MZ
M.B. Zaayer
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1
Power ramp events represent an important challenge to grid stability, motivating the enforcement of ramp limits. For wind farm operation, decisions to respect these limits are based on imperfect forecast data, where errors can lead to deviations from the prescribed limit. In this study, we propose two different methods to mitigate the impact of forecast uncertainties on the operation of ramp-constrained wind farms: the use of a pessimistic forecast, where ramp events are worsened artificially, and the use of a storage system. The two methods are assessed by solving an online dispatch optimization problem for one year of operation. Forecast data are generated from numerical weather prediction models of the ECMWF. The dependence of power production on wind speed and direction changes is captured by an engineering wake deficit model. Results for 20 different offshore sites in Europe show that using a pessimistic forecast reduces the number of ramp events exceeding the limit by one third but increases curtailment by 0.2 percentage points on average. Instead, adding a storage system to the wind farm is more effective at reducing curtailment, proportionally to its size. The impact of forecast errors is mitigated most effectively by combining the two methods.
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Power ramp events represent an important challenge to grid stability, motivating the enforcement of ramp limits. For wind farm operation, decisions to respect these limits are based on imperfect forecast data, where errors can lead to deviations from the prescribed limit. In this study, we propose two different methods to mitigate the impact of forecast uncertainties on the operation of ramp-constrained wind farms: the use of a pessimistic forecast, where ramp events are worsened artificially, and the use of a storage system. The two methods are assessed by solving an online dispatch optimization problem for one year of operation. Forecast data are generated from numerical weather prediction models of the ECMWF. The dependence of power production on wind speed and direction changes is captured by an engineering wake deficit model. Results for 20 different offshore sites in Europe show that using a pessimistic forecast reduces the number of ramp events exceeding the limit by one third but increases curtailment by 0.2 percentage points on average. Instead, adding a storage system to the wind farm is more effective at reducing curtailment, proportionally to its size. The impact of forecast errors is mitigated most effectively by combining the two methods.
The wind energy industry is moving towards larger and more powerful turbines, with next-generation designs expected to operate at blade tip speeds exceeding 100 ms−1. These developments introduce new aerodynamic challenges that have not yet been explored. Here we show, using the IEA 22 MW reference turbine as a case study, that large-scale wind turbines may become susceptible to localised transonic flow effects even under normal operating conditions. By analysing the local inflow conditions along the blade and their operational settings, we identify a significant likelihood of transonic flow onset at high wind speeds above 20 ms−1 in the outer 10% of the blade span. This is particularly driven by the inherently unsteady nature of wind turbine operation. To address this, we propose and demonstrate a Transonic Safe Mode, a framework designed to limit exposure to transonic conditions. Beyond the specific case study, the paper presents a targeted analysis methodology that highlights the additional investigations proposed to assess and ensure a safe design and operation of large-scale wind turbines. In this context, the Transonic Safe Mode offers a pragmatic and forward-looking pathway for next-generation turbines, enabling proactive risk management while focused research efforts continue to close existing knowledge gaps regarding the impact of transonic flow on wind turbine aerodynamics and structural response.
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The wind energy industry is moving towards larger and more powerful turbines, with next-generation designs expected to operate at blade tip speeds exceeding 100 ms−1. These developments introduce new aerodynamic challenges that have not yet been explored. Here we show, using the IEA 22 MW reference turbine as a case study, that large-scale wind turbines may become susceptible to localised transonic flow effects even under normal operating conditions. By analysing the local inflow conditions along the blade and their operational settings, we identify a significant likelihood of transonic flow onset at high wind speeds above 20 ms−1 in the outer 10% of the blade span. This is particularly driven by the inherently unsteady nature of wind turbine operation. To address this, we propose and demonstrate a Transonic Safe Mode, a framework designed to limit exposure to transonic conditions. Beyond the specific case study, the paper presents a targeted analysis methodology that highlights the additional investigations proposed to assess and ensure a safe design and operation of large-scale wind turbines. In this context, the Transonic Safe Mode offers a pragmatic and forward-looking pathway for next-generation turbines, enabling proactive risk management while focused research efforts continue to close existing knowledge gaps regarding the impact of transonic flow on wind turbine aerodynamics and structural response.
As the penetration of renewable energy increases in the generation mix, the problem of power dispatchability becomes more critical. The co-location of storage systems with wind energy is a promising solution to shift power delivery from periods of high wind resource availability to periods of high electricity demand. Producing baseload power from wind farms all or most of the time is an example of such dispatchability. In this work, we present an optimization-based dispatch strategy to produce baseload power. At every time step, an optimization problem is solved to decide the storage operation, maximize revenues on the electricity market and reach a given reliability target. In order to reduce the impact of forecast uncertainties on the reliability of the power delivery, a robust formulation of the dispatch optimization is used, based on a pessimistic version of the forecast. The proposed method is evaluated for 18 offshore sites with a 100 MW wind farm and storage system, for one year of operation. By using a robust dispatch strategy, the reliability increases by up to 0.9 points, with a minor impact on revenues (+2% on average), compared to the reference dispatch strategy using a regular forecast. Our study demonstrates the feasibility of providing a reliable baseload power from wind energy in the presence of forecast uncertainty.
