Unrestricted Wind Farm Layout Design With Optimal Control Considerations

A multiscale numerical framework coupled with control strategies for simulating a wind farm in complex terrain

Optimal design of wind turbine placement in a wind farm is one of the most effective tools to reduce wake power losses by alleviating the wake effect in the wind farm. In comparison to the discrete grid-based wind farm design method, the continuous coordinate method has the property of continuously varying the placement of wind turbines, and hence, is far more capable of obtaining the global optimum solutions. In this paper, the coordinate method was applied to optimize the layout of a real offshore wind farm for both simplified and realistic wind conditions. A new analytical wake model (Jensen-Gaussian model) taking into account the wake velocity variation in the radial direction was employed for the optimization study. The means of handling the irregular real wind farm boundary were proposed to guarantee that the optimized wind turbine positions are feasible within the wind farm boundary, and the discretization method was applied for the evaluation of wind farm power output under Weibull distribution. By investigating the wind farm layout optimization under different wind conditions, it showed that the total wind farm power output increased linearly with an increasing number of wind turbines. Under some particular wind conditions (e.g., constant wind speed and wind direction, and Weibull distribution), almost the same power losses were obtained under the wake effect of some adjacent wind turbine numbers. A common feature of the wind turbine placements regardless of the wind conditions was that they were distributed along the wind farm boundary as much as possible in order to alleviate the wake effect.

This study investigates the effects of the construction and operation of a large Danish offshore wind farm on harbor and gray seal haul-out behavior within a nearby (4 km) seal sanctuary. Time-lapse photography, visual monitoring, and aerial surveys were used to monitor the number of seals on land in daylight hours. Seals were monitored during two preconstruction periods (19 June–31 August 2001 and April–August 2002), a construction period of the wind farm (August 2002–December 2003), and a period of operation of the wind farm (December 2003–December 2004). Monthly aerial surveys were conducted to estimate the proportion of seals in the sanctuary relative to neighboring haul-out sites. From preconstruction to construction and through the first year of operation the number of harbor seals in the sanctuary increased at the same rate as the number of seals at the neighboring haul-out sites. No long-term effects on haul-out behavior were found due to construction and operation of the wind farm. However, a significant short-term decrease was seen in the number of seals present on land during sheet pile driving in or near the wind farm. Acoustic deterrents were utilized simultaneously to avoid hearing damage.

Summary Improving the layout of wind farms is one of the keys to increasing the cost-efficiency of offshore wind projects, which is paramount for the success and acceleration of a sustainable energy transition in our societies. Because field development planning and wind turbine placement share many similarities in terms of the decision-making context, we seek opportunities to leverage the know-how from subsurface field development practices to achieve better farm layout designs in offshore wind projects. In this paper, we present how stochastic optimization technology originally conceived to assist oil and gas practitioners in finding optimal field development strategies can be transferred to support decisions in offshore wind farm developments. We particularly focus on comparing different ways of parametrizing mathematically the wind farm layout. We illustrate the use of our framework in realistic case studies, showing that energy production can indeed be increased by optimizing the turbine placement while respecting all hard constraints imposed on the layout design. Finally, we analyze the results obtained to identify the most effective parametrization approach with respect to the shape of the wind farm license area and the presence of complex geometrical constraints.

Advances in high-performance sensing and signal processing technology enable the development of failure-prognosis tools for wind turbines to detect, diagnose, and predict the system wide effects of failure events. Although prognostics can provide valuable information for proactive actions in preventing system failures, the benefits have not been fully utilized for the operation and maintenance decision-making of wind turbines. This paper presents a generic failure prognosis informed decision-making tool for wind farm operation and maintenance while considering the predictive failure information of an individual turbine and its uncertainty. In the presented approach, the probabilistic damage growth model is used to characterize individual wind turbine performance degradation and failure prognostics, whereas the economic loss measured by monetary values and environmental performance measured by unified carbon credits are considered in the decision-making process. Based on customized wind farm information input, the developed decision-making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize economic and environmental benefits concurrently. The efficacy of the proposed prognosis-informed maintenance strategy is compared with the condition-based maintenance strategy and demonstrated with a wind farm case study.

