Demand Response Application of Battery Swap Station Using A Stochastic Model

The landscape of operating a Battery Swap Station (BSS) and its implications is still not clarified properly. This research paper presents the formulation of a demand response program by BSS based on time of use pricing. Meanwhile, an organized procedure is introduced to obtain the stochastic characteristics of BSS. In this regard, the historical data on the driving patterns are utilized by Monte Carlo simulation system as input data that extracts the hourly swap request and residual capacity which enters BSS by customers discharged batteries. The process contains a queueing algorithm in an artificial BSS to secure the reliability and practicality of the scenarios. Reduction of scenario sets is done by Kantorovich Distance (KD) method to catch tractable data. Eventually, the model key factors are analyzed over various case studies for clarifying the major concerns in BSS application at the distribution level of the power system.