smartbalance·Cloud Technologies · Data
SmartBalance
Cloud Technologies · Data Development
An edge-AI platform that combines solar generation, biogas, storage, and EV charging into a single controllable flexibility resource for distribution grids with a high share of renewables.
Details
General information
- Direction
- Cloud Technologies · Data
- Stage
- Development — Building and iteratively improving the solution.
- Technologies & methods
- Edge AI, LSTM / XGBoost, Deep RL, NVIDIA Jetson Orin, NGSI-LD / OCPP 2.0.1
- Format
- Research and development together with students and the Center's laboratory.
Overview
About the project
Edge AI Multi-Source Power Balancing Service for EV Charging in RES-Rich Distribution Grids. The platform has three layers: an Edge Energy Optimizer — PV and biogas generation forecasting with LSTM/XGBoost and battery dispatch with deep reinforcement learning (Deep RL), running inference on NVIDIA Jetson Orin Nano; a Smart Charging Controller — managing charging stations via OCPP 2.0.1 Smart Charging Profiles; and an integration layer built on the open NGSI-LD / Orion-LD data models and TMForum Open APIs. Target results (TRL 6→7) include lower charging cost and peak consumption, edge-inference latency under 200 ms, and a replicable template for "PV + storage + biogas + EV" sites. The project is carried out within the COP-PILOT programme (Horizon Europe) together with INFOCOM (a manufacturer of charging stations and storage systems, a Siemens Industrial Partner) and the ZNU HPC/ML laboratory; the pilot site is Preveza, Greece.
Stack
Technologies & methods
Edge AILSTM / XGBoostDeep RLNVIDIA Jetson OrinNGSI-LD / OCPP 2.0.1
Interested in this direction?
Let's discuss collaboration, joint research, or a topic for coursework.