G. Lavidas
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Wave power for e-fuels and e-chemicals production
Technical feasibility, economic viability, and regional opportunities
Journal article
(2026)
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Rasul Satymov, Dmitrii Bogdanov, George Lavidas, Shona Pennock, Sarah Kluge, Benjamin Lehner, Christian Breyer
The global energy transition necessitates the defossilisation of the energy-intensive industry and hard-to-abate transport sectors, where direct electrification falls short due to limitations in energy density. Electricity-based fuels and chemicals (e-fuels and e-chemicals) emerge as a viable alternative, offering high energy density and compatibility with existing infrastructure. However, their production hinges on access to vast amounts of low-cost renewable electricity, a challenge for regions with limited land. This study explores wave power as an underexplored source for e-fuel production, focusing on regions with exceptional wave energy: New Zealand, Chile, and Ireland. Using energyHub-LUT, a newly developed optimisation model, the research evaluates the techno-economics of wave power, alongside solar photovoltaics and wind power, for producing e-fuels and e-chemicals. The results show that wave power supplies more stable power for e-fuel production compared to onshore wind power and solar photovoltaics, cutting the need for batteries by 25-100%, depending on location. Chile produces the lowest cost e-fuels when wave power is integrated alongside solar photovoltaics and onshore wind power, thanks to over 7000 full load hours, but its higher capital expenditures render it less competitive than onshore renewable energy. The study concludes that wave power's economic viability is limited even in regions with very high full load hours but also highlights its potential as an alternative where land scarcity hinders large-scale renewable energy projects or in cases of near baseload direct electricity need.
...
The global energy transition necessitates the defossilisation of the energy-intensive industry and hard-to-abate transport sectors, where direct electrification falls short due to limitations in energy density. Electricity-based fuels and chemicals (e-fuels and e-chemicals) emerge as a viable alternative, offering high energy density and compatibility with existing infrastructure. However, their production hinges on access to vast amounts of low-cost renewable electricity, a challenge for regions with limited land. This study explores wave power as an underexplored source for e-fuel production, focusing on regions with exceptional wave energy: New Zealand, Chile, and Ireland. Using energyHub-LUT, a newly developed optimisation model, the research evaluates the techno-economics of wave power, alongside solar photovoltaics and wind power, for producing e-fuels and e-chemicals. The results show that wave power supplies more stable power for e-fuel production compared to onshore wind power and solar photovoltaics, cutting the need for batteries by 25-100%, depending on location. Chile produces the lowest cost e-fuels when wave power is integrated alongside solar photovoltaics and onshore wind power, thanks to over 7000 full load hours, but its higher capital expenditures render it less competitive than onshore renewable energy. The study concludes that wave power's economic viability is limited even in regions with very high full load hours but also highlights its potential as an alternative where land scarcity hinders large-scale renewable energy projects or in cases of near baseload direct electricity need.
Recent studies have demonstrated the merits of spectral-domain (SD) modeling in efficiently addressing nonlinear dynamic behvavior of stand-alone wave energy converters (WECs). However, the potential of the SD modeling approach deserves further exploitation by examining its applicability in simulating the entire wave-to-wire (W2W) process of WEC arrays. This article proposed and verified a SD W2W model of WEC arrays. The WEC arrays are considered as five same-sized heaving cylindrical point absorbers, and they are all equipped with linear Permanent Magnet (PM) generators. The established SD W2W model is verified by being compared with results of a nonlinear time-domain-based W2W model across a variety of operation conditions. The computational efficiency of the two simulation approaches in modeling WEC arrays is also identified and compared. The results suggest that the SD W2W model is associated with a relative error of less than 11 % to the nonlinear time-domain reference, with regard to the estimates of significant statistical performance indicators, such as WEC velocity, absorbed and electrical power of individual power, and total electrical power production of the WEC arrays. At the same time, the SD W2W model presents a high computational efficiency, being around 2000 times faster than the time-domain W2W model of WEC arrays.
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Recent studies have demonstrated the merits of spectral-domain (SD) modeling in efficiently addressing nonlinear dynamic behvavior of stand-alone wave energy converters (WECs). However, the potential of the SD modeling approach deserves further exploitation by examining its applicability in simulating the entire wave-to-wire (W2W) process of WEC arrays. This article proposed and verified a SD W2W model of WEC arrays. The WEC arrays are considered as five same-sized heaving cylindrical point absorbers, and they are all equipped with linear Permanent Magnet (PM) generators. The established SD W2W model is verified by being compared with results of a nonlinear time-domain-based W2W model across a variety of operation conditions. The computational efficiency of the two simulation approaches in modeling WEC arrays is also identified and compared. The results suggest that the SD W2W model is associated with a relative error of less than 11 % to the nonlinear time-domain reference, with regard to the estimates of significant statistical performance indicators, such as WEC velocity, absorbed and electrical power of individual power, and total electrical power production of the WEC arrays. At the same time, the SD W2W model presents a high computational efficiency, being around 2000 times faster than the time-domain W2W model of WEC arrays.
Due to the complexity of the ocean environment and wave energy converter (WEC) system, it has been an effort-demanding work to assess either the power performance or fatigue loads of WECs. This work attempts to apply a data-driven approach to increase the efficiency of the collective prediction of the power and fatigue load of a point-absorber type WEC. Nonlinear time-domain modeling is first established to estimate the power and fatigue loads, which is considered the reference data in this work. To demonstrate the performance of the applied data-driven approach, two prevalent power take-off (PTO) mechanisms are implemented to represent different characteristics of WECs. A data-driven approach, active learning Kriging (AK), is adapted to predict power and fatigue loads collectively, and a new learning function is defined to select the enriched wave cases for the active learning process. Results show that the applied active learning approach can accurately and simultaneously predict power and fatigue loads in both PTO mechanisms. Compared to pure numerical simulation, the proposed method only requires 15 simulations of sea state, and the computational effort is reduced by more than 20 times. The maximum prediction error is less than 2%. The data-driven approach could be a powerful tool for WEC system optimization, considering both power performance and fatigue loads.