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As the penetration of renewable energy increases in the generation mix, the problem of power dispatchability becomes more critical. The co-location of storage systems with wind energy is a promising solution to shift power delivery from periods of high wind resource availability to periods of high electricity demand. Producing baseload power from wind farms all or most of the time is an example of such dispatchability. In this work, we present an optimization-based dispatch strategy to produce baseload power. At every time step, an optimization problem is solved to decide the storage operation, maximize revenues on the electricity market and reach a given reliability target. In order to reduce the impact of forecast uncertainties on the reliability of the power delivery, a robust formulation of the dispatch optimization is used, based on a pessimistic version of the forecast. The proposed method is evaluated for 18 offshore sites with a 100 MW wind farm and storage system, for one year of operation. By using a robust dispatch strategy, the reliability increases by up to 0.9 points, with a minor impact on revenues (+2% on average), compared to the reference dispatch strategy using a regular forecast. Our study demonstrates the feasibility of providing a reliable baseload power from wind energy in the presence of forecast uncertainty.
Report
(2024)
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Samuel Kainz, Julian Quick, Mauricio Souza de Alencar, S. Sanchez Perez Moreno, Katherine Dykes, Christopher Bay, M B Zaaijer, Pietro Bortolotti
This report describes the first version of the regular and irregular IEA-Wind 740-10MW Reference Offshore Wind Plants (v0.1). The two plants have been developed within the second work package of IEA Wind Task 37 on Wind Energy Systems Engineering: Integrated RD&D. The plants aim at acting as reference for future research projects on wind energy, representing modern offshore wind plants. The designs are based on the Borssele III and IV offshore wind plant projects. The associated wind resource, allotted territory, and bathymetry measurements are used to define the site characteristics. 74 IEA 10-MW Reference Wind Turbines are arranged in two suggested layouts that are optimized for maximum annual energy production: one regular grid layout, one irregular layout. These reference wind plants have been described using the WindIO ontology and have been made available through an open-source repository on GitHub.
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This report describes the first version of the regular and irregular IEA-Wind 740-10MW Reference Offshore Wind Plants (v0.1). The two plants have been developed within the second work package of IEA Wind Task 37 on Wind Energy Systems Engineering: Integrated RD&D. The plants aim at acting as reference for future research projects on wind energy, representing modern offshore wind plants. The designs are based on the Borssele III and IV offshore wind plant projects. The associated wind resource, allotted territory, and bathymetry measurements are used to define the site characteristics. 74 IEA 10-MW Reference Wind Turbines are arranged in two suggested layouts that are optimized for maximum annual energy production: one regular grid layout, one irregular layout. These reference wind plants have been described using the WindIO ontology and have been made available through an open-source repository on GitHub.
Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer is exposed to the volatility of market prices, where the price paid per kilowatt-hour of energy would not be constant anymore. The developer might then have to maximize the revenue earned by participating in different energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more profitable for the developer compared to a turbine designed for minimizing the levelized cost of electricity (LCoE). This study is in line with this paradigm shift in the field of turbine and farm design. It is a continuation of a previous study conducted by the same authors , which explicitly focused on the drivers of turbine sizing with respect to LCoE. The goal of this study is to optimize the design for a new set of objective functions and analyze how various day-Ahead market conditions and objectives drive turbine design. A simplified market model that can generate hourly day-Ahead market prices is developed and coupled with a wind-farm-level multidisciplinary design analysis and optimization (MDAO) framework to evaluate key economic indicators of the wind farm. The results show how the optimum turbine design is driven by both the choice of the economic metric and the market scenario. However, an LCoE-optimized design is found to perform well with respect to profitability-based economic metrics like modified internal rate of return (MIRR) or profitability index (PI), indicating a limited need to redesign turbines for a specific day-Ahead market scenario.
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Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer is exposed to the volatility of market prices, where the price paid per kilowatt-hour of energy would not be constant anymore. The developer might then have to maximize the revenue earned by participating in different energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more profitable for the developer compared to a turbine designed for minimizing the levelized cost of electricity (LCoE). This study is in line with this paradigm shift in the field of turbine and farm design. It is a continuation of a previous study conducted by the same authors , which explicitly focused on the drivers of turbine sizing with respect to LCoE. The goal of this study is to optimize the design for a new set of objective functions and analyze how various day-Ahead market conditions and objectives drive turbine design. A simplified market model that can generate hourly day-Ahead market prices is developed and coupled with a wind-farm-level multidisciplinary design analysis and optimization (MDAO) framework to evaluate key economic indicators of the wind farm. The results show how the optimum turbine design is driven by both the choice of the economic metric and the market scenario. However, an LCoE-optimized design is found to perform well with respect to profitability-based economic metrics like modified internal rate of return (MIRR) or profitability index (PI), indicating a limited need to redesign turbines for a specific day-Ahead market scenario.
Large-scale exploitation of offshore wind energy is deemed essential to provide its expected share to electricity needs of the future. To achieve the same, turbine and farm-level optimizations play a significant role. Over the past few years, the growth in the size of turbines has massively contributed to the reduction in costs. However, growing turbine sizes come with challenges in rotor design, turbine installation, supply chain, etc. It is, therefore, important to understand how to size wind turbines when minimizing the levelized cost of electricity (LCoE) of an offshore wind farm. Hence, this study looks at how the rated power and rotor diameter of a turbine affect various turbine and farm-level metrics and uses this information in order to identify the key design drivers and how their impact changes with setup. A multi-disciplinary design optimization and analysis (MDAO) framework is used to perform the analysis. The framework uses low-fidelity models that capture the core dependencies of the outputs on the design variables while also including the trade-offs between various disciplines of the offshore wind farm. The framework is used, not to estimate the LCoE or the optimum turbine size accurately, but to provide insights into various design drivers and trends. A baseline case, for a typical setup in the North Sea, is defined where LCoE is minimized for a given farm power and area constraint with the International Energy Agency 15 MW reference turbine as a starting point. It is found that the global optimum design, for this baseline case, is a turbine with a rated power of 16 MW and a rotor diameter of 236 m. This is already close to the state-of-the-art designs observed in the industry and close enough to the starting design to justify the applied scaling. A sensitivity study is also performed that identifies the design drivers and quantifies the impact of model uncertainties, technology/cost developments, varying farm design conditions, and different farm constraints on the optimum turbine design. To give an example, certain scenarios, like a change in the wind regime or the removal of farm power constraint, result in a significant shift in the scale of the optimum design and/or the specific power of the optimum design. Redesigning the turbine for these scenarios is found to result in an LCoE benefit of the order of 1 %–2 % over the already optimized baseline. The work presented shows how a simplified approach can be applied to a complex turbine sizing problem, which can also be extended to metrics beyond LCoE. It also gives insights into designers, project developers, and policy makers as to how their decision may impact the optimum turbine scale.
