Spatial assessment of wind power potential at global scale. A geographical approach

Bandoc, Georgeta; Prăvălie, Remus; Patriche, Cristian Valeriu; Degeratu, Mircea

doi:10.1016/j.jclepro.2018.07.288

Cited by 55 publications

(53 citation statements)

References 96 publications

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“…We applied Weibull distribution to estimate the most dominant wind speed in the study area. It was found that 1.85 m/s is the most sustained wind speed in the study area, and this falls under the poor wind category [13]; therefore, it is best to used a standalone turbine, which mostly suits remote locations rather than grid connection [47]. Therefore, we consider it to be suitable for agricultural utilization and to support the domestic needs of farmers with no access to electricity from the grid lines [48].…”

Section: Discussionmentioning

confidence: 99%

“…The whole of the country relies on a thermal system as the only source of energy in the country, with a total coverage of only 1%, and mostly within the capital city Juba, in which the area of this study is located [12]. Based on a report published by the South Sudan National Bureau of Statistics, the country's electric power per capita is between 1 and 10 kWh [13]. Due to the current energy insecurity in South Sudan, the government moved to study alternative sources of energy, and currently, the most focus is on hydropower generation due to the availability of remarkable water bodies across the country.…”

Section: Energy In South Sudanmentioning

confidence: 99%

“…Due to the current energy insecurity in South Sudan, the government moved to study alternative sources of energy, and currently, the most focus is on hydropower generation due to the availability of remarkable water bodies across the country. Significant efforts are already being made to develop the energy sector in the country, e.g., Sustainability 2020, 12, 1483 3 of 22 the assessment of potential locations for constructing sizeable hydropower stations, but all are still at the feasibility study level [13]. At the moment, about five locations have been identified and the constructions are expected to end from 2026 to 2040, depending on resource availability.…”

Section: Energy In South Sudanmentioning

confidence: 99%

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Estimation of Small Onshore Wind Power Development for Poverty Reduction in Jubek State, South Sudan, Africa

et al. 2020

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Energy security is one of the challenging issues hindering developmental progress in developing countries. Wind power as a renewable energy source can play a significant role in poverty reduction if adequate information is provided. In this study, multi-approach technics were applied for a better understanding of the wind energy potential in Jubek State, South Sudan. Geographic Information System (GIS), remote sensing, and mathematical equations were applied in identifying suitable locations, potential power per unit area, wind farm layout, design of appropriate turbine size, and utilization of wind energy in both agricultural and domestic sectors. Wind speed, land use land cover, and digital elevation maps of the study area were processed in ArcGIS, MATLAB (Weibull distribution), and Minitab software. The results show that 17,331.4 km 2 (94.64%) of the study area is appropriate for wind power generation, with wind density of about 3.65 W/m 2 and installation capacity about 19,757.79 MW, resulting in an annual energy production of about 7269.29 GWh. With the proposed wind turbine, one ton of various crops and animal products require 1-4 and 2-20 turbines, respectively. Therefore, the step-by-step procedures followed in this study will contribute to poverty reduction through improving agricultural productivity and food quality.Sustainability 2020, 12, 1483 2 of 22 indicated that 3% (9 EJ) of the global energy was spent on agriculture, and up to 20% of the greenhouse gas emissions worldwide. It was generally observed that agricultural yield improvements result in positive energy ratios; this can be justified by the gained in using fossil energy as an input for agricultural activities-the harvested crops contain more energy than the energy consumed in crop production [5].A study was conducted by Cranfield University in the UK in order to review energy requirements for agricultural production from the field to the farm gate. They used the life cycle analysis (LCA) approach, which is a recommended method for estimating the cradle to grave, a product or process of environmental impacts. Selected materials were field crops (maize, wheat, sugarcane, beans, oilseed rape, and potatoes) and three animal products in the form of meat (lamb, poultry, beef, and pork), eggs, and milk [6][7][8]. Besides this, [9] reported a detailed work on the transport cost from farm to plate or distance from the production area to consumption zone. Such work is essential for understanding wasted energy and efficiency at an extent which is beyond the farm boundaries.In the LCA, in order to capture all embodied energies, it is necessary to consider all inputs used in the production of a given product. Therefore, the authors included energy used in fertilizer, buildings, machinery, and pesticides together with diesel and other forms of fuel as direct energy. For estimating inputs for animal production, they included energy for producing feed crops. Breeding overhead was also included in their work. Therefore, the final values presented in Table 1...

