding the qualitative criteriaimage
andlandscape aesthetics
were modelled using the Type II preference function, in which the preference only prevails if the deviation in the performance scores surpasses the indifference thresholdq
. For a further description of the six types of preference functions available in PROMETHEE, see . For illustrative purposes, the preference modelling in this case study universally applies for all stakeholders, whereas for practical applications, a separate determination for each stakeholder might be advisable.Table 2.
Criteria | Orientation |
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We follow [29] when calculating the preference thresholds and estimated
We follow [76] when calculating the preference thresholds and estimatedpil
as the maximum difference between performance scores of all alternatives for each criterion andqil
as 20% of this value. Aggregating the data according to PROMETHEE yields numerical results as shown in as a basis for the calculation of the MAMCA overall flows. Since the crucial part in the MAMCA methodology, just as in other multicriteria decision support methods, is not the generation of hard numerical values but to provide the decision maker with useful information to derive an informed decision, further processing and evaluation of these outranking flows is required.Table 3.
Alternative aj | |||||
---|---|---|---|---|---|
Stakeholder Group sl | PROMETHEE Flows | A1: Status quo |
Steps 6 and 7:
Figure 3 to engage an extensive evaluation. Across all the stakeholders, scenario A3, which utilises a biomass plant in conjunction with wind turbines, performs best, closely followed by the status quo alternative (A1). The combined supply of electricity from biomass and from photovoltaics (A2) performs worst across all the stakeholders, except for the group of inhabitants between 30 and 50. This stakeholder group rates wind turbines and photovoltaic systems’ deployment (A4) noticeably worse than the other groups, given a PROMETHEE net flow of −0.4435. Using PROMETHEE’s unicriterion net flows, we conducted an intrastakeholder analysis to examine the reasons for this negative assessment.
4 to engage an extensive evaluation. Across all the stakeholders, scenario A3, which utilises a biomass plant in conjunction with wind turbines, performs best, closely followed by the status quo alternative (A1). The combined supply of electricity from biomass and from photovoltaics (A2) performs worst across all the stakeholders, except for the group of inhabitants between 30 and 50. This stakeholder group rates wind turbines and photovoltaic systems’ deployment (A4) noticeably worse than the other groups, given a PROMETHEE net flow of −0.4435. Using PROMETHEE’s unicriterion net flows, we conducted an intrastakeholder analysis to examine the reasons for this negative assessment.Figure 4 depicts this intrastakeholder ranking of the group of 30 to 50-year-old inhabitants’ alternatives and reveals how each alternative performs in terms of this group’s criteria set. It is clear that the use of wind power and solar panels is evaluated remarkably negatively in terms of the criterion
5 depicts this intrastakeholder ranking of the group of 30 to 50-year-old inhabitants’ alternatives and reveals how each alternative performs in terms of this group’s criteria set. It is clear that the use of wind power and solar panels is evaluated remarkably negatively in terms of the criterionlandscape aesthetics
, therefore possibly requiring a sensitivity analysis of this criterion’s weights. On the other hand, this knowledge is also a valuable starting point for the communication process with this group of inhabitants and allows the design and implementation of measures that specifically address the landscape aesthetics. The configuration of energy scenario A4 could also be slightly modified or another iteration of the analysis could be undertaken.Figure 34.
Figure 45.