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X Initial Position Research Results
Energy Model at DLR:
REMix 1.
Technological
Impact Assessment
Global Assessment of Renewable Energy Potentials and Energy Modelling: REMix (Renewable Energy Mix)
This project is to identify possible worldwide contributions of renewable energy conversion technologies to electricity supply systems. Laying out future energy supply strategies employing fluctuating resources (i.e.solar radiation, wind) for electricity generation requires spatially and temporally highly resolved information on the potential electricity output of renewable power plants.
Such information is currently not available on a global scale. For that reason the project aims to fulfill the following milestones:
• creation of a global database for the following renewable resources (solar irradiation, wind speed, geothermal heat, biomass, hydro power, wave, tides, ocean currents
•further development of the DLR energy modelling platform REMix (Renewable Energy Mix)
• determination of least-cost power plant portfolios, transmission and storage capacities in predefined regions of investigation
Renewable resources are assessed using satellite data. In this case global horizontal irradiance is provided by NASA.
hours of year
[kWh/m²]
Research
Initial Position X Research
Results
Energy Model at DLR:
REMix 1.
Technological
Impact Assessment
Using the resource database as input for power plant models, electricity generation can be modelled at hourly time steps. The following technologies are assessed: photovoltaics, concentrating solar power, hydro power, wind power, combined heat and power (using both biomass and geothermal), tidal, wave and ocean current power plants.
Further parameters needed for the energy models are electricity and heat demand.
Both data of renewable power output and electricity demand are aggregated within nodes which in turn are used as input for a linear optimization under user defined boundary conditions. All analyses are carried out in a spatial resolution of 0.45°x0.45° (50x50km² at mid-latitudes) at hourly time steps.
TOP: Photovoltaic power plant model taking into account i.e. irradiation and sun elevation.
BOTTOM: For each technology an hourly electricity output will be determined at a spatial resolution of 0.45°x0.45°.
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eGlobal Assessment of Renewable Energy Potentials and Energy Modelling: REMix (Renewable Energy Mix)
hours of year
Results
Initial Position Research X Results
Energy Model at DLR:
REMix 1.
Technological
Impact Assessment
The global database on theoretical renewable energy potentials as well as electricity generation potentials will continuously be updated with data in higher spatial resolution.
Another important part of this project is the modelling of user defined regions to determine a least-cost power plant portfolio yielding
boundary conditions such as a certain share of renewable electricity generation and that the demand be met at all times.
The optimization model calculates the generation portfolio for each hour of a given year, furthermore the cost for the entire system is analysed and finally, necessary transport capacities (for instance high voltage direct current power lines) and storage demand are determined.
Contact: Daniel Stetter (daniel.stetter@dlr.de) Yvonne Scholz (yvonne.scholz@dlr.de) DLR Institute of Technical Thermodynamics Systems Analysis and Technology Assessment Pfaffenwaldring 38-40, 70569 Stuttgart, Germany
-100000 0 100000 200000 300000 400000 500000 600000
4177 4197 4217 4237 4257 4277 4297 4317 4337 Hour MW
Provisional optimization results (region:
Europe and North Africa) showing the
generation portfolio for a summer week of the annual model run.
hydrogen out caes out pumped storage out fossil power plants CSP
PV Wind offshore
Hydropower
Geothermal cogeneration Geothermal
pumped storage in caes in hydrogen in Surplus Load PV
Wind offshore Wind onshore Solid biomass gaseous biomass Storage hydro power Hydropower
Geothermal cogeneration
Global Assessment of Renewable Energy Potentials and
Energy Modelling: REMix (Renewable Energy Mix)
Initial Position
X Initial Position Research Results
Working Group on Renewable Energies – Statistics (AGEE-Stat) 2.
Technological
Impact Assessment
Working Group on Renewable Energies – Statistics (AGEE-Stat)
Marion Ottmüller, Dr. Frank Musiol, Andreas Püttner, Ulrike Zimmer
Members of the working group AGEE-Stat Reliable and current statistical data regarding renewable energies as
well as their future prospects are increasingly important for political decision-making. Therefore, the Working Group on Renewable Energies – Statistics (AGEE-Stat) was set up by the German Environment Ministry in collaboration with the Federal Ministry of Economics and the Federal Agricultural Ministry as an independent specialist body.
The group has been working since 2004 and is coordinated by the Centre for Solar Energy and Hydrogen Research (ZSW).
General Task:
The working group helps to insure that all statistics and data relating to renewable energies are part of a comprehensive, up-to-date and
coordinated system.
BMU
StBA
BMELV BMWi
UBA
AGEB ZSW
FNR
The working group
establishes renewable energies statistics for Germany
creates a basis for Federal Government’s reporting obligations
provides general information and PR
carries out research work (supported by workshops and consultations)
Research
Initial Position X Research
Results
Working Group on Renewable Energies – Statistics (AGEE-Stat) 2.