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Large-scale exploitation of offshore wind energy is deemed essential to provide its expected share to electricity needs of the future. To achieve the same, turbine and farm-level optimizations play a significant role. Over the past few years, the growth in the size of turbines has massively contributed to the reduction in costs. However, growing turbine sizes come with challenges in rotor design, turbine installation, supply chain, etc. It is, therefore, important to understand how to size wind turbines when minimizing the levelized cost of electricity (LCoE) of an offshore wind farm. Hence, this study looks at how the rated power and rotor diameter of a turbine affect various turbine and farm-level metrics and uses this information in order to identify the key design drivers and how their impact changes with setup. A multi-disciplinary design optimization and analysis (MDAO) framework is used to perform the analysis. The framework uses low-fidelity models that capture the core dependencies of the outputs on the design variables while also including the trade-offs between various disciplines of the offshore wind farm. The framework is used, not to estimate the LCoE or the optimum turbine size accurately, but to provide insights into various design drivers and trends. A baseline case, for a typical setup in the North Sea, is defined where LCoE is minimized for a given farm power and area constraint with the International Energy Agency 15 MW reference turbine as a starting point. It is found that the global optimum design, for this baseline case, is a turbine with a rated power of 16 MW and a rotor diameter of 236 m. This is already close to the state-of-the-art designs observed in the industry and close enough to the starting design to justify the applied scaling. A sensitivity study is also performed that identifies the design drivers and quantifies the impact of model uncertainties, technology/cost developments, varying farm design conditions, and different farm constraints on the optimum turbine design. To give an example, certain scenarios, like a change in the wind regime or the removal of farm power constraint, result in a significant shift in the scale of the optimum design and/or the specific power of the optimum design. Redesigning the turbine for these scenarios is found to result in an LCoE benefit of the order of 1 %–2 % over the already optimized baseline. The work presented shows how a simplified approach can be applied to a complex turbine sizing problem, which can also be extended to metrics beyond LCoE. It also gives insights into designers, project developers, and policy makers as to how their decision may impact the optimum turbine scale.
For scenarios of high penetration of renewable energy, it becomes increasingly relevant to improve the dispatchability of supply for wind and solar power plants. Baseload power plants, required to produce a minimum power production at all times, are discussed in this context. The baseload constraint can be satisfied with renewable sources when combined with a storage system but at a high cost. This work studies the design drivers of such a storage system when consisting of short and long-term storage. The capacities of the short-term and long-term storage components are calculated as part of a linear optimization problem with the objective of minimizing the cost of baseload, using a metric based on a net present value formulation. Our analysis, based on 10 locations in Northern Europe, highlights a high sensitivity of optimal storage sizing to storage cost assumptions. In addition, the cost of baseload is found to be correlated to the share of renewable power produced above baseload, but not to the correlation between price and wind power, suggesting arbitrage plays a minor role in the business case.
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For scenarios of high penetration of renewable energy, it becomes increasingly relevant to improve the dispatchability of supply for wind and solar power plants. Baseload power plants, required to produce a minimum power production at all times, are discussed in this context. The baseload constraint can be satisfied with renewable sources when combined with a storage system but at a high cost. This work studies the design drivers of such a storage system when consisting of short and long-term storage. The capacities of the short-term and long-term storage components are calculated as part of a linear optimization problem with the objective of minimizing the cost of baseload, using a metric based on a net present value formulation. Our analysis, based on 10 locations in Northern Europe, highlights a high sensitivity of optimal storage sizing to storage cost assumptions. In addition, the cost of baseload is found to be correlated to the share of renewable power produced above baseload, but not to the correlation between price and wind power, suggesting arbitrage plays a minor role in the business case.
Offshore wind farm optimisation
A comparison of performance between regular and irregular wind turbine layouts
Layout optimisation is essential for improving the overall performance of offshore wind farms. During the past 15 years, the use of yield optimisation algorithms has resulted in a transition from regular to more irregular farm layouts. However, since the layout affects many factors, yield optimisation alone may not maximise the overall performance. In this paper, a comparative case study is presented to quantify the effect of the wind farm layout on the overall performance of offshore wind farms. The case study was performed to investigate two performance indicators: power performance, using yield calculations with windPRO, and wake-induced tower fatigue, using the Frandsen model. It is observed that irregular wind farm layouts have a higher annual energy production compared to regular layouts. Their power production is also more persistent and less sensitive to wind direction, improving predictability and thus the market value of power output. However, one turbine location in the irregular layout has a 24 % higher effective turbulence level, leading to additional tower fatigue. As a result, fatigue-driven tower designs would require increased wall thicknesses, which would result in higher capital costs for all turbine locations. It is demonstrated in this study that layout optimisation using minimum inter-turbine spacing effectively resolves the induced wake issue while maintaining high-yield performance.
