Publications

As part of the project ? Designing Resilient Communities (DRC) : A Consequence - Based Approach for Grid Investment , ? funded by the United States (US) Department of Energy?s (DOE) Grid Modernization Laboratory Consortium (GMLC), Sandia National Labora tories (Sandia) is partnering with a variety of government , industry, and university participants to develop and test a framework for community resilience planning focused on modernization of the electric grid. This report provides a summary of the section of the project focused on h ardware demonstration of ?resilience nodes? concept . Acknowledgements ? SAG members ? P roject partners ? Project team/management ? P roject sponsors ? O ther stakeholders

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.

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

This report is a summary of a 3-year LDRD project that developed novel methods to detect faults in the electric power grid dramatically faster than today’s protection systems. Accurately detecting and quickly removing electrical faults is imperative for power system resilience and national security to minimize impacts to defense critical infrastructure. The new protection schemes will improve grid stability during disturbances and allow additional integration of renewable energy technologies with low inertia and low fault currents. Signal-based fast tripping schemes were developed that use the physics of the grid and do not rely on communication to reduce cyber risks for safely removing faults.

Although there are increasing numbers of distributed energy resources (DERs) and microgrids being deployed, current IEEE and utility standards generally strictly limit their interconnection inside secondary networks. Secondary networks are low-voltage meshed (non-radial) distribution systems that create redundancy in the path from the main grid source to each load. This redundancy provides a high level of immunity to disruptions in the distribution system, and thus extremely high reliability of electric power service. There are two main types of secondary networks, called grid and spot secondary networks, both of which are used worldwide. In the future, primary networks in distribution systems that might include looped or meshed distribution systems at the primary-voltage (mediumvoltage) level may also become common as a means for improving distribution reliability and resilience. The objective of this multiyear project is to increase the adoption of microgrids in secondary networks and meshed distribution systems by developing novel protection schemes that allow for safe reliable operation of DERs in secondary networks. We will address these challenges by working with the appropriate stakeholders of secondary network operators, protection vendors, and standards committee. The outcomes of this project include: a) development and/or demonstration of candidate methods for enabling protection of secondary networks containing high levels of DER; b) development of modeling and testing tools for protection systems designed for use with secondary networks including DERs; and c) development of new industrial partnerships to facilitate widespread results dissemination and eventual commercialization of results as appropriate.

Conservation voltage reduction (CVR) is a common technique used by utilities to strategically reduce demand during peak periods. As penetration levels of distributed generation (DG) continue to rise and advanced inverter capabilities become more common, it is unclear how the effectiveness of CVR will be impacted and how CVR interacts with advanced inverter functions. In this work, we investigated the mutual impacts of CVR and DG from photovoltaic (PV) systems (with and without autonomous Volt-VAR enabled). The analysis was conducted on an actual utility dataset, including a feeder model, measurement data from smart meters and intelligent reclosers, and metadata for more than 30 CVR events triggered by the utility over the year. The installed capacity of the modeled PV systems represented 66% of peak load, but reached instantaneous penetrations reached up to 2.5x the load consumption over the year. While the objectives of CVR and autonomous Volt-VAR are opposed to one another, this study found that their interactions were mostly inconsequential since the CVR events occurred when total PV output was low.

Distributed generation (DG) sources like photovoltaic (PV) systems with advanced inverters are able to perform grid-support functions, like autonomous Volt-VAR that attempts to mitigate voltage issues by injecting or consuming reactive power. However, the Volt-VAR function operates with VAR priority, meaning real power may be curtailed to provide additional reactive power support. Since some locations on the grid may be more prone to higher voltages than others, PV systems installed at those locations may be forced to curtail more power, adversely impacting the value of that PV system. Adaptive Volt-VAR (AVV) could be implemented as an alternative, whereby the Volt-VAR reference voltage changes over time, but this functionality has not been well-explored in the literature. In this work, the potential benefits and grid impacts of AVV were investigated using yearlong quasi-static time-series (QSTS) simulations. After testing a variety of allowable AVV settings, we found that even with aggressive settings AVV resulted in <0.01% real power curtailment and significantly reduced the reactive power support required from the PV inverter compared to conventional Volt-VAR but did not provide much mitigation for extreme voltage conditions. The reactive power support provided by AVV was injected to oppose large deviations in voltage (in either direction), indicating that it could be useful for other applications like reducing voltage flicker or minimizing interactions with other voltage regulating devices.

The increasing availability of advanced metering infrastructure (AMI) data has led to significant improvements in load modeling accuracy. However, since many AMI devices were installed to facilitate billing practices, few utilities record or store reactive power demand measurements from their AMI. When reactive power measurements are unavailable, simplifying assumptions are often applied for load modeling purposes, such as applying constant power factors to the loads. The objective of this work is to quantify the impact that reactive power load modeling practices can have on distribution system analysis, with a particular focus on evaluating the behaviors of distributed photovoltaic (PV) systems with advanced inverter capabilities. Quasi-static time-series simulations were conducted after applying a variety of reactive power load modeling approaches, and the results were compared to a baseline scenario in which real and reactive power measurements were available at all customer locations on the circuit. Overall, it was observed that applying constant power factors to loads can lead to significant errors when evaluating customer voltage profiles, but that performing per-phase time-series reactive power allocation can be utilized to reduce these errors by about 6x, on average, resulting in more accurate evaluations of advanced inverter functions.

The protection systems (circuit breakers, relays, reclosers, and fuses) of the electric grid are the primary component responding to resilience events, ranging from common storms to extreme events. The protective equipment must detect and operate very quickly, generally <0.25 seconds, to remove faults in the system before the system goes unstable or additional equipment is damaged. The burden on protection systems is increasing as the complexity of the grid increases; renewable energy resources, particularly inverter-based resources (IBR) and increasing electrification all contribute to a more complex grid landscape for protection devices. In addition, there are increasing threats from natural disasters, aging infrastructure, and manmade attacks that can cause faults and disturbances in the electric grid. The challenge for the application of AI into power system protection is that events are rare and unpredictable. In order to improve the resiliency of the electric grid, AI has to be able to learn from very little data. During an extreme disaster, it may not be important that the perfect, most optimal action is taken, but AI must be guaranteed to always respond by moving the grid toward a more stable state during unseen events.

