Electric Vehicles (EVs) can, when they are not used for driving, create value for the EV owner, by delivering ancillary services to the transmission system operator. Calculating potential earnings from grid services and charging strategies highly depends on the driving time, driving distance, and time spent at different locations. While few datasets describing EV usage exist, this work is based on one of the most extensive datasets gathered from 7,163 Nissan LEAFs. Using the real driving and charging data it was possible to calculate the value of a specific charging strategy for the individual EV. The EV dataset was used in a simulation based on British electricity transmission network operating codes and frequency measurement data. The outcome is the profit from frequency regulation for each EV in the data-set, which is found to range between 50 and 350 £/year, because of the large difference in the EV usage.

It is essential to secure flexible resources in power systems to increase the proportion of variable renewable energy generation systems. One flexible resource is demand response (DR) of the batteries of electric vehicles (EVs). In this study, we propose an electric vehicle driving simulator using the Markov chain Monte Carlo (MCMC) method and an EV demand response evaluation model. The former is a highly versatile EV driving simulator that can produce a random driving pattern based on actual EV fleet data, taking into account various features. The latter is a residential DR evaluation model that minimizes a household electricity bill based on the simulated fleet data and enables realistic DR operation using three processes: forecasting, planning, and operation. The contribution of this paper is to enable evaluation of realistic EV battery value in various housing and EV utilization combinations. We found that the EV battery control provides an economic benefit of US$62 (5% of the night-charging case cost) with only charge control and US$370 (31%) with charge and discharge control, as the average expected value based on our assumption of evaluation. Because 40-kWh EV batteries have a sufficiently large capacity to store surplus power from a rooftop PV, they can be operated by determining the operation schedule based on a fixed-fee structure without forecasting or planning.

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