By Mert Korkali
The operation of modern electric transmission systems relies increasingly on the use of advanced sensing and synchronized-measurement technologies. These technologies collect and collate a wide variety of data from disperse locations to monitor the health of network, obtain fast and accurate diagnosis of systemic changes, and detect signs of anomalies and instabilities at an early stage. In view of the far-reaching blackout risk from potential damages to critical transmission-grid assets (due to severe disturbances, physical attacks, and extreme weather events), stealthy activities that compromise the operation of wide-area sensing, communication, and control systems could cripple the entire cyber-physical smart-grid infrastructure. To this end, achieving robust event identification, ensuring bulk system observability, and developing actionable intelligence (by fostering data-driven decision making under imminent threat of complex, multifaceted attacks and disruptions) remain key challenges for sustaining a resilient power grid operation.
A power system is a wired, interconnected network that spreads over a geographically wide area. It is constantly monitored with devices that are synchronized via the global positioning system (GPS). Simultaneously recorded, time-tagged, and sampled measurements provided by such devices that are sparsely dispersed over large geographic territories can be beneficial, especially when one needs to observe and analyze dynamic phenomena in a power system. The availability of these high-resolution measurements is critical to monitoring, protection, and control of smart-grid systems. In these systems, communication networks are coupled with an electricity grid, so as to create a two-way power-communications expressway for reliable generation, delivery, and use of electricity.
Essentially, smart grids can be viewed as an outcome of the advancement in digital devices, such as phasor measurement units (PMUs), sensors, and smart meters that are being deployed across power grids. In particular, snapshots of measurements taken from a network of PMUs can be used to take the “pulse” of the power systems. They are extensively used for advanced applications, such as wide-area situational awareness and state estimation, system dynamics monitoring, fault and line-outage localization, and system model validation. While many applications relying on synchronized data have been in place, software vendors need to enhance existing power-system applications using the state-of-the-art techniques in data visualization, advanced computing platforms and algorithms, and multiscale spatial and temporal information processing to ensure grid visibility under impending power-system conditions. These applications are bound to harness cutting-edge data-analytics tools that are capable of handling the vast quantity of data available from PMUs and smart sensors.
Many of the existing models that are integral to improving the grid operator’s insight into the overall system behavior do not sufficiently capture the fast-changing system conditions in a rapidly evolving architecture of electricity grids. With the visibility gained by the system-wide deployment of novel PMUs and intelligent sensors, grid operators can largely observe system changes that have never been observed before. However, there arises a need to build artificial intelligence techniques, which monitor the behavior of the system operator and gracefully represent the decision-making process from the operator’s actual perspective. This would pave the way for dynamic visualization and intelligent control schemes, so that system operators can reliably identify the root causes and the sequence of events that unfold over time in an automated manner.
Recent major blackouts highlight the importance of real-time wide-area situational awareness tools, which are enhanced with predictive analytics capability for the assessment of potential risks and contingencies. Such a necessity is intensified by critical security incidents, e.g., physical attacks and cyber-intrusions on smart-grid infrastructure, in a constantly changing environment full of various threats. Therefore, the time required to analyze the contingencies, perform the corrective switching actions, and make a robust, well-informed decision must be much shorter than the time interval of the subsequent events. Within the evolving operational landscape of power transmission systems, security and observability are, thus, inextricably linked. Real-time analytics, monitoring, and control, if built on high-fidelity measurements and modeling, would offer tremendous benefits for understanding and mitigating the impacts of hybrid security threats and the damages they cause.
The growth in the number of grid devices and the increase in operational complexity pose scalability challenges concerning computational requirements as well as distributed system-level control and coordination. The feasibility of runtime reduction by means of the speedup gained from using parallel computation and model decomposition has been successfully demonstrated in a research setting. This is very promising for the development of robust methodologies and the scalability needed for energy management systems of future smart grids. As new measurement technologies emerge, the data-processing times are likely to be reduced from seconds to sub-seconds, so that grid monitoring and visualization efforts could be shifted to automated control systems. As the smart-grid complexity grows significantly, automated network control and network-wide coordination are expected to play a mission-critical role in managing the future grid operations.
The ever-developing smart grid brings new requirements for the measurement infrastructure of the electric grid. Smart sensing, measurement, and instrumentation technologies will form the foundation for advanced communications, computing, and control capabilities. In the near term, emergent measurement solutions will open up an opportunity for new grid applications, thanks to the enhancement in the smart-grid infrastructure. Next-generation measurement technology will likely be characterized by very high level of data granularity, compact size, low cost and power, while being supported by networked control, data communication, and computerized infrastructure. The smart grid will operate on an intelligent platform allowing for fast, distributed and adaptive sensing, communication, computation, control, and protection, as well as advanced dynamic visual analytics for the entire bulk power system in the near future. Emerging power-grid applications will necessitate the development of powerful measurement and monitoring systems, along with the adoption of new strategies to ensure data quality. With new sources of uncertainty in grid data, exponential growth of information, changing grid topology, and increasing interdependence between electric grids and communications networks, the smart grid will apparently require a wide-ranging application of big data analytics, network science, and high-performance scientific computing.
Mert Korkali, IEEE Member, is a power system research engineer at Lawrence Livermore National Laboratory in Livermore, CA. In his existing role, he conducts research related to extreme event modeling, critical infrastructure resilience, network interdiction, and optimization under uncertainty applied to power systems. Previously, he was a postdoctoral research associate at the University of Vermont. He received B.S. degrees in electrical and electronics engineering and in industrial engineering from Bahçeşehir University, Istanbul, Turkey, in 2008. He received M.S. and Ph.D. degrees in electrical engineering from Northeastern University, Boston, MA, in 2010 and 2013, respectively. His current research interests lie at the broad interface of state estimation, transient analysis, cascading failures, and defense planning for large-scale power grids. Dr. Korkali has recently been recognized as Exceptional Reviewer of IEEE Transactions on Power Delivery.