Power Grid Reconfiguration for Preemptive DER Resilience Dispatch
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Solution Overview
Problem
Traditional power grid preparations for extreme weather events are reactive and do not proactively harden the grid to absorb the impact of such events, leading to high uncertainty and inefficiency in maintaining operational resilience.
Innovation Solution
Implement preemptive ranked resiliency measures using probabilistic optimization for the placement and real-time dispatch of distributed energy resources, optimizing switch status and network topology to enhance the power system's ability to withstand and recover from severe weather events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive preparation measures are taken for extreme weather events, then the power system can respond to damage after it occurs, but the system cannot proactively harden itself to absorb the impact, resulting in higher uncertainty and reduced operational resilience
Solution Approach 1:
The patent implements preliminary action by performing probabilistic optimization computations before extreme weather events occur to determine preemptive ranked resiliency measures. The system calculates optimal switch status configurations, distributed energy resource placements, and dispatch strategies in advance, allowing the power system to be proactively hardened rather than reactively repaired after damage occurs.
2Reliability
If probabilistic optimization is used to compute preemptive ranked resiliency measures, then the power system can optimize network topology and resource dispatch to maximize load served during future events, but the computational complexity and data requirements increase
Solution Approach 1:
The patent applies segmentation by breaking down the complex probabilistic optimization problem into distinct computational components: acquiring asset data and forecast data separately, generating input data as an intermediate step, and computing different ranked resiliency measures (switch status optimization, DER placement, DER dispatch, radiality constraints) as separate optimization objectives. This modular approach manages computational complexity while achieving comprehensive resiliency optimization.
3Productivity
If the power system reconfigures network topology in advance of extreme weather events, then the system can maximize total load served during the event, but the uncertainty in predicting actual event impact increases
Solution Approach 1:
The patent applies parameter changes by using probabilistic optimization that incorporates multiple possible future scenarios with different event impact parameters. The system computes ranked resiliency measures that optimize network topology, switch status, and resource dispatch across a range of probable event conditions, allowing the system to prepare for multiple potential outcomes rather than relying on a single deterministic prediction.
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. In an embodiment, these resiliency measures include the preemptive placement and/or dispatch of distributed energy resources, with accompanying network-switch optimization, within the power system. This preemptive placement and/or dispatch may use a risk-driven optimal power flow model that accounts for the criticality of loads and the failure probability of power lines.