Real-Time Distributed Control for Low-Inertia Power Grids
Real-Time Distributed Control for Low-Inertia Power Grids
批准号:
RGPIN-2017-04008
负责人:
SimpsonPorco, John
金额:
$1.45万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2020
资助国家:
加拿大
项目状态:
已结题
起止时间:
2020-01-01 至 2021-12-31
中文摘要
电力是推动我们的技术文明向前发展的动力。在过去几十年中,气候变化和污染问题促使许多国家采用可持续能源解决方案。我们现在可以从风力涡轮机和太阳能发电场等可再生能源中生产清洁能源。然而,生产电力只是战斗的一半。同样重要的是,我们能够通过电网将电力可靠地传输给消费者。
我们现有的电网是分层的,由几层组成。最低层是配电网,消费者-现在,许多可再生能源-连接在一起。随着化石燃料逐渐被可再生能源所取代,我们用许多更小、更不可预测的能源取代了大型、非常可靠的能源。这种转变正在将全球配电网推向它们根本不适合的运行环境,电网运营商面临着新的稳定性和控制问题。我们有效利用可再生能源的能力目前受到我们控制和优化传统配电网的能力的考验。
该研究计划的长期目标是开发控制和优化策略,以最大限度地提高电网中的可再生能源数量。其关键思想是对电力如何通过电网传输有更深入的数学理解,然后利用这种理解更有效地协调以前独立的发电和控制设备。关键研究将包括开发新的潮流数学模型,量化电网的可再生能源渗透和稳定性限制,以及设计增加这些限制的反馈控制器。真实的协调独立的传感器组、分布式发电机和控制设备将使我们能够最大限度地利用当前的电网基础设施,而不会影响电网的稳定性。通过利用现代反馈控制的工程原理,我们将能够补偿可再生能源发电增加的不确定性的影响,从而实现清洁而可靠的电网。
为了加速小规模可再生能源的广泛采用,这项研究将提供电网控制方面的重要进展,以减少对传统配电网的压力,使公用事业公司能够在不进行昂贵的设备升级的情况下最大限度地利用其资产。这种软件驱动的解决方案是能源难题的重要组成部分。同样重要的是确保在公用事业和私人产品部门都有高素质的人员(HQP),他们具有应对国家能源挑战所需的控制和优化技能。成功参与这一独特的专业计划的HQP将处于过渡到由可再生能源和协调反馈控制供电的21世纪电网的最前沿。
英文摘要
Electric power is what drives our technological civilization forward. Over the past few decades, climate change and pollution issues have propelled many countries to adopt sustainable energy solutions. We can now produce clean power from renewable sources such as wind turbines and solar farms. Yet, producing power is only half the battle. Equally as important is our ability to reliably transmit that power through the power grid to consumers.
Our existing power grid is hierarchical and consists of several layers. The lowest layer is the distribution grid, where consumers -- and now, many renewable power sources -- are connected. As fossil fuels are slowly displaced by renewables, we replace large, very reliable sources of power with many smaller and less predictable sources. This transition is pushing distribution grids worldwide into operating situations they were simply not designed for, and grid operators are facing new stability and control issues. Our ability to effectively use renewable energy is currently bottlenecked by our ability to control and optimize our legacy distribution grids.
The long-term objective of this research program is to develop control and optimization strategies to maximize the amount of renewable energy in the power grid. The key idea is to develop a deeper mathematical understanding of how power is transmitted through the grid, then use this understanding to more efficiently coordinate previously independent generation and control equipment. Critical research will include the development of new mathematical models for power flow, quantifying the renewable penetration and stability limits of the grid, and designing feedback controllers which increase these limits. Coordinating independent groups of sensors, distributed generators, and control equipment in real time will allow us maximize our current grid infrastructure, without compromising grid stability. By leveraging the engineering principles of modern feedback control, we will be able to compensate for the effects of increased uncertainty from renewable generation, leading to a clean yet reliable grid.
To accelerate the wide-spread adoption of small-scale renewable power, this research will provide the essential advances in grid control needed to reduce the stress on our legacy distribution grids, enabling utilities to maximize their assets without costly equipment upgrades. Such software-powered solutions are a vital piece of the energy puzzle. Also central is ensuring there are highly qualified personnel (HQP) with the control and optimization skills necessary to address the country's energy challenges, in both the utility and private product sectors. HQP who successfully participate in this unique, specialized program will be at the forefront of the transition to a 21st century grid powered by renewables and coordinated feedback control.
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Real-Time Distributed Control for Low-Inertia Power Grids
-
批准号:RGPIN-2017-04008
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$3.5万
-
财政年份:2022
-
负责人:SimpsonPorco, John
-
依托单位:
Real-Time Distributed Control for Low-Inertia Power Grids
-
批准号:RGPIN-2017-04008
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$1.75万
-
财政年份:2021
-
负责人:SimpsonPorco, John
-
依托单位:
Real-Time Distributed Control for Low-Inertia Power Grids
-
批准号:RGPIN-2017-04008
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$0.3万
-
财政年份:2020
-
负责人:SimpsonPorco, John
-
依托单位:
Real-Time Distributed Control for Low-Inertia Power Grids
-
批准号:RGPIN-2017-04008
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$1.75万
-
财政年份:2019
-
负责人:SimpsonPorco, John
-
依托单位:
Real-Time Distributed Control for Low-Inertia Power Grids
-
批准号:RGPIN-2017-04008
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$1.75万
-
财政年份:2018
-
负责人:SimpsonPorco, John
-
依托单位:
Real-Time Distributed Control for Low-Inertia Power Grids
-
批准号:RGPIN-2017-04008
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$1.75万
-
财政年份:2017
-
负责人:SimpsonPorco, John
-
依托单位:
Application for Graduate Funding in Control Theory and Quantum Feedback Control
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批准号:404714-2011
-
项目类别:Postgraduate Scholarships - Doctoral
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资助金额:$1.53万
-
财政年份:2013
-
负责人:SimpsonPorco, John
-
依托单位:
Application for Graduate Funding in Control Theory and Quantum Feedback Control
-
批准号:404714-2011
-
项目类别:Postgraduate Scholarships - Doctoral
-
资助金额:$2.29万
-
财政年份:2012
-
负责人:SimpsonPorco, John
-
依托单位:
Application for Graduate Funding in Control Theory and Quantum Feedback Control
-
批准号:404714-2011
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项目类别:Postgraduate Scholarships - Doctoral
-
资助金额:$0.76万
-
财政年份:2011
-
负责人:SimpsonPorco, John
-
依托单位:
Application for Graduate Funding in Control Theory and Quantum Feedback Control
-
批准号:393216-2010
-
项目类别:Postgraduate Scholarships - Master's
-
资助金额:$1.26万
-
财政年份:2010
-
负责人:SimpsonPorco, John
-
依托单位:
Light dynamics in curved coupled waveguide arrays
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批准号:399744-2010
-
项目类别:University Undergraduate Student Research Awards
-
资助金额:$0.33万
-
财政年份:2010
-
负责人:SimpsonPorco, John
-
依托单位:
High finesse reference cavity
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批准号:382681-2009
-
项目类别:University Undergraduate Student Research Awards
-
资助金额:$0.33万
-
财政年份:2009
-
负责人:SimpsonPorco, John
-
依托单位:
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