Collaborative Research: Design and Control of Networked Offshore Hydrokinetic Power-Plants with Energy Storage

合作研究：网络化海上水力储能电站的设计与控制

• 批准号: 1809164
• 负责人: James VanZwieten
• 金额: $ 10.31万
• 依托单位: Florida Atlantic University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1809164&HistoricalAwards=false
• 关键词: Collaborative; Research; Design; Control; Networked

Abstract:Growing a vibrant clean energy industry will lead to major societal benefits by reducing pollution and creating a large number of jobs. In addition to well-established renewables like wind, solar and traditional hydropower, marine currents have the potential to diversify and significantly strengthen the global energy portfolio. This project will conduct basic research focused on extracting electrical power from marine currents using ocean current turbines. These turbines will operate near the sea surface in major offshore currents and will be interconnected in arrays. The project will address fundamental issues related to coordinated, safe, reliable, and robust operation of these turbines within the array for maximum energy production. Networked electrical interconnections between devices and energy storage will also be investigated, with a focus on efficiently feeding electrical power from an array of turbines to an onshore grid. A diverse population of Faculty and student researchers will contribute to this project, creating a heterogeneous network of scholars that will improve the infrastructure for research and education. Important findings from this project will be integrated with active education and outreach programs at Virginia Tech, the University of New Orleans, and Florida Atlantic University which regularly share research outcomes with the public through presentations, workshops, the web, conferences, and summer camp activities. Broader scientific and technological impacts are expected in the energy sector, ocean engineering and technology, marine vehicles and devices, as well as autonomy and control systems.Ocean current turbines will be interconnected in arrays through a local feed-in which sends generated power to a grid. These devices will be affected by flow perturbations such as current shear, turbulence, marine waves, as well as the shear and turbulence generated by neighboring turbines. The overall project goal is to ensure autonomous, reliable and robust coordinated operation of these turbines for maximum energy production and proper power conditioning in the presence of realistic perturbations and sub-system failures. For this purpose fault tolerant flight control, supervisory switching control to manage sub-system failures, formation and network control for ocean current turbine farms with energy storage will be investigated. Novel control systems, which include active blade pitch angle control by leveraging helicopter flight control technology and modern multivariable constrained control techniques will be developed. Control algorithms will be created and evaluated using numerical representations of commercial devices to increase applicability and encourage transfer to the commercial sector. Power conditioning solutions that utilize energy storage will be developed and evaluated to collect power from multiple devices for transfer to shore and grid connection. The project will also investigate energy storage and regeneration through fuel cells or hydrogen internal combustion engines. Basic principles and experimental data series will be combined to create a comprehensive simulation for the entire storage system. The model will be able to provide steady-state performance data for system design and for operations scheduling and control. Modeling and numerical simulation advancements will enable development and testing of controllers on numerical representations with dynamics very similar to commercial marine turbines. These will provide solutions for testing and evaluating the developed controllers, as well as advancements to turbine performance prediction capabilities. Dynamometer and in-water testing data will help advance the developed technologies towards implementation. Methods and tools from diverse disciplines such as control systems theory and technology, hydrodynamics modeling, optimization, as well as numerical computation and analysis will be involved.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

