General assumptions
Some general assumptions, which relate both to the model itself and to specific choices of the analysis, follow:
• All energy produced is assumed to be traded in the day-ahead market. As it was underlined in Chapter 4, even though the trend is toward increasing volumes traded in EPEX, this is not the case today.
• In each scenario, the technology chosen is implemented not only in Germany, but in all the countries modelled.
• Advanced turbines in terms of both lower specific power or higher hub heights are assumed to be installed in each wind area regardless of wind conditions or the IEC class of the turbine model. This reflects the considerations expressed in Section 4.6.
Finally, it has to be underlined that the analysis is limited to the day-ahead market and neglect intra-day market, balancing market, possible bilateral contracts, as well as effects such as forecast errors and grid-related costs.
Chapter 7. Modelling the value of wind in the power system Time resolution
With a medium-term perspective, having a model which can perform investment optimization is fundamental, both to ensure decommissioning of capacity which is not competitive and to optimize the system with additional investments. More- over, since a certain wind penetration in terms of annual onshore generation is fixed exogenously, investment run ensure that the capacity needed to fulfill this require- ment is installed in each scenario, depending on the characteristics of the technology. Investment optimization simulations are characterized by high computational de- mand and a reduction of temporal resolution is usually required [60]. In Balmorel, the simulation features both a selected number of weeks a year and a reduced amount of hours per week, chosen as representative for investment decisions.
On the other hand, as underlined by [61] and [62], the temporal resolution has a great importance when modelling VRES systems and in particular their market value. Only detailed hourly representation of the dispatch can highlight the complex interaction of wind, demand and other generators in the market. In addition, the higher the temporal resolution, the lower the estimated market value of wind energy since more extreme events, which are only present in the high resolution cases, have particularly strong price effects [62].
Given the two requirement for investment and dispatch in terms of temporal reso- lution, the setup of the study is the following:
• Investment optimization with a lower time resolution, to have acceptable run- ning time
• Dispatch optimization performed using the results of the investment simula- tion, with hourly resolution, in order to better capture wind interaction with the market and to correctly assess the MV of wind
Internal grid and congestions in Germany
In Germany, the current EPEX day-ahead market setup consists in one single price zone (together with Austria) [30]. In reality, internal congestion in the German grid sets limitations on the possible flows across the country and in particular in the corridor North-South. This is managed by counter-trading, which is performed by the TSOs after market clearing to make sure the system operates within its limits in terms of NTC [63].
The standard approach for modelling Germany in most of the analysis in Ea Energy Analyses is splitting the country into different market zones and consider the bottle- necks in the system. On the other hand, in few previous analysis in Balmorel, among which [63], Germany has been treated as a single price zone. This corresponds to assume solved all the internal grid congestion and treat Germany as a copper plate. For the sake of this study, which analyses wind generation patterns and the system value of wind, this assumption would be too strong. As a consequence, Germany is modelled with 4 price zones, which are highlighted in Figure 7.1(a). With this setup, internal grid constraints can cause the four zones to have different prices, es- pecially in hours with high wind generation in the North. This modified dispatch in
the four regions is supposed to take into account the TSO congestion management and re-dispatch, as well as the limits imposed in the NTC to other countries. The limitations of NTC to the Nordic countries due to internal congestion is evident in data from [1], while its correlation with RES production in Northern Germany is documented in [64].
The choice of the four zones is a result of an analysis of re-dispatched power plants and congestion management for historical years (already done internally at EA En- ergy Analyses) and is meant to reflect the main grid bottlenecks which are located mainly between DE-NW and DE-CS, as well as between DE-ME and DE-CS. Fig- ure 7.1(b) shows the main expansion projects along these directions.
(a) Modelled regions (b) Planned grid expansions
Figure 7.1: (a): Subdivision of Germany into four regions in the model. (b): Expansion plan for the internal grid in Germany. Source: [65]
Since this is a different setup compared to the actual market in Germany, the re- sultant value of wind has to be understood more in a system and socio-economical sense, rather than a projection of what wind investors will gain in the market.
Hydropower dispatch
It is important to briefly specify how the model performs dispatch of hydropower due to its influence on the results. In the investment runs, a value of hydropower production is fixed for each week. The value is then used in the hourly runs to set the production for the correspondent week. Since the dispatch is different in each scenario, some variations in the annual hydropower production can result from the simulations. These differences in the total level of annual hydropower are taken into account in the total system costs.
Chapter 7. Modelling the value of wind in the power system