Publications

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Often PV hosting capacity analysis is performed for a limited number of distribution feeders. For medium - voltage distribution feeders, previous results generally analyze less than 20 feeders, and then the results are extrapolated out to similar types of feeders. Previous hosting capacity research has often focused on determining a single value for the hosting capacity for the entire feeder, whereas this research expands previous hosting capacity work to investigate all the regions of the feeder that may allow many different hosting capacity values wit h an idea called locational hosting capacity (LHC)to determine the largest PV size that can be interconnected at different locations (buses) on the study feeders. This report discusses novel methods for analyzing PV interconnections with advanced simulati on methods. The focus is feeder and location - specific impacts of PV that determine the locational PV hosting capacity. Feeder PV impact signature are used to more precisely determine the local maximum hosting capacity of individual areas of the feeder. T he feeder signature provides improved interconnection screening with certain zones that show the risk of impact to the distribution feeder from PV interconnections.

This report investigates the fault current contribution from a single large PV system and the impact it has on existing distribution overcurrent protection devices. Assumptions are made about the modeling of the PV system under fault to perform exhaustive steady - state fault analyses throughout distribution feeder models. Each PV interconnection location is tested to determine how the size of the PV system affects the fault current measured by each protection device. This data is then searched for logical conditions that indicate whether a protection device has operated in a manner that will cause more customer outages due to the addition of the PV system. This is referred to as a protection issue , and there are four unique types of issues that have been identified in the study. The PV system size at which any issues occur are recorded to determine the feeder's PV hosting capacity limitations due to interference with protection settings. The analysis is carried out on six feeder models. The report concludes with a discussion of the prevalence and cause of each protection issue caused by PV system fault current.

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This paper describes methods that a distribution engineer could use to determine advanced inverter settings to improve distribution system performance. These settings are for fixed power factor, volt-var, and volt-watt functionality. Depending on the level of detail that is desired, different methods are proposed to determine single settings applicable for all advanced inverters on a feeder or unique settings for each individual inverter. Seven distinctly different utility distribution feeders are analyzed to simulate the potential benefit in terms of hosting capacity, system losses, and reactive power attained with each method to determine the advanced inverter settings.

Smart meters and other the modern distribution measurement devices provide new and more data, but usually they are subject to longer delays and lower reliability than transmission system SCADA. Accurate and robust use of the modern distribution system measurements will be a cornerstone of the future advanced distribution management systems. This paper presents a novel and computationally efficient data processing method for imputing bad and missing load power measurements to create full power consumption data sets. The imputed data periods have a continuous profile with respect to the adjacent available measurements, which is a highly desirable feature for time-series (power flow) analyses. The method is shown to be superior in accuracy to a utility best practice approach. Our simulations use actual AMI data collected from 128 smart meters on the Georgia Tech campus.

Photovoltaic (PV) distributed energy resources (DER) have reached approximately 27 GW in the U.S., and the solar penetration rate continues to increase. This growth is expected to continue, causing challenges for grid operators who must maintain grid stability, reliability, and resiliency. To minimize adverse effects on the performance of electrical power system (EPS) with increasing levels of variable renewable generation, photovoltaic inverters must implement grid-support capabilities, allowing the DER to actively participate in grid support operations and remain connected during short-term voltage and frequency anomalies. These functions include voltage and frequency regulation features that adjust DER active and reactive power at the point of common coupling. To evaluate the risk of these functions conflicting with traditional distribution system voltage regulation equipment, researchers used several methods to quantify EPS-support function response times for autonomous voltage regulation functions (volt-var function). Based on this study, no adverse interactions between PV inverters with volt-var functions and load tap changing transformers or capacitor banks were discovered.

A central control algorithm was developed to utilize photovoltaic system advanced inverter functions, specifically fixed power factor and constant reactive power, to provide distribution system voltage regulation and to mitigate voltage regulator tap operations by using voltage measurements at the regulator. As with any centralized control strategy, the capabilities of the control require a reliable and fast communication infrastructure. These communication requirements were evaluated by varying the interval at which the controller sends dispatch commands and evaluating the effectiveness to mitigate tap operations. The control strategy was demonstrated to perform well for communication intervals faster than the delay on the voltage regulator (30 seconds). The communication reliability, latency, and bandwidth requirements were also evaluated.

Accurate distribution secondary circuit models are needed to effectively monitor and coordinate the distributed energy resources located in the secondary circuits and to enhance overall distribution system operations and planning. Accurate secondary models are also needed to fully leverage the measurement data received from smart meters and distributed energy resources at the customer premises. This paper discusses approaches for creating distribution system secondary low-voltage circuit models utilizing smart meter measurements. This paper also discusses methods to model secondary circuits when the loads and distributed energy resources are only partially metered. The presented methods are demonstrated on a real distribution secondary circuit with smart meter measurements and transformer low voltage measurements. Practical challenges related to real measurement data are discussed.

