Method for predicting short-period microgrid load power interval probability

A technology of load power and probability prediction, applied in the field of electric power system, can solve problems such as uncertainty of fluctuation range and singleness of prediction results

Inactive Publication Date: 2017-11-03
JIANGNAN UNIV
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Problems solved by technology

[0003] At present, the load power prediction methods of microgrid are single-point prediction, only a certain value is given, and the possible fluctuation range of the prediction result cannot be determined.

Method used

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  • Method for predicting short-period microgrid load power interval probability
  • Method for predicting short-period microgrid load power interval probability
  • Method for predicting short-period microgrid load power interval probability

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Embodiment Construction

[0012] The present invention will be further described below with examples in conjunction with the accompanying drawings.

[0013] The short-term wind power interval probability prediction method of the present invention comprises the following steps:

[0014] S101 acquires historical load power data of the microgrid. The microgrid load power sequence contains the actual microgrid load power data for two consecutive months, with a resolution of 1h.

[0015] S102 constructing the optimization criterion CWC combined with the prediction interval coverage PICP and the prediction interval average bandwidth W, including the following calculation steps:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] In the formula: N is the total number of samples, ζ i is the actual load, L i To predict the lower bound, U i To predict the upper bound, R is the target value range of the test sample, that is, the difference between the maximum value and the minimum value of th...

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Abstract

The invention discloses a method for predicting short-period microgrid load power interval probability. The method comprises the following steps: acquiring a plurality of history load power of a microgrid as a sample set; combining prediction interval coverage rate and prediction interval average bandwidth to construct an optimization criterion; establishing a short-period microgrid load interval probability prediction model based on an artificial bee colony recurrent neural network, and carrying out neural network weight threshold value searching and updating on the optimization criterion through an artificial bee colony algorithm; introducing trend operation in a bacteria foraging optimization algorithm into a bee following local search strategy, and introducing an optimal bee source guiding mechanism to carry out optimization on the artificial bee colony algorithm so as to improve the algorithm performance and quicken the rate of convergence. According to the method, through the improvement of the artificial bee colony algorithm, the disadvantage that a traditional artificial bee colony algorithm is slow in rate of convergence and low in precision is preferably overcome, and the load prediction level of the microgrid is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of power systems, and in particular relates to a short-term micro-grid load power interval probability prediction method. Background technique [0002] A microgrid is a small decentralized system that connects distributed power sources, energy storage devices, energy conversion devices, and monitoring and protection devices to supply power to users. Accurate prediction of microgrid load is an important basis for microgrid operation and energy management, and will directly affect microgrid operation strategy. In the field of load forecasting, the current methods for microgrid load forecasting are mainly divided into traditional forecasting methods and modern intelligent forecasting methods. Traditional forecasting methods mainly include curve extrapolation method, gray forecasting method, regression analysis method, time series method and load derivation method, etc. This kind of method uses probability the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02J3/00G06N3/00
CPCG06N3/006H02J3/00H02J2203/20Y02P80/14
Inventor 沈艳霞于昕妍
Owner JIANGNAN UNIV
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