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.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.

An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization

Energy issues have always been one of the most significant concerns for scientists worldwide.With the ongoing over exploitation and continued outbreaks of wars,traditional energy sources face the threat of depletion.Wind energy is a readily available and sustainable energy source.Wind farm layout optimization problem,through scientifically arranging wind turbines,significantly enhances the efficiency of harnessing wind energy.Meta-heuristic algorithms have been widely employed in wind farm layout optimization.This paper introduces an Adaptive strategy-incorporated Integer Genetic Algorithm,referred to as AIGA,for optimizing wind farm layout problems.The adaptive strategy dynamically adjusts the placement of wind turbines,leading to a substantial improvement in energy utilization efficiency within the wind farm.In this study,AIGA is tested in four different wind conditions,alongside four other classical algorithms,to assess their energy conversion efficiency within the wind farm.Experimental results demonstrate a notable advantage of AIGA.

High-Resolution Micro-Siting Technique for Large Scale Wind Farm Outside of Japan Using LES Turbulence Model

The spatial distribution of wind speed varies greatly over steep complex terrain, thus the selection of an optimal site in such terrain for wind turbine construction requires great care. We have developed a numerical model for simulating unsteady flows called RIAM-COMPACT (Research Institute for Applied Mechanics, Kyushu University, COM putational Prediction of Airflow over Complex Terrain), which is based on the LES (Large-Eddy Simulation) technique. The computational domain of RIAM-COMPACT can be varied from several meters to several kilometers, and the model is able to predict airflow over complex terrain with high accuracy. The present paper discusses the application of RIAM-COMPACT to the micro-siting of wind turbines at sites outside Japan. The results from two case studies will be presented.

考虑不同约束区间的风电场布局分级优化

为了提高风电场布局优化中尾流模型预测精度,以便能够较好预测风机安装台数和最大限度降低度电成本,本文基于改进三维尾流模型,考虑风力机处于不同约束区间,采用分级优化策略对2 km×2 km风电场进行精确布局优化。首先,利用网格法对风电场进行第一阶优化;其次,考虑不同约束区间,采用坐标法对第一级优化结果进行第二阶优化。研究表明:与Jensen尾流模型相比,改进后的三维尾流模型可以更合理地优化风电场布局;考虑收敛速度及度电成本,当约束区域为l_(imit)=150、200 m时,第二阶算法迭代速度较快,优化后其度电成本为0.8498/W,相比第一阶优化结果,降低了3.33%。

海上风电场布局协同优化算法

在海上风电场中,风机位置及间距选择会影响风机的发电效率,而风机间电缆线路拓扑会影响风场的电网规划,两者间相互影响和制约。针对海上风电场主要设备间的相互关系进行研究,分析并量化风机尾流对发电效率的影响和电缆拓扑连接对总体成本的影响。从全生命周期的角度出发,以海上风电场度电成本最低为优化目标,建立海上风机选址及电缆线路拓扑度电成本协同优化模型,并对相关案例进行分析,结果表明:所提出的协同优化模型相对于常规独立优化模型具有更好的度电成本优化效果,对未来我国大规模开展海上风电场规划具有重要指导意义。

复杂地形风电场微观选址的GA-PSO混合算法研究

提出一种结合改进遗传算法(GA)和粒子群算法(PSO)的GA-PSO混合算法对复杂地形的风力机排布方案进行优化。以湖南省某实际复杂地形为对象,开展风场全风向数值模拟,结合长期观测风资料评估区域的潜在风能分布,提出考虑网格预处理、时变变异率、唯一化和并行化的改进GA(IGA)对风力机排布方案进行优化,在此基础上利用PSO算法进行进一步优化,并针对尾流模型和目标函数对优化结果的影响进行不确定性分析。结果表明,在复杂地形风电场微观选址方面,所提GA-PSO算法比贪婪算法、GA、IGA分别改善16.4%、12.9%和5.1%。

