Motivation

Simpler models are most of the time preferred to larger and complex ones, as they are easier to build and interpret. Despite this, there are very good reasons for expanding the ELECTRA-CH framework to the larger ELECTRA framework:

Switzerland is a small economy that is closely interlinked with Europe and the rest of the world.

Although Switzerland may not have a noticeable impact on the rest of the world (although, this may not be fully true for the electricity sector), the rest of the world influences to a great extent the Swiss economy through trade.

This inter-linkage is even more pronounced for the electricity sector. Indeed, with the liberalization of the electricity market, electricity is traded all across Europe. It is traded extensively, from long-term contracts, negotiated years in advance, to short-long-term trading through spot and intraday mar-kets. Furthermore, electrons know no international borders. The electricity grid is actually one grid, although some parts can be sectioned-off in case of an emergency. Additionally, with the appearance of renewable technologies, the inter-connection of the whole European network becomes more and more important. The Swiss electricity market can thus not be analyzed adequately without a sound representation of its integration into the European electricity market.

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In principle, international integration can be modeled through exogenous assumptions (import and export prices, world energy prices, CO2 price etc.), which is the case in the ELECTRA-CH framework.

This is perfectly fine when analyzing Swiss policies. However, to adequately simulate impacts of international climate and energy policy scenarios, exogenous assumptions are often not sufficient.

As bottom-up models do not have the ability to model market instruments appropriately, a multi-regional CGE model (here GEMINI-E3) is needed to simulate such international policies. For example, CO2 prices can be implemented only exogenously in the CROSSTEM model. Endogeneity of CO2 prices is important, however, as policies as the emissions trading scheme involve CO2 emission reduction goals in terms of absolute emissions (caps). In countries like Germany or Italy, the electricity sector, which is part of the ETS, has non-negligible impacts on the total emissions from ETS sectors. Hence, variations in the electricity mix may greatly influence CO2 prices.

In connection with international policies, the electricity mix in neighbouring countries affects Switzer-land also directly through electricity markets. This is where the full CROSSTEM model with endoge-nous trade comes to its full potential: EU policies affect the composition of the electricity mix in the EU Member States and indirectly also in Switzerland.

For simulating and understanding these effects, exogenous trade prices are not informative enough.

As can be seen in Figure 31 in Section 2.1.12.1, trade prices assumptions differ compared to simula-ted trade prices. While the exogenous assumptions do not need to be wrong, they typically do not fully correspond to the modeling structure and scenarios of the current project. The endogenous simulation of trade prices is the much more consistent approach, provided that the model framework represents well the electricity systems of the countries involved. For the scenarios of this project, the seasonal variations embedded in simulated trade prices significantly alter the results for the technology mix. For example, solar becomes less attractive with endogenous trade prices, because export prices are lower in summer. It is replaced by flexible gas power plants which can take advantage of high winter prices (see Figure 30 and the explanation in Section 2.1.12.1).

International policies affect the Swiss economy through further transmission mechanisms:

• CO2 price: If Switzerland joins the European emissions trading scheme (as we assume in our scenarios), EU (climate) policies affect CO2 prices also for the Swiss sectors that are affiliated to the Emissions Trading System (ETS).

• Fuel prices: International climate policies affect fuel prices, which are important determi-nants for energy efficiency and the pace of fuel switch in Switzerland. A change in fuel prices thus also affects the Swiss electricity sector (directly, if gas power plants are imple-mented, or indirectly through demand shifts).

• Terms of trade: Differences in environmental and climate policies between countries affect the cost structure and competitiveness of energy-intensive sectors. This can change world market prices also for certain non-energy commodities and thus influence the terms of trade for Switzerland.

In conclusion, the ELECTRA framework, including

• an explicit representation of electricity supply in Switzerland and the neighbouring count-ries,

• international electricity trade based on endogenous prices,

• a general equilibrium representation of Switzerland, the neighbouring countries, the rest of Europe and the rest of the world, and hence,

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• the possibility to explicitly model Swiss, European, and international policies

endogenizes further important aspects for energy policy analysis, and it is especially needed when analyzing scenarios with a focus on the impact of foreign policies on the Swiss electricity sector and general economy.

3.2.2 Coupling methodology

As described above, the ELECTRA framework consists of three component models (CROSSTEM, GENESwIS and GEMINI-E3), coupled through a soft link. We can divide the coupling of the models into three steps:

1. CROSSTEM is coupled to GEMINI-E3. The result of the convergence between these two models provides

a. the electricity mix (and related costs) for Switzerland and the neighbouring countries, b. the economic impacts on the neighbouring countries, the rest of Europe and rest of

the world,

c. which includes the variation of relevant price indicators (mainly CO2 prices and fossil fuel prices).

2. GENESwIS is updated with world market prices simulated by GEMINI-E3.

3. GENESwIS is coupled to CROSSTEM. The result of the convergence between these two models provides

a. the electricity mix (and related costs) for Switzerland and the neighbouring countries, b. the impacts on the Swiss economy.

First, convergence must be reached for each step (mostly 1 and 3; step 2 is a simple harmonization).

Second, steps 1 to 3 must be iterated upon until the full framework converges.

In the following, we elaborate on the exchange of variables involved in each step of the process (see also Figure 75).

Step 1: CROSSTEM-GEMINI-E3

Electricity generation costs and their components as well as export revenues and import costs for Germany, France, Austria and Italy are extracted from the CROSSTEM model and translated for the GEMINI-E3 model into a) wholesale electricity prices (for each country) and b) input shares for factors and commodities to the respective electricity generation cost functions. The national electri-city demand quantities simulated by GEMINI-E3 are then sent back to become inputs to CROSSTEM.

To account for changes in the economy, factor and intermediate input prices from GEMINI-E3 are used to modify the investment costs and operation and maintenance costs of the different technolo-gies in CROSSTEM. This sequence is iterated upon until the vector of quantities of total electricity demanded for each common model period converges. Checks are performed such that demands also converge for each country.

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Step 2: GEMINI-E3-GENESwIS

World energy prices (fuel prices) and CO2 prices in GENESwIS are exogenously fixed to the price levels provided by GEMINI-E3.

Figure 75: ELECTRA framework: exchange of information between the three component models.

Step 3: GENESwIS-CROSSTEM

Electricity generation costs and their components as well as export revenues and import costs are extracted from the CROSSTEM model and translated for the GENESwIS model into a) the Swiss wholesale electricity price and b) input shares for factors and commodities to the electricity genera-tion cost funcgenera-tion. The sectoral electricity demand quantities simulated by GENESwIS are then sent back to become inputs to CROSSTEM. To account for changes in the economy, factor and intermedi-ate input prices from GENESwIS are used to modify the investment costs and operation and mainte-nance costs of the different technologies in CROSSTEM. This sequence is iterated upon until the vector of quantities of total electricity demanded for each common model period converges.

For steps 1 and 3, the following routines are implemented:

• Link CROSSTEM’s costs to CGE’s electricity technology and price (see section 3.1.1.3.2).

• Translate CGE price feedback into changes to investment and operation and maintenance costs (see section 3.1.1.4.2).

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Before coupling the models, the following procedures are applied:

• Modification of the electricity generation production function in the CGE models (Step 1:

see section 3.2.3.2. Step 3: identical to the modifications described in section 3.1.1.3.1 for the ELECTRA-CH framework.).

• Harmonization of the models (Step 1: see section 3.2.3.2.).

• Introduction of a supply elasticity for electricity in the CGEs (see section 3.1.1.5.2).

• Introduction of a dampening of the demand response in the coupling routine (see section 3.1.1.5.3).

3.2.3 State of the coupling