Open AccessTwo‐level procedure based on HICAGA to determine optimal number, locations and operating points of SVCs in Isfahan–Khuzestan power system to maximise loadability and minimise losses, TVD and SVC installation cost
Author(s) -
BasiriKejani Mohsen,
Gholipour Eskandar
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.2016.0651
Subject(s) - electric power system , static var compensator , operating point , voltage , power (physics) , computer science , control theory (sociology) , mathematical optimization , operating cost , automotive engineering , engineering , reliability engineering , ac power , electronic engineering , mathematics , electrical engineering , control (management) , physics , quantum mechanics , artificial intelligence , waste management
Increasing maximum power system loadability while maintaining bus voltages and line loadings constraints by flexible AC transmission system (FACTS) devices is one of the main issues in power system studies. One of the shunt FACTS devices widely used in power system is static var compensator (SVC). Its performance on power system is highly dependent on its number, location and operating point. This study presents a two‐level procedure to find optimal number, locations, and operating points of SVCs in a power system. At the first level, the goal is to maximise loadability of the power system. At this level, by using a method based on iterative process, applying SVCs to the power system, and increasing load factor, the optimisation algorithm seeks to find locations and operating points of SVCs that fulfil bus voltages and line loading constraints. This process continues until the increase in the number of SVCs does not lead to an increase in loadability. At the second level and for a certain number of SVCs, power system losses, total voltage deviations (TVD), and SVC installation cost are minimised at specific loadability. In this study, hybrid imperialist competitive algorithm and genetic algorithm (HICAGA) have been used for optimisation.