Artificial Neural Network power manager for hybrid PV-wind desalination system

Charrouf, Omar; Betka, Achour; Abdeddaim, Sabrina; Ghamri, A.

doi:10.1016/j.matcom.2019.09.005

Cited by 40 publications

(9 citation statements)

References 33 publications

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“…Using the following equation, the daily water capacity (D WC ) produced by the SWROD unit is obtained 24,41,42 :

D_{WC} = 24 \times ((), \frac{P_{D}}{S_{DC}})

where PD represents the desalination installed power.…”

Section: Methodsmentioning

confidence: 99%

“…This software determines the quality of freshwater permeate, flow permeate rate, and specific energy consumption of a SWROD plant, the quality of feed water (seawater), type of membrane, structure of the vessels, and vessels pressure. 40 Using the following equation, the daily water capacity (D WC ) produced by the SWROD unit is obtained 24,41,42 :…”

Section: Soc T ð þ ≤ Soc Maxmentioning

confidence: 99%

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Modeling, design, and optimization of a cost‐effective and reliable hybrid renewable energy system integrated with desalination using the division algorithm

et al. 2020

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People in the Middle East are facing the problem of freshwater shortages. This problem is more intense for a remote region, which has no access to the power grid. The use of seawater desalination technology integrated with the generated energy unit by renewable energy sources could help overcome this problem. In this study, we refer a seawater reverse osmosis desalination (SWROD) plant with a capacity of 1.5 m 3 /h used on Larak Island, Iran. Moreover, for producing fresh water and meet the load demand of the SWROD plant, three different stand-alone hybrid renewable energy systems (SAHRES), namely wind turbine (WT)/photovoltaic (PV)/battery bank storage (BBS), PV/BBS, and WT/BBS are modeled and investigated. The optimization problem was coded in MATLAB software. Furthermore, the optimized results were obtained by the division algorithm (DA). The DA has been developed to solve the sizing problem of three SAHRES configurations by considering the object function's constraints. These results show that this improved algorithm has been simpler, more precise, faster, and more flexible than a genetic algorithm (GA) in solving problems. Moreover, the minimum total life cycle cost (TLCC = 243 763$), with minimum loss of power supply probability (LPSP = 0%) and maximum reliability, was related to the WT/PV/BBS configuration. WT/PV/BBS is also the best configuration to use less battery as a backup unit (69 units). The batteries in this configuration have a longer life cycle (maximum average of annual battery charge level) than two other configurations (93.86%). Moreover, the optimized results have shown that utilizing the configuration of WT/PV/BBS could lead to attaining a cost-effective and green (without environmental pollution) SAHRES, with high reliability for remote areas, with appropriate potential of wind and solar irradiance.

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“…Using the following equation, the daily water capacity (D WC ) produced by the SWROD unit is obtained 24,41,42 :

D_{WC} = 24 \times ((), \frac{P_{D}}{S_{DC}})

where PD represents the desalination installed power.…”

Section: Methodsmentioning

confidence: 99%

Section: Soc T ð þ ≤ Soc Maxmentioning

confidence: 99%

Modeling, design, and optimization of a cost‐effective and reliable hybrid renewable energy system integrated with desalination using the division algorithm

et al. 2020

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“…The values of the connections between both the encoder and decoder layers are represented by Rw; the masses of the connections between both the hidden layer and output layer are represented by Lw, and the bias of a transistor in the corresponding layers is represented by g 1 and g 2 . The Heaviside value, the symmetrical saturated function with respect, the sigmoid component, the Gaussian feature, the logistic sigmoid operation, and also the spline feature are some of the regularly utilized training algorithms that must be selected suitably for every layer [23]. The symmetrical saturated linear model was applied in both stages in this research.…”

Section: Power Management Systemmentioning

confidence: 99%

Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management

Sahoo

Amirthalakshmi²,

Ramesh³

et al. 2022

International Journal of Photoenergy

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The analysis of different components of a grid-linked hybrid energy system (HES) comprising a photovoltaic (PV) system is presented in this work. Due to the increase of the population and industries, power consumption is increasing every day. Due to environmental issues, traditional power plants alone are insufficient to supply customer demand. In this case, the most important thing is to discover another approach to meet customer demands. Most wealthy countries are now concentrating their efforts on developing sustainable materials and investing considerable amounts of money in product development. Wind, solar, fuel cells, and hydro/water resources are among the most environmentally benign renewable sources. To control the variability of PV generation, this sort of application necessitates the usage of energy storage systems (ESSs). Lithium-ion (Li-ion) batteries are the most often used ESSs; however, they have a short lifespan due to the applied stress. Hybrid energy storage systems (HESSs) started to evolve as a way to decrease the pressure on Li-ion batteries and increase their lifetime. This study represents a great power management technique for a PV system with Li-ion batteries and supercapacitor (SC) HESS based on an artificial neural network. The effectiveness of the suggested power management technique is demonstrated and validated using a conventional PV system. Computational models with short-term and long-term durations were used to illustrate their effectiveness. The findings reveal that Li-ion battery dynamical stress and peak value are reduced, resulting in longer battery life.

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“…The authors concluded positively on the technical feasibility of such systems, provided they operate with a variable capacity. Charrouf et al [13] showed that artificial neural networks can be successfully used to achieve the smooth power management of a reverse osmosis unit fed by hybrid renewable energy sources, solar panels, wind turbines, and battery banks. Peng et al [14] investigated the use of different evolutionary algorithms to determine the optimum size under wind intermittency of a hybrid renewable energy system comprised of a wind turbine, a photovoltaic panel, a battery bank, and reverse osmosis desalination.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Wind Driven RO Plant for Brackish Water Desalination during Wind Speed Fluctuation with and without Battery

et al. 2021

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This work aimed to carry out an optimal investigation of the design and operation of a large capacity reverse osmosis (RO) desalination plant powered by wind energy. Different scenarios involving two design options, such as using storage tanks or batteries, and operation options, such as using variable or fixed feed pressure, were analyzed and optimized. In addition, another operation option, of using a fixed number of RO vessels or a varying number of active RO vessels, was also considered. It was found that an optimized plant using storage tanks can provide a less expensive water cost and a less complicated plant structure. Moreover, the use of a variable feed pressure can help in attenuating the disturbances incurred in the form of wind intermittency. Conversely, the use of fixed feed pressure and constantly supplied power per vessel can run the RO units smoothly, leading to a predictable production rate. However, this requires operating the plant on different active sets of vessels each hour, which mandates additional automatic control systems. The water cost when storage tanks are utilized can be as low as 7.42 $/m3, while it is around 19.7 $/m3 when a battery is used.

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Artificial Neural Network power manager for hybrid PV-wind desalination system

Cited by 40 publications

References 33 publications

Modeling, design, and optimization of a cost‐effective and reliable hybrid renewable energy system integrated with desalination using the division algorithm

Modeling, design, and optimization of a cost‐effective and reliable hybrid renewable energy system integrated with desalination using the division algorithm

Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management

Optimization of Wind Driven RO Plant for Brackish Water Desalination during Wind Speed Fluctuation with and without Battery

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