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

4th ICEM, Bari, Italy

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31.7%

Data source: BMWI a) (2017)


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


Burger (2017)

ANTJE

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Headline in 2017

Sunday, 30 April 2017:

Renewables cover 64 % of Germany’s daily electricity demand


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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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


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



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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


ref impr


←sunrise

sunset→

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Demonstrator

User oriented forecast products


provided by Tobias Reinartz, DWD

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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)


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.


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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Picture source: NASA Worldview: Corrected Reflectance (True Color) Terra/Modis ( https://worldview.earthdata.nasa.gov/)


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gsp+con

rr_sfc

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gsp

con

rr_sfc


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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


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


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

smart ideas for a smart energy sector: user-optimized ... · 6/29/2017 · gross electricity...

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