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Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Properly coordinating turbines, by operating some turbines suboptimally, within a wind farm has the potential to improve overall wind farm performance. Computing the optimal control strategy under varying atmospheric conditions can be computationally intense for large wind farms. As wind power farms increase in size and related models become more complex, computationally efficient algorithms are needed to perform real-time optimization and control. This study proposes a distributed optimization framework and computationally efficient wake steering wind farm control strategy that uses the yaw angle of a turbine to alter the behavior of a turbine wake and minimize turbine interactions. This computational efficiency allows the strategy to be feasible for real-time control.
This paper presents a distributed approach to perform real-time optimization of large wind farms. Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. This paper optimizes the overall power produced by a wind farm by formulating and solving a nonconvex optimization problem where the yaw angles are optimized to allow some turbines to operate in misaligned conditions and shape the aerodynamic interactions in a favorable way. The solution of the nonconvex smooth problem is tackled using a proximal primal-dual gradient method, which provably identifies a first-order stationary solution in a global sublinear manner. By adding auxiliary optimization variables for every pair of turbines that are coupled aerodynamically and properly adding consensus constraints into the underlying problem, a distributed algorithm with turbine-to-turbine message passing is obtained; this allows for turbines to be optimized in parallel using local information rather than information from the whole wind farm. This algorithm is computationally light, as it involves closed-form updates. This approach is demonstrated on a large wind farm with 60 turbines. The results indicate that similar performance can be achieved as with finite-difference gradient-based optimization at a fraction of the computational time and thus approach real-time control/optimization.
The study of wind farms within realistic atmospheric boundary layer (ABL) conditions is critical to understand the governing physics of the system and to design optimal operational protocols. Aerodynamic wake interactions between individual wind turbines typically reduce total wind farm energy production 10-20% and increase the cost of electricity for this resource. Further, in large wind farms, the collective farm efficiency is in part dictated by the interaction between the wind farm and the turbulent ABL and, correspondingly, the vertical transport of kinetic energy into the turbine array. Coriolis forces, arising from the projection of Earth's rotation into a non-inertial rotating Earth-fixed frame, modify the interaction of a wind farm with the ABL. The traditional approximation made in typical ABL simulations assumes that the horizontal component of Earth's rotation is negligible in the atmospheric boundary layer. When including the horizontal component of Earth's rotation, the boundary layer and wind farm physics are a function of the geostrophic wind direction. The influence of the geostrophic wind direction on a wind farm atmospheric boundary layer was characterized using conventionally neutral and stable boundary layer large eddy simulations (LES). In the Northern hemisphere, geostrophic winds from west-to-east establish the horizontal component of Earth's rotation as a sink term in the shear Reynolds stress budget whereas the horizontal component manifests as a source term for east-to-west geostrophic winds. As a result, the magnitude of entrainment of mean kinetic energy into a wind turbine array is modified by the direction of the geostrophic wind, and correspondingly, the boundary layer height and wind speed and direction profiles depend on the geostrophic wind direction. Historically, wind farm control protocols have optimized the performance of individual wind turbines which results in aerodynamic wake interactions and a reduction in wind farm efficiency. Considering the wind farm as a collective, a physics- and data-driven wake steering control method to increase the power production of wind farms is developed. Upwind turbines, which generate turbulent energy-deficit wake regions which impinge on downwind generates, are intentionally yaw misaligned with respect to the incident ABL wind. While the yaw misaligned turbine may produce less power than in yaw aligned operation, the downwind generators may significantly enhance their production, increasing the collective power for the farm. The wake steering method developed combines a physics-based engineering wake model with state estimation techniques based on the assimilation of the wind farm power production data, which is readily available for control decisions at operational wind farms. Analytic gradients are derived from the wake model and leveraged for efficient yaw misalignment set-point optimization. The open-loop wake steering control methodology was tested in a multi-turbine array at a utility-scale operational wind farm, where it statistically significantly increased the power production over standard operation. The analytic gradient-based wind farm power optimization methodology developed can optimize the yaw misalignment angles for large wind farms on the order of seconds, enabling online real-time control. The dynamics of the ABL range from microscale features on the order of meters to mesoscale meteorological scales on the order of hundreds of kilometers. As a result of the broad range of scales and diversity of competing forces, the wind farm interaction with the turbulent ABL is a complex dynamical system, necessitating closed-loop control which is able to dynamically adapt to the evolving wind conditions. In order to rapidly design and improve dynamic closed-loop wind farm controllers, we developed wind farm LES capabilities which incorporate Coriolis and stratification effects and which permit the experimentation of real-time control strategies. Dynamic, closed-loop wake steering controllers are tested in simulations with full Coriolis effects and, altogether, the results indicate that closed-loop wake steering control can significantly increase wind farm power production over greedy operation provided that site-specific wind farm data is assimilated into the optimal control model.
