Bayesian method for estimating Weibull parameters for wind resource assessment in the Equatorial region: a comparison between two-parameter and three-parameter Weibull distributions

The two-parameter Weibull distribution has garnered much attention in the assessment of windenergy potential. The estimation of the shape and scale parameters of the distribution has broughtforth a successful tool for the wind energy industry. However, it may be inappropriate to use thetwo-parameter Weibull distribution to assess energy at every location, especially at sites wherelow wind speeds are frequent, such as the Equatorial region. In this work, a robust technique inwind resource assessment using a Bayesian approach for estimating Weibull parameters is firstproposed. Secondly, the wind resource assessment techniques using a two-parameter Weibulldistribution and a three-parameter Weibull distribution which is a generalized form of twoparameterWeibull distribution are compared. Simulation studies confirm that the Bayesianapproach seems a more robust technique for accurate estimation of Weibull parameters. Theresearch is conducted using data from seven sites in Equatorial region from 1o N of Equator to 19oSouth of Equator. Results reveal that a three-parameter Weibull distribution with non-zero shiftparameter is a better fit for wind data having a higher percentage of low wind speeds (0-1 m/s) andlow skewness. However, wind data with a smaller percentage of low wind speeds and highskewness showed better results with a two-parameter distribution that is a special case of threeparameterWeibull distribution with zero shift parameter. The results also demonstrate that theproposed Bayesian approach and application of a three-parameter Weibull distribution areextremely useful in accurate estimate of wind power and annual energy production.

Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review

Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This is especially the case in Australia, where there has been a massive increase in solar and wind farms in the last 15 years, as well as in roof top solar, both domestic and commercial. For example, in 2020, 27% of the electricity in Australia was from renewable sources, and in South Australia almost 60% was from wind and solar. In the literature, there has been extensive research reported on solar and wind resource, entailing both point and interval forecasts, but there has been much less focus on the forecasting of output from wind and solar systems. In this review, we canvass both what has been reported and also what gaps remain. In the case of the latter topic, there are numerous aspects that are not well dealt with in the literature. We have added discussion on the value of forecasts, rather than just focusing on forecast skill. Further, we present a section on how to deal with conditionally changing variance, a topic that has little focus in the literature. One other topic may be particularly important in Australia at the moment, but may become more widespread. This is how to deal with the concept of a clear sky output from a solar farm when the field is oversized compared to the inverter capacity, resulting in a plateau for the output.

Offshore wind resource assessment using reanalysis data

The growth rate of offshore wind is increasing due to technological advancement and reduction in cost. An approach using mast measured data at coastline and reanalysis data is proposed for offshore wind resource assessment, especially for developing countries. The evaluation of fifth generation European Reanalysis (ERA5) data was performed against measured data using statistical analysis. ERA5 data slightly underestimates wind speed and wind direction with percentage bias of less than 1%. Wind resource assessment of region in Exclusive Economic Zone (EEZ) of Pakistan was performed in terms of wind speed and Wind Power Density (WPD). The range of monthly mean wind speed and WPD in the region was 4.03–8.67 m/second and 73–515 W/m2 respectively. Most-probable wind speed and dominating wind direction on corners and center of the region were found using probability distributions and wind rose diagrams respectively. Most-probable wind speed ranges 4.41–7.64 m/second and dominating wind direction is southwest.

Management of a Dual-Bus AC+DC Microgrid Based on a Wind Turbine with Double Stator Induction Generator

The topology and management of a sustainable dual-bus, AC and DC, microgrid designed to operate connected to a weak grid is presented. AC+DC hybrid microgrids are a robust and cost-competitive solution for poorly connected areas, as can be found in rural or island electrification. The versatile microgrid proposed in this work is developed around a wind turbine based on a particular induction generator with double stator winding and squirrel cage rotor (DWIG). This singular generator is especially suitable for a combined AC+DC coupled microgrid application. One of its stator windings is coupled to the DC bus via a controlled AC/DC converter. The other is directly connected to the AC bus, only during the periods of abundant wind resource. The DWIG is complemented with photovoltaic panels and a hybrid energy storage system, comprising flow batteries assisted by supercapacitors, which converge to the DC Bus. The DC bus exchanges power with the AC bus through an interlinking inverter. The article describes the topology and details the operation of its Supervisory Control system, which gives rise to the five operating modes of the proposed AC+DC DWIG based microgrid. Its performance under different generation conditions and load regimes is thoroughly assessed by simulation.

