Renewable Energy System for Small Water Desalination Plant

Vieira, Rodrigo Padilha; Bellar, M.D.; Cunha, José Paulo V. S.; Oliveira, Tiago Roux; Aluisio, A M Bento

doi:10.1109/icrera.2018.8566750

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Furthermore, the quantity of the water, which is available at the end of the process to mitigate the consumers' needs, depends also on the architecture of the storage system (electrochemical and/or hydraulic storage) which varies according to the nature of the available energy. 51,52 Compared to previous works, the capacity of storage of the presented system is enhanced due to the presence of electrochemical and extra hydraulic storage to meet the technical objective of the optimal design. The phases of water shortage are the problem to resolve in this work thanks to the coupled smart energy management between the stocks of materials and the optimal sizing of the system configuration which involves two fields of optimization: technical and environmental.…”

Section: Resultsmentioning

confidence: 98%

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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Section: Resultsmentioning

confidence: 98%

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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“…At this point, the limitations of fossil fuel reserves and their environmental damages have encouraged the people to find the alternative energy resources, such as solar, wind, hydroelectric, geothermal, biomass, hydrogen, wave, etc. [1][2][3]. Accordingly, researchers have been working about the effective and efficient usage of these resources.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Different Machine Learning Algorithms on the Prediction of Wind Turbine Power Generation

Eyecioğlu

Hangun

Kayışlı

et al. 2019

2019 8th International Conference on Renewable Energy Research and Applications (ICRERA)

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Over the past decade, wind energy has gained more attention in the world. However, owing to its indirectness and volatility properties, wind power penetration has increased the difficulty and complexity in dispatching and planning of electric power systems. Therefore, it is needed to make the high-precision wind power prediction in order to balance the electrical power. For this purpose, in this study, the prediction performance of linear regression, k-nearest neighbor regression and decision tree regression algorithms is compared in detail. k-nearest neighbor regression algorithm provides lower coefficient of determination values, while decision tree regression algorithm produces lower mean absolute error values. In addition, the meteorological parameters of wind speed, wind direction, barometric pressure and air temperature are evaluated in terms of their importance on the wind power parameter. The biggest importance factor is achieved by wind speed parameter. In consequence, many useful assessments are made for wind power predictions.

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Water Production System with Peltier Element and Photovoltaic Cell

Nakajima

Masukawa

2019

2019 8th International Conference on Renewable Energy Research and Applications (ICRERA)

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Renewable Energy System for Small Water Desalination Plant

Cited by 3 publications

References 23 publications

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

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

Performance Comparison of Different Machine Learning Algorithms on the Prediction of Wind Turbine Power Generation

Water Production System with Peltier Element and Photovoltaic Cell

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