Annex 4 analytical methods

The European Electricity Market Model consists of the following building blocks:

Supply side:

For all given technologies (e.g. OCGT, CCGT, thermal) commissioning date defines the efficiency, the self-consumption and the variable OPEX cost for all units. Using the fuel prices as an input total fuel costs are calculated taking into account the above parameters. CO2 costs are based on the calculated emission level and the CO2 quota prices, and all these costs are then added to the total energy tax paid and the variable OPEX.

It is important to note that only short-term marginal costs are taken into account, the model does not analyse whether long-term operation is profitable or not. It is possible, that some units remain operational even if they provide electricity in a few hours per year. Power plant units are available until the end of their (pre-defined) lifetimes.

Demand side: When modelling the electricity system short-term market outcomes - representing only one hour - are simulated. However, typically yearly results are interesting, thus, reference hours are defined to be able to produce yearly outputs. In the EEMM 90 reference hours are used: there are six types of hours representing yearly differences, another 4 that represent daily seasonality and the 24 types of hours created this way are further separated to gain 90 representing hours. This latter separation is carried out in a way to gain as homogenous groups as possible, so the demand of all 8760 hours could be represented very well with the given 90 reference hours.

Demand is calculated for each reference hour as the arithmetic mean of the demand of the hours it represents in 2018, and then adjusted according to the level of yearly demand assumed for the modelled year. Yearly demand forecasts are based on EU’s PRIMES modelling for each country.

When modelling a whole year, the model runs 90 times, and outputs are saved for all reference hours separately. From these outputs yearly results are calculated taking into account the weight of all reference hours (based on how many hours they represent). This way yearly baseload prices, import-export positions and production of each unit are generated as outputs.

Cross-border trade

Power flow is ensured by 104 interconnectors between the countries, where each country is treated as a single node, thus no domestic power system constraint is taken into account. NTC values are used to indicate trading possibilities, seasonal differences are included in the modelling based on historical data from ENTSO-E Transparency Platform. Future investments are assumed based on data from ENTSO-E’s latest Ten-Year Network Development Plan (TYNDP).

Equilibrium

The model calculates the simultaneous equilibrium allocation in all markets with the following properties:

• Producers maximize their short-term profits given the prevailing market prices.
• Total domestic consumption is given by the aggregate electricity demand function in each country.
• Electricity transactions (export and import) occur between neighbouring countries until market prices are equalized or transmission capacity is exhausted.
• Energy produced and imported is in balance with energy consumed and exported.

Given our assumptions about demand and supply, market equilibrium always exists and is unique in the model.

Electricity product prices

The calculated market equilibrium is a static one: it only describes situations with the same demand, supply, and transmission characteristics. However, these market features are constantly in motion. As a result, short run equilibrium prices are changing as well.

To simulate the price development of more complex electricity products, such as those for base load or a peak load delivery, we perform several model runs with typical market parameters and take the weighted average of the resulting short term (hourly) prices.

Data for the modelling scenarios is derived from publicly available sources.

• NTC capacity data based on ENTSO database, including the ENTSO-E latest TYNDP projects effect on NTCs
• Supply side database are based on national regulators, system operators, and individual power company and plant websites. These information are cross-checked with aggregated database (ENTSO-E, Eurostat)
• Natural gas price forecast is based on EGMM (European Gas Market Modelling) modelling results
• Other fuel prices (coal and oil) and CO2 price forecast are based on international organizations (IMF, IEA, European Commission, etc.)
• Demand based on ENTSO-E fact database, forecasted consumptions are based on PRIMES projections

Outputs of modelling are the wholesale electricity wholesale market prices per country per reference hours and from this information also the yearly base and peakload prices can be determined. Electricity trades between countries and production and CO2 emission of all producers are also calculated. Based on those outputs the model also calculates welfare on country and stakeholder level (consumer, producer, traders).

The EEMM is a partial equilibrium microeconomic model. It assumes fully liberalised and perfectly competitive electricity markets. The model was used to evaluate the infrastructure developments triggered by the implementation of the TEN-E Regulation by measuring benefits (e.g. socio-economic welfare derived from the higher trading opportunities allowed by the new infrastructure). The main purpose of the modelling carried out for this study was to monetise the realised and potential benefits (in term of socio-economic welfare change) of the projects of common interest (PCIs). In addition, based on modelling outcomes, several indicators were calculated in order to illustrate the effect of the TEN-E Regulation on market integration, competition, CO2 emission reduction and RES integration.

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