Real-time charging optimization method for electric vehicle cluster based on clustering control

Abstract

The invention discloses a real-time charging optimization method for electric vehicle cluster based on clustering control, and relates to the field of real-time charging in electric vehicle cluster, which comprising the following steps of: step 1, based on a distributed layered control framework, performing a queue division on the EV cluster by taking the charging end time as a judgment amount; step 2, according to the charging characteristics of EV cluster, the queue division of EV cluster is conducted by taking the end of charging as a criterion; step 2, a real-time optimization model of charging and discharging power for EV cluster with the objective of minimizing daily load fluctuation is solved, real-time optimization of charging and discharging power of EV cluster is carried out, andsecurity constraints are checked by distribution network; step 3, on the basis of an EV cluster scheduling result, the charging and discharging costs of EV owners are considered, a power allocation model in EV cluster is solved, the optimal power allocation for charging and discharging power of a single EV in the EV cluster is obtained, so that the total output of the EV cluster is as close as possible to the scheduling result. By means of the device, EV charge and discharge distributed layered control framework is adopted, thereby realizing the real-time control of charging and discharging in EV cluster.

Description

technical field [0001] The invention relates to the field of real-time charging in electric vehicle clusters, in particular to a group control-based real-time charging optimization method for electric vehicle clusters. Background technique [0002] With the expansion of electric vehicle (EV) scale, its charging demand and charging load distribution will show regularity. If the distribution network system manages the charge and discharge loads based on the overall characteristics of the EV population, it can reduce the complexity of the communication network and the difficulty of optimization calculations. Scholars have conducted research on EV charging control from the perspective of clusters. Literature "Aggregation Model-Based Optimization for Electric Vehicle Charging Strategy [J]" (J.Z, X.W, K.M, et al. IEEE Transactions on Smart Grid, 2013, 4(2): 1058-1066) and "Based on parameter serialization technology Electric vehicle group response control strategy [J]" (Wang Ran...

AI Technical Summary

Problems solved by technology

[0006] 1. When studying the overall charging and discharging control method of the EV cluster, the energy distribution of a single EV within the cluster is not considered;

[0007] 2. For the research on the power allocation method within the cluster, the economic cost of EV individual and the actual power constraint characteristics are rarely involved;

[0008] 3. The optimal control of charging and discharging of EV clusters is mainly based on day-ahead optimization, while there are few studies on the application of cluster response and cluster optimization in real-time control and scheduling methods

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