Collaborative Research: EAGER-DynamicData: Machine Intelligence for Dynamic Data-Driven Morphing of Nodal Demand in Smart Energy Systems

合作研究:EAGER-DynamicData:智能能源系统中节点需求动态数据驱动变形的机器智能

基本信息

  • 批准号:
    1462404
  • 负责人:
  • 金额:
    $ 7.33万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-09-01 至 2017-08-31
  • 项目状态:
    已结题

项目摘要

The electric power grid is the indispensable infrastructure for power delivery and distribution. It is a system of high complexity and heterogeneity comprised of a variety of interconnected systems, subsystems, generators, and loads. In addition, it is a dynamic system with evolving characteristics that suffers from several infrastructure limitations, which if not handled properly, may lead to instabilities with severe consequences including costly brownouts and blackouts. However, advancements in information and data-driven technologies offer the necessary ground for developing tools that efficiently monitor grid infrastructure and manage electricity flows in ways that achieve and maintain high performance and reliability in grid operations. Towards that end, coupling power systems with information systems converts traditional electric energy delivery infrastructures into interconnected hybrid energy-data systems called smart energy systems, where power flow is controlled via information signals. Dynamic data available in smart energy systems includes, but is not limited to, hourly user energy consumption measurements from smart meters, electricity pricing signals, system voltage readings from GPS-synchronized measuring units scattered throughout the network that can take hundreds of readings per second, and data from weather stations. Thus, due to grid complexity, a tremendous amount of information is not only generated but also transferred throughout the grid, and grid participants, such as customers, utility companies, and grid operators, are exposed to multiple heterogeneous data streams coming from various sources. In this data intensive environment, participants are being engaged to make fast real-time decisions regarding morphing of their load demand and consumption behavior patterns. Nodal load forecasting is identified as a key point for developing future smart energy systems and electricity markets. The principal theme of this research is the fast and optimal nodal load morphing in smart energy systems that takes into account big volumes of dynamically varying data.In particular, this research addresses the problem of management and processing of big data within the framework of Dynamic Data Driven Systems (DDDS) as applied to nodal load morphing. The focus of this study will be the development of a set of new intelligent and self-adaptive algorithms for online big data processing and fast real-time decision-making in smart energy infrastructures. The main feature of the current research is the integration of machine learning DDDS with dynamic optimization methods to solve the computational problem of forecasting optimal or near-optimal shapes of a load in a timely manner accounting for multiple streams of continuously incoming data and their inherent uncertainty. Emphasis will be given in handling and processing incentive signals and more particularly electricity pricing signals as a major factor in load morphing. Furthermore, extensive testing and verification of the developed algorithms will be performed on real-time simulated scenarios obtained with the GridLAB-D software simulator. In short, the proposed research for nodal load morphing will enable a new and transformative approach towards efficient, inexpensive, and fast processing of big data as applied to smart energy systems.
电网是输配电不可缺少的基础设施。它是一个高度复杂和异构的系统,由各种相互连接的系统、子系统、发电机和负载组成。此外,它是一个动态系统,具有不断发展的特征,受到一些基础设施的限制,如果处理不当,可能会导致不稳定,造成严重后果,包括代价高昂的限电和停电。然而,信息和数据驱动技术的进步为开发工具提供了必要的基础,这些工具可以有效地监控电网基础设施,并以实现和保持电网运行的高性能和可靠性的方式管理电流。为此,电力系统与信息系统的耦合将传统的电力输送基础设施转变为相互连接的混合能源数据系统,称为智能能源系统,其中电力流动通过信息信号控制。智能能源系统中可用的动态数据包括,但不限于,每小时用户能源消耗测量从智能电表,电价信号,系统电压读数从gps同步测量单元分散在整个网络,每秒可以读取数百个读数,和数据从气象站。因此,由于网格的复杂性,不仅要生成大量的信息,而且还要在整个网格中传输,并且网格参与者(如客户、公用事业公司和网格运营商)将暴露于来自各种来源的多个异构数据流。在这种数据密集型环境中,参与者需要根据其负载需求和消费行为模式的变化做出快速的实时决策。节点负荷预测被认为是发展未来智能能源系统和电力市场的关键。本研究的主要主题是考虑到大量动态变化数据的智能能源系统中的快速和最优节点负载变形。特别是,本研究解决了应用于节点负载变形的动态数据驱动系统(DDDS)框架下的大数据管理和处理问题。本研究的重点将是开发一套新的智能自适应算法,用于智能能源基础设施的在线大数据处理和快速实时决策。当前研究的主要特点是将机器学习DDDS与动态优化方法相结合,以解决考虑到连续输入的多流数据及其固有的不确定性,及时预测负载最优或近最优形状的计算问题。重点将放在处理和处理激励信号,特别是作为负荷变化主要因素的电价信号。此外,将在GridLAB-D软件模拟器获得的实时模拟场景上对开发的算法进行广泛的测试和验证。简而言之,提出的节点负载变形研究将为应用于智能能源系统的高效、廉价和快速处理大数据提供一种新的变革性方法。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Real-Time Rejection and Mitigation of Time Synchronization Attacks on the Global Positioning System
  • DOI:
    10.1109/tie.2017.2787581
  • 发表时间:
    2018-08-01
  • 期刊:
  • 影响因子:
    7.7
  • 作者:
    Khalajmehrabadi, Ali;Gatsis, Nikolaos;Taha, Ahmad F.
  • 通讯作者:
    Taha, Ahmad F.
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Nikolaos Gatsis其他文献

Model Explainable AI Method for Fault Detection in Inverter-Based Distribution Systems
基于逆变器的配电系统故障检测的模型可解释人工智能方法
Modeling and studying the impact of dynamic reactive current limiting in grid-following inverters for distribution network protection
建模并研究动态无功电流限制对并网逆变器对配电网保护的影响
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Reynaldo S. Gonzalez;V. Aryasomyajula;K. S. Ayyagari;Nikolaos Gatsis;M. Alamaniotis;Sara Ahmed
  • 通讯作者:
    Sara Ahmed
Receding Horizon Control for Drinking Water Networks: The Case for Geometric Programming
饮用水网络的后退地平线控制:几何规划案例
Power Control With Imperfect Exchanges and Applications to Spectrum Sharing
不完善交换的功率控制及其在频谱共享中的应用
Co-Optimization of Interdependent Water and Power Distribution Networks
相互依赖的供水和供电网络协同优化

Nikolaos Gatsis的其他文献

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{{ truncateString('Nikolaos Gatsis', 18)}}的其他基金

CAREER: Optimal Interdependent Operation of Electricity Distribution Grids and Water Distribution Systems in Smart Cities
职业:智慧城市中配电网和供水系统的最佳相互依存运行
  • 批准号:
    1847125
  • 财政年份:
    2019
  • 资助金额:
    $ 7.33万
  • 项目类别:
    Continuing Grant
Integrated Framework for Detection and Mitigation of GPS Spoofing Attacks in Smart Grids
智能电网中 GPS 欺骗攻击检测和缓解的集成框架
  • 批准号:
    1719043
  • 财政年份:
    2017
  • 资助金额:
    $ 7.33万
  • 项目类别:
    Standard Grant
CIF: Small: Collaborative Research: From Communication to Power Networks: Adaptive Energy Management for Power Systems with Renewables
CIF:小型:合作研究:从通信到电力网络:可再生能源电力系统的自适应能源管理
  • 批准号:
    1421583
  • 财政年份:
    2014
  • 资助金额:
    $ 7.33万
  • 项目类别:
    Standard Grant

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