United States offshore wind energy atlas: availability, potential, and economic insights based on wind speeds at different altitudes and thresholds and policy-informed exclusions

von Krauland, Anna-Katharina; Long, Qirui; Enevoldsen, Peter; Jacobson, Mark Z.

doi:10.1016/j.ecmx.2023.100410

Cited by 4 publications

(2 citation statements)

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“…The study results showed that investment in wind farms in eastern Iran could be associated with high profitability. The same methods were applied by Alham et al [24], Mostafaeipou et al [25], and Feng et al [17]. The studies show that the PVC and UCE will differ for each site or wind farm.…”

Section: Economic Feasibility Of Wind Energymentioning

confidence: 97%

“…Among other potential energy sources, this study considers wind energy as that energy source [16]. Having the capability to plan more strategically will ultimately lower the LCOE of projects, enhance the certainty around long-term target-setting, and accelerate the deployment [17]. Many scholars have adopted scientific and advanced intelligent algorithms from the relevant research literature at home and abroad based on cost prediction of power engineering projects [18].…”

Section: Introductionmentioning

confidence: 99%

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Economic feasibility of onshore wind energy potential for electricity generation in Zambia

Mutale,

Wang,

Yasir

et al. 2023

Preprint

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Zambia recently experienced extended hours of load shedding due to reduced hydroelectricity generation attributed to poor rainfall patterns. The inability to meet energy demand has influenced the government to consider alternative energy sources. This study aims to assess the economic feasibility of the eight sites using a 25-turbine layout for a 4 MW generic wind turbine at 130m hub height. An economic evaluation mathematical model was developed to analyze the economic feasibility using net present value, simple payback period, internal rate of return, and levelized cost of electricity (LCOE). The study shows that the Lusaka wind farm was the most economical with 386 GWh energy yield analysis (EYA), wind speed of 8 m/s, NPV of 316 million dollars, SPP of 2.9 years, IRR of 82%, and LCOE of $0.182/ kWh. The results also show that the other seven sites are economically feasible, with Petauke being the lowest. The study's economic sensitivity analysis conducted by varying the average electricity tariff shows that Lusaka was the most financially viable site. Therefore, policymakers should develop cost-reflective feed-in tariff (FiT) schemes and power purchase agreements for the country to have cost-reflective electricity tariffs that will motivate independent power producers to invest in Zambia.

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Section: Economic Feasibility Of Wind Energymentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Economic feasibility of onshore wind energy potential for electricity generation in Zambia

Mutale,

Wang,

Yasir

et al. 2023

Preprint

View full text Add to dashboard Cite

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Evaluation of Hub‐Height Wind Forecasts Over the New York Bight

Myers,

Van Ormer,

Turner

et al. 2024

Wind Energy

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As offshore wind energy development accelerates in the United States, it is important to assess the accuracy of hub‐height wind forecasts from numerical weather prediction models over the ocean. Leveraging approximately 2 years of Doppler lidar observations from buoys in the New York Bight, we evaluate 80‐m wind speed forecasts from two weather models: the High‐Resolution Rapid Refresh (HRRR) atmospheric model and the Global Forecast System (GFS) coupled atmosphere‐ocean model. These models have different horizontal grid spacing, vertical layering, initialization methods, and parameterizations of boundary layer mixing and surface–atmosphere interactions. Despite these differences, the models demonstrate similar and highly skillful short‐term forecasts at three measurement sites. At the Hudson Southwest location that provides a full year of data, their performance is statistically indistinguishable: root mean square error (RMSE) = 2.1 m/s and the Pearson correlation coefficient for 24‐h forecasts of both models, and RMSE = 2.6 m/s and 0.83 for 48‐h forecasts. Twenty‐four‐hour forecasts also exhibit skill in predicting quiescent winds and winds associated with maximum turbine power. By Day 10, GFS forecasts on average have almost no skill. The short‐term forecast skill by the HRRR and GFS does not strongly depend on season or time of day, yet we find some dependence of the models' performance on near‐surface stability. Additionally, 4‐ to 14‐day forecasts by the GFS exhibit lower RMSE during summer relative to other seasons. The high skill of the HRRR and GFS short‐term forecasts establishes confidence in their utility for offshore wind energy maintenance and operation.

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