Wind farm layout optimization using area dimensions and definite point selection techniques

Wind turbines are the biggest rotating machines on earth, operating in the lowest part of the earth boundary layer. Designing the layout scheme of wind farms is a challenging job to researchers, as there are many design objectives and constraints due to the multiple wake phenomena. This paper proposes an area rotation method to find the optimum dimensions of the wind farm shape, where maximum area could face the free stream velocity. Afterwards, a novel method called Definite Point Selection (DPS) is developed to place the turbines in order to operate at maximum, while providing obligatory space between adjacent turbines for operation safety. This method can be used to identify the zero wake effect points at wind farm. The result from this study shows that the proposed method is more effective to increase the overall power of a wind farm than the previous methods. Also, the power output of the wind farm by using combined area rotation and DPS methods was increased even when using the same number of wind turbines.     

Offshore wind farm layout optimization using mathematical programming techniques

Offshore wind power is a renewable energy of growing relevance in current electric energy systems, presenting favorable wind conditions in comparison with the sites on land. However, the higher energy yield has to compensate the increment in installation and maintenance costs, thus the importance of optimizing resources. One relevant aspect to increase profitability is the wind farm layout. The aim of this paper is to propose a new method to maximize the expected power production of offshore wind farms by setting the appropriate layout, i.e. minimizing the wake effects. The method uses a sequential procedure for global optimization consisting of two steps: i) an heuristic method to set an initial random layout configuration, and ii) the use of nonlinear mathematical programming techniques for local optimization, which use the random layout as an initial solution. The method takes full advantage of the most up-to-date mathematical programming techniques while performing a global optimization approach, which can be easily parallelized. The performance of the proposed procedure is tested using the German offshore wind farm Alpha Ventus, located in the North Sea, yielding an increment of expected annual power production of 3.52% with respect to the actual configuration. According to current electricity prices in Germany, this constitutes an expected profit increment of almost 1 M€ per year.     

Wind farm power optimization including flow variability

A model-based optimisation approach is used to investigate the potential gain of wind-farm power with a cooperative control strategy between the wind turbines. Based on the Jensen wake model with the Katic wake superposition rule, the potential gain for the Nysted offshore wind farm is calculated to be 1.4–5.4% for standard choices 0.4 ≥ k ≥ 0.25 of the wake expansion parameter. Wake model fits based on short time intervals of length 15sec ≤ T ≤ 10 min within three months of data reveal a strong wake flow variability, resulting in rather broad distributions for the wake expansion parameter. When an optimized wind-farm control strategy, derived from a fixed wake parameter, is facing this flow variability, the potential gain reduces to 0.3–0.5%. An omnipotent control strategy, which has real-time knowledge of the actual wake flow, would be able to increase the gain in wind-farm power to 4.9%.     

Optimization of wind farm turbines layout using an evolutive algorithm

The optimum wind farm configuration problem is discussed in this paper and an evolutive algorithm to optimize the wind farm layout is proposed. The algorithm's optimization process is based on a global wind farm cost model using the initial investment and the present value of the yearly net cash flow during the entire wind-farm life span. The proposed algorithm calculates the yearly income due to the sale of the net generated energy taking into account the individual wind turbine loss of production due to wake decay effects and it can deal with areas or terrains with non-uniform load-bearing capacity soil and different roughness length for every wind direction or restrictions such as forbidden areas or limitations in the number of wind turbines or the investment. The results are first favorably compared with those previously published and a second collection of test cases is used to proof the performance and suitability of the proposed evolutive algorithm to find the optimum wind farm configuration.     