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Layout optimisation is essential for improving the overall performance of offshore wind farms. During the past 15 years, the use of yield optimisation algorithms has resulted in a transition from regular to more irregular farm layouts. However, since the layout affects many factors, yield optimisation alone may not maximise the overall performance. In this paper, a comparative case study is presented to quantify the effect of the wind farm layout on the overall performance of offshore wind farms. The case study was performed to investigate two performance indicators: power performance, using yield calculations with windPRO, and wake-induced tower fatigue, using the Frandsen model. It is observed that irregular wind farm layouts have a higher annual energy production compared to regular layouts. Their power production is also more persistent and less sensitive to wind direction, improving predictability and thus the market value of power output. However, one turbine location in the irregular layout has a 24 % higher effective turbulence level, leading to additional tower fatigue. As a result, fatigue-driven tower designs would require increased wall thicknesses, which would result in higher capital costs for all turbine locations. It is demonstrated in this study that layout optimisation using minimum inter-turbine spacing effectively resolves the induced wake issue while maintaining high-yield performance.
Onshore wind potentials are commonly mapped with site selection criteria that either fully include or exclude land for wind farms. However, current research rarely addresses the variability of these criteria, possibly resulting in overly conservative or optimistic potentials. This paper proposes a method to account for the variability of site selection criteria in resource assessments. We distinguish between static and flexible, non-binary criteria and assess onshore wind's technical and economic potential with bias-corrected ERA5 data, 28 turbine power curves, and a turbine-specific cost model. For Indonesia, we show that our flexible mapping approach improves the transparency of resource potential assessments and could contribute to more informed and useful recommendations. These recommendations could address the (1) calibration of site exclusion thresholds, (2) dilemmas of preferring one land type over others, (3) location-specific challenges of wind farm deployment, and (4) more direct support schemes for affected stakeholders and wind farm operators.. We report a technical potential of 207–1,994 TWh/year in Indonesia, which could cover more than 50% of 2030 electricity demand on all islands. LCOEs range between 5.8 and 24.5 US¢(2021)/kWh with an economic potential of 16 TWh/year, which improves to 31–212 TWh/year with a carbon tax of 100 US$(2021)/tCO2e.
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Onshore wind potentials are commonly mapped with site selection criteria that either fully include or exclude land for wind farms. However, current research rarely addresses the variability of these criteria, possibly resulting in overly conservative or optimistic potentials. This paper proposes a method to account for the variability of site selection criteria in resource assessments. We distinguish between static and flexible, non-binary criteria and assess onshore wind's technical and economic potential with bias-corrected ERA5 data, 28 turbine power curves, and a turbine-specific cost model. For Indonesia, we show that our flexible mapping approach improves the transparency of resource potential assessments and could contribute to more informed and useful recommendations. These recommendations could address the (1) calibration of site exclusion thresholds, (2) dilemmas of preferring one land type over others, (3) location-specific challenges of wind farm deployment, and (4) more direct support schemes for affected stakeholders and wind farm operators.. We report a technical potential of 207–1,994 TWh/year in Indonesia, which could cover more than 50% of 2030 electricity demand on all islands. LCOEs range between 5.8 and 24.5 US¢(2021)/kWh with an economic potential of 16 TWh/year, which improves to 31–212 TWh/year with a carbon tax of 100 US$(2021)/tCO2e.
To limit the consequences of climate change, generation from renewables coupled with large scale electrification is necessary. However, the deployment of renewables has its own challenges and not all sectors can be electrified. Hydrogen production from wind energy emerges as a promising solution that can alleviate these challenges. The current costs of green hydrogen production are high due to the high costs of electricity used for electrolysis. This study looks into the benefits of optimizing a turbine specifically for hydrogen production and the reduction in the Levelized Cost of Hydrogen (LCoH) compared to the use of conventional Levelized Cost of Energy (LCoE) optimized turbine. The case presented shows that turbines designed specifically for hydrogen production tend to have a higher specific power but these provide only a marginal advantage over using LCoE-optimized turbines for hydrogen production. Oversizing the electrolyzer compared to the turbine was shown to be a good design strategy. In the future, designing turbines specifically for hydrogen production could have certain benefits, depending on how the electrolyzer efficiencies, hydrogen production costs and the hydrogen market evolve.
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To limit the consequences of climate change, generation from renewables coupled with large scale electrification is necessary. However, the deployment of renewables has its own challenges and not all sectors can be electrified. Hydrogen production from wind energy emerges as a promising solution that can alleviate these challenges. The current costs of green hydrogen production are high due to the high costs of electricity used for electrolysis. This study looks into the benefits of optimizing a turbine specifically for hydrogen production and the reduction in the Levelized Cost of Hydrogen (LCoH) compared to the use of conventional Levelized Cost of Energy (LCoE) optimized turbine. The case presented shows that turbines designed specifically for hydrogen production tend to have a higher specific power but these provide only a marginal advantage over using LCoE-optimized turbines for hydrogen production. Oversizing the electrolyzer compared to the turbine was shown to be a good design strategy. In the future, designing turbines specifically for hydrogen production could have certain benefits, depending on how the electrolyzer efficiencies, hydrogen production costs and the hydrogen market evolve.
