Greedy robust wind farm layout optimization with feasibility guarantee

This article proposes a method with light data requirements for generating robust wind farm layouts. Robustness in this work is quantified as the lowest energy conversion efficiency of the wind farm across all wind directions. A quadratic integer programming formulation for generating robustness-maximizing layouts is presented. Small instances of the proposed formulation can be solved to optimality using branch and bound. A modified greedy algorithm that guarantees solution feasibility with regards to inter-turbine safety distance is proposed to find solutions to larger problem instances. A series of experiments were conducted using real world wind data collected at two sites to demonstrate the trade-offs in power generation between robust layouts and power output maximizing layouts. The results show a loss of around 1.1% in hourly power generation in return for an increase in minimum power output of 1% to 45% across all directions for robust layouts generated in the experiments. The increase in robustness largely depends on the shape and orientation of the wind farm relative to the dominant wind direction, as well as the difference between the average wind speed at the site of the wind farm and rated wind speed of the turbines.     

Wind farm layout design optimization through multi-scenario decomposition with complementarity constraints

This article presents a multi-scenario decomposition with complementarity constraints approach to wind farm layout design to maximize wind energy production under region boundary and inter-turbine distance constraints. A complementarity formulation technique is introduced such that the wind farm layout design can be described with a continuously differentiable optimization model, and a multi-scenario decomposition approach is proposed to ensure efficient solution with local optimality. To combine global exploration and local optimization, a hybrid solution algorithm is presented, which combines the multi-scenario approach with a bi-objective genetic algorithm that maximizes energy production and minimizes constraint violations simultaneously. A numerical case study demonstrates the effectiveness of the proposed approach.     

A tight upper bound for quadratic knapsack problems in grid-based wind farm layout optimization

The 0-1 quadratic knapsack problem (QKP) in wind farm layout optimization models possible turbine locations as nodes, and power loss due to wake effects between pairs of turbines as edges in a complete graph. The goal is to select up to a certain number of turbine locations such that the sum of selected node and edge coefficients is maximized. Finding the optimal solution to the QKP is difficult in general, but it is possible to obtain a tight upper bound on the QKP's optimal value which facilitates the use of heuristics to solve QKPs by giving a good estimate of the optimality gap of any feasible solution. This article applies an upper bound method that is especially well-suited to QKPs in wind farm layout optimization due to certain features of the formulation that reduce the computational complexity of calculating the upper bound. The usefulness of the upper bound was demonstrated by assessing the performance of the greedy algorithm for solving QKPs in wind farm layout optimization. The results show that the greedy algorithm produces good solutions within 4% of the optimal value for small to medium sized problems considered in this article.     

Network-based optimization techniques for wind farm location decisions

This study aims to find appropriate locations for wind farms that can maximize the overall energy output while controlling the effects of wind speed variability. High wind speeds are required to obtain the maximum possible power output of a wind farm. However, balancing the wind energy supplies over time by selecting diverse locations is necessary. These issues are addressed using network-based models. Hence, actual wind speed data are utilized to demonstrate the advantages of the proposed approach.     

漂浮式风电场的基础形式和发展趋势

阐述了海上风电场的分类,并对漂浮式风电场风电机组的浮式基础作了详细的介绍。随着风电开发技术的成熟,风机容量大型化、垂直轴风机的应用及建立非并网风电多元化应用系统将是以后漂浮式风电场发展的趋势。     

论我国海上风电场建设重大工程问题

发展海上风电具有重大战略意义,应予高度重视,加大支持力度;发展海上风电面临严峻挑战,应予科学应对,切忌浮躁冒进。我国海上风电发展,必须坚持科学发展理念,坚持高起点上的理性、有序发展,坚持创新和特色结合的跨越式、可持续发展。成本、效益、风险制约海上风电的发展,通过科技进步,风电核心技术掌握和规划建设能力的提高,可以有效解决重大制约发展的因素,确保海上风电健康发展。针对我国海上风电又好又快发展,建设高质量、高效益的海上风电场,提出了若干建议。     

