A Datadriven Convexoptimization Method for Estimating Load Changes This paper presents an optimizationbased method to detect the occurrence, estimate the magnitude, and identify the location of load changes in the power system. The proposed method relies on measurements of only frequency at the output of synchronous generators along with a reducedorder power system dynamical model that captures locational effects of load disturbances on generator frequency dynamics. These locational aspects are retained in the estimation model by incorporating linearized powerflow balance into differential equations that describe synchronousgenerator dynamics. The sparsity structure of loadchange disturbances is leveraged so that only a limited number of measurements are needed to estimate load changes. Furthermore, a convex relaxation of the problem ensures that it can be solved online in a computationally efficient manner. Timedomain simulations involving the Western Electricity Coordinating Council 9bus test system demonstrate the accuracy of the proposed method.

Dynamic Power Network State Estimation with Asynchronous Measurements The operation of distribution networks is becoming increasingly volatile, due to fast variations of renewables and, hence, netloading conditions. To perform a reliable state estimation under these conditions, this paper considers the case where measurements from meters, phasor measurement units, and distributed energy resources are collected and processed in real time to produce estimates of the state at a fast time scale. Streams of measurements collected in real time and at heterogenous rates render the underlying processing asynchronous, and poses severe strains on workhorse state estimation algorithms. In this work, a realtime state estimation algorithm is proposed, where data are processed on the fly. Starting from a regularized leastsquares model, and leveraging appropriate linear models, the proposed scheme boils down to a linear dynamical system where the state is updated based on the previous estimate and on the measurement gathered from a few available sensors. The estimation error is shown to be always bounded under mild condition. Numerical simulations are provided to corroborate the analytical findings.