6 papers
Learning the Weather-Grid Nexus via Weather-to-Voltage (W2V) Predictive Modeling
Sol Lim, Min-Seung Ko, Farnaz Safdarian +1
This paper proposes a weather-to-voltage (W2V) predictive modeling framework to learn the underlying weather-grid nexus. Unlike existing approaches on weather-informed grid operati…
Wide-Area Power System Oscillations from Large-Scale AI Workloads
Min-Seung Ko, Hao Zhu
This paper develops a new dynamic power profiling approach for modeling AI-centric datacenter loads and analyzing their impact on grid operations, particularly their potential to i…
TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling
Fatima Al-Janahi, Min-Seung Ko, Hao Zhu
Modeling dynamical systems is crucial across the science and engineering fields for accurate prediction, control, and decision-making. Recently, machine learning (ML) approaches, p…
LACE-S: Toward Sensitivity-consistent Locational Average Carbon Emissions via Neural Representation
Young-ho Cho, Min-Seung Ko, Hao Zhu
Carbon-aware grid optimization relies on accurate locational emission metrics to effectively guide demand-side decarbonization tasks such as spatial load shifting. However, existin…
PGLib-CO2: A Power Grid Library for Real-Time Computation and Optimization of Carbon Emissions
Young-ho Cho, Min-Seung Ko, Hao Zhu
Achieving a sustainable electricity infrastructure requires the explicit integration of carbon emissions into power system modeling and optimization. However, existing open-source…
Mitigation of Datacenter Demand Ramping and Fluctuation using Hybrid ESS and Supercapacitor
Min-Seung Ko, Jae Woong Shim, Hao Zhu
This paper proposes a hybrid energy storage system (HESS)-based control framework that enables comprehensive power smoothing for hyperscale AI datacenters with large load variation…