Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine for optimal allocation of DG in radial distribution network

• Published in: International journal of electrical power & energy systems 2016-01, Vol.74, p.348-373
• Main Authors: Sharma, Sharmistha, Bhattacharjee, Subhadeep, Bhattacharya, Aniruddha
• Format: Article
• Language: English
• Subjects: Distributed generations; Power losses; Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (QOSIMBO-Q); Voltage profile; Voltage stability
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2015.07.034

•The paper aims to find the optimal location and size of DG in distribution system.•Quasi-Oppositional SIMBO-Q (QOSIMBO-Q) is applied for solution.•Results show improved power loss, voltage stability and voltage profile.•Performance of QOSIMBO-Q has been compared with other soft computing techniques.•Performance of QOSIMBO-Q revealed to be more encouraging to solve the problem. Optimal allocation of Distributed Generations (DGs) is one of the major problems of distribution utilities. Optimum locations and sizes of DG sources have profoundly created impact on system losses, voltage profile, and voltage stability of a distribution network. In this paper Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (QOSIMBO-Q) has been applied to solve a multi-objective function for optimal allocation and sizing of DGs in distribution systems. The objective is to minimize network power losses, achieve better voltage regulation and improve the voltage stability within the frame-work of the system operation and security constraints in radial distribution systems. The limitation of SIMBO-Q algorithm is that it takes large number of iterations to obtain optimum solution in large scale real systems. To overcome this limitation and to improve computational efficiency, quasi-opposition based learning (QOBL) concept is introduced in basic SIMBO-Q algorithm. The proposed QOSIMBO-Q algorithm has been applied to 33-bus and 69-bus radial distribution systems and results are compared with other evolutionary techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Optimization (TLBO) and Quasi-Oppositional Teaching Learning Based Optimization (QOTLBO). Numerical studies represent the effectiveness and out-performance of the proposed QOSIMBO-Q algorithm.