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Details of Grant 

EPSRC Reference: EP/P03277X/1
Title: Aggregative charging control of electric vehicle populations
Principal Investigator: Margellos, Professor K
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Honeywell MathWorks Siemens plc (UK)
Department: Engineering Science
Organisation: University of Oxford
Scheme: First Grant - Revised 2009
Starts: 01 January 2018 Ends: 30 April 2019 Value (£): 100,415
EPSRC Research Topic Classifications:
Artificial Intelligence Sustainable Energy Networks
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
02 Aug 2017 Engineering Prioritisation Panel Meeting 2 August 2017 Announced
Summary on Grant Application Form
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.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Further Information:  
Organisation Website: http://www.ox.ac.uk