Both the general wind modelling and the method developed are meant to describe in a simple way the effects of turbine aggregation and being to some extent empirical certainly hold limits and inaccuracies. Beside the general limitations given by the high level representation of a system model like Balmorel, it is important to mention: • No consideration of complex terrains and roughness when extrapolating speeds
at different heights
• Importance of location chosen as representative, since only one time series is representing the region or country
• Limited number of locations chosen for each region
• Even capacity distribution inside the regions considered when calculating smoothen- ing
• Missing correction of the biases given by reanalysis data when calculating the smoothening of the power curves
• Implementation of smoothened power curve calculated with renalysis data in a model which uses Mesoscale data
• Cut-off at high wind speeds not modelled
All in all, these limits are considered acceptable and part of the trade-off between accuracy and simplicity and they could always be addressed in future studies.
Part III
Results & Sensitivity
9
Results
This section describes the results of the simulations and analyses performed. All monetary results are expressed in real terms in 2015-e.
Firstly, general results for the entire system and for Germany, such as installed capacities and annual production will be for the central scenario Sp300, in order to characterize the system conditions. Secondly, results for the specific power sce- narios are presented in detail, starting with system results, followed by total costs assessment and value of wind results. Consequently, selected results for hub height scenarios are shown in a more concise way.
Indeed, as it will be shown, varying specific power of turbines showed more pro- nounced impacts on the system and markets than hub heights.
As already mentioned, most of the results will be shown for Germany, focus country of this analysis. However, as a comparison, selected results will be presented for Denmark, since it is the country with the highest wind penetration in Europe and it has been modelled more in detail than other EU countries.
9.1
General system results
System results for the entire geography represented are shown for the 300 W/m2 scenario as central case.
Figure 9.1 expresses the total installed capacity in the entire system. The trend up to 2030 is an increased RES-E capacity and a gradual decommissioning of both nuclear and conventional plants. The largest decommissioning of fossil fuel plants takes place in 2020, first year in which the model can choose to dismiss capacity. This is the result of the lower capacity factors these plants can achieve in the market, due to the pressure of RES generators, which are dispatched first due to their lower marginal cost. It has to be noted that coal sees the largest reduction in the installed capacity, while natural gas is less affected.
The results for the annual generation (Figure 9.2) confirm this trend, with a reduc- tion in the output of conventional plants and a sharp increase in RES-E generation. 69
Figure 9.1: Evolution of installed capacity for the entire system, Sp300 sce-
nario.
Figure 9.2: Evolution of annual generation for the entire system, Sp300 sce-
nario.
Table 9.1 expresses the percentage of annual generation for each group of plants. The effect of nuclear decommissioning of Germany and the lower pace of reinvestments in other countries reduces the nuclear share from almost 30% in 2015 to 19% in 2030. Fossil fuels halve their generation in the 15 years span. On the other hand, RES
Chapter 9. Results
sees a very large increase in their output, which results in a share almost doubled by 2030.
While hydro power and biomass are almost constant throughout the years, solar and wind (VRES) are the key contributers to the RES development over time. In 2030, VRES production makes up 36% of the annual generation in the system studied.
2015 2020 2025 2030
Nuclear 29% 23% 19% 19%
Fossil Fuels 40% 40% 34% 23%
RES 32% 38% 47% 58%
VRES [% total] 10% 17% 26% 36%
Table 9.1: Share of total generation per type of generators: Nuclear, Fossil
fuels and RES.
German power system
When looking at Germany, the effect of nuclear phase-out and the large development in wind and solar are reflected in the installed capacity in Figure 9.3 (Sp300). It can be noted that in 2020, first year in which investment and decommissioning are allowed, about 13 GW of Natural gas, 5.5 GW of Coal and 2.5 GW of Oil are decommissioned, meaning these plants were not profitable in the model. Being a model output, these results of decommissioning do not take into account if the pace is reasonable or not with a 5 years time horizon.
Figure 9.3: Evolution of Installed capacity in Germany in Sp300 scenario.
Wholesale electricity price
As mentioned in the model description, Balmorel performs an economic dispatch simulating the market closure every hour in each of the regions in the model. The resultant prices for each year and scenario can be visualized in a "heat map". Figure 9.4 shows an example of such a map, describing the prices in 2030 in the reference Sp300 Scenario. The lines connecting each model region represent the total annual electricity flow in TWh.
Figure 9.4: Map of average electricity prices in 2030 for Sp300 reference
Chapter 9. Results