2019 - Member of Academia Europaea
2008 - IEEE Fellow For contributions to optimization techniques for power systems
2004 - Fellow of the Royal Academy of Engineering (UK)
Yonghua Song mostly deals with Electric power system, Simulation, Automotive engineering, Electric vehicle and Smart grid. His Electric power system study incorporates themes from Wind power, Automatic frequency control, Power control and Vehicle-to-grid. The various areas that Yonghua Song examines in his Simulation study include Linear programming and Distributed computing.
His Automotive engineering research is multidisciplinary, incorporating elements of Grid parity, Distributed generation, Photovoltaics and Energy storage. His work on Charging station is typically connected to Urban area as part of general Electric vehicle study, connecting several disciplines of science. His study focuses on the intersection of Smart grid and fields such as Peak demand with connections in the field of Smart meter, Price signal, Demand response, Load profile and Electrification.
His primary areas of study are Electric power system, Mathematical optimization, Wind power, Automotive engineering and Electricity. His study in Electric power system is interdisciplinary in nature, drawing from both Reliability engineering, Automatic frequency control, Control theory and Renewable energy. His research investigates the connection between Mathematical optimization and topics such as Probabilistic logic that intersect with issues in Extreme learning machine.
His studies deal with areas such as Control engineering, Simulation and Energy storage as well as Wind power. In his study, Electrical engineering is inextricably linked to Electric vehicle, which falls within the broad field of Automotive engineering. His work in Electricity tackles topics such as Energy consumption which are related to areas like Demand response.
His primary areas of investigation include Control theory, Electric power system, Smart grid, Mathematical optimization and Reliability engineering. His Electric power system research includes themes of Automotive engineering and Renewable energy. His research integrates issues of Bidding, CVAR and Energy storage in his study of Automotive engineering.
His research investigates the link between Renewable energy and topics such as Hydropower that cross with problems in Wind power. Yonghua Song focuses mostly in the field of Smart grid, narrowing it down to matters related to Demand response and, in some cases, Telecommunications, Energy engineering, Energy management system, Voltage regulation and Tap changer. His work deals with themes such as Distribution networks, Power dispatch and Robustness, which intersect with Mathematical optimization.
Yonghua Song mainly focuses on Smart grid, Reliability engineering, Demand response, State and Telecommunications. His Smart grid research is multidisciplinary, incorporating perspectives in Efficient energy use, Energy supply, Energy market, Artificial intelligence and Management control system. His study in the field of Hot spare is also linked to topics like Influence diagram.
The Demand response study combines topics in areas such as Photovoltaics, Tap changer, Voltage regulation, Voltage and Energy management system. Yonghua Song combines subjects such as Blackout, Cascading failure, Electric power system and Sequence optimization with his study of State. As part of his studies on Telecommunications, Yonghua Song often connects relevant subjects like Energy engineering.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Flexible AC Transmission Systems (FACTS)
Yong Hua Song;Allan T. Johns.
(1999)
Modern Power Systems Analysis
Xi-Fan Wang;Yonghua Song;Malcolm Irving.
(2008)
Photovoltaic and solar power forecasting for smart grid energy management
Can Wan;Jian Zhao;Yonghua Song;Zhao Xu.
CSEE Journal of Power and Energy Systems (2015)
Decentralized Vehicle-to-Grid Control for Primary Frequency Regulation Considering Charging Demands
Hui Liu;Zechun Hu;Yonghua Song;Jin Lin.
IEEE Transactions on Power Systems (2013)
Vehicle-to-Grid Control for Supplementary Frequency Regulation Considering Charging Demands
Hui Liu;Zechun Hu;Yonghua Song;Jianhui Wang.
IEEE Transactions on Power Systems (2015)
A Comprehensive Review on the Development of Sustainable Energy Strategy and Implementation in China
Xia Yang;Yonghua Song;Guanghui Wang;Weisheng Wang.
IEEE Transactions on Sustainable Energy (2010)
PEV Fast-Charging Station Siting and Sizing on Coupled Transportation and Power Networks
Hongcai Zhang;Scott J. Moura;Zechun Hu;Yonghua Song.
IEEE Transactions on Smart Grid (2018)
Evaluation of Achievable Vehicle-to-Grid Capacity Using Aggregate PEV Model
Hongcai Zhang;Zechun Hu;Zhiwei Xu;Yonghua Song.
IEEE Transactions on Power Systems (2017)
Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function
Haiyan Lu;Pichet Sriyanyong;Yong Hua Song;Tharam Dillon.
International Journal of Electrical Power & Energy Systems (2010)
Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation
Can Wan;Jin Lin;Jianhui Wang;Yonghua Song.
IEEE Transactions on Power Systems (2017)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
Tsinghua University
Zhejiang University
Tsinghua University
University of California, Berkeley
University of Bath
Southwest Jiaotong University
Technical University of Denmark
Mälardalen University
Hong Kong Polytechnic University
University of Denver
Norwegian University of Science and Technology
University of Bergen
University of Tokyo
University of Naples Federico II
Alnylam Pharmaceuticals (United States)
Lehigh University
Shivaji University
North Dakota State University
University of Copenhagen
Peter MacCallum Cancer Centre
University of Colorado Boulder
University of Melbourne
Icahn School of Medicine at Mount Sinai
National Institutes of Health
University of Freiburg
Duke University