Optimization-Based Network Reduction for Transmission Systems via Community Detection
Conference Year
2024
Abstract
Addressing the escalating complexity of electrical networks with myriad components, nodes, and connections poses significant computational and analytical challenges. Building upon the foundations laid by the "Opti-Kron" model, our research introduces an advanced Mixed Integer Linear Programming (MILP) based algorithm for optimal network reduction, tailored for power flow studies in complex electrical networks. A key innovation in our approach is the incorporation of Community Detection techniques to cluster the network targeted for reduction. This strategy allows us to apply the network reduction algorithm to each cluster separately, facilitating a unified and scalable solution for large networks.
Primary Faculty Mentor Name
Samuel Chevalier
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Electrical Engineering
Primary Research Category
Engineering and Math Science
Optimization-Based Network Reduction for Transmission Systems via Community Detection
Addressing the escalating complexity of electrical networks with myriad components, nodes, and connections poses significant computational and analytical challenges. Building upon the foundations laid by the "Opti-Kron" model, our research introduces an advanced Mixed Integer Linear Programming (MILP) based algorithm for optimal network reduction, tailored for power flow studies in complex electrical networks. A key innovation in our approach is the incorporation of Community Detection techniques to cluster the network targeted for reduction. This strategy allows us to apply the network reduction algorithm to each cluster separately, facilitating a unified and scalable solution for large networks.