11 papers
Admittance Sensitivity-Informed Modular GP for Scalable Topology-Adaptive Power-Flow Learning
Henrique O. Caetano, Carlos Dias Maciel, Rahul K. Gupta
Data-driven approaches for learning power flow models suffer from weak generalization across varying network topologies and limited computational scalability. Existing methods typi…
Admittance Matrix Concentration Inequalities for Understanding Uncertain Power Networks
Samuel Talkington, Cameron Khanpour, Rahul K. Gupta +5
This paper presents conservative probabilistic bounds for the spectrum of the admittance matrix and classical linear power flow models under uncertain network parameters; for examp…
bayesgrid: An Open-Source Python Tool for Generating Probabilistic Synthetic Transmission-Distribution Grids Using Bayesian Hierarchical Models
Henrique O. Caetano, Rahul K. Gupta, Carlos D. Maciel
In this work, we present bayesgrid, an open-source python toolbox for generating synthetic power transmission-distribution systems for any geographical location worldwide, using th…
AC-Informed DC Optimal Transmission Switching via Admittance Sensitivity-Augmented Constraints and Repair Costs
Rahul K. Gupta
AC optimal transmission switching (AC-OTS) is a computationally challenging problem due to the nonconvexity and nonlinearity of AC power-flow (PF) equations coupled with a large nu…
Ramping-aware Enhanced Flexibility Aggregation of Distributed Generation with Energy Storage in Power Distribution Networks
Hyeongon Park, Daniel K. Molzahn, Rahul K. Gupta
Power distribution networks are increasingly hosting controllable and flexible distributed energy resources (DERs) that, when aggregated, can provide ancillary support to transmiss…
Optimizing Parameters of the LinDistFlow Power Flow Approximation for Distribution Systems
Babak Taheri, Rahul K. Gupta, Daniel K. Molzahn
The DistFlow model accurately represents power flows in distribution systems, but the model's nonlinearities result in computational challenges for many applications. Accordingly,…