Smart ideas for a smart energy sector: user-optimized ... · 6/29/2017 · Gross electricity...
Transcript of Smart ideas for a smart energy sector: user-optimized ... · 6/29/2017 · Gross electricity...
4th ICEM, Bari, Italy 29 June 2017
Andrea Steiner, German Weather Service (DWD), Research and Development
Smart ideas for a smart energy sector: user-optimized weather and climate information
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Outline
German Energiewende
Status of Renewable Energies (RE)
Energy Meteorology
RE-Projects at DWD (nowcasting to short range forecasts)
RE aspects and the DWD Modelling System
User oriented products
New cooperations
Summary
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31.7%
Data source: BMWI a) (2017)
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Renewable Energies in Germany
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Gross electricity generation in Germany
11.1%
24.2%
3.1%
20.4%
41.2%
Hydropower
Biomass
Photovoltaics (PV) (41.2 GWp)
Household waste
Wind power
(50 GWp)
2016
Natural gas
Oil
Others
Lignite
Nuclear energy
Hard coal
Gross electricity generation in Germany
Renewables
Data source: BMWI b) (2017)
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Headlines in 2016
PV: exceeded monthly nuclear power generation in May 2016 (as in July 2015), maximal production on 08 May 2016 with 28.5 GW
Wind: exceeded monthly hard coal power generation in February 2016, maximal production on 08 February 2016 with 36.6 GW
Christmas 2016: negative electricity prices for 3 – 4 days with wind power production continuously around 30 GW
© DWD
DWD Analysis, 24. 12. 2016, 00 UTC
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Burger (2017)
ANTJE
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Headline in 2017
Sunday, 30 April 2017:
Renewables cover 64 % of Germany’s daily electricity demand
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Data source: ENTSO-E Transparency Platform (2017)
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Energy Meteorology
Power forecasts
Power system management Renewable energy trading
Maintenance
NWP forecasts
New applications, new requirements, new challenges for short range forecasts
New collaborations
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Renewable Energy Projects
ORKA II
ORKA
2017 – 2021 New partners:
2016 – 2018
2012 – 2016
2012 – 2015
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Renewable Energy Projects
ORKA
ORKA II
Main Objectives:
Improve numerical weather and power forecasts for wind and photo-voltaic energy on time scales from nowcasting to short range forecasts
For trading power, for grid management and for dynamic line rating
Explore the use of power measurements in NWP
Advance the use of probabilistic forecasts, uncertainty information
Strengthen the collaboration / understanding between energy sector & NWP
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DWD’s operational NWP system
ICON deterministic 13 / 6.5 km (Europe)
Global ensemble prediction ICON-EPS 40 members 40 / 20 km (Europe)
Regional ensemble prediction COSMO-DE-EPS 20 members 2.8 km
COSMO-DE deterministic 2.8 km
Model physics
perturbations
provided by Michael Buchhold, DWD
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Regional ensemble data assimilation KENDA 40 members 2.8 km
EnVAR Hybrid DA-system
Global ensemble data assimilation ICON-EDA 40 members 40/20 km
Po
st Pro
cessin
g, P
rod
uct ge
ne
ration
MOS
RE aspects are considered in every step of the system development
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Heppelmann (2017)
PBL diurnal cycle generates wind power production ramps
NWP deficits concerning diurnal cycle and nocturnal Low Level Jets
Boundary Layer Diurnal Cycle
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Heppelmann (2017)
PBL diurnal cycle generates wind power production ramps
NWP deficits concerning diurnal cycle and nocturnal Low Level Jets
Improved PBL physics, new physics perturbations for the ensemble
Boundary Layer Diurnal Cycle
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Vogt (2017)
7 Low Level Jet Nights in July 2014
RMSE of wind power forecast vs. lead time
NWP improvement also visible in power forecast
Boundary Layer Diurnal Cycle
