Design and development of novel computing platforms to enable vehicle-to-grid ancillary services for large-scale electric vehicle charging
This 12-month project (running from September 2023 to September 2024) aims to investigate and develop near real-time smart meter data and information exchange between distribution system operators (DSO) and electricity suppliers to facilitate large-scale electric vehicles (EV) charging. The novel, scalable, and secure computing platforms developed with hybrid cloud and edge nodes in conjunction with advanced smart meter data analytics i.e., federated multi-view regression with data streams will unlock greater operational flexibility for transmission and distribution systems, by assessing the temporal grid-to-vehicle demand and vehicle-to-grid generation in response to time-of-use tariffs. Furthermore, the ability to manage system assets with near real-time information would bring significant cost savings that can benefit EV drivers through time-of-use tariffs. Currently, randomised delay function is embedded to smart chargepoints to avoid large power swings but the charging experience is compromised.
By 2030, it is expected that that up to 10 million vehicles or a quarter of all road vehicles will need to be zero emission at the tailpipe. The Committee on Climate Change estimates that EV will comprise up to 37% of road vehicles by 2030. The government expects that as a minimum there will be around 300,000 public chargepoints in the UK, and this number could be double depending on EV adoption.
There are several key challenges that must be addressed in this project:
- Smart meter data is not made live to DSO: DSO has agreed on a process through Smart Data Communications Company to collect the smart meter data monthly.
- Scalability: This is a key factor for computing platforms where the system must effectively process several demands regardless of the growing number of nodes (i.e., smart meters).
- Grid stability: The government has mandated a randomised delay function to help address grid stability concerns arising from smart charging.
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Related Research Group(s)
Brunel Interdisciplinary Power Systems - Power systems analysis for transmission and distribution networks, smart grids; congestion monitoring in transmission networks; simulation and analysis of new energy markets; optimisation of the design and operation of electrical networks; condition monitoring of power station and power system plant; energy-efficient designs for underground electric power cables.
AI Social and Digital Innovation - Social, economic and strategic effects of AI and associated technologies. Impact of AI and related technologies on societies, organisations and individuals.
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Project last modified 09/10/2023