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Due to the complexity of the ocean environment and wave energy converter (WEC) system, it has been an effort-demanding work to assess either the power performance or fatigue loads of WECs. This work attempts to apply a data-driven approach to increase the efficiency of the collective prediction of the power and fatigue load of a point-absorber type WEC. Nonlinear time-domain modeling is first established to estimate the power and fatigue loads, which is considered the reference data in this work. To demonstrate the performance of the applied data-driven approach, two prevalent power take-off (PTO) mechanisms are implemented to represent different characteristics of WECs. A data-driven approach, active learning Kriging (AK), is adapted to predict power and fatigue loads collectively, and a new learning function is defined to select the enriched wave cases for the active learning process. Results show that the applied active learning approach can accurately and simultaneously predict power and fatigue loads in both PTO mechanisms. Compared to pure numerical simulation, the proposed method only requires 15 simulations of sea state, and the computational effort is reduced by more than 20 times. The maximum prediction error is less than 2%. The data-driven approach could be a powerful tool for WEC system optimization, considering both power performance and fatigue loads.
Wave-to-Wire (W2W) modeling simulates the whole operation process of wave energy converters (WECs), which plays a pivotal role in the systematic design and optimization of WECs. Existing W2W models are predominantly constructed based on time-domain (TD) analysis to coherently incorporate relevant nonlinearities. However, TD models require a high computational cost, which hinders the design iterations of WECs. As a newly emerging alternative approach, spectral-domain (SD) modeling has demonstrated the applicability of describing the W2W process while efficiently covering nonlinear effects through statistical linearization. This study aims to develop an SD W2W modeling approach for WECs coupled with a gearbox and rotary generator. The application of the proposed model is exemplified in two case studies: (1) a point absorber with a rack-pinion system and a rotary generator; (2) a flap-type WEC with a revolving gearbox and a rotary generator. The simulation results obtained by the SD W2W model are compared against a higher-fidelity nonlinear TD W2W model to verify its accuracy across a variety of sea states. A good agreement between the two modeling approaches is observed, in which the maximum relative error is below 7 % with regard to the estimation of important system outputs. Meanwhile, the computational efficiency of the SD W2W model is thousands of times higher than the TD modeling approach.
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Wave-to-Wire (W2W) modeling simulates the whole operation process of wave energy converters (WECs), which plays a pivotal role in the systematic design and optimization of WECs. Existing W2W models are predominantly constructed based on time-domain (TD) analysis to coherently incorporate relevant nonlinearities. However, TD models require a high computational cost, which hinders the design iterations of WECs. As a newly emerging alternative approach, spectral-domain (SD) modeling has demonstrated the applicability of describing the W2W process while efficiently covering nonlinear effects through statistical linearization. This study aims to develop an SD W2W modeling approach for WECs coupled with a gearbox and rotary generator. The application of the proposed model is exemplified in two case studies: (1) a point absorber with a rack-pinion system and a rotary generator; (2) a flap-type WEC with a revolving gearbox and a rotary generator. The simulation results obtained by the SD W2W model are compared against a higher-fidelity nonlinear TD W2W model to verify its accuracy across a variety of sea states. A good agreement between the two modeling approaches is observed, in which the maximum relative error is below 7 % with regard to the estimation of important system outputs. Meanwhile, the computational efficiency of the SD W2W model is thousands of times higher than the TD modeling approach.
Wave energy arrays are essential for reducing the Levelised Cost of Energy, yet the performance of traditional mono-device arrays is often limited by destructive hydrodynamic interactions and directional sensitivity. This work focuses on ”mixed arrays,” wherein different types and geometries of wave energy converters operating in different degrees of freedom (point Absorber and a flap) are deployed within the same array to exploit complementary device dynamics, whilst reducing spatial requirements. Using a weakly non-linear frequency-domain model utilising the solver HAMS-MREL, a systematic comparison is performed across 3360 cases considering varying array sizes, spacings, wave directions, and control strategies (active and passive). Comparison of array performance is based on the well known q-factor and a new geometry dependent metric (M-factor). The results demonstrate that mixed arrays can outperform mono-device arrays by leveraging favourable hydrodynamic cross-coupling and radiated wave-field alignment. For a 10-device staggered configuration, mixed arrays achieved a peak q-factor of 1.6 and an M-factor of 2.25 under regular waves, showing a 175% increase in point absorber heave response under displacement constraints and 34% increase in flap excitation forces. Performance is sensitive to the spacing-to-wavelength ratio, mixed arrays exhibit superior directional robustness, and reduced efficiency collapse compared to mono-flap arrays. The findings suggest that mixed-device configurations can provide a robust alternative for optimising energy capture, reducing spatial requirements, offering new collaboration opportunities and contributing to the viability of wave energy arrays.
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Wave energy arrays are essential for reducing the Levelised Cost of Energy, yet the performance of traditional mono-device arrays is often limited by destructive hydrodynamic interactions and directional sensitivity. This work focuses on ”mixed arrays,” wherein different types and geometries of wave energy converters operating in different degrees of freedom (point Absorber and a flap) are deployed within the same array to exploit complementary device dynamics, whilst reducing spatial requirements. Using a weakly non-linear frequency-domain model utilising the solver HAMS-MREL, a systematic comparison is performed across 3360 cases considering varying array sizes, spacings, wave directions, and control strategies (active and passive). Comparison of array performance is based on the well known q-factor and a new geometry dependent metric (M-factor). The results demonstrate that mixed arrays can outperform mono-device arrays by leveraging favourable hydrodynamic cross-coupling and radiated wave-field alignment. For a 10-device staggered configuration, mixed arrays achieved a peak q-factor of 1.6 and an M-factor of 2.25 under regular waves, showing a 175% increase in point absorber heave response under displacement constraints and 34% increase in flap excitation forces. Performance is sensitive to the spacing-to-wavelength ratio, mixed arrays exhibit superior directional robustness, and reduced efficiency collapse compared to mono-flap arrays. The findings suggest that mixed-device configurations can provide a robust alternative for optimising energy capture, reducing spatial requirements, offering new collaboration opportunities and contributing to the viability of wave energy arrays.
