This paper introduces innovative optimization and deep learning techniques to enhance the prediction of complex wake dynamics in the downstream wind velocity of tilted wind turbines. Traditional methods for calibrating the Bastankhah wake model often lead to increased errors in wind velocity distribution due to overfitting of the local wake characteristics. To address this issue, we propose an additional global optimization step to reduce errors in wind velocity predictions with respect to various wake parameters. Despite this improvement, the Bastankhah model's axisymmetric Gaussian wake shape limits its accuracy for complex wake structures. Therefore, we also propose a deep learning approach, which demonstrates promising results by accurately modeling complex wake shapes across a broader range of tilt angles with minimal computational cost. The deep learning approach achieves near-identical predictions to high-fidelity large-eddy simulations, representing a promising advancement in wake modeling.

Wind farm layout optimization (WFLO) studies often aim to maximize the annual energy production (AEP) of a wind farm by choosing an arrangement of turbines that minimizes wake interactions. One way to reduce the cost of WFLO studies is by using more computationally efficient AEP models. The cost of standard AEP modeling approaches, based on the numerical integration of low-fidelity engineering wake models, scales poorly with the number of simulated discrete wind conditions. A second way to reduce cost when using a gradient-based algorithm is to supply exact gradient information instead of finite-difference estimates. However, analytical functions for the derivatives of AEP with respect to turbine positions are not always available in the conventional modeling approach. FLOWERS is a computationally inexpensive, analytical model for wind farm AEP that is specifically developed for WFLO applications. In this paper, we analyze the performance of the FLOWERS AEP model with analytic gradients in a layout optimization study compared with a reference optimization framework across three wind farm case studies. We find that the FLOWERS-based approach reduces computation time by a factor of 50–4000 and improves optimal AEP by about 0.3% with less than half of the variability in AEP across instances with randomized initial conditions. We also find the optimal layouts to be insensitive to model parameter tuning, making FLOWERS-based layout optimization a streamlined, user-friendly approach.

A major cost in the study of wind farm layout optimization is the repeated evaluation of the annual energy production (AEP). The current approach to estimating AEP requires a large set of flow simulations to be performed that cover each discrete wind speed and direction combination contained within the wind rose, followed by a probability-weighted sum of the power production resulting from each simulation. Even with inexpensive engineering wake models, this numerical integration scheme can lead to high computational costs. In this paper, we derive an analytical formulation for estimating farm AEP across every wind direction, based on a Gaussian wake velocity model, which reduces the number of wind farm simulations to a single function evaluation. As a result, we find that the Gaussian-FLOWERS approach reduces the time for AEP calculations by more than two orders of magnitude with a small trade-off in accuracy when compared to a conventional approach. This massive reduction in computation cost is useful to reduce overall costs in wind farm layout optimization studies.

Wake steering strategies are employed to increase the overall power production of wind farms by deflecting wakes of upstream turbines away from downstream ones. The gain in net power comes at the expense of increased fatigue loads experienced by downstream turbines. In this work we investigate performance and fatigue loading characteristics of a small farm consisting of five aligned International Energy Agency Wind Technology Collaboration Programme 15 MW wind turbines. A parametric study is performed where, for every wind direction from −20 to 20°, the yaw misalignment angle varies from −25 to 25°. This setup allows us to investigate asymmetries and identify optimal conditions for a given wind direction. In general, we find that positive yaw configurations are preferred and that yaw configurations that result in attractive power differences when compared to a baseline no-yaw scenario (25 %) come with significant increase in fatigue loading (we use the standard deviation and damage-equivalent load (DEL) of the blade-root, low-speed shaft, and tower-base moments as proxies for fatigue load). We find that for any given positive wind inflow angle, yaw angles between −2.5 and 15° yield power differences of 10 %–20 % over a no-yaw baseline, and positive yaw is preferred because of lower fatigue loading. For any given negative wind inflow angles, positive yaw also results in lower magnitudes of standard deviation and DEL for the channels investigated. A small power loss of up to 2 % is observed for some positive yaw angles under negative wind directions (as compared to symmetric negative yaw and positive wind cases), but improvements in terms of loads exceed 25 % and may be enough to justify a positive yaw configuration under negative winds as well. We show that such behavior can be explained by partial waking and the direction of the rotation of the rotor.