基于三次修正Jensen尾流模型的海上风电场布局优化

目前修正的Jensen尾流模型均没有同时修正尾流初始半径和尾流初始风速,无法准确预测海上风电场的实际尾流风速。基于二次修正Jensen尾流模型基础上,提出三次修正Jensen尾流模型,并通过海上风电场风洞实验数据验证该修正尾流模型的正确性。其次,考虑全尾流及部分尾流面积,将该三次修正Jensen尾流模型应用到海上风电场的布局优化中,以风力机的坐标及数量为优化变量,以度电成本为优化目标函数,并利用樽海鞘算法对海上风电场的风力机布局进行优化设计。优化结果表明:对比采用改进Jensen模型指导风电场布局,采用三次修正Jensen模型时,风电场的布局更加均匀;对比考虑全尾流的布局,当考虑部分尾流布局时,度电成本降低了2.16%。

大模型辅助的大型海上风电场集电系统拓扑优化

集电系统拓扑优化是大型海上风电场规划建设的核心问题,本质上是一个涉及多约束、多目标的复杂混合整数优化问题。针对该问题,提出了一种基于大语言模型(large language model,LLM)辅助的大型海上风电场集电系统拓扑优化方法。首先,基于大语言模型辅助对风电机组群进行聚类,通过链式提示法使LLM理解优化目标,并利用LLM将大型海上风电场分割为若干小型区域,以降低优化问题维度,提升求解速度和质量。然后,构建集电系统拓扑优化模型,基于混合整数线性规划求解器,获得海上风电场的最优集电系统拓扑设计方案。最后,利用1个含有75台风电机组的大型海上风电场系统进行方法性能验证,仿真结果表明:与传统优化技术相比,所提方法获得的聚类风机数量更加均衡,在考虑拓扑功率损耗的同时,生成的拓扑方案经济性最优。LLM在集电系统拓扑辅助优化中具有较高的有效性,为海上风电场集电系统拓扑设计优化提供了一种新思路。

计及禁区影响的风电场布局优化研究

本研究基于查表法判别风电机组与禁区的相对位置,而后将该方法嵌入基于改进帝王蝶算法的风电场布局优化模型中,求解同时满足禁区约束和输出功率最大化的布局结果。应用本模型优化含禁区的2 km×2 km风电场布局,得到布局方案及其适应度数值。结果表明,优化的布局方案能够有效避开禁区,且相比无禁区情况下的布局,有禁区时布局方案的适应度削弱0.5%。与传统帝王蝶、粒子群算法和差分自适应帝王蝶算法对比,进行30次单独运行,本算法在多次结果中均得到满足约束的布局方案,表现出较好的全局搜索能力以及寻优精度。

A review of offshore wind farm layout optimization and electrical system design methods

There is more wind with less turbulence offshore compared with an onshore case,which drives the development of the offshore wind farm worldwide.Since a huge amount of money is required for constructing an offshore wind farm,many types of research have been done on the optimization of the offshore wind farm with the purpose of either minimizing the cost of energy or maximizing the total energy production.There are several factors that have an impact on the performance of the wind farm,mainly the energy production of wind farm which is highly decided bythe wind condition of construction area and micro-siting of wind turbines(WTs),as well as the initial investment which is influenced by both the placement of WTs and the electrical system design,especially the scheme of cable connection layout.In this paper,a review of the state-of-the-art researches related to the wind farm layout optimization as well as electrical system design including cable connection scheme optimization is presented.The most significant factors that should be considered in the optimization work of the offshore wind farm is highlighted after reviewing the latest works,and the future needs are specified.

