scholarly journals Scenarios for offshore wind power production for Central California Call Areas

Wind Energy ◽  
2021 ◽  
Author(s):  
Yi‐Hui Wang ◽  
Ryan K. Walter ◽  
Crow White ◽  
Matthew D. Kehrli ◽  
Benjamin Ruttenberg
Keyword(s):  
Wind Power ◽  
Power Production ◽  
Offshore Wind ◽  
Central California ◽  
Offshore Wind Power

scholarly journals Norwegian hindcast archive (NORA3) – A validation of offshore wind resources in the North Sea and Norwegian Sea

2021 ◽  
Author(s):  
Ida Marie Solbrekke ◽  
Asgeir Sorteberg ◽  
Hilde Haakenstad
Keyword(s):  
Wind Power ◽  
Power Production ◽  
Offshore Wind ◽  
Norwegian Sea ◽  
Offshore Wind Power ◽  
Wind Speeds ◽  
The North ◽  
The North Sea ◽  
Wind Events ◽  
Simulated Wind

Abstract. A new high-resolution (3 km) numerical mesoscale weather simulation spanning the period 2004–2018 is validated for offshore wind power purposes for the North Sea and Norwegian Sea. The NORwegian hindcast Archive (NORA3) was created by dynamical downscaling, forced with state-of-the-art hourly atmospheric reanalysis as boundary conditions. A validation of the simulated wind climatology has been carried out to determine the ability of NORA3 to act as a tool for planning future offshore wind power installations. Special emphasis is placed on evaluating offshore wind power-related metrics and the impact of simulated wind speed deviations on the estimated wind power and the related variability. The general conclusion of the validation is that the NORA3 data is rather well suited for wind power estimates, but gives slightly conservative estimates on the offshore wind metrics. Wind speeds are typically 5 % (0.5 ms−1) lower than observed wind speeds, giving an underestimation of offshore wind power of 10 %–20 % (equivalent to an underestimation of 3 percentage point in the capacity factor), for a selected turbine type and hub height. The model is biased towards lower wind power estimates because of overestimation of the frequency of low-speed wind events (< 10 ms−1) and underestimation of high-speed wind events (> 10 ms−1). The hourly wind speed and wind power variability are slightly underestimated in NORA3. However, the number of hours with zero power production (around 12 % of the time) is fairly well captured, while the duration of each of these events is slightly overestimated, leading to 25-year return values for zero-power duration being too high for four of the six sites. The model is relatively good at capturing spatial co-variability in hourly wind power production among the sites. However, the observed decorrelation length was estimated to be 432 km, whereas the model-based length was 19 % longer.

scholarly journals Spatial and temporal variation of offshore wind power and its value along the Central California Coast

2019 ◽  
Vol 1 (12) ◽  
pp. 121001 ◽  
Author(s):  
Yi-Hui Wang ◽  
Ryan K Walter ◽  
Crow White ◽  
Matthew D Kehrli ◽  
Stephen F Hamilton ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Wind Power ◽  
Offshore Wind ◽  
Spatial And Temporal Variation ◽  
California Coast ◽  
Central California ◽  
Offshore Wind Power

scholarly journals An evaluation of offshore wind power production by floatable systems: A case study from SW Portugal

Energy ◽  
2017 ◽  
Vol 131 ◽  
pp. 239-250 ◽  
Author(s):  
A. Pacheco ◽  
E. Gorbeña ◽  
C. Sequeira ◽  
S. Jerez
Keyword(s):  
Wind Power ◽  
Power Production ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Sw Portugal

scholarly journals The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea

2021 ◽  
Vol 6 (5) ◽  
pp. 1205-1226
Author(s):  
Christoffer Hallgren ◽  
Stefan Ivanell ◽  
Heiner Körnich ◽  
Ville Vakkari ◽  
Erik Sahlée
Keyword(s):  
Random Forest ◽  
Baltic Sea ◽  
Wind Power ◽  
Power Production ◽  
Offshore Wind ◽  
Post Processing ◽  
The Baltic Sea ◽  
Offshore Wind Power ◽  
The Baltic ◽  
Forecast Quality

