Optimal Placement and Sizing of Distributed Generation using Flower Pollination Algorithm for Power Loss Reduction Maximization in Distribution Networks

Sabarinath, G; Manohar, T. Gowri

doi:10.1109/icpeices.2018.8897436

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…However, power losses during allocation of DG are not addressed with this collaborative DG system. The investigational analysis on the losses due to DG allocation and power system sizing has been addressed in [18] where fuzzy logic scheme is introduced to analyze the power losses. The performance of both the IEEE 69 bus system and the IEEE 33 bus system is analyzed, with the fuzzy logic scheme-based DG system having lower power losses.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of solar and wind distributed generation using particle swarm optimization technique

Rekha¹,

Byalihal²

2023

IJECE

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<span lang="EN-US">Power demand in the current days is increasing more and more where the conventional power generation systems are failing to meet these power demands due to less availability of non-renewable resources. Hence, many of the researchers are working on the distributed generation (DG) by using renewable resources like wind and solar. The penetration towards wind, solar DG faced challenging situations during power generation due to uncertainty in the wind speed and solar radiation. Recent studies have predicted that the combination of both solar and wind can lead to better performance. However, the sizing and placement of DG systems is necessary to achieve efficiency otherwise the systems may lead to adverse effects in distribution networks. This paper introduced the solar DG, wind DG and hybrid (solar and wind) DG system. The particle swarm optimization technique is used to size and place the DG because of its parallel search capability. Also, the combination of wind-solar DG gives better DG sizing in the respective DG location. The voltage profile of these DG systems has shown better results for the efficient power system. In comparison to conventional DG systems, the suggested hybrid DG system is capable of minimizing power loss and maintaining voltage profile.</span>

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Section: Introductionmentioning

confidence: 99%

Optimal allocation of solar and wind distributed generation using particle swarm optimization technique

Rekha¹,

Byalihal²

2023

IJECE

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“…Com o objetivo de minimizar as perdas de potência ativa, em (Sabarinath and Manohar, 2018), propõe-se o power loss index (PLI), para a alocação das unidades de GD juntamente com a metaheurística flower pollination algorithm (FPA). Para validação da metodologia, são testados diferentes tipos de tecnologias de GDs nos sistemas IEEE 33 e 69 barras.…”

Section: Introductionunclassified

Análise Comparativa entre PSO e Gradiente Descendente para o Problema do Despacho de Geração Distribuída

Silva

Coelho

Peres

2020

Anais Do Simpósio Brasileiro De Sistemas Elétricos 2020

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O despacho ótimo de Geração Distribuída (GD), em sistemas de distribuição de energia elétrica consiste em se determinar o montante de potência a ser injetada de forma a se obter benefícios operacionais. Redução das perdas ativas, aprimoramento do perfil de tensão e redução da emissão de poluentes são alguns destes potenciais benefícios. Neste trabalho, realiza-se uma comparação entre o Particle Swarm Optimization (PSO) e o método do gradiente descendente para a otimização do despacho de GD. O objetivo principal aqui considerado é a minimização das perdas ativas, mantando-se a tensão dentro de limites regulatórios. Todas as simulações são realizadas com duas GDs conectadas ao sistema IEEE 13 barras. O cálculo do fluxo de potência trifásico desequilibrado é feito pelo software OpenDSS, (Open Distribution System Simulator). Os resultados obtidos mostram que ambas as ferramentas são eficazes na resolução do problema proposto, com o PSO encontrando menores perdas e o gradiente convergindo mais rapidamente.

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A Review on Optimum Location and Sizing of DGs in Radial Distribution System

Tejaswi

Swathika

2023

Integrated Green Energy Solutions Volume 2

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Optimal Placement and Sizing of Distributed Generation using Flower Pollination Algorithm for Power Loss Reduction Maximization in Distribution Networks

Cited by 5 publications

References 12 publications

Optimal allocation of solar and wind distributed generation using particle swarm optimization technique

Optimal allocation of solar and wind distributed generation using particle swarm optimization technique

Análise Comparativa entre PSO e Gradiente Descendente para o Problema do Despacho de Geração Distribuída

A Review on Optimum Location and Sizing of DGs in Radial Distribution System

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