Composite Approach for the Early Detection, Assessment, and Visualization of the Risk of Instability in the Control of Smart Transmission Grids

Abstract

The present chapter tackles the problem of determining and presenting the relevant information which can assist a power system operator in assessing the risk of blackout due to instability in a timely, efficient, and effective manner. Algorithmic approaches and numerical indicators that can help identify the instant status of the degree of operating reliability are also presented. Three concepts to detect, quantify, and visualize the risk of blackout are introduced and extensively illustrated with speedometer diagrams, stability trending charts, and snapshots taken directly from supervisory control and data acquisition (SCADA) system user interfaces. IntroductionThe increase of consumption of electric energy during the past decades has led to an expansion of synchronously operated AC systems as well as to higher voltage levels. All around the world, technical and economic advantages of combined operation have brought forth the interconnection of adjoining grids. With a view to needed reduction of CO 2 emissions, there will be a significant change in utilized resources as well as the energy mix. As in the past, transmission and distribution network structures were developed in accordance with the then available generation and load structures -to address the forecasted resources and energy mix, structural and operational changes will have to be applied (Eichler et al. 2012) to move towards the smart grid of the future.Blackouts do not happen out of the blue and without prior signals of distress. Unplanned transmission outages, decaying voltages, and other events that push a power system towards an unsafe modus operandi usually develop slowly; however, when the unsafe status is reached, the events occur almost instantly within milliseconds and hence do not leave sufficient time for the operator to react. This is the

Fast Computation of the Steady-State Stability Limit

“…Updated information about the current implementation and use of Siemens Spectrum Power QuickStab is provided in the Chap. 4 of this book (Eichler et al 2014). …”

PowerFactory-Python based assessment of frequency and transient stability in power systems dominated by power electronic interfaced generation

“…The ROCOF is defined analytically as shown in (1) and for the computation of the frequency derivative, some current practice is to compute the ROCOF in two ways: the first one is with the use of the approximation taken from [10] given by (2) for qualitative assessment of the frequency performance within the time window of the system inertial response and, the second is by computing the slope of the frequency decrease in a fixed time window of 0.5s after a disturbance.…”

“…The electrical power grid is a massive and complex system with a non-linear dynamic performance, which can be excited by different types of disturbances, and can manifest in different forms of stability phenomena [1]. The future power system has in its dynamic behaviour the main challenge to properly expand with the massive inclusion of power electronic interfaced generation (PEIG) [2], which due to their output variability, and decoupling from the transmission network, impacts the overall stability of the system. Thus, motivating a revision of the approaches used in monitoring and control tasks.…”

Hybrid method for power system transient stability prediction based on two‐stage computing resources

“…In modern power systems, the occurrence of disturbances is more likely to enlarge and contribute to severe stability problems due to expanding of grid structure and increasing diversity of generation types such as wind and solar power. This challenge involved with operation requires more efficient methodologies for stability prediction [1]. Meanwhile, with the construction of smart grid, the advanced measurement, communication, and computation techniques are available for implementation of online stability prediction [2].…”