scholarly journals Sensitivity analysis of mesoscale simulations to physics parameterizations: a case study of storm Ciara over the Belgian North Sea using WRF-ARW

2021 ◽  
Author(s):  
Adithya Vemuri ◽  
Sophia Buckingham ◽  
Wim Munters ◽  
Jan Helsen ◽  
Jeroen van Beeck
Keyword(s):  
Sensitivity Analysis ◽  
Temporal Resolution ◽  
North Sea ◽  
Wind Direction ◽  
Offshore Wind ◽  
Wave Radiation ◽  
Energy Yield ◽  
Physics Parameterizations ◽  
Belgian North Sea

Abstract. The Weather, Research and Forecasting (WRF) model includes a multitude of physics parameterizations to account for atmospheric dynamics and interactions such as turbulent fluxes within the planetary boundary layer (PBL), long and short wave radiation, hydrometeor representation in microphysics, cloud ensemble representation in cumulus, amongst others. A sensitivity analysis is conducted in order to identify the optimal WRF-physics set-up and impact of temporal resolution of re-analysis dataset for the event of sudden changes in wind direction that can become challenging for reliable wind energy operations. In this context, Storm Ciara has been selected as a case study to investigate the influence of a broad combination of different interacting physics-schemes on quantities of interest that are relevant for energy yield assessment. Of particular relevance to fast transient weather events, two different temporal resolutions (1-hourly and 3-hourly) of the lateral boundary condition's re-analysis dataset, ERA5, are considered. Physics parameterizations considered in this study include: two PBL schemes (MYNN2.5 and scale-aware Shin Hong PBL), four cumulus schemes (Kain-Fritsch, Grell-Devenyi, and scale-aware Grell-Freitas and multi-scale Kain-Fritsch,) and three microphysics schemes (WSM5, Thompson and Morrison) coupled with two geospatial configurations for WRF simulation domains. The resulting WRF predictions are assessed by comparison to observational RADAR reflectivity data on precipitation. In addition, SCADA data on wind direction and wind speed from an offshore wind farm located in the Belgian North Sea is considered to assess modeling capabilities for local wind behavior at farm level. For precipitation, results are shown to be very sensitive to model setup, but no clear trends can be observed. For wind-related variables on the other hand, results show a definite improvement in accuracy when both scale-aware cumulus and PBL parameterizations are used in combination with 1-hourly temporal resolution reanalysis data and extended domain sizes.

A methodology for optimizing modeling configuration in the numerical modeling of oil concentrations in underwater blowouts: a North Sea case study

2021 ◽  
Author(s):  
Andrés Martínez
Keyword(s):  
Sensitivity Analysis ◽  
Numerical Modeling ◽  
High Resolution ◽  
North Sea ◽  
Oil And Gas ◽  
Data Bases ◽  
Lagrangian Trajectory ◽  
Number Of Particles

<p><strong>A METHODOLOGY FOR OPTIMIZING MODELING CONFIGURATION IN THE NUMERICAL MODELING OF OIL CONCENTRATIONS IN UNDERWATER BLOWOUTS: A NORTH SEA CASE STUDY</strong></p><p>Andrés Martínez<sup>a,*</sup>, Ana J. Abascal<sup>a</sup>, Andrés García<sup>a</sup>, Beatriz Pérez-Díaz<sup>a</sup>, Germán Aragón<sup>a</sup>, Raúl Medina<sup>a</sup></p><p><sup>a</sup>IHCantabria - Instituto de Hidráulica Ambiental de la Universidad de Cantabria, Avda. Isabel Torres, 15, 39011 Santander, Spain</p><p><sup>* </sup>Corresponding author: [email protected]</p><p>Underwater oil and gas blowouts are not easy to repair. It may take months before the well is finally capped, releasing large amounts of oil into the marine environment. In addition, persistent oils (crude oil, fuel oil, etc.) break up and dissipate slowly, so they often reach the shore before the cleanup is completed, affecting vasts extension of seas-oceans, just as posing a major threat to marine organisms.</p><p>On account of the above, numerical modeling of underwater blowouts demands great computing power. High-resolution, long-term data bases of wind-ocean currents are needed to be able to properly model the trajectory of the spill at both regional (open sea) and local level (coastline), just as to account for temporal variability. Moreover, a large number of particles, just as a high-resolution grid, are unavoidable in order to ensure accurate modeling of oil concentrations, of utmost importance in risk assessment, so that threshold concentrations can be established (threshold concentrations tell you what level of exposure to a compound could harm marine organisms).</p><p>In this study, an innovative methodology has been accomplished for the purpose of optimizing modeling configuration: number of particles and grid resolution, in the modeling of an underwater blowout, with a view to accurately represent oil concentrations, especially when threshold concentrations are considered. In doing so, statistical analyses (dimensionality reduction and clustering techniques), just as numerical modeling, have been applied.</p><p>It is composed of the following partial steps: (i) classification of i representative clusters of forcing patterns (based on PCA and K-means algorithms) from long-term wind-ocean current hindcast data bases, so that forcing variability in the study area is accounted for; (ii) definition of j modeling scenarios, based on key blowout parameters (oil type, flow rate, etc.) and modeling configuration (number of particles and grid resolution); (iii) Lagrangian trajectory modeling of the combination of the i clusters of forcing patterns and the j modeling scenarios; (iv) sensitivity analysis of the Lagrangian trajectory model output: oil concentrations,  to modeling configuration; (v) finally, as a result, the optimal modeling configuration, given a certain underwater blowout (its key parameters), is provided.</p><p>It has been applied to a hypothetical underwater blowout in the North Sea, one of the world’s most active seas in terms of offshore oil and gas exploration and production. A 5,000 cubic meter per day-flow rate oil spill, flowing from the well over a 15-day period, has been modeled (assuming a 31-day period of subsequent drift for a 46-day modeling). Moreover, threshold concentrations of 0.1, 0.25, 1 and 10 grams per square meter have been applied in the sensitivity analysis. The findings of this study stress the importance of modeling configuration in accurate modeling of oil concentrations, in particular if lower threshold concentrations are considered.</p>