Optimal Placement of Wind Turbines in Wind Farm Layout Using Particle Swarm Optimization

An optimal geographical location of wind turbines can ensure the optimum total energy output of a wind farm.This study introduces a new solution to the optimization of wind farm layout(WFLO)problem based on a three-step strategy and particle swarm optimization as the main method.The proposed strategy is applied to a certain WFLO to generate highly efficient optimal output power.Three case scenarios are considered to formulate the non-wake and wake effects at various levels.The required wind turbine positions within the wind farm are determined by the particle swarm optimization method.The rule of thumb,which determines the wind turbine spacing,is thoroughly considered.The MATLAB simulation results verify the proposed three-step strategy.Moreover,the results are compared with those of existing research works,and it shows that the proposed optimization strategy yields a better solution in terms of total output power generation and efficiency with a minimized objective function.The efficiencies of the three case studies considered herein increase by 0.65%,1.95%,and 1.74%,respectively.Finally,the simulation results indicate that the proposed method is robust in WFLO design because it further minimizes the objective function.

Bi-objective Layout Optimization for Multiple Wind Farms Considering Sequential Fluctuation of Wind Power Using Uniform Design

The fluctuation of wind power brings great challenges to the secure,stable,and cost-efficient operation of the power system.Because of the time-correlation of wind speed and the wake effect of wind turbines,the layout of wind farm has a significant impact on the wind power sequential fluctuation.In order to reduce the fluctuation of wind power and improve the operation security with lower operating cost,a bi-objective layout optimization model for multiple wind farms considering the sequential fluctuation of wind power is proposed in this paper.The goal is to determine the optimal installed capacity of wind farms and the location of wind turbines.The proposed model maximizes the energy production and minimizes the fluctuation of wind power simultaneously.To improve the accuracy of wind speed estimation and hence the power calculation,the timeshifting of wind speed between the wind tower and turbines’locations is also considered.A uniform design based two-stage genetic algorithm is developed for the solution of the proposed model.Case studies demonstrate the effectiveness of this proposed model.

考虑馈线交叉规避的海上风电场海缆路径优化

海上风电场集电拓扑优化是一个大规模非凸、非线性优化问题,难以求得最优解,将其分解为分区内部拓扑优化及馈线路径优化两部分:在分区内部以经济性为目标函数,在考虑海缆选型的情况下得到最优拓扑方式;在馈线部分,针对海缆交叉问题,考虑到风机实际建设工程约束,提出了分区规则化方案,借助改进Dijkstra算法实现了从传统线-线交叉判断到线-面交叉判断的转变,避免了跨立实验无法判断海缆交叉的情况。最后,以海上风电场作为算例,将所提算法与其他算法拓扑结果进行对比,验证算法的可行性及优越性。

基于自适应遗传-粒子群优化算法的风电场微观选址优化

为了减小尾流效应对风电场发电量的影响,提高风能利用率,提出了一种自适应权重的遗传-粒子群优化算法(genetic-particle swarm optimization algorithm,GA-PSO)。首先,以风电场单位发电成本为目标函数,风机坐标为优化变量,通过在优化变量的速度更新中加入惯性权重,以改变算法的寻优速度;其次,在WASP软件选址的基础上,对风电机组进行布局优化;进而,将计算结果与遗传算法(genetic algorithm,GA)、萤火虫算法(firefly algorithm,FA)和粒子群(particle swarm algorithm,PSO)优化算法进行对比。结果表明:运用PGOA算法优化后的风电场单位发电成本为2016元/GWh,减少了232元/GWh,年发电量为82.633 GWh,比优化前提高了8.538 GWh,同时尾流损失减小了1.12%。可见研究结论对未来的风电场微观选址具有一定指导意义。

海上相邻风电场间的“尾流效应”实例分析

[目的]研究海上相邻风电场间的“尾流效应”对发电损失的影响。[方法]利用海上风电场实际运行SCADA数据结合激光雷达同期实测测风数据,基于不同的风向扇区范围和风电场实际排布进行尾流效应场景分类,开展实际运行相邻风电场间(20D以上间距)的真实尾流电量损失分析工作。[结果]结果表明:对于规则排布的海上大型风电场,基于实际运行SCADA数据,对各机组发电量进行归一化,可以较好地反映海上风能资源分布特征及各机组发电能力的差异;高度集中的单一扇区条件下,处于下风向的相邻风电场受上风向相邻场区的“尾流效应”影响明显,发电产能较自由流降幅明显;相邻风场间随着缓冲带距离的增加,下风向场区机组尾流电量衰减比随之降低,缓冲带需达到一定的距离,对于风速的恢复有明显的作用,发电产能才能够有所提升;本案例不同场景下,缓冲带距离在23D~44D之间,尾流损失电量降幅在27%~4%之间。[结论]基于相邻风电场实际运行数据开展尾流分析可为后续海上大型风电基地规划设计和机组排布优化设计提供指导。