A novel control strategy approach to optimally design a wind farm layout

Recently wind energy has become one of the most important alternative energy sources and is growing at a rapid rate because of its renewability and abundancy. For the clustered wind turbines in a wind farm, significant wind power losses have been observed due to wake interactions of the air flow induced by the upstream turbines to the downstream turbines. One approach to reduce power losses caused by the wake interactions is through the optimization of wind farm layout, which determine the wind turbine positions and control strategy, which determine the wind turbine operations. In this paper, a new approach named simultaneous layout plus control optimization is developed. The effectiveness is studied by comparison to two other approaches (layout optimization and control optimization). The results of different optimizations, using both grid based and unrestricted coordinate wind farm design methods, are compared for both ideal and realistic wind conditions. Even though the simultaneous layout plus control optimization is theoretically superior to the others, it is prone to the local minima. Through the parametric study of crossover and mutation probabilities of the optimization algorithm, the results of the approach are generally satisfactory. For both simple and realistic wind conditions, the wind farm with the optimized control strategy yield 1–3 kW more power per turbine than that with the self-optimum control strategy, and the unrestricted coordinate method yield 1–2 kW more power per turbine than the grid based method.     

Wind plant system engineering through optimization of layout and yaw control

Recent research has demonstrated exciting potential for wind plant control systems to improve the cost of energy of wind plants. Wind plant controls seek to improve global wind plant performance over control systems in which each turbine optimizes only its individual performance by accounting for the way wind turbines interact through their wakes. Although these technologies can be applied to existing wind plants, it is probable that the maximum benefit would be derived by designing wind plants with these capabilities in mind. In this paper, we use system engineering approaches to perform coupled wind plant controls and position layout optimizations of a model wind plant. Using several cost metrics, we compare the results of this optimization to the original plant and to plants in which the control or layout is optimized separately or sequentially. Results demonstrate that the benefit of this coupled optimization can be substantial, but it depends on the particular constraints of the optimization. Copyright © 2015 John Wiley & Sons, Ltd.     

The impact of land use constraints in multi-objective energy-noise wind farm layout optimization

Recently the environmental impact of onshore wind farms is receiving major attention from both governments and wind farm designers. As land is more extensively exploited for wind farms, it is more likely for wind turbines to be in proximity with human dwellings, infrastructure (e.g. roads, transmission lines), and natural habitats (e.g. rivers, lakes, forests). This proximity makes significant portions of land unusable for the designers, introducing a set of land-use constraints. In this study, we conduct a constrained and continuous-variable multi-objective optimization that considers energy and noise as its objective functions, based on Jensen's wake model and the ISO-9613-2 noise standard. A stochastic evolutionary algorithm (NSGA-II) solves the optimization problem, while the land-use constraints are handled with static and dynamic penalty functions. Results of this study illustrate the effect of constraint severity and spatial distribution of unusable land on the trade-off between energy generation and noise production.     

Prediction of natural gas hydrate inhibitor vaporization rate using particle swarm optimization approach

Natural gas is a very important source of energy. In natural gas processing, accurate prediction of methanol loss to the vapor phase during natural gas hydrate inhibition is necessary to compute the total methanol injection rate required to effectively prevent the formation of natural gas hydrate. A reliable prediction tool that has the capability to accurately predict methanol losses to the vapor phase is thus needed. In order to address this matter, the current research was aimed at assessing the ability and feasibility of a robust computational intelligence paradigm. Based on a total of 326 dataset collected from the reliable literature, methanol loss to the vapor phase was predicted using artificial neural network (ANN) linked with particle swarm optimization (PSO) which is employed to determine the optimal values of the ANN weights. Success of the introduced hybrid intelligence model (or PSO-ANN) was confirmed with overall mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²) values of 0.16421, 0.33210, and 0.99696, respectively.     

Wind farm power prediction: a data‐mining approach

In this paper, models for short‐ and long‐term prediction of wind farm power are discussed. The models are built using weather forecasting data generated at different time scales and horizons. The maximum forecast length of the short‐term prediction model is 12 h, and the maximum forecast length of the long‐term prediction model is 84 h. The wind farm power prediction models are built with five different data mining algorithms. The accuracy of the generated models is analysed. The model generated by a neural network outperforms all other models for both short‐ and long‐term prediction. Two basic prediction methods are presented: the direct prediction model, whereby the power prediction is generated directly from the weather forecasting data, and the integrated prediction model, whereby the prediction of wind speed is generated with the weather data, and then the power is generated with the predicted wind speed. The direct prediction model offers better prediction performance than the integrated prediction model. The main source of the prediction error appears to be contributed by the weather forecasting data. Copyright © 2008 John Wiley & Sons, Ltd.     

