研究方向
研究方向
源荷预测与协同优化
面向现代电力系统的源荷预测与数据驱动协同优化研究。
本方向关注电力系统源荷预测与协同优化。
相关项目与论文涵盖新能源出力分析、时间序列预测、强化学习及储能控制。
相关项目
国家电网有限公司华东分部：基于统计学特征的华东电网新能源出力特性分析与应用技术服务
基于统计学特征开展华东电网新能源出力特性分析与应用技术服务。
查看项目相关论文
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
基于出力特征的华东电网新能源统计学特性研究
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