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Chen-Ching Liu
Researcher at Virginia Tech
Publications - 274
Citations - 14290
Chen-Ching Liu is an academic researcher from Virginia Tech. The author has contributed to research in topics: Electric power system & Electricity market. The author has an hindex of 57, co-authored 269 publications receiving 12126 citations. Previous affiliations of Chen-Ching Liu include Washington State University & Purdue University.
Papers
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Proceedings ArticleDOI
ICT modeling for integrated simulation of cyber-physical power systems
TL;DR: The objective is to implement the ICT model for real-time interactions with both the power grid and transmission operator and the proposed co-simulation framework is validated with the IEEE 39-bus system.
Journal ArticleDOI
Resilience-Motivated Distribution System Restoration Considering Electricity-Water-Gas Interdependency
TL;DR: In this article, a distribution system service restoration method considering the electricity-water-gas interdependency is proposed, which provides electricity, water, and natural gas supplies to critical customers in the desired ratio according to their needs after an extreme event.
Journal ArticleDOI
Distribution System Resilience Under Asynchronous Information Using Deep Reinforcement Learning
TL;DR: A Reinforcement Learning model that learns how to efficiently restore a distribution system after a major outage is proposed based on a Monte Carlo Tree Search to expedite the training process and provides a robust decision-making tool for asynchronous and partial information scenarios.
Proceedings ArticleDOI
Optimal transmission path search in power system restoration
Wei Sun,Chen-Ching Liu +1 more
TL;DR: In this paper, the authors formulated the transmission path search as a Mixed Integer Linear Programming (MILP) problem to find the shortest transmission path to energize transmission network using cranking power from available generators.
Proceedings ArticleDOI
Anomaly extraction and correlations for power infrastructure cyber systems
TL;DR: A systematic approach is established to identify properties that can be extracted to detect malicious activities in the cyber-based control systems, representing the anomalous cyber- or power-related events among substation networks.