Smart Grid Edge AI Platform for Real-Time DER Load Management
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Solution Overview
Problem
Decentralized, bidirectional smart utility grids face challenges in managing changing load characteristics due to the integration of distributed energy resources (DERs), requiring advanced data processing and management capabilities at the grid edge to optimize electrical input/output and visualize electrical conditions in real-time.
Innovation Solution
A smart grid distributed operations platform (SGDOP) is implemented, utilizing edge devices with GPU processing capabilities for distributed edge computing and AI, enabling real-time data processing and integration of machine learning models to manage DERs and visualize electrical conditions, supported by an IoT platform for software deployment and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed energy resources (DERs) are integrated into utility distribution grids to enable decentralized bidirectional smart grids, then the system's adaptability and energy distribution capability are improved, but the complexity of managing changing load characteristics and data processing requirements increases
Solution Approach 1:
The patent segments the centralized grid management function into distributed edge computing nodes deployed at multiple locations within the utility distribution grid. Each edge device independently processes local data and executes AI models, dividing the complex management task into smaller, manageable units that operate autonomously while contributing to overall grid optimization
Solution Approach 2:
The patent introduces an intermediary IoT platform that bridges the gap between DERs and the centralized grid control system. This platform provides standardized interfaces for device connectivity, data exchange, and coordination, simplifying the integration complexity while enabling decentralized operation and bidirectional communication
2Productivity
If traditional metering devices are used for data collection in smart grids, then the system structure remains simple, but the processing speed and real-time data analysis capability are insufficient
Solution Approach 1:
The patent merges the traditional metering device with GPU-accelerated edge computing capabilities into a unified smart edge device. This combination integrates data collection, real-time processing, and AI model execution into a single device, enabling high-speed processing while managing complexity through standardized deployment interfaces
Solution Approach 2:
The patent designs the edge device with multi-functional capabilities that serve multiple purposes: data collection from DERs, real-time load prediction, DER balancing, visualization, and application deployment. This universal device replaces multiple specialized components, improving productivity while the standardized interface manages the apparent complexity
3Extent of automation
If machine learning models are deployed for load prediction and DER balancing, then the intelligence and optimization capability of the grid is improved, but the computational requirements and data processing burden increase
Solution Approach 1:
The patent implements local quality by deploying AI models at the edge devices closest to the data sources and decision points. Each edge device executes models tailored to its local conditions and load characteristics, enabling automated decision-making locally without requiring continuous cloud connectivity or centralized processing of all data
Solution Approach 2:
The patent applies preliminary action by pre-training and deploying optimized AI models to edge devices before they are needed for real-time decision-making. The models are prepared in advance with historical data and local characteristics, so when real-time predictions are needed, the edge devices can execute them with minimal computational overhead and energy consumption
Data Source
AI summary
Systems and methods for a smart grid distributed operations platform. The system can include a data processing system comprising one or more processors, coupled with memory. The data processing system can identify a network connection with edge devices located on an electricity distribution grid, wherein each of the edge devices comprise a processor and memory. The data processing system can identify, based on a configuration associated with the edge devices, an application configured to perform a function to manage delivery of electricity via the electricity distribution grid. The data processing system can select a model from models trained with machine learning based on a local environment attribute associated with a location on the electricity distribution grid at which the edge devices are located. The data processing system can provide, for execution on the edge devices, the application configured with the model to perform the function to manage delivery of electricity.