Abstract. With a rapidly increasing capacity of electricity generation from wind power, the demand for accurate power production forecasts is growing. To date, most wind power installations have been onshore and thus most studies on production forecasts have focused on onshore conditions. However, as offshore wind power is becoming increasingly popular it is also important to assess forecast quality in offshore locations. In this study, forecasts from the high-resolution numerical weather prediction model AROME was used to analyze power production forecast performance for an offshore site in the Baltic Sea. To improve the AROME forecasts, six post-processing methods were investigated and their individual performance analyzed in general as well as for different wind speed ranges, boundary layer stratifications, synoptic situations and in low-level jet conditions. In general, AROME performed well in forecasting the power production, but applying smoothing or using a random forest algorithm increased forecast skill. Smoothing the forecast improved the performance at all wind speeds, all stratifications and for all synoptic weather classes, and the random forest method increased the forecast skill during low-level jets. To achieve the best performance, we recommend selecting which method to use based on the forecasted weather conditions. Combining forecasts from neighboring grid points, combining the recent forecast with the forecast from yesterday or applying linear regression to correct the forecast based on earlier performance were not fruitful methods to increase the overall forecast quality.

scholarly journals A simple methodology to detect and quantify wind power ramps

2020 ◽  
Author(s):  
Bedassa R. Cheneka ◽  
Simon J. Watson ◽  
Sukanta Basu
Keyword(s):  
Wind Power ◽  
Large Range ◽  
Power Production ◽  
Offshore Wind ◽  
Production Data ◽  
Ramp Rate ◽  
Offshore Wind Power ◽  
Abstract Knowledge ◽  
Ramp Events ◽  
Energy Community

Abstract. Knowledge about the expected duration and intensity of wind power ramps is important when planning the integration of wind power production into an electricity network. How to detect and classify wind power ramps is not straightforward due to the large range of events that are observed and the stochastic nature of the wind. The development of an algorithm that can detect and classify wind power ramps is thus of some benefit to the wind energy community. In this study, we describe a relatively simple methodology using a wavelet transform to discriminate ramp events above stochastic variations using randomly shuffled wind power surrogates. To illustrate our approach, we used aggregated Belgian offshore wind power production data to characterise wind power ramps over a period of 10 days. We further illustrate the utility of the methodology by extracting distributions of ramp rates and their duration using two years of wind power production data. This brief study showed that there was a strong correlation between ramp rate and ramp duration, especially for up-ramps, that the majority of ramp events were less than 15 hours with a median duration of around eight hours and that ramps with a duration of more than a day were rare.

scholarly journals Mitigation of offshore wind power intermittency by interconnection of production sites

2020 ◽  
Vol 5 (4) ◽  
pp. 1663-1678
Author(s):  
Ida Marie Solbrekke ◽  
Nils Gunnar Kvamstø ◽  
Asgeir Sorteberg
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Large Scale ◽  
Power Production ◽  
Offshore Wind ◽  
Pressure System ◽  
Electricity Grid ◽  
Offshore Wind Power ◽  
Power Smoothing ◽  
Low Pressure System

Abstract. This study uses a unique set of hourly wind speed data observed over a period of 16 years to quantify the potential of collective offshore wind power production. We address the well-known intermittency problem of wind power for five locations along the Norwegian continental shelf. Mitigation of wind power intermittency is investigated using a hypothetical electricity grid. The degree of mitigation is examined by connecting different configurations of the sites. Along with the wind power smoothing effect, we explore the risk probability of the occurrence and duration of wind power shutdown due to too low or high winds. Typical large-scale atmospheric situations resulting in long term shutdown periods are identified. We find that both the wind power variability and the risk of not producing any wind power decrease significantly with an increasing array of connected sites. The risk of no wind power production for a given hour is reduced from the interval 8.0 %–11.2 % for a single site to under 4 % for two sites. Increasing the array size further reduces the risk, but to a lesser extent. The average atmospheric weather pattern resulting in wind speed that is too low (too high) to produce wind power is associated with a high-pressure (low-pressure) system near the production sites.