海上风电场建设重大工程问题探讨

三峡集团公司承担了国家"十一五"科技支撑计划项目中近海风电场选址及风电机组运行、维护技术开发和近海风电机组施工、测试专用设备的研制等有关课题的研究工作,在江苏近海和潮间带建设了多台具有自主知识产权的试验风机。通过研究测风、风资源评估、基础结构、运输吊装、运行维护等海上施工关键技术,以及推动大型海上风力发电机组的研发,探索降低海上风电开发成本的措施。以海水淡化为切入点,开展风电的非并网应用研究,寻求解决风电消纳问题的新方法。     

土基海流耦合条件下海上风电场塔架支撑结构动力特性初探

针对海上风力机塔柱支撑结构受到土基、海洋流体作用的复杂特点,开展多介质耦合条件下塔柱支撑结构动力特性研究。在考虑流固耦合基础上,进一步考虑海床土基弹性条件对塔柱结构动力特性的影响。最后得出了水流、土基与塔柱结构三合一综合动力特性分析结果,初步取得了变化规律。研究结果对海上风电场塔架支撑结构的抗振设计及进一步的研究提供了重要基础性参考依据。     

海上风电场基础形式及配套施工技术

根据不同的水深及地质条件,结合已建成的海上风电场基础形式及施工方法,介绍和研究了重力式、单桩、群桩、设置沉箱、沉井及吸力式筒形基础等几种形式。对不同的基础形式,分别提出了自升式平台、浅吃水半潜驳、打桩船及整体浮运吊装等相应的基础施工方法。根据风机机组类型,对塔筒和风机的安装也做了介绍。     

上海东海大桥海上风电场规划建设关键技术研究

通过对上海东海大桥海上风电场的选址、风电场升压方式、基础选型设计、桩基试验、基础施工、风机安装等技术问题的研究和实践,提出了海上风电场选址原则、接入电网方式、具有防撞能力的高桩混凝土承台基础形式、风机基础结构分析方法、桩的承载力试验手段、高桩承台施工和风机整体吊装方案等,为海上风电场规划建设提供借鉴。     

海上风机桩式基础结构形式综合模糊优选

针对海上风机基础设计中经常遇到复杂的方案优化选型问题,将多因素、多级模糊优选理论引入到基础的设计选型中。针对影响因素复杂、确定隶属函数主观因素较强的情况,成功引入因素的优先关系法来确定优选矩阵的隶属度,较好地解决了确定隶属函数的人为影响。通过此优选模型成功地将影响基础设计选型的13种主要因素和4种桩基基础设计形式进行了多级模糊综合优选决策,得到了比较理想的决策结果,为海上风机基础设计选型提供了新的思路。     

基础冲刷对海上风电场塔架支撑系统动力特性的影响分析

针对海上风力机塔柱支撑结构受到土基、海洋流体的复杂作用的特点,建立三维有限元数值模型,开展不同海床冲刷深度条件下支撑系统结构的动态特性,探讨其变化规律,为海上风力机支撑系统的动力稳定设计提供科学依据。     

Design optimization of ironless multi-stage axial-flux permanent magnet generators for offshore wind turbines

Direct-driven ironless-stator machines have been reported to have low requirements on the strength of the supporting structures. This feature is attractive for offshore wind turbines, where lightweight generators are preferred. However, to produce sufficient torque, ironless generators are normally designed with large diameters, which can be a challenge to the machine’s structural reliability. The ironless multi-stage axial-flux permanent magnet generator (MS-AFPMG) has the advantages of ironless machines but a relatively small diameter. The objective of this article is to present the design optimization and performance investigation of the ironless MS-AFPMG. An existing design strategy, which employs two- and three-dimensional static finite element analyses and genetic algorithm for machine optimization, is improved with the aim of reducing the calculation load and calculation time. This improved design strategy is used to investigate the optimal ironless MS-AFPMG. Some intrinsic features of this kind of machine are revealed.