A real-time charging optimization method for electric vehicle clusters based on cluster control
An optimization method and technology for electric vehicles, applied in the direction of electric vehicle charging technology, electric vehicles, charging stations, etc., can solve the problems of not considering the energy distribution of a single EV, less research on application cluster response and cluster optimization, etc., to alleviate the problem. The effect of calculating the burden, reducing frequent movements, and prolonging life
Active Publication Date: 2022-05-31
STATE GRID SHANDONG ELECTRIC POWER COMPANY RIZHAOPOWER SUPPLY +1
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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
Method used
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Embodiment approach
[0094] Step 1.1, inside the EV cluster, subdivide into each charging queue according to the driving characteristics of the EV;
[0095] Step 1.2, according to the power characteristics of a single EV in the charging queue, allocate the corresponding charging and discharging power;
[0097] Step 1.4, the cluster controller merges the EVs newly accessed in each time period into the corresponding charging and discharging queues, and provides
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[0102] D=t
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[0105] Step 1.1.3, the real-time scheduling model minimizes load fluctuations, and models the charging and discharging queues.
[0106] Step 1.1.3 specifically includes: modeling each charge-discharge queue as a virtual battery, the power of which varies with time,
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[0129] Among them, and are the charge and discharge power limits of the kth electric vehicle, respectively.
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The invention discloses a real-time charging optimization method for electric vehicle clusters based on grouping control, which relates to the field of real-time charging in electric vehicle clusters, including: step 1, based on a distributed hierarchical control framework, according to the charging characteristics of EV clusters, charging The end moment is the discriminative quantity to divide the EV clusters into queues; step 2, solve the real-time optimization model of the charging and discharging power of the EV clusters with the goal of minimizing daily load fluctuations, optimize the charging and discharging power of the EV clusters in real time, and conduct the real-time optimization of the charging and discharging power of the EV clusters. Safety constraint verification; step 3, on the basis of the EV cluster scheduling results, considering the charging and discharging costs of EV owners, solve the internal power distribution model of the EV cluster, and obtain the optimal power allocation of the charging and discharging power of a single EV in the EV cluster, so that the EV The total output of the cluster is as close as possible to the scheduling result. The invention adopts the EV charging and discharging distributed layered control framework, and realizes the real-time control of EV cluster charging and discharging.
Description
A real-time charging optimization method for electric vehicle clusters based on group control technical field The present invention relates to the real-time charging field in the electric vehicle cluster, in particular to a kind of electric vehicle based on group control A real-time charging optimization method for vehicle clusters. Background technique [0002] With the expansion of electric vehicle (EV) scale, its charging demand and charging load distribution will show regularity. match If the power grid system manages the charging and discharging load based on the overall characteristics of the EV population, it can reduce the complexity of the communication network and reduce the cost of electricity. Optimize calculation difficulty. Some scholars have studied EV charging control from the perspective of cluster. Document "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(...
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IPC IPC(8): B60L58/10B60L58/12B60L53/10
CPCY02T10/40Y02T90/14
Inventor 王晓梅卢芳艾芊卢京祥杨思渊赵佃云韩邦东袁晓峰郑加丽钱栋
Owner STATE GRID SHANDONG ELECTRIC POWER COMPANY RIZHAOPOWER SUPPLY
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