Marine resources such as wind and wave are expected to play an important role in decarbonising the UK’s energy network, as part of the global transition from fossil fuels to renewable energy. However, potential increases in global weather systems variability due to climate change cast doubt on the long-term sustainability of offshore renewable development and the ambitions of the UK government to rapidly expand current capacity. As such, growing interest in co-located wind–wave systems is being paid as a means of enhancing the climate resilience of the future energy network. This study investigates the impacts of climate change on wind and wave resources in the UK from 2015 to 2100, using available CMIP6 datasets and numerical wave modelling using SWAN. In doing so, an initial assessment of the potential for co-located infrastructure is undertaken to inform future research into its role in strengthening the climate resilience of ocean renewable generation. The results reveal gradual reductions in resource availability under a high emission scenario, with statistically significant annual trends detected across most of the study area. Reductions in average annual wind energy reach −16.0% in coastal areas of Northern Ireland towards the end of the century, while decreases in wave energy of more than 25% are projected in certain regions of the North Sea. These trends are primarily driven by seasonal reductions during summer months, with decreases in average wind power during these months as much as −29.0% and decreases in wave power of over −40%. In addition, increases in resource variability from the mid-century onwards suggest that climate change is likely to negatively affect the availability of the UK’s wind and wave energy resources in the long term.
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Marine resources such as wind and wave are expected to play an important role in decarbonising the UK’s energy network, as part of the global transition from fossil fuels to renewable energy. However, potential increases in global weather systems variability due to climate change cast doubt on the long-term sustainability of offshore renewable development and the ambitions of the UK government to rapidly expand current capacity. As such, growing interest in co-located wind–wave systems is being paid as a means of enhancing the climate resilience of the future energy network. This study investigates the impacts of climate change on wind and wave resources in the UK from 2015 to 2100, using available CMIP6 datasets and numerical wave modelling using SWAN. In doing so, an initial assessment of the potential for co-located infrastructure is undertaken to inform future research into its role in strengthening the climate resilience of ocean renewable generation. The results reveal gradual reductions in resource availability under a high emission scenario, with statistically significant annual trends detected across most of the study area. Reductions in average annual wind energy reach −16.0% in coastal areas of Northern Ireland towards the end of the century, while decreases in wave energy of more than 25% are projected in certain regions of the North Sea. These trends are primarily driven by seasonal reductions during summer months, with decreases in average wind power during these months as much as −29.0% and decreases in wave power of over −40%. In addition, increases in resource variability from the mid-century onwards suggest that climate change is likely to negatively affect the availability of the UK’s wind and wave energy resources in the long term.
Wave energy arrays are essential for reducing the Levelised Cost of Energy, yet the performance of traditional mono-device arrays is often limited by destructive hydrodynamic interactions and directional sensitivity. This work focuses on ”mixed arrays,” wherein different types and geometries of wave energy converters operating in different degrees of freedom (point Absorber and a flap) are deployed within the same array to exploit complementary device dynamics, whilst reducing spatial requirements. Using a weakly non-linear frequency-domain model utilising the solver HAMS-MREL, a systematic comparison is performed across 3360 cases considering varying array sizes, spacings, wave directions, and control strategies (active and passive). Comparison of array performance is based on the well known q-factor and a new geometry dependent metric (M-factor). The results demonstrate that mixed arrays can outperform mono-device arrays by leveraging favourable hydrodynamic cross-coupling and radiated wave-field alignment. For a 10-device staggered configuration, mixed arrays achieved a peak q-factor of 1.6 and an M-factor of 2.25 under regular waves, showing a 175% increase in point absorber heave response under displacement constraints and 34% increase in flap excitation forces. Performance is sensitive to the spacing-to-wavelength ratio, mixed arrays exhibit superior directional robustness, and reduced efficiency collapse compared to mono-flap arrays. The findings suggest that mixed-device configurations can provide a robust alternative for optimising energy capture, reducing spatial requirements, offering new collaboration opportunities and contributing to the viability of wave energy arrays.
...
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Wave energy arrays are essential for reducing the Levelised Cost of Energy, yet the performance of traditional mono-device arrays is often limited by destructive hydrodynamic interactions and directional sensitivity. This work focuses on ”mixed arrays,” wherein different types and geometries of wave energy converters operating in different degrees of freedom (point Absorber and a flap) are deployed within the same array to exploit complementary device dynamics, whilst reducing spatial requirements. Using a weakly non-linear frequency-domain model utilising the solver HAMS-MREL, a systematic comparison is performed across 3360 cases considering varying array sizes, spacings, wave directions, and control strategies (active and passive). Comparison of array performance is based on the well known q-factor and a new geometry dependent metric (M-factor). The results demonstrate that mixed arrays can outperform mono-device arrays by leveraging favourable hydrodynamic cross-coupling and radiated wave-field alignment. For a 10-device staggered configuration, mixed arrays achieved a peak q-factor of 1.6 and an M-factor of 2.25 under regular waves, showing a 175% increase in point absorber heave response under displacement constraints and 34% increase in flap excitation forces. Performance is sensitive to the spacing-to-wavelength ratio, mixed arrays exhibit superior directional robustness, and reduced efficiency collapse compared to mono-flap arrays. The findings suggest that mixed-device configurations can provide a robust alternative for optimising energy capture, reducing spatial requirements, offering new collaboration opportunities and contributing to the viability of wave energy arrays.
Obtaining the hydrodynamic pressure distribution on the wetted surface of floating structures is a critical step in structural analysis and is commonly achieved through pressure regeneration based on predicted global dynamic responses. Using derived hydrodynamic coefficients, various dynamic modeling approaches, including Cummins-equation-based nonlinear time-domain modeling, statistical linearization, and Lorentz linearization, can be applied to solve for the global dynamics of structures subjected to specific wave conditions. These dynamic modeling approaches differ in both computational efficiency and modeling fidelity. Despite their widespread use, a systematic comparison of these approaches, particularly between statistical and Lorentz linearization in predicting global dynamics and regenerated pressure fields, remains limited. This study addresses this gap by conducting a comparative study of linear-potential-flow-based dynamic modeling approaches using a generic cylindrical floater, incorporating a representative nonlinear external machinery effect through different modeling approaches. The resulting global responses are used to regenerate hydrodynamic pressure distributions, showing that all the dynamic modeling approaches agree well under low wave steepness. As wave steepness increases, the prediction performance of statistical linearization, Lorentz linearization, and a simplified Lorentz linearization, gradually decreases relative to the nonlinear time-domain model. Among these, the statistical linearization approach provides results closer to the nonlinear time-domain model than both Lorentz-based linearization methods, particularly in capturing global dynamics and reconstructing hydrodynamic pressure distributions under relatively high wave steepness. Given its high computational efficiency, the statistical linearization approach has the potential to be further developed as an efficient alternative modeling for estimating dynamic responses and hydrodynamic pressure distributions.
