Abstract
The daily load profiles modeling is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a flow-based generative network is proposed to model daily load profiles of the distribution network. Firstly, the real samples are used to train a series of reversible functions that map the probability distribution of real samples to the prior distribution. Then, the new daily load profiles are generated by taking the random number obeying the Gaussian distribution as the input data of these reversible functions. Compared with existing methods such as explicit density models, the proposed approach does not need to assume the probability distribution of real samples, and can be used to model different loads only by adjusting the structure and parameters. The simulation results show that the proposed approach not only fits the probability distribution of real samples well, but also accurately captures the spatial-temporal correlation of daily load profiles. The daily load profiles with specific characteristics can be obtained by simply classification.
Highlights
Taking a large number of daily load profiles as the input of the Newton-Raphson method, the probability distribution of voltage and power loss is obtained, which is of great significance for the economic operation and stability analysis of the distribution network [2]–[4]
The random variable z has the same dimension as the real sample x, since the non-linear independent component estimation (NICE) model uses a series of reversible transformations to obtain the probability distribution of daily load profiles
WORK Modeling daily load profiles can help capture the uncertainty of power load, which is of great significance to the optimization and operation of the power system
Summary
THE PRINCIPLE OF NICE Normally, the generative networks are designed to learn the probability distribution X ∼ Pdata(x) of real daily load profiles x. When the training process is over, the random numbers are obtained by sampling probability distribution Z ∼ Pz(z), and the new daily load profiles are generated by the inverse function f −1(z). It can be seen from formula (4) that this transformation is reversible, it is relatively simple and difficult to fit very complex non-linear relationships To this end, the normalizing flow is proposed to estimate the probability distribution of real samples. The random variable z has the same dimension as the real sample x, since the NICE model uses a series of reversible transformations to obtain the probability distribution of daily load profiles.
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