Publications

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The research presented in this report compares several real - time control strategies for the power output of a large number of PV distributed throughout a large distribution feeder circuit. Both real and reactive power controls are considered with the goal of minimizing network over - voltage violations caused by large amounts of PV generation. Several control strategies are considered under various assumptions regarding the existence and latency of a communication network. The control parameters are adjusted to maximize the effectiveness of each control. The controls are then compared based on their ability to achieve multiple objectiv es. These objectives include minimizing the total number of voltage violations , minimizing the total amount of PV energy curtailed or reactive power generated, and maximizing the fairness of any control action among all PV systems . The controls are simulat ed on the OpenDSS platform using time series load and spatially - distributed irradiance data.

To address the lack of knowledge of local solar variability, we have developed, deployed, and demonstrated the value of data collected from a low-cost solar variability sensor. While most currently used solar irradiance sensors are expensive pyranometers with high accuracy (relevant for annual energy estimates), low-cost sensors display similar precision (relevant for solar variability) as high-cost pyranometers, even if they are not as accurate. In this work, we list variability sensor requirements, describe testing of various low-cost sensor components, present a validation of an alpha prototype, and show how the variability sensor collected data can be used for grid integration studies. The variability sensor will enable a greater understanding of local solar variability, which will reduce developer and utility uncertainty about the impact of solar photovoltaic installations and thus will encourage greater penetrations of solar energy.

Most utilities use a standard small generator interconnection procedure (SGIP) process that includes a screen for placing potential PV interconnection requests on a fast track that do not require more detailed study. One common screening threshold is the 15% of peak load screen that fast tracks PV below a certain size. This paper performs a technical evaluation of the screen compared to a large number of simulation results for PV on 40 different feeders. Three error metrics are developed to quantify the accuracy of the screen for identifying interconnections that would cause problems or incorrectly sending a large number of allowable systems for more detailed study.

Increasing number s of PV on distribution systems are creating more grid impacts , but it also provides more opportunities for measurement, sensing, and control of the grid in a distributed fashion. This report demonstrates three software tools for characterizing and controlling distribution feeders by utilizing large numbers of highly distributed current, voltage , and irradiance sensors. Instructions and a user manual is presented for each tool. First, the tool for distribution system secondary circuit parameter estimation is presented. This tool allows studying distribution system parameter estimation accuracy with user-selected active power, reactive power, and voltage measurements and measurement error levels. Second, the tool for multi-objective inverter control is shown. Various PV inverter control strategies can be selected to objectively compare their impact on the feeder. Third, the tool for energy storage for PV ramp rate smoothing is presented. The tool allows the user to select different storage characteristics (power and energy ratings) and control types (local vs. centralized) to study the tradeoffs between state-of-charge (SOC) management and the amount of ramp rate smoothing.

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The third solicitation of the California Solar Initiative (CSI) Research, Development, Demonstration and Deployment (RD&D) Program established by the California Public Utility Commission (CPUC) is supporting the Electric Power Research Institute (EPRI), National Renewable Energy Laboratory (NREL), and Sandia National Laboratories (SNL) with collaboration from Pacific Gas and Electric (PG&E), Southern California Edison (SCE), and San Diego Gas and Electric (SDG&E), in research to improve the Utility Application Review and Approval process for interconnecting distributed energy resources to the distribution system. Currently this process is the most time - consuming of any step on the path to generating power on the distribution system. This CSI RD&D solicitation three project has completed the tasks of collecting data from the three utilities, clustering feeder characteristic data to attain representative feeders, detailed modeling of 16 representative feeders, analysis of PV impacts to those feeders, refinement of current screening processes, and validation of those suggested refinements. In this report each task is summarized to produce a final summary of all components of the overall project.

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Many utilities today have a large number of interconnection requests for new PV installations on their distribution networks. Interconnections should be approved in a timely manner but without compromising network reliability. It is thus important to know a network's PV hosting capacity, which defines the upper bound of PV sizes that pose no risk to the network. This paper investigates how implementing reactive power control on the PV inverter impacts the PV hosting capacity of a distribution network. A local Volt-Var droop control is used and simulations are performed in OpenDSS and Matlab. Multiple feeders are tested and it is found that the control greatly improves the overall hosting capacity of the feeder as well as the locational hosting capacity of most voltage constrained buses.

This project is part of the third solicitation of the California Solar Initiative (CSI3) Research, Development, Demonstration, and Deployment Program created by the California Public Utilities Commission (CPUC) in 2006 to support solar research in California. The program focuses on research to improve the utility application review and approval process for interconnecting distributed energy resources such as solar to the distribution system. The CSI3 program is supporting EPRI, National Renewable Energy Laboratory (NREL), and Sandia National Laboratories (SNL) in their collaboration on the process with Pacific Gas and Electric (PG&E), Southern California Edison (SCE), and San Diego Gas and Electric (SDG&E). At present, the application review and approval process is the most time-consuming of any step on the path to generating power for delivery through the distribution system.

