Wind Farm Layout Design Using Cuckoo Search Algorithms

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for a better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization (PSO) algorithms, which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and PSO algorithms for the given test scenarios in terms of yearly power output and efficiency.     

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems. The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream. Wind farm layout optimization (WFLO) aims to reduce the wake effect for maximizing the power outputs of the wind farm. Nevertheless, the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm, which severely affect power conversion efficiency. Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios. Thus, a chaotic local search-based genetic learning particle swarm optimizer (CGPSO) is proposed to optimize large-scale WFLO problems. CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms. The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance, stability, and robustness. To be specific, a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local. It improves the solution quality. The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.      

Wind turbine micrositing by using the firefly algorithm

Traditionally energy has been a burning issue of mankind, however, this trend has changed with the advent of clean technologies such as wind power. It is common knowledge that wind turbines need to be installed in an open, unobstructed area to obtain the maximal power output. This document attempts to solve the problem of optimization of the layout of large wind farms by the use of nature inspired algorithms. Particular reference is made to the use of the firefly algorithm. A good comparison is made with the past approaches of the use of spread sheets and GA's for optimization.     

Optimized design of patrol path for offshore wind farms based on genetic algorithm and particle swarm optimization with traveling salesman problem

With the rapid expansion of global offshore wind power market, the research on improving the full life cycle income and reducing the construction and operation and maintenance costs has attracted the attention of scholars in the industry. In view of the different aging degree and maintenance cycle of wind turbines, this paper studies the optimized design of patrol path for offshore wind farms based on genetic algorithm (GA) and particle swarm optimization (PSO) with traveling salesman problem (TSP). Firstly, the problem of patrol routing planning in offshore wind farms is described as the traveling salesman problem of shortest route optimization. Secondly, the GA and PSO algorithms are simulated and verified separately, and the patrol path distance is taken as the objective function. Finally, through simulation experiments, the optimized patrol path performances of PSO and GA are compared, which can help to find a shortest route and reduce the operation and maintenance costs.     

Evolutionary computation approaches for real offshore wind farm layout: A case study in northern Europe

This paper presents the layout optimization of a real offshore wind farm in northern Europe, using evolutionary computation techniques. Different strategies for the wind farm design are tested, such as regular turbines layout or free turbines disposition with fixed number of turbines. Also, different layout quality models have been applied, in order to obtain solutions with different characteristics of high energy production and low interlink cost. In all the cases, evolutionary algorithms are developed and detailed in the paper. The experiments carried out in the real problem show that the free design with fixed number of turbines is more appropriate and obtains better quality layouts than the regular design.     

Optimum layout design of onshore wind farms considering stochastic loading

Renewable energy technologies are developing rapidly, while in the last decade great interest is encountered in the use of wind energy, especially due to the energy crisis and serious environmental problems appeared from the use of fossil fuels and therefore a large number of wind farms have been installed around the world. On the other hand the ability of nature inspired algorithms to efficiently handle combinatorial optimization problems was proved by their successful implementation in many fields of engineering sciences. In this study, a new problem formulation for the optimum layout design of onshore wind farms is presented, where the wind load is implemented using stochastic fields. For this purpose, a metaheuristic search algorithm based on a discrete variant of the harmony search method is used for solving the problem at hand. The farm layout problem is by nature a constrained optimization problem, and the contribution of the wake effects is significant; therefore, in two formulations presented in this study the influence of wind direction is also taken into account and compared with the scenario that the wake effect is ignored. The results of this study proved the applicability of the proposed formulations and the efficiency of combining metaheuristic optimization with stochastic wind loading for dealing with the problem of optimal layout design of wind farms.     

Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms

Wind energy has become the world’s fastest growing energy source. Although wind farm layout is a well known problem, its solution used to be heuristic, mainly based on the designer experience. A key in search trend is to increase power production capacity over time. Furthermore the production of wind energy often involves uncertainties due to the stochastic nature of wind speeds. The addressed problem contains a novel aspect with respect of other wind turbine selection problems in the context of wind farm design. The problem requires selecting two different wind turbine models (from a list of 26 items available) to minimize the standard deviation of the energy produced throughout the day while maximizing the total energy produced by the wind farm. The novelty of this new approach is based on the fact that wind farms are usually built using a single model of wind turbine. This paper describes the usage of multi-objective evolutionary algorithms (MOEAs) in the context of power energy production, selecting a combination of two different models of wind turbine along with wind speeds distributed over different time spans of the day. Several MOEAs variants belonging to the most renowned and widely used algorithms such as SPEA2 NSGAII, PESA and msPEA have been investigated, tested and compared based on the data gathered from Cancun (Mexico) throughout the year of 2008. We have demonstrated the powerful of MOEAs applied to wind turbine selection problem (WTS) and estimate the mean power and the associated standard deviation considering the wind speed and the dynamics of the power curve of the turbines. Among them, the performance of PESA algorithm looks a little bit superior than the other three algorithms. In conclusion, the use of MOEAs is technically feasible and opens new perspectives for assisting utility companies in developing wind farms.     

动态评价粒子群优化及风电场微观选址

提出了动态评价方法处理一类约束优化问题.将目标函数值和约束违反量进行动态归一化处理,再进行加权求和,动态评价解的优化性能.不仅解决了惩罚因子确定困难的问题,而且增加了优化算法的多样性,提高了优化算法搜索全局最优解的能力.将动态评价方法引入粒子群算法,求解风电场微观选址优化问题.仿真结果表明,动态评价方法提高了风电场发电量和风能利用效率.此外,该方法可广泛应用于其他优化算法以求解约束优化问题.     

基于CNN-LSTM及深度学习的风电场时空组合预测模型

为了更好地预测风电场的风电功率,提取风电场相邻站点之间时空信息和潜在联系,提出了一种基于卷积神经网络（CNN）、互信息（mutual information,MI）法、长短时记忆网络（LSTM）、注意力机制（AT）和粒子群优化（PSO）的短期风电场预测模型（MI-CNN-ALSTM-PSO）。CNN用于提取不同站点的空间特征,LSTM则用于获取多个站点的风电数据的时间依赖信息,据此设计CNN-LSTM时空预测模型,并结合深度学习算法,如MI特征选择、AT注意力机制、PSO参数优化,对模型进一步改进。通过两个海岛风电场的实验数据分析可知,所提模型具有最优的统计误差,CNN-LSTM模型可以高效提取风电场时空信息并进行时间序列预测,而结合深度学习算法（MI、AT和PSO）后的组合模型能进一步提高风电功率预测精度和稳定性。     

Swarm-based intelligent optimization approach for layout problem

Layout problem is a kind of NP-Complete problem. It is concerned more and more in recent years and arises in a variety of application fields such as the layout design of spacecraft modules, plant equipment, platforms of marine drilling well, shipping, vehicle and robots. The algorithms based on swarm intelligence are considered powerful tools for solving this kind of problems. While usually swarm intelligence algorithms also have several disadvantages, including premature and slow convergence. Aiming at solving engineering complex layout problems satisfactorily, a new improved swarm-based intelligent optimization algorithm is presented on the basis of parallel genetic algorithms. In proposed approach, chaos initialization and multi-subpopulation evolution strategy based on improved adaptive crossover and mutation are adopted. The proposed interpolating rank-based selection with pressure is adaptive with evolution process. That is to say, it can avoid early premature as well as benefit speeding up convergence of later period effectively. And more importantly, proposed PSO update operators based on different versions PSO are introduced into presented algorithm. It can take full advantage of the outstanding convergence characteristic of particle swarm optimization (PSO) and improve the global performance of the proposed algorithm. An example originated from layout of printed circuit boards (PCB) and plant equipment shows the feasibility and effectiveness of presented algorithm.     

