Endogenisation of sustainability indicators in energy system models

29 tion compared to dedicated external cost studies such as ExternE [39]. The external costs of environmental flows are not physical properties and are therefore based on monetary valua-tion. This step includes value choices, such as discounting and equity weighting, which can be made in different ways. Thus, the monetisation of environmental flows bears value-related un-certainties in addition to the unun-certainties related to the quantification of the environmental flows, which are described in Section 2.4.2.2.

2.5.2.4. Literature review

An overview of studies which have integrated externalities in PE energy system models is pre-sented in Appendix, Table 26. Most of the listed studies focus on one country or region, while the studies by Rafaj [15] and Kypreos et al. [40] are based on a multi-regional model. The major-ity of the listed studies address LAP as well as GHG emissions, but Kosugi et al. [41] also analyse the externalities related to land use. There are studies focussing on the electricity sector [15, 42-46], while others address the entire energy sector [40, 47, 48] or even the whole economy [41].

Roeder instead focuses on the external costs of the passenger car sector [49]. The modelling of the emissions is either based on direct (on-site) emissions [15, 42, 47], LCA [40, 41, 43-45, 48, 49] or upstream and operating emissions [46]. Many studies presented in Appendix, Table 26 use external cost data from the European research projects ExternE and NEEDS. The other stud-ies draw information from their previous work or other sources.

The three combined methods discussed so far (Sections 2.3 to 2.5) are based on a cost minimi-sation framework, in which a single internal cost objective is optimised and other indicators are calculated ex-post. With these combined methods, the same (set of) energy system transfor-mation(s) can be analysed based on different types of indicators. The endogenisation of sustain-ability indicators in the PE energy system model instead leads to a new (set of) energy system transformation pathway(s), which is quantified for each (set of) objective(s). This combined method is described and analysed in the next section.

2.6. Endogenisation of sustainability indicators in energy system mod-els

PE energy system models are based on cost minimisation, which is expected to approximate the real world decisions and developments (Section 2.1). Energy system pathways based on the optimisation of (combined) sustainability indicators instead represent developments under

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different policy objectives. The endogenisation of sustainability objectives such as energy carri-er imports or CO2 emissions leads to new scenarios, which can be compared with each other and the least-cost pathway, if the other boundary conditions remain unchanged.

2.6.1. Formalisation of the combined method

Endogenisation of sustainability indicators in PE energy system models can be formalised as follows (Figure 14):

1) Definition and quantification of the sustainability indicators for each technology 2) Scenario description

3) Scenario quantification with single or multiple objectives 4) Result calculation and interpretation

Figure 14: Illustration of the methodological steps for the endogenisation of sustainability indica-tors in energy system models

The approach for endogenisation of sustainability criteria in energy system models is set up analogous to the bottom-up multi-criteria analysis of energy system scenarios presented in Sec-tion 2.4: In the first two steps, the scenarios are described and the sustainability indicators are quantified on technology level and introduced in the energy system model using its features for the consideration of environmental aspects (Section 2.1). But, as opposed to the ex-post

analy-2.6. Endogenisation of sustainability indicators in energy system models ______________________________________________________________________________________________________________

31 sis, the indicators are endogenised, i.e. introduced in the objective function of the PE energy system model. Accordingly, the objective function is altered to allow for the optimisation of (combinations of) the respective sustainability objectives. After the scenario quantification in the third step, the total indicator values are aggregated to scenario level and interpreted in the fourth step.

2.6.2. Discussion of the combined method

2.6.2.1. Regional allocation of impacts

The impacts are quantified and implemented as described in Section 2.4.2.3. Therefore, the im-pacts are allocated to the region in which they actually occur, i.e. they are not defined from a consumption perspective.

2.6.2.2. Endogenisation of the energy system’s own energy use

PE energy system models allocate energy supply technologies to exogenously defined energy service demands based on cost minimisation. The energy service demands are derived from socio-economic drivers and are assumed to include all energy demands of the respective re-gions, i.e. also all demands for the production and disposal of the energy system technologies.

The energy used for the operation of the energy system (the energy system’s own energy use) is either also included in the exogenous energy service demands or in the energy system process-es themselvprocess-es (e.g. in transport and distribution (T&D) efficienciprocess-es)².

