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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Energy Res. | doi: 10.3389/fenrg.2019.00128

Probabilistic optimal power flow calculation method based on adaptive diffusion kernel density estimation

Guoqing Li1,  Weihua Lu1, Jing Bian1*, Fang Qin2 and Ji Wu2
  • 1Northeast Electric Power University, China
  • 2China Electric Power Research Institute (CEPRI), China

In order to accurately evaluate the influence of the uncertainty and correlation of photovoltaic (PV) output and load on the running state of power system, a probabilistic optimal power flow calculation method based on adaptive diffusion kernel density estimation is proposed in paper. Firstly, based on the distribution characteristics of PV output, the adaptive diffusion kernel density estimation model of PV output is constructed, which can fit the distribution of arbitrary distribution of PV power. This model can improve the local adaptability of PV output model and reflect the uncertainty and volatility of PV output more accurately. Secondly, The Kendall rank correlation coefficient and the least Euclidean distance are used as correlation measure and index of fitting to select the optimal Copula function, and the joint probability distribution model of PV output and load is constructed. After extracting the correlated PV output and load samples, a probabilistic optimal power flow calculation method considering the correlation of PV output and load is proposed. Finally, simulation studies are conducted with the measured data of a PV power plant of China and the IEEE 30-bus power system. The results show that considering the correlation between PV output and load can improve the accuracy of probabilistic optimal power flow calculation and effectively reduce the power generation cost of power system.

Keywords: Adaptive, diffusion kernel density, Copula theory, Correlation, probabilistic optimal power flow

Received: 31 Jul 2019; Accepted: 30 Oct 2019.

Copyright: © 2019 Li, Lu, Bian, Qin and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jing Bian, Northeast Electric Power University, Jilin, 132012, China, bj_jjj@163.com