Global estimations of wind energy potential considering seasonal air density changes

Ulazia, Alain; Sáenz, Jon; Ibarra-Berastegi, Gabriel; González-Rojí, Santos J.; Carreno-Madinabeitia, Sheila

doi:10.1016/j.energy.2019.115938

Cited by 98 publications

(39 citation statements)

References 37 publications

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“…Ignoring this can lead to wrong estimations of wind-energy potential, as also pointed out in other studies [33]. In the particular case of the Lebanese offshore WPD 178 , errors moved from −3% in winter to +4% in summer.…”

Section: Discussionmentioning

confidence: 53%

“…In recent publications [13,32,33], the authors developed a technique to seasonally estimate the influence of instantaneous air density changes on the capacity factor of a given turbine. Floors et al [34] emphasized, like us, the importance of air density, and also used ERA5 to study its effects.…”

Section: Introductionmentioning

confidence: 99%

“…Along these lines, ERA5 is the most valuable tool that can be used not only to estimate Lebanese offshore-wind-energy potential, but also for any other geographical region, either offshore or onshore. These aspects explain its widespread use for wind power density estimations [12,33].…”

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confidence: 98%

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Evaluation of Lebanon’s Offshore-Wind-Energy Potential

Ibarra-Berastegi

Ulazia

Sáenz

et al. 2019

JMSE

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The only regional evaluation of Lebanese wind-energy potential (National Wind Atlas) dates back to 2011 and was carried out by a United Nations agency. In this work, data from the most recent reanalysis (ERA5) developed at the European Center for Medium Range Weather Forecast (ECMWF), corresponding to the 2010–2017 period, were used to evaluate Lebanese offshore-wind-energy potential. In the present study, wind power density associated to a SIEMENS 154/6 turbine was calculated with a horizontal resolution of 31 km and 1 hour time steps. This work incorporated the impact of air density changes into the calculations due to the seasonal evolution of pressure, temperature, and humidity. Observed average offshore air density ρ 0 was 1.19 kg / m 3 for the 2010–2017 period, but if instead of ρ 0 , hourly ρ values were used, seasonal oscillations of wind power density ( W P D ) represented differences in percentage terms ranging from −4% in summer to +3% in winter. ERA5 provides hourly wind, temperature, pressure, and dew-point temperature values that allowed us to calculate the hourly evolution of air density during this period and could also be used to accurately evaluate wind power density off the Lebanese coast. There was a significant gradient in wind power density along the shore, with the northern coastal area exhibiting the highest potential and reaching winter values of around 400 W / m 2 . Finally, this study suggests that the initial results provided by the National Wind Atlas overestimated the true offshore-wind-energy potential, thus highlighting the suitability of ERA5 as an accurate tool for similar tasks globally.

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Section: Discussionmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Lebanon’s Offshore-Wind-Energy Potential

Ibarra-Berastegi

Ulazia

Sáenz

et al. 2019

JMSE

Self Cite

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“…However, when the WF system is equipped with a BESS. Objective function (3) is changed to (9), where the output power of the WF system is the sum of the output power of all WTGs and BESS, as given in (10). The main purpose of BESS is to reduce the output power fluctuations and keep the large power output of the WF system.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The minimization of the power deviation for the set-points of WTGs helps the output power of WTGs much smoother and therefore avoids unnecessary internal power fluctuations. Finally, different case studies are also analyzed to show the effectiveness of the proposed method.Energies 2019, 12, 4242 2 of 18 studies have suggested different approaches to increase the output power and service reliability of the WF system, such as pitch misalignment detection [7], identification of defective anemometers [8], estimation of wind energy production considering varying air density [9], optimization of the position of WTGs in the WF system [10][11][12], optimization of the operation of WTGs to reduce power loss [13,14], or reduction of wake loss in WF systems [15,16]. The authors in [11] have proposed a new WF layout optimization model for maximizing the equivalent power of a WF system using particle swarm optimization algorithm.…”

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confidence: 99%

Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System

et al. 2019

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In this paper, a multi-objective optimization method is proposed to determine trade-off between conflicting operation objectives of wind farm (WF) systems, i.e., maximizing the output power and minimizing the output power fluctuation of the WF system. A detailed analysis of the effects of different objective’s weight values and battery size on the operation of the WF system is also carried out. This helps the WF operator to decide on an optimal operation point for the whole system to increase its profit and improve output power quality. In order to find out the optimal solution, a two-stage optimization is also developed to determine the optimal output power of the entire system as well as the optimal set-points of wind turbine generators (WTGs). In stage 1, the WF operator performs multi-objective optimization to determine the optimal output power of the WF system based on the relevant information from WTGs’ and battery’s controllers. In stage 2, the WF operator performs optimization to determine the optimal set-points of WTGs for minimizing the power deviation and fulfilling the required output power from the previous stage. The minimization of the power deviation for the set-points of WTGs helps the output power of WTGs much smoother and therefore avoids unnecessary internal power fluctuations. Finally, different case studies are also analyzed to show the effectiveness of the proposed method.

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High Altitude Turbine (HAT): The Future of Wind Energy

2022

Wave, Wind and Current Power Generation

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Global estimations of wind energy potential considering seasonal air density changes

Cited by 98 publications

References 37 publications

Evaluation of Lebanon’s Offshore-Wind-Energy Potential

Evaluation of Lebanon’s Offshore-Wind-Energy Potential

Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System

High Altitude Turbine (HAT): The Future of Wind Energy

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