Probabilistic load flow algorithm of distribution networks with distributed generators and electric vehicles integration

Bowen Zhou, Xiao Yang, Dongsheng Yang, Zhile Yang, Tim Littler, Hua Li

Research output: Contribution to journalArticle

2 Citations (Scopus)
60 Downloads (Pure)

Abstract

Probabilistic Load Flow (PLF) calculations are important tools for analysis of the steady-state operation of electrical energy networks, especially for electrical energy distribution networks with large-scale distributed generators (DGs) and electric vehicle (EV) integration. Traditional PLF has used the Cumulant Method (CM) and Latin Hypercube Sampling (LHS) method. However, traditional CM requires that each input variable be independent of one another, and the Cholesky decomposition adopted by the traditional LHS has limitations in that it is only applicable for positive definite matrices. To solve these problems, taking into account the Q-MCS theory of LHS, this paper proposes a CM PLF algorithm based on improved LHS (ILHS-CM). The cumulants of the input variables are obtained based on sampling results. The probability distribution of the output variables is obtained according to the Gram-Charlier series expansion. Moreover, DGs, such as wind turbines, photovoltaic (PV) arrays, and EVs integrated into the electrical energy distribution networks are comprehensively considered, including correlation analysis and dynamic load flow analysis for EV-coordinated charging. Four scenarios are analyzed based on the IEEE-30 node network, including with/without DGs and EVs, error analysis and performance evaluation of the proposed algorithm, correlation analysis of DGs and EVs, and dynamic load flow analysis with EV integration. The results presented in this paper demonstrate the effectiveness, accuracy, and practicability of the proposed algorithm.

Original languageEnglish
Article number4234
JournalEnergies
Volume12
Issue number22
DOIs
Publication statusPublished - 06 Nov 2019

Keywords

  • Correlation
  • Dynamic load flow
  • EV integration
  • Optimal charging
  • Probabilistic Load Flow
  • Random walk

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Probabilistic load flow algorithm of distribution networks with distributed generators and electric vehicles integration'. Together they form a unique fingerprint.

Cite this