Climate forecasting for energy: online workshop: 4 Dec 2020

Scientific meetings
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Workshop details and call for presentations

  • scroll down for presented material in subsequent postings
  • details of the session recordings will be announced by S2S4E in due course (this remark timestamped 2020‑12‑19)

Friday 4 December 2020 10:00 +0100 (CET)

The S2S4E project and the Open Energy Modelling Initiative (openmod) are running a joint one‑day workshop on improving the uptake and use of subseasonal to seasonal (S2S) climate forecasts in energy system models and allied decision-making.

S2S4E is the Climate Services for Clean Energy project, funded under Horizon 2020.

Subseasonal to seasonal climate forecasts look forward weeks to months and have the potential to support better management of weather-related risk amid the growth of renewable energy. Examples of possible applications include: maintenance scheduling, energy trading, security of supply estimation, and storage management. Effective use of S2S forecasts for energy can help to reduce risk, enhance profitability, and accelerate the transition to renewable energy.

One aim of the workshop is to improve connections between the energy modeling and climate forecasting communities. To date there has been relatively little overlap. Energy system modelers, in particular, are encouraged to propose 8 minute lightning talks that describe their use of climate information or alternatively outline ways in which climate scientists might better package such information to meet modeling needs. To underscore that last point: agenda setting by articulating currently unmet requirements and use‑cases is sought.

Several of the presentations from climate scientists will explain current and future capabilities as well as indicate emerging roles for S2S climate forecast information. Established public climate databases will also be reviewed.

Outline

The following table provides an outline of the event. Please see the official flier for full details of the presentations and speakers. All timestamps are +0100 (CET). The official agenda is summarized below.

Time Item Topic
S2S4E project Subseasonal and seasonal climate forecasts for energy
10:00 Welcome
10:05 Presentations Climate information and application to energy analysis
11:10 Panel discussion Supporting the use of climate forecasts in energy
11:30 Break
12:00 Presentations Research advances and emerging opportunities
13:00 Lunch break + Posters
S2S4E/openmod Joint session
14:00 Welcome
14:10 Presentations Practical use of climate data for energy analysis
15:00 Break
15:20 Presentations Energy modelers describe their use of climate information
16:20 Panel discussion Bridging the information divide (more below)
16:50 Wrap‑up
Informal
17:00 Virtual drinks + Posters
18:00 Close

Research presentations and posters from energy system modelers

The 15:20 time slot is for five lightning talk presentations from energy system modelers and energy analysts. Each presentation is allotted 8 minutes followed by 2 minutes for questions.

Please describe your presentation in the posting below using the same system that was used for the openmod mini‑workshops held earlier this year. If more than five proposals are received, the scientific committee will adjudicate.

In addition, posters are sought. The S2S4E project are handling the submission of titles and artwork. The posters sessions will take place using the spatial.chat platform whereby attendees can push their assigned avatars toward individual posters in order to engage with the presenter and anybody else who happens to be standing nearby.The call for presentations also closes on Friday 27 November 2020 at 17:00 +0100 (CET). PDF files should likewise be forwarded on or before Wednesday 2 December 2020 to s2s4e@bsc.es.

Contributors are encouraged to add Creative Commons CC BY 4.0 licenses to their material to facilitate their dissemination and reuse.

Background

Under the Paris climate agreement, governments committed to limit the global temperature rise this century to well below 2°C. Decarbonizing energy is widely seen as a major step toward in achieving this commitment, and reliance on renewable electricity generation — particularly from wind and solar — therefore continues to increase. Renewable energy production is weather-dependent and it can therefore be difficult to anticipate how much electricity will be produced at any given time in advance. Integration of renewables thus poses new challenges for the management, operation, and design of power systems. Increasingly skillful subseasonal to seasonal (S2S) climate forecasts on timescales of weeks to months ahead therefore have the potential to support better management of weather-related risk amid the growth of non‑firm renewable energy.

