# Optimal DG allocation and sizing in power system networks using swarm-based algorithms

**Authors:** Kayode Adetunji, Ivan Hofsajer, Ling Cheng  
**arXiv:** [2002.08089](https://arxiv.org/abs/2002.08089)  
**Date:** 2020-02-19  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2002.08089v1/

## Key points

- Distributed Generation (DG) units are crucial for enhancing grid architecture and efficiency
- Careful planning and execution of DG unit installation is necessary to avoid voltage instability and excessive costs
- Researchers developed particle swarm optimization (PSO) and whale optimization algorithm (WOA) to optimize DG unit placement and sizing
- Adetunji et al. used PSO and WOA along with loss sensitivity factors (LSF) to identify ideal locations for DG units in IEEE 14-bus and 30-bus test systems
- Both PSO and WOA algorithms showed promising results, with WOA excelling in real power loss reduction while PSO was more efficient in reducing total DG unit size
- MATLAB environment was used for simulations on an Intel(R) Core(TM) i7-3520M @ 2.90 GHz processor
- Parameters were selected to focus on minimizing power loss and improving voltage profiles within the network
- Two-stage procedure involved identifying potential buses using LSF technique followed by meta-heuristic optimization for optimal DG unit placement and sizing
- Study highlights the effectiveness of PSO and WOA algorithms in optimizing allocation and sizing of DG units in transmission networks

## Summary

In the realm of power system networks, Distributed Generation (DG) units play a crucial role in enhancing the overall architecture and efficiency of the grid. These units are instrumental in bolstering power supply to networks, but their installation must be carefully planned and executed to avoid potential drawbacks such as voltage instability and excessive investment costs. To address this challenge, researchers have developed two swarm-based meta-heuristic algorithms, namely particle swarm optimization (PSO) and whale optimization algorithm (WOA), aimed at optimizing the placement and sizing of DG units for transmission network planning. In their study, Adetunji et al. utilized these algorithms alongside a supportive technique known as loss sensitivity factors (LSF) to pinpoint ideal locations for DG unit deployment within the IEEE 14-bus and 30-bus test systems. The comparison results revealed that both PSO and WOA algorithms yielded promising solutions, with each showcasing strengths in different performance metrics. For instance, WOA demonstrated superior real power loss reduction considering techno-economic factors in both test systems compared to PSO. On the other hand, PSO exhibited a more efficient total reduction in DG unit size across the bus systems. The research was conducted using MATLAB environment with simulations executed on an Intel(R) Core(TM) i7-3520M @ 2.90 GHz processor. The parameters employed in the algorithms were meticulously selected to focus on minimizing power loss and improving voltage profiles within the network. A two-stage procedure was adopted for optimal DG unit placement and sizing, beginning with identifying potential buses using LSF technique followed by meta-heuristic optimization. Overall, this study sheds light on the efficacy of PSO and WOA algorithms in optimizing the allocation and sizing of DG units within transmission networks. By leveraging these advanced computational techniques, researchers aim to mitigate operational challenges while maximizing energy efficiency in modern power systems.