Energy Utilities
enterpriseTarget: Grid Operator
If you are a grid operator dealing with unstable power flows from solar and wind — this project developed AI forecasting and Galileo-enabled PMUs that ensure a stable supply/demand balance.
RESPONDENT · Project
AI-Powered Renewable Energy Forecasting and Grid Synchronization System
energyPilotedTRL 7
Imagine trying to run a city's power grid when the sun hides behind clouds and wind stops blowing unexpectedly. This system acts like a high-tech weather crystal ball that tells power companies exactly how much energy they will get and how much people will use. It also uses satellite timing to keep the entire electrical network ticking in perfect unison, preventing crashes.
By the numbers
16
additional PV parks added for algorithm training
8
consortium partners
2
realized pilots
The business problem
What needed solving
Renewable energy is unpredictable and fluctuates based on weather, making it hard to integrate into old power grids. This causes instability and imbalances between how much power is made and how much is used.
The solution
What was built
An AI/ML power generation and demand forecasting platform and Galileo-enabled Phasor Measurement Unit (PMU) prototypes for precise grid timing.
Audience
Who needs this
Transmission System Operators (TSOs)Distribution System Operators (DSOs)Solar and Wind Farm OperatorsSmart Grid Hardware Manufacturers
Business applications
Who can put this to work
Renewable Energy Production
mid-sizeTarget: Solar Park Operator
If you are a solar park operator dealing with unpredictable energy output — this project developed a forecasting module tested on 16 PV parks that predicts generation based on Copernicus satellite data.
Electrical Hardware Manufacturing
SMETarget: Smart Meter/PMU Manufacturer
If you are a hardware manufacturer dealing with timing drifts in grid monitoring — this project developed PMU prototypes integrated with Galileo receivers for precise synchronization.
Frequently asked
Quick answers
What is the cost or pricing for this solution?
Based on available project data, no specific pricing or cost structures are provided.
Can this be scaled to an industrial level?
Yes, the project demonstrated scalability by adding 16 additional PV parks to the forecasting algorithm and implementing two distinct pilots.
Who owns the IP and how is it licensed?
Based on available project data, specific IP and licensing terms are not disclosed.
How is the system integrated into existing grids?
Integration is achieved through a forecasting module integrated into the RESPONDENT platform and the use of Galileo-enabled Phasor Measurement Units (PMUs).
What is the timeline for deployment?
The project period ran from 2022-11-01 to 2025-04-30, with the forecasting module finalized in the 7th quarter.
Consortium
Who built it
The consortium is heavily industry-driven, with 6 SMEs and 2 research entities across 3 countries (EL, ES, IE). With a 75% industry ratio, the project is geared toward commercial application rather than pure academic research, focusing on practical deployment via SMEs.
How to reach the team
Contact FUTURE INTELLIGENCE EREVNA TILEPIKINONIAKON KE PLIROFORIAKON SYSTIMATON EPE in Greece
Next steps
Talk to the team behind this work.
Contact us to explore licensing the Galileo-enabled PMU prototypes or the AI forecasting module.
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