Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: A case study for Rio de Janeiro State

Tavares, Luiz Filipe de Assis; Shadman, Milad; Assad, Luiz Paulo de Freitas; Estefen, Segen F.

doi:10.1016/j.energy.2021.122767

Cited by 21 publications

(7 citation statements)

References 38 publications

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“…Atmospheric reanalysis demonstrates good performance compared to offshore observations [25,26]. Reanalysis can demonstrate similar or superior performance compared to meso-scale models for offshore applications in regions far from topographical influences [27,28].…”

Section: Introductionmentioning

confidence: 94%

Exploring the Complementarity of Offshore Wind Sites to Reduce the Seasonal Variability of Generation

et al. 2022

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Wind energy is a powerful resource contributing to the decarbonization of the electric grid. However, wind power penetration introduces uncertainty about the availability of wind energy. This article addresses the complementarity of remote offshore wind sites in Brazil, demonstrating that strategic distribution of wind farms can significantly reduce the seasonality and the risk of periods without generation and reduce dependence on fossil sources. Field observations, atmospheric reanalysis, and simplified optimization methods are combined to demonstrate generation improvement considering regions under environmental licensing and areas not yet considered for offshore development. Aggregated power results demonstrate that with the relocation of wind turbines, a 68% reduction of the grid seasonal variability is possible, with a penalty of only 9% of the generated energy. This is accomplished through optimization and the inclusion of the northern region, which presents negative correlations with all other stations. More specifically, the north and northeast of Brazil have large seasonal amplitudes. However, out-of-phase wind regimes with a strong negative correlation (R < −0.6) and high-capacity factors (CF) during the peak seasons occur in Jan-Feb-Mar in the north (CF > 0.5) and in Aug-Sep-Oct in the northeast (CF > 0.7). These complementary regimes allow for the introduction of the concept of Reserve Wind Power (RWP) plants, wind farms that can be viewed as “reserve sources” for energy security. These can replace the contracts of thermal reserve plants, with resulting economic and environmental advantages. Our analysis suggests that RWP plants can be 20 to 32% cheaper than thermal reserves in the current market.

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Section: Introductionmentioning

confidence: 94%

Exploring the Complementarity of Offshore Wind Sites to Reduce the Seasonal Variability of Generation

et al. 2022

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“…ERA5 is a popular global reanalysis model (Hersbach et al, 2020) utilised for wind energy resource assessments in a variety of ways, including as a standalone product, as input boundary conditions to higher-resolution model runs, and as a reference dataset for MCP with local observations (Olauson, 2018;Soares et al, 2020;Hayes et al, 2021;de Assis Tavares et al, 2022). ERA5 is developed by the European Centre for Medium-Range Weather Forecasts and provides meteorological data from 1950-present at 1-hour temporal resolution with a horizontal grid spacing of 0.25° (Hersbach et al, 2020).…”

Section: Reanalysis Model For Long-term Correctionmentioning

confidence: 99%

Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment

Sheridan,

Duplyakin,

Phillips

et al. 2024

Preprint

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Abstract. Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems have shown similar advantages for more recent assessments. While a full twelve months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with one or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how low can you go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the United States. On average, correction with even one month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes within 0.21 m s-1 of the median bias magnitudes achieved using the standard twelve months of data for correction. However, in cases when the shortest observational periods (one to two months) used for correction are misrepresentative of the longer-term trends in the wind resource, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of mean absolute error and correlation when using six months or less of observational data for correction.

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“…Wind power density is a key parameter widely used for assessing the potential for wind energy generation in a given location. This method that does not take into account the specific turbine technology [65,66].…”

Section: Wind Power Densitymentioning

confidence: 99%

An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi

Placide,

Lollchund

2024

BMC Environ Sci

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Background The Weather Research and Forecasting (WRF) Model is an exceptional software for mesoscale climate modeling. It is extensively used to simulate key meteorological variables, including temperature, rainfall, and wind. Methods This study thoroughly examined the effectiveness of the WRF model in generating precise wind data for assessing the potential of wind power in Burundi. A meticulous evaluation of various combinations of model physics parameterization schemes was conducted to ensure accuracy. By comparing the simulated data with measurements from four meteorological stations and utilizing statistical metrics such as root-mean-square error (RMSE) and bias, the accuracy of the WRF model was determined. Results The findings of the study uncovered that utilizing WRF Single-Moment 3-Class (WSM3) for microphysics, Grell-Devenyi ensemble for cumulus physics, and Yonsei University for planetary boundary layer yields highly accurate wind data results for Burundi. Furthermore, the WRF model was utilized to create detailed seasonal and annual mean wind maps with a high resolution. Conclusion These maps demonstrated that the western part of Burundi experiences higher wind speeds (ranging from 4 to 9.7 m/s) during the dry seasons revealing the potential for wind energy harvesting in the different areas of Burundi.

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Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: A case study for Rio de Janeiro State

Cited by 21 publications

References 38 publications

Exploring the Complementarity of Offshore Wind Sites to Reduce the Seasonal Variability of Generation

Exploring the Complementarity of Offshore Wind Sites to Reduce the Seasonal Variability of Generation

Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment

An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi

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