scholarly journals Impact of Electrical Topology, Capacity Factor and Line Length on Economic Performance of Offshore Wind Investments

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3191 ◽  
Author(s):  
Sadik Kucuksari ◽  
Nuh Erdogan ◽  
Umit Cali
Keyword(s):  
Sensitivity Analysis ◽  
Transmission Line ◽  
Economic Performance ◽  
Wind Farm ◽  
Line Length ◽  
Capacity Factor ◽  
Offshore Wind ◽  
Farm Size ◽  
Energy Yield ◽  
Levelized Cost Of Electricity

In this study, an economic performance assessment of offshore wind investments is investigated through electrical topology, capacity factor and line length. First, annual energy yield production and electrical system losses for AC and DC offshore wind configurations are estimated by using Weibull probability distributions of wind speed. A cost model for calculating core energy economic metrics for offshore wind environment is developed by using a discount cash flow analysis. A case study is then conducted for a projected offshore wind farm (OWF) rated 100 MW and 300 MW sizes situated in the Aegean sea. Finally, a sensitivity analysis is performed for AC and DC OWFs with three different capacity factors (e.g., 45%, 55% and 60%) and various transmission line lengths ranging from 20 km to 120 km. The OWF is found to be economically viable for both AC and DC configurations with the estimated levelized cost of electricity (LCOE) ranging from 88.34 $/MWh to 113.76 $/MWh and from 97.61 $/MWh to 126.60 $/MWh, respectively. LCOEs for both options slightly change even though the wind farm size was increased three-fold. The sensitivity analysis reveals that, for further offshore locations with higher capacity factors, the superiority of AC configuration over the DC option in terms of LCOE reduces while the advantage of DC configuration over the AC option in terms of electrical losses is significant. Losses in the AC and DC configurations range from 3.75% to 5.86% and 3.75% to 5.34%, respectively, while LCOEs vary between 59.90 $/MWh and 113.76 $/MWh for the AC configuration and 66.21 $/MWh and 124.15 $/MWh for the DC configuration. Capacity factor was found to be more sensitive in LCOE estimation compared to transmission line length while line length is more sensitive in losses estimation compared to capacity factor.

scholarly journals Analysing uncertainties in offshore wind farm power output using measure–correlate–predict methodologies

2020 ◽  
Vol 5 (2) ◽  
pp. 601-621
Author(s):  
Michael Denis Mifsud ◽  
Tonio Sant ◽  
Robert Nicholas Farrugia
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Power Output ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Offshore Wind ◽  
Energy Yield ◽  
Offshore Wind Farm ◽  
Power And Energy

