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Integrative modeling and novel particle swarm-based optimal design of wind farms.

机译：基于风场的集成建模和新型粒子群优化设计。

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摘要

To meet the energy needs of the future, while seeking to decrease our carbon footprint, a greater penetration of sustainable energy resources such as wind energy is necessary. However, a consistent growth of wind energy (especially in the wake of unfortunate policy changes and reported under-performance of existing projects) calls for a paradigm shift in wind power generation technologies. This dissertation develops a comprehensive methodology to explore, analyze and define the interactions between the key elements of wind farm development, and establish the foundation for designing high-performing wind farms. The primary contribution of this research is the effective quantification of the complex combined influence of wind turbine features, turbine placement, farm-land configuration, nameplate capacity, and wind resource variations on the energy output of the wind farm. A new Particle Swarm Optimization (PSO) algorithm, uniquely capable of preserving population diversity while addressing discrete variables, is also developed to provide powerful solutions towards optimizing wind farm configurations.;In conventional wind farm design, the major elements that influence the farm performance are often addressed individually. The failure to fully capture the critical interactions among these factors introduces important inaccuracies in the projected farm performance and leads to suboptimal wind farm planning. In this dissertation, we develop the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology to model and optimize the performance of wind farms. The UWFLO method obviates traditional assumptions regarding (i) turbine placement, (ii) turbine-wind flow interactions, (iii) variation of wind conditions, and (iv) types of turbines (single/multiple) to be installed. The allowance of multiple turbines, which demands complex modeling, is rare in the existing literature. The UWFLO method also significantly advances the state of the art in wind farm optimization by allowing simultaneous optimization of the type and the location of the turbines. Layout optimization (using UWFLO) of a hypothetical 25-turbine commercial-scale wind farm provides a remarkable 4.4% increase in capacity factor compared to a conventional array layout. A further 2% increase in capacity factor is accomplished when the types of turbines are also optimally selected. The scope of turbine selection and placement however depends on the land configuration and the nameplate capacity of the farm. Such dependencies are not clearly defined in the existing literature. We develop response surface-based models, which implicitly employ UWFLO, to quantify and analyze the roles of these other crucial design factors in optimal wind farm planning.;The wind pattern at a site can vary significantly from year to year, which is not adequately captured by conventional wind distribution models. The resulting ill-predictability of the annual distribution of wind conditions introduces significant uncertainties in the estimated energy output of the wind farm. A new method is developed to characterize these wind resource uncertainties and model the propagation of these uncertainties into the estimated farm output. The overall wind pattern/regime also varies from one region to another, which demands turbines with capabilities uniquely suited for different wind regimes. Using the UWFLO method, we model the performance potential of currently available turbines for different wind regimes, and quantify their feature-based expected market suitability. Such models can initiate an understanding of the product variation that current turbine manufacturers should pursue, to adequately satisfy the needs of the naturally diverse wind energy market.;The wind farm design problems formulated in this dissertation involve highly multimodal objective and constraint functions and a large number of continuous and discrete variables. An effective modification of the PSO algorithm is developed to address such challenging problems. Continuous search, as in conventional PSO, is implemented as the primary search strategy; discrete variables are then updated using a nearest-allowed-discrete-point criterion. Premature stagnation of particles due to loss of population diversity is one of the primary drawbacks of the basic PSO dynamics. A new measure of population diversity is formulated, which unlike existing metrics capture both the overall spread and the distribution of particles in the variable space. This diversity metric is then used to apply (i) an adaptive repulsion away from the best global solution in the case of continuous variables, and (ii) a stochastic update of the discrete variables. The new PSO algorithm provides competitive performance compared to a popular genetic algorithm, when applied to solve a comprehensive set of 98 mixed-integer nonlinear programming problems.
机译：为了满足未来的能源需求，在设法减少我们的碳足迹的同时，有必要更大程度地渗透诸如风能之类的可持续能源。但是，风能的持续增长（尤其是在不幸的政策变化和现有项目的业绩不佳之后），要求风力发电技术发生范式转变。本文为探索，分析和定义风电场发展关键要素之间的相互作用提供了一种综合的方法，为设计高性能风电场奠定了基础。这项研究的主要贡献是有效量化了风力涡轮机功能，涡轮机位置，农田配置，铭牌容量以及风力资源变化对风力发电场能量的综合影响。还开发了一种新的粒子群优化（PSO）算法，该算法独特地能够在解决离散变量的同时保留种群多样性，从而为优化风电场配置提供了强大的解决方案。在传统风电场设计中，影响风电场性能的主要因素是：通常单独处理。无法完全捕获这些因素之间的关键相互作用会导致预计的农场绩效出现重大不准确性，并导致风电场规划欠佳。本文开发了无限制的风电场布局优化（UWFLO）方法，以对风电场的性能进行建模和优化。 UWFLO方法消除了有关（i）涡轮机放置，（ii）涡轮机-风流相互作用，（iii）风况变化以及（iv）要安装的涡轮机类型（单/多）的传统假设。在现有文献中很少需要允许复杂建模的多涡轮机。通过允许同时优化涡轮机的类型和位置，UWFLO方法还大大提高了风电场优化的最新水平。假设的25涡轮商业规模风电场的布局优化（使用UWFLO）与传统的阵列布局相比，容量因子显着提高了4.4％。当涡轮机的类型也被最佳选择时，容量系数又增加了2％。但是，涡轮机的选择和布置范围取决于土地配置和农场的铭牌容量。现有文献中没有明确定义这种依赖性。我们开发了基于响应面的模型，该模型隐含地使用UWFLO来量化和分析这些其他关键设计因素在最佳风电场规划中的作用。;站点的风型每年之间可能会有很大变化，这是不充分的由常规的风分布模型捕获。造成的年度风况分布的不可预测性给风电场的估计能量输出带来了很大的不确定性。开发了一种新方法来表征这些风能资源的不确定性，并对这些不确定性在估计的农场产量中的传播进行建模。整个区域的风向/风向也有所不同，这要求涡轮机具有独特地适合于不同风况的能力。使用UWFLO方法，我们对当前可用的涡轮机在不同风况下的性能潜力进行建模，并量化其基于特征的预期市场适用性。这样的模型可以启动对当前涡轮机制造商应追求的产品变化的理解，以充分满足自然多样化的风能市场的需求。论文中提出的风电场设计问题涉及高度多模态的目标和约束函数以及很大的连续变量和离散变量的数量。开发了对PSO算法的有效修改以解决此类难题。与传统的PSO中一样，连续搜索是主要的搜索策略；然后使用最近允许的离散点标准更新离散变量。由于种群多样性的丧失而导致的粒子过早停滞是基本PSO动力学的主要缺点之一。制定了一种新的人口多样性测度，与现有的测度不同，它既捕获了总体扩散，又捕获了可变空间中粒子的分布。然后使用该分集度量来（i）在连续变量的情况下应用远离最佳全局解的自适应排斥，以及（ii）离散变量的随机更新。当应用于解决98组混合整数非线性规划问题的综合问题时，与流行的遗传算法相比，新的PSO算法具有竞争优势。

著录项

  • 作者

    Chowdhury, Souma.;

  • 作者单位

    Rensselaer Polytechnic Institute.;

  • 授予单位 Rensselaer Polytechnic Institute.;
  • 学科 Alternative Energy.;Engineering Aerospace.;Engineering Mechanical.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 258 p.
  • 总页数 258
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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