In this research, we explored the potential to reduce the cost of floating wind farms by adopting an integrated approach to optimally size semi-submersible substructures accounting for materials, fabrication and installation-logistics-related costs. A trade-off between manufacturing and installation costs was identified. This trade-off is driven by the growth of shipyard costs when the size of the structure increases, counteracting the reduction of fabrication costs achieved with a larger semi-submersible footprint. For the reference scenario, accounting for this trade-off yields a design that is a few tenths of a percent cheaper than when minimising only fabrication costs. However, the obtained design has a considerably smaller footprint than the fabrication-only case. The sensitivity of this trade-off to different installation strategies affecting the required storage area at the shipyard was assessed. When fabrication costs are dominant, the advantage of accounting for installation costs in the design process is negligible. Instead, larger storage area requirements increase the cost reduction achieved by optimising the semi-submersible while simultaneously accounting for fabrication and installation costs. The coupling effect remained significant for all the cases considered in a further sensitivity analysis of key parameters affecting the cost-optimal design. Furthermore, we identified several different designs that provide enough hydrostatic restoring moment in pitch to counteract the thrust-induced overturning moment within a small cost range from the most cost-effective one. This result suggests that additional criteria than minimising manufacturing and installation costs could drive the final design choice.
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In this research, we explored the potential to reduce the cost of floating wind farms by adopting an integrated approach to optimally size semi-submersible substructures accounting for materials, fabrication and installation-logistics-related costs. A trade-off between manufacturing and installation costs was identified. This trade-off is driven by the growth of shipyard costs when the size of the structure increases, counteracting the reduction of fabrication costs achieved with a larger semi-submersible footprint. For the reference scenario, accounting for this trade-off yields a design that is a few tenths of a percent cheaper than when minimising only fabrication costs. However, the obtained design has a considerably smaller footprint than the fabrication-only case. The sensitivity of this trade-off to different installation strategies affecting the required storage area at the shipyard was assessed. When fabrication costs are dominant, the advantage of accounting for installation costs in the design process is negligible. Instead, larger storage area requirements increase the cost reduction achieved by optimising the semi-submersible while simultaneously accounting for fabrication and installation costs. The coupling effect remained significant for all the cases considered in a further sensitivity analysis of key parameters affecting the cost-optimal design. Furthermore, we identified several different designs that provide enough hydrostatic restoring moment in pitch to counteract the thrust-induced overturning moment within a small cost range from the most cost-effective one. This result suggests that additional criteria than minimising manufacturing and installation costs could drive the final design choice.
This is not yet another study into better modelling or optimiser selection for OWFLO. Instead, this study aims to provide insight into what performance can be expected from offshore wind farm layout optimisation(OWFLO) and to know when further optimising is not justifiable anymore. The study consists of three parts. All three parts make use of a referent. (The definition of the term'referent' as used here is given in the paper.) The first part uses the referent to find and understand the characteristics of the OWFLO problem. Wind farms with 9, 25 and 64 turbines have been optimised 100 times with the referent. The results show a small spread in the performance of the found optimised layouts, indicating that many local optima exist with similar performances in an OWFLO problem. The second part compares performances from optimised layouts with 25 turbines resulting from optimisations with alternative implementation choices, evaluated by the referent model. The difference in performance resulting from the alternative optimisers indicates that improvement of a state-of-the-art optimiser is not expected to lead to much better results. The third part explores the need to improve the analysis by adding a phenomenon currently not considered in OWFLO. The influence of neighbouring wind farms(NBWFs) on layout optimisation without including atmospheric stability is investigated. It is evident that adding NBWFs for accurate energy yield assessments is necessary. However, for layout optimisation, the benefit of including NBWFs is not apparent.
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This is not yet another study into better modelling or optimiser selection for OWFLO. Instead, this study aims to provide insight into what performance can be expected from offshore wind farm layout optimisation(OWFLO) and to know when further optimising is not justifiable anymore. The study consists of three parts. All three parts make use of a referent. (The definition of the term'referent' as used here is given in the paper.) The first part uses the referent to find and understand the characteristics of the OWFLO problem. Wind farms with 9, 25 and 64 turbines have been optimised 100 times with the referent. The results show a small spread in the performance of the found optimised layouts, indicating that many local optima exist with similar performances in an OWFLO problem. The second part compares performances from optimised layouts with 25 turbines resulting from optimisations with alternative implementation choices, evaluated by the referent model. The difference in performance resulting from the alternative optimisers indicates that improvement of a state-of-the-art optimiser is not expected to lead to much better results. The third part explores the need to improve the analysis by adding a phenomenon currently not considered in OWFLO. The influence of neighbouring wind farms(NBWFs) on layout optimisation without including atmospheric stability is investigated. It is evident that adding NBWFs for accurate energy yield assessments is necessary. However, for layout optimisation, the benefit of including NBWFs is not apparent.
The potential technical benefits of wind-storage hybrids, mainly arbitrage, imbalance reduction, and frequency support, are convincing enough to launch demonstration projects. However, a quantitative analysis of these benefits, including economic considerations, is lacking. The aim of this study is to establish at what costs such technical benefits can be achieved, and whether developers reap sufficient economic advantage to make the development of such hybrid plants attractive. A wind-storage power plant is simulated for arbitrage, imbalance revenue maximization, and secondary frequency support using the Internal Rate of Return as a parameter to measure the economic performance. It is found that, for a wind-farm developer, deploying batteries just for arbitrage and/or imbalance revenue maximization does not improve profitability at current levels of battery costs. However, there is a strong economic incentive for a wind farm developer to deploy batteries to participate in the secondary frequency market.
