Robust Probabilistic Load Flow in Microgrids considering Wind Generation, Photovoltaics and Plug-in Hybrid Electric Vehicles

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Hamid Reza Baghaee (Author) Other; Ali Parizad (Author) Other; Pierluigi Siano (Author) Other; Miadreza Shafie-khah (Author) Other; Gerardo J. Osório (Author) Other; João P. S. Catalão (Author) FEUP

Conference proceedings International

Title: Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018

Pages: 978-983

16th IEEE International Conference on Industrial Informatics, INDIN 2018

18 July 2018 through 20 July 2018

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

INSPEC

Other information

Authenticus ID: P-00P-TEH

DOI: 10.1109/indin.2018.8472010

Abstract (EN): The power demand uncertainties and intrinsic intermittent characteristics of wind and photovoltaic (PV) distributed energy resources (DERs) make the conventional load flow methods inefficient in active distribution networks (ADNs) and microgrids. Some statistical tools such as Monte Carlo simulation (MCS) are always a reliable solution. However, statistical tools are time-consuming and rather useless in large power systems. In this paper, a new method is proposed for robust probabilistic load flow (PLF) in microgrids and ADNs, including renewable energy resources (RERs), based on singular value decomposition (SVD) unscented Kalman filtering. The probability density functions (PDFs) and cumulative distribution functions (CDFs) for some of the ADN variables are compared with the other reported PLF methods for different test systems and the results validate the robustness, efficiency and accuracy of the proposed method.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6