首页|Multi-agent based sine-cosine algorithm for optimal integration of DERs with consideration of existing OLTC in distribution networks

Multi-agent based sine-cosine algorithm for optimal integration of DERs with consideration of existing OLTC in distribution networks

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  • NSTL
  • Elsevier
In this work, a modified version of the sine-cosine algorithm (SCA) has been developed to solve complex optimization problems. This proposed modified algorithm integrates the multi-agent system and sine-cosine algorithm and is termed as multi-agent sine-cosine algorithm (MA-SCA). This work also proposes a simplified strategy for the self-learning operator and modification in inversion operator. These proposed modifications have been validated by implementing the proposed MA-SCA algorithm on standard functions and comparing results with other reported optimization methods. Furthermore, in this work, MA-SCA algorithm has also been applied to optimally deploy the distributed energy resources (DERs) and shunt capacitors (SCs) in the distribution network with and without consideration of the existing on-load tap changing transformer (OLTC). The considered objectives in this optimization problem are reduction of cost of annual energy loss (CAEL) and minimization of voltage deviation under different loading conditions. To demonstrate the efficacy of the MA-SCA algorithm, it has been implemented on IEEE 33 bus radial distribution network (DN) and real-life Indian 108 bus radial DN. The comparison of obtained results with the results obtained by using other optimization methods has been carried out, and it indicates that MA-SCA algorithm provides the improved solution. (C) 2021 Elsevier B.V. All rights reserved.

Sine-cosine algorithmMulti-agent systemDistributed energy resourcesOn-load tap changing transformerPOWER LOSS MINIMIZATIONOPTIMAL PLACEMENTGENERATION ALLOCATIONDISTRIBUTION-SYSTEMSREACTIVE POWERLOSS REDUCTIONDG ALLOCATIONOPTIMIZATIONRECONFIGURATIONRELIABILITY

Patel, C. D.、Tailor, T. K.

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Nirma Univ

2022

10.1016/j.asoc.2021.108387

Applied Soft Computing

Applied Soft Computing

EISCI
ISSN：1568-4946
年,卷(期)：2022.117
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