Multi-time scale forecast for schedulable capacity of EVs based on big data and machine learning

The application of large-scale electric vehicles (EVs) into the future smart grid may bring about serious power quality problems. But EVs can provide ancillary services for the power system as distributed energy resources through Vehicle-to-grid (V2G) technology. The fast and accurate prediction of schedulable capacity (SC) of EVs is the first step to enable this benefit. In this paper, two different time scales schedulable capacity forecasting (SCF) methods are proposed, including the real-time SCF method and a day head SCF method. Firstly, the real time of SCF of EVs is calculated by collecting the real-time charging or discharging data of individual EVs and a distributed data parallel calculation method to solve problems of the mass data process and storage problem. Secondly, the large amount of results of the real-time SCF, is used as historical data, to train the SCF models one day ahead by parallel decision tree regression algorithm (PDTR) and parallel random forest regression algorithm (PRFR). These proposed methods are evaluated by the real time operation data involving 521 EVs in the filed in China on Spark. The test results show the advantage of using big data technology in the ability and speed of dealing with large-scale data, and PDTR is faster and better than PRFR in one day ahead SCF.