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Obtaining the hydrodynamic pressure distribution on the wetted surface of floating structures is a critical step in structural analysis and is commonly achieved through pressure regeneration based on predicted global dynamic responses. Using derived hydrodynamic coefficients, various dynamic modeling approaches, including Cummins-equation-based nonlinear time-domain modeling, statistical linearization, and Lorentz linearization, can be applied to solve for the global dynamics of structures subjected to specific wave conditions. These dynamic modeling approaches differ in both computational efficiency and modeling fidelity. Despite their widespread use, a systematic comparison of these approaches, particularly between statistical and Lorentz linearization in predicting global dynamics and regenerated pressure fields, remains limited. This study addresses this gap by conducting a comparative study of linear-potential-flow-based dynamic modeling approaches using a generic cylindrical floater, incorporating a representative nonlinear external machinery effect through different modeling approaches. The resulting global responses are used to regenerate hydrodynamic pressure distributions, showing that all the dynamic modeling approaches agree well under low wave steepness. As wave steepness increases, the prediction performance of statistical linearization, Lorentz linearization, and a simplified Lorentz linearization, gradually decreases relative to the nonlinear time-domain model. Among these, the statistical linearization approach provides results closer to the nonlinear time-domain model than both Lorentz-based linearization methods, particularly in capturing global dynamics and reconstructing hydrodynamic pressure distributions under relatively high wave steepness. Given its high computational efficiency, the statistical linearization approach has the potential to be further developed as an efficient alternative modeling for estimating dynamic responses and hydrodynamic pressure distributions.
Conference paper
(2026)
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Ardemia Acampora, Harishankar Sivaprasad, George Lavidas, Apostolos Tsouvalas
The deployment of floating offshore wind turbines requires the installation of anchoring systems in deep-water environments, where pile driving operations may be performed using fully submerged impact hammers. While underwater noise from offshore pile driving has been widely studied, most existing approaches assume impacts occurring in air and may not entirely capture these emerging installation configurations. Furthermore, current assessment frameworks are primarily based on pressure-related acoustic metrics and do not explicitly account for particle motion. This study investigates the vibro-acoustic response of a driven pile under submerged impact conditions, with particular focus on particle motion in both the water column and the seabed. A physics-based modelling approach is adopted to analyse the coupled pile–soil–fluid system, capturing both the radiated acoustic field in the water and the vibration field transmitted through the seabed. The response is described in terms of particle velocity, enabling a consistent representation across both media. The results provide insight into the mechanisms governing energy transmission into the seabed, supporting a more comprehensive assessment of environmental effects in deep-water pile installation.
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The deployment of floating offshore wind turbines requires the installation of anchoring systems in deep-water environments, where pile driving operations may be performed using fully submerged impact hammers. While underwater noise from offshore pile driving has been widely studied, most existing approaches assume impacts occurring in air and may not entirely capture these emerging installation configurations. Furthermore, current assessment frameworks are primarily based on pressure-related acoustic metrics and do not explicitly account for particle motion. This study investigates the vibro-acoustic response of a driven pile under submerged impact conditions, with particular focus on particle motion in both the water column and the seabed. A physics-based modelling approach is adopted to analyse the coupled pile–soil–fluid system, capturing both the radiated acoustic field in the water and the vibration field transmitted through the seabed. The response is described in terms of particle velocity, enabling a consistent representation across both media. The results provide insight into the mechanisms governing energy transmission into the seabed, supporting a more comprehensive assessment of environmental effects in deep-water pile installation.
This study examines the potential contribution of marine renewable generators in Greece, in order to achieve a 100% renewable energy system by 2050. Using PyPSA-Eur, a cost-optimization model of the European energy system, possible energy transition pathways are explored, across five-year intervals from 2030 to 2050. For each five-year target, a new cost assumption dataset is used, one that follows estimated cost reduction learning rates. This version of the model is called PyPSA-Eur-MREL, and is modified to include marine power generators, i.e. floating wind, wave, tidal and floating solar, but also high fidelity climate data, in the scale of 5.5 km
2 for wind and 4 km
2 for wave resources. Three different approaches were employed in this investigation: greenfield, generator constrained, and a high-load scenario inspired by Greece's National Energy and Climate Plan (NECP). The analysis focused on generator capacity and performance, the levels of utilization and availability of each energy carrier and the land-use impact of onshore and offshore generators. While the first two scenarios exhibit similar overall system capacities, they differ in land-use requirements, with the constrained case installing more bottom-fixed wind turbines (1.2 GW), thereby reducing land occupation. The high-load scenario introduces floating wind turbines (4.5 GW), however, the scale of onshore installations remains substantial, covering nearly one-third of Greece's total land area.
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This study examines the potential contribution of marine renewable generators in Greece, in order to achieve a 100% renewable energy system by 2050. Using PyPSA-Eur, a cost-optimization model of the European energy system, possible energy transition pathways are explored, across five-year intervals from 2030 to 2050. For each five-year target, a new cost assumption dataset is used, one that follows estimated cost reduction learning rates. This version of the model is called PyPSA-Eur-MREL, and is modified to include marine power generators, i.e. floating wind, wave, tidal and floating solar, but also high fidelity climate data, in the scale of 5.5 km 2 for wind and 4 km 2 for wave resources. Three different approaches were employed in this investigation: greenfield, generator constrained, and a high-load scenario inspired by Greece's National Energy and Climate Plan (NECP). The analysis focused on generator capacity and performance, the levels of utilization and availability of each energy carrier and the land-use impact of onshore and offshore generators. While the first two scenarios exhibit similar overall system capacities, they differ in land-use requirements, with the constrained case installing more bottom-fixed wind turbines (1.2 GW), thereby reducing land occupation. The high-load scenario introduces floating wind turbines (4.5 GW), however, the scale of onshore installations remains substantial, covering nearly one-third of Greece's total land area.
Numerical modelling plays a pivotal role in the design and optimization of wave energy converters. Spectral-domain (SD) modelling has recently received significant research interest as a newly emerging numerical tool. SD modelling is commonly characterized as an extension of frequency-domain (FD) modelling but can incorporate nonlinearities. Thereby, it combines high computational efficiency and adequate accuracy. Previous studies have demonstrated the applicability of SD modelling to a variety of nonlinear hydrostatic/hydrodynamic effects, including viscous drag force, nonlinear hydrostatic force, nonlinear mooring force, etc. However, there also exist influential nonlinear effects in the power generation phase in wave energy conversion. For instance, previous studies have demonstrated that the current limit of the electrical generator could impact the PTO force and the dynamics of the whole system. Therefore, it is necessary to further develop the SD modelling to cover the entire wave-to-wire process in WECs.
