Optimal sizing of a stand-alone hybrid photovoltaic/wind system using new grey wolf optimizer considering reliability

Hybrid energy systems (HESs) comprising photovoltaic (PV) arrays and wind turbines (WTs) are remarkable solutions for electrifying remote areas. These areas commonly fulfil their energy demands by means of a diesel genset (DGS). In the present study, a novel computational intelligence algorithm called supply-demand-based optimization (SDO) is applied to the HES sizing problem based on long-term cost analysis. The effectiveness of SDO is investigated, and its performance is compared with that of the genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), grasshopper optimization algorithm (GOA), flower pollination algorithm (FPA), and big-bang-big-crunch (BBBC) algorithm. Three HES scenarios are implemented using measured solar radiation, wind speed, and load profile data to electrify an isolated village located in the northern region of Saudi Arabia. The optimal design is evaluated on the basis of technical (loss of power supply probability [LPSP]) and economic (annualized system cost [ASC]) criteria. The evaluation addresses two performance indicators: surplus energy and the renewable energy fraction (REF). The results reveal the validity and superiority of SDO in determining the optimal sizing of an HES with a higher convergence rate, lower ASC, lower LPSP, and higher REF than that of the GA, PSO, GWO, GOA, FPA, and BBBC algorithms. The performance analysis also reveals that an HES comprising PV arrays, WTs, battery banks, and DGS provides the best results: 238.

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“…The REF is the total amount of primary energy from the RER that is supplied to the load. It can be calculated as follows 46 :…”

Section: Renewable Energy Fraction Modelmentioning

confidence: 99%

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

et al. 2020

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“…The optimal design of solar-wind-battery system using evolutionary algorithm (DA) has been studied by minimizing capital investment and repair and maintenance costs of equipment and the probability of notsupplied load constraint and the effects of the mentioned variations on the optimal size of equipment and the cost of the hybrid system have been evaluated [21]. In [22], the optimization of a solar-wind hybrid system with a battery bank has been investigated to minimize the annual cost of the system by considering the power balance constraint using the grey wolf optimizer (GWO) algorithm. In this study, the effect of equipment availability was evaluated by providing a reliability model [22].…”

Section: Introductionmentioning

confidence: 99%

“…In [22], the optimization of a solar-wind hybrid system with a battery bank has been investigated to minimize the annual cost of the system by considering the power balance constraint using the grey wolf optimizer (GWO) algorithm. In this study, the effect of equipment availability was evaluated by providing a reliability model [22]. The optimal design of a solar-diesel-battery system is presented using the cuckoo search algorithm (CSA) by minimizing the system powergeneration cost and improving the reliability of the load [23].…”

Section: Introductionmentioning

confidence: 99%

Designing of stand-alone hybrid PV/wind/battery system using improved crow search algorithm considering reliability index

Moghaddam

Bigdeli

Moradlou

et al. 2019

Int J Energy Environ Eng

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In this paper, the design of a hybrid renewable energy PV/wind/battery system is proposed for improving the load supply reliability over a study horizon considering the Net Present Cost (NPC) as the objective function to minimize. The NPC includes the costs related to the investment, replacement, operation, and maintenance of the hybrid system. The considered reliability index is the deficit power-hourly interruption probability of the load demand. The decision variables are the number of PV panels, wind turbines and batteries, capacity of transferred power by inverter, angle of PV panels, and wind tower height. To solve the optimization problem, a new algorithm named improved crow search algorithm (ICSA) is proposed. The design of the system is done for Zanjan city, Iran based on real data of solar radiation and wind speed of this area. The performance of the proposed ICSA is compared with crow search algorithm (CSA) and particle swarm optimization methods in different combinations of system. This comparison shows that the proposed ICSA algorithm has better performance than other methods.

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“…[9][10][11][12][13][14][15][16] The significant aim of the present work is to provide an efficient PEMFC model according to Amphlett equation model that simulates the practical performance of the cell at various operational circumstances. [31][32][33][34][35][36] So in this article, SBO is used to establish variables of PEMFC model. In the earlier decade, metaheuristic approaches developing as optimization issue have applied by many researchers.…”

Section: Introductionmentioning

confidence: 99%

“…The mentioned advantage of SBO method is the main reason of researcher to use in many problems of electrical engineering. [31][32][33][34][35][36] So in this article, SBO is used to establish variables of PEMFC model.…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

2019

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Summary This study proposes precise modeling for the proton exchange membrane fuel cells which present desirable advantages compared with other energy management systems. The presented model can be applied for the simulation of the actual behavior of the proton exchange membrane fuel cells such as the electrical, electrochemical, and mechanical. In the present literature, a newly presented optimizer namely Satin Bowerbird is implemented for the evaluation of the proton exchange membrane fuel cell performance criteria. The Satin Bowerbird optimizer is an evolutionary algorithm that imitates the mating process of the Bowerbirds in the mating season. The Satin Bowerbird optimizer is applied to the different commercial benchmark of proton exchange membrane fuel cell stack to assess the performance of the proposed algorithm. The statistical study is also carried out to show the superiority of the proposed method compared with other schemes. The standard deviation for the Satin Bowerbird optimizer is obtained 0.0941 which is the lowest value amongst the other well‐known approaches. Also, the lowest sum of squared error is calculated for the proposed algorithm. Moreover, the validation of the presented method is done with the experimental data which shows good agreement between the experimental and modeling data.

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Optimal sizing of a stand-alone hybrid photovoltaic/wind system using new grey wolf optimizer considering reliability

Cited by 66 publications

References 23 publications

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

Designing of stand-alone hybrid PV/wind/battery system using improved crow search algorithm considering reliability index

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

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