Probabilistic load flow methodology for distribution networks including loads uncertainty. (March 2019)

• Record Type: Journal Article
• Authors: Gruosso, Giambattista; Maffezzoni, Paolo; Zhang, Zheng; Daniel, Luca
• Is Part Of: International journal of electrical power & energy systems. Volume 106(2019)
• Page Start: 392
• Page End: 400
• Publication Date: 2019-03
• Subjects: Distribution network -- Load uncertainty -- Polynomial chaos -- Probabilistic load flow -- Variability analysis; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2018.10.023
• Languages: English
• ISSNs: 0142-0615

Abstract

Abstract: Distribution grids probabilistic analysis is an essential step in order to assess the daily network operability under uncertain and stress conditions. It is also functional to the development of new services that require load growth capacity or to the exploitation of new energy resources affected by uncertainty. Efficient numerical tools able to forecast the possible scenarios while accounting for loads and sources uncertainty are thus of paramount importance. The majority of available uncertainty-aware predictive tools are based on Monte Carlo analysis which allows probabilistic evaluations of the network state at the price of time consuming simulations. In this paper, a much more efficient simulation framework is presented. The proposed approach relies on the generalized Polynomial Chaos algorithm and deterministic Power Flow analysis and allows achieving an at least 100 × acceleration compared to standard Monte Carlo analysis for the same accuracy.