Linhan Fang

PhD Student
2024.08 - present
Email
lfang7obfuscate@cougarnet.uh.edu
LinkedIn

Linhan Fang is a current PhD Student with the Department of Electrical and Computer Engineering at RPG lab at the University of Houston, Houston, TX, USA.

Education

  • Ph.D., Electrical Engineering, University of Houston, Houston, TX, USA, 2024.08 - present
  • M.S., Energy Engineering, The University of Hong Kong, Hong Kong, China, 2024
  • B.S., Agricultural Electrification, Nanjing Agricultural University, Nanjing, China, 2023

Research Interests

  • Distribution system operations and reliability
  • Grid integration of DER and EV
  • Voltage regulation
  • Machine learning, deep learning
Papers (with link)

Hosting Capacity Assessment and Enhancement for Edge Data Centers in Active Distribution Networks

Net Load Forecasting Using Machine Learning with Growing Renewable Power Capacity Features: A Comparative Study of Direct and Indirect Methods

A Two-Stage Hierarchical GNN for Computationally Efficient Reduced Optimal Power Flow

Grid Operational Benefit Analysis of Data Center Spatial Flexibility: Congestion Relief, Renewable Energy Curtailment Reduction, and Cost Saving

Diagnosis-Driven Co-planning of Network Reinforcement and BESS for Distribution Grid with High Penetration of Electric Vehicles

Data-Driven EV Charging Load Profile Estimation and Typical EV Daily Load Dataset Generation

Cable Degradation Estimation and Remaining Useful Life Prediction for Distribution Networks with High EV Penetration

A Reliability-Cost Optimization Framework for EV and DER Integration in Standard and Reconfigurable Distribution Network Topologies

Optimal BESS Sizing and Placement for Mitigating EV-Induced Voltage Violations: A Scalable Spatio-Temporal Adaptive Targeting Strategy

A Black Start Strategy for Hydrogen-integrated Renewable Grids with Energy Storage Systems

Analysis of Learning-based Offshore Wind Power Prediction Models with Various Feature Combinations

Resources (with link)

Multi-Year Residential Level-2 EV Charging Load Dataset at One-Hour Resolution