Rewarding Prosumers for
Contributing Green Energy to the Grid
by Certifying their Energy Production

Solution of Frontier Oost for the challenge of Nuon/Vattenfall

WINNING TEAM: FRONTIER OOST

THE CHALLENGE

CREATE THE KEY ENABLER FOR ENERGY TRANSITION

Feeding green energy into the system requires a certification process, as all producers must be able to prove and verify that their energy is truly green. Small-scale green energy production, for instance by households with solar panels, currently cannot be labeled and certified as ‘green’ to be fed back into the system as such: after all, who can prove it was really the solar panel producing the added energy?

This means there are currently a lot of ‘grey’ energy producers trading on the green energy market. Can we come up with a solution for smaller producers to certify their green energy, to have their data validated in (near) real-time?

To answer this challenge, Vattenfall has invited the grid and energy data companies to work alongside the teams and possibly create a European standard. The goal was to create one of the key facilitators to leapfrog the energy transition, with an open protocol to ensure broad adoption and no additional hardware.

SOLUTION

#odysseyhack

Rewarding prosumers for contributing green energy to the grid by certifying their energy production

There is a giant black hole under the radar of green energy that is being produced by prosumers, as a household with solar panels right now is too small to be a ‘certified green energy producer’. These small producers should be rewarded for their ‘green’ energy, selling it at a higher price than ‘grey’ energy. The solution incentivizes them to produce more green energy than they need themselves.

The Frontier Oost solution predicts the green production, to certify the part we didn’t know before. Four components we built that combine to the larger puzzle piece required by the problem.

(1): Data aggregator – This component aggregates the required data on household level. It uses satellite images for the recognition of the amount of solar panels, which gets aggravated with historical weather data. Finally, we connect this component to the smart meter data gathered by an energy supplier.

(2): Energy Generation Prediction model – As only a relatively small percentage of households share their data, our prediction model fills in the gaps.

(3): Data enrichment component – Prosumers can enrich the data model themselves by supplying their unshared smart meter data in our front-end.

(4): Certificate publication & P2P exchange  – After a prosumer has supplied their data, their certificates are minted and send to them on our pan-European green certificate blockchain, where they can store, transfer, and burn them to use them.

Using AI, it (1) automatically determines the amount of solar panels on a house’s roof using a trained Convolutional Neural Network and (2) fill in the blanks of our prediction model to boost the confidence level required for the certification process. It automates the process of finding deciding variables, and calculates a reliable energy prediction.

Using blockchain, the solution enables the prosumer to claim their rightful green energy certificates. These get claimed on a certificate blockchain, that enables the prosumer to store and transfer them at low cost, and without a trusted third party.

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