Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid

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

doi:10.3390/en13081933

Abstract

Vehicle-to-grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from a vehicle tracking service, we assessed the technique’s ability to learn and predict when a fleet of 48 vehicles was parked close to charging stations and compared this with two moving average techniques. We found the ability of all three approaches to predict when individual vehicles could potentially connect to charging stations to be comparable, resulting in the same set of 30 vehicles identified as good candidates to participate in a vehicle-to-grid service. We concluded that this was due to the relatively small feature set and that machine learning techniques were likely to outperform averaging techniques for more complex feature sets. We also explored the ability of the approaches to predict total vehicle availability and found that automated machine learning achieved the best performance with an accuracy of 91.4%. Such technology would be of value to vehicle-to-grid aggregation services.

Highlights

A key function of an electricity grid operator is to balance supply and demand to ensure that the power produced always matches the power required
We made the following specific contributions; firstly, we demonstrated the ability of the models to predict when vehicles are parked close to V2G charging stations with high accuracy, which is necessary to underpin the assessment of the capacity available to a V2G aggregation service during future trading windows; secondly, we demonstrated a method of analysing a dataset retrieved from a vehicle tracking service to support the identification of vehicles that are strong candidates for use in a V2G service; thirdly, we demonstrated that simple prediction strategies, such as moving averages, could yield comparable performance to more complex machine learning techniques, which is of value to help bootstrap V2G services when large training datasets are not initially available
The cumulative moving average (CMA) and exponential moving average (EMA) averaging techniques did not consider and yet their performance was equivalent during both periods, and this feature had little impact the term, and yet their performance was equivalent during both periods, and this feature had on overall vehicle behaviour

Summary

Introduction

A key function of an electricity grid operator is to balance supply and demand to ensure that the power produced always matches the power required. Electricity can be traded ahead of time; in the day-ahead market, for example, generators and suppliers agree contracts for the delivery of energy typically during hour periods on the following day [1]. Vehicle-to-grid (V2G) is a technology that allows electric vehicles to contribute to such flexibility services by discharging or curtailing demand when required [2,3]. This capability has the potential to help manage the additional load on the grid resulting from the influx of electric vehicles, to help manage supply fluctuations inherent to renewable energy sources and to contribute to ambitious sustainability targets introduced by many cities around the world, including Nottingham in the UK [4]

Methods

Results

Discussion

Conclusion