To analyze and coordinate the operation of distribution systems with rapidly increasing amounts of PV, more accurate distribution system models are required, especially for the distribution system secondary (low-voltage) circuits down to the point of common coupling for distributed PV. There is a growing need for automated procedures to calibrate the distribution system secondary circuit models that are typically either not modeled at all or are modeled with a lower level of detail than the better modeled medium-voltage systems. This report presents an accurate, flexible, and computationally efficient method to use measurement data to estimate secondary circuit series impedance parameters in existing utility feeder models. The parameter estimation method assumes well-modeled primary circuit models, known secondary circuit topologies, and AMI active power, and reactive power measurements at all the loads in the secondary circuit. The method also requires AMI voltage measurement at most of the loads in the secondary circuit but can handle loads that do not have voltage measurements. No existing secondary circuit model information is needed, except for topology. The method is based on the well-known linearized voltage drop approximation and linear regression. The performance of the method is demonstrated on a three-phase test circuit with ten different secondary circuit topologies and on the Georgia Tech campus distribution system with AMI data. The developed method can be utilized to improve existing utility feeder models for more accurate analysis and operation with ubiquitous distributed PV interconnected on the low-voltage circuits.

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The many new distributed energy resources being installed at the distribution system level require increased visibility into system operations that will be enabled by distribution system state estimation (DSSE) and situational awareness applications. Reliable and accurate DSSE requires both robust methods for managing the big data provided by smart meters and quality distribution system models. This paper presents intelligent methods for detecting and dealing with missing or inaccurate smart meter data, as well as the ways to process the data for different applications. It also presents an efficient and flexible parameter estimation method based on the voltage drop equation and regression analysis to enhance distribution system model accuracy. Finally, it presents a 3-D graphical user interface for advanced visualization of the system state and events. Moreover, we demonstrate this paper for a university distribution network with the state-of-the-art real-time and historical smart meter data infrastructure.

Accurately representing the local solar variability at timescales relevant to distribution grid operations (30-s and shorter) is essential to modeling the impact of solar photovoltaics (PV) on distribution feeders. Due to a lack of available high-frequency solar data, some distribution grid studies have used synthetically-created PV variability or measured PV variability from a different location than their study location. In this work, we show the importance of using accurate solar PV variability inputs in distribution studies. Using high-frequency solar irradiance data from 10 locations in the United States, we compare the ramp rate distributions at the different locations, use a quantitative metric to describe the solar variability at each location, and run distribution simulations using representative 1-week samples from each location to demonstrate the impact of locational solar variability on the number of voltage regulator tap change operations. Results show more than a factor of 3 difference in the number of tap change operations between different PV power variability samples based on irradiance from the different locations. Errors in simulated number of tap changes of up to -70% were found when using low-frequency (e.g., 15-min) solar variability.

This manual provides the documentation of the MATLAB toolbox of functions for using OpenDSS to simulate the impact of solar energy on the distribution system. The majority of the functio ns are useful for interfacing OpenDSS and MATLAB, and they are of generic use for commanding OpenDSS from MATLAB and retrieving information from simulations. A set of functions is also included for modeling PV plant output and setting up the PV plant in th e OpenDSS simulation. The toolbox contains functions for modeling the OpenDSS distribution feeder on satellite images with GPS coordinates. Finally, example simulations functions are included to show potential uses of the toolbox functions. Each function i n the toolbox is documented with the function use syntax, full description, function input list, function output list, example use, and example output.

The use of residential PV grid-tie inverters to supply reactive power as a benefit to the distribution grid has been widely proposed, however, there is little insight into how much of a benefit can be achieved from this control under varying system operating points. This paper seeks to demonstrate the effectiveness of a linearized versus nonlinear reactive power dispatch solution on a highly unbalanced distribution feeder under differing load profiles, insolation levels, and penetration rates of PV in the feeder. The results are analyzed to determine the system operating points that are favorable to reactive power control and the overall effectiveness of each solution in realistic feeder states.

With rising adoption of solar energy, it is increasingly important for utilities to easily assess potential interconnections of photovoltaic (PV) systems. In this analysis, we show the maximum feeder voltage due to various PV interconnections and provide visualizations of the PV impact to the distribution system. We investigate the locational dependence of PV hosting capacity by examining the impact of PV system size on these voltages with regard to PV distance and resistance to the substation. We look at the effect of increasing system size on line loading and feeder violations. The magnitude of feeder load is also considered as an independent variable with repeated analyses to determine the effect on the PV impact analysis. A technique is presented to determine and visualize the maximum capacity for possible PV installations for distribution feeders.

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This manual provides the documentation of the MATLAB toolbox of functions for using OpenDSS to simulate the impact of solar energy on the distribution system. The majority of the functions are useful for interfacing OpenDSS and MATLAB, and they are of generic use for commanding OpenDSS from MATLAB and retrieving information from simulations. A set of functions is also included for modeling PV plant output and setting up the PV plant in the OpenDSS simulation. The toolbox contains functions for modeling the OpenDSS distribution feeder on satellite images with GPS coordinates. Finally, example simulations functions are included to show potential uses of the toolbox functions. Each function in the toolbox is documented with the function use syntax, full description, function input list, function output list, example use, and example output.