Annual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free-stream wind conditions. Physics-based estimates of wind farm power production typically rely on low-fidelity engineering wake models that approximate the steady-state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed-form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient-based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction.

To help meet the need for inexpensive green hydrogen, a framework for wind turbine design optimization specifically for hydrogen production has been developed. This framework optimizes wind turbines by minimizing a levelized cost of hydrogen (LCOH) objective. Preliminary case study results show a 12.7% decrease in LCOH compared to a baseline reference wind turbine designed using a traditional method of minimizing the levelized cost of energy (LCOE) and a 1.53% decrease in LCOH when compared to an LCOE optimized turbine using our framework. The LCOH optimized turbine has a larger rotor compared to the baseline and LCOE optimized turbines, where the increased turbine cost is offset by increased hydrogen production. This case study focuses on a single wind turbine-electrolyzer system, demonstrating that significant cost savings can be achieved by using new optimization objectives. Further cost savings may be had with plant-level optimizations and by including other technologies such as solar panels and battery storage.

This report reviews the results and main conclusions of the NOWRDC project "Wind Farm Control and Layout Optimization for U.S. Offshore Wind Farms." A key finding is that wake steering should be considered a valuable tool for increasing the energy production of wind farms, especially in the event the farms are designed to maximize the energy production of a given boundary area.

Hydrogen (H2) is an efficient energy carrier and storage mechanism that can supply both stationary and transport energy demand. Rapidly declining renewable energy generation costs; technology innovations in wind, solar, battery storage, and electrolysis; and a global push for more sustainable and secure energy have driven increased interest in green H2 production. In this study, we develop an H2 scenario analysis tool to assist in rapid, high-resolution insights into future, green H2 pathways to achieve policy goals and market competitiveness. Using this tool, we estimate H2 production and costs for U.S., off-grid scenarios given varying policy and cost scenarios from 2025–2035. Results indicate that achieving economically competitive green H2 production (below $2/kg) is possible in 2030 with no policy incentives (one site achieves this target), while increasing policy support to include wind and green H2 production tax credits enables widespread economic viability sooner, with sub-$2/kg LCOH targets achieved by 2025 and 51.7% of sites achieving this target by 2035. Maximizing policy support through prevailing wage and apprenticeship credit multipliers enable widespread economic viability, including sub-$1/kg of green H2 by 2025 and even negative pricing by 2035. Regions with lowest LCOH values correspond to high wind resource areas and capacity factors. Achieving decarbonization goals with green H2 depends on technology cost reductions and policy support, with a maximum average LCOH reduction of $3.10 between no and maximum policy support scenarios, and a maximum average LCOH reduction of $5.86 between current, conservative technology costs and 2035 projected technology cost assumptions.

Selecting a wind farm layout optimization method is difficult. Comparisons between optimization methods in different papers can be uncertain due to the difficulty of exactly reproducing the objective function. Comparisons by just a few authors in one paper can be uncertain if the authors do not have experience using each algorithm. In this work we provide an algorithm comparison for a wind farm layout optimization case study between eight optimization methods applied, or directed, by researchers who developed those algorithms or who had other experience using them. We provided the objective function to each researcher to avoid ambiguity about relative performance due to a difference in objective function. While these comparisons are not perfect, we try to treat each algorithm more fairly by having researchers with experience using each algorithm apply each algorithm and by having a common objective function provided for analysis. The case study is from the International Energy Association (IEA) Wind Task 37, based on the Borssele III and IV wind farms with 81 turbines. Of particular interest in this case study is the presence of disconnected boundary regions and concave boundary features. The optimization methods studied represent a wide range of approaches, including gradient-free, gradient-based, and hybrid methods; discrete and continuous problem formulations; single-run and multi-start approaches; and mathematical and heuristic algorithms. We provide descriptions and references (where applicable) for each optimization method, as well as lists of pros and cons, to help readers determine an appropriate method for their use case. All the optimization methods perform similarly, with optimized wake loss values between 15.48 % and 15.70 % as compared to 17.28 % for the unoptimized provided layout. Each of the layouts found were different, but all layouts exhibited similar characteristics. Strong similarities across all the layouts include tightly packing wind turbines along the outer borders, loosely spacing turbines in the internal regions, and allocating similar numbers of turbines to each discrete boundary region. The best layout by annual energy production (AEP) was found using a new sequential allocation method, discrete exploration-based optimization (DEBO). Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem, given that they are applied correctly.