Fast detection and isolation of faults in a DC microgrid is of particular importance. Fast tripping protection (i) increases the lifetime of power electronics (PE) switches by avoiding high fault current magnitudes and (ii) enhances the controllability of PE converters. This paper proposes a traveling wave (TW) based scheme for fast tripping protection of DC microgrids. The proposed scheme utilizes a discrete wavelet transform (DWT) to calculate the high-frequency components of DC fault currents. Multiresolution analysis (MRA) using DWT is utilized to detect TW components for different frequency ranges. The Parseval energy of the MRA coefficients are then calculated to demonstrate a quantitative relationship between the fault current signal energy and coefficients’ energy. The calculated Parseval energy values are used to train a Support Vector Machine classifier to identify the fault type and a Gaussian Process regression engine to estimate the fault location on the DC cables. The proposed approach is verified by simulating two microgrid test systems in PSCAD/EMTDC.

Increased penetration of Distributed Energy Resources and microgrids have fundamentally changed the operation al characteristics of Low Voltage (LV) network systems. Current LV network protection philosophy and practice are due for a significant re vamp to keep up with changing operating conditions. This workshop invites four of the major LV network users in the US to discuss the challenges they face today and the new technologies they have been experimenting with in light of this workshop discussion, use cases for further hardware-in-the-loop testing efforts are proposed to evaluate new LV network protection solutions.

Downtown low-voltage (LV) distribution networks are generally protected with network protectors that detect faults by restricting reverse power flow out of the network. This creates protection challenges for protecting the system as new smart grid technologies and distributed generation are installed. This report summarizes well-established methods for the control and protection of LV secondary network systems and spot networks, including operating features of network relays. Some current challenges and findings are presented from interviews with three utilities, PHI PEPCO, Oncor Energy Delivery, and Consolidated Edison Company of New York. Opportunities for technical exploration are presented with an assessment of the importance or value and the difficulty or cost. Finally, this leads to some recommendations for research to improve protection in secondary networks.

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

Communication-assisted adaptive protection can improve the speed and selectivity of the protection system. However, in the event, that communication is disrupted to the relays from the centralized adaptive protection system, predicting the local relay protection settings is a viable alternative. This work evaluates the potential for machine learning to overcome these challenges by using the Prophet algorithm programmed into each relay to individually predict the time-dial (TDS) and pickup current (IPICKUP) settings. A modified IEEE 123 feeder was used to generate the data needed to train and test the Prophet algorithm to individually predict the TDS and IPICKUP settings. The models were evaluated using the mean average percentage error (MAPE) and the root mean squared error (RMSE) as metrics. The results show that the algorithms could accurately predict IPICKUP setting with an average MAPE accuracy of 99.961%, and the TDS setting with a average MAPE accuracy of 94.32% which is sufficient for protection parameter prediction.

Adaptive protection is defined as a real-time system that can modify the protective actions according to the changes in the system condition. An adaptive protection system (APS) is conventionally coordinated through a central management system located at the distribution system substation. An APS depends significantly on the communication infrastructure to monitor the latest status of the electric power grid and send appropriate settings to all of the protection relays existing in the grid. This makes an APS highly vulnerable to communication system failures (e.g., broken communication links due to natural disasters as well as wide-range cyber-attacks). To this end, this paper presents the addition of local adaptive modular protection (LAMP) units to the protection system to guarantee its reliable operation under extreme events when the operation of the APS is compromised. LAMP units operate in parallel with the conventional APS. As a backup, if APS fails to operate because of an issue in the communication system, LAMP units can accommodate a reliable fault detection and location on behalf of the protection relay. The performance of the proposed APS is verified using IEEE 123 node test system.

Interest in the application of DC Microgrids to distribution systems have been spurred by the continued rise of renewable energy resources and the dependence on DC loads. However, in comparison to AC systems, the lack of natural zero crossing in DC Microgrids makes the interruption of fault currents with fuses and circuit breakers more difficult. DC faults can cause severe damage to voltage-source converters within few milliseconds, hence, the need to quickly detect and isolate the fault. In this paper, the potential for five different Machine Learning (ML) classifiers to identify fault type and fault resistance in a DC Microgrid is explored. The ML algorithms are trained using simulated fault data recorded from a 750 VDC Microgrid modeled in PSCAD/EMTDC. The performance of the trained algorithms are tested using real fault data gathered from an operational DC Microgrid located on the Kirtland Air Force Base. Of the five ML algorithms, three could detect the fault and determine the fault type with at least 99% accuracy, and only one could estimate the fault resistance with at least 99% accuracy. By performing a self-learning monitoring and decision making analysis, protection relays equipped with ML algorithms can quickly detect and isolate faults to improve the protection operations on DC Microgrids.

Many Electric Power Systems (EPS) already include geographically dispersed photovoltaic (PV) systems. These PV systems may not be co-located with highest-priority loads and, thus, easily integrated into a microgrid; rather PV systems and priority loads may be far away from one another. Furthermore, because of the existing EPS configuration, non-critical loads between the distant PV and critical load(s) cannot be selectively disconnected. To achieve this, the proposed approach finds ideal switch locations by first defining the path between the critical load and a large PV system, then identifies all potential new switch locations along this path, and finally discovers switch locations for a particular budget by finding the ones the produce the lowest Loss of Load Probability (LOLP), which is when load exceed generation. Discovery of the switches with the lowest LOLP involves a Particle Swarm Optimization (PSO) implementation. The objective of the PSO is to minimize the microgird’s LOLP. The approach assumes dynamic microgrid operations, where both the critical and non-critical loads are powered during the day and only the critical load at night. To evaluate the approach, this paper includes a case study that uses the topology and Advanced Metering Infrastructure (AMI) data from an actual EPS. For this example, the assessment found new switch locations that reduced the LOLP by up to 50% for two distant PV location scenarios.

