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

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

Energy, 2021, vol. 221, issue C

Abstract: 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.

Keywords: Vehicle-to-grid; V2G; Deep learning; CNN-LSTM network; Machine learning; Neural networks (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:221:y:2021:i:c:s0360544221000621

DOI: 10.1016/j.energy.2021.119813

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