The increasing penetration of weather-dependent renewable energy generation calls
for high-resolution modeling of the possible future energy mixes to support the energy
strategy and policy decisions. Simulations relying on the data of only a few years,
however, are not only unreliable but also unable to quantify the uncertainty resulting
from the year-to-year variability of the weather conditions. This paper presents a
new method based on artificial neural networks that map the relationship between the
weather data from atmospheric reanalysis and the photovoltaic and wind power generation
and the electric load. The regression models are trained based on the data of the
last 3 to 6 years, and then they are used to generate synthetic hourly renewable power
production and load profiles for 42 years as an ensemble representation of possible
outcomes in the future. The modeled profiles are post-processed by a novel variance-correction
method that ensures the statistical similarity of the modeled and real data and thus
the reliability of the simulation based on these profiles.
The probabilistic modeling enabled by the proposed approach is demonstrated in two
practical applications for the Hungarian electricity system. First, the so-called
Dunkelflaute (dark doldrum) events, are analyzed and categorized. The results reveal
that Dunkelflaute events most frequently happen on summer nights, and their typical
duration is less than 12 h, even though events ranging through multiple days are also
possible. Second, the renewable energy supply is modeled for different photovoltaic
and wind turbine installed capacities. Based on our calculations, the share of the
annual power consumption that weather-dependent renewable generation can directly
cover is up to 60% in Hungary, even with very high installed capacities and overproduction,
and higher carbon-free electricity share targets can only be achieved with an energy
mix containing nuclear power and renewable sources. The proposed method can easily
be extended to other countries and used in more detailed electricity market simulations
in the future.
