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

Kiehbadroudinezhad, Mohammadali; Rajabipour, Ali; Čada, Michael; Khanali, Majid

doi:10.1002/er.5628

Cited by 30 publications

(22 citation statements)

References 54 publications

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“…A wind turbine converts the mechanical energy of wind into electrical energy [31]. Generally, two influential parameters play a critical role in the amount of power output of WT, that is, wind speed and height of the WT hub that could be modeled using Equation (1) [26]:…”

Section: Modeling Of Wind Turbine (Wt)mentioning

confidence: 99%

“…In the current study, the power required by the desalination plant is determined by ROSA software. This software could also assess the desalination plant's specific energy consumption, freshwater and seawater quality, membrane type, flow permeate rate, and structure and pressure of the vessels [26]. In this study, the daily water capacity that desalination could generate is estimated as follows [33]:…”

Section: Modeling Of Seawater Reverse Osmosis Desalination (Swrod)mentioning

confidence: 99%

“…For solving this problem, novel algorithms are proposed and developed to optimize the multi-objective problems. Recently, Kiehbadroudinezhad et al [26] developed a novel algorithm named division algorithm (DA) to optimize reliable and cost-effective desalination based on renewable energy resources. They claimed that in solving problems, DA is flexible, simple, precise, and fast compared to GA.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Wind Energy Battery Storage Microgrid by Division Algorithm Considering Cumulative Exergy Demand for Power-Water Cogeneration

Kiehbadroudinezhad

Merabet

Hosseinzadeh-Bandbafha

2021

Energies

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This study investigates the use of division algorithms to optimize the size of a desalination system integrated with a microgrid based on a wind turbine plant and the battery storage to supply freshwater based on cost, reliability, and energy losses. Cumulative exergy demand is used to identify and minimize the energy losses in the optimized system. Division algorithms are used to overcome the drawback of low convergence speed encountered by the well-known method genetic algorithm. The findings indicated that there is a positive relationship between cost, cumulative exergy, and reliability. More specifically, when the loss of power supply probability is 10%, compared to when it is 0%, the total cumulative exergy demand and total life cycle cost are reduced by 34.76% when the battery is full and 45.44% when the battery is empty and there is a 44.43% decrease in total life cycle cost, respectively. However, the more reliable system, the less exergy is lost during the production of 1 m3 freshwater by desalination integrated into wind turbine plant.

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Section: Modeling Of Wind Turbine (Wt)mentioning

confidence: 99%

Section: Modeling Of Seawater Reverse Osmosis Desalination (Swrod)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of Wind Energy Battery Storage Microgrid by Division Algorithm Considering Cumulative Exergy Demand for Power-Water Cogeneration

Kiehbadroudinezhad

Merabet

Hosseinzadeh-Bandbafha

2021

Energies

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“…The produced power of each wind turbine at time t ( P wt, t ) is obtained as follows:

P_{wt, t} = ({, \begin{matrix} 0 & V_{t} \leq V_{i} \\ \begin{matrix} \frac{P_{r, wt} . {((), V_{t})}^{3}}{((), {V_{r}}^{3} - {V_{i}}^{3})} - \frac{P_{r, wt} . {V_{i}}^{3}}{((), {V_{r}}^{3} - {V_{i}}^{3})} & V_{i} < V_{t} < V_{r} \\ P_{r, wt} & V_{r} \leq V_{t} < V_{o} \\ 0 & V_{t} \geq V_{o} \end{matrix} \end{matrix}),

where P r ,wt is the wind generator rated power and V t is the wind speed. Also, V i , V o , and V r are the cut‐in, cut‐off, and the rated speeds, respectively 44‐46 . A turbine starts generating power at V i and achieves its rated power at V r .…”

Section: Mathematical Modelmentioning

confidence: 99%

Dynamic optimization of solar‐wind hybrid system connected to electrical battery or hydrogen as an energy storage system