<strong class="journal-contentHeaderColor">Abstract.</strong> Mesoscale meteorological phenomena, including Atmospheric Gravity Waves, or Trapped Lee Waves (TLWs) can result from flow over topography or coastal transition in the presence of stable atmospheric stratification, particularly with strong capping inversions. Satellite images show that topographically forced TLWs frequently occur around near-coastal offshore wind farms. Yet current understanding of how they interact with individual turbines and whole farm energy output is limited. This parametric study investigates the potential impact of TLWs on a UK near-coastal offshore wind farm, Westermost Rough (WMR) resulting from westerly &ndash; south-westerly flow over topography in the Southeast of England. Computational fluid dynamics (CFD) modelling (using ANSYS-CFX) of TLW situations based on real atmospheric conditions at WMR was used to better understand turbine level and whole wind farm performance in this parametric study based on real inflow conditions. These simulations indicated that TLWs have the potential to significantly alter the windspeeds experienced by and the resultant power output of individual turbines and the whole wind farm. The location of the wind farm in the TLW wave cycle was an important factor in determining the magnitude of TLW impacts, given the expected wavelength of the TLW. Where the TLW trough was coincident with the wind farm, the turbine windspeeds and power outputs were more substantially reduced compared with when the TLW peak was coincident with the location of the wind farm. These reductions were mediated by turbine windspeeds and wake losses being superimposed on the TLW. However, the same initial flow conditions interacting with topography under different atmospheric stability settings produce differing near wind farm flow. Factors influencing the flow within the wind farm under the different stability conditions include differing: hill and coastal transition recovery, windfarm blockage effects and wake recovery. Determining how much of the differences in windspeed and power output in the wind farm resulted from the TLW is an area for future development.

The electrical systems needed for offshore wind farms to collect power from wind turbines-and transmit it to shore- will be a significant cost element of these systems. This paper describes the development of a simplified model of the cost and performance of such systems. The performance prediction accounts for losses as a function of the power produced in the wind farm and the length and size of the cables. The cost prediction is flexibly formulated so wind farm configurations can be evaluated by parameters such as the number of wind turbines, wind turbine size, turbine array configuration and spacing, and distance from shore. The collection system-the medium-voltage electrical grid within the wind farm, and the transmission system-the high-voltage electrical connection to an on-shore transmission line-are treated independently in the model. Data sources for the model and limitations of the data are discussed, and comparison is made to costs reported by others. The choice of transmission system technology is also addressed. This electrical system model is intended for integration into a more comprehensive model of offshore wind farm design, cost, and performance that will be used for parametric studies and optimization of wind farm configurations. Because some concepts for future offshore wind installations in deep water use floating platforms, this paper briefly discusses the application of submarine cable technology to nonfixed termination points, a departure from current practice. Introduction The National Wind Technology Center (NWTC) of the National Renewable Energy Laboratory (NREL) in Golden, Colorado, has undertaken a series of concept studies to evaluate the cost and performance of offshore wind farms. The product of these studies will be a comprehensive model of offshore wind farm design, cost, and performance suitable for parametric studies and optimization of wind farmconfigurations. The overall goal of this effort is to helpidentify technology pathways for offshore wind energy development and deployment in the United States. Offshore wind farms present an attractive option because they allow for larger wind turbines that operate in higher wind resources than land-based [1,2]. Offshore installations are also more expensive, so an understanding of their performance, cost, and optimal configurations is needed. This paper is an overview of one of these concept studies. It focuses on the power losses in wind farm electrical power collection and transmission systems, as well as the costs of the system components and their installation. A hypothetical system was based loosely on the Horns Rev offshore wind farm in Denmark. Inquiries were made with manufacturers about electrical and cost data on the required components, which were then compiled in a spreadsheet model. The performance prediction accounts for losses as a function of power output of the wind farm and length and size of the cables. The cost prediction is flexibly formulated so wind farm configurations can be evaluated by parameters such as the number of wind turbines, wind turbine size, turbine array configuration and spacing, and distance from shore.

Abstract. Wind farm layout optimization usually aims at maximizing annual energy production by placing wind turbines in a strategic way to avoid wake losses. However, this might not lead to optimal profits because of the volatility of electricity prices. Moreover, with the growing unpredictability and variability of future power systems due to the increase in renewable electricity production, wind farm operators will have a more important role in balancing the system through participation in reserve markets. This study presents a new formulation for wind farm layout optimization where the objective function aims at maximizing revenues from both day-ahead and reserve markets. It uses stochastic gradient descent for the optimization and probabilistic forecasts for wind power and electricity prices. The new formulation is applied to a test case based on a real-life offshore wind farm in Belgium. An important conclusion is that annual revenues are expected to increase in a significant way when accounting for participation in reserve markets, while exhibiting a lower supplied energy production. Moreover, layouts optimized for revenue maximization with reserve participation tend to show better yearly revenues than when considering the day-ahead market only in the objective function. Expected revenues are also higher by 0.18 % on average for the new methodology than when using the maximization of annual energy production, widely used in the literature, as the objective function.

