Optimal allocation of distributed generation for power flow enhancement in distribution network

Jayaraman Panneerselvam, Srividhya; Krishnamoorthi, Yogitha; Ramamoorthy, Suresh; Selvaraj, Jaisiva

doi:10.1002/oca.3031

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…The model's objective function minimizes the potential investment's combined capital expenditure (CAPEX) and operational expenditure (OPEX). Operational expenditure (OPEX), in (23), is calculated as the sum of the grid power consumption (P grid t ) multiplied by the corresponding coefficient (a 1 t ) for each time period (t) within the optimization horizon (T). The coefficient (a 1 t ) accounts for the cost associated with grid power consumption at each time period.…”

mentioning

confidence: 99%

Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO

El Sayed,

Poyrazoglu

2024

Energies

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The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO’s computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid’s physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability.

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mentioning

confidence: 99%

Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO

El Sayed,

Poyrazoglu

2024

Energies

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Optimal installation of DG in radial distribution network using arithmetic optimization algorithm

Dey,

Roy

2024

Adv Control Appl

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The purpose of this work is an arrangement of distributed generation (DG) in radial distribution networks (RDNs) in an adequate way. An efficient method, named arithmetic optimization algorithm (AOA) is used for that purpose. The intention for effective DG posting is to decrease the power loss and upgrade the voltage shape and voltage steadiness magnification in RDNs. The demonstration of the suggested AOA method has been made on 33‐bus, 69‐bus, 85‐bus, and 118‐bus RDNs for optimum orientation and capacity of DGs with distinct power factors (unity, fixed, and optimal). By using this projected technique, the execution of RDNs is improved with respect to voltage stability maximization, voltage fluctuation mitigation, and also reduction of real power loss. The AOA method which is recommended here, grants a better solution than several optimization techniques found in the literature. This recommended AOA technique provides the percentage improvement of power loss for case 1 to case 3 (65.50%, 86.48%, and 94.43%), (69.16%, 90.80%, and 98.10%), (44.57%, 66.61%, and 80.26%), and (60.34%, 88.80%, and 90.31%) for 33, 69, 85, and 118‐bus systems, respectively. The percentage improvement in voltage deviation minimization for case 1 to case 3 are (99.53%, 99.83%, and 99.84%), (99.88%, 99.91%, and 99.92%), and (97.53%, 98.98%, and 99.16%) for 33, 69, and 118‐bus systems, respectively. For different test systems (33, 69, and 118‐bus) improvements in voltage stability index maximization for case 1 to case 3 are (31.33%, 31.80%, and 31.81%), (30.15%, 31.25%, and 31.25%), and (37.78%, 39.23%, and 40.88%), respectively.

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Renewable Energy Based EV Charging Station Employed with Machine Learning Approach

K M,

et al. 2024

2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC)

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Optimal allocation of distributed generation for power flow enhancement in distribution network

Cited by 4 publications

References 37 publications

Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO

Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO

Optimal installation of DG in radial distribution network using arithmetic optimization algorithm

Renewable Energy Based EV Charging Station Employed with Machine Learning Approach

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