CAREER: Transforming Distribution System Situational Awareness via Continuous-Time Adaptive Data Fusion

职业：通过连续时间自适应数据融合改变配电系统态势感知

• 批准号: 2143021
• 负责人: Yuzhang Lin
• 金额: $ 50万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143021&HistoricalAwards=false
• 关键词: CAREER; Transforming; Distribution; System; Situational

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).In the path to the clean energy future of the nation, the electric power industry is witnessing a massive shift from centralized fossil fuel generation to distributed renewable energy generation. Power distribution systems are responsible for connecting customers to the main grid and balancing the instantaneous generation-load mismatch at each customer. As power flows in the distribution systems become highly volatile and bidirectional, it is of crucial importance to gain situational awareness in real time such that grid operators can assess and enhance the renewable energy hosting capacity, and customers can reliably, resiliently, and efficiently buy/sell electricity to meet their needs. As a variety of data sources are being populated in distribution systems, the fundamental question remains how to extract and integrate the information to construct a complete picture of distribution system operation. Existing methods have not fully considered the complicated measurement environment pertaining to power distribution systems, and cannot produce accurate and reliable results when the measurements have diverse sampling rates, sampling times, accuracy classes, and with limited communication support. This project will develop transformative concepts and methodologies to comprehensively address the outstanding challenges in tracking the operating states of distribution systems. The outcomes of the project will fully bring out the potential of various sensor assets for distribution system situational awareness, which will serve as the foundation of intelligent decision making processes for accommodating the volatile but pervasive distributed renewable energy generation across the grid. The project will feature a Seeable Electrical Energy Distribution (SEED) program to integrate research with education. It will develop a simulation and visualization platform for a close-to-real synthetic distribution system “operating” in real time 24/7, providing educational experience to the wide public that has not been possible without entering control rooms of a utility company. The platform will also serve as a public data portal for researchers around the nation, facilitating data availability and research reproducibility across the whole technical community.State estimation is a key technology for enabling the situational awareness of distribution systems and massive integration of distributed renewable energy generation. The existing distribution system state estimation methods largely inherit mature concepts from state estimation of high-voltage transmission systems, and do not fully consider or address the unique complicated measurement environment in distribution systems, including the unknown continuous-time state transition model, asynchronous and multi-rate measurements, unknown and time-varying measurement error statistics, and limited sampling rates and communication bandwidth. This project will propose a revolutionary distribution system state estimation paradigm that will transform the situational awareness of distribution systems for accommodating massive and pervasive renewable energy integration and demand response. We will develop new concepts and methodologies that result in a holistic solution allowing 1) learning-based continuous-time system dynamics modeling, 2) seamless fusion of asynchronous and multi-rate measurements arriving at any continuous time instants, 3) adaptive near-optimal estimation under unknown and time-varying measurement error statistics, and 4) proactive scheduling of sensor sampling times to maximize observability and minimize communication congestion using clustering. With the continuous-time data fusion feature, the proposed paradigm will replace the conventional discrete-time step-by-step estimation paradigm and reshape the field of distribution system state estimation. In a unique Seeable Electrical Energy Distribution (SEED) program, generative adversarial network will be exploited to synthesize distributed renewable energy and load data, which cannot be distinguished from real-world data yet do not have proprietary issues and can be freely distributed and reused by the research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。在通往国家清洁能源未来的道路上，电力行业正在见证从集中式化石燃料发电到分布式可再生能源发电的巨大转变。配电系统负责将客户连接到主电网，并平衡每个客户的瞬时发电负载失配。随着配电系统中的电力流动变得高度不稳定和双向，真实的实时获得态势感知至关重要，这样电网运营商可以评估和提高可再生能源托管能力，客户可以可靠，弹性和有效地购买/出售电力以满足他们的需求。随着各种数据源被填充在配电系统中，基本问题仍然是如何提取和整合信息，以构建配电系统运行的全貌。现有的方法没有充分考虑配电系统复杂的测量环境，并且当测量具有不同的采样率、采样时间、精度等级以及有限的通信支持时，不能产生准确和可靠的结果。该项目将开发变革性的概念和方法，以全面解决跟踪配电系统运行状态的突出挑战。该项目的成果将充分发挥各种传感器资产在配电系统态势感知方面的潜力，这将成为智能决策过程的基础，以适应电网中不稳定但普遍存在的分布式可再生能源发电。该项目将采用Seeable Electric Energy Distribution（SEED）计划，将研究与教育相结合。它将为一个接近真实的综合配电系统开发一个模拟和可视化平台，该系统以真实的时间24/7“运行”，为广大公众提供教育体验，而这在不进入公用事业公司控制室的情况下是不可能的。该平台还将作为全国各地研究人员的公共数据门户，促进整个技术社区的数据可用性和研究再现性。状态估计是实现配电系统态势感知和分布式可再生能源发电大规模集成的关键技术。现有的配电系统状态估计方法在很大程度上继承了高压输电系统状态估计的成熟概念，没有充分考虑或解决配电系统特有的复杂测量环境，包括未知的连续时间状态转移模型、异步和多速率测量、未知和时变的测量误差统计以及有限的采样率和通信带宽。该项目将提出一种革命性的配电系统状态估计范式，该范式将改变配电系统的态势感知，以适应大规模和普遍的可再生能源集成和需求响应。我们将开发新的概念和方法，从而形成一个整体解决方案，允许1）基于学习的连续时间系统动态建模，2）在任何连续时刻到达的异步和多速率测量的无缝融合，3）在未知和时变测量误差统计下的自适应接近最优估计，以及4）传感器采样时间的主动调度，以使用聚类来最大化可观测性并最小化通信拥塞。该方法具有连续时间数据融合的特点，将取代传统的离散时间逐步估计方法，并将重塑配电系统状态估计领域。在一个独特的Seeable Electric Energy Distribution（SEED）计划中，将利用生成对抗网络来合成分布式可再生能源和负载数据，无法与真实的区别开来-该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持。优点和更广泛的影响审查标准。