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Section: Discussionmentioning

confidence: 99%

Section: Energy In South Sudanmentioning

confidence: 99%

Section: Energy In South Sudanmentioning

confidence: 99%

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Estimation of Small Onshore Wind Power Development for Poverty Reduction in Jubek State, South Sudan, Africa

et al. 2020

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“…Because the GM (1,1) model only uses single time series data and it cannot reflect the interaction between multiple variables, some scholars have proposed a MGM (1, n), where n is the number of variables. The MGM (1, n) is not a simple combination of GM (1,1), but a generalization from the univariate GM (1,1) to the multivariate case by solving n differential equations simultaneously [27].…”

Section: Multivariate Grey Modelmentioning

confidence: 99%

“…The problem of energy shortage and environmental degradation in the world is becoming more and more serious. Wind energy as environmentally friendly and renewable energy has attracted increasing attention [1]. The cumulative installed capacity of global wind power has also steadily increased in recent years [2].…”

Section: Introductionmentioning

confidence: 99%

Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Xiao

Hua

2020

Algorithms

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Due to the harsh working environment of wind turbines, various types of faults are prone to occur during long-term operation. Misalignment faults between the gearbox and the generator are one of the latent common faults for doubly-fed wind turbines. Compared with other faults like gears and bearings, the prediction research of misalignment faults for wind turbines is relatively few. How to accurately predict its developing trend has always been a difficulty. In this paper, a combined forecasting model is proposed for misalignment fault prediction of wind turbines based on vibration and current signals. In the modelling, the improved Multivariate Grey Model (IMGM) is used to predict the deterministic trend and the Least Squares Support Vector Machine (LSSVM) optimized by quantum genetic algorithm (QGA) is adopted to predict the stochastic trend of the fault index separately, and another LSSVM optimized by QGA is used as a non-linear combiner. Multiple information of time-domain, frequency-domain and time-frequency domain of the wind turbine's vibration or current signals are extracted as the input vectors of the combined forecasting model and the kurtosis index is regarded as the output. The simulation results show that the proposed combined model has higher prediction accuracy than the single forecasting models.Algorithms 2020, 13, 56 For the complex non-linear system, a single forecasting model is not enough to obtain ideal prediction results. Therefore, in order to predict the mechanical fault accurately, the combined forecasting model has attracted more and more attention from scholars. For example, in Ref.[18], the improved Grey Model (GM (1,1)) and the Back Propagation (BP) neural network optimized by Genetic Algorithm (GA) were used as the single forecasting models. The minimum sum of error squares was used as the combination principle to assign appropriate weight coefficients to them. The combined forecasting model had a smaller prediction error. Ref. [19] proposed a calculation method of combined weight coefficients for the unequal weight of error. The combined forecasting model was constructed by Multivariate Grey Model (MGM (1, n)) and Extreme Learning Machine (ELM) neural network. The combined forecasting model was more suitable for predicting the trend of the bearing fault. In Ref.[20], according to the minimum variance principle, Support Vector Machine (SVM) and grey model were combined to make up the shortcomings of single forecasting models. In Ref. [21], SVM was used as the combiner of forecasting models. The Kalman filter, BP neural network and SVM model were used as single forecasting models. The prediction errors of the single forecasting models were larger than that of the combined model. In Ref. [22], the BP neural network was used to determine the weight coefficients of each single forecasting model. The combined forecasting model using GM (1,1,θ) optimized by Particle Swarm Optimization (PSO) algorithm and SVM optimized by PSO achieved better prediction accuracy for the short-term load of...

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Potential impact of global warming on wind power production in Central Asia

Turgali

Kopeyeva

Dikhanbayeva

et al. 2021

Env Prog and Sustain Energy

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Global warming is affecting the Earth's surface temperature and consequently the intensity of near-surface airstreams and wind power potential in many regions. Conversely, global wind power installed capacity is continuously growing during the last years. The economic viability and sustainability of centralized wind projects relies heavily on wind resources, efficient technologies, and an attractive and dynamic Feed-in-Tariff (FIT). Therefore, this study assesses the potential effect of global warming on the life-cycle techno-economic viability of future wind power projects in Central Asia, specifically in Kazakhstan. Hence, the Ereymentau Wind Power Plant (WPP) in the Northern region of Kazakhstan was selected as case study. Historic data showed that monthly averaged wind speed and ambient pressure can be treated as statistically stationary, and only the air density drop due to temperature increase of 0.2 C per decade is assumed in the present study. Our results predict a reduction in Net Present Value, Benefit-Cost Ratio, and Internal Rate of Return of 29 million KZT, of 0.0015, and 0.03%, respectively, in the lifetime of Ereymentau WPP. Moreover, an Equity payback increase of 0.4 years and a drop of 987 tCO 2e in GHG emission reductions are projected. Consequently, our results indicate that global warming, despite of being a global problem of significance, would have a negligible impact in ongoing plans to develop the wind potential in Northern regions of Eurasian countries sharing similar conditions to those present in Ereymentau-Kazakhstan.

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Spatial assessment of wind power potential at global scale. A geographical approach

Cited by 55 publications

References 96 publications

Estimation of Small Onshore Wind Power Development for Poverty Reduction in Jubek State, South Sudan, Africa

Estimation of Small Onshore Wind Power Development for Poverty Reduction in Jubek State, South Sudan, Africa

Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Potential impact of global warming on wind power production in Central Asia

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