A new constraint handling method for wind farm layout optimization with lands owned by different owners

For wind farm optimizations with lands belonging to different owners, the traditional penalty method is highly dependent on the type of wind farm land division. The application of the traditional method can be cumbersome if the divisions are complex. To overcome this disadvantage, a new method is proposed in this paper for the first time. Unlike the penalty method which requires the addition of penalizing term when evaluating the fitness function, it is achieved through repairing the infeasible solutions before fitness evaluation. To assess the effectiveness of the proposed method on the optimization of wind farm, the optimizing results of different methods are compared for three different types of wind farm division. Different wind scenarios are also incorporated during optimization which includes (i) constant wind speed and wind direction; (ii) various wind speed and wind direction; and (iii) the more realistic Weibull distribution. Results show that the performance of the new method varies for different land plots in the tested cases. Nevertheless, it is found that optimum or at least close to optimum results can be obtained with sequential land plot study using the new method for all cases. It is concluded that satisfactory results can be achieved using the proposed method. In addition, it has the advantage of flexibility in managing the wind farm design, which not only frees users to define the penalty parameter but without limitations on the wind farm division.     

Wind farm voltage dip measurements

This paper is based on continuous measurements of voltages and currents from three wind farms for a period of 1 year, and the focus is on voltage dips. The purpose is to get an overview of the characteristics and rate of voltage dips, which occur in the wind farms and to study the wind turbine responses to voltage dips. In each of the wind farms there is one measurement point at a single wind turbine and one for measuring the contribution from the whole wind farm. Different wind turbine technologies are used in the three wind farms; fixed speed turbines with directly connected induction generators in wind farm 1 and variable speed turbines with power electronics converters and synchronous generators in wind farms 2 and 3. Voltage dips are evaluated according to the standard EN 50160, by considering the durations and residual voltages of the positive sequence component voltage dips. Some examples of voltage dip events with corresponding responses in active and reactive power are shown and discussed with a view to the different technologies. Copyright © 2013 John Wiley & Sons, Ltd.     

Wind farm progress in Denmark

The paper presents a status of wind power in Denmark and on the technical and industrial achievements. The present total installed capacity is be the end of 1995 approx. 630 MW, and the contribution to the electric energy generation in Denmark is approx. 4%.This presentation is to a large extent based on Andersen 1995a and Andersen 1995b.     

直驱式波浪能发电场的布局优化研究

文章研究了直驱式波浪能发电场的优化布局问题。将直驱式波浪能发电装置简化为地形,利用Mike21软件计算波浪能发电场内的波浪分布,进而将其作为波浪能发电场电气模型的输入,仿真获得波浪能发电场输出功率。在此基础上,基于波浪的传输特性,对波浪能发电场内各发电装置进行优化布局,以保证发电场的平均输出功率,同时改善功率输出的波动性。得到优化布局方案后,分别在规则波和不规则波两种情况下验证了方案的有效性。     

Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique

Shell-and-tube heat exchangers (STHEs) are the most common type of heat exchangers that find widespread use in numerous industrial applications. Cost minimization of these heat exchangers is a key objective for both designer and users. Heat exchanger design involves complex processes, including selection of geometrical parameters and operating parameters. The traditional design approach for shell-and-tube heat exchangers involves rating a large number of different exchanger geometries to identify those that satisfy a given heat duty and a set of geometric and operational constraints. However, this approach is time-consuming and does not assure an optimal solution. Hence the present study explores the use of a non-traditional optimization technique; called particle swarm optimization (PSO), for design optimization of shell-and-tube heat exchangers from economic view point. Minimization of total annual cost is considered as an objective function. Three design variables such as shell internal diameter, outer tube diameter and baffle spacing are considered for optimization. Two tube layouts viz. triangle and square are also considered for optimization. Four different case studies are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using PSO technique are compared with those obtained by using genetic algorithm (GA).     

