Resiliency Oriented Scenario Generation Framework for Natural Gas Infrastructure

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Lahogue, Malo

Advisor dc:contributor.advisor

• Saurabh, Amin

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/157024

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/157024