Demand-side Price Responsive EV Charging Strategy based on Stochastic Model

This paper is primarily aimed at building a stochastic model considering the probabilistic distribution of relative factors in charging electric vehicles (EVs), including the start time of charging progress, the initial state of charge, and the length of charging. Following a brief introduction to the battery market and the common battery types utilized in the EV industry, the charging characteristics of a single battery are carefully analyzed. Four user groups are classified depending on the purpose of their use of EVs: commuting, business, public service, and taxi. The distribution of the stochastic parameters of each group is analyzed using probability density functions. The Monte Carlo method is employed to model the daily load pattern of multiple EVs with three different penetration levels: 10%, 20%, and 30%. To optimize the results, a state variable is also introduced which reflects the state of charge (SOC). By utilizing a management strategy, the impact of real-time electricity rates on the distribution of the initial SOC of EVs is analyzed by comparing the coordinated and uncontrolled load profiles.