A space variable-scale scheduling method for digital vehicle-to-grid platform under distributed electric energy storage

Abstract

With the widespread application of digital technology in the intelligent transportation field, the vehicle-to-grid (V2G) pattern, which utilizes electric vehicles (EVs) as the distributed storage resources of electric energy, has won increasingly more attention from authorities and decision-makers to promote the sustainable development of regenerative clean energy. This paper aims to study the interaction and integration problem between EVs and grid for the digital V2G platform, so as to support load aggregators making scheduling decisions. According to the variable-scale data analysis theory, a space–time scale space model is established to describe the demand response behavioral feature of EV users, and the lightning-Scale Transformation method (LST) is also proposed. Compared to the original unidirectional scale transformation modes, the LST is capable of adjusting the space and time observation scale collaboratively. A vehicle-to-grid scheduling method based on the lightning-Scale Transformation (V2G-LST) is put forward, in order to make scheduling plans by considering EV users’ behavioral feature and charger resource constraints. We collect the real dataset from 863 Beijing charging stations using the APIs of Amap, as well as the investigation data of the State Grid Corporation of China to verify the efficacy of our proposed method. Comparative experimental results verify that the V2G-LST could obtain the lower dispatch cost and higher EV charger usage rate than the maximum dispatchable quantity first method EVC-MDQ under both charging and discharging scenarios.