Cohort: 1 

Location: Rueil-Malmaison, France 

Application Title: AI-enabled microgrid systems for energy optimization and carbon reduction 

Team Members: Philippe RAMBACH, Chief AI Officer; Aditya CHANGAVALLI, Product Owner; Navdeep AHUJA, Microgrid Offer Management Director; Vanya IGNATOVA, Senior Principal, AI Product Manager; Jacques KLUSKA, Principal, Responsible AI Specialist

The challenge 

Buildings are a major source of energy use and carbon emissions. Heating, cooling, lighting and electrical equipment account for a significant share of global electricity demand, particularly in commercial and industrial facilities. Managing this energy efficiently has become more difficult as buildings incorporate rooftop solar, battery storage and electric vehicle charging. 

Most building energy systems still rely on fixed schedules and rule-based controls. These systems operate according to predefined settings that change infrequently. They do not respond well to real-time conditions such as weather shifts, fluctuating electricity prices or changes in occupancy. When tariffs rise or grid conditions become unstable, facility managers often have limited visibility and must intervene manually. 

As energy systems become more distributed, these static approaches lead to higher costs and underuse of on-site renewable resources. They also limit the ability of organisations to reduce emissions consistently across large property portfolios.

The solution 

AI-driven control for on-site microgrids 

Schneider Electric developed an AI-enabled platform designed to manage energy across facilities that combine local generation and flexible consumption. 

For readers unfamiliar with the term, a microgrid is a local energy system that can operate while connected to the main grid or independently if needed. It may include solar panels, batteries and controllable loads such as heating or charging equipment. 

Schneider Electric’s platform connects a cloud-based optimisation engine with a local edge controller installed at each site. Together, they coordinate how energy is generated, stored and consumed throughout the day, with the goal of reducing cost and emissions without disrupting operations. 

How it works 

Predictive optimisation with local execution 

At the core of the system is a model predictive control (MPC) engine. MPC is a control method that uses forecasts to decide how a system should behave over a short future horizon. In this case, the engine considers site-specific data such as weather forecasts, expected demand and electricity prices. 

Every few minutes, the optimiser recalculates an operating plan. It decides when to draw power from the grid, when to use on-site generation and when to charge or discharge batteries. These decisions are executed locally by the edge controller, allowing fast response even if connectivity is limited. 

The AI models update continuously using incoming data. This allows the system to adapt to changing conditions without manual tuning or custom engineering for each site. 

How it differs from conventional approaches 

Moving beyond static energy rules 

Traditional building energy management systems apply fixed heuristics that are updated infrequently. They react to conditions after changes occur and cannot anticipate future constraints. 

Schneider Electric’s approach treats each microgrid as a dynamic system. By forecasting conditions and adjusting control actions in a closed loop, the platform responds more quickly to changes in weather or pricing. Because the intelligence layer is shared across sites, deployments do not require bespoke configuration, which supports scaling across diverse facilities. 

Impact 

• 14% reduction in energy consumption across deployed sites 
• 28% reduction in CO₂ emissions per site per year across 97 locations 
• Approximately $430,000 in annual net savings at a pilot site 

Why it matters 

Making distributed energy systems easier to manage 

As organisations add renewable generation and flexible loads, energy management becomes more complex. Schneider Electric’s platform shows how AI-based control can help facilities use existing assets more effectively. 

By shifting from static rules to predictive optimisation, the system supports lower emissions and reduced costs while maintaining operational stability across different types of sites.