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer time scales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting Model (WRF) 4.2.1 to two different SST products—a daily, spatially coarser resolution data set (the Operational Sea Surface Temperature and Ice Analysis, or OSTIA), and an hourly, spatially finer resolution product (SSTs from the Geostationary Operational Environmental Satellite 16, or GOES-16). We find that in the Mid-Atlantic, although OSTIA SSTs validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height wind characterization performance on monthly time scales. Additionally, during flagged events that show statistically significant wind speed deviations between the two simulations, the GOES-16-forced simulation outperforms that forced by OSTIA.

Integration of Wind Energy and Desalination Systems: A Review Study

Desalination is a well-established technology used all over the world to mitigate freshwater scarcity. Wind-powered reverse osmosis plants are one of the most promising alternatives for renewable energy desalination, particularly for coastal areas and islands. Wind energy can satisfy the high energy consumption of desalination while reducing costs and CO2 emissions. However, the mismatch between the intermittent availability of the wind resource and the desalination’s power demand makes the integration between the two technologies critical. This paper presents a review of wind-powered desalination systems, focusing on the existing topologies and technological advances. An overview of the advantages and disadvantages are analysed based on the theoretical and experimental cases available in the scientific literature. The goal of this work is to show the current status of wind-powered desalination and to present the technical challenges that need to be overcome in order to ensure a sustainable freshwater source.

The Sensitivity of the Fitch Wind Farm Parameterization to a Three-Dimensional Planetary Boundary Layer Scheme

Wind plant wake impacts can be estimated with a number of simulation methodologies, each with its own fidelity and sensitivity to model inputs. In turbine-free mesoscale simulations, hub-height wind speeds can significantly vary with the choice of a planetary boundary layer (PBL) scheme. However, the sensitivity of wind plant wakes to a PBL scheme has not been explored because, until now, wake parameterizations were only compatible with one PBL scheme. We couple the Fitch wind farm parameterization with the new NCAR 3DPBL scheme and compare the resulting wakes to those simulated with a widely used PBL scheme. First, we simulate a wind plant in a pseudo-steady state under idealized stable, neutral, and unstable conditions with two PBL schemes: MYNN and the NCAR 3DPBL. For these idealized scenarios, MYNN consistently predicts internal wakes that are 0.25–1.5 m s−1 stronger than internal 3DPBL wakes. However, because MYNN predicts stronger inflow winds than the 3DPBL, MYNN predicts average capacity factors that are as large as 13 percentage points higher than with the 3DPBL, depending on the stability. To extend this sensitivity study, we conduct a month-long case study with both PBL schemes centered on the Vineyard Wind 1 lease area in the mid-Atlantic United States. Under stable and unstable conditions averaged across the month, MYNN again predicts stronger internal waking—by about 0.25 m s−1. However, again due to stronger plant inflow wind speeds in MYNN, the 3DPBL generates 4.7 %–7.8 % less power than MYNN in August 2020, depending on the turbine build-out scenario. Differences between PBL schemes can be even larger for individual instances in time. These simulations suggest that PBL schemes represent a meaningful source of modeled wind resource uncertainty; therefore, we recommend incorporating PBL variability into future wind plant planning sensitivity studies as well as wind forecasting studies.

Validation of Wind Resource and Energy Production Simulations for Small Wind Turbines in the United States

Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, and MyWindTurbine.com, which are seen to overestimate actual annual capacity factors by 2.5, 4.2, and 11.5 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produce more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.