Maximizing DER Hosting Capacity Using Convex Inner Approximations
Conference Year
2024
Abstract
The real-time disaggregation of distributed energy resources (DERs) is increasingly important, as it must be cognizant of the existing grid constraints. This study presents a convex inner approximation (CIA) methodology that dynamically maximizes the nodal hosting capacity of the feeders for flexible demand while guaranteeing safe operation at all times. The inner approximation captures the maximum feasible region within the highly non-convex solution set of AC power flow equations by quantifying the nodal injections in radial networks.The approach is validated using an IEEE 37-bus network. Future research aims to expand CIA to meshed, unbalanced networks, and 3-phase models.
Primary Faculty Mentor Name
Mads Almassalkhi
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Electrical Engineering
Primary Research Category
Engineering and Math Science
Maximizing DER Hosting Capacity Using Convex Inner Approximations
The real-time disaggregation of distributed energy resources (DERs) is increasingly important, as it must be cognizant of the existing grid constraints. This study presents a convex inner approximation (CIA) methodology that dynamically maximizes the nodal hosting capacity of the feeders for flexible demand while guaranteeing safe operation at all times. The inner approximation captures the maximum feasible region within the highly non-convex solution set of AC power flow equations by quantifying the nodal injections in radial networks.The approach is validated using an IEEE 37-bus network. Future research aims to expand CIA to meshed, unbalanced networks, and 3-phase models.