Iterative Sequential Optimization Method for Maximizing the Wind Farm Generation Considering the Wake Effect

Wind turbine wakes modeling and applications: Past, present, and future

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

Wake-effect Minimising Optimal Control of Wind Farms, with Load Reduction

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.

The paper represents a comprehensive review of the wind farm layout and reliability assessment of the wind farm integrated electrical power system. The authors have done a review on the proliferation of renewable energy which raises the uncertainties in the electrical power system. The uncertainties including wind speed and wake effect are important to deal with when an isolated microgrid is considered. The scenario becomes vigilant when the wind farms are integrated with the main grid. Due to uncertainties, the study of reliability evaluation of a wind integrated power system would become significant to analyse the electrical power system behaviour effectively. So, the paper discusses the layout optimisation methods of wind turbines considering the uncertainty parameters, mainly the wake effect. In this regard, the different wake models and optimisation methods based on a single‐objective and multi‐objective functions are reviewed in detail with the proper comparisons. The paper serves as a better illustration of the competency of these optimisation methods on the optimal wind turbine location on a wind farm. Furthermore, the paper extends the view on the reliability and cost assessment, and reliability improvement techniques of the wind integrated power system. This article provides comprehensive information, yields an attractive and subsequent tool for research requirements for the researchers to design the wind farm layout, and assessed the reliability of a wind integrated power system.

Large scale wind power penetration has a significant impact on the reliability of the electric generation systems. A wind farm consists of a large number of wind turbine generators (WTGs). A major difficulty in modeling wind farms is that the WTG not have an independent capacity distribution due to the dependence of the individual turbine output on the same energy source, the wind. In this paper, a model of the wind farm output power considering multi-wake effects is established according to the probability distribution of the wind speed and the characteristic of the wind generator output power: based on the simple Jenson wake effect model, the wake effect with wind speed sheer model and the detail wake effect model with the detail shade areas of the upstream wind turbines are discussed respectively. Compared to the individual wake effect model, this model takes the wind farm as a whole and considers the multi-wakes effect on the same unit. As a result the loss of the velocity inside the wind farm is considered more exactly. Furthermore, considering the features of sequentially and self-correlation of wind speed, an auto-regressive and moving average (ARMA) model for wind speed is built up. Also the reliability model of wind farm is built when the output characteristics of wind power generation units, correlation of wind speeds among different wind farms, outage model of wind power generation units, wake effect of wind farm and air temperature are considered. Simulation results validate the effectiveness of the proposed models. These models can be used to research the reliability of power grid containing wind farms, wind farm capacity credit as well as the interconnection among wind farms

Many old wind farms currently consist of outdated wind turbines with small-capacity and low-efficiency. Wind farm repowering (WFR) is an effective approach to revitalize the old wind farms by replacing the outdated turbines with advanced ones. This paper proposes a novel WFR optimization framework to maximize the annual energy production (AEP) of the repowered wind farms. In this framework, the wake effect is assessed by a three-dimensional Gaussian wake model and the equivalent inflow wind speed is calculated by a rotor discretization method based on a sunflower algorithm. Additionally, the WFR solutions are optimized using a discrete particle swarm optimization algorithm. In this study, a wind farm containing 25 2 MW turbines will be repowered by some 5 MW turbines. Two WFR optimization cases are investigated in this wind farm: maintaining the rated capacity of the wind farm and keeping a fixed number of the new turbines. The results of the first case show that the WFR optimization substantially increases the aerodynamic efficiency and AEP of the wind farm. The highest increment occurs when the wind farm is completely repowered by the 5 MW turbines. The second case provides an optimal number of turbines to replace, considering both the economic factors and aerodynamic efficiency. The fundamental physics of the wake effect is deeply analyzed in each case. This study demonstrates that the proposed framework can provide valuable guidance for the practical WFR.

Layout optimization of a wind farm considering grids of various resolutions, wake effect, and realistic wind speed and wind direction data: A techno-economic assessment

Through wind farm control, the negative impact of the wake effect on power production could be mitigated. In previous research on wind farm control, the centralized optimization method was mostly employed due to its simplicity. However, centralized optimization control methods will face difficulties for large wind farms, in which more complex wake coupling relationships among wind turbines will make the optimization highly inefficient. In order to boost the efficiency of the optimization, the weighted graph wake decoupling (WGWD) method is proposed in the current research, which decomposes the complex optimization problem into accessible sub-problems solved in parallel. A wake weighted graph portraying the wake coupling relationship among wind turbines has been established. The decoupling approach applied on a weighted graph partitions the wind farm into uncoupled subsets of wind turbines, simplifying the optimization problem. By using distributed optimization method, the yaw angles of wind turbines in each subset have been optimized to maximize the power generation of the subset. The results show that the proposed method has high computational efficiency and can also improve wind farm total power production. Taking advantage of its high efficiency, the proposed wind farm optimization control method is suitable for future real-time active yaw control of large wind farms.

