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B. Baki
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The increasing global demand for wind power underscores the importance of understanding and characterizing extreme ramp events, which are significant fluctuations in wind power generation over short periods that pose challenges for grid integration. This study focuses on modeling frontal low-level jets (FLLJs) and associated extreme ramp-down events, particularly their impact on wind power production at Belgium offshore wind farms. Using the Weather Research and Forecasting (WRF) model, we analyzed five cases of extreme wind power ramp-down events, including in-depth analysis of two cases and generalization of three additional cases. We assessed the sensitivity of various model configurations, including initial and boundary condition (IC/BC) datasets (ERA5 and CERRA), the activation of Fitch wind farm parameterization (WFP), planetary boundary layer (PBL) schemes, and single- versus nested-domain configuration. Our findings indicate that CERRA IC/BCs provide a superior representation of atmospheric flow compared to ERA5, resulting in more accurate predictions of ramp timing, intensity, and FLLJ characteristics. The WFP significantly impacts wind power output by modeling turbine interactions and wake effects, leading to slightly lower wind speeds. The scale-aware Shin and Hong PBL scheme yielded a stronger FLLJ core at higher altitudes with a more pronounced jet nose, although wind speeds below 200 m were lower compared to the Mellor–Yamada–Nakanishi–Niino 2.5 scheme. Single-domain configuration proved more effective in simulating wind power ramps but had higher core heights and higher wind speeds below 200 m, resulting in a diffused jet profile. Our analysis highlights that reliable simulation of extreme ramps associated with FLLJs using a single-domain configuration could reduce computational costs. Further, the FLLJs and associated extreme ramps can be predicted 1 d in advance, offering substantial benefits for operational efficiency in wind energy management.
...
The increasing global demand for wind power underscores the importance of understanding and characterizing extreme ramp events, which are significant fluctuations in wind power generation over short periods that pose challenges for grid integration. This study focuses on modeling frontal low-level jets (FLLJs) and associated extreme ramp-down events, particularly their impact on wind power production at Belgium offshore wind farms. Using the Weather Research and Forecasting (WRF) model, we analyzed five cases of extreme wind power ramp-down events, including in-depth analysis of two cases and generalization of three additional cases. We assessed the sensitivity of various model configurations, including initial and boundary condition (IC/BC) datasets (ERA5 and CERRA), the activation of Fitch wind farm parameterization (WFP), planetary boundary layer (PBL) schemes, and single- versus nested-domain configuration. Our findings indicate that CERRA IC/BCs provide a superior representation of atmospheric flow compared to ERA5, resulting in more accurate predictions of ramp timing, intensity, and FLLJ characteristics. The WFP significantly impacts wind power output by modeling turbine interactions and wake effects, leading to slightly lower wind speeds. The scale-aware Shin and Hong PBL scheme yielded a stronger FLLJ core at higher altitudes with a more pronounced jet nose, although wind speeds below 200 m were lower compared to the Mellor–Yamada–Nakanishi–Niino 2.5 scheme. Single-domain configuration proved more effective in simulating wind power ramps but had higher core heights and higher wind speeds below 200 m, resulting in a diffused jet profile. Our analysis highlights that reliable simulation of extreme ramps associated with FLLJs using a single-domain configuration could reduce computational costs. Further, the FLLJs and associated extreme ramps can be predicted 1 d in advance, offering substantial benefits for operational efficiency in wind energy management.
Marine renewables in Energy Systems
Impacts of climate data, generators, energy policies, opportunities, and untapped potential for 100% decarbonised systems
Journal article
(2025)
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George Lavidas, Lefteris Mezilis, Matías Alday G., Harish Baki, Jian Tan, Avni Jain, Tabea Engelfried, Vaibhav Raghavan
The Energy Transition requires meticulous planning, taking into consideration economic, technical, social, and resource constraints. In Europe ambitious targets have been set for system electrification, however, integrating the potential of marine renewables have not been thoroughly investigated. This study extends the framework of PyPSA-Eur into PyPSA-Eur-MREL that for the first time incorporates all marine renewables, using high resolution datasets, that uncover the potential of marine renewables. Marine renewables are modelled in terms of power estimations, deployment strategies and revised packing density, and expected benefits for 2030, and 2050 across all European Countries are quantified. Higher spatio-temporal data have an immediate impact in estimates, and reduction of energy storage by 73%. Wind energy has a reduced installation capacity by 50%, but the higher fidelity of resource matches production to demand and reduces curtailments up to 60%. System costs with high resolution data are 40% reduced to 160 billion € for a 2030 100% renewable reliant system. The benefits of having more marine renewables are not limited to cost and more efficient demand matching, reduced energy storage, but it also with the area required to decarbonise the system. The results are encouraging and outline the importance and further need for marine renewable energies.