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High penetration of solar photovoltaics can have a significant impact on the power flows and voltages in distribution systems. In order to support distribution grid planning, control and optimization, it is imperative for utilities to maintain an accurate database of the locations and sizes of PV systems. This paper extends previous work on methods to estimate the location of PV systems based on knowledge of the distribution network model and availability of voltage magnitude measurement streams. The proposed method leverages the expected impact of solar injection variations on the circuit voltage and takes into account the operation and impact of changes in voltage due to discrete voltage regulation equipment (VRE). The estimation model enables determining the most likely location of PV systems, as well as voltage regulator tap and switching capacitors state changes. The method has been tested for individual and multiple PV system, using the Chi-Square test as a metric to evaluate the goodness of fit. Simulations on the IEEE 13-bus and IEEE 123-bus distribution feeders demonstrate the ability of the method to provide consistent estimations of PV locations as well as VRE actions.

Renewable energy has become a viable solution for reducing the harmful effects that fossil fuels have on our environment, prompting utilities to replace traditional synchronous generators (SG) with more inverter-based devices that can provide clean energy. One of the biggest challenges utilities are facing is that by replacing SG, there is a reduction in the systems' mechanical inertia, making them vulnerable to frequency instability. Grid-forming inverters (GFMI) have the ability to create and regulate their own voltage reference in a manner that helps stabilize system frequency. As an emerging technology, there is a need for understanding their dynamic behavior when subjected to abrupt changes. This paper evaluates the performance of a GFMI when subjected to voltage phase jump conditions. Experimental results are presented for the GFMI subjected to both balanced and unbalanced voltage phase jump events in both P/Q and V/f modes.

Recent trends in PV economics and advanced inverter functionalities have contributed to the rapid growth in PV adoption; PV modules have gotten much cheaper and advanced inverters can deliver a range of services in support of grid operations. However, these phenomena also provide conditions for PV curtailment, where high penetrations of distributed PV often necessitate the use of advanced inverter functions with VAR priority to address abnormal grid conditions like over- and under-voltages. This paper presents a detailed energy loss analysis, using a combination of open-source PV modeling tools and high-resolution time-series simulations, to place the magnitude of clipped and curtailed PV energy in context with other operational sources of PV energy loss. The simulations were conducted on a realistic distribution circuit, modified to include utility load data and 341 modeled PV systems at 25% of the customer locations. The results revealed that the magnitude of clipping losses often overshadows that of curtailment but, on average, both were among the lowest contributors to total annual PV energy loss. However, combined clipping and curtailment loss are likely to become more prevalent as recent trends continue.

High penetration of distributed energy resources presents challenges for monitoring and control of power distribution systems. Some of these problems might be solved through accurate monitoring of distribution systems, such as what can be achieved with distribution system state estimation (DSSE). With the recent large-scale deployment of advanced metering infrastructure associated with existing SCADA measurements, DSSE may become a reality in many utilities. In this paper, we present a sensitivity analysis of DSSE with respect to phase mislabeling of single-phase service transformers, another class of errors distribution system operators are faced with regularly. The results show DSSE is more robust to phase label errors than a power flow-based technique, which would allow distribution engineers to more accurately capture the impacts and benefits of distributed PV.

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This article is the first in a two-part series on the influence of inverter-based resources (IBRs) s on microgrid protection. In part one, the focus is on microgrids deployed on radial circuits. This article discusses some of the challenges related to the protection of IBR-based microgrids and presents some ongoing research and solutions in the area. The different controls for IBRs are discussed to present how their short current signatures and dynamic response under faults impact microgrid protection. Recently, microgrids have gained much attention in the electric power industry due to their capability for improving power system reliability and resiliency, their impact on increasing the use of renewable resources, the reduced cost of distributed energy resource (DER) equipment, and the continuing evolution of applicable codes and standards.

The integration of renewable and distributed energy resources to the electric power system is expected to increase, particularly at the distribution level. As a consequence, the grid will become more modular consisting of many interconnected microgrids. These microgrids will likely evolve from existing distribution feeders and hence be unbalanced in nature. As the world moves towards cleaner and distributed generation, microgrids that are 100% inverter sourced will become more commonplace. To increase resiliency and reliability, these microgrids will need to operate in both grid-connected and islanded modes. Protection and control of these microgrids needs to be studied in real-time to test and validate possible solutions with hardware-in-the-loop (HIL) and real communication delays. This paper describes the creation of a real-time microgrid test bed based on the IEEE 13-bus distribution system using the RTDS platform. The inverter models with grid-forming and grid-following control schemes are discussed. Results highlighting stable operation, power sharing, and fault response are shown.

The goal of this paper is to utilize machine learning (ML) techniques for estimating the distribution circuit topology in an adaptive protection system. In a reconfigurable distribution system with multiple tie lines, the adaptive protection system requires knowledge of the existing circuit topology to adapt the correct settings for the relay. Relays rely on the communication system to identify the latest status of remote breakers and tie lines. However, in the case of communication system failure, the performance of adaptive protection system can be significantly impacted. To tackle this challenge, the remote circuit breakers and tie lines' status are estimated locally at a relay to identify the circuit topology in a reconfigurable distribution system. This paper utilizes Support Vector Machine (SVM) to forecast the status of remote circuit breakers and identify the circuit topology. The effectiveness of proposed approach is verified on two sample test systems.

Distribution system model accuracy is increasingly important and using advanced metering infrastructure (AMI) data to algorithmically identify and correct errors can dramatically reduce the time required to correct errors in the models. This work proposes a data-driven, physics-based approach for grouping residential meters downstream of the same service transformer. The proposed method involves a two-stage approach that first uses correlation coefficient analysis to identify transformers with errors in their customer grouping then applies a second stage, using a linear regression formulation, to correct the errors. This method achieved >99% accuracy in transformer groupings, demonstrated using EPRI's Ckt 5 model containing 1379 customers and 591 transformers.

