Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2

Bouchekara, Houssem R. E. H.; Sha’aban, Yusuf A.; Shahriar, Mohammad Shoaib; Ramli, Makbul A.M.; Mas’ud, Abdullahi Abubakar

doi:10.3390/su15032525

Cited by 3 publications

(3 citation statements)

References 74 publications

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“…Sustainability has not been consistently applied to all aspects of wind energy. Given the significant capital investment and large-scale production involved in Wind Turbine Generators (WTG) [10], the scientific community has focused its efforts on applying sustainability criteria to the design and location of wind farms [11][12][13][14], as well as the positioning and design of WTGs [15][16][17]. Over the past decades, sustainability has conventionally been evaluated in terms of the environmental impacts derived from human actions.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Design of Onshore Wind Turbine Foundations

Masanet,

Navarro,

Collado

et al. 2024

J. Phys.: Conf. Ser.

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In recent times, wind power has emerged as a prominent contributor to electricity production. Minimizing the costs and maximizing sustainability of wind energy is required to improve its competitiveness against other non-renewable energy sources. This communication offers a practical approach to assess the sustainability of wind turbine generator foundations from a 3-dimensional holistic point of view. Specifically, the main goal of this study is to analyse the life cycle impacts of one shallow foundation design comparing three different concrete alternatives: conventional concrete, concrete with 66-80% of blast furnace slags and concrete with 20% fly ash, and then to apply a Multi-Criteria Decision-Making model based on TOPSIS method to evaluate and compare the resulting sustainability of each alternative considered. The study results in a methodology for quantifying sustainability rather than simply qualifying it. Therefore, this methodology can be employed for design optimization, such as geometry and materials, with a sustainable perspective in mind. Specifically in this study, concrete with blast furnace slags emerges as the top-ranked sustainable alternative, followed by conventional concrete in second place, and fly ash option in third position.

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Section: Introductionmentioning

confidence: 99%

Sustainable Design of Onshore Wind Turbine Foundations

Masanet,

Navarro,

Collado

et al. 2024

J. Phys.: Conf. Ser.

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“…Power plant scheduling and unit commitment cannot be efficiently and reliably managed without accurate load forecasting [ 7 ]. Additionally, accurate load forecasting can be used for renewable energy sizing [ 8 ] and layout [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Predictive models for short-term load forecasting in the UK’s electrical grid

Sha’aban

2024

PLoS ONE

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There are global efforts to deploy Electric Vehicles (EVs) because of the role they promise to play in energy transition. These efforts underscore the e-mobility paradigm, representing an interplay between renewable energy resources, smart technologies, and networked transportation. However, there are concerns that these initiatives could burden the electricity grid due to increased demand. Hence, the need for accurate short-term load forecasting is pivotal for the efficient planning, operation, and control of the grid and associated power systems. This study presents robust models for forecasting half-hourly and hourly loads in the UK’s power system. The work leverages machine learning techniques such as Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR) to develop robust prediction models using the net imports dataset from 2010 to 2020. The models were evaluated based on metrics like Root Mean Square Error (RMSE), Mean Absolute Prediction Error (MAPE), Mean Absolute Deviation (MAD), and the Correlation of Determination (R2). For half-hourly forecasts, SVR performed best with an R-value of 99.85%, followed closely by GPR and ANN. But, for hourly forecasts, ANN led with an R-value of 99.71%. The findings affirm the reliability and precision of machine learning methods in short-term load forecasting, particularly highlighting the superior accuracy of the SVR model for half-hourly forecasts and the ANN model for hourly forecasts.

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“…In recent years, climate change and global warming have emerged as major environmental and economic concerns, with obvious implications for the upward trend in energy prices [9][10][11], especially using microwave devices or special plants. Concerns about climate change, global warming, and energy supply have grown in tandem with the world's excessive energy consumption [12][13][14][15][16][17][18][19][20][21]. Bringing electricity to far places via transmission lines is a huge hassle and a waste of money.…”

Section: Introductionmentioning

confidence: 99%

Using genetic algorithm for optimal sizing of stand-alone hybrid energy system

Abed,

ALRubeei

et al. 2024

Heritage and Sustainable Development

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When planning a hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources—those that rely on fossil fuels—the primary considerations are the total cost of the system and the CO? emissions. In this paper, we will investigate the typical hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources involving a detailed simulation process that may require specific inputs, models, and data. Then, we employed dual optimization methods: genetic algorithm (GA) and particle swarm optimization (PSO). The consequences of GA and PSO execution in the bus timetabling problem depict that the GA algorithm is better at finding the optimal solution in terms of accuracy and iteration. Additionally, the GA algorithm is also superior to the straightforwardness of the techniques used. So, in this work, we employed a Genetic Algorithm Optimization (GA)–-based optimal sizing technique for HES configurations that include sustainability wind turbines (WTs), battery storage (BS), and diesel generators (DGs). HES improved power delivery to a rural community in the Wasit Province, Iraq, situated at 46° - 36° and 32° - 31° in the country's southeastern central region. Throughout the project's 25-year lifespan, the optimization primarily aims to minimize the total cost (CT) and total CO? emissions (ECO2T). The outcomes demonstrate that the GA algorithm may, with continuous electricity supply, minimize the objectives while meeting the load demand.

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Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2

Cited by 3 publications

References 74 publications

Sustainable Design of Onshore Wind Turbine Foundations

Sustainable Design of Onshore Wind Turbine Foundations

Predictive models for short-term load forecasting in the UK’s electrical grid

Using genetic algorithm for optimal sizing of stand-alone hybrid energy system

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