Optimal Allocation and Sizing of Photovoltaic-Battery Energy Storage Systems in Distribution Network for Power Loss Minimization

Amraj, Natin Janj; Boonrach, Nattapong; Bhumkittipich, Krischonme

doi:10.1109/icpei52436.2021.9690692

Cited by 4 publications

(1 citation statement)

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“…The literature highlights diverse strategies, with a particular focus on optimal capacitor placemen (Devabalaji & Ravi, 2014;Abraham & Oluwafemi, 2022;Abou El-Ela et al, 2016;Muhtazaruddin et al, 2014) , DG (Distributed Generation) siting (A. Alam, Gupta, Bindal, Siddiqui, & M. Zaid, 2018;Amraj, Boonrach, & K. Bhumkittipich, 2021;Siregar, Yuwaldi, Tarmizi, & Akhyar, 2023;Roy, Srivastava, & Dixit, 2020;Venkatraman & Khaitan, 2016;Sridevi, Rani, & B. L. Rao, 2019), and integrated approaches combining both capacitor and DG placement (Imran & Kowsalya, 2014;Chege, K. Murage, & K. Kihato, 2018;Jamil, 2023;Kaur & Jain, 2016). Capacitor placement and DG placement studies have employed methods such as genetic algorithms (Siregar et al, 2023), Tabu search (Rugthaicharoencheep et al, 2012), and harmonic search algorithms (Kona & Kollu, 2017), with a strong emphasis on renewable energy solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Power Loss Minimization and Voltage Profile Improvement of Radial Distribution Network Through the Installation of Capacitor and Distributed Generation (DG)

Mahato,

Poudel,

Mandal

et al. 2024

AAES

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The growing demand for electricity has raised concerns about power dissipation in distribution systems. To mitigate these losses, capacitors and distributed generator (DGs), particularly solar PV are strategically placed within the system. This project is committed to reducing power losses and improving the voltage profile through an in-depth analysis, optimizing the placement of capacitor and DG along the distribution feeder. The application of forward and backward sweep (FBS) algorithms assists load flow analysis in distribution networks with high R/X ratios, while the incorporation of the Genetic Algorithm (GA) within MATLAB identifies optimal locations & size for capacitors and DGs inside the large solution space of this complex, nonlinear optimization problem. Test outcomes, conducted on an IEEE 33-bus test system as its convincing representation of medium sized distribution network providing a versatile platform for evaluating proposed methodologies with practical implementation, showcase load flow examination, improvements in voltage profiles and minimized energy dissipation. The methodology is further applied to the real distribution network of the Sallaghari-Thimi 11 kV feeder in Bhaktapur, Nepal, sustaining the approach's effectiveness in mitigating power losses and increasing voltage profiles. Distributed generation with capacitor outperforms capacitors, and DG integration in the power system results in significant reductions of 72.91% in real power loss and 63.45% in reactive power loss, with a notable 6.542% increase in voltage magnitude. Application of these strategies in the Thimi Sallaghari 11 kV feeder demonstrates significant power loss saving (up to 82.72%) and worthy improvements in voltage profiles (up to 5.32%), focusing on their effectiveness in enhancing operational efficiency. This approach provides a practical solution for optimizing capacitor and solar PV distributed generator placement in distribution networks considering various case scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

Power Loss Minimization and Voltage Profile Improvement of Radial Distribution Network Through the Installation of Capacitor and Distributed Generation (DG)

Mahato,

Poudel,

Mandal

et al. 2024

AAES

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Distributionally Robust Co-optimization of the Soft Open Points Allocation

Xiaotang,

Xinhua,

Xiaojun

et al. 2023

2023 8th Asia Conference on Power and Electrical Engineering (ACPEE)

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Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods

et al. 2023

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Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the “Dual-Carbon” target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations.

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Optimal Allocation and Sizing of Photovoltaic-Battery Energy Storage Systems in Distribution Network for Power Loss Minimization

Cited by 4 publications

References 5 publications

Power Loss Minimization and Voltage Profile Improvement of Radial Distribution Network Through the Installation of Capacitor and Distributed Generation (DG)

Power Loss Minimization and Voltage Profile Improvement of Radial Distribution Network Through the Installation of Capacitor and Distributed Generation (DG)

Distributionally Robust Co-optimization of the Soft Open Points Allocation

Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods

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