The use of climate data in energy applications (particularly S2S forecasts) is a rapidly developing field of research and innovation, but major challenges remain due to the complexity of information involved. This workshop therefore seeks to address this by discussing:

  • the science basis of climate forecasting
  • the use of climate data in energy modelling and decision making
  • state-of-the-art research advances in the use of climate data in energy modelling

Organizing committee

The scientific organizing committee comprises: David Brayshaw and Hannah Bloomfield (Energy Meteorology Group, University of Reading, United Kingdom), Robbie Morrison @robbie.morrison, Ekaterina Fedotova @ekatef, Alex Kies @alexkies, Anne Fouilloux, and Martin Dorenkamper (openmod), Isadora Jimenez, Albert Soret, and Andria Nicodemou (Barcelona Supercomputing Center, Spain), and Erlend Hermansen and Jana Sillmann (Centre for International Climate and Environmental Research, Norway).

Recording permissions

The workshop will be recorded and the recordings will be made available after the event through the S2S4E channels (website, YouTube, and social media).

Participants can license their contribution under a Creative Commons CC BY 4.0 license at the time of registration. This will permit the associated video recording to be uploaded to YouTube for a wider audience and downstream reuse. In addition, presenters can optionally license their presentations and posters under a Creative Commons CC BY 4.0 license. This will also facilitate their dissemination and reuse.

Attendees and presenters should note that open licensing is optional.

About the openmod

The Open Energy Modelling Initiative (openmod) is a loose network of energy system modelers and energy analysts also interested in advancing open science. The openmod began with a workshop in Berlin, Germany in September 2014. Its most recent (pre‑covid) three‑day workshop, again in Berlin, attracted 190 researchers. The openmod currently has circa 700 participants on its mailing list and on its discussion forum. For more background:

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Presentations

Only those PDFs with open access licensing can be made available for download from this site.

Morning presentations

The presentation sessions at 10:05 and 12:00 were administered by the S2S4E project. Unlike the afternoon session, the presentations are listed without abstracts.

Afternoon presentations

Lightning talk presentations for the 15:20 time slot.

COP26 climate data hackathon brainstorm

  1. COP26 climate data hackathon. The final ten minute slot is now devoted to discussions on a climate data hackathon scheduled for 22–26 March 2021. There will be an initial “challenge brainstorm” event on 25 February 2021 for the community to generate ideas to take forward and develop during the main hackathon event.

    The hackathon is motivated by the next COP26 climate negotiations in Glasgow and is supported by the UK Met Office, University of Oxford and Reading University. This brainstorming session seeks input from participants regarding the format and goals of the data hackathon, who might potentially contribute, and how to make contact.

List of afternoon presentations

  1. Paul Westermann @pwest. Building energy surrogate models that span multiple climate zones. Machine learning surrogate models are being trained on building energy simulation in- and output data. Their key advantage is their computational efficiency, which allows modellers to explore building design performance in fractions of a second. However, these surrogate models are currently bound to the specific building energy simulation model, that was used for generating the training data set. In this study, we show how we can break that boundary by using a deep convolutional neural network which can process large annual hourly weather data. This allows the surrogate model to expand over all climates and modellers can assess the impact of climate on the building energy performance rapidly.

    To showcase the use of surrogate models they span multiple climates, we host our surrogate models on the platform www.buildingenergy.ninja. The surrogate models take building design parameters and annual hourly weather data as inputs, and produce annual hourly building loads as outputs.

  2. Adriaan Hilbers @ahilbers. Efficient quantification of the impact of climate uncertainty in energy system models. Recent studies indicate that the effects of climate uncertainty in energy system models should not be ignored. For example, running the same model with different years of demand and weather data (e.g. 2018 vs. 2019) may lead to significant spreads in outputs, and picking the “wrong year” of climate data may lead users to suboptimal energy strategy. For this reason, quantifying the impact of climate uncertainty in energy system models (creating confidence or prediction intervals) allows more robust decision-making. The standard approach involves running a model multiple times using different samples of demand and weather data. However, this is infeasible in many energy settings due to limitations in data (many different samples unavailable) or computing (many expensive model runs infeasible). In this presentation, we introduce a method that runs models across shorter samples and rescales uncertainty bounds in a statistically robust way, reducing both the data and computational burden. The paper, models, data and sample code can be found here.