In this paper, a SD model is derived to simulate the wave-to-wire process of a point absorber WEC. A mechanical PTO system coupled with a rotary permanent-magnet generator is considered for the WEC. Representative nonlinear effects of the wave-to-wire process are incorporated, including viscous drag force, nonlinear PTO force, and the current limit of the generator. A nonlinear time-domain (TD) wave-to-wire model is established correspondingly to serve as the accuracy reference because it is inherently associated with higher modelling fidelity. The dynamic response and the power performance of the proposed SD model are verified against those of the nonlinear TD wave-to-wire model. Additionally, the computational efficiency of the proposed SD model and the TD model is identified and compared. ...
In this paper, a SD model is derived to simulate the wave-to-wire process of a point absorber WEC. A mechanical PTO system coupled with a rotary permanent-magnet generator is considered for the WEC. Representative nonlinear effects of the wave-to-wire process are incorporated, including viscous drag force, nonlinear PTO force, and the current limit of the generator. A nonlinear time-domain (TD) wave-to-wire model is established correspondingly to serve as the accuracy reference because it is inherently associated with higher modelling fidelity. The dynamic response and the power performance of the proposed SD model are verified against those of the nonlinear TD wave-to-wire model. Additionally, the computational efficiency of the proposed SD model and the TD model is identified and compared. ...
Numerical modelling plays a pivotal role in the design and optimization of wave energy converters. Spectral-domain (SD) modelling has recently received significant research interest as a newly emerging numerical tool. SD modelling is commonly characterized as an extension of frequency-domain (FD) modelling but can incorporate nonlinearities. Thereby, it combines high computational efficiency and adequate accuracy. Previous studies have demonstrated the applicability of SD modelling to a variety of nonlinear hydrostatic/hydrodynamic effects, including viscous drag force, nonlinear hydrostatic force, nonlinear mooring force, etc. However, there also exist influential nonlinear effects in the power generation phase in wave energy conversion. For instance, previous studies have demonstrated that the current limit of the electrical generator could impact the PTO force and the dynamics of the whole system. Therefore, it is necessary to further develop the SD modelling to cover the entire wave-to-wire process in WECs.
In this paper, a SD model is derived to simulate the wave-to-wire process of a point absorber WEC. A mechanical PTO system coupled with a rotary permanent-magnet generator is considered for the WEC. Representative nonlinear effects of the wave-to-wire process are incorporated, including viscous drag force, nonlinear PTO force, and the current limit of the generator. A nonlinear time-domain (TD) wave-to-wire model is established correspondingly to serve as the accuracy reference because it is inherently associated with higher modelling fidelity. The dynamic response and the power performance of the proposed SD model are verified against those of the nonlinear TD wave-to-wire model. Additionally, the computational efficiency of the proposed SD model and the TD model is identified and compared.
In this paper, a SD model is derived to simulate the wave-to-wire process of a point absorber WEC. A mechanical PTO system coupled with a rotary permanent-magnet generator is considered for the WEC. Representative nonlinear effects of the wave-to-wire process are incorporated, including viscous drag force, nonlinear PTO force, and the current limit of the generator. A nonlinear time-domain (TD) wave-to-wire model is established correspondingly to serve as the accuracy reference because it is inherently associated with higher modelling fidelity. The dynamic response and the power performance of the proposed SD model are verified against those of the nonlinear TD wave-to-wire model. Additionally, the computational efficiency of the proposed SD model and the TD model is identified and compared.
This article investigates the methodology and applicability of the statistical linearization (SL) method to incorporating multi-variate non-differentiable nonlinearities, with a focus on floating renewable energy devices. The SL method serves as a highly competitive approach for analyzing floating renewable energy structures, such as wave energy converters (WECs) and floating wind energy turbines, because it inherently combines adequate accuracy and high computational efficiency. The origin of high accuracy comes from its incorporation of nonlinear effects through statistically linearized representations. Yet, the statistically linearized solutions have only been derived and verified for a limited number of nonlinearities of floating renewable energy devices, mostly simply-formed and differentiable in their mathematical expressions. However, floating renewable energy devices usually exhibit a complex dynamic mechanism, in which the relevant nonlinear effects could appear to be highly complex for linearization process to describe. These nonlinear effects could make a significant impact on the system dynamics, exemplified by external machinery force saturation and nonlinear hydrostatics of floaters with a non-uniform geometry. To push forward the boundary of the SL method, it is crucial to demonstrate how it applies to nonlinearities of different features. In this paper, the existing SL method is extended to address the nonlinear effects expressed as multi-variate non-differentiable functions. Several case studies are carried out to exemplify the application of the extended SL approach to the concerned nonlinearities in floating renewable energy devices. The accuracy and computational efficiency of the extended SL approach are evaluated by verifying against the corresponding nonlinear time-domain (TD) and linear frequency-domain (FD) models. Despite the complexity of the given nonlinearities, the relative errors of the SL approach are no more than 6 % while its computational time is comparable to the FD model, being thousands of times faster than the TD model. Comparatively, the FD model leads to a relative error of over 70% in some cases.
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This article investigates the methodology and applicability of the statistical linearization (SL) method to incorporating multi-variate non-differentiable nonlinearities, with a focus on floating renewable energy devices. The SL method serves as a highly competitive approach for analyzing floating renewable energy structures, such as wave energy converters (WECs) and floating wind energy turbines, because it inherently combines adequate accuracy and high computational efficiency. The origin of high accuracy comes from its incorporation of nonlinear effects through statistically linearized representations. Yet, the statistically linearized solutions have only been derived and verified for a limited number of nonlinearities of floating renewable energy devices, mostly simply-formed and differentiable in their mathematical expressions. However, floating renewable energy devices usually exhibit a complex dynamic mechanism, in which the relevant nonlinear effects could appear to be highly complex for linearization process to describe. These nonlinear effects could make a significant impact on the system dynamics, exemplified by external machinery force saturation and nonlinear hydrostatics of floaters with a non-uniform geometry. To push forward the boundary of the SL method, it is crucial to demonstrate how it applies to nonlinearities of different features. In this paper, the existing SL method is extended to address the nonlinear effects expressed as multi-variate non-differentiable functions. Several case studies are carried out to exemplify the application of the extended SL approach to the concerned nonlinearities in floating renewable energy devices. The accuracy and computational efficiency of the extended SL approach are evaluated by verifying against the corresponding nonlinear time-domain (TD) and linear frequency-domain (FD) models. Despite the complexity of the given nonlinearities, the relative errors of the SL approach are no more than 6 % while its computational time is comparable to the FD model, being thousands of times faster than the TD model. Comparatively, the FD model leads to a relative error of over 70% in some cases.
