Optimal Allocation of Wind DG with Time Varying Voltage Dependent Loads Using Bio-Inspired: Salp Swarm Algorithm

Ahmed, Ali; Nadeem, Muhammad Faisal; Sajjad, Intisar Ali; Bo, Rui; Khan, Irfan

doi:10.1109/icomet48670.2020.9074118

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…Type of loads connected to the feeder greatly affect the grid intakes [23] therefore, hourly load demand is presented in Fig. 1 [24] for different load models. TVVD loads are modeled by incorporating voltage-dependent characteristics of load with time varying characteristics for any time interval (t) and are given as,…”

Section: Load and Wind Speed Modeling A Load Modelingmentioning

confidence: 99%

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A Novel Framework to Determine the Impact of Time Varying Load Models on Wind DG Planning

Ahmed

Khan

et al. 2021

IEEE Access

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Distributed Generation (DG) based on Renewable Energy Sources (RES) are considered as an effective and economical technology for the advancement of an Electric Power System (EPS) to fulfill the load demand. Mostly, studies pertaining to DG planning are performed while considering constant load demand and DG generation. However, these considerations may provide misleading and inconsistent values for loss reduction, voltage profile, power quality, and other operational parameters. Therefore, this paper proposes a novel framework to determine the impact of different Time Varying Voltage Dependent (TVVD) load models on wind DG planning study. Firstly, wind DG optimal allocation is performed using Salp Swarm Algorithm (SSA) for different TVVD load models. Afterwards, impact of different TVVD load models on wind DG planning is investigated. Comparative evaluation of various impact indices, real and reactive power (losses and intakes), penetration level, and apparent power support provided due to integration of wind DG are discussed for various TVVD loads. The analysis of results indicates that TVVD loads have a significant impact on performance of distribution system and DG planning studies. INDEX TERMS Distributed generation, impact indices, salp swarm algorithm, time varying voltage dependent loads.

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Section: Load and Wind Speed Modeling A Load Modelingmentioning

confidence: 99%

“…3. 3) Again, perform load flow after integrating wind DG at an optimal bus to find hourly P loss and Q loss for each TVVD load model to compute hourly P in , Q in and S in using (23), (24), and (25). 4) Compute the difference between S in , Q in , P in , Q loss , P loss and MVA sys before and after wind DG integration.…”

Section: Wind Dg Penetration Levelmentioning

confidence: 99%

A Novel Framework to Determine the Impact of Time Varying Load Models on Wind DG Planning

Ahmed

Khan

et al. 2021

IEEE Access

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“…Shockley diode equation provides (7) and ( 8), Here, I represents output current obtained from respective PV model. I diode is the diode current as explained by the Shockley equation in (2).…”

Section: Double-diode Modelmentioning

confidence: 99%

“…Electric energy plays a vital role in the existence of various sectors such as industries, agriculture, manufacturing, and housing. Fossil fuels have been used significantly for many years to produce electric energy, but increasing fuel prices and reduction in their availability focused the attention of researchers towards sustainable sources of energy [1,2]. Fossil fuels such as oil, coal, and gas are also responsible for carbon emission resulting in global environmental hazards [3].…”

Section: Introductionmentioning

confidence: 99%

An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters

et al. 2021

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The efficiency of PV systems can be improved by accurate estimation of PV parameters. Parameter estimation of PV cells and modules is a challenging task as it requires accurate operation of PV cells and modules followed by an optimization tool that estimates their associated parameters. Mostly, population-based optimization tools are utilized for PV parameter estimation problems due to their computational intelligent behavior. However, most of them suffer from premature convergence problems, high computational burden, and often fall into local optimum solution. To mitigate these limitations, this paper presents an improved variant of particle swarm optimization (PSO) aiming to reduce shortcomings offered by conventional PSO for estimation of PV parameters. PSO is improved by introducing two strategies to control inertia weight and acceleration coefficients. At first, a sine chaotic inertia weight strategy is employed to attain an appropriate balance between local and global search. Afterward, a tangent chaotic strategy is utilized to guide acceleration coefficients in search of an optimal solution. The proposed algorithm is utilized to estimate the parameters of the PWP201 PV module, RTC France solar cell, and a JKM330P-72 PV module-based practical system. The obtained results indicate that the proposed technique avoids premature convergence and local optima stagnation of conventional PSO. Moreover, a comparison of obtained results with techniques available in the literature proves that the proposed methodology is an efficient, effective, and optimal tool to estimate PV modules and cells’ parameters.

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“…Em (Truong et al, 2019), um novo algoritmo, baseado na meta-heurística Symbiotic Organisms Search (SOS), foi implementado para determinar a quantidade, a localização e o dimensionamento ótimo de DGs de SDEEs radiais, de modo a reduzir as perdas elétricas ativas. Já em (Ahmed et al, 2020) utilizou-se um algoritmo recentemente proposto, chamado Salp Swarm Algorithm (SSA), para otimizar a alocação de GDs eólicos. Para isso, foram considerados parâmetros relacionados a desvio de tensão, perdas e estabilidade.…”

Section: Introductionunclassified

Alocação de Geradores Distribuídos em Sistemas de Distribuição de Energia Elétrica Utilizando Algoritmo GRASP-VNS

Silva

Zvietcovich

Baracho

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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Este trabalho apresenta a implementação das meta-heuristicas, Greedy Randomized Adaptive Search Procedure (GRASP) e Variable Neighborhood Search (VNS) para a alocação ótima de Geradores Distribuídos (GDs) e seu dimensionamento em redes de distribuição com operação radial, visando a redução das perdas elétricas ativas e, simultaneamente, à melhoria dos níveis de tensão. Nesta metodologia, o algoritmo GRASP é utilizado para a construção de possíveis soluções do problema, enquanto o VNS se aplica à uma etapa de busca local, na qual realiza-se um refinamento no espaço de soluções para aprimorar a convergência do processo de busca. Para validação da técnica, a metodologia foi aplicada em duas redes, uma de 33 barras do IEEE e outra de 135 barras. Dos resultados obtidos, pôde-se verificar a eficácia da metodologia em encontrar soluções de boa qualidade com perdas mínimas e consideráveis melhorias nos perfis de tensão.

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Optimal Allocation of Wind DG with Time Varying Voltage Dependent Loads Using Bio-Inspired: Salp Swarm Algorithm

Cited by 13 publications

References 19 publications

A Novel Framework to Determine the Impact of Time Varying Load Models on Wind DG Planning

A Novel Framework to Determine the Impact of Time Varying Load Models on Wind DG Planning

An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters

Alocação de Geradores Distribuídos em Sistemas de Distribuição de Energia Elétrica Utilizando Algoritmo GRASP-VNS

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