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Distributed photovoltaic (PV) projects must go through an interconnection study process before connecting to the distribution grid. These studies are intended to identify the likely impacts and mitigation alternatives. In the majority of the cases, system impacts can be ruled out or mitigation can be identified without an involved study, through a screening process or a simple supplemental review study. For some proposed projects, expensive and time-consuming interconnection studies are required. The challenges to performing the studies are twofold. First, every study scenario is potentially unique, as the studies are often highly specific to the amount of PV generation capacity that varies greatly from feeder to feeder and is often unevenly distributed along the same feeder. This can cause location-specific impacts and mitigations. The second challenge is the inherent variability in PV power output which can interact with feeder operation in complex ways, by affecting the operation of voltage regulation and protection devices. The typical simulation tools and methods in use today for distribution system planning are often not adequate to accurately assess these potential impacts. This report demonstrates how quasi-static time series (QSTS) simulation and high time-resolution data can be used to assess the potential impacts in a more comprehensive manner. The QSTS simulations are applied to a set of sample feeders with high PV deployment to illustrate the usefulness of the approach. The report describes methods that can help determine how PV affects distribution system operations. The simulation results are focused on enhancing the understanding of the underlying technical issues. The examples also highlight the steps needed to perform QSTS simulation and describe the data needed to drive the simulations. The goal of this report is to make the methodology of time series power flow analysis readily accessible to utilities and others responsible for evaluating potential PV impacts.

High frequency irradiance variability measured on the ground is caused by the formation, dissipation, and passage of clouds in the sky. Variability and ramp rates of PV systems are increasingly important to understand and model for grid stability as PV penetration levels rise. Using satellite imagery to identify cloud types and patterns can predict irradiance variability in areas lacking sensors. With satellite imagery covering the entire U.S., this allows for more accurate integration planning and power flow modelling over wide areas. Satellite imagery from southern Nevada was analyzed at 15 minute intervals over a year. Methods for image stabilization, cloud detection, and textural classification of clouds were developed and tested. High Performance Computing parallel processing algorithms were also investigated and tested. Artificial Neural Networks using imagery as inputs were trained on ground-based measurements of irradiance to model the variability and were tested to show some promise as a means for predicting irradiance variability. Artificial Neural Networks, cloud texture analysis, and cloud type categorization can be used to model the irradiance and variability for a location at a one minute resolution without needing many ground based irradiance sensors. © 2012 IEEE.

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Clear sky models estimate the terrestrial solar radiation under a cloudless sky as a function of the solar elevation angle, site altitude, aerosol concentration, water vapor, and various atmospheric conditions. This report provides an overview of a number of global horizontal irradiance (GHI) clear sky models from very simple to complex. Validation of clear-sky models requires comparison of model results to measured irradiance during clear-sky periods. To facilitate validation, we present a new algorithm for automatically identifying clear-sky periods in a time series of GHI measurements. We evaluate the performance of selected clear-sky models using measured data from 30 different sites, totaling about 300 site-years of data. We analyze the variation of these errors across time and location. In terms of error averaged over all locations and times, we found that complex models that correctly account for all the atmospheric parameters are slightly more accurate than other models, but, primarily at low elevations, comparable accuracy can be obtained from some simpler models. However, simpler models often exhibit errors that vary with time of day and season, whereas the errors for complex models vary less over time.

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Design and operation of the electric power grid (EPG) relies heavily on computational models. High-fidelity, full-order models are used to study transient phenomena on only a small part of the network. Reduced-order dynamic and power flow models are used when analysis involving thousands of nodes are required due to the computational demands when simulating large numbers of nodes. The level of complexity of the future EPG will dramatically increase due to large-scale deployment of variable renewable generation, active load and distributed generation resources, adaptive protection and control systems, and price-responsive demand. High-fidelity modeling of this future grid will require significant advances in coupled, multi-scale tools and their use on high performance computing (HPC) platforms. This LDRD report demonstrates SNL's capability to apply HPC resources to these 3 tasks: (1) High-fidelity, large-scale modeling of power system dynamics; (2) Statistical assessment of grid security via Monte-Carlo simulations of cyber attacks; and (3) Development of models to predict variability of solar resources at locations where little or no ground-based measurements are available.

High frequency irradiance variability measured on the ground is caused by the formation, dissipation, and passage of clouds in the sky. If we can identify and associate different cloud types/patterns from satellite imagery, we may be able to predict irradiance variability in areas lacking sensors. With satellite imagery covering the entire U.S., this allows for more accurate integration planning and power flow modeling over wide areas. Satellite imagery from southern Nevada was analyzed at 15 minute intervals over a year. Methods for image stabilization, cloud detection, and textural classification of clouds were developed and tested. High Performance Computing parallel processing algorithms were also investigated and tested. Artificial Neural Networks using imagery as inputs were trained on ground-based measurements of irradiance to model the variability and were tested to show some promise as a means for predicting irradiance variability.