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Neighbours must share a transformer.
P2P ENERGY
MARKETPLACE
GERMANY
2026
Stromteilung: helping neighbours
share solar power.
I designed the MVP and its price calculator for a new German energy-sharing law. It won the EU Energy Hackathon 2026 in Berlin.
THE PROBLEM
A new law, but no way to use it.
Since June 2026, people with solar panels in Germany can sell their extra power to neighbours. Today they get about 7.71 ct/kWh from the grid, while a café nearby pays around 39 ct.
But there's no place to find each other, and grid fees and taxes still apply. Nobody could tell if sharing was worth it.
KEY FEATURE
Show the real price for your postcode.
The first thing people ask is "what will it cost me?" So the landing page starts with a calculator. Enter a postcode and see the full price, both sides' gain, and your local grid operator.
To make it work anywhere, I built a dataset with Claude that prices all 10,813 German postcodes against their real grid operator. Claude read the price sheets; I checked the regional boundaries and fixed the ones it got wrong.
28.4 ct vs 38.9 ct
What a Munich buyer pays per kWh, against basic supply.
10 ct vs 7.71 ct
What the seller earns, against the feed-in tariff.
10,813 postcodes
Priced against their real grid operator, so the calculator works anywhere in Germany.
WHAT CHANGED
Reading the law changed the design.
The first flow matched people by transformer number, like most energy-sharing ideas. The actual text of § 42c said otherwise.
What we assumed
What the law says
So we changed the design
Anyone in the same grid area can trade, often a whole city.
Your postcode now detects your grid area.
The 4.5 km radius decides who can trade.
It only decides whether electricity tax is waived, for plants up to 2 MW.
Sellers enter plant size, and the tax saving shows only when both rules are met.
A big savings headline sells it.
Grid fees always apply, so savings are only on the shared part.
The calculator shows the full cost, not a single save-X% number.
What we assumed
Neighbours must share a transformer.
The 4.5 km radius decides who can trade.
A big savings headline sells it.
What the law says
Anyone in the same grid area can trade, often a whole city.
It only decides whether electricity tax is waived, for plants up to 2 MW.
Grid fees always apply, so savings are only on the shared part.
So we changed the design
Your postcode now detects your grid area.
Sellers enter plant size, and the tax saving shows only when both rules are met.
The calculator shows the full cost, not a single save-X% number.
WHAT WE BUILT
The MVP: postcode to trade in 4 steps.
German and English, light and dark, desktop and mobile. Behind it: user journeys, task flows, 92 screens and states, and a design system.
- 1
Check
Price calculator
Enter a postcode. See your grid operator, the full cost and what each side gains.
Add screensPrice calculator - 2
Join
Onboarding
Pick a role. Sellers add plant size and prices; buyers add monthly demand.
Add screensOnboarding - 3
Match
Listings
Sellers manage their listings. Buyers see sellers in their grid area, sorted by cheapest or top rated.
Add screensListings - 4
Connect
Contact and rate
One tap opens WhatsApp with the seller. Buyers rate the trade out of 5.
Add screensContact and rate
Add screensPrice calculator
THE OUTCOME
Won the EU Energy Hackathon 2026.
We presented Stromteilung in Berlin and won. The design was recognised by Ekaterina Zaharieva, EU Commissioner for Startups, Research and Innovation.
1st
EU Energy Hackathon 2026
10,813
postcodes priced
92
screens and states
What comes next
1
Talk to real users
Plant owners leaving feed-in tariffs, and cafés paying household rates.
2
Live meter data
Connect smart meters so both sides see real usage.
3
Contracts and payments
Move from connecting people to a full trade in one place.
WHAT I'M TAKING WITH ME
Looking back: 3 lessons.
1
Read the law before drawing the map.
Our first matching model came from the common idea of energy sharing, not from the text of § 42c.
2
Price is the hook. Honesty keeps it.
Showing the full cost makes the saving look smaller, and easier to believe.
3
AI scales the data. Local knowledge corrects it.
Claude priced 10,813 postcodes fast. Checking grid boundaries region by region made the numbers right.
NEXT CASE STUDY
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