Two-Stage Optimal Scheduling Strategy for Large-Scale Electric Vehicles

Abstract

This paper proposes a two-stage scheduling strategy for large-scale electric vehicles to reduce the adverse impact of the uncontrolled charging of the electric vehicles on the grid. Based on the statistical data of private car travel, the uncontrolled charging demand of individual electric vehicles and their aggregation are simulated. In the first stage, the electric vehicles and thermal power units are jointly scheduled. To minimize the total cost and standard deviation of the total load curve, the charging and discharging load guiding curve of the electric vehicles and the optimal output plans of the thermal power units in each period of the scheduled day are formulated. In the second stage, the electric vehicle load management and control centre formulates specific charging and discharging plans for the users through rolling optimization to follow the guiding load curve. The cost of vehicle discharge compensation is considered to improve the willingness of users to participate in scheduling and the user satisfaction. To avoid the ''dimension disaster'' caused by the centralized dispatching of large numbers of electric vehicles, the K-means clustering algorithm is used to divide the vehicles into different groups. Next, each group is scheduled as a unit, and the model is solved by using the particle swarm optimization algorithm. By comparing the optimization results of different scenarios, the feasibility and effectiveness of the proposed strategy are verified.

A Calculation Model of Charge and Discharge Capacity of Electric Vehicle Cluster Based on Trip Chain

“…It can be seen that the energy-charged in [38] are close to the results in this paper while the peak charging load appears in the early evening. This is because this model does not consider the charge demand in the working area.…”

“…Compared with the model in this paper, the charge demand curve of the model in [38] has two peaks with higher peak values and higher energy-charged. This is because the model assumes that the initial charging SOC is concentrated around 0.6, but ignores the impact of the driving mileage on the charge demand.…”

“…An EV charge demand model is proposed in [38], which assumes that the start charging time of EVs follows a normal distribution N(17.5, 3.4 2 ), and the daily mileage follows a lognormal distribution log-N(3.4, 0.5 2 ). Another EV charge demand model is proposed in [39], which assumes that 30% of the EVs in the cluster will charge in the working area where the start charging time follows a normal distribution N(9, 0.5 2 ), and 70% will charge in the residential area where the start charging time follows a normal distribution N(19, 1.5 2 ).…”

“…This is because this model does not consider the charge demand in the working area. Model in [38] Model in [39] Model in this paper [38] 39.1 In [39] 50.5…”

Save or Waste: Real Data Based Energy-Efficient Driving

“…In this section, by taking advantage of k-means clustering, participated drivers are partitioned into 4 typical categories. K-means clustering is one of the most commonly used clustering algorithms [29] [30]. In kmeans clustering, each observation belongs to the category with the nearest mean.…”