Overall design optimization of wind farms

An Evolutive Algorithm (EA) for wind farm optimal overall design is presented. The algorithm objective is to optimize the profits given an investment on a wind farm. Net Present Value (NPV) will be used as a figure of the revenue in the proposed method. To estimate the NPV is necessary to calculate the initial capital investment and net cash flow throughout the wind farm life cycle. The maximization of the NPV means the minimization of the investment and the maximization of the net cash flows (to maximise the generation of energy and minimise the power losses). Both terms depend mainly on the number and type of wind turbines, the tower height and geographical position, electrical layout, among others. Besides, other auxiliary costs must be to keep in mind to calculate the initial investment such as the cost of auxiliary roads or tower foundations. The difficulty of the problem is mainly due to the fact that there is neither analytic function to model the wind farm costs nor analytic function to model net generation. The complexity of this problem arises not only from a technical point of view, due to strong links between its variables, but also from a purely mathematical point of view. The problem consists of both discrete and continuous variables, being therefore an integer-mixed type problem. The problem exhibits manifold optimal solutions (convexity), some variables have a range of non allowed values (solutions space not simply connected) and others are integers. This fact makes the problem non-derivable, preventing the use of classical analytical optimization techniques.     

Cuckoo search for wind farm optimization with auxiliary infrastructure

This paper focuses on the optimization problem of a wind farm layout. This area of research is currently receiving widespread attention, as optimal positioning of the turbines promotes the financial viability of the wind farm and enhances the competitiveness of wind projects in the energy market. In this work, cuckoo search (CS), a modern population‐based metaheuristic optimization algorithm, is used. The objective is to find the turbine layout and types that maximize the net present value of the wind farm, while constraints on the turbine positions have to be met. The following constraints are considered: Firstly, the minimum distance between turbines for safe operation; secondly, a realistic wind farm shape including forbidden zones for installation and the existing infrastructure. Furthermore, the optimization of the wind farm includes an algorithm to find the least expensive layout of the wind farm roads and the electrical collector system. The algorithm is based on Dijkstra's shortest path and Prim's minimum spanning tree algorithms. The test results indicate that the infrastructure cost has a significant effect on the optimum wind farm solution. A genetic algorithm, commonly applied to wind farm micro‐siting problems, is used to benchmark the performance of the CS. The results show that the CS is capable of consistently finding better solutions than the genetic algorithm.     

Possible power of down‐regulated offshore wind power plants: The PossPOW algorithm

This paper proposes a method for real‐time estimation of the possible power of an offshore wind power plant when it is down‐regulated. The main purpose of the method is to provide an industrially applicable estimate of the possible (or reserve) power. The method also yields a real‐time power curve, which can be used for operation monitoring and wind farm control. Currently, there is no verified approach regarding estimation of possible power at wind farm scale. The key challenge in possible power estimation at wind farm level is to correct the reduction in wake losses, which occurs due to the down‐regulation. Therefore, firstly, the 1‐second wind speeds at the upstream turbines are estimated, since they are not affected by the reduced wake. Then they are introduced into the wake model, adjusted for the same time resolution, to correct the wake losses. To mitigate the uncertainties due to dynamic changes within the large offshore wind farms, the algorithm is updated at every turbine downstream, considering the local axial and lateral turbulence effects. The PossPOW algorithm uses only 1‐Hz turbine data as inputs and provides possible power output. The algorithm is trained and validated in Thanet and Horns Rev‐I offshore wind farms under nominal operation, where the turbines are following the optimum power curve. The results indicate that the PossPOW algorithm performs well; in the Horns Rev‐I wind farm, the strict power system requirements are met more than 70% of the time over the 24‐hour data set on which the algorithm was evaluated.     

