Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy

Gualtieri, Giovanni; Secci, Sauro

doi:10.1016/j.renene.2013.07.003

Cited by 43 publications

(28 citation statements)

References 26 publications

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“…According to this standard, the annual energy production can be estimated by assuming 100% availability of the wind turbines and by using different reference wind speed frequency distributions, such as the Weibull and the Rayleigh density functions, where the latter is a particular case of a Weibull distribution with a shape factor of 2. Weibull distributions have been commonly proposed in the literature to model wind resources, taking into account that 10 min or hourly averaged wind speeds throughout a year are the result of a considerable degree of random variation [14][15][16]20,21]. Weibull distributions have been thus applied to characterize PDF for wind speeds, mainly when wind speed data are restricted to a specific geographical location with a unique meteorological tower (also known as a met mast).…”

Section: Characterization Of Probability Density Function For Wind Pomentioning

confidence: 99%

Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures

et al. 2016

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Abstract:The Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.

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Section: Characterization Of Probability Density Function For Wind Pomentioning

confidence: 99%

Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures

et al. 2016

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“…Where, is the velocity of the wind at a height , is the reference speed known to a reference height known and is the length of roughness 9 . Table 1 shows the roughness factor from the description of the terrain, where "x" represents the characteristic distance between obstacles and "H" is the height of the main obstacle 10 .…”

Section: ( ) ( )mentioning

confidence: 99%

Wind Potential Assessment in the Department of Meta Colombia

Solano¹,

Campo²,

Ballesteros³

2018

IJCTR

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In this research, the assessment of eolic potential in the plane zone of department of Meta-Colombia was carried out, and economic prefactibility analysis was done about the electricity system from wind velocity data at a reference height (z =10m). Five different types of power turbines between 330 and 3000 kW were studied in order to analyze their technical and economic feasibility. The evaluated zone presented an energy potential of 4.9075 GWh/year, using a 3000 kW (Vestas V-112) turbine at a height of 90 m. The production cost of kWh under this condition was approximate 123.67$/Kw-h.

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“…The Weibull distribution has been used in our paper to model the uncertainty of wind speed [26], [27]. Note that other more appropriate approaches exist in literature for considering wind uncertainty [28]. However, our simpified wind uncertainty modeling is used in order to focus on the mathematical formulation of transmission and generation expansion coordination which is the primary goal of this paper.…”

Section: The Constraints Of Wind Power Unitsmentioning

confidence: 99%

Sequential Coordination of Transmission Expansion Planning With Strategic Generation Investments

Tohidi

Hesamzadeh

Regairaz

2017

IEEE Trans. Power Syst.

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Abstract-This paper proposes mathematical models for sequential coordination of transmission expansion planning with strategic generation investments. The proactive and reactive coordinations are modeled and studied. The interaction between transmission company (Transco) and strategic generation companies (Gencos) is modeled using the sequential-move game. This is while the interaction between the strategic Gencos is modeled as a simultaneous-move game. In the proactive coordination, the Transco expands its future transmission capacities taking into account the strategic investments by Gencos. In the reactive coordination, strategic Gencos move first and expand their future generation capacities and then Transco expands the transmission capacity. The proactive coordination is modeled as a mixedinteger bilevel linear program (MIBLP) and the reactive coordination is modeled as a mixed-integer linear program (MILP). The MIBLP has binary variables in both upper and lower levels. The Moore-Bard algorithm is parallelized and used to solve the MIBLP. The mathematical models and the parallelized MooreBard algorithm are tested on 3-bus and 6-bus example systems and the modified IEEE-RTS96. Also, the IEEE 118-bus test system is studied using a heuristic version of the Moore-Bard algorithm.

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Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy

Cited by 43 publications

References 26 publications

Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures

Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures

Wind Potential Assessment in the Department of Meta Colombia

Sequential Coordination of Transmission Expansion Planning With Strategic Generation Investments

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