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Topology identification in transmission systems has historically been accomplished using SCADA measurements. In distribution systems, however, SCADA measurements are insufficient to determine system topology. An accurate system topology is essential for distribution system monitoring and operation. Recently there has been a proliferation of Advanced Metering Infrastructure (AMI) by the electrical utilities, which improved the visibility into distribution systems. These measurements offer a unique capability for Distribution System Topology Identification (DSTI). A novel approach to DSTI is presented in this paper which utilizes the voltage magnitudes collected by distribution grid sensors to facilitate identification of the topology of the distribution network in real-time using Linear Discriminant Analysis (LDA) and Regularized Diagonal Quadratic Discriminant Analysis (RDQDA). The results show that this method can leverage noisy voltage magnitude readings from load buses to accurately identify distribution system reconfiguration between radial topologies during operation under changing loads.

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The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modeling and impact analysis. Unlike conventional scenario-based studies, quasi-static time-series (QSTS) simulations can realistically model time-dependent voltage controllers and the diversity of potential impacts that can occur at different times of year. However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1-second resolution is often required, which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled. This computational burden is a clear limitation to the adoption of QSTS simulations in interconnection studies and for determining optimal control solutions for utility operations. The solutions we developed include accurate and computationally efficient QSTS methods that could be implemented in existing open-source and commercial software used by utilities and the development of methods to create high-resolution proxy data sets. This project demonstrated multiple pathways for speeding up the QSTS computation using new and innovative methods for advanced time-series analysis, faster power flow solvers, parallel processing of power flow solutions and circuit reduction. The target performance level for this project was achieved with year-long high-resolution time series solutions run in less than 5 minutes within an acceptable error.

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This paper discusses the findings from an EPRI industry survey mapping the state, current practices, and future needs of distribution load modeling in the U.S. and internationally. The paper provides a benchmark for distribution utilities and a view of the current industry state and future needs for researchers and other readers. The survey found the parameters and measurements available and utilized for load modeling to vary widely between the utilities and data types. Loads were found to be largely modeled based on different load allocation methods. While distribution planning was found to focus on assessing peak load conditions, some utilities evaluate other time instances and/or explore time-series assessments. Simple grid edge and voltage sensitivity models were found common. The identified future needs include access for additional data, as well as methods to process and utilize the increasing data, handle masked load, and perform time-series load modeling.

The recent growth of sensing devices on the distribution system, such as smart meter deployment, has enabled a wide variety of data-driven distribution system model calibration algorithms. A challenge associated with developing algorithms for model calibration tasks is the determination of parameters for a particular algorithm. This work proposes a method for parameter selection utilizing silhouette score analysis that allows these parameters to be tuned on a per-feeder basis. This method leverages cluster analysis and the distance matrices often produced by phase identification methods. The proposed method was tested on 5 feeders from 2 different utilities to select the number of clusters used in a spectral clustering phase identification algorithm. A synthetic dataset was then used to validate the method with the phase identification algorithm performing with 100% accuracy.

This paper presents a new method for detecting power quality disturbances, such as faults. The method is based on the dynamic mode decomposition (DMD)-a data-driven method to estimate linear dynamics whose eigenvalues and eigenvectors approximate those of the Koopman operator. The proposed method uses the real part of the main eigenvalue estimated by the DMD as the key indicator that a power quality event has occurred. The paper shows how the proposed method can be used to detect events using current and voltage signals to distinguish different faults. Because the proposed method is window-based, the effect that the window size has on the performance of the approach is analyzed. In addition, a study on the effect that noise has on the proposed approach is presented.

As a result of the increase in penetration of inverter-based generation such as wind and solar, the dynamics of the grid are being modified. These modifications may threaten the stability of the power system since the dynamics of these devices are completely different from those of rotating generators. Protection schemes need to evolve with the changes in the grid to successfully deliver their objectives of maintaining safe and reliable grid operations. This paper explores the theory of traveling waves and how they can be used to enable fast protection mechanisms. It surveys a list of signal processing methods to extract information on power system signals following a disturbance. The paper also presents a literature review of traveling wave-based protection methods at the transmission and distribution levels of the grid and for AC and DC configurations. The paper then discusses simulations tools to help design and implement protection schemes. A discussion of the anticipated evolution of protection mechanisms with the challenges facing the grid is also presented.

Advanced solar PV inverter control settings may not be reported to utilities or may be changed without notice. This paper develops an estimation method for determining a fixed power factor control setting of a behind-the-meter (BTM) solar PV smart inverter. The estimation is achieved using linear regression methods with historical net load advanced metering infrastructure (AMI) data. Notably, the BTM PV power factor setting may be unknown or uncertain to a distribution engineer, and cannot be trivially estimated from the historical AMI data due to the influence of the native load on the measurements. To solve this, we use a simple percentile-based approach for filtering the measurements. A physics-based linear sensitivity model is then used to determine the fixed power factor control setting from the sensitivity in the complex power plane. This sensitivity parameter characterizes the control setting hidden in the aggregate data. We compare several loss functions, and verify the models developed by conducting experiments on 250 datasets based on real smart meter data. The data are augmented with synthetic quasi-static-timeseries (QSTS) simulations of BTM PV that simulate utility-observed aggregate measurements at the load. The simulations demonstrate the reactive power sensitivity of a BTM PV smart inverter can be recovered efficiently from the net load data after applying the filtering approach.

Due to the existence of DC-DC converters, fast-tripping fault location in DC power systems is of particular importance to ensure the reliable operation of DC systems. Traveling wave (TW) protection is one of the promising approaches to accommodate fast detection and location of faults in DC systems. This paper proposes a numerical approach for a DC system fault location using the concept of TWs. The proposed approach is based on multiresolution analysis to calculate the TW signal's wavelet coefficients for different frequency ranges, and then, the Parseval theorem is used to calculate the energy of wavelet coefficients. A curve-fitting approach is used to find the best curve that fits the Parseval energy as a function of fault location for a set of curve-fitting datapoints. The identified Parseval energy curves are then utilized to estimate the fault location when a new fault is applied on a DC cable. A DC test system simulated in PSCAD/EMTDC is used to verify the performance of the proposed fault location algorithm.

