Optimal Siting and Sizing of Renewable Energy and Energy Storage Systems Using Hippo Swarm Optimization for Profit Maximization in Distribution Networks.
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| Title: | Optimal Siting and Sizing of Renewable Energy and Energy Storage Systems Using Hippo Swarm Optimization for Profit Maximization in Distribution Networks. |
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| Authors: | Chinh, Nguyen Cong1 (AUTHOR) chinhnc@tlu.edu.vn, Minh Y, Nguyen1 (AUTHOR), Ponce-Silva, Mario1 (AUTHOR) mario.ps@cenidet.tecnm.mx |
| Source: | International Transactions on Electrical Energy Systems. 5/7/2026, Vol. 2026, p1-17. 17p. |
| Subject Terms: | *Energy storage, *Profit maximization, *Optimization algorithms, *Solar energy, *Renewable energy sources, *Electric power distribution grids, *Wind power, *Mathematical optimization |
| Abstract: | This study proposes an optimization framework based on the hippo swarm optimization (HSO) for determining the optimal location and capacity of energy storage systems (ESSs), solar power distributed generation units (SGUs), and wind power distributed generation units (WGUs) in the radial distribution grid, considering time‐varying power generation and consumption. The main purpose of this study is to maximize total profit by reducing total investment, operation, and maintenance (IOM) costs and increasing total power generation revenue for units in the long‐term project. The obtained solution indicates that, thanks to the penetration of units, the total profit from applying the introduced method reaches $7.4966 million over the 20‐year project life cycle and corresponds to a reduction in total grid operating costs of up to 36.1% compared to the initial network. This result outperforms four other methods, including improved particle swarm optimization (IPSO), intelligent water drops (IWD), sunflower optimization (SFO), and salp swarm (SSA), on the same objective function and established constraints. In addition, the study also analyzes and demonstrates the technical benefits from the penetration of units such as decreasing line power loss by 76.12%, improving node voltage profile from the range [0.9092, 1.00 (pu)] to the range [0.9616, 1.0482 (pu)], and reducing the line current magnitude with the highest reduction of up to 33.09%, leading to reduced power congestion in distribution lines. These results demonstrate that the introduced method is sufficiently robust to tackle the optimization problem and achieve both economic and technical benefits. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | This study proposes an optimization framework based on the hippo swarm optimization (HSO) for determining the optimal location and capacity of energy storage systems (ESSs), solar power distributed generation units (SGUs), and wind power distributed generation units (WGUs) in the radial distribution grid, considering time‐varying power generation and consumption. The main purpose of this study is to maximize total profit by reducing total investment, operation, and maintenance (IOM) costs and increasing total power generation revenue for units in the long‐term project. The obtained solution indicates that, thanks to the penetration of units, the total profit from applying the introduced method reaches $7.4966 million over the 20‐year project life cycle and corresponds to a reduction in total grid operating costs of up to 36.1% compared to the initial network. This result outperforms four other methods, including improved particle swarm optimization (IPSO), intelligent water drops (IWD), sunflower optimization (SFO), and salp swarm (SSA), on the same objective function and established constraints. In addition, the study also analyzes and demonstrates the technical benefits from the penetration of units such as decreasing line power loss by 76.12%, improving node voltage profile from the range [0.9092, 1.00 (pu)] to the range [0.9616, 1.0482 (pu)], and reducing the line current magnitude with the highest reduction of up to 33.09%, leading to reduced power congestion in distribution lines. These results demonstrate that the introduced method is sufficiently robust to tackle the optimization problem and achieve both economic and technical benefits. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20507038 |
| DOI: | 10.1155/etep/7427325 |