Wind farm design and analysis heavily rely on computationally efficient engineering models that are evaluated many times to find an optimal solution. A recent article compared the state-of-the-art Gauss-curl hybrid (GCH) model to historical data of three offshore wind farms. Two points of model discrepancy were identified therein: poor wake predictions for turbines experiencing a lot of wakes and wake interactions between two turbines over long distances. The present article addresses those two concerns and presents the cumulative-curl (CC) model. Comparison of the CC model to high-fidelity simulation data and historical data of three offshore wind farms confirms the improved accuracy of the CC model over the GCH model in situations with large wake losses and wake recovery over large inter-turbine distances. Additionally, the CC model performs comparably to the GCH model for single- and fewer-turbine wake interactions, which were already accurately modeled. Lastly, the CC model has been implemented in a vectorized form, greatly reducing the computation time for many wind conditions. The CC model now enables reliable simulation studies for both small and large offshore wind farms at a low computational cost, thereby making it an ideal candidate for wake-steering optimization and layout optimization.

Wind will be a foundational energy source in the electricity grid at the heart of a future integrated energy system, replacing traditional electricity generators powered by fossil fuels and providing grid reliability services in addition to energy. Future capabilities and functions of the wind energy sector will evolve apace with the future expansion and needs of global energy infrastructure; however, wind turbines designed today will not be able to provide the services needed to form and stabilize the grid as a majority supplier. In 2017, organizers for the IEA Wind Technical Experts Meeting (TEM) #89 Grand Vision for Wind Energy workshop assembled a group of experts to consider the question of how to enable a future in which wind energy supplies more than 50% of global electricity consumption. More than 70 experts representing 15 countries attended the workshop and provided diverse perspectives for the Grand Vision for Wind Energy. The IEA Wind TEM #109 was a subsequent gathering that was convened Feb. 28-March 1, 2023, in Boulder, Colorado, USA. The IEA Wind TEM #109 meeting aimed to bring together the leaders of all working groups and the IEA Wind Technology Collaboration Programme (TCP) to identify gaps in scientific knowledge, design, and deployment practice as well as identify recommendations for collaborative pathways, initiatives, and prioritized long-term research needs that can be addressed by IEA Wind. This report captures the outcomes of this meeting of international experts: five Grand Challenge areas (The Atmosphere, The Turbine, The Plant and Grid, Environmental Co-Design, and Social Science). In addition, meeting participants identified eight crosscutting topic areas that are discusses within this report (Environment-Turbine, Turbine-Atmosphere, Atmosphere-Grid/Plant, Grid/Plant-Turbine, Grid/Plant-Environment, Atmosphere-Environment, Turbine-Social, and Social-Grid/Plant).

