Abstract
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.
Highlights
Photovoltaic (PV) power generation has developed rapidly in recent years due to its advantages of green environmental protection and renewable capacity
A probabilistic optimal power flow (POPF) calculation method based on adaptive diffusion kernel density estimation is proposed
The following conclusions can be drawn: 1. The traditional Gaussian kernel function is transformed by linear diffusion, and an adaptive diffusion kernel density estimation method suitable for arbitrary distributed PV
Summary
Photovoltaic (PV) power generation has developed rapidly in recent years due to its advantages of green environmental protection and renewable capacity. 2. Based on the Copula theory and taking the least Euclidean distance as index of fitting, the joint probability distribution model of PV output and load is obtained, and Kendall rank correlation coefficient is selected to describe the correlation between them. The corresponding PV output and load data are respectively connected to buses No 29 and No 30, and the Monte Carlo probability optimal power flow calculation result with a sampling size of 30,000 times is used as an accurate result to judge the error of the proposed method. Load can improve the accuracy of system power generation cost calculation The closer the other buses are to the relevant PV and load buses, the stronger the impact on their operation
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