Optimal Allocation of PV Systems on Unbalanced Networks Using Evolutionary Algorithms

Wenlei Bai, Wen Zhang, Fanlin Meng, Richard Allmendinger, Kwang Y. Lee

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As the distributed energy resources (DERs) increasingly penetrate the unbalanced distribution network, it becomes challenging to accommodate such penetration technically and economically. Therefore, this paper tackles an optimal allocation of PV systems (locations and sizes) to maximize the penetration while minimizing voltage violation. It is challenging because the problem is a mixed integer nonlinear programming (MINLP) problem with non-linear and non convex properties. In addition, the network is unbalanced which brings burdens on solving load flows. Computational intelligent methods, particularly evolutionary algorithms (EAs) have proven its efficiency and robustness in large optimization problems and thus, this paper explores two EAs on the problem with the help of a robust unbalanced load flow algorithm. A comparative study is conducted on particle swarm optimization (PSO) and artificial bee colony (ABC) based on IEEE 13 and 37 bus systems. Optimal allocation based on peak hour and day ahead scenarios are considered. After 30 times run, the test cases have shown that both EAs are successful and yet ABC generally converges to better solution and yet with larger statistical deviations on solutions.

Original languageEnglish
Title of host publication2023 IEEE Symposium Series on Computational Intelligence (SSCI 2023)
Publication statusPublished - 1 Jan 2024


  • Artificial bee colony
  • Distributed energy resources
  • Evolutionary algorithms
  • Particle swarm optimization PV allocation
  • Unbalanced network


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