...
The Energy Transition requires meticulous planning, taking into consideration economic, technical, social, and resource constraints. In Europe ambitious targets have been set for system electrification, however, integrating the potential of marine renewables have not been thoroughly investigated. This study extends the framework of PyPSA-Eur into PyPSA-Eur-MREL that for the first time incorporates all marine renewables, using high resolution datasets, that uncover the potential of marine renewables. Marine renewables are modelled in terms of power estimations, deployment strategies and revised packing density, and expected benefits for 2030, and 2050 across all European Countries are quantified. Higher spatio-temporal data have an immediate impact in estimates, and reduction of energy storage by 73%. Wind energy has a reduced installation capacity by 50%, but the higher fidelity of resource matches production to demand and reduces curtailments up to 60%. System costs with high resolution data are 40% reduced to 160 billion € for a 2030 100% renewable reliant system. The benefits of having more marine renewables are not limited to cost and more efficient demand matching, reduced energy storage, but it also with the area required to decarbonise the system. The results are encouraging and outline the importance and further need for marine renewable energies.
The growing demand for global wind power production, driven by the critical need for sustainable energy sources, requires reliable estimation of wind speed vertical profiles for accurate wind power prediction and comprehensive wind turbine performance assessment. Traditional methods relying on empirical equations or similarity theory face challenges due to their restricted applicability beyond the surface layer. Although recent studies have utilized various machine learning techniques to vertically extrapolate wind speeds, they often focus on single levels and lack a holistic approach to predicting entire wind profiles. As an alternative, this study introduces a proof-of-concept methodology utilizing TabNet, an attention-based sequential deep learning model, to estimate wind speed vertical profiles from coarse-resolution meteorological features extracted from a reanalysis dataset. To ensure that the methodology is applicable across diverse datasets, Chebyshev polynomial approximation is employed to model the wind profiles. Trained on the meteorological features as inputs and the Chebyshev coefficients as targets, the TabNet more-or-less accurately predicts unseen wind profiles for different wind conditions, such as high shear, low shear/well-mixed, low-level jet, and high wind. Additionally, this methodology quantifies the correlation of wind profiles with prevailing atmospheric conditions through a systematic feature importance assessment.
...
The growing demand for global wind power production, driven by the critical need for sustainable energy sources, requires reliable estimation of wind speed vertical profiles for accurate wind power prediction and comprehensive wind turbine performance assessment. Traditional methods relying on empirical equations or similarity theory face challenges due to their restricted applicability beyond the surface layer. Although recent studies have utilized various machine learning techniques to vertically extrapolate wind speeds, they often focus on single levels and lack a holistic approach to predicting entire wind profiles. As an alternative, this study introduces a proof-of-concept methodology utilizing TabNet, an attention-based sequential deep learning model, to estimate wind speed vertical profiles from coarse-resolution meteorological features extracted from a reanalysis dataset. To ensure that the methodology is applicable across diverse datasets, Chebyshev polynomial approximation is employed to model the wind profiles. Trained on the meteorological features as inputs and the Chebyshev coefficients as targets, the TabNet more-or-less accurately predicts unseen wind profiles for different wind conditions, such as high shear, low shear/well-mixed, low-level jet, and high wind. Additionally, this methodology quantifies the correlation of wind profiles with prevailing atmospheric conditions through a systematic feature importance assessment.
Renewable energy project require long term climate information, offshore renewable energies in particular are in need of higher fidelity information as both power production, reliability and survivability rely on them. Existing open source datasets are too coarse, and often do not have the suitable physics based solutions to resolve high fidelity areas.
The deliverable offers an in-depth resource assessment for wind-wave-solar renewable energy resources along the European Atlantic region. The duration of information cover 1990-2021 (including 2021), resulting in 32 years. This deliverable uses state of the art high resolution data for wind and solar, and introduces the European Coasts High resolution Ocean WAVEs (ECHOWAVE) hindcast, a new open source database for wave conditions with superior accuracy. ECHOWAVE covers North Atlantic European waters within the coastal shelf, from intermediate to shallow water relative depths and is specially adjusted to improve the representation of sea states within the area of interest. This translates as a reduction of the uncertainties in the estimation of some of the most important wave parameters for wave energy applications. ...
