Bottom-up ex-post multi-criteria analysis of energy systems on the supply and end-use

For bottom-up ex-post multi-criteria sustainability analysis of energy systems on the technology level, the sustainability indicators are quantified on the resource, conversion and end-use tech-nology levels as depicted in Figure 8 instead of only the end-use techtech-nology level. A set of sus-tainability indicators is quantified for each technology and aggregated to total indicator values for each scenario. These total indicator values are compared with or without full MCDA.

Figure 8: Illustration of the bottom-up quantification of sustainability indicators on the supply and end-use technology levels based on a simplified reference energy system from [18]

2.4. Bottom-up ex-post multi-criteria analysis of energy systems on the supply and end-use technology levels ______________________________________________________________________________________________________________

19 2.4.1. Formalisation of the combined method

The process of performing a bottom-up ex-post multi-criteria sustainability analysis of energy systems on both the supply and end-use technology levels can be broken down into the follow-ing four steps (Figure 9), which are described in more detail in the subsequent paragraphs:

1) Scenario description

2) Technology data selection, criteria definition and specific indicator quantification for each supply and end-use technology

3) Scenario quantification based on cost minimisation

4) Total indicator value quantification per scenario, possible calculation of MCDA results, and interpretation of results

Figure 9: Illustration of the methodological steps of the bottom-up multi-criteria sustainability analysis of energy systems on the supply and end-use technology levels. The result calculation with MCDA is indicated by dashed lines.

As a first step, the energy system scenarios are developed. Together with technology and policy assumptions, the derived energy service demands are implemented into the energy system model. In the second step, the sustainability criteria are defined and corresponding specific in-dicator values are quantified. As the inin-dicator values are quantified on the technology level, they can be integrated in the energy system model framework using its existing features for the

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quantification of environmental indicators (Section 2.1). Depending on the features of the ap-plied energy system modelling framework, it is possible to specify the impacts of each energy system technology per activity, investment and capacity.

The scenarios are quantified using the energy system model in the third step, taking into ac-count the corresponding policies and technological constraints. This step includes the quantifi-cation of the sustainability indicators for each supply and end-use technology (e.g. the air pollu-tant emissions of a technology). In the fourth step, the total indicator values are extracted from the energy system modelling results. Further sustainability indicators can be drawn from the techno-economic results of the energy system model itself (e.g. investment costs and energy carrier imports). In addition to the trade-off analysis of the total indicator values, it is also pos-sible to carry out a full MCDA. In doing so, the total indicator values per scenario are normalised and weighted and the scenarios are ranked by aggregating the normalised and weighted total indicator values. This step is only possible for the comparison of scenario variants, i.e. scenarios with the same energy service demands but different policy and technology assumptions (Sec-tion 2.3.2.4).

2.4.2. Discussion of the combined method

2.4.2.1. Avoiding double-counting impacts with LCA indicators

As described in Section 2.3.2.1, bottom-up quantification of LCA indicators for energy system technologies leads to double-counting (parts of) the impacts of the energy inputs if standard LCA calculation schemes are applied. Therefore, an approach is proposed, which avoids double-counting and which can be divided into the following steps:

1) Matching energy system technologies with their corresponding Life-Cycle Inventory (LCI) datasets

2) Subdividing LCI datasets according to the life-cycle phases

3) Constructing a background LCI database without the energy system of the considered region(s)

4) Calculating the cumulative LCI and conducting Life-Cycle Impact Assessment (LCIA) (if required)

As the first step for the calculation of the LCA-based indicators, one LCI dataset is allocated to each technology in the energy system model. The LCI dataset matches the energy system model

2.4. Bottom-up ex-post multi-criteria analysis of energy systems on the supply and end-use technology levels ______________________________________________________________________________________________________________

21 technology as closely as possible regarding technical, geographical and temporal characteristics.

In the second step, the selected LCI datasets are subdivided into the upstream input from tech-nosphere on the one hand and the residual techtech-nosphere inputs and biosphere flows on the other hand (Figure 10a). The upstream contribution is removed from the LCI dataset in this bottom-up approach, as the impacts of the upstream energy chain are represented by the other processes in the energy system model. For example for hard coal power generation this means that impacts resulting from hard coal extraction and transport are separated.

