Skip to main content

Research Repository

Advanced Search

We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network

Shipman, Rob; Roberts, Rebecca; Waldron, Julie; Naylor, Sophie; Pinchin, James; Rodrigues, Lucelia; Gillott, Mark

We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network Thumbnail


Authors

ROB SHIPMAN Rob.Shipman@nottingham.ac.uk
Associate Professor

Rebecca Roberts

Julie Waldron

Sophie Naylor

MARK GILLOTT MARK.GILLOTT@NOTTINGHAM.AC.UK
Professor of Sustainable Building Design



Abstract

© 2021 Elsevier Ltd Vehicle-to-grid (V2G) services utilise a population of electric vehicle batteries to provide the aggregated capacity required to participate in power and energy markets. Such participation relies on the prediction of available capacity to support the reliable delivery of agreed reserves at a future time. In this work real historical trip data from a fleet of vehicles belonging to the University of Nottingham was used and a simulation developed to show how battery state-of-charge and available capacity would vary if these trips were taken in electric vehicles that were charged at simulated charging station locations. A time series forecasting neural network was developed to predict aggregated available capacity for the next 24-h period given input data from the previous 24 h and its increased predictive capability over a regression model trained using automated machine learning was demonstrated. The simulations were then extended to include delivery of reserves to satisfy the needs of simulated market events and the ability of the model to successfully adapt its predictions to such events was demonstrated. The authors conclude that this ability is of critical importance to the viability and success of future V2G services by supporting trading and vehicle utilisation decisions for multiple market events.

Citation

Shipman, R., Roberts, R., Waldron, J., Naylor, S., Pinchin, J., Rodrigues, L., & Gillott, M. (2021). We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network. Energy, 221, Article 119813. https://doi.org/10.1016/j.energy.2021.119813

Journal Article Type Article
Acceptance Date Jan 6, 2021
Online Publication Date Jan 9, 2021
Publication Date Apr 15, 2021
Deposit Date Jan 20, 2021
Publicly Available Date Mar 28, 2024
Journal Energy
Print ISSN 0360-5442
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 221
Article Number 119813
DOI https://doi.org/10.1016/j.energy.2021.119813
Keywords Vehicle-to-grid, V2G, Deep learning, CNN-LSTM network, Machine learning, Neural networks
Public URL https://nottingham-repository.worktribe.com/output/5234817
Publisher URL https://www.sciencedirect.com/science/article/abs/pii/S0360544221000621

Files




You might also like



Downloadable Citations