The deliverable offers an in-depth resource assessment for wind-wave-solar renewable energy resources along the European Atlantic region. The duration of information cover 1990-2021 (including 2021), resulting in 32 years. This deliverable uses state of the art high resolution data for wind and solar, and introduces the European Coasts High resolution Ocean WAVEs (ECHOWAVE) hindcast, a new open source database for wave conditions with superior accuracy. ECHOWAVE covers North Atlantic European waters within the coastal shelf, from intermediate to shallow water relative depths and is specially adjusted to improve the representation of sea states within the area of interest. This translates as a reduction of the uncertainties in the estimation of some of the most important wave parameters for wave energy applications. ...
Renewable energy project require long term climate information, offshore renewable energies in particular are in need of higher fidelity information as both power production, reliability and survivability rely on them. Existing open source datasets are too coarse, and often do not have the suitable physics based solutions to resolve high fidelity areas.
The deliverable offers an in-depth resource assessment for wind-wave-solar renewable energy resources along the European Atlantic region. The duration of information cover 1990-2021 (including 2021), resulting in 32 years. This deliverable uses state of the art high resolution data for wind and solar, and introduces the European Coasts High resolution Ocean WAVEs (ECHOWAVE) hindcast, a new open source database for wave conditions with superior accuracy. ECHOWAVE covers North Atlantic European waters within the coastal shelf, from intermediate to shallow water relative depths and is specially adjusted to improve the representation of sea states within the area of interest. This translates as a reduction of the uncertainties in the estimation of some of the most important wave parameters for wave energy applications.
The deliverable offers an in-depth resource assessment for wind-wave-solar renewable energy resources along the European Atlantic region. The duration of information cover 1990-2021 (including 2021), resulting in 32 years. This deliverable uses state of the art high resolution data for wind and solar, and introduces the European Coasts High resolution Ocean WAVEs (ECHOWAVE) hindcast, a new open source database for wave conditions with superior accuracy. ECHOWAVE covers North Atlantic European waters within the coastal shelf, from intermediate to shallow water relative depths and is specially adjusted to improve the representation of sea states within the area of interest. This translates as a reduction of the uncertainties in the estimation of some of the most important wave parameters for wave energy applications.
Advancements in floating offshore wind energy are unlocking the potential of the coastal waters of Portugal for the installation of wind farms. A thorough evaluation of coastal effects and variability across different time scales is crucial to ensure successful offshore wind farm investments. State-of-the-art atmospheric reanalysis datasets fall short in explaining the coastal effects due to their modest grid resolution. This study aims to fill this gap by simulating a 31-year wind dataset at a gray-zone resolution of 500 m using the Weather Research and Forecasting model, covering a significant portion of the Portugal coast. The gray-zone refers to grid scales of a few hundred meters, where turbulence is only partially resolved, traditional turbulence modeling breaks down, and large-eddy simulations are computationally prohibitive. The newly generated dataset has been validated with buoy observations and compared against reanalysis datasets, demonstrating improved performance and highlighting its higher fidelity in assessing wind resources. Two wind turbine power curves, the Leanwind 8 megawatt (MW) reference wind turbine (RWT), which has been commercialized, and the International Energy Agency (IEA) 15 MW RWT, which represents future commercialization, are considered in energy production calculations. In the simulated data, the Iberian Peninsula Coastal Jet (IPCJ) emerges as a crucial factor influencing wind maxima, especially during the summer months. The diurnal and annual variability of wind energy resources aligns with the occurrence of IPCJ, highlighting its impact on wind energy generation. The energy production capability of the 15 MW turbine model is demonstrated to be significantly higher, attributed not only to its increased capacity but also to the stronger jet winds near the turbine hub height. Interestingly, wind resources are not monotonically increasing with distance from the coastline, but a tongue-like resource maxima is observed, which is attributed to the IPCJ.
...
