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Novel computing platforms for large-scale electric vehicle charging

Design and development of novel computing platforms to enable vehicle-to-grid ancillary services for large-scale electric vehicle charging

This 12-month project (running from September 2023 to September 2024) aims to investigate and develop near real-time smart meter data and information exchange between distribution system operators (DSO) and electricity suppliers to facilitate large-scale electric vehicles (EV) charging. The novel, scalable, and secure computing platforms developed with hybrid cloud and edge nodes in conjunction with advanced smart meter data analytics i.e., federated multi-view regression with data streams will unlock greater operational flexibility for transmission and distribution systems, by assessing the temporal grid-to-vehicle demand and vehicle-to-grid generation in response to time-of-use tariffs. Furthermore, the ability to manage system assets with near real-time information would bring significant cost savings that can benefit EV drivers through time-of-use tariffs. Currently, randomised delay function is embedded to smart chargepoints to avoid large power swings but the charging experience is compromised.

By 2030, it is expected that that up to 10 million vehicles or a quarter of all road vehicles will need to be zero emission at the tailpipe. The Committee on Climate Change estimates that EV will comprise up to 37% of road vehicles by 2030. The government expects that as a minimum there will be around 300,000 public chargepoints in the UK, and this number could be double depending on EV adoption.

There are several key challenges that must be addressed in this project:

  • Smart meter data is not made live to DSO: DSO has agreed on a process through Smart Data Communications Company to collect the smart meter data monthly.
  • Scalability: This is a key factor for computing platforms where the system must effectively process several demands regardless of the growing number of nodes (i.e., smart meters).
  • Grid stability: The government has mandated a randomised delay function to help address grid stability concerns arising from smart charging.

Meet the Principal Investigator(s) for the project

Dr Chun Sing Lai
Dr Chun Sing Lai - Dr Chun Sing Lai is a Lecturer, Module Leader for EE1618 Devices and Circuits, taught at Chongqing University of Posts and Telecommunication (CQUPT) and Course Director for MSc Electric Vehicle Systems. He is an academic member of the Transnational Education (TNE) CQUPT programme for BEng Electronics and Communications Engineering. He is a member of Brunel Interdisciplinary Power Systems (BIPS) Research Centre. His current interests are in power system optimisation, energy system modeling, data analytics, electric vehicle systems, hybrid powertrains optimisation, and energy economics for renewable energy and storage systems. From 2018 to 2020, Dr Lai was an EPSRC Research Fellow with the Faculty of Engineering and Physical Sciences, University of Leeds as the lead researcher for EP/P022049/1: Generation Integrated Energy Storage - A Paradigm Shift. He is a Visiting Research Fellow at the School of Electronic and Electrical Engineering, University of Leeds and Visiting Research Fellow with the Department of Electrical Engineering, School of Automation, Guangdong University of Technology, China. He is Vice-Chair of the IEEE Smart Cities Publications Committee. Since 2022, Dr Lai is Associate Vice President, Systems Science and Engineering of the IEEE Systems, Man, and Cybernetics Society (IEEE/SMCS). He is a Co-Chair of Intelligent Power And Energy Systems Technical Committee of IEEE SMC Society. Since 2020, Dr Lai is Vice-counsellor for Brunel University London IEEE Student Branch. He is a Member of Early Career Researchers Committee of EPSRC Supergen Energy Storage Network+. He organised the workshop on Smart Grid and Smart City, IEEE SMC 2017 in Canada and a workshop on Blockchain for Smart Grid, IEEE SMC 2018 in Japan. He was a Publications Co-Chair for IEEE International Smart Cities Conference ISC2 (2020 and 2021). Dr Lai is a Technical Programme Chair for IEEE ISC2 2022 and Publications Co-Chair for 2022 the 12th International Conference on Power and Energy Systems (ICPES 2022). He was an Invited Speaker at 2023 6th Asia Conference on Energy and Electrical Engineering (ACEEE 2023) and 2023 6th International Conference on Power and Smart Grid. He has successfully secured funding to lead Standards-Related Activities in 2022-23 from IEEE Technical Activities Board Committee on Standards (TAB CoS). He is a recipient of the IET International Travel Award and Meritorious Service Award from the IEEE SMC Society for "meritorious and significant service to IEEE SMC Society technical activities and standards development" in 2022. Dr Lai is an Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics: Systems (IF: 11.471), IEEE Transactions on Consumer Electronics (IF: 4.414); IET Energy Conversion and Economics, and Frontiers in Energy Research (Smart Grids) (IF: 3.858). He is an Editorial Board member for Sensors (Industrial Sensors, IF: 3.847), Topics Board member for Electronics, and Reviewer Board member for Applied Sciences, as well as Guest Editor for several IEEE and MDPI journals. Dr Lai has co-authored "Smart Energy for Transportation and Health in a Smart City", Wiley, 2023. He is a book editor for "Electrification of Smart Cities", Electronics 2022. He is a Member and Contributor to IEEE Task Force on Enabling Paradigms for High-performance Computing in Wide Area Monitoring Protective and Control Systems. He has contributed to four journal papers that appear on Web of Science as Highly Cited Papers with three as the lead author. He is recognised as the top 2% of world active scientists by survey conducted by Stanford University. Dr Lai is a frequent reviewer for research grant applications such as National Fund for Scientific and Technological Development (FONDECYT), Government of Chile.

Related Research Group(s)

power cables

Brunel Interdisciplinary Power Systems - Power systems analysis for transmission and distribution networks, smart grids; congestion monitoring in transmission networks; simulation and analysis of new energy markets; optimisation of the design and operation of electrical networks; condition monitoring of power station and power system plant; energy-efficient designs for underground electric power cables.


AI Social and Digital Innovation - Social, economic and strategic effects of AI and associated technologies. Impact of AI and related technologies on societies, organisations and individuals.

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Project last modified 09/10/2023