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The potential technical benefits of wind-storage hybrids, mainly arbitrage, imbalance reduction, and frequency support, are convincing enough to launch demonstration projects. However, a quantitative analysis of these benefits, including economic considerations, is lacking. The aim of this study is to establish at what costs such technical benefits can be achieved, and whether developers reap sufficient economic advantage to make the development of such hybrid plants attractive. A wind-storage power plant is simulated for arbitrage, imbalance revenue maximization, and secondary frequency support using the Internal Rate of Return as a parameter to measure the economic performance. It is found that, for a wind-farm developer, deploying batteries just for arbitrage and/or imbalance revenue maximization does not improve profitability at current levels of battery costs. However, there is a strong economic incentive for a wind farm developer to deploy batteries to participate in the secondary frequency market.
Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer could be completely exposed to the volatility of market prices, where the price paid per kWh of energy would not be a constant anymore. The developer might then have to maximize the revenue earned by participating in di_erent energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more pro_table for the developer compared to a turbine designed for minimizing the Levelized Cost of Electricity (LCoE). This study is in line with this paradigm shift in the _eld of turbine and farm design. The goal is to optimize the de- sign for a new set of objective functions and constraints, and analyze the impact of these new designs on the system as a whole. The power density of the turbine is optimized to maximize the Internal Rate of Return (IRR) and is compared to the turbine design optimized for LCoE. A multivariate model is developed to derive the spot price from the existing nationwide wind power and demand forecast. For the future years, the forecasts are scaled up w.r.t the increase in installed wind turbine capacity and demand derived from trends/government targets. Various scenarios are simulated wherein the installed wind turbine capacity and demand are varied. A gradient-free optimization is performed by using the rotor diameter as a design variable while keeping the machine rating constant. Using IRR as an objective function results in larger rotor sizes enabling the turbine to produce a higher power at lower wind speeds, corresponding to times with higher spot prices. The result of a scenario (Target) where the installed wind turbine capacity follows government targets and demand is extrapolated linearly, is shown in Figure 1a. Here, the power density of a 5 MW baseline turbine is optimized for IRR, where the revenue from the Dutch day-ahead market is considered along with the turbine costs. Results for a single (onshore) turbine will be compared with a similar IRR optimization of power density of a turbine in a sample o_shore wind farm. At a wind farm level, the e_ects of power density variations on the farm layout, wake losses, ca- bling costs, etc. are also included. Moreover, insights into the consequences of optimizing the turbines on 'system-friendliness' are provided. Figure 1b illustrates a comparison between the farm capacity factor and farm power ramps. It is observable that while the capacity factor of the farm with a revenue-driven turbine is higher, the power ramps are steeper as well. A system-level trade-o_ is apparent as higher capacity factors ensure a better supply of demand at lower wind speeds while higher ramps need further compensation. This shows how moving beyond LCoE, by only considering energy markets, might not necessarily produce the most system-friendly turbines. To avoid negative implications, this study emphasizes the need to examine the consequences of selecting a revenue-based objective function on the system as a whole.
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Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer could be completely exposed to the volatility of market prices, where the price paid per kWh of energy would not be a constant anymore. The developer might then have to maximize the revenue earned by participating in di_erent energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more pro_table for the developer compared to a turbine designed for minimizing the Levelized Cost of Electricity (LCoE). This study is in line with this paradigm shift in the _eld of turbine and farm design. The goal is to optimize the de- sign for a new set of objective functions and constraints, and analyze the impact of these new designs on the system as a whole. The power density of the turbine is optimized to maximize the Internal Rate of Return (IRR) and is compared to the turbine design optimized for LCoE. A multivariate model is developed to derive the spot price from the existing nationwide wind power and demand forecast. For the future years, the forecasts are scaled up w.r.t the increase in installed wind turbine capacity and demand derived from trends/government targets. Various scenarios are simulated wherein the installed wind turbine capacity and demand are varied. A gradient-free optimization is performed by using the rotor diameter as a design variable while keeping the machine rating constant. Using IRR as an objective function results in larger rotor sizes enabling the turbine to produce a higher power at lower wind speeds, corresponding to times with higher spot prices. The result of a scenario (Target) where the installed wind turbine capacity follows government targets and demand is extrapolated linearly, is shown in Figure 1a. Here, the power density of a 5 MW baseline turbine is optimized for IRR, where the revenue from the Dutch day-ahead market is considered along with the turbine costs. Results for a single (onshore) turbine will be compared with a similar IRR optimization of power density of a turbine in a sample o_shore wind farm. At a wind farm level, the e_ects of power density variations on the farm layout, wake losses, ca- bling costs, etc. are also included. Moreover, insights into the consequences of optimizing the turbines on 'system-friendliness' are provided. Figure 1b illustrates a comparison between the farm capacity factor and farm power ramps. It is observable that while the capacity factor of the farm with a revenue-driven turbine is higher, the power ramps are steeper as well. A system-level trade-o_ is apparent as higher capacity factors ensure a better supply of demand at lower wind speeds while higher ramps need further compensation. This shows how moving beyond LCoE, by only considering energy markets, might not necessarily produce the most system-friendly turbines. To avoid negative implications, this study emphasizes the need to examine the consequences of selecting a revenue-based objective function on the system as a whole.
