Advantages of CROSSTEM over CROSSTEM-CH

This section highlights some of the strengths of a multi-region model like CROSSTEM with endoge-nous electricity trade, over a single region Swiss electricity model like STEM-E or CROSSTEM-CH with exogenous electricity trade price assumptions. To illustrate the differences, the Sc1 scenario of CROSSTEM is compared with the ‘Sc.1 equivalent’ Baseline scenario from the coupled framework (presented in section 1.4.1), as they have identical electricity demand and supply options for Switzer-land. The results are compared with respect to the electricity generation mix and generation sche-dules to understand the underlying drivers.

2.1.12.1 Electricity generation mix

Figure 30 shows the electricity generation mix for Switzerland from both models. Although the tech-nology mix is similar in the near term (2020), there are significant differences in the long run. In 2035 for instance, both models have around 100 PJ from gas-based electricity generation. However, in the Baseline scenario from the CROSSTEM-CH model, around 80% of the gas-based electricity generation

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originates from base-load type plants, whereas in the CROSSTEM model only around 45% is gene-rated by base-load plants, with the majority share from flexible plants. An even larger difference is observed in the year 2050 in terms of generation mix and type of gas plants. While CROSSTEM-CH chooses a considerable quantity of solar PV in the generation mix, no solar PV investments are made in CROSSTEM. Moreover, as seen in 2035, most of the gas plants in CROSSTEM-CH are base-load type (80%), whereas the major part comes from flexible gas plants (54%) in CROSSTEM. There are two main reasons for this difference in technology choice, viz. “load dumping” in the single region model which is not possible in CROSSTEM, and uncertainties in the exogenous import/export prices assump-tion in CROSSTEM-CH.

Figure 30: Electricity generation mix (Switzerland): CROSSTEM-CH (Baseline) vs CROSSTEM (Sc1)

“Load dumping” is a term used to describe the phenomenon of dumping excess electricity to neigh-bouring countries without any knowledge of their markets. In a single region model like CROSSTEM-CH, the electricity imports/exports are exogenously defined. Although there are bounds on total trade volume as well as market share constraints to replicate historical trading patterns with neigh-bouring countries, there is no restriction on the timing of the imports or exports. This means that imported electricity is assumed to be available whenever there is a demand, and electricity can be exported whenever there is an excess generation. In reality, neither of these two conditions are true, but it is a common compromise made in single region models. This issue is partly addressed in the CROSSTEM model, wherein electricity can be imported only when there is excess generation in the surrounding countries. Similarly, the exports are only possible when there is a market (i.e. demand) in the surrounding countries. This is why in the Baseline scenario of CROSSTEM-CH, most of the gas-based generation is produced from base-load plants, which are more efficient (and hence cheaper) than flexible gas plants. In contrast, Switzerland in CROSSTEM has to invest more in flexible gas plants to be able to optimize the trading patterns with the real market of the neighbouring counties (and vice versa).

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The second driver is the assumption on electricity trade price. Figure 31 shows the exogenously given electricity import/export price assumptions²⁰ in the CROSSTEM-CH model versus the marginal cost of electricity in the surrounding countries of Switzerland obtained from CROSSTEM. As can be seen from the figure, the exogenous import/export price assumptions in CROSSTEM-CH are relatively higher in all time slices than in CROSSTEM except for winter weekdays²¹. This implies that exporting electricity in summer or spring is as attractive for CROSSTEM-CH as in the winter season, since it fully ignores the source of import and/or market for export. Whereas in the CROSSTEM, electricity prices in winter are much higher than in the other seasons reflecting high demand across all regions in winter. The differences in trade prices partly explain the reason for the cheap base-load type gas plants and investments in solar PV in CROSSTEM-CH. The high electricity price assumption during the peak hours in summer is based on the historical market trend. Thus, the solar PV becomes an attractive option in CROSSTEM-CH to generate excess electricity since it is assumed that there is a market to export the electricity from solar PV. However, the full framework (CROSSTEM) indicates that there is no market to import the summer electricity generated from solar PV in Switzerland. This was validated by running the CROSSTEM-CH model using the marginal costs from the Sc1 scenario of CROSSTEM. In this case, the generation mix from CROSSTEM-CH resembles that of the Sc1 scenario – i.e. no solar PV based electricity generation due to lower summer electricity prices. However, trade volumes and trade patterns in the CROSSTEM-CH model were still very different compared to CROSSTEM, reasons for which are explained in the following subsection.

Figure 31: Electricity import/export costs for Switzerland - CROSSTEM (endogenous) vs CROSSTEM-CH (exogenous)

2.1.12.2 Electricity generation schedule

Figure 32 shows the differences in the electricity generation schedules in Switzerland between the two models on a summer (Figure 32a) and winter (Figure 32b) weekday in 2050. As mentioned before, the import/export trade profiles in CROSSTEM-CH are purely driven by the exogenous trade price assumptions, whereas in CROSSTEM, the trade is endogenous, and import/export patterns are highly dependant on supply options and demand in neighbouring countries (Figure 25).

20 An hourly price assumption is estimated based on annual cost of electricity supply from the ADAM model. The methodology is explained in Kannan/Turton 2011.

21 It should be noted that the import prices adopted from the ADAM model were for a stringent climate scenario, resulting in high import prices (Fraunhofer 2010).

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(a) (b)

Figure 32: Electricity generation schedules - CROSSTEM-CH vs CROSSTEM

In the summer, CROSSTEM-CH dispatches all the flexible hydro from 08:00-16:00, supplementing to the solar PV generation to export maximum electricity during these peak hours, with imports re-quired to meet the demand in early morning and late evenings. In CROSSTEM on the other hand, most of the imports occur during the early morning hours which is simultaneously also exported (see Figure B 13 in Appendix B for more details), with no export during the daytime hours 08:00-16:00.

Dam/pumped hydro and flexible gas plants are scheduled during the evening hours to be exported from 16:00-00:00.

In winter (b), electricity is imported almost throughout the day in CROSSTEM-CH, except for the two peak hours between 09:00-12:00 and 17:00-19:00, where electricity price is assumed to be high. All the flexible generation is scheduled for these hours, thereby maximizing export trade revenue. In CROSSTEM, marginal costs of electricity in winter are very high, which makes it very attractive for Switzerland to export electricity. Hence, full capacity of the installed gas plants is scheduled in winter, supplemented by dam and pumped hydro. As mentioned before, this import/export pattern for Switzerland in CROSSTEM is only possible because of matching conditions in the surrounding countries (see Figure B 14 in Appendix B). Hence, the profiles from CROSSTEM are more consistent than those obtained from CROSSTEM-CH, which would have similar import/export patterns for all scenarios (see Figure 58 in section 3.1.3.4).

The limited set of scenarios presented in Section 2.1.11.1 (Figure 20) with the assumed set of boundary conditions shed powerful insights on the development of the Swiss electricity system, which would not have been possible with a single region model. Thus, the CROSSTEM is very a powerful tool to explore different boundary conditions of the neighbouring countries to generate insights for policy decisions.

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