Open AccessPossibilistic uncertainty assessment in the presence of optimally integrated solar PV-DG and probabilistic load model in distribution network
Author(s) -
Shradha Singh Parihar,
Nitin Malik
Publication year - 2022
Publication title -
facta universitatis. series electronics and energetics/facta universitatis. series: electronics and energetics
Language(s) - English
Resource type - Journals
eISSN - 2217-5997
pISSN - 0353-3670
DOI - 10.2298/fuee2201071p
Subject(s) - photovoltaic system , gaussian , renewable energy , mathematical optimization , control theory (sociology) , probabilistic logic , interval (graph theory) , voltage , reliability engineering , computer science , mathematics , statistics , engineering , electrical engineering , physics , control (management) , quantum mechanics , combinatorics , artificial intelligence
To integrate network load and line uncertainties in the radial distribution network (RDN), the probabilistic and possibilistic method has been applied. The load uncertainty is considered to vary as Gaussian distribution function whereas line uncertainty is varied at a fixed proportion. A voltage stability index is proposed to assign solar PV-DG optimally followed by application of PSO technique to determine the optimal power rating of DG. Standard IEEE 33- and 69-bus RDN are considered for the analysis. The impact of various uncertainties in the presence of optimally integrated solar PV-DG has been carried out on 69-bus network. The results obtained are superior to fuzzy-arithmetic algorithm. Faster convergence characteristic is obtained and analyzed at different degree of belongingness and realistic load models. The narrower interval width indicates that the observed results are numerically stable. To improve network performance, the technique takes into account long-term changes in the load profile during the planning stage. The significant drop in network power losses, upgraded bus voltage profile and noteworthy energy loss savings are observed due to the introduction of renewable DG. The results are also statistically verified.