21 citations · 22 across the 6 of their papers we have counts for
6 papers
Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market
Houxuan Zhou, Sriram Prasad, Chenghao Huang +2
Accurate electricity price forecasting (EPF) is essential for market participants to support operational planning and risk management, yet remains challenging due to strong volatil…
Incentive-Aligned Vehicle-to-Vehicle Energy Trading via Nash-Integrated Multi-Agent Reinforcement Learning
Yujin Lin, Yue Yang, Hao Wang
Vehicle-to-vehicle (V2V) energy trading enables decentralized peer-to-peer energy exchange among electric vehicles (EVs), reducing grid dependency while monetizing surplus capacity…
A Unified Variational Imputation Framework for Electric Vehicle Charging Data Using Retrieval-Augmented Language Model
Jinhao Li, Hao Wang
The reliability of data-driven applications in electric vehicle (EV) infrastructure, such as charging demand forecasting, hinges on the availability of complete, high-quality charg…
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…
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…