Open AccessOptimal sizing and sitting of DG with load models using soft computing techniques in practical distribution system
Author(s) -
Bohre Aashish Kumar,
Agnihotri Ganga,
Dubey Manisha
Publication year - 2016
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.2015.1034
Subject(s) - sizing , particle swarm optimization , reliability engineering , distributed generation , reliability (semiconductor) , computer science , genetic algorithm , electric power system , mathematical optimization , ac power , voltage , power (physics) , engineering , control theory (sociology) , control (management) , algorithm , mathematics , renewable energy , art , artificial intelligence , visual arts , physics , electrical engineering , quantum mechanics
In the deregulated power market environment, distributed generation (DG) is an effective approach to manage performance, operation and control of the distribution system. Methods available in the literature for DG planning are often not able to simultaneously provide technical and economical benefits. Therefore an effective methodology is developed to improve the technical as well as economical benefits as compared with the existing approaches. This study reports the optimal installation of multi‐DG in the standard 33‐bus, 69‐bus radial distribution systems and 54‐bus practical radial distribution system. Several performance evaluation indices such as active and reactive power loss indices, voltage deviation index, reliability index and shift factor indices are used to develop a novel multi‐objective function (MOF). A new set of equations is developed for representing different practical load models. A novel MOF has been solved to find optimal sizing and placement of DGs using genetic algorithm and particle swarm optimisation technique. The comparative result analysis is also discussed for both techniques. The result analysis reveals that system losses, energy not supplied, system MVA intakes are reduced, whereas available transfer capability, voltage profile, reliability and cost benefits are improved for the case with‐DGs in the distribution system.