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The wide variety of inverter control settings for solar photovoltaics (PV) causes the accurate knowledge of these settings to be difficult to obtain in practice. This paper addresses the problem of determining inverter reactive power control settings from net load advanced metering infrastructure (AMI) data. The estimation is first cast as fitting parameterized control curves. We argue for an intuitive and practical approach to preprocess the AMI data, which exposes the setting to be extracted. We then develop a more general approach with a data-driven reactive power disaggregation algorithm, reframing the problem as a maximum likelihood estimation for the native load reactive power. These methods form the first approach for reconstructing reactive power control settings of solar PV inverters from net load data. The constrained curve fitting algorithm is tested on 701 loads with behind-the-meter (BTM) PV systems with identical control settings. The settings are accurately reconstructed with mean absolute percentage errors between 0.425% and 2.870%. The disaggregation-based approach is then tested on 451 loads with variable BTM PV control settings. Different configurations of this algorithm reconstruct the PV inverter reactive power timeseries with root mean squared errors between 0.173 and 0.198 kVAR. [ABSTRACT FROM AUTHOR]

Secondary systems comprise the localized groups of nodes located downstream of low-voltage service transformers. Models of secondary electric power distribution systems are often unknown or unavailable, motivating the use of measurements to model them. This paper studies the structure of sensitivity matrices relating changes in nodal voltage magnitudes to changes in active and reactive power injections in distribution systems with multiple radial secondary systems. In such problem settings, we propose that voltage sensitivity matrices take on a block diagonal—or localized —structure. Each sensitivity submatrix represents a localized model of the secondary system, which allows each secondary to be modeled individually. This contrasts with feeders that only model the primary network, which admits dense voltage sensitivity matrices with rapidly decreasing singular values. We propose efficient algorithms that exploit the structure of secondary distribution networks for measurement-based modeling of distribution systems. Specifically, we show that this structure can provide speed and accuracy improvements in model-free hosting capacity and state estimation algorithms. Intuitive physical explanations and simulations using a test case with multiple secondary system topologies verify the proposed matrix structure. • Block diagonally dominant structure can emerge in voltage sensitivity matrices. • This structure can emerge in distribution systems with multiple secondary circuits. • A localization metric based on eigenvalue inclusion sets can quantify this structure. • Localization can reveal system topology and invertibility of its sensitivity matrix. • Enforcing localized structure can improve data-driven distribution system analysis. [ABSTRACT FROM AUTHOR]

We propose an efficient method for the assessment of localized voltage-constrained distributed generation hosting capacity given an engineer's choice or estimate of a power factor control setpoint for each customer. The advantage of the proposed method is that it extends recent methods to perform HCA using only advanced metering infrastructure (AMI) measurements with no prior knowledge of the network or its topology. This is achieved by developing efficient decision-making algorithms based on linear algebra and optimization techniques. The performance of the method is benchmarked against conventional model-based hosting capacity analysis (HCA) techniques, and we demonstrate the success of the method on several real-world AMI datasets. We find that comparable performance is achieved to model-based methods. Calculating HC from AMI datasets has the potential to avoid modeling errors common in circuit models. • A method to assess localized hosting capacity without the network model is developed. • The method requires measurements of the voltage magnitudes and net power demands. • Reactive power control parameters are incorporated to improve physical realism. • The reactive power control parameters can be chosen or estimated. [ABSTRACT FROM AUTHOR]