Jiangjiao Xu
Principal Investigator · Lecturer
Biography
Jiangjiao Xu is a lecturer and master’s supervisor in Electrical Engineering within the Division of Electrical Engineering at Shanghai University of Electric Power. His research connects artificial intelligence with modern power systems, with an emphasis on intelligent forecasting, collaborative optimization, and online monitoring and fault diagnosis for distribution networks.
Education and Experience
He received his PhD from Durham University in 2019 and conducted postdoctoral research at the University of Exeter from 2019 to 2022. Public university materials also record a 2014 Durham full PhD scholarship and in-service postdoctoral research at Tongji University from 2025 to 2027.
Academic Service and Recognition
Public institutional information lists Jiangjiao Xu as Vice Chair of the IEEE Smart Village China Committee and a young editorial-board member of Smart Power. He has served as a reviewer for Nature Energy and IEEE Transactions journals, and has held publicity-chair, technical-program co-chair, and special-session-chair roles for IEEE PES and related conferences. Public conference pages specifically list him as Special Session Chair for IEEE EI² 2026, Chair of PCCE 2026 Track 2, and Publicity Chair and Poster Session 1 Session Chair for SPIES 2025.
The university profile lists the Shanghai Overseas High-Level Talent programme, the Shanghai Higher Education talent programme, and the Pudong “Mingzhu Jingying” talent programme. It also reports more than 20 papers as first or corresponding author and more than seven IEEE Transactions papers; these figures are retained as institutional profile statements rather than independently audited metrics.
Public Project Record
The published project record includes projects led through the Shanghai Municipal Education Commission, State Grid Shanghai, and State Grid East China Branch. The verified project pages below provide the authoritative project periods and descriptions.
Source: Shanghai University of Electric Power faculty profile.
A university mentor profile also records participation as a main researcher in seven UK and European projects associated with UKRI, EPSRC, INTERREG, and the European Regional Development Fund. The public source does not provide project names or detailed roles, so they are not represented as separate X-Laboratory project records.
Public university news records a 2023 report on new-type power systems during a university–industry activity in Zhuji and participation in a 2024 Karachi TP1000 overhead-line training programme. These are listed as public academic and technical activities, not as additional research projects.
Research Interests
- Artificial Intelligence and Large Language Model Applications
- Source–Load Forecasting and Computing–Power Collaborative Optimization
- Online Monitoring and Fault Diagnosis for Distribution Networks
Publications
Decarbonizing Long-haul Heavy-duty Electric Trucks with Co-optimized Corridor Charging Hubs
Distributed deep reinforcement learning for large-scale hybrid hydrogen/electricity refueling optimization with a data-driven electrolyzer model
Nonstationary Multioutput Forecasting Framework With Adaptive Location-Aware Bayesian Sampling for Data-Scarce Smart Grids
Privacy-preserving federated ADMM for distributed OPF meets communication failures
Semantic-probabilistic co-optimization framework for distributed non-linear optimal power flow
Smart Generation Control for Interconnected Power System Based on Self-Learning Reward Function
A Preference-Based Online Reinforcement Learning With Embedded Communication Failure Solutions in Smart Grid
Distributed Deep Reinforcement Learning for Data-Driven Water Heater Model in Smart Grid
Meta Reinforcement Learning Based Adaptive and Interpretable Energy Storage Control Meets Dynamic Scenarios
Multiscale Feature Fusion Transformer With Hybrid Attention for Insulator Defect Detection
Research on Statistical Characteristics of Renewable Energy in East China Power Grid Based on Output Features
An Automated Few-Shot Learning for Time-Series Forecasting in Smart Grid Under Data Scarcity
Multioutput Framework for Time-Series Forecasting in Smart Grid Meets Data Scarcity
Preference based multi-objective reinforcement learning for multi-microgrid system optimization problem in smart grid
ADMM-based OPF Problem Against Cyber Attacks in Smart Grid
Electric Water Heaters Management via Reinforcement Learning With Time-Delay in Isolated Microgrids
Multi-objective Reinforcement Learning Based Multi-microgrid System Optimisation Problem
ADMM-Based Distributed OPF Problem Meets Stochastic Communication Delay
ADMM-based Coordinated Decentralized Voltage Control Meets Practical Communication Systems
The Coordinated Voltage Control Meets Imperfect Communication System
Projects
Data-Driven Knowledge-Graph-Based Anomaly Diagnosis and Operations Platform for Metering Equipment
A State Grid Shanghai project on data-driven knowledge graphs for metering-equipment anomaly diagnosis and operations.
View projectResearch on a Large-Language-Model-Empowered Research and Innovation Platform for Smart Grids
A Shanghai Municipal Education Commission project on a large-language-model-empowered research and innovation platform for smart grids.
View projectStatistical Characterization and Application Analysis of Renewable Energy Output in the East China Power Grid
An East China Branch of State Grid project on statistical characterization and application analysis of renewable-energy output.
View project