This paper discusses a solar photovoltaic (PV) DC microgrid system consisting of a PV array, a battery, DC-DC converters, and a load, where all these elements are simulated in MATLAB/Simulink environment. The design and testing entail the functions of a boost converter and a bidirectional converter and how they work together to maintain stable control of the DC bus voltage and its energy management. Furthermore, the boost converter operates under Maximum Power Point Tracking (MPPT) settings to maximize the power that the PV array can output. The control algorithm can successfully maintain the output power of the PV array at its maximum point and can respond well to changes in input irradiance. This is shown in detail in the results section.

Distribution systems with high levels of solar PV may experience notable changes due to external conditions, such as temperature or solar irradiation. Fault detection methods must be developed in order to support these changes of conditions. This paper develops a method for fast detection, location, and classification of faults in a system with a high level of solar PV. The method uses the Continuous Wavelet Transform (CWT) technique to detect the traveling waves produced by fault events. The CWT coefficients of the current waveform at the traveling wave arrival time provide a fingerprint that is characteristic of each fault type and location. Two Convolutional Neural Networks are trained to classify any new fault event. The method relays of several protection devices and doesn't require communication between them. The results show that for multiple fault scenarios and solar PV conditions, high accuracy for both location and type classification can be obtained.

The rising penetration levels of photovoltaic (PV) systems within distribution networks has driven considerable interest in the implementation of advanced inverter functions, like autonomous Volt- Var, to provide grid support in response to adverse conditions. Quasi-static time-series (QSTS) analyses are increasingly being utilized to evaluate advanced inverter functions on their potential benefits to the grid and to quantify the magnitude of PV power curtailment they may induce. However, these analyses require additional modeling efforts to appropriately capture the time-varying behavior of circuit elements like loads and PV systems. The contribution of this paper is to study QSTS-based curtailment evaluations with different load allocation and PV modeling practices under a variety of assumptions and data limitations. A total of 24 combinations of PV and load modeling scenarios were tested on a realistic test circuit with 1,379 loads and 701 PV systems. The results revealed that the average annual curtailment varied from the baseline value of 0.47% by an absolute difference of +0.55% to -0.43 % based on the modeling scenario.

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Networked microgrids are clusters of geographically-close, islanded microgrids that can function as a single, aggregate island. This flexibility enables customer-level resilience and reliability improvements during extreme event outages and also reduces utility costs during normal grid operations. To achieve this cohesive operation, microgrid controllers and external connections (including advanced communication protocols, protocol translators, and/or internet connection) are needed. However, these advancements also increase the vulnerability landscape of networked microgrids, and significant consequences could arise during networked operation, increasing cascading impact. To address these issues, this report seeks to understand the unique components, functions, and communications within networked microgrids and what cybersecurity solutions can be implemented and what solutions need to be developed. A literature review of microgrid cybersecurity research is provided and a gap analysis of what is additionally needed for securing networked microgrids is performed. Relevant cyber hygiene and best practices to implement are provided, as well as ideas on how cybersecurity can be integrated into networked microgrid design. Lastly, future directions of networked microgrid cybersecurity R&D are provided to inform next steps.

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Distributed photovoltaic (PV) systems equipped with advanced inverters can control real and reactive power output based on grid and atmospheric conditions. The Volt-Var control method allows inverters to regulate local grid voltages by producing or consuming reactive power. Based on their power ratings, the inverters may need to curtail real power to meet the reactive power requirements, which decreases their total energy production. To evaluate the expected curtailment associated with Volt-Var control, yearlong quasi-static time-series (QSTS) simulations were conducted on a realistic distribution feeder under a variety of PV system design considerations. Overall, this paper found that the amount of curtailed energy is low (< 0.55%) compared to the total PV energy production in a year but is affected by several PV system design considerations.

Distribution system models play a critical role in the modern grid, driving distributed energy resource integration through hosting capacity analysis and providing insight into critical areas of interest such as grid resilience and stability. Thus, the ability to validate and improve existing distribution system models is also critical. This work presents a method for identifying service transformers which contain errors in specifying the customers connected to the low-voltage side of that transformer. Pairwise correlation coefficients of the smart meter voltage time series are used to detect when a customer is not in the transformer grouping that is specified in the model. The proposed method is demonstrated both on synthetic data as well as a real utility feeder, and it successfully identifies errors in the transformer labeling in both datasets.

Calibrating distribution system models to aid in the accuracy of simulations such as hosting capacity analysis is increasingly important in the pursuit of the goal of integrating more distributed energy resources. The recent availability of smart meter data is enabling the use of machine learning tools to automatically achieve model calibration tasks. This research focuses on applying machine learning to the phase identification task, using a co-association matrix-based, ensemble spectral clustering approach. The proposed method leverages voltage time series from smart meters and does not require existing or accurate phase labels. This work demonstrates the success of the proposed method on both synthetic and real data, surpassing the accuracy of other phase identification research.

This paper proposes an optimal relay placement approach for microgrids. The proposed approach considers both grid-connected and islanded microgrid modes. The algorithm separately calculates the System Average Interruption Frequency Index (SAIFI) of a microgrid in each operating mode. Then, two weighting factors corresponding to different operating modes are used to calculate the overall SAIFI of the microgrid. The objective is to find the optimal relay locations such that the microgrid overall SAIFI is minimized. The power electronics interfaces associated with distributed energy resources may be classified as grid following or grid forming. As opposed to grid-following distributed energy resources (DERs) such as typical solar inverters, grid-forming inverters are able to control the microgrid voltage and frequency at the point of their interconnection. Therefore, these DERs can facilitate the formation of sub-islands in the microgrid when the protective relays isolate a portion of the microgrid. If there is at least one grid-forming DER available in a sub-island, that sub-island can continue supplying its local load. The exchange market algorithm (EMA) is used for optimizing functions. The effectiveness of the proposed optimal relay placement approach is verified using an 18-bus microgrid and IEEE 123-bus test system.

