Probabilistic load flow for distribution systems with uncertain PV generation

Kabir, M.N., Mishra, Y., & Bansal, R.C. (2016) Probabilistic load flow for distribution systems with uncertain PV generation. Applied Energy, 163, pp. 343-351.

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Large integration of solar Photo Voltaic (PV) in distribution network has resulted in over-voltage problems. Several control techniques are developed to address over-voltage problem using Deterministic Load Flow (DLF). However, intermittent characteristics of PV generation require Probabilistic Load Flow (PLF) to introduce variability in analysis that is ignored in DLF. The traditional PLF techniques are not suitable for distribution systems and suffer from several drawbacks such as computational burden (Monte Carlo, Conventional convolution), sensitive accuracy with the complexity of system (point estimation method), requirement of necessary linearization (multi-linear simulation) and convergence problem (Gram–Charlier expansion, Cornish Fisher expansion). In this research, Latin Hypercube Sampling with Cholesky Decomposition (LHS-CD) is used to quantify the over-voltage issues with and without the voltage control algorithm in the distribution network with active generation. LHS technique is verified with a test network and real system from an Australian distribution network service provider. Accuracy and computational burden of simulated results are also compared with Monte Carlo simulations.

Impact and interest:

5 citations in Scopus
2 citations in Web of Science®
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ID Code: 91599
Item Type: Journal Article
Refereed: Yes
Keywords: Photovoltaic (PV), Distribution networks, Probabilistic Load Flow (PLF), Coordinated control algorithm, Latin Hypercube Sampling (LHS)
DOI: 10.1016/j.apenergy.2015.11.003
ISSN: 0306-2619
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Renewable Power and Energy Systems Engineering (excl. Solar Cells) (090608)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 04 Jan 2016 23:47
Last Modified: 06 Jan 2016 04:21

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