Optimal Siting of Distributed Generation Unit in Power Distribution System considering Voltage Profile and Power Losses

Aman, Muhammad Aamir; Ren, Xin Cheng; Tareen, Wajahat Ullah Khan; Khan, Muhammad Abbas; Anjum, Muhammad Rizwan; Hashmi, Anas; Ali, Haider; Bari, Inam; Khan, Jehanzeb; Ahmad, Sheeraz

doi:10.1155/2022/5638407

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The differences relate to the formulation of the problem, the technique, and the assumptions used. Several techniques are described in [7] as analytical approaches, [8] numerical programming, and [9,10] heuristics. Each strategy has pros and cons that depend on the information and system being used.…”

Section: Critical Review Of Related Workmentioning

confidence: 99%

A Holistic Approach to Voltage Profile Improvement and Power Loss Mitigation through Distributed Generation Placement and Sizing

Aman,

Ahmad,

Khan

et al. 2023

Preprint

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One of the rapidly developing research areas in the power system is the integration of distributed generation (DG) with the distribution system. The size and location of DG sources had a considerable impact on power system networks. Artificial intelligence (AI) techniques can be used to address multidimensional problems relating to DG size and location in distribution systems. Heuristic optimization offers a reliable and effective method for solving complicated real-world problems. This work focuses on a hybrid approach that combines the two heuristic optimization methods i.e., Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for the optimal siting and sizing of DG in distribution systems. This hybrid approach combines ideas from GA and PSO and generates individuals in a new generation using both PSO mechanisms and the procedures found in GA. An extensive performance analysis of the IEEE 14 busbar standard test system is conducted to demonstrate the viability of the suggested methodologies. In the designated locations, DG is placed, and the outcomes have been verified. The results indicated that the right placement of DG injection enhances the voltage profile and lowers the distributed system’s power losses. These techniques offer unique methods for determining the location of the DG unit, demonstrating the potential of such a computational techniques to reduce computing time and complexity while simultaneously reducing human errors associated with hit-and-trail methods.

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Section: Critical Review Of Related Workmentioning

confidence: 99%

A Holistic Approach to Voltage Profile Improvement and Power Loss Mitigation through Distributed Generation Placement and Sizing

Aman,

Ahmad,

Khan

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, an analytical method was proposed to assess the impact of optimal DG integration on the active and reactive losses in DS using particle swarm optimization [5]. The authors in [6] presented a comprehensive analysis of optimal DG location and size for a residential feeder intending to mainly improve loss reduction and the overall voltage profile. To demonstrate the superiority of the suggested strategy when compared to other methods, the authors of this research examined several cases employing various types of DGs.…”

Section: Introductionmentioning

confidence: 99%

Optimal DG Integration in Power Distribution Systems Using Coronavirus Herd Immunity Optimizer

2022

IJIES

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In this study, the problem of finding an optimal location and size of a distributed generator (DG) in distribution systems with considering operational distribution system constraints is proposed with the objective of maximizing DG hosting capacity (MDHC), reducing system loss, and improving voltage stability index (VSI). The proposed objective function is formulated as a multi-objective mixed-integer nonlinear optimization in order to solve it simultaneously. To solve this problem, the coronavirus herd immunity optimizer (CHIO), a bio-inspired metaheuristic optimization method, is herein proposed to simultaneously tackle a discrete and continuous DG integration problem in distribution systems. Extensive simulations on an IEEE 69-node system with different load levels and DG numbers are performed using MATLAB software to evaluate the efficacy of the proposed method. The simulation results demonstrate that the proposed method efficiently improves overall distribution system performance when compared to different DG numbers and load levels. Furthermore, the CHIO optimization method shows encouraging results and almost obtains the best results in all proposed cases when compared with well-known metaheuristic optimization methods such as genetic algorithm (GA), the hunger games search (HGS), the chaotic neural network algorithm (CNNA), and the water cycle algorithm (WCA). The CHIO can successfully offer a notable solution for the DG integration problem, and the obtained results, for example in case 1, revealed outperforming the CHIO compared to other methods in terms of the MDHC (i.e., 99.999 %), voltage profile improvement (i.e., the minimum voltage magnitude of 0.9696 p.u), VSI improvement (i.e., 29.16 %), and system loss reduction (i.e., 66.95 %) compared with the base case, respectively.

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“Optimal placement and sizing of distributed generation in power distribution system: a comprehensive review”

Kumar

Verma

Choudhary

et al. 2023

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Optimal Siting of Distributed Generation Unit in Power Distribution System considering Voltage Profile and Power Losses

Cited by 4 publications

References 23 publications

A Holistic Approach to Voltage Profile Improvement and Power Loss Mitigation through Distributed Generation Placement and Sizing

A Holistic Approach to Voltage Profile Improvement and Power Loss Mitigation through Distributed Generation Placement and Sizing

Optimal DG Integration in Power Distribution Systems Using Coronavirus Herd Immunity Optimizer

“Optimal placement and sizing of distributed generation in power distribution system: a comprehensive review”

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