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Gyu Seob Lee

Seoul National University · 工学

研究室紹介

Professor Gyu Seob Lee's research lab specializes in smart energy systems, focusing on demand response, electric vehicle charging optimization, and grid flexibility integration. The lab develops data-driven and distributed control strategies to enhance the efficiency and stability of power systems, particularly through HVAC systems and medium-voltage DC charging stations. Their work emphasizes real-world applications using empirical data and advanced algorithms such as artificial neural networks and consensus-based optimization. The lab's research contributes significantly to the integration of renewable energy and sustainable urban energy management.

demand responseelectric vehicle chargingHVAC optimizationgrid flexibilitydistributed control

Research Overview

Papers
3
Total Citations
1
Papers (5y)
3
Primary Field
工学

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
3total
2014
2026
Citations per year (5y)
1total
20142026

Selected Papers

3
1
Article|1 citations·2014
Health Status in Adult Patients with Copd in Korea
E.S. Kim, B.J. Lee, Gyu-Sub Lee, AR. Jung, Hye-Min Hwang
SJR Q1Value in Health
General Health ProfessionsHealth Professions
2
Preprint|0 citations·2026
A Successive Linear/Convex Programming Framework under a Voltage–Current Representation for Loss-Minimizing Optimal Power Flow
Seungchan Jo, Jae Young Oh, Gyu-Sub Lee
SSRN Electronic JournalOA
Electrical and Electronic EngineeringEngineering
3
Article|0 citations·2026
Distributed Optimal Charging Strategy of MVDC Electric Vehicle Ultra-fast Charging Station to Manage Peak Load and Consumer Convenience
Seong-Cheon Cho, Jae-Won Chang, Gyu-Sub Lee
SJR Q1IEEE Transactions on Transportation Electrification

This paper proposes a distributed optimal charging strategy for medium-voltage direct current (MVDC) electric vehicle ultra-fast charging stations (EV UFCS) that simultaneously addresses the convenience of electric vehicle (EV) owners and the management of peak load conditions. The method employs a consensus-based algorithm to enable efficient data exchange of EV information over a sparse communication network. Unlike conventional optimization techniques, the proposed strategy generates active p

Electrical and Electronic EngineeringEngineering

Research Areas

Electrical and Electronic EngineeringGeneral Health Professions

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