Collaborative Research: Factor-Graph Approach to Monitoring and Failure Assessment in Smart-Grid Networks

协作研究：智能电网网络监控和故障评估的因子图方法

• 批准号: 1029348
• 负责人: Marija Ilic
• 金额: $ 30万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1029348&HistoricalAwards=false
• 关键词: Collaborative; Research; Factor; Graph; Approach

The proposal is motivated by the need to introduce probabilistic concepts for sensing and managing changing electric energy systems. The very nature of many new distributed resources, including responsive demand, is highly variable and hard to predict. Moreover, the system may be prone to cyber-security threats which represent low-probability high-impact events. This challenges the fundamental assumptions underlying today?s operations and planning which are by and large deterministic. In particular, a holistic stochastic formulation is needed to state the problem of sensing and communications as an integral part of supply/demand dispatch during normal conditions as well as during failures. Failures could be caused by either forced outages or intended attacks on portions of the system. The probabilities of events threatening the integrity of the system and the ability to balance supply and demand are generally dependent on system conditions and are not determined once for good. In order to begin to fill this gap, it is proposed in this project to view the changing electric power grid as an electric network with many highly variable distributed resources, micro-solar and micro-wind plants, in particular. Very many distributed loads are also varying and must be monitored; moreover, they are also responsive to the sensed and communicated information. The key idea in this proposal is to represent such future grid as a factor graph. Once this is done, it becomes possible to draw on formal communications theory methods to compute the probabilities of portions of the system being in certain states of interest. This, in turn, creates the basis for an affordable sensors and communications architecture design in support of novel operating practices. It is key to bring probabilistic reasoning into supply and demand balancing because without such proactive tracking of system state the worst-case design approach to ensuring reliable services becomes unacceptably inefficient, and, at the same time, does not provide information about the likelihood of the worst-case service scenario. The approach proposed in this project brings together the probability estimates based on the sensed and communicated information. These estimates are then used to introduce a self-dispatch by different distributed resources with minimal coordination by the system operators, as already proposed by one of the co-PIs for balancing supply and demand during normal conditions. In collaboration with the communications and security co-PIs on this project, the self-dispatch concept will be further generalized to probabilistically account for equipment failures. These failures could be either forced equipment outages, or initiated by cyber attacks on the power grid. The objective is to provide proof-of-concept illustrations of probabilistic self-dispatch facilitated by the factor-graph-enabled probability estimates about the severity of system state. The effects of sensors and communications on the overall performance will be illustrated using the grid of the Hawaiian island of Oahu as the experimental example.Intellectual merit: The fundamental intellectual novelty in this proposal is the penetration of probabilistic reasoning into the field of power-grid monitoring and control. The project will apply factor-graph and secure-belief-propagation formalisms to pave the way towards distributed monitoring, assessment, safe-guarding, control and risk management in power grids ? the goal being a proof-of-concept illustration of probabilistic self-dispatch on a portion of the power grid of the Hawaiian island of Oahu.Broader impact: The research activities in this project will lead to methods for efficient utilization of scattered renewable energy resources. This will contribute to the accelerated pace of transformation towards a clean/renewable energy economy. The co-PIs will continue to be engaged in integrating the participation of under-represented groups in engineering through research experience programs for undergraduates under the umbrella of the Native Hawaiian Science and Engineering Mentorship Program (NHSEMP).

该提案的动机是需要引入概率的概念，用于感测和管理不断变化的电能系统。许多新的分布式资源的本质，包括响应式需求，是高度可变的，难以预测。此外，该系统可能容易受到网络安全威胁，这些威胁代表低概率高影响事件。这挑战了今天的基本假设？的操作和规划，这是由和大的确定性。特别是，需要一个整体的随机制定状态的问题，传感和通信的供应/需求调度在正常情况下，以及在故障期间的一个组成部分。故障可能是由于系统的某些部分被强制中断或被有意攻击而导致的。威胁系统完整性和平衡供需能力的事件的概率通常取决于系统条件，并且不会一次确定。为了开始填补这一空白，在本项目中提出将变化中的电网视为具有许多高度可变的分布式资源的电力网络，特别是微型太阳能和微型风力发电厂。许多分布的负载也在变化，必须加以监测;此外，它们也对感测到的和传送的信息作出响应。在这个建议的关键思想是表示这种未来的网格作为一个因素图。一旦这样做了，就有可能利用正式的通信理论方法来计算系统的某些部分处于某些感兴趣状态的概率。这反过来又为支持新操作实践的负担得起的传感器和通信架构设计奠定了基础。将概率推理引入供需平衡至关重要，因为如果没有对系统状态进行这种主动跟踪，确保可靠服务的最坏情况设计方法将变得效率低下，无法接受，同时也无法提供有关最坏情况的可能性的信息服务场景。该项目提出的方法汇集了基于感知和通信信息的概率估计。然后，这些估计用于通过不同的分布式资源引入自调度，其中系统运营商的协调最小，正如已经提出的一个合作PI用于在正常条件下平衡供需。在本项目中，与通信和安全co-PI合作，将进一步推广自调度概念，以概率解释设备故障。这些故障可能是被迫的设备停机，也可能是由电网上的网络攻击引发的。我们的目标是提供概念验证说明的概率自调度促进因素图启用概率估计系统状态的严重性。传感器和通信对整体性能的影响将说明使用夏威夷的Oakland岛的网格作为experimental examples.Intellectual merit：在这个建议的基本知识的新奇性是渗透到电网监测和控制领域的概率推理。该项目将采用因素图和安全信念传播形式主义铺平道路，分布式监测，评估，安全保卫，控制和风险管理在电网？目标是一个概念验证的说明概率自我调度的一部分，夏威夷的夏威夷岛的电网。更广泛的影响：在这个项目中的研究活动将导致分散的可再生能源资源的有效利用的方法。这将有助于加快向清洁/可再生能源经济转型的步伐。co-PI将继续通过夏威夷土著科学与工程导师计划（NHSEMP）的保护伞下的本科生研究经验计划，参与整合代表性不足的群体参与工程。