Technological
Impact Assessment
Working Group on Renewable Energies – Statistics (AGEE-Stat)
Marion Ottmüller, Dr. Frank Musiol, Andreas Püttner, Ulrike Zimmer
The Centre for Solar Energy and Hydrogen Research (ZSW) collects and evaluates data from different sources
The working group decides which data will be published
The working group meets normally 5 times for two days per year
There is permanent contact between ZSW-statisticians and members of the working group
The group harmonizes data with the Working Group on Energy Balances (AGEB)
AGEE-Stat continues to develop data basis and calculation tools
Important annual publications:
First assessment of renewables-development in the previous year (end of February)
New edition of „Renewables in Figures“ with detailed data (June)
Updated edition of „Renewables in Figures“ with latest new data for the previous year (December)
Results
Initial Position Research X Results
Working Group on Renewable Energies – Statistics (AGEE-Stat) 2.
Technological
Impact Assessment
Working Group on Renewable Energies – Statistics (AGEE-Stat)
Marion Ottmüller, Dr. Frank Musiol, Andreas Püttner, Ulrike Zimmer
Dr. Frank Musiol frank.musiol@zsw-bw.de Further information on AGEE-Stat and on renewable energies may be found on the BMU website:
www.erneuerbare- energien.de
2.1 0.2
3.5 3.1
4.8
7.0 5.9
7.4 15.1
9.5
12 14
18
0 5 10 15 20 25 30 35
Share of total final energy consumtion
Share of gross electricity consumption
Share of final energy consumtion for heat
Share of fuel consumption Share of primary energy consumption
Share in [%]
1998 2000 2002 2004 2006 2007 2008 2020 minimum 30
German Government Targets
Renewable energy sources as a share of energy supply in Germany
Source: BMU Brochure „Renewable energy sources in figures – national and international development“, KI III 1, Version: June 2009
Initial Position
X Initial Position Research Results
Wind Power prediction using RNNs
3.
Technological
Impact Assessment
Wind Power Prediction using Recurrent Neural Networks
Martin Felder (ZSW), Anton Kaifel (ZSW), Alex Graves (TU München)
As the impact of wind turbine generator (WTG) output on the power grids increases with each installed turbine, it becomes more and more important to have accurate estimates of wind energy yield on different forecast horizons ranging from minutes to several days.
Roughly speaking, from shortest to longest horizons these forecasts are used to
• control wind turbine plant parameters,
• stabilize the grid by planning ahead the use of slower power plants,
• optimize trade strategies on the energy markets.
Obviously, wind power depends strongly on wind speed, with lesser factors like air pressure and turbulence also playing a certain role.
Hence, forecasts from operational Numerical Weather Prediction (NWP) form the basis for predicting wind power. To convert pre- dicted wind in NWP grid boxes into power at a particular WTG re- quires complex physical or statistical models. Interestingly, the best systems are huge molochs combining several NWPs with several conversion models. Can we do better, or maybe make them better?
Research
Initial Position X Research
Results
Wind Power prediction using RNNs
3.
Technological
Impact Assessment
Wind Power Prediction using Recurrent Neural Networks
Martin Felder (ZSW), Anton Kaifel (ZSW), Alex Graves (TU München)
Machine learning techniques have proven effective at forecasting the power output of wind turbine generators. However, predictions
typically use a single input vector of NWP forecasts, disregarding the potentially informative history of previous inputs.
Another issue is that prediction uncertainty is often provided only when NWP ensembles are available, from which a probabilistic interpretation can be constructed.
We address these shortcomings by using mixture density recurrent neural networks (RNNs) to forecast a time-dependent probability distribution over power outputs. As dynamical systems, RNNs are inherently more difficult to control than stationary, feed-forward methods. On the other hand, their behaviour being radically different from both static methods and traditional time series analysis makes them ideal for combination with existing methods.
In contrast to many existing systems providing fixed horizon
forecasts, our RNN predicts a complete analytical description of the wind power distribution every 10 min up to 48 h ahead.
An RNN consisting of input, hidden and output layer, unravelled in time. At each time step, NWP forecasts and the previous
internal state determine a probability density function parameterization, which is then interpreted as a wind power distribution.
Results
Initial Position Research X Results
Wind Power prediction using RNNs
3.
Technological
Impact Assessment
Wind Power Prediction using Recurrent Neural Networks
Martin Felder (ZSW), Anton Kaifel (ZSW), Alex Graves (TU München)
The company Natenco has kindly provided us with target values in the form of 10-minute average wind power readings for several German wind farms, spanning July 2007 to December 2008.
Historical NWP forecasts from the NASA GFS-4 and DWD COSMO- EU analyses for the same time range are used as input data.
After an initial transient phase of about 24 h, in which the RNN is presented with current power readings, we switch to feeding it NWP forecasts. Due to its evolving internal state, the RNN can smoothly produce predictions in 1 h or even 10 min intervals, while new forecasts come in only every 3 h.
We have already achieved prediction accuracy on par with a well- tuned feed-forward network approach, and will demonstrate good performance on short and long-term predictions shortly. An analysis of the new method's properties and validation of the modelled probability densities is currently being prepared.
Corresponding author: Dr. Martin Felder <martin.felder@zsw-bw.de>
Example 48-hour wind power prediction by an RNN using two Gaussian components to represent the PDF at each forecast horizon.
