Simultaneous Reconfiguration, Optimal Placement of DSTATCOM, and Photovoltaic Array in a Distribution System Based on Fuzzy-ACO Approach

In this paper, a combination of a fuzzy multiobjective approach and ant colony optimization (ACO) as a metaheuristic algorithm is used to solve the simultaneous reconfiguration and optimal allocation (size and location) of photovoltaic (PV) arrays as a distributed generation (DG) and distribution st...

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Bibliographic Details
Published in:IEEE transactions on sustainable energy 2015-01, Vol.6 (1), p.210-218
Main Authors: Bagheri Tolabi, Hajar, Ali, Mohd Hasan, Rizwan, M.
Format: Article
Language:English
Subjects:
Ant colony optimization (ACO)
Arrays
distribution static compensator (DSTATCOM)
distribution system
Equations
Fuzzy logic
fuzzy sets
Linear programming
Optimization
photovoltaic (PV)
Photovoltaic systems
Reactive power
reconfiguration
Resource management
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Summary:In this paper, a combination of a fuzzy multiobjective approach and ant colony optimization (ACO) as a metaheuristic algorithm is used to solve the simultaneous reconfiguration and optimal allocation (size and location) of photovoltaic (PV) arrays as a distributed generation (DG) and distribution static compensator (DSTATCOM) as a distribution flexible ac transmission system (DFACT) device in a distribution system. The purpose of this research includes loss reduction, voltage profile (VP) improvement, and increase in the feeder load balancing (LB). The proposed method is validated using the IEEE 33-bus test system and a Tai-Power 11.4-kV distribution system as a real distribution network. The results proved that simultaneous reconfiguration and optimal allocation of PV array and DSTATCOM unit leads to significantly reduced losses, improved VP, and increased LB. Obtained results have been compared with the base value and found that simultaneous placement of PV and DSTATCOM along with reconfiguration is more beneficial than separate single-objective optimization. Also, the proposed fuzzy-ACO approach is more accurate as compared to ACO and other intelligent techniques like fuzzy-genetic algorithm (GA) and fuzzy-particle swarm optimization (PSO).
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2014.2364230