  3. Ekaterina Fedotova @ekatef. Climate change impacts on the energy system under the fossil fuel curse. I’ll highlight the research works assessing the climate change impacts on the Russian energy system. A series of studies has addressed the following questions:

    • integral impacts of the warming climate on the national energy system
    • evolution of the renewable energy potential under the climate change
    • possible climate change effects on the renewables integration into power systems

    It has been shown that, while rapidly warming winters result in a significant heating demand decrease, the climate change associated shift in the load patterns is likely to create additional obstacles in escaping the fossil fuel trap. A combination of the energy systems simulation with reliable climate data seems to be crucial in resolving this issue.

  4. Adriaan Hilbers @ahilbers. Open energy system modeling for climate scientists and others. A tutorial presentation on the types of models developed and used within the open energy modeling community. It includes a simple tutorial on how climate and weather data is used in energy system models, as well as introducing a simple energy system model, designed as an introduction to the topic for climate scientists. The model, data and tutorial are available on GitHub under an MIT license:

  5. Bruno Schyska @Bruno. The sensitivity of power system expansion models on
    meteorological parameters    . Power system expansion models are a widely used tool
    for planning power systems, especially considering the integration of renewable
    resources. Studies using these models form the basis for far-reaching political
    decisions. The backbone of power system models is an optimization problem, which
    depends on a number of economic and technical parameters. Although these parameters
    contain significant uncertainties, a consistent way to quantify the sensitivity to
    these uncertainties does not yet exist. Here, we analyze and quantify the
    sensitivity of a power system expansion model to the meteorological parameter time
    series based on a novel misallocation metric. We find that the sensitivity to the
    weather data is in the same order of magnitude as the sensitivity to the definition
    of cost. By comparing different climatic periods we can, additionally, identify
    representative weather years and periods which should rather not be used for
    expansion planning. A preprint of the corresponding paper can be found
    here.

    • Schyska, Bruno U, Alex Kies, Markus Schlott, Lueder von Bremen, and Wided Medjroubi (4 December 2020). The sensitivity of power system expansion models on meteorological parameters — Presentation. Oldenburg, Germany: Institute of Networked Energy Systems, German Aerospace Center (DLR). Presented at the Climate Forecasting for Energy online workshop. CC‑BY‑4.0 license.
    • 2020-schyska-etal-sensitivity-power-system-expansion-models-meteorological-parameters.pdf (1.2 MB)
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Additional resources

This posting lists some additional resources.

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16:20 panel discussion on “information bridges”

The following panel discussion is scheduled for Friday 4 December 2020 at 16:20 +0100 (CET) using the Zoom platform. Registration is required — see the top posting for details.

Topic: Bridging the information divide between climate science and energy modeling

The idea is to explore the information connections between climate scientists and energy system modelers and analysts. In more concrete terms, these bridges may include:

  Type Comment
1 data bridges piping data from one model type to the next
2 educational bridges helping the communities connect academically
3 research collaborations establishing better and deeper partnerships
4 joint end-user support providing results and context to industry and for public policy
5 distributed data architectures supporting reproducible workflows and complex data integration

On the last point 5, the energy modeling community has been engaging with the DBpedia Databus project.

Panelists

Panelist Affiliation
Roberta Boscolo
 		 Climate and Energy Science Officer
World Meteorological Organization (WMO), Switzerland
Alberto Troccoli
 		 Founder and Managing Director
World Energy and Meteorology Council (WEMC), United Kingdom
Sofia Simões
 		 Head of Unit, Resource Economics Unit
National Laboratory of Energy and Geology (LNEG), Portugal
Ralph Evins
@ralphevins 		Director, Energy in Cities lab
University of Victoria (UVic), Canada

The panel will be moderated by Robbie Morrison @robbie.morrison and Ekaterina Fedotova @ekatef. With questions taken via the Zoom chat dialog in the first instance.

