Open AccessProbabilistic approach for optimal planning of distributed generators with controlling harmonic distortions
Author(s) -
Abdelsalam Abdelazeem A.,
ElSaadany Ehab F.
Publication year - 2013
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2012.0769
Subject(s) - distributed generation , total harmonic distortion , probabilistic logic , renewable energy , mathematical optimization , computer science , electric power system , wind power , matlab , voltage , control theory (sociology) , power (physics) , engineering , electrical engineering , mathematics , physics , control (management) , quantum mechanics , artificial intelligence , operating system
In this study, a probabilistic planning approach is proposed for optimally allocating different types of distributed generator (DG) (i.e. wind‐based DG, solar DG and non‐renewable DG) into a harmonic polluted distribution system so as to minimise the annual energy losses and reduce the harmonic distortions. The proposed planning methodology takes into consideration the intermittent nature of the renewable resources, load profile and the technical constraints of the system. The objective function is the total system annual power loss. The constraints include voltage limits at different buses (slack and load buses) of the system, feeder capacity, total harmonic distortion (THD) limits and maximum penetration limit of DG units. The optimisation process is achieved using the genetic algorithm optimisation method. This proposed approach has been applied to a typical rural distribution system with different scenarios including all possible combinations of distributed energy resources. The simulation results using Matlab programming environment show that significant reductions in the energy losses and THD are achieved for all the proposed scenarios. Also simulation results depict that the proposed method is robust and computationally efficient.