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
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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:
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[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
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[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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