EAGER: Topology Control for Enhancing the Reliability of the National Power Grid

EAGER:拓扑控制增强国家电网可靠性

基本信息

项目摘要

Nowadays, fulfilling the demand for electric power is of utmost importance from economic, security, and societal standpoints. Paradoxically, there are very few control actions available to guarantee the power grid's reliability (i.e., to prevent unexpected power outages). This is due in part to the energy industry's conception of the electric transmission network as a static entity. In most situations, the corrective actions in response to a contingency event (i.e., a component failure) consist only of dispatching unused generator capacity and of starting up additional generation units. Conversely, topology control is an innovative approach that equips grid operators with the ability to switch each transmission line's on/off status. Previous research has shown that finding and implementing the best grid configuration (i.e., the best grid topology), in conjunction with the best respective generator dispatch, can decrease energy production/delivery costs. This EArly-Grant for Exploratory Research (EAGER) award supports fundamental research to enhance both the proactive and reactive reliable operation of the power grid without costly infrastructure investments. Specifically, this research will show that controlling the grid's topology can enhance the grid's reliability. In addition, this research will also develop the procedures required to find the most reliable grid topology in response to changes in energy demand. Thus, the primary societal impact of this research is to increase the capability to prevent and resolve unexpected blackouts, which account for approximately $80 billion in losses each year for U.S. businesses and consumers. This research involves several disciplines including power systems, parallel computing and optimization.The specific aim of this award is to enhance the transmission grid reliability. This will be accomplished by incorporating the innovative concept of topology control into representative mathematical programs to optimize the minute-by-minute operations of large-scale power systems. Specifically, the problem that needs to be solved every few minutes is a mixed integer nonlinear problem (MINLP). Three directions are planned to solve the MINLPs that arise when including topology control into the energy system's operations: (1) Combine alternative expressions for modeling alternate current power flows; (2) Devise new decomposition algorithms and combine them with parallel computing; (3) Exploit the fact that, to improve the reliability of the power grid, one needs to solve a MINLP every few minutes; however, since consecutive problem instances are very similar to each other, one can reuse several properties of previous optimal solutions (for example, binding constraints and other properties such as transmission-line switches that have been recurrently effective).
如今,从经济、安全和社会的角度来看,满足对电力的需求是至关重要的。奇怪的是,几乎没有控制动作可用于保证电网的可靠性(即,以防止意外断电)。这部分是由于能源行业将输电网络视为静态实体。在大多数情况下,响应应急事件的纠正措施(即,组件故障)仅包括调度未使用的发电机容量和启动附加的发电单元。相反,拓扑控制是一种创新的方法,它使电网运营商能够切换每条输电线路的开/关状态。以前的研究表明,找到和实现最佳网格配置(即,最佳电网拓扑)结合最佳的相应发电机调度,可以降低能量生产/输送成本。 EARLY探索性研究奖(EAGER)支持基础研究,以增强电网的主动和被动可靠运行,而无需昂贵的基础设施投资。具体而言,本研究将表明,控制网格的拓扑结构可以提高网格的可靠性。此外,这项研究还将开发必要的程序,以找到最可靠的电网拓扑结构,以应对能源需求的变化。因此,这项研究的主要社会影响是提高预防和解决意外停电的能力,这些停电每年为美国企业和消费者造成约800亿美元的损失。该研究涉及多个学科,包括电力系统,并行计算和优化。该奖项的具体目标是提高输电网的可靠性。这将通过将拓扑控制的创新概念纳入代表性的数学程序来实现,以优化大规模电力系统的逐分钟操作。具体来说,每隔几分钟需要解决的问题是混合整数非线性问题(MINLP)。在将拓扑控制引入电力系统运行时,MINLP问题的求解主要有三个方向:(1)联合收割机交流潮流模型的替代表达式;(2)设计新的分解算法,并将其与并行计算相结合;(3)利用每隔几分钟就需要求解一次MINLP的事实,以提高电网的可靠性;然而,由于连续的问题实例彼此非常相似,因此可以重用先前最佳解决方案的若干属性(例如,绑定约束和其他属性,诸如已经循环有效的传输线开关)。

项目成果

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Erick Moreno-Centeno其他文献

Exact QR factorizations of rectangular matrices
矩形矩阵的精确 QR 分解
  • DOI:
    10.1007/s11590-024-02095-z
  • 发表时间:
    2024-02-22
  • 期刊:
  • 影响因子:
    1.100
  • 作者:
    Christopher Lourenco;Erick Moreno-Centeno
  • 通讯作者:
    Erick Moreno-Centeno

Erick Moreno-Centeno的其他文献

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{{ truncateString('Erick Moreno-Centeno', 18)}}的其他基金

Elements: Software: Roundoff-Error-Free Algorithms for Large-Scale, Sparse Systems of Linear Equations and Optimization
要素:软件:大规模稀疏线性方程系统的无舍入误差算法和优化
  • 批准号:
    1835499
  • 财政年份:
    2019
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
EAGER: Optimization without Round-off Errors
EAGER:无舍入误差的优化
  • 批准号:
    1252456
  • 财政年份:
    2012
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant

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