The objective of this research is to establish a foundational framework for smart grids that enables significant penetration of renewable DERs and facilitates flexible deployments of plug-and-play applications, similar to the way users connect to the Internet. The approach is to view the overall grid management as an adaptive optimizer to iteratively solve a system-wide optimization problem, where networked sensing, control and verification carry out distributed computation tasks to achieve reliability at all levels, particularly component-level, system-level, and application level.
Intellectual merit. Under the common theme of reliability guarantees, distributed monitoring and inference algorithms will be developed to perform fault diagnosis and operate resiliently against all hazards. To attain high reliability, a trustworthy middleware will be used to shield the grid system design from the complexities of the underlying software world while providing services to grid applications through message passing and transactions. Further, selective load/generation control using Automatic Generation Control, based on multi-scale state estimation for energy supply and demand, will be carried out to guarantee that the load and generation in the system remain balanced.
Broader impact. The envisioned architecture of the smart grid is an outstanding example of the CPS technology. Built on this critical application study, this collaborative effort will pursue a CPS architecture that enables embedding intelligent computation, communication and control mechanisms into physical systems with active and reconfigurable components. Close collaborations between this team and major EMS and SCADA vendors will pave the path for technology transfer via proof-of-concept demonstrations.
The much heralded objective of deploying PMUs with global positioning system (GPS) synchronization provides unprecedented opportunities to achieve wide-area monitoring, protection and control. Given the potentially powerful capability of synchrophasor measurement at improving situational awareness, a large number of other intelligent electronic devices with phasor measurement unit (PMU) functionality, such as frequency monitoring network and frequency disturbance recorder, are rapidly being brought online. Consequently, it has become an important problem to manage and process the increasing amount of data from synchrophasors. As an illustration, China now has coverage from about 1717 PMUs; in the United States, there were about 500 PMUs installed by July 2012, and another 800 are anticipated by the end of 2014. As for the data storage concern, one phasor data concentrator collecting data from 100 PMUs of 20 measurements each at 30 Hz sampling rate generates over 50 GB data each day. With the increasing deployments of such high accuracy time-synchronized data, several fundamental questions arise regarding real-time PMU storage, data processing, and utilization: (1) What is the underlying dimensionality of the massive PMU data in wide-area power systems? (2)Will the underlying dimensionality change as system operating conditions change? (3) Can such a change of dimensionality indicate an anomaly in power system real-time operations? (4) Are there any fundamental connections between the PMU-based data-driven analytics and the physical model-based analysis of power systems? We have studied the fundamental dimensionality of synchrophasor data, and proposed an online application for early anomaly detection using the reduced dimensionality. We have analyzed the dimensionality of the phasor measurement unit (PMU) data under both normal and abnormal conditions. Our analysis suggests an extremely low underlying dimensionality despite the large dimension of the raw data. Based on this, we have developed a novel early event detection (NEED) algorithm based on the change of core subspaces at the occurrence of an event is then proposed. We have conducted numerical simulations based on both synthetic and realistic Texas and Eastern Interconnections PMU data.
