Using Machine Learning Methods to Determine Extreme Weather-Related Outage Scenarios for Optimal Siting and Sizing of Distributed Generation and Energy Storage Facilities to Enhance Distribution Network Resiliencey, M.Sc. Thesis Sharif University of Technology ; Hosseini, Hamid (Supervisor)
Abstract
Natural disasters such as storms, earthquakes, floods, etc. are among the factors that can damage the elements of the power grid and cause them to leave the grid. The result of these outages will be a widespread power outage, which will create many economic and social problems. Therefore, in recent years, the concept of resiliencey has been introduced in power system studies, which examines the extensive outputs in the network as a result of natural disasters.One way to improve the power system resiliency is to strengthen the network by upgrading existing equipment, installing new equipment, and modifying the operation of the network. Among these, the installation of distributed generation...
Cataloging briefUsing Machine Learning Methods to Determine Extreme Weather-Related Outage Scenarios for Optimal Siting and Sizing of Distributed Generation and Energy Storage Facilities to Enhance Distribution Network Resiliencey, M.Sc. Thesis Sharif University of Technology ; Hosseini, Hamid (Supervisor)
Abstract
Natural disasters such as storms, earthquakes, floods, etc. are among the factors that can damage the elements of the power grid and cause them to leave the grid. The result of these outages will be a widespread power outage, which will create many economic and social problems. Therefore, in recent years, the concept of resiliencey has been introduced in power system studies, which examines the extensive outputs in the network as a result of natural disasters.One way to improve the power system resiliency is to strengthen the network by upgrading existing equipment, installing new equipment, and modifying the operation of the network. Among these, the installation of distributed generation...
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