Edge AI Energy Orchestration for Decentralized Grid Control
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Solution Overview
Problem
The energy market is transitioning from a centralized model to a decentralized one, requiring a platform that facilitates management and improvement of legacy infrastructure to coordinate with distributed systems, including energy generation, storage, and consumption.
Innovation Solution
An AI-based energy edge platform that integrates AI, IoT, and blockchain technologies to manage and optimize energy generation, storage, and consumption, utilizing intelligent data layers, smart contracts, adaptive digital twins, and energy orchestration systems for efficient energy utilization and transaction management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a centralized energy management model is used, then infrastructure control is simplified, but system flexibility and adaptability to distributed resources deteriorates
Solution Approach 1:
The patent segments the energy management system into multiple edge computing nodes distributed across the energy infrastructure. Each node independently processes data and executes decisions locally, eliminating the need for a single centralized control point while maintaining coordinated operation across the entire system. This segmentation enables both simplified local control and enhanced system flexibility.
Solution Approach 2:
The patent introduces a new dimensional approach by deploying AI models at the edge dimension (distributed locations) rather than solely at the cloud dimension (centralized location). This multi-dimensional architecture allows the system to maintain centralized coordination capabilities while simultaneously achieving distributed adaptability and flexibility.
2Productivity
If AI models are deployed at the edge, then real-time energy optimization is improved, but computational resource requirements at distributed locations increase
Solution Approach 1:
The patent implements partial deployment of AI functionality at the edge, where only inference operations are performed at distributed edge nodes while training and model updates occur centrally. This partial action approach enables real-time optimization benefits without requiring full computational capabilities at each edge location, thus balancing performance improvement with resource constraints.
3Adaptability or versatility
If distributed energy systems are integrated, then system adaptability and resilience are improved, but coordination complexity and communication overhead increase
Solution Approach 1:
The patent introduces edge computing nodes as intermediary components that mediate between distributed energy resources and the centralized system. These intermediaries aggregate data from multiple distributed sources, perform local processing, and communicate consolidated information upward, thereby reducing communication overhead and coordination complexity while maintaining system adaptability.
Data Source
AI summary
An AI-based platform for enabling intelligent orchestration and management of at least one operating process is provided herein. The AI-based platform includes an artificial intelligence system that is configured to generate a prediction of an energy pattern associated with the at least one operating process. The AI-based platform is also configured to manage the at least one operating process based on the prediction of the energy pattern.