Electric Vehicle Charging Simulation Framework Considering Traffic, User, and Power Grid

Abstract

The traffic and user have significant impacts on the electric vehicle (EV) charging load but are not considered in the existing research. We propose a novel integrated simulation framework considering the traffic, the user, and power grid as well as the EV traveling, parking and charging based on cellular automaton (CA). The traffic is modeled by the traffic module of the proposed framework based on CA, while the power grid and user are modeled in the EV charging module. The traffic flow, user' s charging preference, user' s charging satisfaction, and the total supply capability (TSC) in the surveyed region are considered in the proposed framework. Two cases are carried out to show the interactions between the user and power grid. It is shown that the proposed framework can accurately simulate the interactions among traffic situation, user' s behavior and TSC，which are significantly lacking in the existing research. The proposed framework is scalable in considering additional interrelated elements.

Review on Optimization of Forecasting and Coordination Strategies for Electric Vehicle Charging

“…The SOC distribution must be considered when determining the power demand. Many factors affect the SOC distribution, e.g., the distance traveled by the EVs, the market share of different types of EVs, the charging rate, and the initial SOC after the EV is connected to the power grid [22]. Many studies have shown that a more accurate forecast of the SOC can significantly reduce the number of decision variables, reduce the time for formulating charging strategies, and maximize the energy utilization [23].…”

A Three-Stage Stochastic Framework for Smart Electric Vehicle Charging

“…Because for each set of EV load profile(generated from individual one-way travel pattern) samples are i.i.d. [50] which naturally works on Gibbs sampler and load profile probability distribution are not time sensitive since the overall traffic conditions will not change rapidly in certain districts. Thus, a high-dimension probability distribution for EV load profile including TOA, TOD and ETC is demonstrated in this study.…”

Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning

“…The impact of 6 Teslas (Model S and X) which joined only in the last third of the trial is clearly evident from the increases in both the peak power and the peak-to-average power ratio. [53,[58][59][60][61]. The simulation results are readily tested against real data to refine the models and reveal useful insights.…”

“…To simplify further analysis and guide design, the results can be represented stochastically, i.e., in terms of a characteristic probability distribution function, mean, and variance [31,50,53,55,65,[70][71][72][73][74][75]. (iii) Agent-based computer simulations of EV travel and charging patterns attempt to predict the value of key parameters, such as charging time, location, and the associated impacts on the electricity grid, and to test the sensitivity of those parameters to factors such as the mix of EVs and charging options, the topology and scale of the road and electricity networks, driver behaviours, the cost to charge, and various other factors of interest [53,[58][59][60][61]. The simulation results are readily tested against real data to refine the models and reveal useful insights.…”