scholarly journals The impact of weather patterns on offshore wind power production

2020 ◽  
Vol 1618 ◽  
pp. 062032
Author(s):  
Bedassa R Cheneka ◽  
Simon J Watson ◽  
Sukanta Basu
Keyword(s):  
Wind Power ◽  
Power Production ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Weather Patterns ◽  
The Impact

scholarly journals The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea

2021 ◽  
Author(s):  
Christoffer Hallgren ◽  
Stefan Ivanell ◽  
Heiner Körnich ◽  
Ville Vakkari ◽  
Erik Sahlée
Keyword(s):  
Random Forest ◽  
Baltic Sea ◽  
Wind Power ◽  
Power Production ◽  
Offshore Wind ◽  
Post Processing ◽  
The Baltic Sea ◽  
Offshore Wind Power ◽  
The Baltic ◽  
Forecast Quality

Abstract. With a rapidly increasing capacity of electricity generation from wind power, the demand for accurate power production forecasts is growing. To date, most wind power installations have been onshore and thus most studies on production forecasts have focused on onshore conditions. However, as offshore wind power is becoming increasingly popular it is also important to assess forecast quality in offshore locations. In this study, forecasts from the high-resolution numerical weather prediction model AROME was used to analyze power production forecast performance for an offshore site in the Baltic Sea. To improve the AROME forecasts, six post-processing methods were investigated and their individual performance analyzed in general as well as for different wind speed ranges, boundary layer stratifications, synoptic situations and in low-level jet conditions. In general, AROME performed well in forecasting the power production, but applying smoothing or using a random forest algorithm increased forecast skill. Smoothing the forecast improved the performance at all wind speeds, all stratifications and for all synoptic weather classes, the random forest method increased the forecast skill during low-level jets. To achieve the best performance, we recommend to select which method to use based on the forecasted weather conditions. Combining forecasts from neighbouring grid points, combining the recent forecast with the forecast from yesterday or applying linear regression to correct the forecast based on earlier performance were not fruitful methods to increase the overall forecast quality.

scholarly journals Offshore wind power intermittency: The effect of connecting production sites along the Norwegian continental shelf

2020 ◽  
Author(s):  
Ida Marie Solbrekke ◽  
Nils Gunnar Kvamstø ◽  
Asgeir Sorteberg
Keyword(s):  
Wind Speed ◽  
Continental Shelf ◽  
Wind Power ◽  
Large Scale ◽  
Power Production ◽  
Offshore Wind ◽  
Pressure System ◽  
Electricity Grid ◽  
Offshore Wind Power ◽  
Norwegian Continental Shelf

Abstract. This study uses a unique set of hourly wind speed data observed over a period of 16 years to quantify the potential of collective offshore wind power production. We address the well-known intermittency problem of wind power for five locations along the Norwegian continental shelf. Mitigation of wind power intermittency is investigated using a hypothetical electricity grid. The degree of mitigation is examined by connecting different configurations of the sites. Along with the wind power smoothing effect, we explore the risk probability of the occurrence and duration of wind power shut-down. Typical large-scale atmospheric situations resulting in long term shut-down periods are identified. We find that both the wind power variability and the risk of not producing any wind power decrease significantly with an increasing array of connected sites. The risk of no wind power production for a given hour is reduced from 10 % for a single site to under 4 % for two sites. Increasing the array-size further reduces the risk, but to a lesser extend. The average atmospheric weather pattern resulting in wind speed that is too low (too high) to produce wind power is associated with a high- (low-) pressure system near the production sites.

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