Flexibility characterization and analysis of electric vehicle clusters based on real user behavior data 11The short version of the paper was presented at ICAE2023, Doha, Qatar, Dec 3–5, 2023. This paper is a substantial extension of the short version of the conference paper

With the increase in electric vehicles (EVs), they have begun to be valued as a new flexibility resource. Characterizing the flexibility of EVs is conducive to fully utilizing their flexibility to support the operation of electric power systems. A flexibility characterization method for EVs based on real user behavior data was proposed. First, the raw data are cleaned to obtain a dataset for each EV user. Then, a clustering algorithm is used to mine the plug-in time habit of each EV user, and an EV flexibility potential index system is proposed to quantify the flexibility of EV users with adjustable capacity, on-grid time, and charging power under the plug-in time habit. Furthermore, based on the flexibility indexes, the EV flexibility region under each plug-in time habit was constructed. Finally by setting the generators reasonably, the flexibility characterization from one EV user to the EV user cluster that includes large EVs is realized with the help of the zonotope similar. The flexibility of 6903 EV users in Nanjing, China, was characterized. The results showed that the similarity was improved by 0.566 by resetting the generator. Simultaneously, the results demonstrate the flexibility of the EV cluster in different periods. For example, there is a capacity adjustment range of approximately 75–150 MWh in 20:00–22:00, and approximately 15–20 MWh in 2:00–4:00 for the power grid to carry out day-ahead dispatching of EVs to assist the operation of the power grid.