Optimizing wind farm cable layout considering ditch sharing

Wind power is becoming an important source of electrical energy production. In an onshore wind farm (WF), the electrical energy is collected at a substation from different wind turbines through electrical cables deployed over ground ditches. This work considers the WF layout design assuming that the substation location and all wind turbine locations are given, and a set of electrical cable types is available. The WF layout problem, taking into account its lifetime and technical constraints, involves selecting the cables to interconnect all wind turbines to the substation and the supporting ditches to minimize the initial investment cost plus the cost of the electrical energy that is lost on the cables over the lifetime of the WF. It is assumed that each ditch can deploy multiple cables, turning this problem into a more complex variant of previously addressed WF layout problems. This variant turns the problem best fitting to the real case and leads to substantial gains in the total cost of the solutions. The problem is defined as an integer linear programming model, which is then strengthened with different sets of valid inequalities. The models are tested with four WFs with up to 115 wind turbines. The computational experiments show that the optimal solutions can be computed with the proposed models for almost all cases. The largest WF was not solved to optimality, but the final relative gaps are small.     

A Game Theory‐Based Coordination and Optimization Control Methodology for a Wind Power‐Generation Hybrid Energy Storage System

The installation of an energy storage system to smooth the fluctuations of wind power output at a certain wind farm can improve the electric quality of wind power connected to the grid. In order to reduce the capacity of the energy storage system and the loss of the battery and make full use of the advantages of the super‐capacitor, a game theory‐based coordination and optimization control methodology for a wind power‐generation and storage system (WPGSS) is presented in this paper. Aiming to maximize the WPGSS's overall profit, the methodology, taking the smoothing effect of the active power, the cost of the hybrid energy storage system (HESS), and the earnings of wind power connected to grid into consideration, builds a coordination and optimization control model based on the ensemble empirical mode decomposition (EEMD) algorithm combined with game theory. In the model, the low‐pass filtering signal obtained by the EEMD is used to smooth the fluctuations of wind power output, and the band‐pass filtering signal and high‐pass filtering signal obtained by the EEMD are used to achieve energy distribution among the HESS. Cooperative game theory is introduced to determine the filter order of the EEMD according to the state of charge (SOC) of the HESS and to achieve the coordination and optimization control of the WPGSS taking the maximization of the WPGSS's overall profit as the game's goal constraint conditions. The genetic algorithm (GA) and particle swarm optimization (PSO) are adopted to solve the model's optimal solution, and the simulation tests were realized to verify the effectiveness of the proposed method, which can provide a theoretical basis for the coordination and optimization control of the WPGSS.     

Adjoint-based model predictive control for optimal energy extraction in waked wind farms

In this paper, a model predictive control (MPC) is proposed for wind farms to minimize wake-induced power losses. A constrained optimization problem is formulated to maximize the total power production of a wind farm. The developed controller employs a two-dimensional dynamic wind farm model to predict wake interactions in advance. An adjoint approach as an efficient tool is utilized to compute the gradient of the performance index for such a large-scale system. The wind turbine axial induction factors are considered as the control inputs to influence the overall performance by taking the wake interactions into account. A layout of a 2 × 3 wind farm is considered in this study. The parameterization of the controller is discussed in detail for a practical optimal energy extraction. The performance of the adjoint-based model predictive control (AMPC) is investigated with time-varying changes in wind direction. The simulation results show the effectiveness of the proposed approach. The computational complexity of the developed AMPC is also outlined with respect to the real time control implementation.     

Synthesis of nonlinear controller for wind turbines stability when providing grid support

This paper presents a new nonlinear polynomial controller for wind turbines that assures stability and maximizes the energy produced while imposing a bound in the generated power derivative in normal operation (guarantees a smooth operation against wind turbulence). The proposed controller structure also allows eventually producing a transient power increase to provide grid support, in response to a demand from a frequency controller. The controller design uses new optimization over polynomials techniques, leading to a tractable semidefinite programming problem. The ability of the wind turbine to increase its power under partial load operation has been analysed. The aforementioned optimization techniques have allowed quantifying the maximum transient overproduction that can be demanded to the wind turbine without violating minimum speed constraints (that could lead to unstable behaviour), as well as the total generated energy loss. The ability to evaluate this shortfall has permitted the development of an optimization procedure in which wind farm overproduction requirements are divided into individual turbines, assuring that the total energy loss in the wind farm is minimum, while complying with the maximum demanded power constraints. Copyright © 2013 John Wiley & Sons, Ltd.     