Advancements in floating offshore wind energy are unlocking the potential of the coastal waters of Portugal for the installation of wind farms. A thorough evaluation of coastal effects and variability across different time scales is crucial to ensure successful offshore wind farm investments. State-of-the-art atmospheric reanalysis datasets fall short in explaining the coastal effects due to their modest grid resolution. This study aims to fill this gap by simulating a 31-year wind dataset at a gray-zone resolution of 500 m using the Weather Research and Forecasting model, covering a significant portion of the Portugal coast. The gray-zone refers to grid scales of a few hundred meters, where turbulence is only partially resolved, traditional turbulence modeling breaks down, and large-eddy simulations are computationally prohibitive. The newly generated dataset has been validated with buoy observations and compared against reanalysis datasets, demonstrating improved performance and highlighting its higher fidelity in assessing wind resources. Two wind turbine power curves, the Leanwind 8 megawatt (MW) reference wind turbine (RWT), which has been commercialized, and the International Energy Agency (IEA) 15 MW RWT, which represents future commercialization, are considered in energy production calculations. In the simulated data, the Iberian Peninsula Coastal Jet (IPCJ) emerges as a crucial factor influencing wind maxima, especially during the summer months. The diurnal and annual variability of wind energy resources aligns with the occurrence of IPCJ, highlighting its impact on wind energy generation. The energy production capability of the 15 MW turbine model is demonstrated to be significantly higher, attributed not only to its increased capacity but also to the stronger jet winds near the turbine hub height. Interestingly, wind resources are not monotonically increasing with distance from the coastline, but a tongue-like resource maxima is observed, which is attributed to the IPCJ.
Renewable energy project require long term climate information, offshore renewable energies in particular are in need of higher fidelity information as both power production, reliability and survivability rely on them. Existing open source datasets are too coarse, and often do not have the suitable physics based solutions to resolve high fidelity areas.
While for power production different datasets may be needed wind speeds, solar radiation, ambient temperature, metocean conditions, the common thread is that all information provided by often open source free dataset carry large deviations that can be catastrophic or under-estimate power production significantly.
This deliverable aims to bridge the gap and offer the EU-SCORES project three custom models wind, solar, wave and are then used for ultra high-fidelity assessment (sub 500m). The physical parametrisation are specifically calibrated with the need of EU-SCORES, and the models are intensively validated against in-situ and satellite information. This report describes the different models used for the construction of the open-source databases for wind-wave-solar information, from 1990-2021. All models have been developed, calibrated and validated against in-situ measurements, providing with the uncertainty levels for the hindcasts.
...
Renewable energy project require long term climate information, offshore renewable energies in particular are in need of higher fidelity information as both power production, reliability and survivability rely on them. Existing open source datasets are too coarse, and often do not have the suitable physics based solutions to resolve high fidelity areas.
While for power production different datasets may be needed wind speeds, solar radiation, ambient temperature, metocean conditions, the common thread is that all information provided by often open source free dataset carry large deviations that can be catastrophic or under-estimate power production significantly.
This deliverable aims to bridge the gap and offer the EU-SCORES project three custom models wind, solar, wave and are then used for ultra high-fidelity assessment (sub 500m). The physical parametrisation are specifically calibrated with the need of EU-SCORES, and the models are intensively validated against in-situ and satellite information. This report describes the different models used for the construction of the open-source databases for wind-wave-solar information, from 1990-2021. All models have been developed, calibrated and validated against in-situ measurements, providing with the uncertainty levels for the hindcasts.
Journal article
(2024)
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P. Jyoteeshkumar Reddy, Sandeep Chinta, Harish Baki, Richard Matear, John Taylor
In Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, parameter uncertainty in physics parameterization schemes significantly impacts model output. Our study adopts a Bayesian probabilistic approach, building on prior research that identified temperature (T) and relative humidity (Rh) as sensitive to three key WRF parameters during southeast Australia's extreme heat events. Using Gaussian process regression-based Bayesian Optimisation (G-BO), we accurately estimated the optimal distributions for these parameters. Results show that the default values are outside their corresponding optimal distribution bounds for two of the three parameters, suggesting the need to reconsider these default values. Additionally, the robustness of the optimal parameter distributions is validated by their application to an independent extreme heat event, not included in the optimisation process. In this test, the optimized parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions.
...
In Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, parameter uncertainty in physics parameterization schemes significantly impacts model output. Our study adopts a Bayesian probabilistic approach, building on prior research that identified temperature (T) and relative humidity (Rh) as sensitive to three key WRF parameters during southeast Australia's extreme heat events. Using Gaussian process regression-based Bayesian Optimisation (G-BO), we accurately estimated the optimal distributions for these parameters. Results show that the default values are outside their corresponding optimal distribution bounds for two of the three parameters, suggesting the need to reconsider these default values. Additionally, the robustness of the optimal parameter distributions is validated by their application to an independent extreme heat event, not included in the optimisation process. In this test, the optimized parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions.
Wind ramps, or rapid changes in wind speed, are a crucial aspect of atmospheric dynamics and have significant implications for various wind energy applications. For example, wind ramps tend to increase uncertainty in power output predictions. Furthermore, they also induce fatigue damage to wind turbines.
