1 citations · 1 across the 6 of their papers we have counts for
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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…
Carbon-aware Market Participation for Building Energy Management Systems
Young-ho Cho, Mohamad Chehade, Fatima Al-Janahi +3
Tackling climate change requires the rapid and deep decarbonization of electric power systems. While energy management systems (EMSs) play a central role in this transition, conven…
Sparse Neural Approximations for Bilevel Adversarial Problems in Power Grids
Young-ho Cho, Harsha Nagarajan, Deepjyoti Deka +1
The adversarial worst-case load shedding (AWLS) problem is pivotal for identifying critical contingencies under line outages. It is naturally cast as a bilevel program: the upper l…
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…
Data-driven Modeling of Linearizable Power Flow for Large-scale Grid Topology Optimization
Young-ho Cho, Hao Zhu
Effective power flow (PF) modeling critically affects the solution accuracy and computational complexity of large-scale grid optimization problems. Especially for grid optimization…
Should we use model-free or model-based control? A case study of battery management systems
Mohamad Fares El Hajj Chehade, Young-ho Cho, Sandeep Chinchali +1
Reinforcement learning (RL) and model predictive control (MPC) each offer distinct advantages and limitations when applied to control problems in power and energy systems. Despite…