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Yue Wang

Senior Lecturer

yue.wang@chi.ac.uk

Yue has 8+ years of experience implementing technically advanced research and economically feasible solutions to real-life projects on renewable energy, electric transportation and smart energy management. To be more specific, after she graduated from University of Strathclyde in 2010, she worked closely with various wind turbine operators from 2010 to 2013 during her PhD and carried out original research of condition monitoring and fault prognosis on modern wind turbines, both onshore and offshore, based on their SCADA data. Since 2014, she focused on the integration of electric vehicles, renewable energy sources, and energy storage in the smart grid, aiming to demonstrate its technical and economic feasibility, and to facilitate the transition to a low carbon and sustainable economy in European cities.

Professional

  • Managing guest editor for a special issue in Renewable Energy Journal on ‘Renewable Energy to Drive Sustainable Electric Transport: Synergies, Challenges and Opportunities’.
  • Member of the International Scientific Committee of EFEA (IEEE International Symposium of Environment friendly energies and applications) 2018 and 2020.
  • Invited speaker to top universities in China, such as Hebei University of Technology.
  • Best paper award in UPEC Conference 2018.

Publications

Journal Papers

  • E. Bentley, R. Kotter, Y. Wang, R. Das, G. Putrus, J. van der Hoogt, E. van Bergen, J. Warmerdam, R. Heller and B. Jablonska, Pathways to energy autonomy – challenges and opportunities, International Journal of Environmental Sciences, 2019. (accepted for publication)
  • Y. Wang, D. Infield, Markov Chain Monte Carlo simulation of electric vehicle use for network integration studies, International Journal of Electrical Power & Energy Systems, Volume 99, 2018. (Q1)
  • D. Mathew, C. Rani, M. Rajesh Kumar, Y. Wang, R. Binns and K. Busawon, Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters, IEEE Journal of Photovoltaics, vol. 8, no. 1, pp. 248-256, 2018. (Q1)
  • Y. Cao, H. Song, O. Kaiwartya, O. Lei, Y. Wang, and G. Putrus, Electric Vehicle Charging Recommendation and Enabling ICT Technologies: Recent Advances and Future Directions. IEEE COMSOC MMTC Communications – Frontiers, 2017.
  • M. Derick, C. Rani, M. Rajesh, M.E. Farrag, Y. Wang, K. Busawon, An improved optimization technique for estimation of solar photovoltaic parameters, Solar Energy, Volume 157, Pages 116-124, 2017. (Q1)
  • Y. Wang, D. Infield, and S. Gill, Smart charging for electric vehicles to minimize charging cost, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, pp.1-15, 2016.
  • Y. Wang, D. G. Infield, B. Stephen, and S. J. Galloway, Copula-based model for wind turbine power curve outlier rejection, Wind Energy, doi: 10.1002/we.1661, 2013. (Q1)
  • Y. Wang, D. Infield, Supervisory control and data acquisition data-based non-linear state estimation technique for wind turbine gearbox condition monitoring, Renewable Power Generation, IET, vol.7, no.4, pp.350-358, July 2013.

Conference Proceedings

  • I.T. Vadium, R. Das, Y. Wang, G. Putrus and R. Kotter, Electric vehicle Carbon footprint reduction via intelligent charging strategies, 8th International Conference on Modern Power Systems, Romania, May, 2019.
  • R. Van den Hoed, J. Van der Hoogt, B. Jablonska, E. Van Bergen, R. Prateek, G. Putrus, R. Kotter, R. Das, Y. Wang, Lessons Learnt – A Cross-case Analysis of Six, Real-time Smart Charging and V2X Operational Pilots in the North Sea Region, 32nd Electric Vehicle Symposium (EVS32), Lyon, France, May 2019.
  • R. Das, Y. Wang, G. Putrus, and K. Busawon, Modelling the State of Charge of Lithium-ion batteries, 53rd International Universities Power Engineering Conference (UPEC), Glasgow, Sep. 2018. (Best paper award)
  • M. Nicoli, R. Das, Y. Wang, G. Putrus, R. Turri, and R. Kotter, A Smart Grid Modelling Tool for Evaluating Optimal Control of Electric Vehicles, 53rd International Universities Power Engineering Conference (UPEC), Glasgow, Sep. 2018.
  • S. Nagamitsu, R. Gondo, N. Nagaoka, A.A.A. Al-karakchi, G. Putrus, and Y. Wang, A Battery Diagnostic Method for Smart EV Charger Employing a Pulse Current Method for Prolonging Battery Life Time, 53rd International Universities Power Engineering Conference (UPEC), Glasgow, Sep. 2018.
  • Y. Wang, D. Infield, Optimal demand side response to real time price signal using electric vehicles, International Conference on Renewable Power Generation (RPG 2015), Beijing, 2015. 
  • Y. Wang, S. Huang, and D. Infield, Investigation of the potential for electric vehicles to support the domestic peak load, Electric Vehicle Conference (IEVC), IEEE, Dec. 2014.
  • Y. Wang, D. Infield, Multi-machine Based Wind Turbine Gearbox Condition Monitoring Using Nonlinear State Estimation Technique, EWEA Barcelona 2014.
  • Y. Wang, D. G. Infield, B. Stephen, and S. J. Galloway, Power Curve Based Online Condition Monitoring for Wind Turbines, COMADEM Helsinki 2013.
  • B. Stephen, S. Gill, S. Galloway, Y. Wang, D. McMillan and D. Infield, Wind turbine operation anomaly detection using copula statistics, EWEA Vienna 2013.
  • Y. Wang, D. Infield, Neural network modelling with autoregressive inputs for wind turbine condition monitoring, SuperGen 2nd international Conference, Hangzhou 2012.
  • Y. Wang, D. Infield, SCADA data based nonlinear state estimation technique for wind turbine gearbox condition monitoring, EWEA Copenhagen 2012.

 

Research

  • Application of artificial intelligence and data mining techniques to wind turbine condition monitoring and fault prognosis
  • Investigation of driving patterns and development of optimal smart charging and vehicle-to-grid (V2G) solutions using optimisation techniques
  • Techno-economic evaluation of renewable energy investment and business model development to facilitate the adoption of renewables and electric vehicles
  • Investigation of smart grid and micro-energy market to improve energy efficiency and cost benefit for involved stakeholders

PHD Supervision

With the increasing penetrations of renewable energy and electrified transport, this project aims to integrate the renewable power generation and electric vehicles into the current and future power networks. Smart charging and vehicle to grid techniques will be investigated to increase the energy autonomy, reduce carbon footprint and alleviate grid stress. In particular, more effective smart energy management strategies will be developed for different clusters of users with distinct behaviours. 

Drop me an email to discuss further if this is of interest.