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Projects: Projects for Investigator
Reference Number EP/P03277X/1
Title Aggregative charging control of electric vehicle populations
Status Completed
Energy Categories Energy Efficiency(Transport) 75%;
Other Power and Storage Technologies(Electricity transmission and distribution) 25%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 100%
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Professor K Margellos
No email address given
Engineering Science
University of Oxford
Award Type Standard
Funding Source EPSRC
Start Date 01 January 2018
End Date 30 April 2019
Duration 16 months
Total Grant Value £100,415
Industrial Sectors Energy
Region South East
Programme Energy : Energy, NC : ICT, NC : Maths
Investigators Principal Investigator Professor K Margellos , Engineering Science, University of Oxford (100.000%)
  Industrial Collaborator Project Contact , Siemens plc (0.000%)
Project Contact , The MathWorks Inc, USA (0.000%)
Project Contact , Honeywell UOP, USA (0.000%)
Web Site
Abstract The ambitious targets in the United Kingdom for increasing the share of renewable energy sources integrated to the network, and the need for providing affordable, resilient and clean energy, call for a paradigm shift in energy systems operations. Electric vehicles offer the means to address these challenges and achieve uninterrupted operation by deferring their demand in time and acting as dynamic storage devices. As a result, their number is expected to increase rapidly over the next years, leading to a "green car revolution". This constitutes an opportunity for modernizing energy systems operation, but will unavoidably give rise to coordination and scheduling issues at a population level so that cost savings are achieved and reliability is ensured. The latter is of significant importance to prevent from undesirable disruptions of service. This project will address this problem using tools at the intersection of control theory, optimization and machine learning, allowing for a decentralized computation of the electric vehicle charging strategies, while preventing vehicles from sharing information about their local utility functions and consumption patterns that is considered to be private. We will develop algorithms capable of dealing both with cooperative and non-cooperative vehicle behaviours in large fleets of vehicles, and immunize the resulting strategies against uncertainty due to unpredictability in the vehicles' driving behaviour and due to the presence of renewable energy sources. The presence of an algorithmic tool with these features will allow for scalable charging solutions amenable to problems of practical relevance, will provide insight on the mechanism driving the response of large populations of electric vehicles, and embed robustness in the resulting charging schedules. As such, the proposed project will offer the means for reliable system operation and facilitate the integration of higher shares of renewable energy sources
Publications (none)
Final Report (none)
Added to Database 15/02/18