This paper presents a heuristic building block for wind farm layout optimization algorithms. For each pair of wake-interacting turbines, a vector is defined. Its magnitude is proportional to the wind speed deficit of the waked turbine due to the waking turbine. Its direction is chosen from the inter-turbine, downwind, or crosswind directions. These vectors can be combined for all waking or waked turbines and averaged over the wind resource to obtain a vector, a "pseudo-gradient", that can take the role of gradient in classical gradient-following optimization algorithms. A proof-of-concept optimization algorithm demonstrates how such vectors can be used for computationally efficient wind farm layout optimization. Results for various sites, both idealized and realistic, illustrate the types of layout generated by the proof-of-concept algorithm. These results provide a basis for a discussion of the heuristic's strong points-speed, competitive reduction in wake losses, and flexibility-and weak points-partial blindness to the objective and dependence on the starting layout. The computational speed of pseudo-gradient-based optimization is an enabler for analyses that would otherwise be computationally impractical. Pseudo-gradient-based optimization has already been used by industry in the design of large-scale (offshore) wind farms.
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This paper presents a heuristic building block for wind farm layout optimization algorithms. For each pair of wake-interacting turbines, a vector is defined. Its magnitude is proportional to the wind speed deficit of the waked turbine due to the waking turbine. Its direction is chosen from the inter-turbine, downwind, or crosswind directions. These vectors can be combined for all waking or waked turbines and averaged over the wind resource to obtain a vector, a "pseudo-gradient", that can take the role of gradient in classical gradient-following optimization algorithms. A proof-of-concept optimization algorithm demonstrates how such vectors can be used for computationally efficient wind farm layout optimization. Results for various sites, both idealized and realistic, illustrate the types of layout generated by the proof-of-concept algorithm. These results provide a basis for a discussion of the heuristic's strong points-speed, competitive reduction in wake losses, and flexibility-and weak points-partial blindness to the objective and dependence on the starting layout. The computational speed of pseudo-gradient-based optimization is an enabler for analyses that would otherwise be computationally impractical. Pseudo-gradient-based optimization has already been used by industry in the design of large-scale (offshore) wind farms.
We present an analysis of three datasets of 10min metocean measurement statistics and our resulting recommendations to both producers and users of such datasets. Many of our recommendations are more generally of interest to all numerical measurement data producers. The datasets analyzed originate from offshore meteorological masts installed to support offshore wind farm planning and design: the Dutch OWEZ and MMIJ and the German FINO1. Our analysis shows that such datasets contain issues that users should look out for and whose prevalence can be reduced by producers. We also present expressions to derive uncertainty and bias values for the statistics from information typically available about sample uncertainty. We also observe that the format in which the data are disseminated is sub-optimal from the users' perspective and discuss how producers can create more immediately useful dataset files. Effectively, we advocate using an established binary format (HDF5 or netCDF4) instead of the typical text-based one (comma-separated values), as this allows for the inclusion of relevant metadata and the creation of significantly smaller directly accessible dataset files. Next to informing producers of the advantages of these formats, we also provide concrete pointers to their effective use. Our conclusion is that datasets such as the ones we analyzed can be improved substantially in usefulness and convenience with limited effort.
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We present an analysis of three datasets of 10min metocean measurement statistics and our resulting recommendations to both producers and users of such datasets. Many of our recommendations are more generally of interest to all numerical measurement data producers. The datasets analyzed originate from offshore meteorological masts installed to support offshore wind farm planning and design: the Dutch OWEZ and MMIJ and the German FINO1. Our analysis shows that such datasets contain issues that users should look out for and whose prevalence can be reduced by producers. We also present expressions to derive uncertainty and bias values for the statistics from information typically available about sample uncertainty. We also observe that the format in which the data are disseminated is sub-optimal from the users' perspective and discuss how producers can create more immediately useful dataset files. Effectively, we advocate using an established binary format (HDF5 or netCDF4) instead of the typical text-based one (comma-separated values), as this allows for the inclusion of relevant metadata and the creation of significantly smaller directly accessible dataset files. Next to informing producers of the advantages of these formats, we also provide concrete pointers to their effective use. Our conclusion is that datasets such as the ones we analyzed can be improved substantially in usefulness and convenience with limited effort.
The construction and management of a wind farm involve many disciplines. It is hard for a single designer or developer to keep an overview of all the relevant concepts, models, and tools. Nevertheless, this is needed when performing integrated modeling or analysis. To help researchers keep this overview, we have created WESgraph (the Wind Energy System graph), a knowledge base for the wind farm domain, implemented as a graph database. It currently contains 1222 concepts and 1725 relations between them. This paper presents the structure of this graph database – content stored in nodes and the relationships between them – as a foundational ontology, which classifies the domain's concepts. This foundational ontology partitions the domain in two: a part describing physical aspects and a part describing mathematical and computational aspects. This paper also discusses a number of generally difficult cases that exist when adding content to such a knowledge base. This paper furthermore discusses the potential applications of WESgraph and illustrates its use for computation pathway discovery – the application that triggered its creation. It also contains a description of our practical experience with its design and use as well as our thoughts about the community use and management of this tool.
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The construction and management of a wind farm involve many disciplines. It is hard for a single designer or developer to keep an overview of all the relevant concepts, models, and tools. Nevertheless, this is needed when performing integrated modeling or analysis. To help researchers keep this overview, we have created WESgraph (the Wind Energy System graph), a knowledge base for the wind farm domain, implemented as a graph database. It currently contains 1222 concepts and 1725 relations between them. This paper presents the structure of this graph database – content stored in nodes and the relationships between them – as a foundational ontology, which classifies the domain's concepts. This foundational ontology partitions the domain in two: a part describing physical aspects and a part describing mathematical and computational aspects. This paper also discusses a number of generally difficult cases that exist when adding content to such a knowledge base. This paper furthermore discusses the potential applications of WESgraph and illustrates its use for computation pathway discovery – the application that triggered its creation. It also contains a description of our practical experience with its design and use as well as our thoughts about the community use and management of this tool.
