Jinglin Yu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jinglin Yu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Huan Zhao
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, China
1 shared publication - Junhua Zhao
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China
1 shared publication - Xuanang Gui
School of Science and Engineering, The Chinese University of Hong Kong–Shenzhen, Shenzhen 518172, China
1 shared publication - Yuheng Cheng
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China
1 shared publication - Zifan Liu
Department of Electronic and Computer Engineering, School of Engineering, Hong Kong University of Science and Technology, Hong Kong SAR 999077, China
1 shared publication
Latest Publications
- Open Access
- 11 June 2026
- Published by MDPI AG in Energy Storage and Applications
- Vol. 3 (2) , 9
Flexible Mobile Battery Energy Storage System Control Considering Traffic Congestion Risk
The volatility of renewable energy generation and nodal electricity prices provides an arbitrage opportunity for Mobile Battery Energy Storage Systems (MBESS) leveraging both temporal and spatial advantages. However, the inherent high complexity and strong randomness of both power and transportation systems lead to complex risks for MBESS control. Existing works mainly consider the market price risk and ignore the transportation system risk caused by traffic congestion. Specifically, they are constrained by two critical limitations: (1) decisions can only be made upon arrival at a destination, making the agent unresponsive on the road, and (2) traffic congestion risk is neither quantified nor controlled, leading to suboptimal routing strategies. To address these limitations, the MBESS needs more flexible “on the road” decision making and multiple risk management capabilities. Guided by this objective, a flexible deep reinforcement learning-based MBESS control framework is proposed, considering both market and traffic congestion risk. First, dynamic routing ability is integrated with the MBESS agent to provide more flexibility in making decisions, regardless of whether the agent has reached the designated location or not. Second, two risk metrics are proposed to quantitatively assess the traffic congestion risk based on moving time, and then the agent can make decisions considering both market and traffic congestion risk. Finally, considering the inefficiency of learning caused by introducing multiple risks, a risk curriculum learning method is proposed to improve the training efficiency and reduce learning costs. These components are unified in the Multiple Risk Estimation SDDPG (MRE-SDDPG) algorithm, which jointly maximizes profitability while controlling electricity price and traffic congestion risk. Simulations in the IEEE 30 bus environment show that the proposed framework can increase profit by 8.6% while reducing the traffic time by 15.8% on average, demonstrating the superiority of our design in considering traffic congestion risk.
- 11 June 2026
- Published by MDPI AG in Energy Storage and Applications
- Vol. 3 (2) , 9
Open AccessFlexible Mobile Battery Energy Storage System Control Considering Traffic Congestion Risk
The volatility of renewable energy generation and nodal electricity prices provides an arbitrage opportunity for Mobile Battery Energy Storage Systems (MBESS) leveraging both temporal and spatial advantages. However, the inherent high complexity and strong randomness of both power and transportation systems lead to complex risks for MBESS control. Existing works mainly consider the market price risk and ignore the transportation system risk caused by traffic congestion. Specifically, they are constrained by two critical limitations: (1) decisions can only be made upon arrival at a destination, making the agent unresponsive on the road, and (2) traffic congestion risk is neither quantified nor controlled, leading to suboptimal routing strategies. To address these limitations, the MBESS needs more flexible “on the road” decision making and multiple risk management capabilities. Guided by this objective, a flexible deep reinforcement learning-based MBESS control framework is proposed, considering both market and traffic congestion risk. First, dynamic routing ability is integrated with the MBESS agent to provide more flexibility in making decisions, regardless of whether the agent has reached the designated location or not. Second, two risk metrics are proposed to quantitatively assess the traffic congestion risk based on moving time, and then the agent can make decisions considering both market and traffic congestion risk. Finally, considering the inefficiency of learning caused by introducing multiple risks, a risk curriculum learning method is proposed to improve the training efficiency and reduce learning costs. These components are unified in the Multiple Risk Estimation SDDPG (MRE-SDDPG) algorithm, which jointly maximizes profitability while controlling electricity price and traffic congestion risk. Simulations in the IEEE 30 bus environment show that the proposed framework can increase profit by 8.6% while reducing the traffic time by 15.8% on average, demonstrating the superiority of our design in considering traffic congestion risk.