Different numerical modeling methods have been developed and applied to evaluate a variety of performance indicators of wave energy converters (WECs), including the power performance, structural loads, levelized cost of energy, etc. Based on the modeling fidelity, the commonly used numerical modeling approaches can be classified as linear modeling, weakly nonlinear modeling and fully nonlinear modeling approaches. Each method differs in accuracy and computational efficiency, making them suitable for different stages of WEC design. However, the selection of modeling approach could significantly impact evaluation outcomes. For instance, simplified linear models may underestimate structural loads or overestimate energy production in some operational conditions, potentially leading to less cost-effective designs. Given the widespread utilization of these models, it is essential to understand the uncertainties brought by them in performance evaluations. This work is dedicated to benchmarking different linear-potential-flow-based numerical models for evaluating the systematic performance of WECs. Three representative numerical modeling approaches are considered in this work, including linear frequency-domain modeling, statistically linearized spectral-domain modeling and Cummins equation-based nonlinear time-domain modeling. A generic point absorber WEC is considered as the research reference in this work, and different sea sites are taken into account. The numerical models are utilized to predict critical performance indicators, including power performance, the annual energy production, the capacity factor, the levelized cost of energy and the PTO fatigue loads. By comparing the results, this work identifies the uncertainties associated with different modeling approaches in evaluating WEC performance.
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Different numerical modeling methods have been developed and applied to evaluate a variety of performance indicators of wave energy converters (WECs), including the power performance, structural loads, levelized cost of energy, etc. Based on the modeling fidelity, the commonly used numerical modeling approaches can be classified as linear modeling, weakly nonlinear modeling and fully nonlinear modeling approaches. Each method differs in accuracy and computational efficiency, making them suitable for different stages of WEC design. However, the selection of modeling approach could significantly impact evaluation outcomes. For instance, simplified linear models may underestimate structural loads or overestimate energy production in some operational conditions, potentially leading to less cost-effective designs. Given the widespread utilization of these models, it is essential to understand the uncertainties brought by them in performance evaluations. This work is dedicated to benchmarking different linear-potential-flow-based numerical models for evaluating the systematic performance of WECs. Three representative numerical modeling approaches are considered in this work, including linear frequency-domain modeling, statistically linearized spectral-domain modeling and Cummins equation-based nonlinear time-domain modeling. A generic point absorber WEC is considered as the research reference in this work, and different sea sites are taken into account. The numerical models are utilized to predict critical performance indicators, including power performance, the annual energy production, the capacity factor, the levelized cost of energy and the PTO fatigue loads. By comparing the results, this work identifies the uncertainties associated with different modeling approaches in evaluating WEC performance.
In line with the global shift to transition away from fossil fuels to sustainable energy sources, tidal stream energy has emerged as a promising renewable option. This study investigates the tidal stream energy resources along the Dutch coast and focuses on the impact of Mean Sea Level (MSL) rise on the future resource potential. A THETIS high-resolution unstructured model is used. The model is validated against sea surface elevations, and the Dutch tidal stream resource uncertainties are well defined. The validated model is used to evaluate the tidal stream energy potential of the Netherlands, regions in the Wadden Sea and Westerschelde in Zeeland display noteworthy potential, evidenced by maximum average flow velocities of 1.3 m/s and maximum average energy densities of 1600 W/m2 for the Wadden Sea and maximum average flow velocities of 0.75 m/s and maximum average energy densities of 300 W/m2 for the Westerschelde. Forecasting the 2050 tidal stream resource, considering a projected 118 mm MSL rise, results indicate persistent energy characteristics, with minimal fluctuations in average velocities and average energy density when compared to the 2016 model. In the Wadden Sea and Zeeland, respectively, only marginal changes of +25 W/m2, and +8 W/m2 are observed in average energy density for those same locations. Furthermore, the inclusion of long-term constituents has negligible effects on the 2050 results, emphasising the stability of the tidal stream energy source. Tidal stream energy in the Netherlands stands out as a reliable and resilient energy source, demonstrating consistency in the face of projected MSL rise. Such predictability has the potential to contribute significantly to fostering a sustainable and secure energy system in the Netherlands, while aligning with global efforts to combat climate change.
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In line with the global shift to transition away from fossil fuels to sustainable energy sources, tidal stream energy has emerged as a promising renewable option. This study investigates the tidal stream energy resources along the Dutch coast and focuses on the impact of Mean Sea Level (MSL) rise on the future resource potential. A THETIS high-resolution unstructured model is used. The model is validated against sea surface elevations, and the Dutch tidal stream resource uncertainties are well defined. The validated model is used to evaluate the tidal stream energy potential of the Netherlands, regions in the Wadden Sea and Westerschelde in Zeeland display noteworthy potential, evidenced by maximum average flow velocities of 1.3 m/s and maximum average energy densities of 1600 W/m2 for the Wadden Sea and maximum average flow velocities of 0.75 m/s and maximum average energy densities of 300 W/m2 for the Westerschelde. Forecasting the 2050 tidal stream resource, considering a projected 118 mm MSL rise, results indicate persistent energy characteristics, with minimal fluctuations in average velocities and average energy density when compared to the 2016 model. In the Wadden Sea and Zeeland, respectively, only marginal changes of +25 W/m2, and +8 W/m2 are observed in average energy density for those same locations. Furthermore, the inclusion of long-term constituents has negligible effects on the 2050 results, emphasising the stability of the tidal stream energy source. Tidal stream energy in the Netherlands stands out as a reliable and resilient energy source, demonstrating consistency in the face of projected MSL rise. Such predictability has the potential to contribute significantly to fostering a sustainable and secure energy system in the Netherlands, while aligning with global efforts to combat climate change.
