Numerical Simulation of the Integrated Solar/Hybrid Desalination System

Aldali, Yasser; Morad, K.; Elminshawy, Nabil A.S.; Ahwide, Farag

doi:10.51646/jsesd.v6i2.47

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…( 6) and (7) to generate another solution. Subsequently, a new solution for the CSPSO is generated as shown in Eq (8). After finishing all iterations, when the stopping criterion is met, the CSPSO-based tracker stops and provides the optimum solution, which is the duty cycle's optimal value in relation to the GMPP.…”

Section: ( )mentioning

confidence: 99%

“…The suggested technique is capable of tracking GMPP with an average efficiency of 99.925% and an average tracking time of 0.13 s in all shading patterns studied. in producing electricity from a variety of sources, such as PV/grid, PV/wind, PV/diesel, PV/ concentrated solar power (CSP), PV/wind/diesel, and PV/wind/battery combinations [4][5][6][7][8]. Moreover, solar photovoltaic has many qualities, including its clean energy, ease of installation, and environmentally friendly nature; however, despite these qualities, it also faces challenges.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid of Meta-Heuristic Techniques Based on Cuckoo Search and Particle Swarm Optimizations for Solar PV Systems Subjected to Partially Shaded Conditions

Nouh,

Almalih,

Faraj

et al. 2024

jsesd

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Solar energy has a significant role in meeting rising energy demand while reducing environmental impact. Solar radiation and temperature are important factors on which PV energy production depends, but its optimal operation point is influenced by variations in the aforementioned environmental factors. The nonlinear behavior of the solar system and the variable nature of environmental conditions make determining the optimal operation point difficult. To overcome these difficulties, maximum power point tracking (MPPT) finding techniques are used to extract the optimal power from the photovoltaic energy system. The behavior of MPPT varies for different weather conditions, such as partial shading conditions (PSC), and uniform irradiance conditions. Conventional techniques are simple, quick, and efficient for tracing the MPP quickly, but they are limited to uniform weather conditions. In addition, these techniques don't achieve the Global Maxima (GM) and mostly stay stuck at the Local Maxima (LM). The Meta-Heuristic techniques aid in finding the GM, but their primary disadvantage is that they take a longer time to trace the Global Maxima. This study addresses the problem by combining Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms, leading to a hybrid (CSPSO) technique to extract the global maximum (GM). To verify the effectiveness of the suggested technique, its performance is examined under three different irradiance patterns for different PV array configurations (such as 3S and 4S3P) through MATLAB simulation. The outcomes of CSPSO are compared with the prior well-known Meta-Heuristic techniques such as Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Crow Search Algorithm (CSA). The results show the suggested technique excels over other techniques in terms of accuracy, tracking efficiency, and tracking speed. The suggested technique is capable of tracking GMPP with an average efficiency of 99.925% and an average tracking time of 0.13 s in all shading patterns studied.

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Section: ( )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid of Meta-Heuristic Techniques Based on Cuckoo Search and Particle Swarm Optimizations for Solar PV Systems Subjected to Partially Shaded Conditions

Nouh,

Almalih,

Faraj

et al. 2024

jsesd

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show abstract

“…Fresh water is necessary for human existence and contemporary cultural advancement. Unfortunately, a sizable section of the world's population still does not have proper access to it [1,2]. It is essential to have access to clean and safe water to sustain good hygiene, sanitation, and general health.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Management of Hybrid Wind Turbine-Diesel-Battery System for Reverse Osmosis Seawater Desalination in NEOM City

Hassan,

Rezk,

Youssef

et al. 2024

International Journal of Energy Research

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Optimal sizing and management of hybrid wind turbine-diesel-battery system for reverse osmosis seawater desalination in NEOM city is the objective of the paper. Therefore, the paper explored the different factors to optimize and introduce a technoeconomic evaluation and energy management of a stand-alone wind turbine (WT) system, diesel generator (DG), and battery storage (BS). The suggested WT/DG/BS system is implemented to feed seawater reverse osmosis (SWRO) unit in NEOM. The necessitated desalinated water per day is 100 m3. To determine the optimal size of WT/DG/BS corresponding to the minimum cost of energy (COE) and net present cost, two different ratings of the SWRO units (SWRO-100 and SWRO-150), three control dispatch strategies (load following, cycle charging, and combined dispatch), and five types of batteries are considered. HOMER software is performed to simulate and optimize the WT/DG/BS. The optimization results indicated that the best battery storage is the Trojan SAGM battery. In this case, the COE ranged between $0.337/kWh and $0.564/kWh. The lowest COE of $0.377/kWh is obtained when using a combined control strategy and SWRO-100 unit, whereas the worst COE of $0.564/kWh is obtained when using load following control strategy and SWRO-150 unit. The best option of the WT/DG/BS system to supply the SWRO unit is option number 26. This system includes one wind turbine of 90 kW, DG of 25 kW, 47 Trojan SAGM batteries, a 23.8 kW converter, a SWRO-100 unit, and a combined control strategy. The net present cost and the initial cost are $950,725 and $221,495, respectively. The annual operating cost and annual consumed fuel are $56,409 and 36,396 L, respectively. Compared with using only a 25 kW diesel generator, the COE reduced from $0.373/kWh (using only DG/BS) to $0.337/kWh (using the best option) by around 9.65%. Under this condition, the values for the internal rate of return, return on investment, and simple payback are 11%, 7.8%, and 8.3 years, respectively.

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Numerical Simulation of the Integrated Solar/Hybrid Desalination System

Cited by 2 publications

References 14 publications

Hybrid of Meta-Heuristic Techniques Based on Cuckoo Search and Particle Swarm Optimizations for Solar PV Systems Subjected to Partially Shaded Conditions

Hybrid of Meta-Heuristic Techniques Based on Cuckoo Search and Particle Swarm Optimizations for Solar PV Systems Subjected to Partially Shaded Conditions

Optimal Sizing and Management of Hybrid Wind Turbine-Diesel-Battery System for Reverse Osmosis Seawater Desalination in NEOM City

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