What challenges did Allego face before AI implementation?

Managing grid stability with growing EV adoption and variable energy demand Aligning EV charging schedules with the availability of renewable energy sources Providing real-time prioritization for different vehicle types (e.g., buses vs. cars) Ensuring affordability and convenience of charging for end users Remote monitoring and servicing of 9,500+ charging points across Europe

What was the impact of the AI solution?

Improved scheduling and prioritization of EV charging sessions Increased adaptability of charging stations to grid constraints and renewable energy supply/demand Enhanced user affordability and flexible charging options for EV drivers Enabled real-time dynamic reprioritization, benefiting fleet and public transport vehicles Reduced manual intervention through automated, remote monitoring of charging points

Source

MicrosoftWent darkProductionEvidence: Medium50/100

Allego optimizes EV networks with Azure Digital Twins

Use case typeEnergy optimizationUpdated Jun 13, 2026

Allego, a Europe-wide electric vehicle charging provider, leverages Azure Digital Twins to manage and optimize charging stations, improve scheduling, prioritize vehicles, and align energy consumption with grid constraints. This smart solution enhances both user affordability and grid reliability and adaptability. The system takes into account renewable energy supply and demand dynamics to streamline EV operations.

Organization: Allego
Industry: Energy & Utilities
Location: Netherlands
Published: September 2018

Reported outcomes

Strategic outcomes

New product / capabilityOptimized EV charging network managementBetter decisions & insightImproved charging scheduling and prioritizationSpeed & agilityEnabled real-time dynamic reprioritizationScale & capacityEnabled scalable remote monitoring

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Allego
Provider: Microsoft
Maturity: Production
Linked source: azure.microsoft.com

cars) Ensuring affordability and convenience of charging for end users Remote monitoring and servicing of 9,500+ charging points across Europe Deployed Azure Digital Twins to model and manage the entire EV charging ecosystem Integrated real-time data sources including grid constraints and renewable resource availability Implemented dynamic charging schedule optimization based on vehicle type, demand, and grid capacity Enabled remote monitoring and servicing for scalability across thousands of ch

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 5

1Real-time Optimization of EV Charging Schedules
2Automated Prioritization of Vehicle Charging (e.g., prioritizing buses)
3Remote Monitoring and Management of Charging Stations

Managing grid stability with growing EV adoption and variable energy demand
Aligning EV charging schedules with the availability of renewable energy sources
Providing real-time prioritization for different vehicle types (e.g., buses vs. cars)
Ensuring affordability and convenience of charging for end users
Remote monitoring and servicing of 9,500+ charging points across Europe

Deployed Azure Digital Twins to model and manage the entire EV charging ecosystem
Integrated real-time data sources including grid constraints and renewable resource availability
Implemented dynamic charging schedule optimization based on vehicle type, demand, and grid capacity
Enabled remote monitoring and servicing for scalability across thousands of charging points

Technologies

Azure Digital Twins

Improved scheduling and prioritization of EV charging sessions
Increased adaptability of charging stations to grid constraints and renewable energy supply/demand
Enhanced user affordability and flexible charging options for EV drivers
Enabled real-time dynamic reprioritization, benefiting fleet and public transport vehicles
Reduced manual intervention through automated, remote monitoring of charging points

Sources & evidence1

Evidence: Medium50/100Evidence strength