An Explicit Battery Discharging Model to Enable Vehicle to Grid Services

2017 ◽  
pp. 69-78
Author(s):  
Sandford Bessler
Keyword(s):  
Grid Services ◽  
Vehicle To Grid

scholarly journals Quantifying electric vehicle battery degradation from driving vs. vehicle-to-grid services

2016 ◽  
Vol 332 ◽  
pp. 193-203 ◽  
Author(s):  
Dai Wang ◽  
Jonathan Coignard ◽  
Teng Zeng ◽  
Cong Zhang ◽  
Samveg Saxena
Keyword(s):  
Electric Vehicle ◽  
Grid Services ◽  
Vehicle To Grid ◽  
Electric Vehicle Battery ◽  
Battery Degradation

scholarly journals Coordinated Autonomous Vehicle Parking for Vehicle-to-Grid Services: Formulation and Distributed Algorithm

2018 ◽  
Vol 9 (5) ◽  
pp. 4356-4366 ◽  
Author(s):  
Albert Y.S. Lam ◽  
James J.Q. Yu ◽  
Yunhe Hou ◽  
Victor O.K. Li
Keyword(s):  
Distributed Algorithm ◽  
Autonomous Vehicle ◽  
Grid Services ◽  
Vehicle To Grid

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

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Rob Shipman ◽  
Sophie Naylor ◽  
James Pinchin ◽  
Rebecca Gough ◽  
Mark Gillott
Keyword(s):  
Machine Learning ◽  
Electric Vehicles ◽  
Supply And Demand ◽  
Learning Model ◽  
Grid Services ◽  
Vehicle To Grid ◽  
Learning Capacity ◽  
Machine Learning Model ◽  
Storage Resource ◽  
Ev Charging

AbstractThe 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.

Coordinating Vehicle-to-Grid Services With Energy Trading

2012 ◽  
Vol 3 (1) ◽  
pp. 453-462 ◽  
Author(s):  
Ali T. Al-Awami ◽  
Eric Sortomme
Keyword(s):  
Grid Services ◽  
Energy Trading ◽  
Vehicle To Grid
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