Overall Optimization for Offshore Wind Farm Electrical System

Based on particle swarm optimization (PSO), an optimization platform for offshore wind farm electrical system (OWFES) is proposed in this paper, where the main components of an offshore wind farm and key technical constraints are considered as input parameters. The offshore wind farm electrical system is optimized in accordance with initial investment by considering three aspects: the number and siting of offshore substations (OS), the cable connection layout of both collection system (CS) and transmission system (TS) as well as the selection of electrical components in terms of voltage level and capacity. Because hundreds of optimization variables, continuous or discrete, are involved in the problem, a mix integer PSO (MIPSO) is required to obtain the solution. The fuzzy C‐means clustering (FCM) algorithm is used to partition the wind farm into several sub regions. The collection system layout in each sub region as well as the connection scheme between offshore substations are optimized by an adaptive PSO‐minimum spanning tree algorithm (APSO‐MST) which has been proposed in a previous work. The simulation results show that the proposed optimization platform can find an optimized layout that save 3.01% total cost compared with the industrial layout, and can be a useful tool for OWFES design and evaluation. Copyright © 2016 John Wiley & Sons, Ltd.     

Combining hydro and variable wind power generation by means of pumped-storage under economically viable terms

Pumped hydro storage (PHS) systems which are located at isolated regions and are able to exploit the rejected wind energy amounts produced by local wind farms, seem to gain interest worldwide and to become essential in regard to higher shares of renewable-generated electricity. Despite the high wind potential encountered in many Greek island regions, the wind energy contribution to the electrification of these areas is significantly restricted due to imposed electrical grid limitations. In this context, the current work examines the economic viability of a wind-based PHS system (wind-hydro solution) which provides the local electrical grid of an Aegean Sea island, Lesbos, with guaranteed energy amounts during the peak load demand periods. Based on the maximization of the project’s net present value, the optimum system configuration is proposed while many other feasible solutions are revealed. According to the results obtained the implementation of this project demonstrates excellent technical and economic performance, while at the same time renewable energy sources (RES) contribution is doubled reaching almost 20% of the Lesbos island electrical energy consumption.     

基于改进型运行策略的独立微电网容量优化配置

建立独立微电网对于解决离岸岛屿等传统供电不便地区的可靠自主供用电问题具有非常显著的实用性。针对风/光/蓄/柴独立微电网,提出了一种考虑系统净负荷大小的改进型运行控制策略,通过权重法将系统经济性和环保性指标整合并加入可靠性经济惩罚共同作为优化目标,考虑多种约束条件,建立基于改进型运行策略的风/光/蓄/柴独立微电网容量优化配置模型,并采用混合量子遗传算法(HQGA)对我国沿海某岛地区独立微网实施优化配置。结果表明,与采用常规运行策略的配置结果相比,所提运行策略具有正确性和优越性。     

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.     

Guidelines for assessment of investment cost for offshore wind generation

Offshore wind generation represents a key element for development of renewable energy, thanks to higher availability of energy source and lower presence of constraints. However, the feasibility of offshore wind farms has to be carefully evaluated, due to remarkable economical efforts required. In this paper, economic issues concerning costs in pre-investment and investment stages for offshore wind farms exploiting alternating-current transmission system are analysed. Single cost centres are detailed, taking into account technical features and current equipment exploitation. The aim is to formulate a general model to evaluate the total investment depending on wind farm layout. The model is employed to determine the most suitable connection solution for a 150-MW test wind farm, accounting for different connection schemes and the presence of an offshore or onshore substation. Further tests are run to evaluate cost variation for larger wind farms with different nominal voltage levels.     

Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms

In this paper a novel evolutionary algorithm for optimal positioning of wind turbines in wind farms is proposed. A realistic model for the wind farm is considered in the optimization process, which includes orography, shape of the wind farm, simulation of the wind speed and direction, and costs of installation, connection and road construction among wind turbines. Regarding the solution of the problem, this paper introduces a greedy heuristic algorithm which is able to obtain a reasonable initial solution for the problem. This heuristic is then used to seed the initial population of the evolutionary algorithm, improving its performance. It is shown that the proposed seeded evolutionary approach is able to obtain very good solutions to this problem, which maximize the economical benefit which can be obtained from the wind farm.     