Wind energy systems are central contributors to renewable energy generation, and their technology is continuously improved and updated. Without losing sight of theory, Control of Large Wind Energy Systems demonstrates how to implement concrete control systems for modern wind turbines, explaining the reasons behind choices and decisions. This book provides an extended treatment of different control topics divided into three thematic parts including modelling, control and implementation. Solutions for real-life difficulties such as multi-parameter tuning of several controllers, curve fitting of nonlinear power curves, and filter design for concrete signals are also undertaken. Examples and a case study are included to illustrate the parametrization of models, the control systems design with problems and possible solutions. Advice for the selection of control laws, calculation of specific parameters, which are necessary for the control laws, as the sensitivity functions, is given, as well as an evaluation of control performance based on indices and load calculation. Control of Large Wind Energy Systems covers methodologies which are not usually found in literature on this topic, including fractional order PID and nonlinear PID for pitch control, peak shaving control and extremum seeking control for the generator control, yaw control and shutdown control. This makes it an ideal book for postgraduate students, researchers and industrial engineers in the field of wind turbine control. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Among the various renewable energy sources, wind power has proven effective for large-scale energy production. To increase wind power production, it is essential not only to increase the number of wind farms but also to operate them efficiently. Conventionally, for a given wind condition, each individual wind turbine maximizes its own power production without taking into consideration the conditions of other wind turbines. Under this greedy control strategy, the wake formed by the upstream wind turbine, resulting in reduced wind speed and increased turbulence intensity inside the wake, would affect and lower the power production of downstream wind turbines. This thesis investigates a cooperative wind farm control approach to optimally coordinate the control actions (i.e., the operational conditions) of the wind turbines. The optimally coordinated control actions minimize the wake interference among the wind turbines and would therefore increase the total wind farm power production. To determine the optimum coordinated control actions, two methods are discussed in this thesis. First, the optimum coordinated control actions for wind turbines are determined using an analytical approach by employing mathematical optimization. In this approach, the total wind farm power is expressed as a function of the control actions of all the wind turbines. The wind farm power function is then maximized using sequential quadratic programming to determine the optimum coordinated control actions for the wind turbines. The effectiveness of the cooperative control strategy is studied using an example wind farm site and available wind data. For the second approach, the optimum coordinated control actions of the wind turbines are derived using the input (control actions of wind turbines) and output (wind farm power) data of a target wind farm. For real-time, data-driven wind farm control, an optimization algorithm should be able to improve target wind farm power production by executing as few trial actions as possible using the wind farm power monitoring data. To achieve this goal, a Bayesian Ascent (BA) algorithm is developed by incorporating into the Bayesian Optimization framework a trust region strategy that regulates the search domain. Numerical simulations using the wind farm power function show that the BA algorithm can be as effective as the analytical approach. Wind tunnel experiments with scaled wind turbines are conducted to further demonstrate the effectiveness of the data-driven BA algorithm for real-time control. Experimental results show that the BA algorithm can achieve a monotonic increase in the total wind farm power production using a small number of trial actions and demonstrate the potential of the BA algorithm for the real-time wind farm control problem.