Wave energy as a renewable resource has been shown to have immense potential. Up to date research has predominately focused on sector focused approaches, with devices being explored in small applications, or solely on their own merit. However, when discussing the integration to energy systems with high share of renewable energies, large levels of electrical interconnectivity and grid balancing infrastructure, the role of wave energy is often “lost”. This work further refines the approach of integrating wave energy in the European and UK energy system, highlighting potential future pathways to improve wave energy considerations.
This study assesses the input source of climate data for evaluation of wave energy production and economics. The comparison of ERA5 and a high-fidelity wave dataset across all Europe, the ECHOWAVE database, show a clear benefit for wave energy. When ERA5 is utilised to assess the potential of wave energy, the usefulness and integration of wave energy is not reflected in the system. This works shows that a proper representation of wave energy in terms of climate, power production methodologies and economic considerations. ...
This study assesses the input source of climate data for evaluation of wave energy production and economics. The comparison of ERA5 and a high-fidelity wave dataset across all Europe, the ECHOWAVE database, show a clear benefit for wave energy. When ERA5 is utilised to assess the potential of wave energy, the usefulness and integration of wave energy is not reflected in the system. This works shows that a proper representation of wave energy in terms of climate, power production methodologies and economic considerations. ...
Wave energy as a renewable resource has been shown to have immense potential. Up to date research has predominately focused on sector focused approaches, with devices being explored in small applications, or solely on their own merit. However, when discussing the integration to energy systems with high share of renewable energies, large levels of electrical interconnectivity and grid balancing infrastructure, the role of wave energy is often “lost”. This work further refines the approach of integrating wave energy in the European and UK energy system, highlighting potential future pathways to improve wave energy considerations.
This study assesses the input source of climate data for evaluation of wave energy production and economics. The comparison of ERA5 and a high-fidelity wave dataset across all Europe, the ECHOWAVE database, show a clear benefit for wave energy. When ERA5 is utilised to assess the potential of wave energy, the usefulness and integration of wave energy is not reflected in the system. This works shows that a proper representation of wave energy in terms of climate, power production methodologies and economic considerations.
This study assesses the input source of climate data for evaluation of wave energy production and economics. The comparison of ERA5 and a high-fidelity wave dataset across all Europe, the ECHOWAVE database, show a clear benefit for wave energy. When ERA5 is utilised to assess the potential of wave energy, the usefulness and integration of wave energy is not reflected in the system. This works shows that a proper representation of wave energy in terms of climate, power production methodologies and economic considerations.
In order to reduce the Levelized Cost Of Energy (LCOE) of Wave Energy Converters (WECs) and make them competitive with conventional energy sources, they would need to be deployed in large numbers as farms similar to Offshore Wind. Given their significant capacity, Offshore wind turbines are often placed at large distances apart, to reduce destructive wake effects, while maintaining a high energy density per unit area. However, WECs within a farm, are much smaller with much lower capacities and stronger inter device interactions due to the presence of a highly dense fluid. Therefore, larger number of WECs can be deployed in closer proximity to produce comparable energy density per unit area. As we move towards hybrid systems with floating solar, wind and wave energy amongst others, efficiency in deployment within an area becomes key. Conventional wave farm concepts that have been extensively studied such as 1) wave farms of different types of WECs (Point Absorber, Attenuator, Flap etc) also referred to as homogeneous arrays (same device in multiple numbers) and 2) wave farms with different sizes and drafts of one type of WEC also referred to as heterogeneous arrays. To date, studies have focused on multiple devices with similar geometries interacting through the same degrees of freedom. With this research, the authors explore mixed wave energy farms, which are wave farms utilizing different types of wave energy converters in the same farm. With the focus on the hydrodynamics and power produced by wave energy converters, this research provides for the first time insights into the interaction of devices, with varying geometries and degrees of freedom, thus entering an entirely new domain of wave farm research.
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In order to reduce the Levelized Cost Of Energy (LCOE) of Wave Energy Converters (WECs) and make them competitive with conventional energy sources, they would need to be deployed in large numbers as farms similar to Offshore Wind. Given their significant capacity, Offshore wind turbines are often placed at large distances apart, to reduce destructive wake effects, while maintaining a high energy density per unit area. However, WECs within a farm, are much smaller with much lower capacities and stronger inter device interactions due to the presence of a highly dense fluid. Therefore, larger number of WECs can be deployed in closer proximity to produce comparable energy density per unit area. As we move towards hybrid systems with floating solar, wind and wave energy amongst others, efficiency in deployment within an area becomes key. Conventional wave farm concepts that have been extensively studied such as 1) wave farms of different types of WECs (Point Absorber, Attenuator, Flap etc) also referred to as homogeneous arrays (same device in multiple numbers) and 2) wave farms with different sizes and drafts of one type of WEC also referred to as heterogeneous arrays. To date, studies have focused on multiple devices with similar geometries interacting through the same degrees of freedom. With this research, the authors explore mixed wave energy farms, which are wave farms utilizing different types of wave energy converters in the same farm. With the focus on the hydrodynamics and power produced by wave energy converters, this research provides for the first time insights into the interaction of devices, with varying geometries and degrees of freedom, thus entering an entirely new domain of wave farm research.
The deployment of marine renewables (MRE) is important for transitioning to a low-carbon energy system. However, their performance is highly dependent on the deployment location, making the selection of feasible sites critical for large-scale implementation. To contribute meaningfully to Europe’s renewable energy strategy and support a carbon-neutral energy system by 2050, the environmental performance of MREs must be taken into account in site selection, beyond the typical economic and technical aspects. Therefore, this study presents a geospatial analysis of the climate change mitigation potential of two wave energy converters, floating offshore photovoltaics, and floating wind turbines in northern European coastal waters. By combining a detailed life cycle assessment model of the four MREs with spatial data, the distribution of their life cycle global warming impact and carbon payback periods is assessed across multiple regions. The results show significantly varying impact levels of the different MREs, with carbon-neutral deployment not guaranteed at every location. Wave energy converters only partially reach carbon neutrality, while floating photovoltaics fail to do so across the entire study area. Floating wind turbines can be considered carbon-neutral nearly across their entire theoretical application area. The findings highlight the importance of taking into account site-specific environmental performance of MREs in order to ensure a positive contribution to climate change mitigation. By providing spatially explicit maps of MREs’ global warming impacts and carbon payback periods, this study enables as the first of its kind the inclusion of climate change mitigation considerations in the site selection process for MREs.
