Power Grid Resilience Ranking Before Extreme Weather Events
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Solution Overview
Problem
Traditional power grid preparations for extreme weather events are reactive and do not proactively harden the system to absorb the impact of such events, leading to high uncertainty and inefficiency in maintaining operational resilience.
Innovation Solution
Computing preemptive ranked resiliency measures using probabilistic optimization to enhance operational resilience by optimizing power network parameters such as load curtailment, network topology, and electrical islanding before the event occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive preparation measures are used (assessing inventory, procuring hardware, scheduling repair crews after weather events are forecasted), then the power system can respond to damage, but the system cannot proactively absorb or mitigate the impact of extreme weather events in advance
Solution Approach 1:
The system performs preliminary probabilistic optimization computations before extreme weather events occur to determine optimal preemptive resiliency measures. The optimization model evaluates multiple potential actions (load curtailment, network reconfiguration, electrical islanding) and selects the best course of action in advance, transforming reactive preparation into proactive hardening of the power system.
Solution Approach 2:
The system identifies and implements actions that counteract the expected impact of extreme weather events before they occur. By computing optimal resiliency measures that preemptively adjust network topology, curtail loads, or create electrical islands, the system actively works against the anticipated damage rather than merely responding to it after occurrence.
2Reliability
If proactive preparedness measures are implemented to harden the power grid before extreme weather events, then the system's ability to absorb and adapt to events improves, but the uncertainty in capturing the impact of extreme weather events increases
Solution Approach 1:
The probabilistic optimization model incorporates feedback loops that continuously refine predictions of extreme weather event impacts. By comparing predicted outcomes with actual system responses and updating probability distributions, the system reduces uncertainty over time and improves the precision of impact assessments for future events.
Solution Approach 2:
The system transforms uncertain qualitative assessments of weather impacts into quantifiable probabilistic parameters. By expressing weather event impacts as probability distributions and optimizing resiliency measures based on these parameters, the system converts measurement uncertainty into a structured mathematical framework that can be objectively analyzed and optimized.
3Loss of time
If preemptive resiliency measures such as load curtailment, network reconfiguration, or electrical islanding are implemented before extreme weather events, then downtime and costs are reduced, but the complexity of determining which measures to implement increases
Solution Approach 1:
The complex optimization problem is segmented into distinct resiliency measure categories (load curtailment, network reconfiguration, electrical islanding), each with its own sub-model and decision variables. This segmentation allows the system to evaluate multiple types of measures independently while maintaining overall optimization coherence, reducing the computational complexity of determining which specific measures to implement.
Solution Approach 2:
The system transforms the complex qualitative decision of which resiliency measures to implement into a parametric optimization problem with defined objective functions and constraints. By expressing the selection of resiliency measures in terms of optimizable parameters and probability distributions, the system reduces decision complexity while maintaining the ability to evaluate multiple competing measures systematically.
Data Source
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AI summary
When an extreme weather event is forecast, an operator of a power system will typically prepare reactive measures, to facilitate the restoration of the power system after it has been damaged by the event. Disclosed embodiments utilize probabilistic optimization to identify and rank resiliency measures in advance of such an event, such that proactive measures may be implemented before the event, to increase the operational resilience of the power system during the event. This enables the power system to better withstand and recover more quickly from the event, thereby reducing downtime (e.g., power outages) and costs. Exemplary resiliency measures include, without limitation, load curtailment, network topology optimization, preemptive electrical islanding, and distributed energy resource (DER) dispatch.