Probabilistic Load Flow Algorithm of Distribution Networks with Distributed Generators and Electric Vehicles Integration

Bowen Zhou, Xiao Yang, Dongsheng Yang, Zhile Yang, Tim Littler and Hua Li
Additional contact information
Bowen Zhou: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Xiao Yang: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Dongsheng Yang: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Zhile Yang: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Tim Littler: School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT9 5AH, UK
Hua Li: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Energies, 2019, vol. 12, issue 22, 1-24

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.

Keywords: Probabilistic Load Flow; random walk; correlation; EV integration; optimal charging; dynamic load flow (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/22/4234/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/22/4234/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:22:p:4234-:d:284213

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().