This book presents advanced studies on the conversion efficiency, mechanical reliability, and the quality of power related to wind energy systems. The main concern regarding such systems is reconciling the highly intermittent nature of the primary source (wind speed) with the demand for high-quality electrical energy and system stability. This means that wind energy conversion within the standard parameters imposed by the energy market and power industry is unachievable without optimization and control. The book discusses the rapid growth of control and optimization paradigms and applies them to wind energy systems: new controllers, new computational approaches, new applications, new algorithms, and new obstacles.
Wind plant control strategies, including axial induction and wake steering control, aim to improve the performance of wind farms, including increasing energy production and decreasing turbine loads. This paper presents a linear model of wake characteristics for use with a distributed model predictive control method for the purpose of optimizing axial induction and yaw misalignment setpoints. In particular, we use an iterative, distributed control method with nearest neighbor communication to coordinate turbine control actions that account for wake interactions between turbines. Simulations of the model and controller are performed on a 2x3 array of turbines using a modified version of the FLOw Redirection and Induction in Steady-state (FLORIS) model to dynamically track the relevant wake parameters. Preliminary results show the controller's ability to follow an arbitrary wind farm power reference signal for the purpose of providing active power control (APC) ancillary services for power grid stability. This efficient distributed control strategy can enable real-time wind farm optimization and control, even for very large scale farms.
Wind farms are clusters of wind turbines deployed over a relatively small area. During operations, the wake from upstream turbines may impinge on trailing turbines causing a decrease in power production. Wind farm control strategies aim at mitigating the effect of wake interactions. In this dissertation, model-free control strategies for wind farm power maximization have been evaluated using numerical simulations of the flow through wind farms. A model-free approach does not require a priori assumptions on the physical system, but learns on-line the system dynamics, avoiding modeling uncertainties. The control strategies are based on extremum-seeking control (ESC), a real-time gradient-based optimization algorithm. Either the turbine generator torque or the rotor yaw angle is used as the control parameter tuned by ESC to optimize the wind farm power production. The generator torque adjusts the turbine angular speed and the momentum deficit in the trailing wake, while the yaw angle serves to vary the direction of the wake and avoid trailing turbines. We first consider several implementations of ESC and assess their performances and practical feasibility. Both torque- and yaw-based ESC enhance power production, but the latter has a larger margin for improvement. For idealised turbine arrays, ESC achieves a potential power improvement of at least 7–8% compared to operations with design settings for an isolated turbine. After this calibration, we perform an optimization study for a real wind farm and obtain a quantitative evaluation of the impact of the control strategy in annual energy production. Large-eddy simulations with rotating actuator disk are used, in the first place, to provide a virtual wind farm to test the control algorithms. Additionally, the numerical data are investigated to gain a physical insight on the mechanisms underlying the performance improvement and broaden the impact of the optimization.
The reduction of greenhouse gas emissions is a major governmental goal worldwide. The main target, hopefully by 2050, is to move away from fossil fuels in the electricity sector and then switch to clean power to fuel transportation, buildings and industry. This book discusses important issues in the expanding field of wind farm modeling and simulation as well as the optimization of hybrid and micro-grid systems. Section I deals with modeling and simulation of wind farms for efficient, reliable and cost-effective optimal solutions. Section II tackles the optimization of hybrid wind/PV and renewable energy-based smart micro-grid systems.
Daniel Lückehe presents different approaches to optimize locations of multiple wind turbines on a topographical map. The author succeeds in significantly improving placement solutions by employing optimization heuristics. He proposes various real-world scenarios that represent real planning situations. Advanced evolutionary heuristics for the turbine placement optimization create not only highly optimized solutions but also significantly different solutions to give decision-makers optimal choices. As a matter of fact, wind turbines play an important role towards green energy supply. An optimal location is essential to achieve the highest possible energy efficiency.