Power Grid Outage Sequencing Using Geospatial Weather Forecasts
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Solution Overview
Problem
Current power grid planning systems fail to account for non-power system factors such as geospatial location, weather conditions, field crew risk inspection inputs, and load criticality when determining the timing and sequence of emergency outage and restoration events.
Innovation Solution
A computing system that selects transmission assets for outage or restoration, receives forecasted weather conditions, determines outage or restoration priority based on geospatial locations, and generates a sequence of instructions for powering down or restoring the assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to create outage and restoration plans, then flexibility in decision-making is maintained, but the accuracy and efficiency of planning are reduced
Solution Approach 1:
An automated planning system acts as an intermediary between weather forecast data and grid operators, processing multiple data sources (weather forecasts, geospatial asset locations, load criticality) to generate optimized outage and restoration sequences. This intermediary system resolves the contradiction by handling the complexity of integrating multiple factors automatically while providing precise, data-driven planning recommendations to operators.
2Reliability
If current planning systems are used that do not account for non-power system factors, then the planning process is simpler, but the effectiveness of emergency operations is reduced
Solution Approach 1:
The system merges previously separate planning considerations (weather forecasts, geospatial asset locations, load criticality, field crew risk inputs) into a unified automated planning process. By combining these diverse data sources and factors into a single integrated system, the patent achieves comprehensive operational effectiveness while managing complexity through automation rather than manual coordination of multiple factors.
3Productivity
If automated decision support mechanisms are implemented to create optimal strategies, then the efficiency and accuracy of outage planning improve, but the complexity of the system increases
Solution Approach 1:
The automated planning system performs self-service by automatically ingesting weather forecast data, comparing it with geospatial locations of transmission assets, determining outage priorities, and generating sequenced instructions without requiring manual intervention for each decision. This self-service capability drives planning efficiency while the system manages its own complexity through automated data processing and algorithmic decision-making.
4Object-affected harmful factors
If the timing and sequence of outage events are optimized using multiple factors, then public safety and grid stability are improved, but the difficulty of creating successful sequences increases
Solution Approach 1:
The system performs preliminary action by proactively analyzing weather forecasts and pre-determining optimal outage sequences before severe weather events occur. By comparing forecasted weather conditions with asset locations in advance and creating pre-planned outage and restoration sequences, the system reduces public safety risks while managing the complexity of sequence planning through automated algorithms that consider all relevant factors simultaneously.
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.