Time series probabilistic power flow calculation of distribution network based on nonparametric kernel density estimation

Tao, Yigang; Xu, Min; Guo, Zuogang; Zhang, Junchen; Cui, Zhiwei; Tan, Yonghong; Lei, Jinyong

doi:10.1109/cipae55637.2022.00013

Cited by 1 publication

(1 citation statement)

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“…Analytical methods [16,17] seek to derive analytical equations for the probabilistic distribution of system variables using probability density functions, providing faster computation but limited applicability to simplified power system models. Time-Series [18] analysis relies on historical data to predict future system behavior, but it may overlook new trends and depend on data quality and availability. Sensitivity analysis investigates the effect of parameter changes on system output.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Load Flow Analysis Using Nonparametric Distribution

Bin,

Abbas,

Shahzad

et al. 2023

Sustainability

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In the pursuit of sustainable energy solutions, this research addresses the critical need for accurate probabilistic load flow (PLF) analysis in power systems. PLF analysis is an essential tool for estimating the statistical behavior of power systems under uncertainty. It plays a vital part in power system planning, operation, and dependability studies. To perform accurate PLF analysis, this article proposes a Kernel density estimation with adaptive bandwidth for probability density function (PDF) estimation of power injections from sustainable energy sources like solar and wind, reducing errors in PDF estimation. To reduce the computational burden, a Latin hypercube sampling approach was incorporated. Input random variables are modeled using kernel density estimation (KDE) in conjunction with Latin hypercube sampling (LHS) for probabilistic load flow (PLF) analysis. To test the proposed techniques, IEEE 14 and IEEE 118 bus systems are used. Two benchmark techniques, the Monte Carlo Simulation (MCS) method and Hamiltonian Monte Carlo (HMC), were set side by side for validation of results. The results illustrate that an adaptive bandwidth kernel density estimation with the Latin hypercube sampling (AKDE-LHS) method provides better performance in terms of precision and computational efficiency. The results also show that the suggested technique is more feasible in reducing errors, uncertainties, and computational time while depicting arbitrary distributions of photovoltaic and wind farms for probabilistic load flow analysis. It can be a potential solution to tackle challenges posed by sustainable energy sources in power systems.

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Section: Introductionmentioning

confidence: 99%