Abstract. This paper investigates the uncertainties resulting from different measure–correlate–predict (MCP) methods to project the power and energy yield from a wind farm. The analysis is based on a case study that utilises short-term data acquired from a lidar wind measurement system deployed at a coastal site in the northern part of the island of Malta and long-term measurements from the island's international airport. The wind speed at the candidate site is measured by means of a lidar system. The predicted power output for a hypothetical offshore wind farm from the various MCP methodologies is compared to the actual power output obtained directly from the input of lidar data to establish which MCP methodology best predicts the power generated. The power output from the wind farm is predicted by inputting wind speed and direction derived from the different MCP methods into windPRO® (https://www.emd.dk/windpro, last access: 8 May 2020). The predicted power is compared to the power output generated from the actual wind and direction data by using the normalised mean absolute error (NMAE) and the normalised mean-squared error (NMSE). This methodology will establish which combination of MCP methodology and wind farm configuration will have the least prediction error. The best MCP methodology which combines prediction of wind speed and wind direction, together with the topology of the wind farm, is that using multiple linear regression (MLR). However, the study concludes that the other MCP methodologies cannot be discarded as it is always best to compare different combinations of MCP methodologies for wind speed and wind direction, together with different wake models and wind farm topologies.

scholarly journals Analysing Uncertainties in Offshore Wind Farm Power Output using Measure Correlate Predict Methodologies

2019 ◽  
Author(s):  
Michael Denis Mifsud ◽  
Tonio Sant ◽  
Robert Nicholas Farrugia
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Power Output ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Offshore Wind ◽  
Energy Yield ◽  
Offshore Wind Farm ◽  
The Mean

Abstract. This paper investigates the uncertainties resulting from different Measure-Correlate-Predict methods to project the power and energy yield from a wind farm. The analysis is based on a case study that utilizes short-term data acquired from a LiDAR wind measurement system deployed at a coastal site in the northern part of the island of Malta and long-term measurements from the island’s international airport. The wind speed at the candidate site is measured by means of a LiDAR system. The predicted power output for a hypothetical offshore wind farm from the various MCP methodologies is compared to the actual power output obtained directly from the input of LiDAR data to establish which MCP methodology best predicts the power generated. The power output from the wind farm is predicted by inputting wind speed and direction derived from the different MCP methods into windPRO® (https://www.emd.dk/windpro). The predicted power is compared to the power output generated from the actual wind and direction data by using the Mean Squared Error (MSE) and the Mean Absolute Error (MAE) measures. This methodology will establish which combination of MCP methodology and wind farm configuration will have the least prediction error. The best MCP methodology which combines prediction of wind speed and wind direction, together with the topology of the wind farm, is that using Artificial Neural Networks. However, the study concludes that the other MCP methodologies cannot be discarded as it is always best to compare different combinations of MCP methodologies for wind speed and wind direction, together with different wake models and wind farm topologies.

Ecosystem based modeling and indication of ecological integrity in the German North Sea—Case study offshore wind parks

2011 ◽  
Vol 11 (1) ◽  
pp. 168-174 ◽  
Author(s):  
Benjamin Burkhard ◽  
Silvia Opitz ◽  
Hermann Lenhart ◽  
Kai Ahrendt ◽  
Stefan Garthe ◽  
...  
Keyword(s):  
North Sea ◽  
Ecological Integrity ◽  
Offshore Wind ◽  
Offshore Wind Parks

Stability assessment of VSC-HVDC connected large-scale offshore wind power: A North-Sea region case study

2015 ◽  
Author(s):  
Arjen A. van der Meer ◽  
Mario Ndreko ◽  
Jorrit A. Bos ◽  
Madeleine Gibescu ◽  
Mart A. M. M. van der Meijden ◽  
...  
Keyword(s):  
Wind Power ◽  
North Sea ◽  
Large Scale ◽  
Offshore Wind ◽  
Stability Assessment ◽  
Offshore Wind Power

Ten Demi-Culverins for Aldeburgh: Whitehall, the Dunkirkers, and a Suffolk Fishing Community, 1625–1630

2019 ◽  
Vol 58 (2) ◽  
pp. 315-337 ◽  
Author(s):  
Thomas Cogswell
Keyword(s):  
North Sea ◽  
East Coast ◽  
Central Government ◽  
Fishing Community ◽  
Close Attention ◽  
The North ◽  
The North Sea ◽  
The Impact

AbstractHistorians have not paid close attention to the activities of freebooters operating out of Dunkirk in the late 1620s. This essay corrects that omission by first studying the threat from Dunkirk to England's east coast and then addressing how the central government, counties, and coastal towns responded. A surprisingly rich vein of manuscript material from Great Yarmouth and particularly from the Suffolk fishing community of Aldeburgh informs this case study of the impact of this conflict around the North Sea.

scholarly journals A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1261
Author(s):  
Christopher Gradwohl ◽  
Vesna Dimitrievska ◽  
Federico Pittino ◽  
Wolfgang Muehleisen ◽  
András Montvay ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Failure Detection ◽  
Energy Yield ◽  
Combined Approach ◽  
Levelized Cost Of Electricity ◽  
Term Operation ◽  
Model Based

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

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