Smart grids link various types of energy technologies-such as power electronics, machines, grids, and markets-via communication technology, which leads to a transdisciplinary, multidomain system. Simulation packages for assessing system integration of components typically cover only one subdomain, while simplifying the others. Cosimulation overcomes this by coupling subdomain models that are described and solved within their native environments, using specialized solvers and validated libraries. This article discusses the state of the art and conceptually describes the main challenges for simulating intelligent power systems. This article, part 1 of 2 on this subject, covers fundamental concepts. Part 2 will appear in a future issue of IEEE Electrification Magazine and cover applications.@en

Smart grids link various types of energy technologies, such as power electronics, machines, grids, and markets, via communication technology, which leads to transdisciplinary, multidomain systems. Simulation packages for assessing the system integration of components typically cover only one subdomain, while greatly simplifying the others. Cosimulation overcomes this by coupling subdomain models that are described and solved within their native environments, using specialized solvers and validated libraries. This article discusses the state of the art and conceptually describes the main challenges for simulating intelligent power systems. The article "Cosimulation of Intelligent Power Systems: Fundamentals, Software Architecture, Numerics, and Coupling," published in the March 2017 issue of this magazine [88], covered the fundamental concepts of this topic, and this follow-up article covers the applied aspects of the subject.@en

Predictive mitigation of undesired events has long been seen as a supportive complement to corrective mitigation that could relax the stringent requirements on the corrective actions and increase reliability of the overall system. This article describes one such predictive measure, i.e. the use of faster than real-time simulation in detecting faults and predicting the dynamic behavior for the resilient operation of future smart grid systems. A predictive mitigation strategy is proposed for a fault induced dynamic voltage recovery (FIDVR) event. These events, although rare, are typically addressed with under voltage load shedding schemes (UVLS) which leave significant portion of load under-supplied. We show that, by using the digital faster than real-time replica, the minimal level of UVLS can be determined on-the fly as the event develops while ensuring only the minimal amount of load shed.@en

Predictive analysis of post fault system dynamic behavior can be a vital resource for better control and reliability improvement of the overall system. This article presents methods for predictive analysis of Fault Induced Dynamic Voltage Recovery (FIDVR) event using a faster than real-time digital replica of a power system. The methods proposed include use of quick algorithms for detection of FIDVR events and metrics for predicting dynamic behavior of the power system impacted by the detected FIDVR event. We show that, using a digital faster than real-time replica, the FIDVR event can be detected in required time and that the transient voltage deviation index (TVDI) can be quickly calculated. @en

A substantial increase in renewable energy in-feed to the primary grid as well as demand growth poses a challenge for transmission system operators (TSOs) to perform maintenance activities while addressing security of supply. A computationally efficient outage scheduling algorithm which is customizable in terms of area and time selection is proposed in this paper. Benders decomposition approach under different demand and wind scenarios, spanning two-stage stochastic programming approach is used. An accurate schedule while fulfilling both maintenance and network constraints is validated on a modified IEEE RTS-24 bus system in GAMS environment. A cost comparison analysis is also performed in this study.@en

The situational grid awareness is becoming increasingly important for power system operations due to smaller operational margins, wide range of uncertainties entailed by renewables and highly critical infrastructure failures due to potential cyber-attacks. In this chapter, we look at some of the state-of-the-art technologies to monitor events in a power system under normal operating condition, followed by detection algorithms for regular business risk events, such as faults and equipment failures, and finally, we look into methods for quantifying vulnerability of under the rare and men-orchestrated cyber-attacks. First, we outline an architecture of a central piece of today’s grid awareness system, Wide Area Monitoring, Protection and Control technology. Next, we review an event detection method used to identify and record faults and failures in the grid. Finally, we present a method for vulnerability assessment of grids under cyber-attacks.@en

Faster detection of faults, can lead to better and more efficient control of power system against cascading failures triggered by the faults. This article presents a novel detection algorithm using statistical methods for the detection of events that lead to FIDVR. Furthermore we show that the algorithm is computationally efficient and quick enough to provide the control inputs to an UVLS scheme using in-feed PMU data. Thus, it is ensured that the minimal level of load to be shed is determined on-the-fly as the event develops. @en