The slides with Creative Commons CC‑BY‑4.0 licenses are able to be distributed:

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Poster sessions

The poster sessions and the final drinks will be run using the spatial.chat platform. There are a couple things to watch out for:

  • the Chrome browser is recommended but not essential
  • you may need to close Zoom COMPLETELY to allow spatial.chat access to your camera and microphone — otherwise make sure your mic is muted and your camera off in Zoom before switching applications
  • the organizers will have little badge icons and can be approached for assistance
  • space is limited so please LEAVE the session when you’ve finished wandering around
  • it is also considered antisocial to park your bubble over the top of posters and obscure their contents

More hints from the organizers:

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The following posters were suitably licensed for distribution:

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Official agenda

The official agenda for reference (click on images to enlarge and cycle through). Timestamps in +0100 (CET) and the day is Friday 4 December 2020.

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s2s4e-openmod-event-agenda-afternoon
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Poll results

An optional anonymous mentimeter poll comprising 7 questions was held during the event (click and click again to enlarge):

  1. Which domains do you work in?
  2. Which contexts do you work in?
  3. Do you consider the influence of a changing climate in your [energy system] models?
  4. (For climate scientists) How would your rate your knowledge of energy system modeling?
  5. How important is it that scientific models are open source?
  6. How important is it that public interest data be open licensed?
  7. And how would you rate the quality of metadata in the datasets you normally encounter?

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And the native Inkscape 1.0 SVG file for those who might need access to higher quality graphics:

My thanks to Isadora Jimenez, Barcelona Supercomputing Center for helping set up the poll.

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Climate forecast information for energy system analysts

The following two presentations provide excellent background for energy system modelers wishing to incorporate climate forecast information into their models — and particularly operational models:

The following diagram (adapted from Andrea’s presentation) shows where the sub-seasonal to seasonal time horizons fit:

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Recordings of the two presentations mentioned above should be available shortly and I will post back here when they are published.

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Collected downloads

This posting collects all the downloadable material in one place for convenience. All the material listed is licensed Creative Commons CC‑BY‑4.0.

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Participation

Zoom collects information on registrations and on participation:

Participation and duration

attribute value
registrations 400
participants 285
stayed 20 minutes or more  264
cumulative hours 927

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Plot of sorted person number versus duration. The nominal duration excludes the final hour for posters and virtual drinks. Some participants may have left the workshop during the breaks and logged on again later.

Regional spread

The geographic spread of registrations was diverse, bearing in mind adverse timezones and language issues:

count region
79 Germany
41 United Kingdom
33 USA
30 Spain
27 Italy
16 France, Poland
14 Denmark, Norway
10 Austria, Sweden
8 Belgium
7 India
6 Finland, Switzerland, Turkey
5 Netherlands
4 Canada, Croatia, Luxembourg, Portugal
3 Australia, Czechia, Nigeria
2 Bangladesh, Brazil, Indonesia, Ireland, Israel, South Africa
1 Albania, Argentina, Burkina Faso, China, Ecuador, Egypt, Haiti, Hungary, Jordan, Kazakhstan, Kenya, Latvia, Malaysia, Mexico, Romania, Russian Federation, Senegal, Serbia, Singapore, Sudan, Tunisia

CC BY 4.0 open license status

Registrants were asked:

  • The meeting will be recorded and we would like to distribute the online recordings under an open license. Do you consent to your contributions being released under a Creative Commons CC BY 4.0 license?

And 90% opted to open license:

response count
Yes 366
No 33
No (but perhaps next time!) 1
Text and images licensed under CC BY 4.0.  Data licensed under CC0 1.0.  Code licensed under MIT.  Site terms of service.  Openmod mailing list.  Openmod wiki. Openmod YouTube.