Power Grid Outage Sequencing Using Weather and Geospatial Priority
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Solution Overview
Problem
Current power grid planning systems fail to account for non-power system factors such as geospatial location of assets, weather conditions, field crew risk, and load criticality when determining optimal strategies for severe weather-driven outage and restoration events.
Innovation Solution
A computing system that automatically determines the timing and sequence of outages and restorations for transmission assets based on forecasted weather conditions, geospatial locations, and other critical factors, using data-driven algorithms to prioritize and sequence outage and restoration actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated decision support mechanisms are implemented to optimize outage and restoration sequences, then grid stability and public safety are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the power grid into multiple transmission assets with individual geospatial locations and vulnerability assessments. Each asset is evaluated separately based on weather exposure, crew availability, and load criticality, allowing complex decisions to be broken down into manageable components that can be processed algorithmically
Solution Approach 2:
The system performs preliminary vulnerability assessments and weather forecasting before severe weather events occur. Transmission assets are pre-rated based on their susceptibility to weather conditions, and restoration sequences are pre-planned based on predicted weather patterns, enabling proactive rather than reactive grid management
Solution Approach 3:
The system incorporates field crew risk inspection inputs and real-time weather data updates to continuously refine outage and restoration sequences. Feedback from crew assessments of asset vulnerability and actual weather conditions adjusts the automated decision-making process to optimize grid reliability dynamically
2Measurement precision
If geospatial location data and weather forecasting are integrated into planning systems, then outage prioritization accuracy is improved, but data processing time and computational requirements increase
Solution Approach 1:
Geospatial locations of transmission assets are mapped and weather vulnerability assessments are completed in advance of severe weather events. Historical weather data and asset characteristics are pre-analyzed to create baseline vulnerability ratings, reducing the computational burden during time-critical outage decision-making
Solution Approach 2:
The system divides the analysis into discrete geospatial zones and individual transmission assets, allowing parallel processing of vulnerability assessments. Each asset's weather exposure is calculated independently based on its specific location and characteristics, enabling efficient computation of prioritization scores across the entire grid
3Object-affected harmful factors
If transmission assets are outed proactively before severe weather events, then asset protection and public safety are improved, but unnecessary power shutdowns and economic loss increase
Solution Approach 1:
The system applies differentiated outage decisions to specific transmission assets based on their individual geospatial locations, vulnerability ratings, and local weather forecasts. Rather than blanket outages, only assets in high-risk zones with confirmed severe weather exposure are outed, while assets in lower-risk areas maintain normal operation
Solution Approach 2:
Proactive outages are timed and targeted based on forecasted weather events rather than implemented as preventive measures for all assets. The system schedules outages just before predicted severe weather arrival at specific locations, minimizing the duration and scope of power shutdowns while still protecting vulnerable assets
4Productivity
If field crew resources are optimized based on vulnerability assessments, then restoration efficiency is improved, but coordination complexity and scheduling demands increase
Solution Approach 1:
The restoration process is segmented into discrete tasks assigned to specific transmission assets based on their restoration priority. Field crews are allocated to specific geospatial zones and asset groups rather than being deployed broadly, allowing parallel restoration operations across multiple priority zones while simplifying crew coordination within each zone
Solution Approach 2:
Restoration sequences and field crew assignments are pre-planned based on vulnerability assessments and predicted weather patterns. Priority restoration lists are generated in advance, identifying which assets should be restored first based on their vulnerability to weather events and their importance to grid stability, enabling efficient crew deployment when weather events occur
Data Source
AI summary
Provided is a system and method that can determine the timing and sequence of transmission assets on a power grid to be outaged and/or restored based on severe weather. In one example, the method may include selecting a group of transmission assets of a power grid for outage, receiving forecasted weather conditions for a geospatial area that includes the group of transmission assets, determining an outage time and priority among the group of transmission assets based on geospatial locations of the forecasted weather conditions and geospatial locations of the group of transmission assets, and generating a sequence of instructions for powering down the group of transmission assets based on the determined outage time and priority among the group of transmission assets and storing the sequence of instructions in memory.