Rapid identification of worst‐case conditions: improved planning of active distribution grids. Issue 9 (26th June 2017)

• Record Type: Journal Article
• Authors: Wiest, Pascal; Gross, Daniel; Rudion, Krzyzstof; Probst, Alexander
• Is Part Of: IET generation, transmission & distribution. Volume 11:Issue 9(2017)
• Page Start: 2412
• Page End: 2417
• Publication Date: 2017-06-26
• Subjects: load flow -- sensitivity analysis -- probability -- distribution networks -- power grids; rapid identification -- worst‐case condition -- planning -- active distribution grids -- distribution factors -- AC power flow equations -- superposition theorem -- renewable energy sources -- probabilistic methods -- network expansion planning -- comprehensive probabilistic load flow -- high‐voltage distribution grid -- in‐line contingency analysis -- sensitivity analysis; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2017.0148
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : This study proposes a new, fast and efficient method to identify worst‐case conditions that result in a maximum loading of a particular line in active distribution grids. For this purpose, distribution factors are formulated based on the AC power flow equations and superposition theorem. Caused by the high penetration of volatile renewable energy sources, probabilistic methods are necessary in network expansion planning, since the predefined load and generation conditions are no longer suitable. The proposed rapid identification method is compared with the comprehensive probabilistic load flow (PLF) analysis in a real high‐voltage distribution grid. The identified worst‐case scenarios, using both approaches, differ slightly; nevertheless, the results of an in‐line contingency analysis are quite similar to the PLF, which shows the applicability of the method. An additionally performed sensitivity analysis proves the high accuracy of the proposed approach.