A comprehensive evaluation of the wind resource characteristics to investigate the short term penetration of regional wind power based on different probability statistical methods

Nedaei, Mojtaba; Assareh, Ehsanolah; Walsh, Philip R.

doi:10.1016/j.renene.2018.05.077

Cited by 31 publications

(24 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a more comprehensive study, Nedaei et al (2018) fitted 46 different PDF and suggested that Wakeby performs better than other distribution functions [20]. They did not mention the method of estimating parameters.…”

Section: Review Of the Literaturementioning

confidence: 99%

A Comparative Study of Different Wind Speed Distribution Models for Accurate Evaluation of Onshore Wind Energy Potential: A Case Study on the Southern Coasts of Iran

Filom¹,

Radfar²,

Panahi³

2020

Preprint

View full text Add to dashboard Cite

Wind power output is highly dependent on the wind speed at the selected site, therefore wind-speed distribution modeling is the most important step in the assessment of wind energy potential. This study aims at accurate evaluation of onshore wind energy potential in seven coastal cities in the south of Iran. Six Probability Distribution Functions (PDFs) were examined over representative stations. It has been deduced that the Weibull function, which was the most used PDF in similar studies, was only applicable to one station. Here, Gamma offered the best fit for three stations and for the other ones, Generalized Extreme Value (GEV) performed better. Considering the ranking of six examined PDFs and the simplicity of Gamma, it was identified as the effective function in the southern coasts of Iran bearing in mind the geographic distribution of stations. Besides, six turbine power curve functions were contributed to investigate the capacity factor. That was very important, as using only one function could cause under- or over-estimation. Then, stations were classified based on the National Renewable Energy Laboratory system. Last but not least, examining a range of wind turbines enabled scholars to extend this study into the practice and prioritize development of stations considering budget limits.

show abstract

Section: Review Of the Literaturementioning

confidence: 99%

A Comparative Study of Different Wind Speed Distribution Models for Accurate Evaluation of Onshore Wind Energy Potential: A Case Study on the Southern Coasts of Iran

Filom¹,

Radfar²,

Panahi³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Diurnal statistics are also widely calculated if a target region is close to water mass or mountain (Weisser and Foxon, ; Ko et al ., ; Belu and Koracin, ). When wind direction is investigated, wind rose is frequently used (Kim and Kim, ; Ramadan, ; Nedaei et al ., ).…”

Section: Introductionmentioning

confidence: 97%

Physical mechanisms of surface wind variability at Incheon Airport

Park

Kim

2019

Intl Journal of Climatology

View full text Add to dashboard Cite

The mechanisms of surface wind variability observed by Aerodrome Meteorological Observation System at Incheon International Airport (IIA), located near the western coast of the Korean Peninsula, are investigated using cyclostationary empirical orthogonal function (CSEOF) analysis. Despite the complex nature of surface wind, as various factors contribute to its variance, distinct features of surface wind variability are separated reasonably into CSEOF modes. The physical interpretation of each mode using the ERA‐Interim reanalysis data is accomplished through regression analysis in CSEOF space. In essence, three atmospheric features dominate the surface wind variability: East Asian monsoon, land–sea breeze, and baroclinic waves. East Asian monsoon, the first mode, explains ~12% of the total variance of the surface wind at IIA. The annual reversal of the monsoon circulation due to the thermal contrast between the Asian continent and the western Pacific is captured as the single most dominant source of surface wind variability at IIA. The second CSEOF mode reveals that zonal circulation induced by land–sea breeze dominates the diurnal variation of the surface wind at IIA, and explains ~8% of the total variance. The largest fraction of variability is associated with baroclinic waves, which is separated into many CSEOF modes due to their irregular phase and wavelength, and the locations of vortex centre. Modes 3–10 are all associated with baroclinic waves and they together explain ~42% of the total variance. Baroclinic waves are amplified as they move over the ocean due to strong turbulent flux from the ocean surface induced by the associated upper tropospheric vortices.

show abstract

“…Given the range of ER resources, the vast majority of countries focus on the development and optimization of solar and wind technology [1]. For the particular case of wind energy (WE), a measure of energy flow per unit area [2] and, according to [3], the interest in developing wind farms, focuses on methods to assess resource potential wind and how to increase the efficiency of wind turbines (and with it wind farms), so that this leads to greater profitability of the project, by reducing operation and capital costs, taking into account their generation capacity, annual growth of installed capacity, efficiency and long-term competitive cost [4], [5], [6], [7], [8], [9]. Proof of this is how the WE covers 4% of the generation of electricity in the world, which for 2017 was 539 GW [9] and an estimate of installed wind capacity close to 10,800,000 MW, which could cover all global electricity demand [10], ideal for large cities as for remote areas [11].…”

Section: Introductionmentioning

confidence: 99%

“…Based on the research carried out by [13], wind energy forecasts are the most uncertain due to spatial and temporal variability [2] and predictability of the wind field. To address the randomness of the resource, research has been carried out on the analysis and application of probability distribution functions (PDF) that allow characterizing the wind resource [1], [3], [11], [14], [15], in order to study the feasibility and development of wind generation projects [3], [8], [9], [16]; and, therefore, to reduce the probability that the energy market is characterized by volatile and irregular prices between supply and demand [13]. Of the PDF, the Weibull distribution (of two parameters) has been the most used [1], [2], [4], [6], [7], [8], [10], [14], for the estimation (characterization) of the resource and the production of wind power, due to its simplicity and flexibility in the analysis of a wide range of data, however, according to the cited by [16], this distribution does not It is recommended for calm wind regimes (low wind speeds).…”

Section: Introductionmentioning

confidence: 99%

Statistical methodologies for wind resource analysis, case: Catatumbo region - Norte de Santander, Colombia

Lopez-Rodriguez¹,

Serrano-Rico²,

Florez-Serrano³

2020

REPQJ

View full text Add to dashboard Cite

In this article, we analyze 4 probability distribution functions to characterize the wind resource at northeast zone of Colombia, based on data of taken from existing meteorological stations in three towns in the zone of Norte de Santander, Colombia. The data is sampled each 10 minutes, obtaining average values per hour every day, for each month of the year. The probability distributions studied are Weibull, Rayleigh, Log-Logistics and Gamma. In the case of the Weibull distribution, 5 methods were used to estimate two principal parameters of the distribution; shape and scale. For other distributions only was used one. The distributions for each data set were determined, exposing the best fit for each zone according to the 3 performance indices considered (RMSE, , R2). The main result obtained is the identification of the probability distribution that presents a better adjustment to the existing wind speed data in the zone where the meteorological stations are located. Specifically, it was identified that the probability distributions that best fit the data, for each month of the year, are Log-Logistics and Gamma. Further, the identification of the probability distribution that best fits to the wind speed data, allows to characterize with a greater degree of accuracy the wind potential of a zone.

show abstract

A comprehensive evaluation of the wind resource characteristics to investigate the short term penetration of regional wind power based on different probability statistical methods

Cited by 31 publications

References 33 publications

A Comparative Study of Different Wind Speed Distribution Models for Accurate Evaluation of Onshore Wind Energy Potential: A Case Study on the Southern Coasts of Iran

A Comparative Study of Different Wind Speed Distribution Models for Accurate Evaluation of Onshore Wind Energy Potential: A Case Study on the Southern Coasts of Iran

Physical mechanisms of surface wind variability at Incheon Airport

Statistical methodologies for wind resource analysis, case: Catatumbo region - Norte de Santander, Colombia

Contact Info

Product

Resources

About