AI Edge Orchestration for Distributed Energy Coordination

The patent segments the energy management system into multiple hierarchical levels including cloud-based AI orchestration systems, edge computing devices at distribution points, and local DER control units. This segmentation allows each component to handle specific tasks independently, reducing overall system complexity while maintaining decentralized flexibility.

The patent introduces AI-based orchestration systems and edge computing devices as intermediary layers between centralized grid control and distributed energy resources. These intermediaries manage the complexity of coordinating numerous DERs by implementing intelligent algorithms that optimize energy flow, pricing, and resource allocation without requiring direct centralized control of each individual DER.

The patent segments computational tasks across different infrastructure levels: AI training and heavy computation occur in cloud data centers, while inference and lightweight processing occur at edge devices and local controllers. This segmentation reduces the computational burden on any single component and allows the system to leverage distributed computing resources efficiently.

The patent implements preliminary action by pre-training AI models in cloud environments before deploying them to edge devices. This allows the system to perform computationally intensive model training once in the cloud, then reuse the trained models at the edge for real-time decision-making, significantly reducing ongoing computational requirements at distributed locations.