Wind turbines type and number choice using combinatorial optimization

The paper addresses the problem associated with the optimal wind park design. A combinatorial optimization model for wind turbines type and number choice and placement considering the given wind conditions and wind park area is developed. The wind park investment costs and the total power relation as function of wind turbines number and type are used as optimization criteria. The optimization problem is formulated as a single criterion mixed-integer nonlinear discrete combinatorial task. The different wind park conditions are introduced into optimization tasks formulation as variables relations and restrictions. Two basic wind directions cases are taken into consideration – uniform and predominant wind directions for two wind park area shapes – square and rectangular. The developed wind park design approach was tested numerically by solving of different optimization tasks formulations based on wind turbines real parameters data. The numerical testing shows the applicability of the developed optimization approach. Using it will help to find mathematically reasoned wind turbines choice as contradiction to the heuristic approaches.     

Wind farm modelling for reliability assessment

Increasing exploitation of wind energy requires the development of adequate models and techniques for reliability assessment of wind farms (WFs) and of electric power systems including wind energy conversion systems. A novel approach to WF modelling is investigated for reliability assessment, which is based on the universal generating functions. Such an approach combines the use of the z-transform and composition operators, allowing to account for all the factors affecting the WF performance. The results, both theoretical and numerical, of the case study, give evidence of the effectiveness of the proposed approach in terms of accuracy as well as of flexibility and efficiency of the solving algorithm.     

考虑尾流效应对风电场机组布局的影响分析

尾流效应的存在会导致风电场下风向风能减少,流场湍流度增加,进而影响风电场中位于下风向风机的效率和风轮的使用寿命。文章对尾流效应研究现状进行了概述,利用WASP软件以及风资源数据进行风电场模拟计算,将上下游风机之间间距以及上下游风机连线与主导风向的偏向角作为风机定位坐标,建立了分别由2台、3台、4台风机组成的模型并进行计算。比较在不同风机布局的情况下,风电场内每台风机和风电场的年净发电量以及尾流损失值随风机布局的变化趋势。对比计算结果得出风电场机组布局中风机之间的最佳间距和偏向角的定量值,确定风机尾流效应分析在风电场内机组布局中的重要性,为优化风电机组布局以及提高风电场风能利用率提供理论依据。     

Inverse radiation analysis using repulsive particle swarm optimization algorithm

In this study, an inverse radiation analysis is presented for the estimation of the radiation properties for an absorbing, emitting, and scattering media with diffusely emitting and reflecting opaque boundaries. The repulsive particle swarm optimization (RPSO) algorithm, which is a relatively recent heuristic search method, is proposed as an effective method for improving the search efficiency for unknown radiative parameters. To verify the performance of the RPSO algorithm, it is compared with a basic particle swarm optimization (PSO) algorithm and a hybrid genetic algorithm (HGA) for the inverse radiation problem in estimating the various radiation properties in a two-dimensional irregular medium, when the temperatures are given at only four measurement positions. A finite-volume method is applied to solve the radiative transfer equation of a direct problem to obtain measured temperatures. RPSO is proven to be quite a robust tool for simultaneous estimation of multi-parameters even in a strongly-coupled environment.     

Optimal design of power-system stabilizers using particle swarm optimization

In this paper, a novel evolutionary algorithm-based approach to optimal design of multimachine power-system stabilizers (PSSs) is proposed. The proposed approach employs a particle-swarm-optimization (PSO) technique to search for optimal settings of PSS parameters. Two eigenvalue-based objective functions to enhance system damping of electromechanical modes are considered. The robustness of the proposed approach to the initial guess is demonstrated. The performance of the proposed PSO-based PSS (PSOPSS) under different disturbances, loading conditions, and system configurations is tested and examined for different multimachine power systems. Eigenvalue analysis and nonlinear simulation results show the effectiveness of the proposed PSOPSSs to damp out the local and interarea modes of oscillations and work effectively over a wide range of loading conditions and system configurations. In addition, the potential and superiority of the proposed approach over the conventional approaches is demonstrated.     

Economic dispatch using particle swarm optimization: A review

Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Meta-heuristic optimization techniques especially particle swarm optimization (PSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of PSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper.