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With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment.This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.

While wind assessment periods commonly range from 1 to several years, this is typically based on experience and industry norms. In this investigation, we perform an analysis of the error that can be expected in a wind resource assessment of various lengths of time. In contrast to earlier work measuring the uncertainty of predicted wind speeds, the uncertainty in this evaluation is measured directly in terms of energy and revenue production. As the wind assessment period increased from 30 days to 1 year, the average error increased slightly. However, when the wind assessment period was increased to 2 years, the average error decreased significantly. Simultaneously, the standard deviation of the error distributions decreased and the magnitude of the maximum experimentally obtained error decreased. By understanding how the energy production uncertainty decreases with increasing assessment time, the length of the assessment period can be tailored to match a developer’s risk tolerance.

Driven by the energy auctions system, wind power in Brazil is undergoing a phase of expansion within its electric energy mix. Due to wind’s stochastic nature and variability, the wind measurement campaign duration of a wind farm project is required to last for a minimum of 36 months in order for it to partake in energy auctions. In this respect, the influence of such duration on a measure-correlate-predict (MCP) based wind resource assessment was studied to assess the accuracy of generation forecasts. For this purpose, three databases containing time series of wind speed belonging to a site were considered. Campaigns with durations varying from 2 to 6 years were simulated to evaluate the behavior of the uncertainty in the long-term wind resource and to analyze how it impacts a wind farm power output estimation. As the wind measurement campaign length is increased, the uncertainty in the long-term wind resource diminished, thereby reducing the overall uncertainty that pervades the wind power harnessing. Larger monitoring campaigns implied larger quantities of data, thus enabling a better assessment of wind speed variability within that target location. Consequently, the energy production estimation decreased, allowing an improvement in the accuracy of the energy generation prediction by not overestimating it, which could benefit the reliability of the Brazilian electric system.

Sail-assisted technology can reduce greenhouse-gas emissions by saving the energy consumption of ships with wind energy utilization. The distribution characteristics of marine wind resources are critical to the energy-saving effect of sail-assisted ships. However, due to the lack of effective energy-saving evaluation methods for improving the utilization rate of wind energy, a high potential for wind energy utilization still exists. A novel energy-saving evaluation method based on the wind energy resource analysis of typical ship routes is proposed in this paper. First, a three-degree-of-freedom motion model for sail-assisted ships considering the wing sail forces is constructed. Then, a wind resource acquisition and analysis method based on spatial–temporal interpolation is proposed. On this basis, the wind field probability matrix and wing sail force matrix are established. Ultimately, an energy-saving evaluation method for sail-assisted ships on typical routes is proposed by combining the sailing condition of ships. The case study results show that the energy-saving effect of a wing sail-assisted oil tanker that sailed on the China-to-Middle East route was more than 5.37% in 2021 and could reach 9.54% in a single voyage. It is of great significance to realize the popularization and application of sail-assisted technology, thus improving the greenization of the shipping industry.

Unfortunately, Pakistan is facing an acute energy crisis since the past decade due to the increasing population growth and is heavily dependent on imports of fossil fuels. The shortage of the electricity is 14-18 hours in rural areas and 8-10 hours in urban areas. This situation has been significantly affecting the residential, industrial and commercial sectors in the country. At this time, it is immense challenges for the government to keep the power supply provision continue in the future for the country. In this situation, it has been the increased research to explore renewable energy resources in the country to fulfill the deficit scenario in the state. The renewable energy sector has not penetrated in the energy mix, currently in the upcoming markets. This paper highlights the steps taken by the country in the past and is taking steps at the present time to get rid of from the existing energy crisis when most urban areas are suffering from power outages for 12 hours on regular basis. Until 2009, no single grid interconnected wind established, but now the circumstances are changing significantly and wind farms are contributing to the national grid is the reality now. The initiation of the three wind farms interconnection network and many others in the pipeline are going to be operational soon. The federal policy on wind energy system has recently changed. Surprisingly, the continuing schemes of the wind farm are getting slow. This paper reviews developments in the wind energy sector in the country and lists some suggestions that can contribute to improving the penetration of wind energy in the national energy sector.Article History: Received Dec 16th 2016; Received in revised form May 15th 2017; Accepted June 18th 2017; Available onlineHow to Cite This Article: Kaloi,G.S., Wang, J., Baloch, M.H and Tahir, S. (2017) Wind Energy Potential at Badin and Pasni Costal Line Pakistan. Int. Journal of Renewable Energy Development, 6(2), 103-110.https://doi.org/10.14710/ijred.6.2.103-110

With the rapid development and construction of large-scale wind power bases under the “Carbon Peaking and Carbon Neutrality Goals” target, traditional wind energy resource assessment methods typically rely on a limited amount of wind mast data, providing only limited wind resource analysis results. These methods are incapable of capturing the spatiotemporal distribution of wind energy resources throughout the entire base, thus failing to meet the construction requirements of wind power bases. In this study, the mesoscale WRF (The Weather Research and Forecasting Model) was employed for wind resource simulation in a large wind power base. Based on the terrain, meteorological observation data, and boundary conditions, high-resolution wind field simulation results were generated, providing more comprehensive spatiotemporal distribution information within the Ulanqab region’s wind power base. Through the analysis and comparison of measured data and simulation results at different horizontal resolutions, the model was evaluated. Taking the Ulanqab wind power base as an example, the WRF model was used to study the distribution patterns of key parameters, such as annual average wind speed, turbulence intensity, annual average wind power density, and wind direction. The results indicate that a 4 km horizontal resolution can simultaneously ensure the accuracy of wind speed and wind direction simulations, demonstrating good engineering applicability. The analysis of wind resource characteristics in the Ulanqab wind power base based on the mesoscale model provides reliable reference value and data support for its macro- and micro-siting.

The reliability of ERA5 reanalyses for directly predicting wind resources and energy production has been assessed against observations from six tall towers installed over very heterogeneous sites around the world. Scores were acceptable at the FINO3 (Germany) offshore platform for both wind speed (bias within 1%, r = 0.95−0.96) and capacity factor (CF, at worst biased by 6.70%) and at the flat and sea-level site of Cabauw (Netherlands) for both wind speed (bias within 7%, r = 0.93−0.94) and CF (bias within 6.82%). Conversely, due to the ERA5 limited resolution (~31 km), large under-predictions were found at the Boulder (US) and Ghoroghchi (Iran) mountain sites, and large over-predictions were found at the Wallaby Creek (Australia) forested site. Therefore, using ERA5 in place of higher-resolution regional reanalysis products or numerical weather prediction models should be avoided when addressing sites with high variation of topography and, in particular, land use. ERA5 scores at the Humansdorp (South Africa) coastal location were generally acceptable, at least for wind speed (bias of 14%, r = 0.84) if not for CF (biased by 20.84%). However, due to the inherent sea–land discontinuity resulting in large differences in both surface roughness and solar irradiation (and thus stability conditions), a particular caution should be paid when applying ERA5 over coastal locations.

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO 2 emission intensity factor (kg CO 2 /kWh) of the national electricity system.

The accurate characterization of seasonal and inter-annual site-level wind energy variability is essential during wind project development. Understanding the features and probability of low-wind years is of particular interest to developers and financers. However, a dearth of long-term, hub-height wind observations makes these characterizations challenging, and thus techniques to improve these characterizations are of great value. To improve resource characterization, we explicitly link wind resource variability (at hub-height, and at specific sites) to regional and synoptic scale wind regimes. Our approach involves statistical clustering of high-resolution modeled wind data, and is applied to California for a period covering 1980–2015. With this approach, we investigate the unique meteorological patterns driving low and high wind years at five separate wind project sites. We also find wind regime changes over the 36-year period consistent with global warming: wind regimes associated with anomalously hot summer days increased at half a day per year and stagnant conditions increased at one-third days per year. Despite these changes, the average annual resource potential remained constant at all project sites. Additionally, we identify correlations between climate modes and wind regime frequency, a linkage valuable for resource characterization and forecasting. Our general approach can be applied in any location and may benefit many aspects of wind energy resource evaluation and forecasting.