Resource Assessment Methods in the Offshore Wind Energy Sector

Salvação, N.; Soares, C. Guedes

doi:10.1007/978-3-319-27972-5_7

Cited by 8 publications

(10 citation statements)

References 30 publications

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“…According to the European Environment Agency (EEA), the potential offshore wind energy of Europe was seven times the European energy demand in 2009 [1], which means there was, and still is, much unrealised potential for further offshore renewable energy development. Following investment in renewable energy, 90% of the global offshore wind farms operating in 2014 were located in Northern Europe [2] and, by 2016, the European coasts were host to more than 3500 offshore wind turbines distributed across 81 wind farms, delivering a cumulative total of 12.6 GW of power [3]. It has been predicted that further development will lead to a European production of offshore wind power between 20-50 GW by 2050 [4].…”

Section: Introductionmentioning

confidence: 99%

“…Offshore wind energy has multiple advantages over land-based wind farms. For example, the offshore winds are normally stronger, steadier, and more abundant [2,5,6], which generally leads to lower wind variations and therefore reduces the periods of no electricity generation [2]. Additionally, the offshore annual capacity factor of wind energy can be between 20% and 40% higher than land-based sites [7].…”

Section: Introductionmentioning

confidence: 99%

“…In Europe, the average offshore wind turbine has the capacity to power 3300 average households, in comparison with 1500 for its land-based counterpart [5]. Moreover, inland renewable energy projects can have reduced site availability due to multiple stakeholder interactions, environmental constraints, as well as aesthetic factors, while offshore sites may be more easily situated near coastal population centres [2,4,5,8]. Consequently, the offshore wind energy market grew by 34% in the European Union in 2013, in comparison with 12% for its land-based counterpart [9].…”

Section: Introductionmentioning

confidence: 99%

“…This is also critical for planning the location and Expected Annual energy Production (EAP) of ORE farms. Accurate knowledge of diurnal, seasonal, and intra-annual wind variability is extremely important to improve wind farm efficiency and calculate productivity scenarios to evaluate revenue risks [2].…”

Section: Introductionmentioning

confidence: 99%

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The Potential of Advanced Scatterometer (ASCAT) 12.5 km Coastal Observations for Offshore Wind Farm Site Selection in Irish Waters

et al. 2019

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The offshore wind industry has seen unprecedented growth over the last few years. In line with this growth, there has been a push towards more exposed sites, farther from shore, in deeper water with consequent increased investor risk. There is therefore a growing need for accurate, reliable, met-ocean data to identify suitable sites, and from which to base preliminary design and investment decisions. This study investigates the potential of hyper-temporal satellite remote sensing Advanced Scatterometer (ASCAT) data in generating information necessary for the optimal site selection of offshore renewable energy infrastructure, and hence providing a cost-effective alternative to traditional techniques, such as in situ data from public or private entities and modelled data. Five years of the ASCAT 12.5 km wind product were validated against in situ weather buoys and showed a strong correlation with a Pearson coefficient of 0.95, when the in situ measurements were extrapolated with the log law. Temporal variations depicted by the ASCAT wind data followed the same inter-seasonal and intra-annual variations as the in situ measurements. A small diurnal bias of 0.12 m s−1 was observed between the descending swath (10:00 to 12:00) and the ascending swath (20:30 to 22:30), indicating that Ireland’s offshore wind speeds are slightly stronger in the daytime, especially in the nearshore areas. Seasonal maps showed that the highest spatial variability in offshore wind speeds are exhibited in winter and summer. The mean wind speed extrapolated at 80 m above sea level showed that Ireland’s mean offshore wind speeds at hub height ranged between 9.6 m s−1 and 12.3 m s−1. To best represent the offshore wind resource and its spatial distribution, an operational frequency map and a maximum yield frequency map were produced based on the ASCAT wind product in an offshore zone between 20 km and 200 km from the coast. The operational frequency indicates the percentage of time during which the observed local wind speed is between cut-in (3 m/s) and cut-out (25 m/s) for a standard turbine. The operational frequency map shows that the frequency of the wind speed within the cut-in and cut-off range of wind turbines was between 92.4% and 97.2%, while the maximum yield frequency map showed that between 40.6% and 59.5% of the wind speed frequency was included in the wind turbine rated power range. The results showed that the hyper-temporal ASCAT 12.5 km wind speed product (five consecutive years, two observations daily per satellite, two satellites) is representative of wind speeds measured by in situ measurements in Irish waters, and that its ability to depict temporal and spatial variability can assist in the decision-making process for offshore wind farm site selection in Ireland.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Potential of Advanced Scatterometer (ASCAT) 12.5 km Coastal Observations for Offshore Wind Farm Site Selection in Irish Waters

et al. 2019

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“…In order to perform a wind resource assessment for a region as large as Alaska's offshore zone, the primary options are either to use an atmospheric reanalysis product or to run a numerical weather prediction (NWP) model for a year or more at a higher spatial and temporal resolution than the reanalysis (e.g., [13,14]). Given the regional nature of wind resource assessments, limited-area NWP models like the Weather Research and Forecasting (WRF) model [15,16] are typically used to produce them.…”

Section: Introductionmentioning

confidence: 99%

Wind Resource Assessment for Alaska’s Offshore Regions: Validation of a 14-Year High-Resolution WRF Data Set

et al. 2019

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Offshore wind resource assessments for the conterminous U.S. and Hawai’i have been developed before, but Alaska’s offshore wind resource has never been rigorously assessed. Alaska, with its vast coastline, presents ample potential territory in which to build offshore wind farms, but significant challenges have thus far limited Alaska’s deployment of utility-scale wind energy capacity to a modest 62 MW (or approximately 2.7% of the state’s electric generation) as of this writing, all in land-based wind farms. This study provides an assessment of Alaska’s offshore wind resource, the first such assessment for Alaska, using a 14-year, high-resolution simulation from a numerical weather prediction and regional climate model. This is the longest-known high-resolution model data set to be used in a wind resource assessment. Widespread areas with relatively shallow ocean depth and high long-term average 100-m wind speeds and estimated net capacity factors over 50% were found, including a small area near Alaska’s population centers and the largest transmission grid that, if even partially developed, could provide the bulk of the state’s energy needs. The regional climate simulations were validated against available radiosonde and surface wind observations to provide the confidence of the model-based assessment. The model-simulated wind speed was found to be skillful and with near-zero average bias (−0.4–0.2 m s−1) when averaged over the domain. Small sample sizes made regional validation noisy, however.

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Wind Resource Assessment of a Coastal Site for Offshore Wind Power Generation in India

Saxena,

Mishra,

Rao

2022

Lecture Notes in Electrical Engineering

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Resource Assessment Methods in the Offshore Wind Energy Sector

Cited by 8 publications

References 30 publications

The Potential of Advanced Scatterometer (ASCAT) 12.5 km Coastal Observations for Offshore Wind Farm Site Selection in Irish Waters

The Potential of Advanced Scatterometer (ASCAT) 12.5 km Coastal Observations for Offshore Wind Farm Site Selection in Irish Waters

Wind Resource Assessment for Alaska’s Offshore Regions: Validation of a 14-Year High-Resolution WRF Data Set

Wind Resource Assessment of a Coastal Site for Offshore Wind Power Generation in India

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