Learning capacity: predicting user decisions for vehicle-to-grid services

Shipman, Rob; Naylor, Sophie; Pinchin, James; Gough, Rebecca; Gillott, Mark

Abstract

The electric vehicles (EV) market is projected to continue its rapid growth, which will profoundly impact the demand on the electricity network requiring costly network reinforcements unless EV charging is properly managed. However, as well as importing electricity from the grid, EVs also have the potential to export electricity through vehicle-to-grid (V2G) technology, which can help balance supply and demand and stabilise the grid through participation in flexibility markets. Such a scenario requires a population of EVs to be pooled to provide a larger storage resource. Key to doing so effectively however is knowledge of the users, as they ultimately determine the availability of a vehicle. In this paper we introduce a machine learning model that aims to learn both a) the criteria influencing users when they decided whether to make their vehicle available and b) their reliability in following through on those decisions, with a view to more accurately predicting total available capacity from the pool of vehicles at a given time. Using a series of simplified simulations, we demonstrate that the learning model is able to adapt to both these factors, which allows the required capacity of a market event to be satisfied more reliably and using a smaller number of vehicles than would otherwise be the case. This in turn has the potential to support participation in larger and more numerous market events for the same user base and use of the technology for smaller groups of users such as individual communities.

Citation

Shipman, R., Naylor, S., Pinchin, J., Gough, R., & Gillott, M. (2019). Learning capacity: predicting user decisions for vehicle-to-grid services. Energy Informatics, 2(1), Article 37. https://doi.org/10.1186/s42162-019-0102-2

• Journal Article Type: Article
• Acceptance Date: Dec 2, 2019
• Online Publication Date: Dec 26, 2019
• Publication Date: 2019-12
• Deposit Date: Dec 12, 2019
• Publicly Available Date: Jan 7, 2020
• Journal: Energy Informatics
• Electronic ISSN: 2520-8942
• Publisher: SpringerOpen
• Peer Reviewed: Peer Reviewed
• Volume: 2
• Issue: 1
• Article Number: 37
• DOI: https://doi.org/10.1186/s42162-019-0102-2
• Keywords: Electric vehicles; vehicle-to-grid; smart charging; artificial intelligence; machine learning
• Public URL: https://nottingham-repository.worktribe.com/output/3542596
• Publisher URL: https://link.springer.com/article/10.1186/s42162-019-0102-2?wt_mc=Internal.Event.1.SEM.ArticleAuthorIncrementalIssue&utm_source=ArticleAuthorIncrementalIssue&utm_medium=email&utm_content=AA_en_06082018&ArticleAuthorIncrementalIssue_20191229
• Additional Information: Received: 13 September 2019; Accepted: 3 December 2019; First Online: 26 December 2019; : The authors declare that they have no competing interests.