摘要：发展一个充满活力的清洁能源产业将通过减少污染和创造大量就业机会而带来重大的社会效益。除了风能、太阳能和传统水电等成熟的可再生能源外，海流还具有使全球能源组合多样化并显著加强的潜力。该项目将进行基础研究，重点是利用海流涡轮机从海流中提取电力。这些涡轮机将在主要的近海洋流中靠近海面运行，并将以阵列的形式相互连接。该项目将解决与阵列内这些涡轮机的协调、安全、可靠和稳健运行有关的基本问题，以实现最大的能源生产。设备和能源存储之间的网络电力互连也将被研究，重点是如何有效地将涡轮机阵列的电力输送到陆上电网。不同的教师和学生研究人员将为这个项目做出贡献，创建一个异质的学者网络，这将改善研究和教育的基础设施。该项目的重要发现将与弗吉尼亚理工大学、新奥尔良大学和佛罗里达大西洋大学的积极教育和推广计划相结合，这些大学定期通过演讲、研讨会、网络、会议和夏令营活动与公众分享研究成果。预计在能源部门、海洋工程和技术、海洋车辆和设备以及自主和控制系统方面将产生更广泛的科技影响。洋流涡轮机将通过本地馈电系统将产生的电力送入电网，以阵列形式相互连接。这些装置会受到气流扰动的影响，如电流剪切、湍流、海浪，以及邻近涡轮机产生的剪切和湍流。整个项目的目标是确保这些涡轮机的自主、可靠和强大的协调运行，以实现最大的能源生产，并在实际扰动和子系统故障的情况下进行适当的功率调节。为此，将研究具有储能的海流涡轮电场的容错飞行控制、子系统故障管理的监督切换控制、编队和网络控制。新的控制系统，包括利用直升机飞行控制技术和现代多变量约束控制技术的桨叶俯仰角主动控制。将使用商业设备的数字表示来创建和评估控制算法，以增加适用性并鼓励向商业部门转移。将开发和评估利用储能的电力调节解决方案，以从多个设备收集电力，并将其传输到岸上和电网连接。该项目还将研究通过燃料电池或氢内燃机储存和再生能源。将基本原理和实验数据系列相结合，对整个存储系统进行综合仿真。该模型将能够为系统设计、作业调度和控制提供稳态性能数据。建模和数值模拟的进步将使控制器的动态数值表示的开发和测试非常类似于商业船用涡轮机。这些将为测试和评估开发的控制器提供解决方案，以及提高涡轮机性能预测能力。测功机和水中测试数据将有助于推进已开发技术的实施。方法和工具从不同的学科，如控制系统理论和技术，流体动力学建模，优化，以及数值计算和分析将涉及。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Integrated Path Planning and Tracking Control of Marine Current Turbine in Uncertain Ocean Environments

不确定海洋环境下海流涡轮机综合路径规划与跟踪控制

• DOI: 10.23919/acc53348.2022.9867485
• 发表时间: 2022
• 期刊: 2022 American Control Conference (ACC
• 作者: Hasankhani, Arezoo;Ondes, Tugrul Baris;Tang, Yufei;Sultan, Cornel;Van Zwieten, James
• 通讯作者: Van Zwieten, James

A spatially nonlinear generalised actuator disk model for the simulation of horizontal axis wind and tidal turbines

• DOI: 10.1016/j.energy.2019.116803
• 发表时间: 2020-03
• 期刊: Energy
• 影响因子: 9
• 作者: M. Edmunds;A. Williams;I. Masters;A. Banerjee;James H. VanZwieten

Prognostics With Variational Autoencoder by Generative Adversarial Learning

通过生成对抗学习使用变分自动编码器进行预测

• DOI: 10.1109/tie.2021.3053882
• 发表时间: 2022
• 期刊: IEEE Transactions on Industrial Electronics
• 影响因子: 7.7
• 作者: Huang, Yu;Tang, Yufei;VanZwieten, James
• 通讯作者: VanZwieten, James

CFD VALIDATION OF THE THERMODYNAMIC MODEL OF A COMPRESSED GASEOUS HYDROGEN STORAGE TANK

压缩氢气储罐热力模型的 CFD 验证

• DOI: 10.1615/tfec2021.est.036525
• 发表时间: 2021
• 期刊: 5-6th Thermal and Fluids Engineering Conference (TFEC
• 作者: Rouhi, Shahab;Xiros, N.;Sadeqi, Setare;Ioup, J.;Sultan, C.;VanZwieten, J.
• 通讯作者: VanZwieten, J.

Spatiotemporal Optimization for Vertical Path Planning of an Ocean Current Turbine

• DOI: 10.1109/tcst.2022.3193637
• 发表时间: 2023-03
• 期刊: IEEE Transactions on Control Systems Technology
• 影响因子: 4.8
• 作者: Arezoo Hasankhani;Yufei Tang;James H. VanZwieten;C. Sultan