Weather Generator

Gudrun is speaking with the portuguese engineer Bruno Pousinho.
He has been a student of the Energy Technologies (ENTECH) Master
program. This is an international and interdisciplinary program
under the label of the European Institute of Innovation and
Technology (EIT) inbetween a number of European technical
universities.


Bruno spent his second master year at the Karlsruhe Institute of
Technology (KIT). Gudrun had the role of his supervisor at KIT
while he worked on his Master's thesis at the Chair of Renewable
and Sustainable Energy Systems (ENS) at TUM in Garching. His
direct contact person there was Franz Christange from the group
of Prof. Thomas Hamacher.


Renewable energy systems are a growing part of the energy mix. In
Germany between 1990 and 2016 it grew from 4168 GW to 104024 GW.
This corresponds to an annual power consumption share of 3.4% and
31.7%, respectively. But in the related research this means a
crucial shift. The conventional centralized synchronous machine
dominated models have to be exchanged for decentralized power
electronic dominated networks - so-called microgrids. This needs
collaboration of mechanical and electrical engineers. The
interdisciplinary group at TUM has the goal to work on modeling
future microgrids in order to easily configure and simulate them.


One additional factor is that for most renewable energy systems
it is necessary to have the right weather conditions. Moreover,
there is always the problem of reliability. Especially for
Photovoltaics (PV) and wind turbines Weather phenomena as solar
irradiation, air temperature and wind speed have to be known in
advance in order to plan for these types of systems.


There are two fundamentally different approaches to model weather
data. Firstly the numerical weather and climate models, which
provide the weather forecast for the next days and years.
Secondly, so-called weather generators. The numerical models are
very complex and have to run on the largest computer systems
available. For that in order to have a simple enough model for
planning the Renewable energy resources (RER) at a certain place
weather generators are used. They produce synthetic weather data
on the basis of the weather conditions in the past. They do not
predict/forecast the values of a specific weather phenomenon for
a specific time but provides random simulations whose outputs
show the same or very similar distributional properties as the
measured weather data in the past.


The group in Garching wanted to have a time dynamic analytical
model. The model is time continuous which grant it the ability of
having any time sampling interval. This means it wanted to have a
system of equations for the generation of synthetic weather data
with as few as possible parameters. When Bruno started his work,
there existed a model for Garching (developped by Franz
Christange) with about 60 parameters. The aim of Bruno's work was
to reduce the number of parameters and to show that the general
concept can be used worldwide, i.e. it can adapt to different
weather data in different climate zones. In the thesis the tested
points range from 33º South to 40º North.

In the synthesis of the weather generator the crucial tool is to
use stochastic relations. Mostly the standard normal distribution
is applied and shaped for the rate of change and corelation
between RER. In particular this means that it describes the
fundamental behavior of weather (mean, standard deviation, time-
and cross-correlation) and introduces them into white noise in an
analytical way. This idea was first introduced for crop
estimation by Richardson in 1985. Time-dependence works on
different time scales - through days and through seasons,
e.g..

In the Analysis it is then necessary to parametrize the measured
weather data and to provide a parameter set to the weather
model.

Bruno started his Master course in Lisbon at Instituto Superior
tecnico (IST). In his second year he changed to KIT in Karlsruhe
and put his focus on Energy systems. In his thesis he uses a lot
of mathematics which he learned during his Bachelor education and
had to recall and refresh.


The results of the project are published in the open source model
'solfons' in Github, which uses Python and was developed in
MATLAB.
References

F. Christange & T. Hamacher: Analytical Modeling Concept
for Weather Phenomena as Renewable Energy Resources, in IEEE
International Conference on Renewable Energy Research and
Applications (ICRERA), 2016. doi: 10.1109/ICRERA.2016.7884551

P. Ailliot, D. Allard, P. Naveau, C. D. Beaulieu, R. Cedex:
Stochastic weather generators, an overview of weather type
models, Journal de la Société Française de Statistique, Vol. 156,
No 1, pp. 1-14, 2015.

C.L. Wiegand, A.J. Richardson: Leaf area, light interception,
and yield estimates from spectral components analysis, Agron. J.,
76, 543, 1984.

solfons: Artificial wheater data for energy system modeling,
Software at GitHub.


Podcasts

S. Seier, T. Alexandrin: Blindstrom - Der Energie Podcast,
2016-2017.

M. Völter, V. Hagenmeyer: Stromnetze, ein Überblick, omega
tau Podcast, Episode 246, 2017.

K. A. Zach, L. Bodingbauer: Energiespeicher, PHS186 in der
Physikalischen Soiree, 2013.

F. Trieb, T. Pritlive: Energie der Zukunft, RZ033 im Raumzeit
Podcast, Metaebene Personal Media, 2012.