Determining the Pareto front of distributed generator and static VAR compensator units placement in distribution networks

Bahman Ahmadi, Ramazan Çağlar

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
28 Downloads (Pure)

Abstract

The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.

Original languageEnglish
Pages (from-to)3440-3453
Number of pages14
JournalInternational Journal of Electrical and Computer Engineering
Volume12
Issue number4
DOIs
Publication statusPublished - Aug 2022
Externally publishedYes

Keywords

  • Multi-objective salp swarm
  • Optimal planning
  • Optimization algorithm
  • Pareto efficient
  • Photovoltaic
  • Power distribution planning
  • Wind power generation

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