Utility Repair Crew Deployment Optimization
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Solution Overview
Problem
Existing utility restoration management during severe weather events relies heavily on human intuition and subjective decision-making, leading to inefficiencies such as over- or under-allocation of repair crews, resulting in extended blackouts and excessive costs.
Innovation Solution
A system that uses machine-learning technology to determine node criticality and failure probabilities based on historical data and weather forecasts, optimizing the deployment of repair crews by analyzing network reliability and susceptibility to weather-induced failures, and employing the barycentric coordinates technique for accurate weather weighting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If repair crews are deployed based on human intuition and subjective judgment, then flexibility in decision-making is maintained, but allocation accuracy deteriorates leading to over- or under-allocation
Solution Approach 1:
The patent introduces an automated system as an intermediary between the complex weather data/network topology and the repair crew deployment decisions. This system processes historical weather data, real-time conditions, and network interconnectivity to generate objective recommendations, eliminating the bullwhip effect while preserving operational flexibility through the recommended deployment strategy.
2Reliability
If more repair crews are deployed during major weather events, then service coverage is improved, but cost increases due to over-allocation
Solution Approach 1:
The system performs preliminary analysis of weather forecasts and network vulnerability before the weather event impacts the grid. By pre-identifying at-risk nodes and calculating optimal crew deployment in advance, the system ensures adequate service coverage is prepared while avoiding the cost of deploying crews to locations that ultimately don't need them.
Solution Approach 2:
The patent dynamically adjusts deployment parameters (number of crews, their locations, and timing) based on quantitative analysis of weather severity, node criticality, and historical failure patterns. This replaces static over-allocation with dynamic, optimized allocation that maintains reliability while reducing unnecessary costs.
3Loss of energy
If fewer repair crews are deployed to reduce costs, then cost is reduced, but restoration time increases due to under-allocation
Solution Approach 1:
The system optimizes the parameter of crew quantity by analyzing the relationship between deployment levels and restoration outcomes. It identifies the minimum effective number of crews needed for each scenario based on weather severity and network criticality, eliminating both over-allocation waste and under-allocation delays.
4Productivity
If repair crews are shared across multiple utilities, then resource utilization is improved, but deployment complexity increases
Solution Approach 1:
The patent segments the multi-utility repair crew deployment problem into independent optimization units based on geographic regions, weather event zones, and network criticality areas. Each segment can be optimized independently while maintaining overall resource utilization, reducing the complexity of coordinating shared crews across multiple utilities.
Data Source
AI summary
The disclosed embodiments relate to a system that facilitates deployment of utility repair crews to nodes in a utility network. During operation, the system determines a node criticality for each node in the utility network based on a network-reliability analysis, which considers interconnections among the nodes in the utility network. The system also determines a node failure probability for each node in the utility network based on historical weather data, historical node failure data and weather forecast information for the upcoming weather event. The system uses the determined node criticalities and the determined node failure probabilities to determine a deployment plan for deploying repair crews to nodes in the utility network in preparation for the upcoming weather event. The system then presents the deployment plan to a person who uses the deployment plan to deploy repair crews to be available to service nodes in the utility network.