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This report presents a novel fault detection, characterization, and fault current control algorithm for a standalone solar-photovoltaic (PV) based dc microgrids. The protection scheme is based on the current derivative algorithm. The overcurrent and current directional/differential comparison based protection schemes are incorporated for the dc microgrid fault characterization. For a low impedance fault, the fault current is controlled based on the current/voltage thresholds and current direction. Generally, the droop method is used to control the power-sharing between the converters by controlling the reference voltage. In this article, an adaptive droop scheme is also proposed to control the fault current by calculating a virtual resistance R droop , and to control the converter output reference voltage. For a high impedance fault, differential comparison method is used to characterize the fault. These algorithms effectively control the converter pulsewidth and reduce the flow of source current from a particular converter, which helps to increase the fault clearing time. Additionally, a trip signal is sent to the corresponding dc circuit breaker (DCCB), to isolate the faulted converter, feeder or a dc bus. The dc microgrid protection design procedure is detailed, and the performance of the proposed method is verified by simulation analysis.

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Distribution and transmission protection systems are considered vital parts of modern smart grid ecosystems due to their ability to isolate faulted segments and preserve the operation of critical loads. Current protection schemes increasingly utilize cognitive methods to proactively modify their actions according to extreme power system changes. However, the effectiveness and robustness of these information-driven solutions rely entirely on the integrity, authenticity, and confidentiality of the data and control signals exchanged on the underlying relay communication networks. In this paper, we outline a scalable adaptive protection platform for distribution systems, and introduce a novel blockchain-based distributed network architecture to enhance data exchange security among the smart grid protection relays. The proposed mechanism utilizes a tiered blockchain architecture to counter the current technology limitations providing low latency with better scalability. The decentralized nature removes singular points of failure or contamination, enabling direct secure communication between smart grid relays. We also present a security analysis that demonstrates how the proposed framework prohibits any alterations on the blockchain ledger providing integrity and authenticity of the exchanged data (e.g., realtime measurements/relay settings). Finally, the performance of the proposed approach is evaluated through simulation on a blockchain benchmarking framework with the results demonstrating a promising solution for secure smart grid protection system communication.

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The energy grid becomes more complex with increasing penetration of renewable resources, distributed energy storage, distributed generators, and more diverse loads such as electric vehicle charging stations. The presence of distributed energy resources (DERs) requires directional protection due to the added potential for energy to flow in both directions down the line. Additionally, contingency requirements for critical loads within a microgrid may result in looped or meshed systems. Computation speeds of iterative methods required to coordinate loops are improved by starting with a minimum breakpoint set (MBPS) of relays. A breakpoint set (BPS) is a set of breakers such that, when opened, breaks all loops in a mesh grid creating a radial system. A MBPS is a BPS that consists of the minimum possible number of relays required to accomplish this goal. In this paper, a method is proposed in which a minimum spanning tree is computed to indirectly break all loops in the system, and a set difference is used to identify the MBPS. The proposed method is found to minimize the cardinality of the BPS to achieve a MBPS.

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Timeseries power and voltage data recorded by electricity smart meters in the US have been shown to provide immense value to utilities when coupled with advanced analytics. However, Advanced Metering Infrastructure (AMI) has diverse characteristics depending on the utility implementing the meters. Currently, there are no specific guidelines for the parameters of data collection, such as measurement interval, that are considered optimal, and this continues to be an active area of research. This paper aims to review different grid edge, delay tolerant algorithms using AMI data and to identify the minimum granularity and type of data required to apply these algorithms to improve distribution system models. The primary focus of this report is on distribution system secondary circuit topology and parameter estimation (DSPE).

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Accurate distribution secondary low-voltage circuit models are needed to enhance overall distribution system operations and planning, including effective monitoring and coordination of distributed energy resources located in the secondary circuits. We present a full-scale demonstration across three real feeders of a computationally efficient approach for estimating the secondary circuit topologies and parameters using historical voltage and power measurements provided by smart meters. The method is validated against several secondary configurations, and compares favorably to satellite imagery and the utility secondary model. Feeder-wide results show how much parameters can vary from simple assumptions. Model sensitivities are tested, demonstrating only modest amounts of data and resolutions of data measurements are needed for accurate parameter and topology results.

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Power outages are a challenge that utility companies must face, with the potential to affect millions of customers and cost billions in damage. For this reason, there is a need for developing approaches that help understand the effects of fault conditions on the power grid. In distribution circuits with high renewable penetrations, the fault currents from DER equipment can impact coordinated protection scheme implementations so it is critical to accurately analyze fault contributions from DER systems. To do this, MATLAB/Simulink/RT-Labs was used to simulate the reduced-order distribution system and three different faults are applied at three different bus locations in the distribution system. The use of Real-Time (RT) Power Hardware-in-the-Loop (PHIL) simulations was also used to further improve the fidelity of the model. A comparison between OpenDSS simulation results and the Opal-RT experimental fault currents was conducted to determine the steady-state and dynamic accuracy of each method as well as the response of using simulated and hardware PV inverters. It was found that all methods were closely correlated in steady-state, but the transient response of the inverter was difficult to capture with a PV model and the physical device behavior could not be represented completely without incorporating it through PHIL.

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Successful system protection is critical to the performance of the DC microgrid system. This paper proposes an adaptive droop based fault current control for a standalone low voltage (LV) solar-photovoltaic (PV) based DC microgrid protection. In the proposed method, a DC microgrid fault is detected by the current and voltage thresholds. Generally, the droop method is used to control the power sharing between the converters by controlling the reference voltage. In this paper, this scheme is extended to control the fault current by calculating an adaptive virtual resistance Rdroop, and to control the converter output reference voltage. This effectively controls the converter pulse width, and reduces the flow of source current from a particular converter which helps to increase the fault clearing time. Additionally, a trip signal is sent to the corresponding DC circuit breaker (DCCB), to isolate the faulted converter, feeder or a DC bus. The design procedure is detailed, and the effectiveness of proposed method is verified by simulation analysis.

