Demand Response Asset Failure Detection via Statistical Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing demand response systems face significant challenges in efficiently identifying and addressing mechanical failures in dispatchable demand response assets, leading to degraded program effectiveness due to costly and inefficient on-site inspections or reliance on participant self-reporting.
Innovation Solution
A distributed demand response optimization and management system employing a 2-stage statistical method, combining mixture modeling and Bayesian updates, to automatically detect device failures based on historical meter data and participant baseline load estimates, utilizing machine learning and open automated demand response signaling technology for real-time analysis and dynamic price signal dissemination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical on-site inspections are performed to monitor and fix broken devices, then device failure detection accuracy is improved, but inspection cost and time consumption increase significantly
Solution Approach 1:
The patent replaces physical on-site inspections with an automated statistical analysis system that processes meter data remotely. The operability analysis engine uses mixture models and Bayesian updates to detect device failures through data analysis rather than mechanical inspection, eliminating travel time and manual labor while maintaining high detection accuracy.
Solution Approach 2:
The patent introduces meter data as an intermediary between the device and the inspection process. Instead of directly inspecting devices, the system analyzes meter data that reflects device performance, enabling indirect but accurate failure detection without physical presence at the site.
2Reliability
If physical on-site inspections are scaled up to many thousands of participants, then device failure detection coverage is improved, but inspection cost increases exponentially
Solution Approach 1:
The automated statistical analysis system replaces costly physical inspections with computational analysis of existing meter data. The system can process data from thousands of participants simultaneously using cluster computing, achieving scale without proportional increases in cost.
Solution Approach 2:
The operability analysis engine serves multiple functions: it detects device failures, identifies high-probability failure cases, prioritizes inspection targets, and provides continuous monitoring. This multi-functional system replaces multiple separate inspection activities with a single integrated platform.
3Loss of energy
If waiting for participant self-reporting of failed devices is used, then inspection cost is reduced, but demand response performance deteriorates consistently
Solution Approach 1:
The system implements continuous feedback by monitoring meter data in real-time to detect device failures. When failures are detected, the system automatically notifies participants and prioritizes them for inspection, creating a closed-loop system that maintains high performance while controlling costs through targeted rather than universal inspection.
Solution Approach 2:
The system performs preliminary detection of device failures through statistical analysis before participants self-report. By identifying high-probability failure cases in advance, the system can proactively manage device issues and maintain demand response performance without waiting for participant reporting.
4Productivity
If automated statistical analysis is implemented to identify high-probability device failures, then inspection efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the analysis system into distinct modular components: data collection module, mixture model fitting module, Bayesian update module, and inspection prioritization module. Each module performs a specific function and can be independently developed, tested, and maintained, reducing overall system complexity while maintaining high inspection efficiency.
Data Source
AI summary
The present invention demonstrates a highly distributed demand response optimization and management system for real-time (DROMS-RT) power flow control to support large scale integration of distributed renewable generation into the grid. The system is a cloud-based platform that reduces critical peak power safely and securely. The arrangement is provided with a control and communications platform to allow highly dispatchable demand response (DR) services in timeframes suitable for providing ancillary services to the transmission grid. The services are substantially more efficient than other forms of ancillary service options currently available to manage the intermittency associated with large-scale renewable integration.