Multi-time scale electric vehicle clustering schedulable capacity prediction method
A multi-time scale, electric vehicle technology, applied in electric vehicles, electric vehicle charging technology, forecasting, etc., can solve the problem of not considering multi-time scale power scheduling, low accuracy of probability models, and not considering dispatchable capacity calculation and prediction, etc. question
A multi-time scale, electric vehicle technology, applied in electric vehicles, electric vehicle charging technology, forecasting, etc., can solve the problem of not considering multi-time scale power scheduling, low accuracy of probability models, and not considering dispatchable capacity calculation and prediction, etc. question
CN106203720AActive Publication Date: 2016-12-07HEFEI UNIV OF TECH
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[0062] Multi-time scale electric vehicle cluster dispatchable capacity prediction methods include real-time electric vehicle cluster dispatchable capacity prediction and day-ahead electric vehicle cluster dispatchable capacity prediction, electric vehicle cluster dispatchable capacity includes electric vehicle cluster dispatchable charging capacity and electric vehicle cluster dispatchable capacity discharge capacity.
[0063] In this embodiment, the prediction method is carried out according to the following steps:
[0064] Step 1. Set the forecast time scale t for real-time electric vehicle cluster dispatchable capacity forecast d ; The time scale based on power system real-time scheduling is usually 1 second to 10 minutes, so t d The value ranges from 1 second to 10 minutes; to predict the time scale t d is the time interval to collect the real-time status data of electric vehicles connected to the grid area; t d The smaller the value, the more accurate the real-time ele...
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The invention discloses a multi-time scale electric vehicle clustering schedulable capacity prediction method. The method is characterized in that the method includes the following steps: establishing a real-time electric vehicle clustering schedulable capacity prediction model on a distributive parallel big data processing platform, using real-time state data, acquiring the result of real-time electric vehicle clustering schedulable capacity prediction based on the prediction model; based on the result of real-time electric vehicle clustering schedulable capacity prediction and feature data of symptom feature attributes, performing correlation analysis, extracting the feature data and establishing a data set, establishing the parallel big data algorithm on the distributive parallel big data processing platform, using the big data algorithm and the data set to establish the current electric vehicle clustering schedulable capacity prediction model. According to the invention, the method brings the advantages of big data parallel processing into full play, can provide strong data support for grid multi-time scale scheduling, electric vehicle charging and discharging control and gird reliability.
Description
technical field [0001] The invention relates to a method for predicting the schedulable capacity of an electric vehicle cluster, more specifically, a method for predicting the schedulable capacity of an electric vehicle cluster based on big data. Background technique [0002] Electric vehicles have advantages that traditional vehicles cannot match in terms of environmental protection, cleanliness and energy saving. Due to the great randomness of electric vehicle charging, the access of large-scale electric vehicles will have adverse effects on the power grid, including affecting the power quality of the distribution network and increasing the difficulty of control optimization. The development of Vehicle-to-grid (v2g) technology brings new opportunities for large-scale access of electric vehicles. The electric vehicle cluster connected to a certain grid area can be used as a large distributed energy storage system, which can provide various auxiliary support services for th...
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Patent Timeline
07 Dec 2016
Publication
CN106203720A
- IPC
- G06Q10/04; G06Q50/06; B60L11/18
- CPC
- B60L53/60; B60L55/00; G06Q10/04; G06Q50/06; Y02T10/70; Y02T10/7072; Y02T90/12; Y02T90/14
- Inventors
- 茆美琴; 岳友