Group search optimizer based optimal location and capacity of distributed generations

  • Authors:

  • Qi Kang;Tian Lan;Yong Yan;Lei Wang;Qidi Wu

  • Affiliations:

  • Department of Control Science and Engineering, Tongji University, Shanghai 201804, China and Key Laboratory of Embedded System and Computer-Service of MOE, Tongji University, Shanghai 201804, Chin ...;Department of Control Science and Engineering, Tongji University, Shanghai 201804, China;Department of Control Science and Engineering, Tongji University, Shanghai 201804, China;Department of Control Science and Engineering, Tongji University, Shanghai 201804, China and Key Laboratory of Embedded System and Computer-Service of MOE, Tongji University, Shanghai 201804, Chin ...;Department of Control Science and Engineering, Tongji University, Shanghai 201804, China and Key Laboratory of Embedded System and Computer-Service of MOE, Tongji University, Shanghai 201804, Chin ...

  • Venue:
  • Neurocomputing
  • Year:
  • 2012

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Abstract

This paper presents a novel efficient population-based heuristic approach for optimal location and capacity of distributed generations (DGs) in distribution networks, with the objectives of minimization of fuel cost, power loss reduction, and voltage profile improvement. The approach employs an improved group search optimizer (iGSO) proposed in this paper by incorporating particle swarm optimization (PSO) into group search optimizer (GSO) for optimal setting of DGs. The proposed approach is executed on a networked distribution system-the IEEE 14-bus test system for different objectives. The results are also compared to those that executed by basic GSO algorithm and PSO algorithm on the same test system. The results show the effectiveness and promising applications of the proposed approach in optimal location and capacity of DGs.