Wind turbine control
Open-source software for control education, standardization and compilation
Standardized, easy to use, and preferably open-source research software is an important aspect in supporting and solidifying the wind turbine community. To this end, three contributions in the form of open-source software projects are presented in this paper. First, a community-driven wind turbine baseline controller, the Delft Research Controller (DRC), is presented. The DRC is applicable to high-fidelity simulation software that uses the DISCON controller interface. The controller distinguishes itself by the variety of available control and estimation implementations, its ease of use, and the universal applicability to wind turbine models. Secondly, in the wake of the DRC, the SimulinkDRC graphical controller design and compilation environment has been developed. Users having access to Simulink can benefit from the convenient way of controller development the tool provides. Finally, the FASTTool has been developed for educational purposes, by focusing on the graphical aspect of wind turbine (controller) design. The tool simplifies interaction with the advanced FAST simulation software, by comprehensive visualizations and analysis tools. This paper demonstrates and describes the functionality of all three software projects.
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Standardized, easy to use, and preferably open-source research software is an important aspect in supporting and solidifying the wind turbine community. To this end, three contributions in the form of open-source software projects are presented in this paper. First, a community-driven wind turbine baseline controller, the Delft Research Controller (DRC), is presented. The DRC is applicable to high-fidelity simulation software that uses the DISCON controller interface. The controller distinguishes itself by the variety of available control and estimation implementations, its ease of use, and the universal applicability to wind turbine models. Secondly, in the wake of the DRC, the SimulinkDRC graphical controller design and compilation environment has been developed. Users having access to Simulink can benefit from the convenient way of controller development the tool provides. Finally, the FASTTool has been developed for educational purposes, by focusing on the graphical aspect of wind turbine (controller) design. The tool simplifies interaction with the advanced FAST simulation software, by comprehensive visualizations and analysis tools. This paper demonstrates and describes the functionality of all three software projects.
Motivated by the need to develop reference wind energy systems for optimisation and technology assessment studies, the International Energy Agency Wind Task 37 on Wind Energy Systems Engineering is developing a reference offshore wind power plant at the Dutch offshore wind energy areas Borssele III and IV. This paper presents a comparison between two approaches for developing the preliminary design of an offshore wind plant turbine layout, electrical collection system, and support structures. The first is a sequential approach, where components of the wind farm are optimised sequentially, each with its own objective function, thus neglecting potential interactions between them. The second approach uses Multidisciplinary Design Analysis and Optimisation (MDAO), where all components are jointly optimised with the overall system levelised cost of energy (LCOE) as a global objective function. Studying the cases of regular and irregular layouts, the integrated approach always shows a greater improvement in the LCOE of the final design compared to the design resulting from the traditional sequential approach. The most significant trade-off exploited by the MDAO approach used in this study is between losses in energy production due to turbine wake effects and the costs of electrical cable infrastructure.
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Motivated by the need to develop reference wind energy systems for optimisation and technology assessment studies, the International Energy Agency Wind Task 37 on Wind Energy Systems Engineering is developing a reference offshore wind power plant at the Dutch offshore wind energy areas Borssele III and IV. This paper presents a comparison between two approaches for developing the preliminary design of an offshore wind plant turbine layout, electrical collection system, and support structures. The first is a sequential approach, where components of the wind farm are optimised sequentially, each with its own objective function, thus neglecting potential interactions between them. The second approach uses Multidisciplinary Design Analysis and Optimisation (MDAO), where all components are jointly optimised with the overall system levelised cost of energy (LCOE) as a global objective function. Studying the cases of regular and irregular layouts, the integrated approach always shows a greater improvement in the LCOE of the final design compared to the design resulting from the traditional sequential approach. The most significant trade-off exploited by the MDAO approach used in this study is between losses in energy production due to turbine wake effects and the costs of electrical cable infrastructure.
Multidisciplinary Design Analysis and Optimisation (MDAO) workflows consist of coupled tools driven by an algorithm for a specific purpose, e.g. optimisation. MDAO users may have at their disposal a set of tools of varying levels of fidelity. As a result, many permutations or MDAO workflows may arise, for which no clear methodology exists to evaluate, compare and rank them based on their performance. Our research question is then how to find the most useful MDAO workflows for a given purpose. This paper provides a guideline for solving this multiple criteria decision analysis problem. Our guideline includes a method to define the criteria and metrics for evaluating the performance of MDAO workflows, a strategy to aggregate the scores, and an optimisation algorithm for
categorical variables used to find the best alternatives. We apply this guideline to the offshore wind farm layout optimisation problem to demonstrate its use. This case study evidenced how critical t
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Multidisciplinary Design Analysis and Optimisation (MDAO) workflows consist of coupled tools driven by an algorithm for a specific purpose, e.g. optimisation. MDAO users may have at their disposal a set of tools of varying levels of fidelity. As a result, many permutations or MDAO workflows may arise, for which no clear methodology exists to evaluate, compare and rank them based on their performance. Our research question is then how to find the most useful MDAO workflows for a given purpose. This paper provides a guideline for solving this multiple criteria decision analysis problem. Our guideline includes a method to define the criteria and metrics for evaluating the performance of MDAO workflows, a strategy to aggregate the scores, and an optimisation algorithm for
categorical variables used to find the best alternatives. We apply this guideline to the offshore wind farm layout optimisation problem to demonstrate its use. This case study evidenced how critical t