This work proposes a generic method to utilize customer smart meter measurements to automatically and simultaneously estimate topology, line parameters, and customer and line phasing connections in low voltage (LV) distribution systems. This generic approach is applicable to single, two, and three-phases lines and customers. Hence, it is suitable not only for North American systems but also for European and South American systems. This generic estimation is conducted by using a multiple linear regression model applied to data supplied by customers meters. The acceptance of each estimated parameter is carried out through comparisons with mathematical (e.g., coefficient of determination and relative standard deviation) and physical constraints (e.g., resistances, line length, and conductor X/R ratios). Granularity and sensitivity analyses are also conducted taking into account smart meter data quality (e.g., update ratio, metering errors, resolution, clock desynchronization). The obtained results show the high performance of the method to correctly estimate the system topology, line parameters, and line and customers phasing based on a specification of 15-day sample size with 60-min resolution as a general compromise solution between data acquisition and accuracy.

Smart inverters provide additional control capability to help optimize the operation of distribution systems. This paper proposes a framework for exact optimal active and reactive power dispatch of distributed photovoltaic (PV) generation, switched capacitors, and tap changers in large multi-phase unbalanced distribution systems. The objectives of the optimal dispatch are minimization of the energy loss, PV active power curtailment, and capacitor and tap changer switching operations, in addition to elimination of voltage violations and reverse power flow. The optimization problem is formulated in rectangular coordinates as a nonlinear, nonconvex problem. Effective computational strategies are proposed to allow the application of predictorcorrector primal-dual interior point method to solve optimization problems in real-time with a large number of constraints and variables, including discrete variables corresponding to switched capacitors and tap changers. The accuracy of the numerical solution and the ability to implement the proposed framework are validated using the unbalanced multi-phase IEEE 34-bus and EPRI 2,998-bus distribution systems with 15-minute load and PV data. The results show a significant loss reduction and complete removal of both voltage violations and reverse power flow.

Battery energy storage systems (BESS) are a critical technology for integrating high penetration renewable power on an intelligent electrical grid. As limited energy restricts the steady-state operational state-of-charge (SoC) of storage systems, SoC forecasting models are used to determine feasible charge and discharge schedules that supply grid services. Smart grid controllers use SoC forecasts to optimize BESS schedules to make grid operation more efficient and resilient. This paper presents three advances in BESS SoC forecasting. First, two forecasting models are reformulated to be conducive to parameter optimization. Second, a new method for selecting optimal parameter values based on operational data is presented. Last, a new framework for quantifying model accuracy is developed that enables a comparison between models, systems, and parameter selection methods. The accuracies achieved by both models, on two example battery systems, with each method of parameter selection are then compared in detail. The results of this analysis suggest variation in the suitability of these models for different battery types and applications. The proposed model formulations, optimization methods and accuracy assessment framework can be used to improve the accuracy of SoC forecasts enabling better control over BESS charge/discharge schedules.

Fault location algorithms require the input of voltage and current phasors when estimating the distance to a fault. In the case of momentary faults, phasor calculations are often complicated by the presence of an exponentially decaying DC offset. Fourier filters and cosine filters, popularly used by most phasor estimation algorithms in relays, are successful in filtering out most, but not all, of the exponentially decaying DC offset. This DC offset affects the accuracy of voltage and current phasors and may result in a significant error in location estimates. Therefore, this paper presents a DC offset removal algorithm to improve the fault location estimates of momentary faults. The algorithm uses the RMS-wavelet method for fault detection and estimates voltage and current phasors using non-linear least squares methods. The proposed method uses variable window size in calculating phasors and estimates a single, but more accurate fault location than multiple locations estimated by the Fourier and cosine filters. The method is validated using simulated and actual field data. The location estimates are shown to be accurate within 5.6% in all cases.