基于改进鸟群算法优化聚类的风电场等值建模

为了提高风电场动态等值建模的精确度,采用风力发电机组的12个状态变量作为分群指标,使用改进鸟群算法(Improve Bird Swarm Algorithm,IBSA)搜索最佳聚类中心,通过K-means算法对风电场进行分群处理。在MATLAB/Simulink中搭建详细模型与等值模型,并与传统聚类算法进行对比。实验结果表明,该方法对等值建模的准确度有很大的提高,可以精确地表征风电场的对外特性。     

改进粒子群算法在单体型重构问题中的应用

结合单体型装配问题的计算模型—最少错误纠正模型（MEC）的特定知识,提出了一种求解单体型装配问题的改进粒子群算法。应用改进粒子群算法对真实数据和模拟数据进行数值计算,并且与基础粒子群算法和遗传算法进行比较,数值结果表明所设计的改进粒子群算法在单体型重构率上优于基础粒子群算法和遗传算法。     

风光气储联合发电最优容量配比问题研究

开发和利用农村的风能、光能、沼气是解决偏远农村地区用电量快速增加和偏远山区供电问题的有效途径,该文针对3种可再生能源和储能的联合发电问题,通过研究它们之间的协调互补性,提出了一种新型的"风光气储"多能源互补的微电网供电系统,在该系统中因地制宜地采用沼气和蓄电池作为备用电源,通过改进的鲸鱼算法对"风光气储"联合运行问题进行了优化,仿真实验结果表明在成本最低和弃风弃光率最低这2个目标下都能稳定的运行,对已经有的粒子群算法、遗传算法和鲸鱼算法进行了对比,实验结果表明改进的鲸鱼算法在解决"风光气储"最优容量配比问题上得到了很大的提升。     

求解车间调度问题的自适应混合粒子群算法

针对最小完工时间的流水车间作业调度问题,提出了一种自适应混合粒子群进化算法--AHPSO,将遗传操作有效地结合到粒子群算法中.定义了粒子相似度及粒子能量,粒子相似度阈值随迭代次数动态自适应变化,而粒子能量阈值与群体进化程度及其自身进化速度相关.此外,针对算法运行后期进化速度慢的缺点,提出了一种基于邻域的随机贪心策略进一步提高算法的性能.最后将此算法在不同规模的实例上进行了测试,并与其他几种具有代表性的算法进行了比较,实验结果表明,无论是在求解质量还是稳定性方面都优于其他几种算法,并且能够有效求解大规模车间作业问题.     

基于多目标飞蛾算法的电力系统无功优化研究

鉴于电力需求的日益增长与传统无功优化方法的桎梏,如何更加合理有效地解决电力系统的无功优化问题逐渐成为了研究的热点。提出一种多目标飞蛾扑火算法来解决电力系统多目标无功优化的问题,算法引入固定大小的外部储存机制、自适应的网格和筛选机制来有效存储和提升无功优化问题的帕累托最优解集,算法采用CEC2009标准多目标测试函数来进行仿真实验,并与两种经典算法进行性能的对比分析。此外,在电力系统IEEE 30节点上将该算法与MOPSO,NGSGA-II算法的求解结果进行比较分析的结果表明,多目标飞蛾算法具有良好的性能,并在解决电力系统多目标无功优化问题上具有良好的潜力。     

Ensemble particle swarm optimizer

According to the “No Free Lunch (NFL)” theorem, there is no single optimization algorithm to solve every problem effectively and efficiently. Different algorithms possess capabilities for solving different types of optimization problems. It is difficult to predict the best algorithm for every optimization problem. However, the ensemble of different optimization algorithms could be a potential solution and more efficient than using one single algorithm for solving complex problems. Inspired by this, we propose an ensemble of different particle swarm optimization algorithms called the ensemble particle swarm optimizer (EPSO) to solve real-parameter optimization problems. In each generation, a self-adaptive scheme is employed to identify the top algorithms by learning from their previous experiences in generating promising solutions. Consequently, the best-performing algorithm can be determined adaptively for each generation and assigned to individuals in the population. The performance of the proposed ensemble particle swarm optimization algorithm is evaluated using the CEC2005 real-parameter optimization benchmark problems and compared with each individual algorithm and other state-of-the-art optimization algorithms to show the superiority of the proposed ensemble particle swarm optimization (EPSO) algorithm.