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This paper presents a distributed measurement-based method to determine distributed energy resource (DER) active- and reactive-power setpoints that minimize bus voltage deviations from prescribed references, bus active- and reactive-power deviations from desired values, as well as cost of DER outputs. The proposed method partitions the system into multiple areas and performs per-area computations in parallel, thus mitigating scalability concerns of centralized implementations. A linear sensitivity model for each area is first estimated from measurements via the recursive weighted partial least-squares algorithm. The estimated sensitivity models are then embedded in an optimization problem, the structure of which is amenable to decomposition into per-area subproblems. The subproblems are solved in parallel using consensus-based alternating direction method of multipliers to obtain optimal DER setpoints. Both the estimation and optimization tasks require only the exchange of information at boundary buses among adjacent areas. Numerical simulations involving the IEEE 33-bus distribution test system illustrate the ability of the proposed method to determine optimal DER setpoints that adapt to operating-point changes in a distributed fashion. Additional numerical simulations involving the IEEE 123-bus system demonstrate computational scalability and the application to multi-phase systems in a practical scenario. [ABSTRACT FROM AUTHOR]

This paper proposes to incorporate goodness-of-fit (GoF) metrics as outputs—in addition to the synchronized phasor (or synchrophasor)—from a phasor measurement unit (PMU) to assess the performance quality of synchrophasor-based real-time applications, with specific focus on the accuracy of fault location along a transmission line. Typically, a PMU outputs quantities that describe the phasor-of-best-fit of a time-domain voltage or current signal. While a phasor accurately represents the actual measured signal in sinusoidal steady state, this may not be the case during electrical transients that occur immediately after sudden events, such as a fault. Unaware of this, the real-time application may not locate the fault accurately. By quantifying the accuracy of the phasor as compared with the actual time-domain signal to which the phasor is fit, GoF metrics help to assess the credibility of fault-location results. This hypothesis is verified via extensive case studies, and GoF-based criteria for sufficiently accurate fault-location results are uncovered. [ABSTRACT FROM PUBLISHER]

Generalized injection shift factors (ISFs) are the sensitivities of active-power line flows to active-power bus injections. They are computed without designating an artifactual slack bus in the power network; therefore, conceptually, they reflect sensitivities of power flows more accurately than conventional ISFs, the values of which depend on the choice of the slack bus. This paper derives analytical closed-form expressions for generalized ISFs from a perturbative analysis of the ac circuit equations. In addition, they are computed from a system of linear equations that arise from high-frequency synchronized measurements collected from phasor measurement units. As an application, generalized ISFs are used to predict active-power line flows during the transient period following a contingency by leveraging inertial and governor power flows over appropriate time horizons. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a measurement-based method to compute the power flow Jacobian matrix, from which we can infer pertinent information about the system topology in near real-time. A salient feature of our approach is that it readily adapts to changes in system operating point and topology; this is desirable as it provides power system operators with a way to update, as the system evolves, the models used in many reliability analysis tools. The method uses high-speed synchronized voltage and current phasor data collected from phasor measurement units to estimate entries of the Jacobian matrix through linear total least-squares (TLS) estimation. In addition to centralized TLS-based algorithms, we provide distributed alternatives aimed at reducing computational burden. Through numerical case studies, we illustrate the effectiveness of our proposed Jacobian-matrix estimation approach as compared with the conventional model-based one. [ABSTRACT FROM PUBLISHER]

A method is proposed to detect and identify power system transmission line outages in near real-time. The method exploits the statistical properties of the small random fluctuations in electricity generation and demand that a power system is subject to as time evolves. To detect and identify transmission line outages, a linearized incremental small-signal power system model is used in conjunction with high-speed synchronized voltage phase angle measurements obtained from phasor measurement units. By monitoring the statistical properties of voltage phase angle time-series, line outages are detected and identified using techniques borrowed from the theory of quickest change detection. As illustrated through case studies, the proposed method is effective in detecting and identifying single- and double-line outages in an accurate and timely fashion. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to compute linear sensitivity distribution factors (DFs) in near real time without relying on a power flow model of the system. Specifically, we compute the injection shift factors (ISFs) of a particular line of interest with respect to active power injections at all buses (all other DFs can be determined from ISFs). The proposed ISF estimation method relies on the solution of an underdetermined system of linear equations that arise from high-frequency synchronized measurements obtained from phasor measurement units. We exploit a sparse representation (i.e., one in which many elements are zero) of the vector of desired ISFs via rearrangement by electrical distance and an appropriately chosen linear transformation, and cast the estimation problem into a sparse vector recovery problem. As we illustrate through case studies, the proposed approach provides accurate DF estimates with fewer sets of synchronized measurements than earlier approaches that rely on the solution of an overdetermined system of equations via the least-squares errors estimation method. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a method to compute linear sensitivity distribution factors (DFs) in near real-time. The method does not rely on the system power flow model. Instead, it uses only high-frequency synchronized data collected from phasor measurement units to estimate the injection shift factors through linear least-squares estimation, after which other DFs can be easily computed. Such a measurement-based approach is desirable since it is adaptive to changes in system operating point and topology. We further improve the adaptability of the proposed approach to such changes by using weighted and recursive least-squares estimation. Through numerical examples, we illustrate the advantages of our proposed DF estimation approach over the conventional model-based one in the context of contingency analysis and generation re-dispatch. [ABSTRACT FROM PUBLISHER]