Assoc Prof Dr. Shuli Wen, Shanghai Jiao Tong University, China
š Assoc. Prof. Dr. Shuli Wen is an accomplished academic and researcher at Shanghai Jiao Tong University, China, specializing in Transportation Electrification, Smart Grid, and Renewable Integration. With a Ph.D. in Control Science and Engineering from Harbin Engineering University, Dr. Wen has published extensively, with an h-index of 16 and over 1,142 citations. Their research has led to significant breakthroughs in Maritime Mobile Energy Internet, enhancing port power systems’ stability and ship efficiency. Dr. Wen’s contributions include 33 SCI-indexed papers, 50 patents, and editorial roles in prestigious journals. They are a Senior IEEE member and actively collaborate with industry leaders. š
Publication Profile
Google Scholar
š Education
Dr. Shuli Wen earned a Ph.D. in Control Science and Engineering from Harbin Engineering University in 2016, following a Bachelor’s in Electrical Engineering and Automation from Harbin Institute of Technology in 2010.
Research Focus
Dr. Shuli Wen’s research focuses on optimizing ship power systems, particularly through hybrid PV/diesel/battery configurations and energy storage system (ESS) allocations. š¢ Their pioneering work addresses complex challenges such as energy management in port environments and real-time power fluctuation identification using advanced technologies like LSTM neural networks. Dr. Wen’s contributions in spatiotemporal solar irradiation forecasting and optimal energy management for all-electric ships enhance operational efficiency and sustainability in maritime applications. Their extensive publication record and patent filings underscore their leadership in smart grid integration, renewable energy, and computational intelligence, making significant strides in advancing maritime energy systems.
Publication Top Notes
- Optimal sizing of hybrid PV/diesel/battery in ship power system š Cited by 431, 2015
- Economic allocation for energy storage system considering wind power distribution š Cited by 322, 2014
- Allocation of ESS by interval optimization method considering impact of ship swinging on hybrid PV/diesel ship power system š Cited by 140, 2016
- Real-time identification of power fluctuations based on LSTM recurrent neural network: A case study on Singapore power system š Cited by 122, 2019
- Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network š Cited by 110, 2019
- Optimal sizing of hybrid energy storage sub-systems in PV/diesel ship power system using frequency analysis š Cited by 105, 2017
- Data-driven robust coordination of generation and demand-side in photovoltaic integrated all-electric ship microgrids š Cited by 80, 2019
- A hybrid ensemble model for interval prediction of solar power output in ship onboard power systems š Cited by 65, 2019
- Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route š Cited by 60, 2018
- Coordinated optimal energy management and voyage scheduling for all-electric ships based on predicted shore-side electricity price š Cited by 56, 2020