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Integrated transmission-and-distribution (T&D) modeling is a new and developing method for simulating power systems. Interest in integrated T&D modeling is driven by the changes taking place in power systems worldwide that are resulting in more decentralized power systems with increasingly high levels of distributed energy resources. Additionally, the increasing role of the hitherto passive energy consumer in the management and operation of power systems requires more capable and detailed integrated T&D modeling to understand the interactions between T&D systems. Although integrated T&D modeling has not yet found widespread commercial application, its potential for changing the decades-old power system modeling approaches has led to several research efforts in the last few years that tried to (i) develop algorithms and software for steady-state and dynamic modeling of power systems and (ii) demonstrate the advantages of this modeling approach compared with traditional, separated T&D system modeling. In this paper, we provide a review of integrated T&D modeling research efforts and the methods employed for steady-state and dynamic modeling of power systems. We also discuss our current research in integrated T&D modeling and the potential directions for future research. This paper should be useful for power systems researchers and industry members because it will provide them with a critical summary of current research efforts and the potential topics where research efforts are needed to further advance and demonstrate the utility of integrated T&D modeling.

Many singular value decomposition (SVD) problems in power system computations require only a few largest singular values of a large-scale matrix for the analysis. This letter introduces two fast SVD approaches recently developed in other domains to power systems for speeding up phasor measurement unit (PMU) based online applications. The first method is a randomized SVD algorithm that accelerates computation by introducing a low-rank approximation of a given matrix through randomness. The second method is the augmented Lanczos bidiagonalization, an iterative Krylov subspace technique that computes sequences of projections of a given matrix onto low-dimensional subspaces. Both approaches are illustrated on SVD evaluation within an ambient oscillation monitoring algorithm, namely stochastic subspace identification (SSI).

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