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

• 批准号: 1462393
• 负责人: Lefteri Tsoukalas
• 金额: $ 8.67万
• 依托单位: Purdue University
• 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
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462393&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; DynamicData; Machine

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.

电网是电力输送和分配不可或缺的基础设施。它是一个高度复杂和异质性的系统，包括各种互连的系统，子系统，发电机和负载。此外，这是一个动态系统，具有不断发展的特点，受到几个基础设施的限制，如果处理不当，可能导致不稳定，造成严重后果，包括代价高昂的停电和停电。然而，信息和数据驱动技术的进步为开发有效监测电网基础设施和管理电力流动的工具提供了必要的基础，这些工具可以实现并保持电网运营的高性能和可靠性。为此，将电力系统与信息系统相结合，将传统的电能输送基础设施转换为称为智能能源系统的互连混合能源数据系统，其中通过信息信号控制电力流。智能能源系统中可用的动态数据包括但不限于智能电表每小时的用户能耗测量、电价信号、分散在整个网络中的GPS同步测量单元的系统电压读数（每秒可读取数百个读数）以及来自气象站的数据。因此，由于网格的复杂性，大量的信息不仅产生，而且还在整个网格中传输，并且网格参与者，例如客户，公用事业公司和网格运营商，暴露于来自各种源的多个异构数据流。在这种数据密集型环境中，参与者正在参与做出关于其负载需求和消耗行为模式的变形的快速实时决策。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。本研究的主题是智能能源系统中的快速和最佳的节点负荷变形，考虑到大量的动态变化的数据，特别是，本研究解决的问题，管理和处理的大数据的框架内的动态数据驱动系统（DDDS）应用于节点负荷变形。本研究的重点将是开发一套新的智能和自适应算法，用于智能能源基础设施中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，以及时解决预测负荷的最佳或接近最佳形状的计算问题，从而解决多个连续传入数据流及其固有的不确定性。重点将在处理和处理激励信号，特别是电力定价信号作为一个主要因素，在负荷变形。此外，广泛的测试和验证所开发的算法将进行实时模拟的情况下获得的GridLAB-D软件模拟器。简而言之，拟议的节点负载变形研究将为智能能源系统提供一种新的变革性方法，以实现高效，廉价和快速的大数据处理。