User behaviour informed optimal control for vehicle-home-grid integration

Achieving the global targets on the environment and carbon neutrality heavily relies on fossil-free transport and renewable electric energy sources.

Simply integrating electric vehicle (EV) fleets and variable renewables into the power grid will, however, cause large power fluctuations, associated with frequency deviations, potential risks of instability, and even system failure.

By utilising vehicle battery systems as energy buffers, vehicle-to-home and vehicle-to-grid technology have huge potential to even out the demand spikes but is subject to high dimensionalities, nonlinearities, and stochasticity.

This PhD project will leverage advanced models, optimisation algorithms, and control and communication strategies to address the above challenges fundamentally.

The results will reduce the costs of household electricity and EV ownership, enhance the grid resilience for EV and renewable integration, and maximise energy and resource efficiency.