Electric vehicles with Vehicle-to-Grid capability in the future power system: optimal charging, discharging and trip scheduling

Abstract

As countries electrify road transport and decarbonise electricity, the demand for power is in- creasing while generation becomes more intermittent due to renewable energy uptake. This makes power system operation challenging and expensive. With expected high electric vehicle load, coordinating charging and discharging to balance supply and demand for electricity is important to avoid overburdening the network. Bidirectional charging through Vehicle-to-Grid could unlock significant storage capacity from parked electric vehicles when they are plugged in. This thesis focuses on the opportunities presented by scheduling charging, discharging and travel (trips) for commercial electric vehicle fleet operation to benefit both the fleet and network operators. Opportunities for carbon emission reduction are also considered. Throughout, this thesis integrates real-world data and learning from the Vehicle-to-Grid demonstrator project, E-Flex, of which the author was a contributor. The study identifies economically effi cient ways in which a future system may operate, and highlights barriers to such implementation. A foundational optimisation model is set out and an analysis of examined scenarios is produced, covering various configurations for scheduling charging for 12 trial fleets. The multi-service optimal charging scheduling model developed for this analysis is extended in two ways. Firstly, a novel operational strategy integrating trip scheduling is implemented with future energy and ancillary service prices to understand potential scenarios for commercial fleets. Secondly, to account for randomness in prices and travel data, machine learning is applied to a multi-service charging strategy, showing the benefits of real-time scheduling. This approach utilises the rich data-set provided by the real world project and employs a novel reinforcement learning environment. The thesis contributes to understanding optimal operational strategies for electric vehicles with Vehicle-to-Grid in the future power system, demonstrating the benefits of market participation.