TY - JOUR
T1 - Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network
AU - Ge, Leijiao
AU - Liao, Wenlong
AU - Wang, Shouxiang
AU - Bak-Jensen, Birgitte
AU - Pillai, Jayakrishnan Radhakrishna
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
KW - Daily load profiles
KW - Distribution network
KW - generative network
UR - http://www.scopus.com/inward/record.url?scp=85084802710&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.2989350
DO - 10.1109/ACCESS.2020.2989350
M3 - Journal article
SN - 2169-3536
VL - 8
SP - 77587
EP - 77597
JO - IEEE Access
JF - IEEE Access
M1 - 9075210
ER -