Layout design and wake steering through wind plant control are important and complex components in the design and operation of modern wind power plants. They are currently optimized separately, but with more and more computational and experimental studies demonstrating the gains possible through wake steering, there is a growing need from industry and regulating bodies to combine the layout and control optimization in a co-design process. However, combining these two optimization problems is currently infeasible due to the excessive number of design variables and large solution space. In this article, we present a method that enables the coupled optimization of wind power plant layout and wake steering with no additional computational expense than a traditional layout optimization. We developed a geometric relationship between wind turbines to find an approximate optimal yaw angle, bypassing the need for either a nested or coupled wind plant control optimization. It also provides a significant and immediate improvement to wind power plant design by enabling the co-design of turbine layout and yaw control for wake steering. A small co-designed plant shown in this article produces 0.8 % more energy than its sequentially designed counterpart. This additional energy production comes with no additional infrastructure, turbine hardware, or control software; it is simply the outcome of optimizing the turbine layout and yaw control together, resulting in millions of dollars of additional revenue for the wind power plants of the future.

The research objective for this Atmosphere to Electrons to Grid (A2e2g) project was to design a platform that merges forecasting tools with aerodynamic and economic models to maximize a wind plant's value streams for energy and grid services. The value proposition is that expanding wind plant operation to include grid services allows wind plants to operate in markets for grid services as well as energy markets, increasing revenue streams for wind plant operators while contributing to reliable grid operation. The A2e2g project consists of a platform that integrates 1) forecasting tools to account for weather uncertainty, with 2) aerodynamic wind plant models to account for wake dynamics and wind plant operation, and 3) economic models to advise on operation for a wind power plant that offers grid services in addition to energy. The A2e2g platform is a holistic Python tool with modules that can be run to 1) advise on market participation and 2) control and operate a wind power plant in real time. Figure E-2 shows the different components of the A2e2g platform. The A2e2g framework assumes two stages: the first stage is in day-ahead and the second stage is in real-time. Managing uncertainty is key in the first stage and managing variability is key in the second stage. Detailed descriptions of each component can be found in the main body of the report. The different components have models written and developed in the Python programming language. The code is assembled into a Python package and can be easily downloaded and installed from the A2e2g repository (https://github.com/NREL/a2e2g). The code is open-source and free to use.

This article considers joint active power control of wind turbines and battery storage to follow a plant-level power reference signal. The joint control dynamically curtails the energy from a subset of the wind turbines and stores or withdraws energy from the battery to meet the power reference setpoint while accounting for wind plant aerodynamic interactions, such as wake losses. As a use case, we study the performance of the controller in maintaining a constant power output over hourly periods. A wind plant operating in this way would rely much less on other grid resources to meet its contractual agreements, thereby improving grid reliability, especially in grids with high penetration of wind and solar generation. We compare the operation of the wind plant under joint active power control to standard power-maximizing control with battery support. We present an analysis of the performance of the control system architecture. To study the impact of the battery size on performance, we simulate a 50-MW wind plant supported by batteries ranging from 8 to 64 MWh. We then evaluate the over and undergeneration penalties incurred by the plant during the simulation period.

Renewable energy (RE) generation systems are rapidly being deployed on the grid. In parallel, electrified devices are quickly being added to the grid, introducing additional electric loads and increased load flexibility. While increased deployment of RE generation contributes to decarbonization of the grid, it is inherently variable and unpredictable, introducing uncertainty and potential instability in the grid. One way to mitigate this problem is to deploy utility-scale storage. However, in many cases the deployment of utility-scale battery storage systems remain unfeasible due to their cost. Instead, utilizing the increased amounts of data and flexibility from electrified devices on the grid, advanced control can be applied to shift the demand to match RE generation, significantly reducing the capacity of required utility-scale battery storage. This work introduces the novel forecast-aided predictive control (FAPC) algorithm to optimize this load shifting in the presence of forecasts. Extending upon an existing coordinated control framework, the FAPC algorithm introduces a new electric vehicle charging control algorithm that has the capability to incorporate forecasted information in its control loop. This enables FAPC to better track a realistic RE generation signal in a fully correlated simulation environment. Results show that FAPC effectively shifts demand to track a RE generation signal under different weather and operating conditions. It is found that FAPC significantly reduces the required capacity of the battery storage system compared to a baseline control case.