ref impr
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←sunrise
sunset→
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Demonstrator
User oriented forecast products
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provided by Tobias Reinartz, DWD
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Renewable Energy Projects
Renewable Energy variables and problems have become an integral part of DWDʼs model development targets
Innovative application of extended DWD forecasting system capabilities
2016 – 2020
For ICON see Zängl et. al. 2015
For ICON-ART see Rieger et. al. 2015
ICON + Aerosols and Reactive Trace Gases
©KIT
©DWD, MPI
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ICON-ART
Online coupled extension of the NWP system ICON by ART modules
+6 prognostic equations for mineral dust
Specific number and mass mixing ratio for 3 modes
Transport and diffusion for ART tracer as for atmospheric variables (such as moisture)
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ICON and ART (Aerosols and Reactive Trace Gases)
Illustration from Rieger et. al. 2015
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DUST
Motivation for PerduS
NWP forecast errors due to aerosols
direct, semi-direct indirect effects
Polluted PV modules
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PerduS
Main objective:
Improvement of PV power forecasts during Saharan dust outbreaks on a regional and national scale
Therefore:
Use and improvement of the model system ICON-ART
• dust emission
• optical properties of mineral dust
• washout of aerosols
Consideration of polluted PV panels and cleaning due to precipitation
High quality observations
Quasi-operational application
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PerduS: Washout of Aerosols
Example of specific research topic: washout of mineral dust
Developed at KIT (Rinke, 2008)
λ: scavenging of aerosols by raindrops
Collision Kernel K f(circle area, fall velocity of rain droplet)
Collision Efficiency E
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precip.
PerduS: Washout of Aerosols
Sensitivity concerning the distribution of rain droplets
Class of precipitation:
• light • moderate • heavy
( #/m3)
Marshall-Palmer Raindrop size distribution
Forecasted precipitation: gsp: grid scale con: subgrid scale convective
gsp + con
gsp
con
con
gsp
rr_sfc
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PerduS: Washout of Aerosols
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PerduS: Washout of Aerosols
Picture source: NASA Worldview: Corrected Reflectance (True Color) Terra/Modis ( https://worldview.earthdata.nasa.gov/)
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PerduS: Washout of Aerosols
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PerduS: Washout of Aerosols
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gsp+con
rr_sfc
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PerduS: Washout of Aerosols
gsp
con
rr_sfc
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PerduS: Washout of Aerosols
gsp
con
rr_sfc
Evaluation over extended period
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ICON-EU-(NA)2
40 km
20 km
Outlook PerduS
Introduction of a dedicated assimilation cycle for PerduS forecasts
Introduction of ICON-EU-(NA)2-Nest
Ongoing model improvement
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NWP test bench:
Daily mineral dust forecasts, different long term sensitivity studies
Alert System
Near Future:
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Summary
German Energiewende (31.7 % RE in 2016) poses a new challenge
New target variables, specific important weather situations
Joint improvement of power- and weather forecasts necessary
Close collaborations between energy sector and meteorology needed
Thanks to all contributors:
Projects: EWeLiNE, ORKA, ORKA II, PerduS, grid cast
- Identify model deficiencies
- Develop and test model improvements
- RE-variables and problematic firmly established in DWDʼs development strategies
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Acknowledgements
Project staff:
Permanent staff @ DWD, KIT
