The problem
The rapid spread of residential distributed energy resources, such as rooftop solar, home batteries and electric vehicles, is outpacing utilities’ ability to manage them. Current systems lack real-time, granular visibility into how these resources behave at a local level, so operators cannot reliably predict or prevent microgrid instabilities. Remote communities that rely on microgrids are particularly underserved.
What we did
We helped build GridOps Analytics, a hyper-local microgrid management platform that combines machine learning, IoT data streams and cloud computing. Proprietary algorithms map distributed energy activity block by block in real time, giving operators visibility they cannot currently detect. AI-driven forecasting anticipates local grid behaviour from weather patterns, historical data and live SCADA and IoT feeds, while microgrid optimisation tools help operators build and run resilient local grids. It is offered as an optimisation subscription, with revenue-sharing, public-private partnership and consulting models, and built-in support for provincial and federal regulatory compliance.
The outcome
We help Canadian utilities integrate more renewable energy and keep local grids stable, turning the complexity of distributed resources into actionable insight.