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The deployment of marine renewables (MRE) is important for transitioning to a low-carbon energy system. However, their performance is highly dependent on the deployment location, making the selection of feasible sites critical for large-scale implementation. To contribute meaningfully to Europe’s renewable energy strategy and support a carbon-neutral energy system by 2050, the environmental performance of MREs must be taken into account in site selection, beyond the typical economic and technical aspects. Therefore, this study presents a geospatial analysis of the climate change mitigation potential of two wave energy converters, floating offshore photovoltaics, and floating wind turbines in northern European coastal waters. By combining a detailed life cycle assessment model of the four MREs with spatial data, the distribution of their life cycle global warming impact and carbon payback periods is assessed across multiple regions. The results show significantly varying impact levels of the different MREs, with carbon-neutral deployment not guaranteed at every location. Wave energy converters only partially reach carbon neutrality, while floating photovoltaics fail to do so across the entire study area. Floating wind turbines can be considered carbon-neutral nearly across their entire theoretical application area. The findings highlight the importance of taking into account site-specific environmental performance of MREs in order to ensure a positive contribution to climate change mitigation. By providing spatially explicit maps of MREs’ global warming impacts and carbon payback periods, this study enables as the first of its kind the inclusion of climate change mitigation considerations in the site selection process for MREs.
Journal article
(2025)
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Andreas T. Asiikkis, Vaibhav Raghavan, Dimokratis G.E. Grigoriadis, Andrei V. Metrikine, George Lavidas, Antonis I. Vakis
The accurate modelling of hydrodynamic interactions in dense arrays of Wave Energy Converters (WECs) is critical for optimizing design and predicting energy capture efficiency. This study presents the first time-domain experimental validation of the Boundary Element Method (BEM) multi body solver HAMS-MREL, for WEC arrays. The validation involves a comparative assessment of wave excitation forces from numerical predictions and physical measurements for an array of 5 floaters. Results exhibit good overall agreement, with Normalized Root Mean Square Error (NRMSE) values typically below 10 %, though with some exceptions. The results highlight solver limitations that vary with wave steepness and floater positioning within the array. Additionally, this study presents the first integration of HAMS-MREL with WEC-Sim for time-domain simulations, evaluating the linear HAMS-MREL and the weakly nonlinear WEC-Sim hydrodynamic models across various wave conditions. The comparative study conducted with the Ocean Grazer 4.0 case, a dense array of 18 floaters around a monopile, reveals the conditions under which linear modelling remains valid and when nonlinear approaches become necessary. Despite significant wave excitation force differences at wave steepness above 2 %, power output estimates remain within acceptable limits (∼10 %). These findings offer critical insights into appropriate model selection for different wave conditions.
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The accurate modelling of hydrodynamic interactions in dense arrays of Wave Energy Converters (WECs) is critical for optimizing design and predicting energy capture efficiency. This study presents the first time-domain experimental validation of the Boundary Element Method (BEM) multi body solver HAMS-MREL, for WEC arrays. The validation involves a comparative assessment of wave excitation forces from numerical predictions and physical measurements for an array of 5 floaters. Results exhibit good overall agreement, with Normalized Root Mean Square Error (NRMSE) values typically below 10 %, though with some exceptions. The results highlight solver limitations that vary with wave steepness and floater positioning within the array. Additionally, this study presents the first integration of HAMS-MREL with WEC-Sim for time-domain simulations, evaluating the linear HAMS-MREL and the weakly nonlinear WEC-Sim hydrodynamic models across various wave conditions. The comparative study conducted with the Ocean Grazer 4.0 case, a dense array of 18 floaters around a monopile, reveals the conditions under which linear modelling remains valid and when nonlinear approaches become necessary. Despite significant wave excitation force differences at wave steepness above 2 %, power output estimates remain within acceptable limits (∼10 %). These findings offer critical insights into appropriate model selection for different wave conditions.
As the necessity for the decarbonisation of the global electricity market increases, a range of renewable energy technologies will be implemented, one of which is wave energy. A key step in this process is the thorough quantification of both the power resource at locations of interest, and the impacts of these devices on the natural environment. The present work streamlines these 2 processes into one methodology by investigating the long-term impacts of an array of 20 WECs on the nearshore Dutch wave climate, while also calculating the potential power resource at the site considering intra-array wake effects. Simulations of 10 year duration were conducted in the baseline scenario (no farm present) and with 2 array configurations, using the spectral wave model SWAN on an unstructured mesh. It was demonstrated that the power production of the farm during this period, when wake effects are considered, is calculated to be up to 1.8% less than traditional methods. The presence of the farm is shown to reduce significant wave height and wave power in its lee, with the effects being largely attenuated at the coast. It was shown that the magnitude of the change is dependent on both the period and height of the waves at the farm, and notably the magnitude of the reduction does not increase consistently with the wave height, contradicting the sentiment that wave farms are effective protection mechanisms against damaging high-energy conditions. Furthermore, the present work suggests that changes to the nearshore breaker index may impact longshore currents that are essential for nutrient and sediment transport, the effect of which on the ecosystem is not yet well quantified.
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As the necessity for the decarbonisation of the global electricity market increases, a range of renewable energy technologies will be implemented, one of which is wave energy. A key step in this process is the thorough quantification of both the power resource at locations of interest, and the impacts of these devices on the natural environment. The present work streamlines these 2 processes into one methodology by investigating the long-term impacts of an array of 20 WECs on the nearshore Dutch wave climate, while also calculating the potential power resource at the site considering intra-array wake effects. Simulations of 10 year duration were conducted in the baseline scenario (no farm present) and with 2 array configurations, using the spectral wave model SWAN on an unstructured mesh. It was demonstrated that the power production of the farm during this period, when wake effects are considered, is calculated to be up to 1.8% less than traditional methods. The presence of the farm is shown to reduce significant wave height and wave power in its lee, with the effects being largely attenuated at the coast. It was shown that the magnitude of the change is dependent on both the period and height of the waves at the farm, and notably the magnitude of the reduction does not increase consistently with the wave height, contradicting the sentiment that wave farms are effective protection mechanisms against damaging high-energy conditions. Furthermore, the present work suggests that changes to the nearshore breaker index may impact longshore currents that are essential for nutrient and sediment transport, the effect of which on the ecosystem is not yet well quantified.
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.
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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.