Virtual Power Plant

Real-time telemetry from distributed energy assets forecasts and dispatches power to balance grid demand efficiently.

Business impact

• Grid reliability — Enhances stability by balancing supply and demand with distributed resources
• Energy costs — Reduces costs by optimizing energy usage and avoiding expensive peaker plants
• Customer revenue — Increases earnings for participants by monetizing flexible energy assets
• Carbon emissions — Lowers emissions by integrating renewable energy and reducing fossil fuel reliance
• Grid operational efficiency — Optimizes dispatch and reduces congestion on transmission and distribution networks

Data requirements

• Real-time telemetry from distributed energy devices (Numeric) — Monitors power output, consumption, and battery state for dispatch decisions
• Weather forecasts (Numeric) — Predicts renewable generation variability to optimize resource scheduling
• Electricity market prices (Numeric) — Informs economic dispatch and bidding strategies in energy markets
• Device status and control signals (Structured) — Enables remote control and coordination of distributed assets
• User preferences and participation data (Text) — Incorporates consumer behavior to tailor demand response actions

AI methods and techniques

• Predictive AI — Forecasts energy supply, demand, and market conditions for optimal dispatch
• Agentic AI — Autonomously controls distributed devices to respond dynamically to grid events
• Symbolic AI — Applies rule-based optimization to ensure compliance with operational constraints

AI models and model families

GPT-4o, Claude, Llama, Custom ML models for forecasting and optimization

Industries

Real-world evidence

4 documented case studies on record.

Companies using this: 1KOMMA5°, Iberdrola, Renew Home, Simtel Team.