Open AccessA coordinated planning framework of electric power distribution system: I ntelligent reconfiguration
Author(s) -
Kumar Deepak,
Singh Akansha,
Mishra Sudhansu Kumar,
Jha Rakesh Chandra,
Samantaray Subhransu Ranjan
Publication year - 2018
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2543
Subject(s) - sizing , particle swarm optimization , control reconfiguration , operator (biology) , voltage , mathematical optimization , heuristic , computer science , distributed generation , sensitivity (control systems) , electric power system , power (physics) , engineering , control theory (sociology) , reliability engineering , electronic engineering , algorithm , mathematics , embedded system , electrical engineering , renewable energy , artificial intelligence , control (management) , quantum mechanics , physics , art , repressor , chemistry , visual arts , biochemistry , transcription factor , gene
Summary This paper has proposed a comprehensive coordinated planning framework for solving the network reconfiguration with simultaneous installation of distribution generation (DG) units, with an objective of minimizing the feeder power loss and boosting the voltage profile of the electric distribution system. A meta‐heuristic bit‐shift operator–based particle‐swarm‐optimization (PSO) technique has been used for simultaneous reconfiguration with the optimal siting and sizing of the DG units. The bit‐shift operator–based PSO has been obtained by incorporating a shift operator in the velocity equation of the basic PSO, such that the problem moves in the direction of finding the best optimal reconfigured system. The entire problem has been investigated in the light of both voltage independent and dependent loads, such as residential, industrial, and commercial, to evaluate the performance of the proposed work in a practical scenario. A sensitivity analysis has been applied for finding the optimal location for the DG placement. The efficacy and validation of the proposed method have been tested on a standard IEEE test system under 4 different load models for 5 different cases.