End User
EMOT
Description
The Energy Community Sharing & EV Charging Flexibility Optimisation service implements an AI-driven forecasting and MPC-based optimisation framework to align EV charging demand with locally generated PV electricity and market prices. Using a rolling-horizon Model Predictive Control approach updated every 15 minutes, the service increases renewable self-consumption, reduces peak grid imports, and minimises export losses within energy communities.
Core Capabilities
Predictive & Prescriptive Analytics
Optimization & Decision Support
Business Need
Energy communities with rooftop PV and EV charging face a fundamental optimisation challenge: PV generation peaks during daylight hours when charging demand may not naturally align. Without coordinated scheduling, excess PV is exported at low rates while charging demand draws from the grid at high-cost periods. The service automates this coordination to increase the economic and environmental value of local renewable energy.
Key Performance Indicators
Reduction of PV energy exported to the grid
Reduction of peak electricity imports
Improvement in temporal alignment between PV production and EV charging demand
Comparison against uncontrolled charging baseline
Data Provided
Time-indexed PV generation and EV charging demand forecasts
Optimised EV charging modulation trajectories
Operational indicators for energy imports and exports
Inputs: 15-min electricity data, ACR framework allocation outputs from Leneda platform
TEF
TEF EV