Optimal DG Placement for Power Loss Minimization in Radial Power Distribution Networks using Mixed-Integer Quadratic Programming Method based on Modified DistFlow

Pham, Van N.; Q., Duy; Nguyen, Quyet D.; Nguyen, Thu Thi Hoai

doi:10.1109/gtsd54989.2022.9989014

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Due to rising of pollution to the environment and increasing growth in green energy sources, DG is a beneficial additional energy source for conventional fossil fuel power plants. The design and enhancement of power systems for increased reliability depend on the quantity, size, and kind of DG [5]. Power losses on the network have led to a system imbalance.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

Kuamthab,

Rasyidah Mohamad Idris

2024

Elektrika

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In recent years, most growing nations worldwide have experienced an unavoidably rising demand for electricity, which may result in a dropping of the voltage profile and a reduction in system stability, both of which have the potential to overload the power generation system significantly. One of the ways to reduce loss system network in this study is by distributed generation (DG) allocation, which the DG optimal location and sizing are the two most significant considerations for integration of DG. Improper placement and DG sizing in the power system not only result in increased total power losses but also affect the electrical system’s capability to be function properly. This paper provides a comparative analysis of two meta- heuristic optimization techniques, that is, Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), to examine the optimal placement and sizing of distributed-generation (DG) for photovoltaic systems with the aim of minimizing the losses in interconnected distributed generation, IEEE-30 bus system. The objective function of this study is minimization the power loss with considered some constraints which are voltage limits and DG sizing limits are being covers in this study. The optimization issue has been solved by working with MATLAB/m-files software. The outcomes derived from the simulation results in this study demonstrated that PSO performance of two units of DG is better compared to EP algorithm. The voltage profile was improved drastically, and the power loss was reduced as lowest as 17.4486MW in the first case, 17.4368MW in the second case, and 17.4179MW in the third case.

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Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

Kuamthab,

Rasyidah Mohamad Idris

2024

Elektrika

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Optimization Methods for Optimal Power Flow Problems in Distribution Networks: A Brief Review

Sun

Yang

Cui

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 DG Placement for Power Loss Minimization in Radial Power Distribution Networks using Mixed-Integer Quadratic Programming Method based on Modified DistFlow

Cited by 3 publications

References 14 publications

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

Optimization Methods for Optimal Power Flow Problems in Distribution Networks: A Brief Review

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

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