Resiliency Oriented Scenario Generation Framework for Natural Gas Infrastructure
Name
Lahogue-904629590-Meng-CEE-2024-thesis.pdf
Description
Thesis PDF
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5.33 MB
Format
Adobe PDF
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653caed2d91b89358d4c9f51b5b715a3
Author(s)
Lahogue, Malo
Advisor(s)
Saurabh, Amin
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
Traditionally, NG's impact on power supply has been studied from a reliability perspective, focusing on frequent and low-impact events. Furthermore, power-NG interdependence has been considered at a local scale, with few possibilities for extension to future climate impacts. Our work contributes to a framework for scenario-based resilience quantification of regional power systems under power-NG interdependencies. Specifically, we develop a scenario generation approach to model disruptions in the intra-regional transmission infrastructure as well as supply restrictions due to contingencies in inter-regional NG supply chains. To account for the interregional interdependencies through the import capacity of NG into the regional system, we implement a Long Short-Term Memory (LSTM) model that predicts NG import capacity probability density based on weather conditions along transregional supply pipelines. Our ML model does not require detailed modeling of gas extraction rates and flows along pipelines since such information is not readily available. Furthermore, we develop a sampling procedure to capture low-probability but potentially severe disruption scenarios within the regional transmission infrastructure. To compute the corresponding probabilities, we utilize a physically-based structural reliability model for pipelines.
Crucially, by sampling the scenarios first and then estimating the corresponding probabilities, we account for low-probability ``rare’’ events that can negatively impact the reliability of power supply. The resulting scenario set enables more precise quantification of power system resilience to correlated transmission and supply disruptions in the NG infrastructure. Since we utilize weather data to forecast NG import capacities as well as compute pipeline disruption probabilities, our work is well-suited for the integration of future climate projections in the risk-sensitive planning and resilient operations of power-NG systems.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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