Wind Energy Resource Assessment in Ngaoundere Locality

Kazet, Myrin Y.; Mouangue, Ruben; Kuitche, Alexis; Ndjaka, J.M.B.

doi:10.1016/j.egypro.2016.07.152

Cited by 14 publications

(14 citation statements)

References 11 publications

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“…WRA mostly deals with estimates (often point [20][21][22] ) of wind power or annual energy production (AEP) cubically proportional to the wind speed. MC in WRA is usually used for windfarm layout optimization 23,24 or profitability UA 10,[25][26][27][28][29] often with no SA accompanying.…”

Section: Introductionmentioning

confidence: 99%

Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE

Tsvetkova¹,

Ouarda²

2019

Journal of Renewable and Sustainable Energy

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The present paper bridges mathematical modeling and wind resource assessment (WRA). Sensitivity analysis (SA) links portions of output variance to the variance in each input variable. Global SA (GSA) explores inputs globally. One-at-a-time SA is dominating in WRA, while GSA is often overlooked. Compared to traditional methods, GSA offers potential improvement by the means of the quasi-Monte Carlo (QMC) technique with its elaborate sampling designs enabling faster convergence. The main novelty of this work is twofold: the use of QMC in WRA and Sobol method as a variance-based GSA method in WRA. This paper is among a few using GSA in WRA. Two case studies were conducted. One shows that QMC with sampling based on Sobol sampling outperforms Latin hypercube sampling and pseudorandom sampling. It also shows that in terms of accuracy of results, the brute-force calculation of Sobol sensitivity indices (SI) should be used whenever the model allows it; otherwise, SI can be estimated. Another case study demonstrates a valid GSA study for WRA at Masdar City, United Arab Emirates. The results suggest that the influence of the variance in Weibull parameters on annual energy production (AEP) might be overestimated, while found to be responsible for 2% of AEP uncertainty, and the influence of the variance in air density might be overlooked, while found to account for 94%. WRA studies would benefit greatly from using the QMC and Sobol method. The Sobol method is a universal GSA method, providing valid results for nonlinear models typical for WRA, and QMC provides global scalability, model independence, and flexibility in uncertainty quantification.

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Section: Introductionmentioning

confidence: 99%

Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE

Tsvetkova¹,

Ouarda²

2019

Journal of Renewable and Sustainable Energy

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“…The study established that wind energy at mountain's ridges, hilltops and highlands could be utilized to improve access to cost-effective low carbon electricity. Other studies [27,28] assessed as well wind energy potential in Ngaoundere and Bamenda. Based on published papers, the vast majority these studies were meant to determine or assess wind power potential, simulate WECS output, using various mathematical models.…”

Section: Wind Energymentioning

confidence: 99%

Status of renewable energy in Cameroon

Kidmo

Deli

Bogno

2021

Renew. Energy Environ. Sustain.

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Globally, the continuous increase of energy consumption coupled with the depletion of the limited fossil fuel sources and their negative impacts on the environment, has shifted focus towards renewables for a sustainable development. For the last fifteen years, the world has enjoyed renewables generation capacity increases in a double-digit Terawatt-hours range. Although renewables consumption is fast developing in Asia Pacific, Europe and North America, significant coordination efforts are required among stakeholders in sub-Saharan Africans countries such as Cameroon. In 2018, the total final energy consumption in Cameroon was 7.41 Mtoe, 74.22% of which was from biomass, 18.48% from fossil fuels and 7.30% from electricity. Furthermore, 6977 GWh of electricity was produced, 78.29% of which from the major electricity operator (ENEO S.A. Cameroon) and 21.71% from independent producers (GLOBELEQ, ALTAAQA Sinohydro China and AGGREKO). More than three quarter of electricity produced were consumed by industry (57.04%) and residential (20.74%) sectors. The country's installed electricity generation capacity rose to 1402 MW, 56.15% of which was from hydropower, 43.84% from fossil fuels (17.55% from natural gas and 26.29% from oil) and 0.01% from solar photovoltaic. The promotion of renewable energy is an important part of Cameroon's plan to increase energy security and provide job opportunities to the country. Yet, the lack of proactive and long-term renewable energy policy and laws, in addition to less attention paid to renewable energy training and research, financing mechanisms, and unaffordable costs of renewable energy technologies to the poor population are amongst present issues hindering the development of renewable energy in the country. Hence, this paper aims to highlight the present status of renewable energy exploitation and development in Cameroon.

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“…Therefore, several wind power density determination models have been developed such as the Rayleigh model, Normal, Log Normal, Truncated Normal, Logistic, Log Logistic, Generalised Extreme Value, Nakagami, Inverse Gaussian, Inverse Weibull and Weibull as presented in Table 1 (Akgül et al, 2016;Alavi et al, 2016;Jung & Schindler, 2017;Katinas et al, 2018;Masseran, 2018;Mohammadi et al, 2017;Wang et al, 2016). Among them, Weibull distribution has been found as one of the widely appropriate and accepted approach to statistically assess wind behaviour and potential in any site (Akdağ & Dinler, 2009;Aristide et al, 2015;Chang, 2011;Costa Rocha et al, 2012;Justus & Mikhail, 1976;Kaoga et al, 2014;Kazet et al, 2013;Mohammadi et al, 2016;Mohammadi et al, 2017;Nsouandélé et al, 2016;Ouahabi et al, 2020;Tchinda et al, 2000;Youm et al, 2005). Although the three-parameter Weibull distribution may give a more precise result when there is a high frequency of null winds speeds, the two-parameters Weibull distribution remains the most appropriate model and the most widely used in the wind industry sector provided the more accurate parameters are given (Justus & Mikhail, 1976;Kaoga et al, 2014;Kumar Pandey et al, 2020;Ulrich et al, 2018;Wais, 2017;Zhu, 2020).…”

Section: Introductionmentioning

confidence: 99%

Influence of Weibull parameters on the estimation of wind energy potential

Aziz

Tsuanyo

Nsouandele

et al. 2023

Sustainable Energy res.

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Wind potential estimation is generally evaluated using two-parameter (k, c) Weibull distribution. Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Relative Error (RE) are computed in order to comparatively analyse fourteen methods of determining Weibull parameters. They are the Graphical Method, the Standard Deviation Method, the Empirical Method of Justus, the Empirical Method of Lysen, the Energy Pattern Factor Method, the Maximum Likelihood Method, the Modified Maximum Likelihood Method, the Alternative Maximum Likelihood Method, the Least Square Method, the Weighted Least squares Method, the Curve Fitting Method, the Wind Variability Method, the Moroccan Method and the Median and Quartile Method. These methods have been applied on three different windy sites (slightly, moderately and very windy sites) with hourly wind data over a period of 10 years (2005–2014), measured at 10 m height. As a result, compared to the other methods, Energy Pattern Factor method is the more suitable method applicable to assess the Weibull parameters for all wind speeds. However, the values obtained from RMSE, R2 and RE tests revealed that the WVM and MoroM methods are not suitable while all other methods are acceptable for the estimation of k and c. parameters. The determination of the wind power density and the gap between the predicted standard deviation by each method and the measured standard deviation for all the sites highlighted the relevance of EPFM method and the others methods. Moreover, this work reveals that the Weibull shape factor k decrease with height above ground level, while that of the scale factor c increase with height.

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Wind Energy Resource Assessment in Ngaoundere Locality

Cited by 14 publications

References 11 publications

Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE

Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE

Status of renewable energy in Cameroon

Influence of Weibull parameters on the estimation of wind energy potential

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