• Isaac Flower

  • Theme:Propulsion Electrification
  • Project:High-fidelity spatiotemporal modelling of electric vehicle charging demand at the distribution network scale
  • Supervisor: Furong Li ,Julian Padget ,Chris Brace
  • The Gorgon's Head - Bath University Logo
  • Research journey


Isaac graduated from the University of Bath with a BSc in Natural Sciences (Physics with Chemistry), in which he undertook a year-long placement at Shell Global Solutions as an Electric Mobility intern. For his final year project, Isaac developed a model to predict financial crashes by utilising the self-exciting nature of a financial market, a technique that is used to predict earthquakes and their aftershocks. During his time at Shell, Isaac worked on many projects surrounding electric vehicles and charging infrastructure, including research into smart charging and discharging profiles to reduce battery degradation during DC fast charging, analysing data for the Wireless Charging of Electric Taxis (WiCET) Project, and the creation and management of an electric vehicle database. He also gained considerable expertise in the ISO 15118 charging communication standard, its “Plug and Charge” feature, and the related ecosystem. Outside of university, Isaac is an avid drummer and enjoys powerlifting, swimming, tennis and running.


  • I've been playing the drums for more than half my life
  • I used to swim at a county level
  • I took part in the 2021 Catan world championships
  • I've never seen Game of Thrones

Quantifying the value of EV flexibility through spatiotemporal modelling and optimisation at grid-scale

Isaac's PhD will investigate the definition, quantification, aggregation and optimisation of EV flexibility, and their consequential system values to appropriately reward EV drivers, based on their level of flexibility. Spatiotemporal analysis of charging behaviour will be used to model charging demand in granular detail with realistic assumptions, identify potential vulnerabilities in the distribution network, and assess the degree of misalignment with renewable energy. To fully realise the potential financial and environmental benefits, EV charging will be optimised over a range of spatial (e.g. single EV, EV cluster, street level, town/city level, regional level and national level) and temporal scales (e.g. hour, day, week, month), and against different weather conditions and local demographics. The outcome of this research will inform how charging optimisation and behaviour should evolve with increasing renewable penetration and changing mobility patterns, and the required upgrading in charging and electrical infrastructure.

© Copyright 2021 AAPS CDT, Centre for Doctoral Training in Advanced Automotive Propulsion Systems at the University of Bath