Probabilistic Load Flow Analysis Using Nonparametric Distribution

Li Bin, Rashana Abbas, Muhammad Shahzad () and Nouman Safdar
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Li Bin: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Rashana Abbas: Department of Electrical Engineering and Technology, Muhammad Nawaz Sharif University of Engineering and Technology, Multan 66000, Pakistan
Muhammad Shahzad: Department of Electrical Engineering and Technology, Muhammad Nawaz Sharif University of Engineering and Technology, Multan 66000, Pakistan
Nouman Safdar: Department of Electrical Engineering and Technology, Government College University Faisalabad, Faisalabad 38000, Pakistan

Sustainability, 2023, vol. 16, issue 1, 1-13

Abstract: 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.

Keywords: adaptive kernel density; probabilistic load flow; Latin hypercube sampling; HMC; Monte Carlo (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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