Wind Turbine Operation and Maintenance Video Supportive System Based on the Mobile Internet of Things

<strong class="journal-contentHeaderColor">Abstract.</strong> 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 (less sensitive) to wind direction, improving predictability and thus 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 a minimum inter-turbine spacing effectively resolves the induced wake issue while maintaining high-yield performance.

<strong class="journal-contentHeaderColor">Abstract.</strong> 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 (less sensitive) to wind direction, improving predictability and thus 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 a minimum inter-turbine spacing effectively resolves the induced wake issue while maintaining high-yield performance.

<strong class="journal-contentHeaderColor">Abstract.</strong> 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 (less sensitive) to wind direction, improving predictability and thus 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 a minimum inter-turbine spacing effectively resolves the induced wake issue while maintaining high-yield performance.

This research focuses on prediction of wind farm power for horizons of 0–10 minutes and not more than one hour using statistical methods. These short term predictions are relevant for both transmission system operators, wind farm operators and traders. Previous research indicates that for short time horizons the persistence method performs as well as more complex methods. However, these results are based on accumulated power for an entire wind farm. The contribution in this paper is to develop multi-dimensional linear methods based on measurements of power or wind speed from individual wind turbine in a wind farm. These multi-dimensional methods are compared with the persistence method using real 1 minute average data from the Sheringham Shoal wind farm with 88 turbines. The results show that the use of measurements from individual turbines reduce the prediction errors 5–10% and also improves the prediction error variance estimate compared to the persistence method. We also present convincing examples showing that the predictions follow the wind farm power over a window of an hour.

<strong class="journal-contentHeaderColor">Abstract.</strong> With the increasing growth of wind farm installations, the impact of wake effects from wind turbines on power output, structural loads, and revenue has become a major concern. Hence, there is a need for precise simulation tools to facilitate efficient and cost-effective design and operation of wind farms. To address this need, we present HAWC2Farm, a dynamic and versatile aeroelastic wind farm simulation methodology that combines state-of-the-art engineering models to accurately capture the complex physical phenomena in wind farms. HAWC2Farm models each turbine in a wind farm using the aeroelastic turbine simulator, HAWC2, while sharing a large, high-resolution turbulence box to model atmospheric flow field effects at the farm level, and a modified Dynamic Wake Meandering (DWM) model for capturing wake interactions. The method is computationally efficient and provides valuable insights for wind farm design. It is validated using time series field measurements from the Lillgrund wind farm, including scenarios with yaw steering and a turbine shutdown. HAWC2Farm addresses the challenges of modelling the complex dynamics in wind farms, enabling more accurate, informed and cost-effective design and operation.

Floating offshore wind energy will play a key role in the clean energy transition scenario. The number of projects deploying large-scale wind farms is growing in multiple regions, from Northern Europe to the East Coast of the United States, and extending to the Mediterranean Sea. Offshore wind farms face fewer constraints in layout design, as they do not need to consider orography and can generally be situated in vast, open sea areas. Consequently, offshore wind turbines could be arranged in simple layouts, such as grid patterns or staggered rows, spaced uniformly. However, this regular turbine arrangement would result in significant annual energy production (AEP) losses due to wake-rotor interaction. Although increasing spacing between turbines can mitigate this issue, it is not always feasible due to marine space availability. Moreover, when feasible, it can lead to higher costs for additional cabling and maintenance. This paper aims to introduce a multi-objective wind farm optimization framework that employs a genetic algorithm (NSGA II). This framework seeks to maximize the energy production while minimizing OPEX and CAPEX costs, taking into account the wind resource and bathymetry of a specific region. In the paper, two case studies are presented for the optimization of wind farms in the Mediterranean Sea assuming 15MW wind turbines. AEP evaluation of each individual wind farm is obtained with the open-source library FLORIS, while the optimization algorithm relies on the library PyMoo for multi-objective optimization.