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Smart grid technologies and wide-spread installation of advanced metering infrastructure (AMI) equipment present new opportunities for the use of machine learning algorithms paired with big data to improve distribution system models. Accurate models are critical in the continuing integration of distributed energy resources (DER) into the power grid, however the low-voltage models often contain significant errors. This paper proposes a novel spectral clustering approach for validating and correcting customer electrical phase labels in existing utility models using the voltage timeseries produced by AMI equipment. Spectral clustering is used in conjunction with a sliding window ensemble to improve the accuracy and scalability of the algorithm for large datasets. The proposed algorithm is tested using real data to validate or correct over 99% of customer phase labels within the primary feeder under consideration. This is over a 94% reduction in error given the 9% of customers predicted to have incorrect phase labels.

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Quasi-static time-series (QSTS) simulation provides an accurate method to determine the impact that new PV interconnections including control strategies would have on a distribution feeder. However, the QSTS computational time currently makes it impractical for use by the industry. A vector quantization approach [1- 2] leverages similarities in power flow solutions to avoid re-computing identical power flows resulting in significant time reduction. While previous work arbitrarily quantized similar power flow scenarios, this paper proposes a novel circuit-specific quantization algorithm to balance speed and accuracy. This sensitivity-based method effectively quantizes the power flow scenarios prior to running the quantized QSTS simulation. The results show vast computational time reduction while maintaining specified bounds for the error.

Fast deployment of renewable energy resources in distribution networks, especially solar photovoltaic (PV) systems, have motivated the need for inverter-based voltage regulation. Integration studies are often necessary to fully understand the potential impacts of PV inverter settings on the various elements of the distribution system, including voltage regulators and capacitor banks. A year long quasi-static time series (QSTS) at second-level granularity provides a comprehensive assessment of these impacts, however the computational burden associated with running QSTS limits its applicability. This paper proposes a fast QSTS simulation technique capable of modeling the smart inverter dynamic VAR control functionality and accurately estimating the states of controllable elements including voltage regulators and capacitor banks at each time step. Consequently, the complex interactions between various legacy voltage regulation devices is also captured. The efficacy of the proposed algorithm is demonstrated on the IEEE 13-bus test case with a 98% reduction in computation time.

The increasing penetration of inverter-interfaced resources underscores the need of valid and accurate pv-inverter models for short circuit studies and for the design of proper protection schemes. This paper presents comparison and validation of several inverter models' dynamics under fault scenarios to two commercial inverters using a Power Hardware-in-the-Loop (PHIL) testbed. Nowadays, IEEE1574 compliant inverters with anti-islanding will contribute for several cycles (1.1 p.u.) before they disconnect. As the inverter standards move towards low voltage ride-through (LVRT) capabilities to counteract remote faults, the accurate modeling of inverters using this feature becomes extremely important. One of the purposes of this paper is to compare the dynamic behavior of different inverter models with LVRT capabilities against two commercial inverters with the aid of PHIL simulation environments. Comparisons were made under different fault scenarios using the IEEE 13 node feeder as testing grid. The other purpose is to raise awareness amongst inverter manufacturers on providing accurate and comprehensive inverter simulation models that account for the protection engineers necessities.

As PV penetration on the distribution system increases, there is growing concern about how much PV each feeder can handle. A total of 14 medium-voltage distributions feeders from two utilities have been analyzed in detail for their individual PV hosting capacity and the locational PV hosting capacity at all the buses on the feeder. This paper discusses methods for analyzing PV interconnections with advanced simulation methods to study feeder and location-specific impacts of PV to determine the locational PV hosting capacity and optimal siting of PV. Investigating the locational PV hosting capacity expands the conventional analytical methods that study only the worst-case PV scenario. Previous methods are also extended to include single-phase PV systems, especially focusing on long single-phase laterals. Finally, the benefits of smart inverters with volt-var is analyzed to demonstrate the improvements in hosting capacity.

Distribution system analysis with high penetrations of distributed PV require quasi-static time-series (QSTS) analysis to model the variability introduced on the distribution system, but current QSTS algorithms are prohibitively burdensome and computationally intensive. This paper proposes a variable timestep algorithm to calculate the critical time periods when QSTS simulations should be solved at higher or lower time-resolution and to backtrack for any critical periods that were missed. This variable time-step solver is a new method of performing timeseries simulations with high accuracy while performing the simulation more than 50 times faster. The scalability of the algorithm is demonstrated using a real utility distribution system model with thousands of buses.

The proliferation of photovoltaic (PV) distributed energy resources (DER) on distribution systems have caused concerns about electric power system (EPS) protection schemes, protection configurations, and device coordination. With the EPS designed for power to flow in one direction, the high penetration of PV-based DER has created concerns of grid reliability and protection scheme efficacy. The short-circuit current characteristics of the classical synchronous generator has been well characterized for symmetrical or unsymmetrical short circuit faults, but inverter-based DER dynamic models are not as wellknown and are generally specific to a single inverter manufacturer. There is also uncertainty in how advanced inverter controls like volt-var and low-voltage ride-through capabilities can impact the inverter fault currents. This paper performs laboratory tests to quantify the fault currents of single-phase, three-phase, and grid-forming inverters under a range of gridsupport function operating modes. The results characterize the PV DER sub-transient, transient, and steady-state equivalents. It was found that grid-support functions affect the current contribution from PV inverters.

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Successful system protection is critical to the feasibility of the DC microgrid system. This work focused on identifying the types of faults, challenges of protection, different fault detection schemes, and devices pertinent to DC microgrid systems. One of the main challenges of DC microgrid protection is the lack of guidelines and standards. The various parameters that improve the design of protection schemes were identified and discussed. Due to the absence of physical inertia, the resistive nature of the line impedance affects fault clearing time and system stability during faults. Therefore, the effectiveness of protection coordination systems with communication were also explored. A detailed literature review was done to identify possible grounding schemes and protection devices needed to ensure seamless power flow of grid-connected DC microgrids. Ultimately, it was identified that more analyses and experimentation are needed to develop optimized fault detection schemes with reduced fault clearing time.