基于蚁狮算法的风电集群储能容量配置优化方法

随着风电渗透率提高,风电并网对电网安全稳定运行影响更加显著。风电配备储能可有效改善风电出力波动性和不确定性,增强可调度性。该文建立以风电集群联合储能系统售电收益最高为优化目标的模型,采用蚁狮算法算法进行求解,得到风电集群功率备用、储能功率和容量最优配置方案,对比蚁狮算法、遗传算法和粒子群算法优化结果,分析储能电池单位成本和寿命对优化结果影响。最后以中国东北某风电集群作为算例,验证了所提算法与模型的有效性。     

Two Alternative Modeling Approaches for the Evaluation of Wind Farm Active and Reactive Power Performances

Based on the stator-flux-oriented electromechanical model of a doubly fed induction machine (DFIM), this paper presents two different approaches to wind farm modeling. Particularly, the overall control system of the DFIM is modeled in detail, so that tuning equations that allow adjusting the parameters of its proportional-integral (PI) compensators straightforwardly are provided. In addition to validating the wind generator model proposed, experimental tests carried out on a 660-kW doubly fed induction generator (DFIG) prove that such a control system makes it possible to govern separately its stator-side or net active and reactive powers. The two wind farm models proposed, which are assumed to be made up of DFIM type generators exclusively, are mainly devised to assist the design and test of closed-loop control algorithms for the wind farm total power factor. They generally imply significantly different computational loads, so that the second may be regarded as a simplified version of the first one. The net active and reactive power performances of both wind farm models are finally compared via simulation, which allows identifying the stronger and weaker points associated to each model     

An integrated model for performance simulation of hybrid wind–diesel systems

Stand-alone hybrid systems have turned into one of the most promising ways to handle the electrification requirements of numerous isolated consumers worldwide. The proposed wind–diesel–battery hybrid system consists of a micro-wind converter, a small diesel-electric generator—basically operating as a back up energy production system—and a lead-acid battery bank that stores the wind energy surplus during high wind speed periods. In this context the present work is focused on presenting a detailed mathematical model describing the operational behavior of the basic hybrid system components, along with the representative calculation results based on the developed mathematical model. Accordingly, an integrated numerical algorithm is built to estimate the energy autonomy configuration of the hybrid system under investigation. Using the proposed numerical algorithm, the optimum configuration selection procedure is verified by carrying out an appropriate sensitivity analysis. The proposed methodology may equally well be applied to any other remote consumer and wind potential type, in order to estimate the optimum wind–diesel hybrid system configuration that guarantees long-term energy autonomy.     

A simplified model for estimating yearly wind fraction in hybrid-wind energy systems

This article presents a simplified algorithm to estimate the yearly wind fraction, the fraction of energy demand provided by wind generator, in a hybrid-wind system (typically a PV-wind) with battery storage. The novel model is drawn based on the simulation results, using 8-year long measured hour-by-hour wind speed data from five different locations throughout the world. The simulation program simulates the battery state of voltage (SoV) and is able to predict the wind fraction for a period of time, typically monthly or yearly. The yearly wind fraction values obtained from the simulations are plotted against the ratio of energy to load for various battery storage capacities to obtain wind fraction curves. The novel method correlates the yearly wind fraction with the parameters of the Weibull distribution function, thus, offering a general methodology. The yearly wind fraction curves are mathematically represented using a 2-parameter model. The novel algorithm is validated by comparing the simulated wind fraction values with those calculated from the simplified algorithm. The standard error of estimation of the WF from the simplified algorithm is further presented for each battery capacity.     

Optimisation of electrical system for offshore wind farms via genetic algorithm

An optimisation platform based on genetic algorithm (GA) is presented, where the main components of a wind farm and key technical specifications are used as input parameters and the electrical system design of the wind farm is optimised in terms of both production cost and system reliability. The power losses, wind power production, initial investment and maintenance costs are considered in the production cost. The availability of components and network redundancy are included in the reliability evaluation. The method of coding an electrical system to a binary string, which is processed by GA, is developed. Different GA techniques are investigated based on a real example offshore wind farm. This optimisation platform has been demonstrated as a powerful tool for offshore wind farm design and evaluation.     

Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

Most wind turbines within wind farms are set up to face a pre-determined wind direction. However, wind directions are intermittent in nature, leading to less electricity production capacity. This paper proposes an algorithm to solve the wind farm layout optimization problem considering multi-angular (MA) wind direction with the aim of maximizing the total power generated on wind farms and minimizing the cost of installation. A two-stage genetic algorithm (GA) equipped with complementary sampling and uniform crossover is used to evolve a MA layout that will yield optimal output regardless of the wind direction. In the first stage, the optimal wind turbine layouts for 8 different major wind directions were determined while the second stage allows each of the previously determined layouts to compete and inter-breed so as to evolve an optimal MA wind farm layout. The proposed MA wind farm layout is thereafter compared to other layouts whose turbines have focused site specific wind turbine orientation. The results reveal that the proposed wind farm layout improves wind power production capacity with minimum cost of installation compared to the layouts with site specific wind turbine layouts. This paper will find application at the planning stage of wind farm.     

基于TPNT含风电场发输电组合系统可靠性评估

为克服含风电场可靠性评估中需已知风速分布函数的缺点,提出了一种基于三阶多项式正态变换(TPNT)的非序贯蒙特卡洛模拟法评估含风电场发输电系统的可靠性。在已知风速历史数据或风速分布函数的情况下,通过TPNT构建风速随机变量与标准正态分布变量的关系,进而利用标准正态分布函数的性质产生具有任意数量的具有指定相关性的风速样本,并应用于风电场接入的发输电系统可靠性计算中。通过算例分析验证了TPNT应用于发输电系统可靠性计算中的适用性。在此基础上,从风速相关性、额定容量、风资源强度和风电场位置四个角度分析了风电场接入对可靠性的影响。为含风电场发输电系统可靠性的评估提供了新思路。     

Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

Wind energy is an important source of renewable energy with significant untapped potential around the world. However, the cost of wind energy production is high, and efforts to lower the cost of energy generation will help enable more widespread use of wind energy. Yaw error reduces the efficiency of turbines as well as lowers the reliability of key components in turbines. Light detection and ranging (LIDAR) devices can correct the yaw error; however, they are expensive, and there is a trade‐off between their costs and benefits. In this study, a stochastic discrete‐event simulation was developed that models the operation of a wind farm. We maximize the net present value (NPV) changes associated with using LIDAR devices in a wind farm and determine the optimum number of LIDAR devices and their associated turbine stay time as a function of number of turbines in the wind farm for specific turbine sizes. The outcome of this work will help wind farm owners and operators make informed decisions about purchasing LIDAR devices for their wind farms.     

基于改进D-K聚类算法的直驱型风电场动态等值建模

研究永磁同步发电机(PMSG)的低电压穿越(LVRT)特性,提出一种基于改进D-K聚类算法的直驱型风电场等值建模方法。首先,根据风电场内各台PMSG卸荷电路的导通情况对风电场进行首次分群。其次,将卸荷电路未导通的PMSG故障期间的机端电压值作为分群指标,应用改进D-K聚类算法对风电场进行聚类等值。该算法解决了传统K-均值聚类算法k值需提前给定和依赖于初始聚类中心的问题。最后,以某实际风电场为例进行仿真分析,结果表明该等值模型与传统等值模型相比能更准确地反映直驱型风电场的动态和暂态运行特性。     

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.     

Rapid sizing of a hydrogen-battery storage for an offshore wind farm using convex programming

This paper carries out a comprehensive analysis on an offshore wind farm equipped with a hybrid storage comprised of hydrogen and battery, from the perspective of economic effectiveness. To rapidly evaluate the system economy, a computationally efficient convex program that takes the nonlinear storage efficiencies into account is provided, which can simultaneously and synergistically optimize the storage sizing and energy management over a long offshore wind cycle. In the analysis, a case study on the optimal configuration and operation of the hybrid storage is thoroughly investigated, answering what the scalings are and how the storage functions in the offshore wind farm. Comparisons to other offshore wind farms with none or only one storage type further demonstrate the advantage of combining hydrogen plant and battery. Influences of the offshore wind electricity price of grid parity and hydrogen price on the system economies, in the terms of total annual cost, net annual profit and hydrogen production cost, are also discussed, revealing sensitivity and dependency of the scalings. Finally, this paper presents the future potential of applying hydrogen plant in the offshore wind farm, from the angles of hydrogen production cost and energy saving.