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.

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 Associating Synoptic-Scale Weather Patterns with Aggregated Offshore Wind Power Production and Ramps

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3903
Author(s):  
Bedassa R. Cheneka ◽  
Simon J. Watson ◽  
Sukanta Basu
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Power Production ◽  
Low Pressure ◽  
Offshore Wind ◽  
Self Organizing Map ◽  
Pressure System ◽  
Weather Patterns ◽  
Low Pressure System

Large-scale weather patterns and their variability can influence both the amount of wind power production and its temporal variation, i.e., wind power ramps. In this study, we use a self-organizing map to cluster hourly sea level pressure into a discrete number of weather patterns. The dependency of wind power production and wind power ramps on these weather patterns is studied for the Belgian offshore wind farm fleet. A newly developed wavelet-surrogate ramp-detection algorithm is used for the identification of wind power ramps. It was observed that low-pressure systems, southwesterly and northeasterly wind flows are often associated with high levels of wind power production. Regarding wind power ramps, the type of transition between weather patterns was shown to determine whether ramp up or ramp down events would occur. Ramp up events tend to occur due to the transition from a high-pressure to a low-pressure system, or the weakening of the intensity of a deep low-pressure system. The reverse is associated with ramp down events.

scholarly journals Research on the Influence of Development Scenarios on the OLCOE of Wind Power: A Case Study of China

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Chao Liang ◽  
Jing Zhang ◽  
Yongqian Liu ◽  
Jie Yan ◽  
Wei He
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Offshore Wind ◽  
Power Cost ◽  
Offshore Wind Power ◽  
Development Scenarios ◽  
Low Wind Speed ◽  
The Impact

To achieve a high penetration of renewable energy, wind power development in China has gradually moved to diverse manifestation (e.g., centralized onshore, low wind speed, and offshore wind power). However, preexisting studies regarding wind power cost neglect to consider the respective characteristics of different development scenarios. In this paper, the overall levelized cost of energy (OLCOE) model is established for different scenarios. Taking China’s wind farm data as an example, the impact of development scenarios and wind power permeability on OLCOE and its cost components is quantitatively analysed. The results show that, (1) in the low penetration scenario, low wind speed power has the best economy and is beneficial to the conventional units; (2) the large-scale development of offshore wind power requires a reduction in the cost of offshore wind turbines and submarine cables; and (3) at present, onshore centralized wind power has economic advantages, but there is little room for its cost reduction.

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.

Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

2020 ◽  
Author(s):  
Do-Eun Choe ◽  
Gary Talor ◽  
Changkyu Kim
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Weather Data ◽  
Local Wind ◽  
Offshore Wind Power ◽  
The Future

Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

Impact of weather regimes on wind power variability in western Europe

2020 ◽  
Author(s):  
Ricardo García-Herrera ◽  
Jose M. Garrido-Perez ◽  
Carlos Ordóñez ◽  
David Barriopedro ◽  
Daniel Paredes
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Large Scale ◽  
Western Europe ◽  
Power Production ◽  
Large Scale Circulation ◽  
Weather Regimes ◽  
The Uk

&lt;p&gt;&lt;span&gt;&lt;span&gt;We have examined the applicability of a new set of 8 tailored weather regimes (WRs) to reproduce wind power variability in Western Europe. These WRs have been defined using a substantially smaller domain than those traditionally used to derive WRs for the North Atlantic-European sector, in order to maximize the large-scale circulation signal on wind power in the region of study. Wind power is characterized here by wind capacity factors (CFs) from a meteorological reanalysis dataset and from high-resolution data simulated by the Weather Research and Forecasting (WRF) model. We first show that WRs capture effectively year-round onshore wind power production variability across Europe, especially over northwestern / central Europe and Iberia. Since the influence of the large-scale circulation on wind energy production is regionally dependent, we have then examined the high-resolution CF data interpolated to the location of more than 100 wind farms in two regions with different orography and climatological features, the UK and the Iberian Peninsula. &lt;/span&gt;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;&lt;span&gt;The use of WRs allows discriminating situations with varied wind speed distributions and power production in both regions. In addition, the use of their monthly frequencies of occurrence as predictors in a multi-linear regression model allows explaining up to two thirds of the month-to-month CF variability for most seasons and sub-regions. These results outperform those previously reported based on Euro-Atlantic modes of atmospheric circulation. The improvement achieved by the spatial adaptation of WRs to a relatively small domain seems to compensate for the reduction in explained variance that may occur when using yearly as compared to monthly or seasonal WR classifications. In addition, our annual WR classification has the advantage that it allows applying a consistent group of WRs to reproduce day-to-day wind speed variability during extreme events regardless of the time of the year. As an illustration, we have applied these WRs to two recent periods such as the wind energy deficit of summer 2018 in the UK and the surplus of March 2018 in Iberia, which can be explained consistently by the different combinations of WRs.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

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