Alternatively, the energy use of the energy system technologies can be endogenised using an LCA-based approach. If – for example – the direct (on-site) CO2 emissions of the energy system are optimised, low-CO2 conversion technologies such as photovoltaic (PV) power plants are expected to be part of the optimal solution as they do not emit CO2. Nevertheless, they require energy for the production of the components and the installation. In order to better represent such developments, the energy system’s own energy use (including both operation and infra-structure contributions) can be endogenised in the energy system model. If such an approach is taken, the energy used in the supply chains of each energy system technology is explicitly

2 According to 2011 statistics, the average own energy use for the operation of the energy sector was 7 % of the total produced electricity (observed range 0%-44%) and 7% of the total produced heat (observed range 0-39%) [50].

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sented as a set of energy inputs (electricity, heat, freight transport and feedstock demands) as illustrated in Figure 15.

Figure 15: Illustration of the endogenous life-cycle energy inputs on technology level

The life-cycle energy inputs can be derived from the standard formulation of LCA indicators (Box 1 in Section 2.3.2.1):

𝑠 = 𝐴⁻¹𝑓

where s is the supply vector, A is the technosphere matrix and f is the demand vector. The sup-ply vector represents the cumulative inputs from technosphere required to satisfy one unit of demand. By summing up over the electricity, heat, freight transport and feedstock inputs, re-spectively, the life-cycle energy inputs can be calculated. Analogous to Section 2.5.2.2, it is fur-ther possible to disaggregate the cumulative energy inputs into to the regional contributions by summing up only over the respective regional energy inputs.

For the implementation in the energy system model, industrial electricity and heat, freight transport and feedstock end-use technologies and according energy carriers are defined, which represent the mixes of the respective time period and region. An illustration of the approach is presented in Figure 16.

If such an approach is implemented, the energy service demands must be adapted: The energy service demands of the base year are lowered according to the life-cycle amounts of energy used by the energy system technologies to avoid double-counting the energy flows related to the en-ergy system technologies. For the future time periods, an assumption about the share of the

2.6. Endogenisation of sustainability indicators in energy system models ______________________________________________________________________________________________________________

33 energy service demands due to energy technologies is required for each time period and region.

This share can – for example – be estimated based on the cost optimal scenario (without endog-enous energy flows). After these adjustments, the scenarios can be quantified based on other objectives and the residual energy service demands and the energy demands from the energy technologies are satisfied.

Figure 16: Illustration of the modelling of the endogenous energy inputs on energy system level

2.6.2.3. Modelling uncertainties and limitations

There are uncertainties in the indicator quantification in bottom-up analysis as listed in Section 2.4.2.2. Further modelling limitations could also be determined, which are described in the sub-sequent paragraphs.

TIMES- and MARKAL-based PE energy system models are directed to cost minimisation and thus have detailed cost characteristics implemented on technology level. This ensures that the

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solving algorithms lead to cost-efficient solutions without dissipation (“leakages”), although the modelling equations are formulated inequalities. If the modelling paradigm is changed to the optimisation of another sustainability objective, leakages can occur as the costs are no longer (the only) part of the objective function and the modelling equations are still formulated as ine-qualities. For example, a CO2 minimal solution can include the construction of electric capacity which is not used. The reason is that the capacity does not have sustainability impacts (no di-rect, i.e. on-site, CO2 emissions) and that its construction costs are not included in the objective function so that the technology is selected even though it is not required to satisfy the electricity demand. Such leakages must be avoided to reach credible results. One approach is to change the model’s inequalities to equalities. But in large-scale models such as the GMM model, this ap-proach leads to difficulties for the solver to find feasible solutions due to numerical problems even if it has been proven that feasible solutions exist.

For the optimisation of multiple weighted objectives in the objective function (analogous to the WSA in MCDA), the weighting is carried out concurrent to the optimisation. The individual ob-jectives usually must be scaled as it is likely that they are not on the same scale. Without scaling, the objective with the largest order of magnitude would dominate the other objectives and thus the solution. These scaling factors however not only influence the results of the modelling but they also interfere with the weighting factors so that no robust results for such a WSA applica-tion could be found.

The introduction of new technologies on the end-use level (e.g. more end-use technologies) in existing large models such as the GMM model changes the results as the model has more oppor-tunities to satisfy the respective energy service demand. The induced changes in one end-use sector can in turn influence other end-use sectors. Similarly, if new end-use technologies such as the ones for industrial electricity and heat as well as freight transport are implemented in exit-ing models, the previous end-use technologies are shifted to the conversion sector (Figure 16).

Such modifications change the interactions between conversion technologies, end-use technolo-gies and energy service demands, and lead to changes in the modelling results.