23 citations · 100 across the 12 of their papers we have counts for
6 papers · 1 filter
Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets
Jinhao Li, Changlong Wang, Yanru Zhang +1
The battery energy storage system (BESS) has immense potential for enhancing grid reliability and security through its participation in the electricity market. BESS often seeks var…
Attentive Convolutional Deep Reinforcement Learning for Optimizing Solar-Storage Systems in Real-Time Electricity Markets
Jinhao Li, Changlong Wang, Hao Wang
This paper studies the synergy of solar-battery energy storage system (BESS) and develops a viable strategy for the BESS to unlock its economic potential by serving as a backup to…
Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information
Jinhao Li, Ruichang Zhang, Hao Wang +3
Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization. However, the variable nature of RERs introduces uncertaint…
Cross-Entropy-Based Approach to Multi-Objective Electric Vehicle Charging Infrastructure Planning
Jinhao Li, Yu Hui Yuan, Qiushi Cui +1
Pure electric vehicles (PEVs) are increasingly adopted to decarbonize the transport sector and mitigate global warming. However, the inadequate PEV charging infrastructure may hind…
Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning
Zhuo Wei, Frits de Nijs, Jinhao Li +1
The rapid adoption of residential solar photovoltaics (PV) has resulted in regular overvoltage events, due to correlated reverse power flows. Currently, PV inverters prevent damage…
Deep Reinforcement Learning for Wind and Energy Storage Coordination in Wholesale Energy and Ancillary Service Markets
Jinhao Li, Changlong Wang, Hao Wang
Wind energy has been increasingly adopted to mitigate climate change. However, the variability of wind energy causes wind curtailment, resulting in considerable economic losses for…