14 citations · 50 across the 18 of their papers we have counts for
18 papers
Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients
Shourya Bose, Kibaek Kim
The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting (STLF) models. In response to privacy concerns, fede…
Differentially Private Distributed Convex Optimization
Minseok Ryu, Kibaek Kim
This paper considers distributed optimization (DO) where multiple agents cooperate to minimize a global objective function, expressed as a sum of local objectives, subject to some…
Polytopic Superset Algorithm for Nonlinear Robust Optimization
Bowen Li, Kibaek Kim, Sven Leyffer
Nonlinear robust optimization (NRO) is widely used in different applications, including energy, control, and economics, to make robust decisions under uncertainty. One of the class…
Linearized Optimal Power Flow for Multiphase Radial Networks with Delta Connections
Geunyeong Byeon, Minseok Ryu, Kibaek Kim
This paper proposes a linear approximation of the alternating current optimal power flow problem for multiphase distribution networks with voltage-dependent loads connected in both…
Two-Stage Distributionally Robust Conic Linear Programming over 1-Wasserstein Balls
Geunyeong Byeon, Kaiwen Fang, Kibaek Kim
This paper studies two-stage distributionally robust conic linear programming under constraint uncertainty over type-1 Wasserstein balls. We present optimality conditions for the d…
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning
Minseok Ryu, Youngdae Kim, Kibaek Kim +1
Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in…
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates
Minseok Ryu, Kibaek Kim
Differential privacy (DP) techniques can be applied to the federated learning model to statistically guarantee data privacy against inference attacks to communication among the lea…
A mixed complementarity problem approach for steady-state voltage and frequency stability analysis
Youngdae Kim, Kibaek Kim
We present a mixed complementarity problem (MCP) approach for a steady-state stability analysis of voltage and frequency of electrical grids. We perform a theoretical analysis prov…
Accelerated Computation and Tracking of AC Optimal Power Flow Solutions using GPUs
Youngdae Kim, Kibaek Kim
We present a scalable solution method based on an alternating direction method of multipliers and graphics processing units (GPUs) for rapidly computing and tracking a solution of…
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow
Sihan Zeng, Alyssa Kody, Youngdae Kim +2
With the increasing penetration of distributed energy resources, distributed optimization algorithms have attracted significant attention for power systems applications due to thei…
Numerical Performance of Different Formulations for Alternating Current Optimal Power Flow
Sayed Abdullah Sadat, Kibaek Kim
Alternating current optimal power flow (ACOPF) problems are nonconvex and nonlinear optimization problems. Utilities and independent service operators (ISO) require ACOPF to be sol…
Leveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition
Youngdae Kim, François Pacaud, Kibaek Kim +1
We present the implementation of a trust-region Newton algorithm ExaTron for bound-constrained nonlinear programming problems, fully running on multiple GPUs. Without data transfer…
Differentially Private Federated Learning via Inexact ADMM
Minseok Ryu, Kibaek Kim
Differential privacy (DP) techniques can be applied to the federated learning model to protect data privacy against inference attacks to communication among the learning agents. Th…
On the Tightness of the Lagrangian Dual Bound for Alternating Current Optimal Power Flow
Weiqi Zhang, Kibaek Kim, Victor M. Zavala
We study tightness properties of a Lagrangian dual (LD) bound for the nonconvex alternating current optimal power flow (ACOPF) problem. We show an LD bound that can be computed in…
A Privacy-Preserving Distributed Control of Optimal Power Flow
Minseok Ryu, Kibaek Kim
We consider a distributed optimal power flow formulated as an optimization problem that maximizes a nondifferentiable concave function. Solving such a problem by the existing distr…
Data-driven two-stage conic optimization with zero-one uncertainties
Anirudh Subramanyam, Mohamed El Tonbari, Kibaek Kim
We address high-dimensional zero-one random parameters in two-stage convex conic optimization problems. Such parameters typically represent failures of network elements and constit…
Data Centers as Dispatchable Loads to Harness Stranded Power
Kibaek Kim, Fan Yang, Victor M. Zavala +1
We analyze how both traditional data center integration and dispatchable load integration affect power grid efficiency. We use detailed network models, parallel optimization solver…
A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties
Victor M. Zavala, Kibaek Kim, Mihai Anitescu +1
We argue that deterministic market clearing formulations introduce arbitrary distortions between day-ahead and expected real-time prices that bias economic incentives and block div…