Power distribution network probabilistic load flow obtaining method and device considering wind power uncertainty

Abstract

The invention relates to a power distribution network probabilistic load flow obtaining method considering wind power uncertainty, and the method comprises the following steps: S1, constructing a network structure of a bidirectional generative adversarial network: employing an artificial neural network of a full connection layer for the network structures of an encoder, a generator and a discriminator, and employing a LeakyReLU activation function for the full connection layer, wherein an output layer of the generator uses a Tanh function, an output layer of the discriminator uses a Sigmoid activation function, a Dropout layer is added behind a full connection layer of the discriminator, and a batch standardization layer is added before each layer of input of the encoder, the generator andthe discriminator; S2, training the bidirectional generative adversarial network in the step S1; S3, after the bidirectional generative adversarial network is trained, intercepting a generator as a generation model, and inputting one-dimensional random noise obeying Gaussian distribution to obtain wind power data conforming to original data probability distribution; S4, inputting the node load and the obtained wind power data into a probabilistic load flow calculation model, and calculating output node voltage and branch power. According to the method, the result of probabilistic load flow calculation is obtained more accurately in the environment of considering the uncertainty of wind power output.

Description

technical field [0001] The invention belongs to the technical field of distribution network probabilistic power flow calculation with distributed power sources, and in particular relates to a distribution network probability power flow acquisition method and device considering wind power uncertainty. Background technique [0002] Under the situation of energy shortage and global warming, a large number of distributed power sources are connected to the distribution network, making the traditional distribution network evolve into a complex network of multiple power sources, and the output of distributed power sources dominated by wind power is random and intermittent , which has a negative impact on the planned operation and economic dispatch of the power distribution system. Therefore, accurately describing the uncertainty of wind power has become a hot issue. [0003] Probabilistic power flow calculation can comprehensively reflect the influence of uncertainty on system ope...

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Problems solved by technology

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art, to provide a distribution network probabilistic power flow calculation method considering the uncertainty of wind power, and to use the powerful expression ability of Bidirectional Generative Adversarial Networks (BIGAN) to find out a given The internal statistical laws of the observation data do not require probabilistic modeling, and the generated data can well reflect the spatio-temporal characteristics of the actual power generation unit while ensuring diversity, thereby overcoming the poor accuracy of traditional probability models, complex solution parameters and multidimensional random variable correlation Difficult to describe, in the environment of considering the uncertainty of wind power output, the results of probabilistic power flow calculation can be obtained more accurately

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