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

• Published in: IEEE transactions on sustainable energy Vol. 6; no. 1; pp. 210 - 218
• Main Authors: Bagheri Tolabi, Hajar, Ali, Mohd Hasan, Rizwan, M.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant colony optimization (ACO); Arrays; distribution static compensator (DSTATCOM); distribution system; Equations; Fuzzy logic; fuzzy sets; Linear programming; Optimization; photovoltaic (PV); Photovoltaic systems; Photovoltaics; Reactive power; reconfiguration; Resource management; distribution system; reconfiguration; fuzzy sets; Ant colony optimization (ACO); photovoltaic (PV); distribution static compensator (DSTATCOM)
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2014.2364230

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).