DWD: Dr. Andreas Röpnack, Tobias Reinartz, Dr. Stefan Declair, Tobias Heppelmann, David Hansmeier, IWES-colleagues, TSOs, DSOs Alumni.: Dr. Kristina Lundgren, Dr. Carmen Köhler, Isabel Metzinger, Dr. Vanessa Fundel, Dr. Harald Kempf, Dr. Annika Schomburg, Andrea Steiner, Alexandros Bouras, Dr. Tobias Tröndle, Zied Ben Bouallegue, Dr. Gernot Vogt
DWD: Jonas von Schumann, Dr. Regina Kohlhepp, emsys-colleagues, TSOs, DSOs
DWD: Vanessa Bachmann, Andrea Steiner, Florian Filipitsch, KIT: Frank Wagner, Gholamali Hoshyaripour, meteocontrol-colleagues
ORKA, ORKA II
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Literature BMWI a), 2017: Zeitreihen zur Entwicklung der erneuerbaren Energien in Deutschland, Bundesministerium für Wirtschaft und Energie,
Arbeitsgruppe Erneuerbare Energien – Statistik, 45, http://www.erneuerbare-energien.de/EE/Navigation/DE/Service/ Erneuerbare_Energien_in_Zahlen/Zeitreihen/zeitreihen.html (last accessed 06 June 2017)
BMWI b), 2017: For a future of green energy, Bundesministerium für Wirtschaft und Energie, Working Group on Energy Balances, online: http://www.bmwi.de/Redaktion/EN/Dossier/renewable-energy.html (last accessed 06 June 2017)
Burger, B., 2017: Stromerzeugung in Deutschland im Jahr 2016, online: https://www.ise.fraunhofer.de/content/dam/ise/de/ documents/publications/studies/Stromerzeugung_2016.pdf (last accessed 12 June 2017)
Heppelmann, T., A. Steiner, S. Vogt, 2017: Application of numerical weather prediction in wind power forecasting: Assessment of the diurnal cycle. Meteorologische Zeitschrift, 26, 319-331
Rieger, D., Bangert, M., Bischoff Gauss, I., Förstner, J., Lundgren, K., Reinert, D., Schröter, J., Vogel, H., Zängl, G., Ruhnke, R., and Vogel, B., 2015: ICON–ART 1.0 – a new online-coupled model system from the global to regional scale, Geosci. Model Dev., 8, 1659–1676
Rieger, D., Steiner, A., Bachmann, V., Gasch, P., Förstner, J., Deetz, K., Vogel, B., and Vogel, H.: Impact of the 4 April 2014 Saharan dust outbreak on the photovoltaic power generation in Germany, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-441, in review, 2017.
Rinke, R., 2008: Parametrisierung des Auswaschens von Aerosolpartikeln durch Niederschlag, PhD-Thesis, University Karlsruhe Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-
M:Description of the non-hydrostatic dynamical core, Q. J. Roy. Meteor. Soc., 141, 563–579, doi:10.1002/qj.2378, 2015.
Data source: ENTSO-E (European Network of Transmission System Operators) Transparency Platform, 2017: Central collection and publication of
electricity generation, transportation and consumption data and information for the pan-European market, online: https://transparency.entsoe.eu/, (last accessed 12 June 2017)
Related contributions @ ICEM 2017: Bachmann, Vanessa: The Project PerduS: Improvements in photovoltaic power forecasts during Saharan dust episodes over Europe. Talk in
the session „Weather and Climate Services for Energy – Radiation and Wind“ Wagner, Frank: Influence of Desert Dust Outbreaks on Radiation and Photovoltaic Systems. Talk in the session „Weather and Climate
Services for Energy – Radiation and Wind“ Schumann, Jonas: Optimized COSMO-DE Ensemble forecasts for renewable energies and current-carrying capacity, poster presentation Reinartz, Tobias: Graphical user interface for weather forecast products optimized for the needs of renewable energy industry, poster
presentation
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Summary
German Energiewende (31.7 % RE in 2016) poses a new challenge
New target variables, specific important weather situations
Joint improvement of power- and weather forecasts necessary
Close collaborations between energy sector and meteorology needed
Projects: EWeLiNE, ORKA, ORKA II, PerduS, grid cast
- Identify model deficiencies
- Develop and test model improvements
- RE-variables and problematic firmly established in DWDʼs development strategies
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Thank You!
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Special weather situations
WIND:
Low pressure systems, fronts
Diurnal cycle, Low Level Jets, vertical mixing
Winter positive bias
(Icing)
SOLAR:
Convective events
Subscale clouds after cold front passage
Low stratus clouds
Aerosol Optical Depth
Solar eclipses
(Snow / Dust on PV panels)
NWP forecast uncertainty propagate into power forecasts