Electric vehicle charging scheduling method considering demand matching degree

An electric vehicle and scheduling method technology, applied in data processing applications, instruments, forecasting, etc., can solve the problems of under-response, over-response frequently, unable to find the optimal solution, large fluctuation of electric vehicle load, etc. Reduced negative impacts, high population diversity, and mitigation of under-response effects

Pending Publication Date: 2022-03-01
国网重庆市电力公司营销服务中心 +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] On the one hand, there are studies considering the Electric Vehicle Aggregator (EVA) mechanism to explore the charging scheduling of electric vehicles, but the load of electric vehicles fluctuates greatly, and the charging load of electric vehicles predicted by EVA is not accurate enough, so it is difficult to guarantee the charging load of electric vehicles. The charging power of the grid matches the demand power of the grid, resulting in frequent under-response and over-response problems
On the other hand, when solving the dispatching model of electric vehicles, the convergence speed of the solution algorithm is not fast enough to find the optimal solution, resulting in an uneconomical dispatching scheme

Method used

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  • Electric vehicle charging scheduling method considering demand matching degree
  • Electric vehicle charging scheduling method considering demand matching degree
  • Electric vehicle charging scheduling method considering demand matching degree

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0085] A charging scheduling method for electric vehicles considering demand matching, comprising the following steps,

[0086] Step 1: Establish the electric vehicle charging load prediction model of the travel chain; and input the sorted information into the electric vehicle charging load prediction model to obtain the prediction results;

[0087] Step 2: Establish an electric vehicle dispatching model of demand matching degree;

[0088] Step 3: Input the prediction result in step 1 into the dispatching model in step 2, solve it by fuzzy cuckoo algorithm, and obtain the dispatching plan.

[0089] The working principle / working process of the present invention is as follows: first, establish the electric vehicle charging load prediction model of the travel chain and the electric vehicle dispatching model of the demand matching degree, sort out the system information required by the electric vehicle prediction model; secondly, input the sorted information Then, input the predi...

Embodiment 2

[0091] The process of establishing a travel chain electric vehicle charging load forecasting model is as follows: figure 2 As shown, the specific steps are as follows:

[0092] Step 1.1. Establish a space-time model of vehicle travel

[0093] The prediction model investigates the travel data of residents, and divides the above-mentioned travel purposes into four categories according to the classification of activity types: home (Home, H), work (Work, W), shopping and eating (Shopping&Eating, SE), and other affairs (Other Family / Personal Errands, O). Each user of an electric private car randomly transfers between these four types of destinations, and the end time of the trip and the driving distance are independent of each other and do not interfere with each other;

[0094] For the distribution of the end time of the trip, the Weibull probability function is used to fit the end time of each trip, namely:

[0095]

[0096] In the formula, x is a random variable, k is a s...

Embodiment 3

[0133] Establish an electric vehicle dispatching model of demand matching degree, and implement it according to the following steps:

[0134] Step 2.1. Establish electric vehicle (EV) charging model

[0135] When optimizing scheduling, in order to give full play to the energy storage characteristics of electric vehicle batteries, this patent establishes an electric vehicle charging model. The model is expressed as

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142] Respectively, the charging power of the i-th electric vehicle in the t scheduling period; is the rated discharge power of the i-th electric vehicle; is the state of charge (SOC) of the i-th vehicle in the period t; is the capacity of the battery of vehicle i; η c is the charging efficiency of electric vehicles; Δt is the time interval between two optimal dispatches; t i,arr is the period when the i-th electric vehicle starts to be dispatched;, t i, d ep Indicates the period w...

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Abstract

The invention discloses an electric vehicle charging scheduling method considering the demand matching degree, relates to the technical field of electric power system control, and solves the problems of under response, over response, low solving convergence speed and the like in the electric vehicle charging scheduling process, and the method comprises the following steps: establishing an electric vehicle charging load prediction model of a trip chain; inputting the sorted information into a model for predicting the charging load of the electric vehicle to obtain a prediction result; establishing an electric vehicle scheduling model of the demand matching degree; inputting the prediction result in the step 1 into the scheduling model in the step 2, and solving through a fuzzy cuckoo algorithm to obtain a scheduling scheme; according to the method, the improved trip chain is used for predicting the load of the electric vehicle, the charging load of the electric vehicle in different time and space is predicted, various factors such as the number of the electric vehicle and the battery capacity can be considered, and the prediction result is more real and reasonable.

Description

technical field [0001] The invention relates to the technical field of electric power system control, in particular to an electric vehicle charging scheduling method considering demand matching degree. Background technique [0002] With the continuous increase of EV (Electric Vehicle) scale, when a large number of EVs are charged during peak load hours, the safe operation of the power grid is facing increasing risks. [0003] On the one hand, there are studies considering the Electric Vehicle Aggregator (EVA) mechanism to explore the charging scheduling of electric vehicles, but the load of electric vehicles fluctuates greatly, and the charging load of electric vehicles predicted by EVA is not accurate enough, so it is difficult to guarantee the charging load of electric vehicles. The charging power of the grid matches the demand power of the grid, resulting in frequent under-response and over-response problems. On the other hand, when solving the electric vehicle dispatchi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/06G06Q10/04G06N3/00
CPCG06Q10/06315G06Q10/04G06N3/006G06Q50/06
Inventor 徐婷婷胡晓锐吴高林龙方家程超贻张雨晴朱彬龙羿汪会财池磊李智王敏谢晓念谢涵袁秀娟
Owner 国网重庆市电力公司营销服务中心
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