This paper presents an approach for generating simplified secondary circuit models with limited SCADA and PV micro-inverter measurement data. The proposed method is computationally efficient and can be utilized with typically available measurement data. The method is applied to models of three real U.S. utility feeders with PV micro-inverter measurements. The proposed simplified secondary circuit modeling approach decreases the PV voltage simulation errors in all the three feeders compared to using generic secondary circuit models. This paper also presents approaches for improving the feeder voltage regulating device model set points by utilizing the PV voltage measurements.

As PV penetration on the distribution system increases, there is growing concern about how much PV each feeder can handle. A total of 216 medium-voltage distributions feeders have been analyzed in detail for their individual PV hosting capacity and the locational PV hosting capacity at all the buses on the feeder. A statistical analysis is performed on the hosting capacity results in order to compare correlation with feeder load, percent of issues caused, and the variation for different feeder voltages. Due to the large number of distribution systems simulated, the analysis provides novel insights into each of these areas. Investigating the locational PV hosting capacity also expands the conventional analytical methods that study only the worst-case PV scenario.

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Efficient management and coordination of distributed energy resources with advanced automation schemes requires accurate distribution system modeling and monitoring. Big data from smart meters and photovoltaic (PV) micro-inverters can be leveraged to calibrate existing utility models. This paper presents computationally efficient distribution system parameter estimation algorithms to improve the accuracy of existing utility feeder radial secondary circuit model parameters. The method is demonstrated using a real utility feeder model with advanced metering infrastructure (AMI) and PV micro-inverters, along with alternative parameter estimation approaches that can be used to improve secondary circuit models when limited measurement data is available. The parameter estimation accuracy is demonstrated for both a three-phase test circuit with typical secondary circuit topologies and single-phase secondary circuits in a real mixed-phase test system.

The fourth solicitation of the California Solar Initiative (CSI) Research, Development, Demonstration and Deployment (RD&D) Program established by the California Public Utilities Commission (CPUC) supported the Electric Power Research Institute (EPRI), National Renewable Energy Laboratory (NREL), and Sandia National Laboratories (SNL) with data provided from Pacific Gas and Electric (PG&E), Southern California Edison (SCE), and San Diego Gas and Electric (SDG&E) conducted research to determine optimal default settings for distributed energy resource advanced inverter controls. The inverter functions studied are aligned with those developed by the California Smart Inverter Working Group (SIWG) and those being considered by the IEEE 1547 Working Group. The advanced inverter controls examined to improve the distribution system response included power factor, volt-var, and volt-watt. The advanced inverter controls examined to improve the transmission system response included frequency and voltage ride-through as well as Dynamic Voltage Support. This CSI RD&D project accomplished the task of developing methods to derive distribution focused advanced inverter control settings, selecting a diverse set of feeders to evaluate the methods through detailed analysis, and evaluating the effectiveness of each method developed. Inverter settings focused on the transmission system performance were also evaluated and verified. Based on the findings of this work, the suggested advanced inverter settings and methods to determine settings can be used to improve the accommodation of distributed energy resources (PV specifically). The voltage impact from PV can be mitigated using power factor, volt-var, or volt-watt control, while the bulk system impact can be improved with frequency/voltage ride-through.

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Operating distribution systems with a growing number of distributed energy resources requires accurate feeder models down to the point of interconnection. Many of the new resources are located in the secondary low-voltage distribution circuits that typically are not modeled or modeled with low level of detail. This paper presents a practical and computational efficient approach for estimating the secondary circuit topologies from historical voltage and power measurement data provided by smart meters and distributed energy resource sensors. The accuracy of the algorithm is demonstrated on a 66-node test circuit utilizing real AMI data. The algorithm is also utilized to estimate the secondary circuit topologies of the Georgia Tech distribution system. Challenges and practical implementation approaches of the algorithm are discussed. The paper demonstrates the computational infeasibility of exhaustive secondary circuit topology estimation approaches and presents an efficient algorithm for verifying whether two radial secondary circuits have identical topologies.

This paper examines the accuracy of clustering techniques for predicting hosting capacity. Hosting capacity results for 214 study feeders were used to predict a range of hosting capacities for an addition 7929 feeders using clustering techniques. Several methods were explored to try to improve the accuracy for predicting hosting capacity, including increasing the number of clusters, selecting variables that are highly correlated to hosting capacity for clustering, and weighting highly correlated clustering variables. The average normalized interquartile range (ANIQR) is used to compare the accuracy of several clustering methods for predicting hosting capacity.