Figure 10: Illustration of the disaggregation of LCI datasets for the bottom-up quantification of LCA-based indicators

If it is of interest for the study, the modified LCI dataset can be subdivided into upstream, opera-tion and infrastructure contribuopera-tions (Figure 10b). Impacts from operaopera-tion occur whenever the energy technology is used, while the infrastructure impacts occur whenever there is investment in the respective energy technology. For example in the case of hard coal power generation the infrastructure contribution includes the land use for the power plant, contributions from the

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materials used to build the plant, and residues from decommissioning, while the operation con-tribution consists of natural inputs, smokestack emissions, ash production as well as additional processes and materials used for power generation. The operation and infrastructure impacts can be further subdivided into direct impacts that occur on-site and indirect impacts that occur elsewhere (Figure 10c). The natural inputs and emissions from operation produce direct im-pacts, while materials and wastes produce indirect impacts from operation. The direct impacts related to infrastructure include land use as well as emissions related to construction. The indi-rect impacts of infrastructure include the materials and waste from constructing and decom-missioning the power plant.

The third step represents the preparation of the background LCI database so that double-counting the impacts from the energy system is avoided, i.e. the technosphere matrix A is modi-fied so that it excludes the contributions for the energy system(s) of the region(s) under consid-eration. All energy inputs to all activities in the technosphere matrix are set to zero as illustrat-ed in Figure 11. These energy inputs include industrial electricity and heat generation, freight transport and feedstocks, which are – as opposed to residential and commercial energy, passen-ger car transport and non-commercial biomass – potential inputs to the construction and opera-tion of energy system technologies.

The general LCA formula (Section 2.3.2.1, Box 1) for the calculation of the cumulative environ-mental impacts h as the fourth step is thus changed to:

ℎ′ = 𝐵𝐴^′−1𝑓

where A’ is the modified technosphere matrix, B is the biosphere matrix, f is the functional unit and h’ is the corresponding vector of cumulative environmental impacts. The LCA-based indica-tors can then be implemented in the energy system model according to Section 2.1.

2.4.2.2. Uncertainties in the indicator quantification

The energy system technologies and sustainability indicators are modelled with data from dif-ferent sources. This can lead to uncertainties in the indicator quantification due to deviations in the underlying assumptions. While the energy system model includes techno-economic data for all future time periods and regions under consideration, the corresponding information for oth-er sustainability indicators may be rough regarding the required regional and temporal

resolu-2.4. Bottom-up ex-post multi-criteria analysis of energy systems on the supply and end-use technology levels ______________________________________________________________________________________________________________

23 tion. This also includes data from background databases. If projections are required, further uncertainties are introduced.

Figure 11: Illustration of the modified technosphere matrix A’ in the background LCI database

2.4.2.3. Regional allocation of impacts

This combined method allocates the impacts from a production perspective: i.e. all impacts are allocated to the technology and region that satisfies the energy service demand, independent of which region causes this demand. The life-cycle and energy chain impacts are allocated to the region in which the direct (on-site) impacts occur. This is analogous to the discussion in Section 2.3.2.3.

2.4.2.4. Literature review

In addition to the bottom-up ex-post multi-criteria analysis of energy system scenarios studies discussed in Section 2.3.2.5 and Appendix, Table 24, there are dedicated bottom-up ex-post LCA studies of energy system scenarios (Appendix, Table 25). Except for Brand et al. [35], the listed

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studies focus on the electricity sector, and only two mention double-counting environmental impacts: Garcia-Gusano et al. [36] propose to allocate the LCA-based indicators to the generat-ing capacity level and Brand et al. [35] apply an approach by Stroemman et al. [37] for hybrid life-cycle inventories to avoid double-counting. In the European NEEDS project, Loulou et al.

proposed two approaches for integrating LCA-based indicators in the TIMES model of the Euro-pean electricity system: (i) endogenous modelling of the amounts of materials and fuels directly consumed in the construction, dismantling and upstream phase, or (ii) integration of the cumu-lative impacts of construction and dismantling and their upstream chains [38]. Eventually, ap-proach (ii) was selected, which does not fully avoid double-counting.

While some sustainability indicators such as investment cost are specified in monetary terms by the PE energy system model, other indicators such as environmental and human health impacts due to pollutant emissions are quantified in physical terms or on other scales. This prevents direct comparisons of such impacts with the economic sustainability indicators, energy system costs, and important economic measures such as the GDP. It also prevents aggregation of the multiple sustainability indicators without normalisation. The monetisation of impacts enables such economic comparisons, allows for direct aggregation of multiple indicators and makes physical flows more comprehensible. The combination of PE energy system models and mone-tised environmental flows is therefore discussed in the subsequent section.