Wind power plays a vital role in the global effort towards net zero. A recent figure shows that 93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year increase. The control system is the core of wind farm operations and has an essential influence on the farm’s power capture efficiency, economic profitability, and operation and maintenance cost. However, the inherent system complexities of wind farms and the aerodynamic interactions among wind turbines cause significant barriers to control system design. The wind industry has recognized that new technologies are needed to handle wind farm control tasks, especially for large-scale offshore wind farms. This paper provides a comprehensive review of the development and most recent advances in wind farm control technologies. It covers the introduction of fundamental aspects of wind farm control in terms of system modeling, main challenges and control objectives. Existing wind farm control methods for different purposes, including layout optimization, power generation maximization, fatigue load minimization and power reference tracking, are investigated. Moreover, a detailed discussion regarding the differences and similarities between model-based, model-free and data-driven wind farm approaches is presented. In addition, we highlight state-of-the-art wind farm control technologies based on reinforcement learning—a booming machine learning technique that has drawn worldwide attention. Future challenges and research avenues in wind farm control are also analyzed.

Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines

Huaneng Rudong 300 MW offshore wind farm project is located in eastern China. The wake effect is one of the major concerns for wind farm operators, as all 70 units are plotted in ranks, and the sea surface roughness is low. This paper investigated the wake intensity by combining a field test and a numerical simulation. To carry out further yaw optimization, a Gaussian wake model was adopted. Firstly, a 3D Light Detection and Ranging device (LiDAR) was used to capture the features in both horizontal and vertical directions of the wake. It indicated that Gaussian wake model can precisely predict the characteristics under time average and steady state in the wind farm. The predicted annual energy production (AEP) of the whole wind farm by the Gaussian model is compared with the calculation result of the actuator line (AL)-based LES method, and the difference between the two methods is mostly under 10%.

This paper analyses, from a steady state point of view, the potential benefit of a wind farm control strategy whose main objective is to maximize its total energy yield over its lifetime. Unlike the conventional approach in which each wind turbine operation is optimized individually to maximize its own energy capture, the proposed control strategy aims to optimize the whole system by operating some wind turbines at suboptimum points, so that the wake effect within the wind farm is reduced and therefore the total power generation is maximized. The methodology used to assess the performance of both control approaches is presented and applied to a particular study case consisting of three wind turbines. The paper contains a comprehensive wake model considering single, partial and multiple wake effects. The study also takes into account the Blade element theory and actuator disc theory to accurately compute both power and thrust coefficient of each wind turbine. The results suggest a good potential of the proposed concept, since an increase in the annual energy captured by the wind farm from 0.3% up to 3.7% may be achieved (depending on the wind rose at the wind farm location).

This paper presents a bi-level planning approach to find an optimal layout for a large-scale wind farm (WF) from economic and reliability perspectives. The upper level of the planning approach is formulated as constrained multi-objective optimization (MOO) to obtain the optimal WF layout, where two conflicting objectives are optimized simultaneously. Two objectives for MOO are i) yearly gross energy production and ii) annual loan repayment (for cable and land area of the WF), where the first objective is maximized and the second objective is minimized. Jensen's wake model is used to calculate the wake effect, and to evaluate the multiple wake effect within a WF, a coordinate-transformation-based approach has been adopted in this study. An efficient hybrid multi-objective metaheuristic algorithm, which is based on nondominated sorting multi-objective particle swarm optimization and improved archive multi-objective simulated annealing, has been proposed to find the optimized WF layout. The superiority of the Pareto front obtained by the proposed algorithm for the large-scale WF layout is supported by its comparison with different powerful and well-established MOO algorithms. In the lower level of the proposed approach, economic and reliability analyses of the solutions (WF layout), obtained in the Pareto front by the proposed hybrid algorithm, have been done. In this context, two performance metrics, a) generation economy index and b) expected wind energy generation (EWEG), have been considered. A sequential Monte Carlo simulation has been developed to calculate the EWEG considering random failure of both wind turbines and cables in this paper. The most suitable WF layout is selected by trading off between economy and reliability using a fuzzy decision-making approach.

Investigation of the Impact of Wakes and Stratification on the Performance of an Onshore Wind Farm