Predictive Demand Response for Grid Fault and Load Balancing
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Solution Overview
Problem
Current demand response systems fail to predict faults and conditions leading to power outages, and they do not effectively manage over or under-consumption of energy resources, resulting in suboptimal power grid performance.
Innovation Solution
A computer-implemented method and system that generates demand response events based on grid operations and faults by obtaining grid status information, determining fault conditions, and using energy management system data to predict and manage energy consumption, thereby coordinating flexible resources to balance load and prevent outages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If demand response systems only respond to current grid conditions without prediction capabilities, then system simplicity is maintained, but reliability of power supply deteriorates due to inability to prevent outages
Solution Approach 1:
The system performs preliminary fault prediction by analyzing grid status information, energy management system data, load profiles, and weather conditions before actual faults occur. This advance prediction enables proactive demand response events that prevent outages rather than merely reacting to them, thereby improving reliability while maintaining manageable complexity through targeted predictive analytics.
2Measurement precision
If the system aggregates and analyzes extensive EMS data from multiple sources for fault prediction, then prediction accuracy improves, but information processing complexity increases
Solution Approach 1:
The system segments data processing by handling different data types (load profiles, energy management system data, weather conditions, grid status) through specialized modules. Each module processes specific data segments independently, then integrates results for comprehensive fault prediction. This segmentation improves prediction accuracy through thorough analysis while managing complexity through modular architecture.
3Reliability
If demand response events are generated only after faults occur, then response time is minimized, but loss of power supply increases due to delayed prevention
Solution Approach 1:
The system generates demand response events based on predicted faults before actual outages occur. By analyzing trends in grid status, load profiles, and environmental conditions, the system anticipates potential failures and triggers preventive demand response actions, thereby maintaining power supply continuity and eliminating the time loss associated with reactive responses.
4Stability of the object's composition
If the system coordinates flexible resources across multiple energy management systems for load balancing, then power grid stability improves, but system coordination complexity increases
Solution Approach 1:
The aggregator consolidates data from multiple energy management systems and coordinates flexible resources across them through a unified demand response framework. By merging data streams and coordination functions into a centralized system, the patent achieves improved power grid stability through comprehensive load balancing while managing coordination complexity through integrated control architecture.
Data Source
AI summary
Provided are techniques for predictively generating demand response (DR) events based on grid operations and faults, based on optimization and self-learning routines. The techniques include obtaining grid status information, and determining a fault condition based at least in part on the grid status information. The techniques also include generating a DR event based at least in part on the fault condition, and responsive to generating the DR event, transmitting a notification of the DR event.