In a recent study, DeMarco and Basu (2018; Wind Energy) used long-term observational data from four geographical locations to characterize the tails of the wind ramp probability distribution functions (pdfs). They showed that the pdfs from these various sites (ranging from offshore to complex terrain) portray quasi-universal behavior. The tails of the pdfs are much heavier than the Gaussian pdf and decay faster with increasing time increments. The tail-index
statistics, computed via the so-called Hill plots, exhibited minimal height dependency up to
approximately one hundred meters above the land or sea surface level. However, wind ramp
statistics at higher altitudes at Cabauw (the Netherlands) were quite distinct.
In the present study, we investigate if state-of-the-art reanalysis datasets capture the intrinsic traits of wind ramp pdfs. Specifically, we make use of the newly released Copernicus European Regional ReAnalysis (CERRA) dataset in conjunction with the popular fifth-generation ECMWF reanalysis (ERA5) dataset. These datasets allow us to describe the characteristics of wind ramp pdfs at high altitudes (up to 500 m). Given the disparity of the spatial resolution of CERRA (~5.5 km) and ERA5 (~32 km) datasets, we are also able to demonstrate the impact of spatial resolution on simulated tail index characteristics. Lastly, the influence of natural climate patterns such as El-Nino and La-Nina on wind ramp pdfs are examined.
...
Wind ramps, or rapid changes in wind speed, are a crucial aspect of atmospheric dynamics and have significant implications for various wind energy applications. For example, wind ramps tend to increase uncertainty in power output predictions. Furthermore, they also induce fatigue damage to wind turbines.
In a recent study, DeMarco and Basu (2018; Wind Energy) used long-term observational data from four geographical locations to characterize the tails of the wind ramp probability distribution functions (pdfs). They showed that the pdfs from these various sites (ranging from offshore to complex terrain) portray quasi-universal behavior. The tails of the pdfs are much heavier than the Gaussian pdf and decay faster with increasing time increments. The tail-index
statistics, computed via the so-called Hill plots, exhibited minimal height dependency up to
approximately one hundred meters above the land or sea surface level. However, wind ramp
statistics at higher altitudes at Cabauw (the Netherlands) were quite distinct.
In the present study, we investigate if state-of-the-art reanalysis datasets capture the intrinsic traits of wind ramp pdfs. Specifically, we make use of the newly released Copernicus European Regional ReAnalysis (CERRA) dataset in conjunction with the popular fifth-generation ECMWF reanalysis (ERA5) dataset. These datasets allow us to describe the characteristics of wind ramp pdfs at high altitudes (up to 500 m). Given the disparity of the spatial resolution of CERRA (~5.5 km) and ERA5 (~32 km) datasets, we are also able to demonstrate the impact of spatial resolution on simulated tail index characteristics. Lastly, the influence of natural climate patterns such as El-Nino and La-Nina on wind ramp pdfs are examined.
Machine learning based parameter sensitivity of regional climate models
A case study of the WRF model for heat extremes over Southeast Australia
Journal article
(2023)
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P. Jyoteeshkumar Reddy, Sandeep Chinta, Richard Matear, John Taylor, Harish Baki, Marcus Thatcher, Jatin Kala, Jason Sharples
Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model’s performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global SA method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study’s results will help in further optimising WRF parameters to improve model simulation.
...
Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model’s performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global SA method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study’s results will help in further optimising WRF parameters to improve model simulation.
This Deliverable, 6.1 Renewable Correlation of offshore resources, aims to investigate the potential for correlation between parameters of different renewable energies.
The analysis is based on a first layer on coarse data, and will allow us to identify which resources have more “connectivity”. The resource assessment, even at coarse level, will indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The correlation analysis is expected to showcase the potential of temporal overlaps by the different resources.
The Deliverable examines the overlap of stochastic conditions, the analysis will consider different “time windows” for the base resource, and assess its complementarity with other stochastic renewables. The aim of this deliverable is the estimation of overlap and production of mean maps that indicate to which extend each resource is connected. It is expected that the wind and wave resources will produce higher interest, due to their temporal variability. However, peak solar performance will also be analysed in terms of overlap with wind and/or wave. ...
The analysis is based on a first layer on coarse data, and will allow us to identify which resources have more “connectivity”. The resource assessment, even at coarse level, will indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The correlation analysis is expected to showcase the potential of temporal overlaps by the different resources.
