Optimal Allocation of Distributed Generation and Shunt Capacitors for the Reduction of Total Voltage Deviation and Total Line Loss in Radial Distribution Systems Using Binary Collective Animal Behavior Optimization Algorithm

Khan, Nasim Ali; Ghoshal, Sakti Prasad; Ghosh, Sandip

doi:10.1080/15325008.2014.975384

Cited by 33 publications

(13 citation statements)

References 56 publications

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“…IEEE 33 and 69 node systems Current system No planning [131] No planning Constant load IEEE 33, 69 and 119 node systems Current system No planning [132] No planning Constant load IEEE 33 and 37 node systems Current system No planning [134] No planning Constant load IEEE 12, 33 and 69 node systems Current system No planning Table 3 Advantages and disadvantages of some research works on distribution system optimisation.…”

Section: And 69 Nodes Current Systemmentioning

confidence: 98%

“…Indeed in [36,72,90,92,100,106,108,111,114,116,117,121,123,125,128,131,132,134], the performance of current system has been optimised and in [24][25][26]34,35,[42][43][44]46,47,49,59,61,74,75,77,79,81,94,96,98,102,[142][143][144], expansion planning problem has been solved.…”

Section: And 69 Nodes Current Systemmentioning

confidence: 99%

“…In [24][25][26][34][35][36]42,44,46,59,61,72,74,75,77,79,90,92,96,98,106,108,111,116,117,121,123,125,131,132,134,[142][143][144] loads are considered constant for each planning stage and uncertainties of loads have not been taken into account. In [47,49,81,102] uncertainty of loads has been modelled.…”

Section: And 69 Nodes Current Systemmentioning

confidence: 99%

“…The application of the proposed modified firefly algorithm on IEEE 33 and 37 node systems shows its efficacy. In [134], collective animal behaviour optimisation algorithm has been used to find optimal placement and sizing of DG units and shunt capacitors in order to minimise copper losses and voltage deviations. The simulation results on IEEE 12, 33 and 69 node systems testify that the proposed binary collective animal behaviour algorithm outperforms binary PSO.…”

Section: Review From Viewpoint Of Used Optimisation Algorithms Objecmentioning

confidence: 99%

See 3 more Smart Citations

Optimisation of electric distribution systems: A review

Jordehi¹

2015

Renewable and Sustainable Energy Reviews

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Section: And 69 Nodes Current Systemmentioning

confidence: 98%

Section: And 69 Nodes Current Systemmentioning

confidence: 99%

Section: And 69 Nodes Current Systemmentioning

confidence: 99%

Section: Review From Viewpoint Of Used Optimisation Algorithms Objecmentioning

confidence: 99%

See 2 more Smart Citations

Optimisation of electric distribution systems: A review

Jordehi¹

2015

Renewable and Sustainable Energy Reviews

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“…Por exemplo, em [17] e [25] a alocação simultânea é realizada obrigatoriamente na mesma barra. Já em [29] e [26] as alocações devem possuir o mesmo tamanho.…”

Section: Figura 7: Sistema De 33 Barrasunclassified

Otimização Multiobjetivo para Alocação de Geradores Distribuídos e Banco de Capacitores Fixos

Ribeiro¹,

Barbosa²,

Lemos³

2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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Resumo-Algumas medidas adotadas para melhorar a performance de sistemas de distribuição são a inserção de geração distribuída (GD) e bancos de capacitores (BC). Este trabalho apresenta um modelo de otimização multiobjetivo para o dimensionamento e posicionamento simultâneo ou não desses recursos. O NSGA-II foi utilizado como ferramenta de busca para minimizar as perdas de potência ativa, melhorar o perfil de tensão, aumentar a capacidade de transferência disponível e reduzir o tamanho do empreendimento. As simulações foram realizadas no MATPOWER no sistema de 33 barras. A escolha de pontos extremos da fronteira de Pareto e a composição max-min foram as ferramentas utilizadas para trade-off e decisão final. O ganho de performance do sistema pós-alocação sugere que a inserção simultânea de GD e BC deve ser priorizada.

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An efficient framework for integrating distributed generation and capacitor units for simultaneous grid‐connected and islanded network operations

Leghari

Hassan

Said

et al. 2021

Intl J of Energy Research

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This paper proposes a two-stage planning approach for the efficient utilization of distributed generation (DG) and capacitor banks (CBs) for the simultaneous grid-connected and islanded operations of the balanced distribution networks.The first stage determines the optimal installation locations and capacities of DGs and CBs using an improved variant of the Jaya optimization algorithm (IJaya) to minimize the power losses and voltage deviation during the gridconnected operation of the distribution network. The second stage identifies the optimal operating point of the DG and CB combination to carry the maximum load during the islanded network operation under power supply-demand imbalance conditions. For the second stage, an analytical approach is proposed to identify the best operating power factor for the DG-CB combination, calculating the power loss, and under-utilization of the installed real-reactive power sources. Simulation results on the IEEE 33-bus grid-dependent distribution network demonstrate that the proposed IJaya significantly improved the network performance and outperforms various existing optimization methods. In addition, during the network's islanded operation, maintaining the network power factor equal to the source power factor (pf source ) makes the DG-CB combination operate at their rated capacities.

show abstract

Optimal Allocation of Distributed Generation and Shunt Capacitors for the Reduction of Total Voltage Deviation and Total Line Loss in Radial Distribution Systems Using Binary Collective Animal Behavior Optimization Algorithm

Cited by 33 publications

References 56 publications

Optimisation of electric distribution systems: A review

Optimisation of electric distribution systems: A review

Otimização Multiobjetivo para Alocação de Geradores Distribuídos e Banco de Capacitores Fixos

An efficient framework for integrating distributed generation and capacitor units for simultaneous grid‐connected and islanded network operations

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