Tom Brown

🚨 New paper: Coupling integrated assessment and energy system models 🚨

Planning future energy systems requires bridging two fundamentally different scales: long-term transformation pathways spanning decades, and short-term power system operations unfolding hour by hour.

#EnergySky #PyPSA

🧵

Just mind blowing. Solar and batteries can power this world. Pretty much.

I've added new results to this post on solar and batteries taking over the world, include system cost breakdowns in components (solar, battery, fuel), simulations with much lower battery costs in 2050 (e.g. 50 €/kWh, also 20 €/kWh), curtailment, etc., as well as making data files available. Enjoy!

🇪🇺 New PyPSA-Eur v2026.02.0 release for European open-source energy system modelling!

Many thanks to all of the 34 community contributors to this release for a total of 200 pull requests!

pypsa-eur.readthedocs.io/en/latest/re...

Highlights include:

#PyPSA #OpenSource #OpenData #EnergySky

Better coordination for a more efficient European energy system EU energy efficiency hinges on open data, integrated grid planning and aligned national plans as electrification raises the cost of fragmentation

NEW🚨EU energy markets will be high on the agenda of EU leaders at the upcoming European Council in March. In this new paper, we propose 3 steps to improve the efficiency of the EU energy system:
🖥️ Open data
🔌 Integrate network planning
📚Alignment of national plans

www.bruegel.org/policy-brief...

On a more technical point, it would be good to redo the above analysis with better insolation data than ERA5 and with axis tracking, could make a big difference to some of the results.

That was such a nice paper!

Did you save your old Twitter posts before deleting your account?

Maybe they're saved on nitter.net or archive.org?

I salvaged my old threads to a blog:

nworbmot.org/blog/

Wind is also very helpful. The rest can be covered by existing hydro, longer duration storage or even e-biofuels (that we need for other sectors anyway).

Here are the equivalent graphs with wind, which really helps in the North.

I wasn't making any assumptions in the main thread about per person energy consumption, just the cost of the electricity. But lower down the blog I throw around some rough numbers, assuming 10 MWh/a/person with electrification and equalisation. A bit more than 1 kW/person.

The map for solar-wind-batt is on the blog too:

It's a few posts further down:

bsky.app/profile/nwor...

Sorry, I had to correct a small bug in one graphic, very annoying there's not an edit button. Here's the corrected thread:

bsky.app/profile/nwor...

Full details on assumptions and more results can be found in the blog post:

nworbmot.org/blog/solar-b...

Please check it out first if you have questions. To get you started, here's a snapshot of the main assumptions.

- The more expensive regions are at high latitudes in the North, where seasonality makes it attractive to include wind and other sources.

- We can get far without worrying about the last 5-10%. The last 5-10% could be fossil fuels in the short-term, long-duration storage, or storeable e-biofuels.

What conclusions can we draw?

- Solar and batteries have the potential to dominate electricity supply in most regions of the world, providing cheap and clean power.

- Where there is enough space, this supply can be provided close to demand (only minimal grid costs were included in the results).

If we add wind in the case of 2030, this helps reduce costs particularly in high northern latitudes, where the wind tends to blow more strongly in the winter.

With projected cost reductions for solar and batteries in 2050, 80% of the population are below 60 €/MWh for 90% solar-battery supply, and 93% are below 80 €/MWh for 95% solar-battery supply.

The graph shows the results for 90%, 95% and 99% solar-battery supply. The high-latitude regions in particular have difficulty with the last 1-5% because of low winter sunshine.

For 80% of the population it is below 80 €/MWh. The more expensive locations are all at high latitudes in the North, where low sunshine in the winter increases backup costs. In these regions the addition of wind, existing hydro or other energy sources would help to alleviate the higher costs.

The map shows the total average system cost of providing electricity for a constant hourly demand in 2030 with solar and batteries providing 90% of the electricity and some storeable fuel the final 10%.

More precisely: solar and batteries can supply 90% of electricity for 80% of the world's population at less than 80 €/MWh (including a fuel backup) with 2030 assumptions. Add some wind, existing hydro, or wait a few more years for costs to decline, and the equation just gets better.

Solar and batteries are cheap enough that most people can get most of their electricity from them, and save money. This equation gets better and better over time as their costs decline.

All details in a new blog post: nworbmot.org/blog/solar-b...

Wie gefährlich ist Deutschlands Gasabhängigkeit wirklich? Guten Tag! Herzlich Willkommen zur ersten Ausgabe meines Klima & Energie Newsletters. Nach 2 ½ Jahren weitgehender Funkstille melde ich mich hier mit diesem…

Dass Deutschlands Gasspeicher auf unter 30% Füllstand gesunken sind, hat hier ja eine kleine Diskussion ausgelöst, ob das nun gefährlich ist oder nicht. Ich habe das deshalb zum Anlass genommen für meinen ersten Blog/Newsletter-Beitrag:
steady.page/de/climate-a...

YouTube video by Fabian Neumann Data Science for Energy System Modelling - Lecture 1: Introduction

I have been teaching 'Data Science for Energy System Modelling' at TU Berlin for a while. This semester, I recorded the lectures to accompany the revamped, fully open course website:

🎓Course Website: fneum.github.io/data-science...

🎥 YouTube Playlist: www.youtube.com/watch?v=Bqvb...

What if you could visualise how electricity flows from a wind farm in Scotland to your home in London?

Ok it's not quite that simple, but it got me thinking that there must be a way of visualising the complexity of the electricity transmission grid in Great Britain.

This is my first attempt…

Sorry, should have tagged @agoraew.bsky.social not @agoraind.bsky.social, BlueSky when do we get the edit button?!

Existing stakeholders may find that scary, but it will help the energy transition in the long-run.

Tools like PyPSA-SPICE and others can help lower the barrier to running these tools.

#freethemodels

a) we need lots of folks enabled to think about innovative ways to drive it forward faster and b) we need high levels of general involvement to enable stakeholder buy-in for all the changes it brings. Open modelling can help get more folks involved, **that** is the big selling point.

Open modelling is much more about **process**, about involving existing and new stakeholders in the process of energy system planning. We don't have a perfect roadmap where we're going with the energy transition so

Long-term utopic vision: A set of open models recognised by industry, academia, government and NGOs. - Grid operator X uses model to show network expansion is required under assumptions Y - Academic Z shows changing regulation A would require less grid expansion - Regulator C adapts regulation correspondingly - NGO D shows in the model that stronger efficiency measures at reasonable cost could reduce build-out of onshore wind in an area of high bird and bat biodiversity - Government F takes note, increases incentives for efficiency measures - Public confidence in the Energy Transition rises This is difficult in the current fragmented model landscape, since many models are closed black boxes and there is neither comparability nor common sets of assumptions.

I want to make a few comments and highlight slide 6 in my deck.

Sometimes in the discussion around open energy modelling it's suggested that if everyone just switched from PLEXOS to an open source framework, or put their model data online, then we're done.

That's not how I see it.