High-resolution, quasi-static time series (QSTS) simulations are essential for modeling modern distribution systems with high-penetration of distributed energy resources (DER) in order to accurately simulate the time-dependent aspects of the system. Presently, QSTS simulations are too computationally intensive for widespread industry adoption. This paper proposes to simulate a portion of the year with QSTS and to use decision tree machine learning methods, random forests and boosting ensembles, to predict the voltage regulator tap changes for the remainder of the year, accurately reproducing the results of the time-consuming, brute-force, yearlong QSTS simulation. This research uses decision tree ensemble machine learning, applied for the first time to QSTS simulations, to produce high-accuracy QSTS results, up to 4x times faster than traditional methods.

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Distribution system analysis with ever increasing numbers of distributed energy resources (DER) requires quasistatic time-series (QSTS) analysis to capture the time-varying and time-dependent aspects of the system. Previous literature has demonstrated the benefits of QSTS, but there is limited information available for the requirements and standards for performing QSTS simulations. This paper provides a novel analysis of the QSTS requirements for the input data timeresolution, the simulation time-step resolution, and the length of the simulation. Detailed simulations quantify the specific errors introduced by not performing yearlong high-resolution QSTS simulations.

The rapidly growing penetration levels of distributed photovoltaic (PV) systems requires more comprehensive studies to understand their impact on distribution feeders. IEEE P.1547 highlights the need for Quasi-Static Time Series (QSTS) simulation in conducting distribution impact studies for distributed resource interconnection. Unlike conventional scenario-based simulation, the time series simulation can realistically assess time-dependent impacts such as the operation of various controllable elements (e.g. voltage regulating tap changers) or impacts of power fluctuations. However, QSTS simulations are still not widely used in the industry because of the computational burden associated with running yearlong simulations at a 1-s granularity, which is needed to capture device controller effects responding to PV variability. This paper presents a novel algorithm that reduces the number of times that the non-linear 3-phase unbalanced AC power flow must be solved by storing and reassigning power flow solutions as it progresses through the simulation. Each unique power flow solution is defined by a set of factors affecting the solution that can easily be queried. We demonstrate a computational time reduction of 98.9% for a yearlong simulation at 1-s resolution with minimal errors for metrics including: number of tap changes, capacitor actions, highest and lowest voltage on the feeder, line losses, and ANSI voltage violations. The key contribution of this work is the formulation of an algorithm capable of: (i) drastically reducing the computational time of QSTS simulations, (ii) accurately modeling distribution system voltage-control elements with hysteresis, and (iii) efficiently compressing result time series data for post-simulation analysis.

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A hierarchical control algorithm was developed to utilize photovoltaic system advanced inverter volt-VAr functions to provide distribution system voltage regulation and to mitigate 10-minute average voltages outside of ANSI Range A (0.95-1.05 pu). As with any hierarchical control strategy, the success of the control requires a sufficiently fast and reliable communication infrastructure. The communication requirements for voltage regulation were tested by varying the interval at which the controller monitors and dispatches commands and evaluating the effectiveness to mitigate distribution system over-voltages. The control strategy was demonstrated to perform well for communication intervals equal to the 10-minute ANSI metric definition or faster. The communication reliability impacted the controller performance at levels of 99% and below, depending on the communication interval, where an 8-minute communication interval could be unsuccessful with an 80% reliability. The communication delay, up to 20 seconds, was too small to have an impact on the effectiveness of the communication-based hierarchical voltage control.

Distribution system analysis with high penetrations of distributed energy resources (DER) requires quasi-static time-series (QSTS) analysis to capture the time-varying and time-dependent aspects of the system, but current QSTS algorithms are prohibitively burdensome and computationally intensive. This paper proposes a novel deviation-based algorithm to calculate the critical time periods when QSTS simulations should be solved at higher or lower time-resolution. This predetermined time-step (PT) solver is a new method of performing variable time-step simulations based solely on the input data. The PT solver demonstrates high accuracy while performing the simulation up to 20 times faster.

As the penetration of renewables increases in the distribution systems, and microgrids are conceived with high penetration of such generation that connects through inverters, fault location and protection of microgrids needs consideration. This report proposes averaged models that help simulate fault scenarios in renewable-rich microgrids, models for locating faults in such microgrids, and comments on the protection models that may be considered for microgrids. Simulation studies are reported to justify the models.

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The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modelling and impact analysis. Unlike conventional scenario - based studies , quasi - static time - series (QSTS) simulation s can realistically model time - dependent voltage controllers and the diversity of potential impacts that can occur at different times of year . However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1 - second resolution is often required , which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled . This computational burden is a clear l imitation to the adoption of QSTS simulation s in interconnection studies and for determining optimal control solutions for utility operations . Our ongoing research to improve the speed of QSTS simulation has revealed many unique aspects of distribution system modelling and sequential power flow analysis that make fast QSTS a very difficult problem to solve. In this report , the most relevant challenges in reducing the computational time of QSTS simulations are presented: number of power flows to solve, circuit complexity, time dependence between time steps, multiple valid power flow solutions, controllable element interactions, and extensive accurate simulation analysis.

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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.

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We present a simple algorithm for identifying periods of time with broadband global horizontal irradiance (GHI) similar to that occurring during clear sky conditions from a time series of GHI measurements. Other available methods to identify these periods do so by identifying periods with clear sky conditions using additional measurements, such as direct or diffuse irradiance. Our algorithm compares characteristics of the time series of measured GHI with the output of a clear sky model without requiring additional measurements. We validate our algorithm using data from several locations by comparing our results with those obtained from a clear sky detection algorithm, and with satellite and ground-based sky imagery.

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Utilities are increasingly concerned about the potential negative impacts distributed PV may have on the operational integrity of their distribution feeders. Some have proposed novel methods for controlling a PV system's grid - tie inverter to mitigate poten tial PV - induced problems. This report investigates the effectiveness of several of these PV advanced inverter controls on improving distribution feeder operational metrics. The controls are simulated on a large PV system interconnected at several locations within two realistic distribution feeder models. Due to the time - domain nature of the advanced inverter controls, quasi - static time series simulations are performed under one week of representative variable irradiance and load data for each feeder. A para metric study is performed on each control type to determine how well certain measurable network metrics improve as a function of the control parameters. This methodology is used to determine appropriate advanced inverter settings for each location on the f eeder and overall for any interconnection location on the feeder.

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