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<p>The optimal placement and sizing of distributed generation (DG) units and Static VAR Compensators (SVCs) are essential for enhancing the efficiency and reliability of radial distribution networks (RDN). Existing methods often rely on shunt capacitors, sensitivity-based techniques, which may not address the multi-objective challenges of minimizing power losses, reducing voltage deviations, and improving voltage stability. This paper proposes a Pareto front-based multi-objective chaotic particle swarm optimization with sigmoid-based acceleration coefficients (MO-CPSOS) for optimal placement and sizing of DGs and SVCs. The framework employs adaptive, real-time adjustment of cognitive and social parameters, promoting efficient exploration and faster convergence. Simulation results on IEEE 33-bus, 69-bus, and 119-bus test systems show that MO-CPSOS consistently achieves substantial reductions in power losses, improves voltage profiles, and enhances voltage stability compared to conventional approaches. The optimal solution involves three devices for the 33-bus and 69-bus networks, and six to seven for the 119-bus system, with diminishing returns beyond these points. MO-CPSOS demonstrates scalability and computational efficiency, making it suitable for modern RDNs. Future research may focus on incorporating dynamic load models and extending the approach to integrate economic, environmental, and operational factors for even broader applicability.</p>

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A multi-objective chaotic PSO with sigmoid-based acceleration coefficients approach for location and sizing of DGs and SVC in a distribution network

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Rajesh Murari,
  • Srinivasa Rao Rayapudi

摘要

The optimal placement and sizing of distributed generation (DG) units and Static VAR Compensators (SVCs) are essential for enhancing the efficiency and reliability of radial distribution networks (RDN). Existing methods often rely on shunt capacitors, sensitivity-based techniques, which may not address the multi-objective challenges of minimizing power losses, reducing voltage deviations, and improving voltage stability. This paper proposes a Pareto front-based multi-objective chaotic particle swarm optimization with sigmoid-based acceleration coefficients (MO-CPSOS) for optimal placement and sizing of DGs and SVCs. The framework employs adaptive, real-time adjustment of cognitive and social parameters, promoting efficient exploration and faster convergence. Simulation results on IEEE 33-bus, 69-bus, and 119-bus test systems show that MO-CPSOS consistently achieves substantial reductions in power losses, improves voltage profiles, and enhances voltage stability compared to conventional approaches. The optimal solution involves three devices for the 33-bus and 69-bus networks, and six to seven for the 119-bus system, with diminishing returns beyond these points. MO-CPSOS demonstrates scalability and computational efficiency, making it suitable for modern RDNs. Future research may focus on incorporating dynamic load models and extending the approach to integrate economic, environmental, and operational factors for even broader applicability.