Shams

Ahmadi

2021

Int J Energy Res

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Summary Nowadays, due to the pollution arising from the consumption of fossil fuels, destructive effects of pollutants on humans and the environment, and the reduction of reserves of these energy resources, the tendency to utilize renewable energy to meet energy needs has increased. In this investigation, first, a grid‐connected photovoltaic‐wind‐battery (PWB) hybrid system for an educational building is designed and optimized by implementing a genetic algorithm in MATLAB. The main goal is to minimize the annual total cost (ATC) and find the optimal sizes. The balance between the high costs of renewable electricity generation and the setting up a renewable system is achieved by applying the renewable energy fraction (fRE). In the second part, a photovoltaic‐wind‐fuel cell (PWF) hybrid system is designed and optimized in the same approach. A novel techno‐economic‐environmental comparison is performed to select the battery bank or hydrogen system as energy storage. Considering the environmental penalties, the effect of the environmental cost on the optimal sizes of components and annual total costs is investigated. Owing to the low tariff of the grid electricity as well as the high cost of the renewable systems, results confirms that the use of renewable systems is not cost‐effective. Also, if the authorities and householders intend to implement a storage system to store electricity, battery bank is less expensive than fuel cell system. To apply scenarios to increase renewable energy penetration, a comprehensive sensitivity analysis on the ATCs and optimal sizes is conducted technically and economically. Highlights Designing, optimizing, and comparing of PWB and PWF systems are investigated GA in MATLAB is implemented to determine optimal sizes and minimum ATCs A comprehensive sensitivity analysis is performed on the ATCs and optimal sizes The installing of the hybrid systems with the current tariffs is not cost effective Superiority of the battery bank over the hydrogen system in residential storage

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“…The state of the charge (SOC) of the battery bank, which is averaging 50%, defines the scenarios of the power management of the system. Kiehbadroudinezhad et al, 24 focused on modeling, designing, and optimizing a power generation system for supplying electricity to a reverse osmosis seawater desalination plant. The optimization was developed under the division genetic algorithm.…”

mentioning

confidence: 99%

Smart system management and techno‐environmental optimal sizing of a desalination plant powered by renewables with energy storage

Affi

Cherif

2021

Int J Energy Res

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The present study assessed the performances of a brackish water reverse osmosis desalination unit powered by (PV/Wind/Battery) system with hydraulic storage. The hybrid renewable energy system and the reverse osmosis desalination plant were modeled for a specific site in southern Tunisia using real meteorological and water consumption data for 1 year. A total of 8760 simulations were treated under a dynamic simulator coupled to a smart energy management strategy to reach the hourly water load demand. Although the recognized reliability of the hybrid renewable sources compared to one-power source, their design may still critical. Therefore, in this paper, the authors suggest applying the methodology of optimizing the presented unit using an integrated systemic meta-modeling which is built through the design of experiments tool and defined by an equation involving the decision variables of the optimization as its parameters. This equation outlines the objects of the optimum sizing. The selected optimal design of the proposed unit is predicted by the genetic algorithm NSGA-II based on minimizing two objectives in conflict: Embodied energy and hydraulic loss of power supply. The CPU time of the NSGA-II optimization via the meta-model reached only 15 minutes of treatment (with one day to build the design of experiments) compared to the classic optimization via dynamic simulation. The results of the optimum sizing show that it is possible to consider a low water shortage rate (0% ⩽ LPSP-H < 5%) for a marginal variation of embodied energy rate which does not exceed (7%). K E Y W O R D S design of experiments, hybrid renewable energy, meta-model, multi-objective genetic algorithm, optimization, water desalination, water-energy nexus 1 | INTRODUCTION Climate change affects many sides of the environment and severely emphasizes the issue of water scarcity. Indeed, global warming attacks the planet's humidity and the water's underground basins, and if the water management strategies don't change on the behalf of "rescuing water sources", by 2050 the number of environmental refugees will be doubled five times than the actual number. Currently, the world population is about 7.6 billion, reaching 8.6 billion by 2030 and exceeding 9 billion in 2050 whom will be in increasing need of freshwater. 1,2 Desalting, as it was known during World War II while the freshwater supplies were restricted, is likely to be the

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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

Cited by 30 publications

References 54 publications

Optimization of Wind Energy Battery Storage Microgrid by Division Algorithm Considering Cumulative Exergy Demand for Power-Water Cogeneration

Optimization of Wind Energy Battery Storage Microgrid by Division Algorithm Considering Cumulative Exergy Demand for Power-Water Cogeneration

Dynamic optimization of solar‐wind hybrid system connected to electrical battery or hydrogen as an energy storage system

Smart system management and techno‐environmental optimal sizing of a desalination plant powered by renewables with energy storage

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