The Deliverable examines the overlap of stochastic conditions, the analysis will consider different “time windows” for the base resource, and assess its complementarity with other stochastic renewables. The aim of this deliverable is the estimation of overlap and production of mean maps that indicate to which extend each resource is connected. It is expected that the wind and wave resources will produce higher interest, due to their temporal variability. However, peak solar performance will also be analysed in terms of overlap with wind and/or wave. ...
This Deliverable, 6.1 Renewable Correlation of offshore resources, aims to investigate the potential for correlation between parameters of different renewable energies.
The analysis is based on a first layer on coarse data, and will allow us to identify which resources have more “connectivity”. The resource assessment, even at coarse level, will indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The correlation analysis is expected to showcase the potential of temporal overlaps by the different resources.
The Deliverable examines the overlap of stochastic conditions, the analysis will consider different “time windows” for the base resource, and assess its complementarity with other stochastic renewables. The aim of this deliverable is the estimation of overlap and production of mean maps that indicate to which extend each resource is connected. It is expected that the wind and wave resources will produce higher interest, due to their temporal variability. However, peak solar performance will also be analysed in terms of overlap with wind and/or wave.
The analysis is based on a first layer on coarse data, and will allow us to identify which resources have more “connectivity”. The resource assessment, even at coarse level, will indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The correlation analysis is expected to showcase the potential of temporal overlaps by the different resources.
The Deliverable examines the overlap of stochastic conditions, the analysis will consider different “time windows” for the base resource, and assess its complementarity with other stochastic renewables. The aim of this deliverable is the estimation of overlap and production of mean maps that indicate to which extend each resource is connected. It is expected that the wind and wave resources will produce higher interest, due to their temporal variability. However, peak solar performance will also be analysed in terms of overlap with wind and/or wave.
This Deliverable, 6.2 Renewable Coarse Resource Assessment for the European Region, aims to offer a preliminary overview of the available wind, wave and solar resources across the European Continent. The coarse assessment aims to analyse and assess the current levels of these renewable resources, analysing and discussing the expected variations per regions.
The resource assessment, even at coarse level, can indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The estimated energy densities of wind, wave and solar, are partially the main indicators, we also discuss the impacts of variability, as this is expected to alter the performance of power production, when each resource is utilised by specific technologies.
This report also introduces some of the main statistical approaches and ways to estimate the resource potentials. They will be used and expanded upon in forthcoming Deliverables that will also look into power production, via coupling of high fidelity wind-wave-solar models with specific renewable converters.
Finally, in this Deliverable we discuss the role of open source coarse data and underline their limitations for operational renewable energy projects. ...
The resource assessment, even at coarse level, can indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The estimated energy densities of wind, wave and solar, are partially the main indicators, we also discuss the impacts of variability, as this is expected to alter the performance of power production, when each resource is utilised by specific technologies.
This report also introduces some of the main statistical approaches and ways to estimate the resource potentials. They will be used and expanded upon in forthcoming Deliverables that will also look into power production, via coupling of high fidelity wind-wave-solar models with specific renewable converters.
Finally, in this Deliverable we discuss the role of open source coarse data and underline their limitations for operational renewable energy projects. ...
This Deliverable, 6.2 Renewable Coarse Resource Assessment for the European Region, aims to offer a preliminary overview of the available wind, wave and solar resources across the European Continent. The coarse assessment aims to analyse and assess the current levels of these renewable resources, analysing and discussing the expected variations per regions.
The resource assessment, even at coarse level, can indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The estimated energy densities of wind, wave and solar, are partially the main indicators, we also discuss the impacts of variability, as this is expected to alter the performance of power production, when each resource is utilised by specific technologies.
This report also introduces some of the main statistical approaches and ways to estimate the resource potentials. They will be used and expanded upon in forthcoming Deliverables that will also look into power production, via coupling of high fidelity wind-wave-solar models with specific renewable converters.
Finally, in this Deliverable we discuss the role of open source coarse data and underline their limitations for operational renewable energy projects.
The resource assessment, even at coarse level, can indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The estimated energy densities of wind, wave and solar, are partially the main indicators, we also discuss the impacts of variability, as this is expected to alter the performance of power production, when each resource is utilised by specific technologies.
This report also introduces some of the main statistical approaches and ways to estimate the resource potentials. They will be used and expanded upon in forthcoming Deliverables that will also look into power production, via coupling of high fidelity wind-wave-solar models with specific renewable converters.
Finally, in this Deliverable we discuss the role of open source coarse data and underline their limitations for operational renewable energy projects.