Multi-periods distribution feeder reconfiguration at the presence of distributed generation through economic assessment using a new modified PSO algorithm

Nayeripour, Majid; Mahboubi-Moghaddam, Esmaeil; Khooban, Mohammad Hassan

doi:10.3233/ifs-162144

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…The algorithm of PSO [25] has been used as an efficient method to solve the optimization problem [26,27]. Based on the initial feasible solution set, PSO is used to be compatible with the proposed dynamic network reconfiguration.…”

Section: Decimal Particle Swarm Optimization Algorithm With Loop Searchmentioning

confidence: 99%

Distribution Network Dynamic Reconfiguration Based on Improved Fuzzy C‐Means Clustering with Time Series Analysis

Gao

Chen

et al. 2021

IEEJ Transactions Elec Engng

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The rapid growth of distributed energy resources integrated in distribution systems leads to an increasing need of continuously and automatically changing the system topology to realize the economic operation of distribution networks. This paper proposes an optimization model of dynamic reconfiguration for distribution networks based on a new method of time series analysis. Equivalent daily curve considering time‐varying nature of distributed generator and load demands is divided by an improved fuzzy C‐means clustering algorithm, where the indicator of section function is set to find the optimal reconfiguration time intervals. The uncertainty of distributed generator outputs and load demands is described by the interval algorithm. Then the affine Taylor expansion is adopted to solve the interval power flow equations. The reconfiguration optimization model is solved with decimal particle swarm optimization algorithm based on loop search. The optimal dynamic reconfiguration of a modified 70‐bus test system with distributed generators is carried out and the simulation results demonstrate the effectiveness and superiority of the proposed method. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Decimal Particle Swarm Optimization Algorithm With Loop Searchmentioning

confidence: 99%

Distribution Network Dynamic Reconfiguration Based on Improved Fuzzy C‐Means Clustering with Time Series Analysis

Gao

Chen

et al. 2021

IEEJ Transactions Elec Engng

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“…DG normally includes solar, wind, biomass, combined heat and power, or small rating power plant. The main advantages of DG include low investment cost, managing transmission line congestion [1,2], reduction in greenhouse gas emission [3], minimization of power losses [4,5], possible islanding operation that minimizes interruption time, and increased reliability [6][7][8]. Thus, many researchers are focusing on optimizing DG size or placement in distribution networks, with different objective functions to improve its performance.…”

Section: Introductionmentioning

confidence: 99%

Minimizing reverse current flow due to distributed generation via optimal network reconfiguration

Jamian¹,

Aman²,

Baharuddin³

et al. 2017

Turk J Elec Eng & Comp Sci

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Distributed generation (DG) is widely used to minimize total power losses in distribution networks. However, one of the problems of DG in a grid system is reverse current flow (RCF), which is when the DG output becomes greater than the connected load. Therefore, this paper proposes a multiobjective artificial bee colony (MOABC) algorithm to determine the optimal network reconfiguration for reducing total RCF in DG. The proposed algorithm is tested on 33-bus radial distribution systems in two different scenarios, i.e. base case and with 50% load. The proposed technique can reduce reverse current by up to 93%; however, the total power loss in the system will increase by 7%. Therefore, a suitable weight value is needed in MOABC for balancing the effect of RCF and the power loss value.

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Secure and Reliable Distribution Feeder Reconfiguration in the Presence of Static VAR Compensator

Nayeripour

Mahboubi-Moghaddam

Narimani

et al. 2019

Iran J Sci Technol Trans Electr Eng

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Multi-periods distribution feeder reconfiguration at the presence of distributed generation through economic assessment using a new modified PSO algorithm

Cited by 3 publications

References 22 publications

Distribution Network Dynamic Reconfiguration Based on Improved Fuzzy C‐Means Clustering with Time Series Analysis

Distribution Network Dynamic Reconfiguration Based on Improved Fuzzy C‐Means Clustering with Time Series Analysis

Minimizing reverse current flow due to distributed generation via optimal network reconfiguration

Secure and Reliable Distribution Feeder Reconfiguration in the Presence of Static VAR Compensator

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