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Recent studies have demonstrated the potential of flexible loads in providing frequency response services. However, uncertainty and variability in various weather-related and end-use behavioral factors often affect the demand-side control performance. This work addresses this problem with the design of a demand-side control to achieve frequency response under load uncertainties. Our approach involves modeling the load uncertainties via stochastic processes that appear as both multiplicative and additive to the system states in closed-loop power system dynamics. Extending the recently developed mean square exponential stability (MSES) results for stochastic systems, we formulate multi-objective linear matrix inequality (LMI)-based optimal control synthesis problems to not only guarantee stochastic stability, but also promote sparsity, enhance closed-loop transient performance, and maximize allowable uncertainties. The fundamental trade-off between the maximum allowable (\textit{critical}) uncertainty levels and the optimal stochastic stabilizing control efforts is established. Moreover, the sparse control synthesis problem is generalized to the realistic power systems scenario in which only partial-state measurements are available. Detailed numerical studies are carried out on IEEE 39-bus system to demonstrate the closed-loop stochastic stabilizing performance of the sparse controllers in enhancing frequency response under load uncertainties; as well as illustrate the fundamental trade-off between the allowable uncertainties and optimal control efforts., accepted for publication at the IEEE Transactions on Power Sysems

Contingency analysis has been an integral part of power system planning and operations. Dynamic contingency analysis is often performed with offline simulation studies, due to its intense computational effort. Due to a large number of possible system variations, covering all combinations in planning studies is very challenging. Contingencies must be chosen carefully to cover a wider group of possibilities, while ensuring system security. This paper proposes a method to classify dynamic contingencies into different clusters, according to their behavioral patterns, in particular, with respect to voltage recovery patterns. The most severe contingency from each cluster becomes the representative of other contingencies in the corresponding cluster. Using the information of contingency clusters, a new concept called dynamic voltage control area (DVCA) is derived. The concept of DVCA will address the importance of the location of dynamic reactive reserves. Simulations have been completed on the modified IEEE 162-bus system to test and validate the proposed method.

In this paper, we introduce a novel entropy-based metric to characterize the fault-induced delayed voltage recovery (FIDVR) phenomenon. In particular, we make use of Kullback-Leibler (KL) divergence to determine both the rate and the level of voltage recovery following a fault or disturbance. The computation of the entropy-based measure relies on voltage time-series data and is independent of the underlying system model used to generate the voltage time-series. The proposed measure provides quantitative information about the degree of WECC voltage performance violation for FIDVR phenomenon. The quantitative measure for violation allows one to compare the voltage responses of different buses to various contingencies and to rank order them, based on the degree of violation.

Online monitoring of rotor angle stability in wide area power systems is an important task to avoid out-of-step instability conditions. In recent years, the installation of phasor measurement units (PMUs) on the power grids has increased significantly and, therefore, a large amount of real-time data is available for online monitoring of system dynamics. This paper proposes a PMU-based application for online monitoring of rotor angle stability. A technique based on Lyapunov exponents is used to determine if a power swing leads to system instability. The relationship between rotor angle stability and maximal Lyapunov exponent (MLE) is established. A computational algorithm is developed for the calculation of MLE in an operational environment. The effectiveness of the monitoring scheme is illustrated with a three-machine system and a 200-bus system model.