风电场尾流干涉效应及布局优化研究

为探寻风电场尾流干涉效应及定量预报不同风电场布局下整体输出效能,从工程中预报单台风电机尾流效应使用最普遍的Jensen模型出发,基于风电场布局中上游风力机对下游风力机的遮蔽效应及遮蔽模式,构建尾流干涉效应数学模型,编制风电场尾流干涉效应预报程序,研究齐位排列方式下风电场整体输出效能随风电场规模、行列间距及风向角的变化规律,以及错位排列方式下风电场整体输出效能随错位间距和风向角的变化规律。结果表明:不同风电场规模下整体输出效能均随着行列间距的增大而增大,不同行列间距下整体输出效能均随着风电场规模的增大而减小;不同错位间距和风向角下风电场整体输出效能差异明显,需根据风电场所在地风向来合理安排风力机行列间距,以最大化整体输出效能。研究可为工程中预报任意风电场布局下风电机组尾流干涉效应及合理化设计风电场布局提供技术支撑。

考虑噪声约束的分布式风场布局优化

分布式风电是一种新型风电,其优势在于充分利用用户周围的风能,降低输电成本同时提高供电可靠性。然而,由于靠近用户,分布式风场存在严重的噪声污染。以往的研究对噪声问题常采用降噪设备或者降低发电功率的方法处理,少有学者从优化风机布局的角度研究。对于分布式风场而言,布局优化十分关键且较复杂。因为分布式风场的经济性取决于布局设计,而布局设计又受风力波动、风机尾流损失以及电缆布线等复杂因素影响。因此,本研究结合噪声、风速分布、尾流损失、风机功率曲线以及缆线连接模型,以最大风场年经济效益为目标函数,采用遗传算法和斯坦纳树算法对风机位置分布和电缆布线进行优化,以期提高风场经济性的同时降低噪声影响。

“上大压小”风电场改造项目的风电机组布局优化方案研究

中国早期的风电技术不成熟,部分风电机组存在故障率高、效率低的情况,严重影响了风电场的整体经济效益。以建设在复杂地形的风电场为例,针对采用“上大压小”方式对风电场改造时风电机组的布局问题进行分析,通过Meteodyn WT软件解析Navier-stokes方程,仿真复杂地形条件下风流的运动,再结合OpenWind软件优化风电机组的布局,从而最大限度利用风能资源,使整个风电场发电量最大化,提高风电项目整体经济效益。

风电场大容量风电机组永磁直驱发电机稳态运行参数动态优化

针对目前的风力发电机关键参数优化方法优化效果不佳的问题,提出了一种风电场大容量风电机组永磁直驱发电机(PMDDG)稳态运行参数动态优化方法。抓取PMDDG关键动态参数,利用PID控制使电磁转矩波动在整个控制周期达到理想值。引入目标函数,对影响电压波动的动态电压带宽参数自适应调节,实现电压平稳输出。采用人工蚁群算法将经过的节点在记忆中储存,通过释放外激素实现信息传递,扩大解空间搜索范围,利用正反馈原理在最优路径上快速收敛,实现了PMDDG稳态运行参数的全局动态优化。通过实验验证可知,提出的PMDDG稳态运行参数动态优化策略,能够使电压的输出值始终保持在1(p.u.),优化后的转矩波形与标准转矩波形相比最大误差为0.2 kN·m,具有较强稳态运行可靠性。