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
Traditionally electric grid planning strives to maintain safe, reliable, efficient, and affordable service for current and future customers. As policies, social preferences, and the threat landscape evolve, additional considerations for power system planners are emerging, including decarbonization, resilience, and energy equity and justice. The MOD-Plan framework leverages and extends prior work to provide a framework for integrating incorporating resilience, equity, and decarbonization into integrated distribution system planning.
Broderick, Robert J.; Sandoval, Marcelo S.; Hernández, Jorge H.; Grijalva, Santiago G.; O?Neill-Carrillo, Efraín &.
This work details a project to design reliable, resilient, and cost-effective networked microgrids considering grid constraints and resilience metrics focused on Puerto Rico distribution feeder locations with long outages after Hurricane Maria. The project consisted primarily of modeling and simulation tasks that accomplished the following objectives: 1. Selected 10 distribution feeder models in vulnerable areas. The sample feeders are geographically distributed across Puerto Rico and vary in length to capture the wide variety of feeders on the island. 2. Determined the optimal location and sizing of distributed energy resources (DERs) on the identified distribution feeders. The systems considered as part of the microgrid solutions were solar photovoltaic (PV), battery energy storage systems (BESS) and distributed fossil fuel generation (DFFG). 3. Estimated the cost-benefit of the proposed DER portfolios. 4. Provided a set of final recommendations that inform decision making on how to do targeted planning analysis for microgrids that can supply energy to critical infrastructures.
This guide is meant to assist communities – from residents to energy experts to decision makers – in developing a conceptual microgrid design that meets site-specific energy resilience goals. Using the framework described in this guidebook, stakeholders can come together and start to quantify site-specific vulnerabilities, identify the most significant risks to delivery of electricity, and establish electric outage tolerances across the community. In addition to establishing minimum service needs, this framework encourages communities to consider broader sustainability goals and policy constraints and begin to estimate up-front costs associated with the installation of alternative microgrid solutions. The framework guides a community through data collection and a high-level assessment of its needs, constraints, and priorities, prior to engaging engineers, vendors, and contractors. The first sections of this guidebook provide a high-level primer on electric systems. The latter sections include guidance for step-by-step data gathering and analysis of site conditions. The ultimate product resulting from the stepwise approach is a conceptual microgrid design. A conceptual design is defined as an initial design (10%-20% complete) that considers the specific threats, needs, limitations, and investment options for a given location. Going through this exercise and developing the conceptual microgrid design as a community ensures the same community members who will ultimately live with the solution are the developers of its foundational design. Often, these are also the very same people who understand system tolerances and needs the best and are therefore the ideal candidates for establishing these criteria. Especially when it comes to evaluating critical infrastructure, it is the community that best understands the most critical services. The framework is intended to facilitate a systematic approach to planning for resilience and provide a deeper understanding of how to use a framework to make decisions around microgrid solutions. Like many processes where tradeoffs need to be considered, this is often an iterative process. If this guide serves to help educate and empower communities who are beginning the process of deploying a microgrid, it has met the goal of its authors.
In 2019, Sandia National Laboratories contracted Synapse Energy Economics (Synapse) to research the integration of community and electric utility resilience investment planning as part of the Designing Resilient Communities: A Consequence-Based Approach for Grid Investment (DRC) project. Synapse produced a series of reports to explore the challenges and opportunities in several key areas, including benefit-cost analysis, performance metrics, microgrids, and regulatory mechanisms to promote investments in electric system resilience. This report focuses on regulatory mechanisms to improve resilience. Regulatory mechanisms that improve resilience are approaches that electric utility regulators can use to align utility, customer, and third-party investments with regulatory, ratepayer, community, and other important stakeholder interests and priorities for resilience. Cost-of-service regulation may fail to provide utilities with adequate guidance or incentives regarding community priorities for infrastructure hardening and disaster recovery. The application of other types of regulatory mechanisms to resilience investments can help. This report: characterizes regulatory objective as they apply to resilience; identifies several regulatory mechanisms that are used or can be adapted to improve the resilience of the electric system--including performance-based regulation, integrated planning, tariffs and programs to leverage private investment, alternative lines of business for utilities, enhanced cost recovery, and securitization; provides a case study of each regulatory mechanism; summarizes findings across the case studies; and suggests how these regulatory mechanisms might be improved and applied to resilience moving forward. In this report, we assess the effectiveness of a range of utility regulatory mechanisms at evaluating and prioritizing utility investments in grid resilience. First, we characterize regulatory objectives which underly all regulatory mechanisms. We then describe seven types of regulatory mechanisms that can be used to improve resilience--including performance-based regulation, integrated planning, tariffs and programs to leverage private investment, alternative lines of business for utilities, enhanced cost recovery, and securitization--and provide a case study for each one. We summarize our findings on the extent to which these regulatory mechanisms have supported resilience to date. We conclude with suggestions on how these regulatory mechanisms might be improved and applied to resilience moving forward.
In 2019, Sandia National Laboratories (Sandia) contracted Synapse Energy Economics (Synapse) to research the integration of community and electric grid resilience investment planning as part of the Designing Resilient Communities (DRC): A Consequence-Based Approach for Grid Investment project. Synapse produced a series of reports to explore the challenges and opportunities in several key areas, including benefit-cost analysis (BCA), performance metrics, microgrids, and regulatory mechanisms. This report focuses on BCA. BCA is an approach that electric utilities, electric utility regulators, and communities can use to evaluate the costs and benefits of a wide range of grid resilience investments in a comprehensive and consistent way. While BCA is regularly applied to some types of grid investments, application of BCA to grid resilience investments is in the early stages of development. Though resilience is increasingly cited in connection with grid investment proposals and plans, the resilience- related costs and benefits of grid resilience investments are typically not fully identified, infrequently quantified, and almost never monetized. Without complete assessments of costs and benefits, regulators can be hesitant to approve some types of grid resilience investments. This report provides the first application of the framework developed in the 2020 National Standard Practice Manual for Benefit-Cost Analysis of Distributed Energy Resources (NSPM for DERs) to grid resilience investments. We provide guidance on next steps for implementation to enable grid resilience investments to receive due consideration. We suggest developing BCA principles and standards for jurisdiction-specific BCA tests. We also recommend identifying the resilience impacts of the investments and quantification of these impacts by establishing utility performance metrics for resilience. Proactive integration of grid resilience investments into existing regulatory processes and practices can increase the capacity of jurisdictions to respond to and recover from the consequences of extreme events. 1 National Energy Screening Project. 2020. National Standard Practice Manual for Benefit-Cost Analysis of Distributed Energy Resources.
Synapse Energy Economics has conducted structured interviews to better characterize the current landscape of resilience planning within and across jurisdictions. Synapse interviewed representatives of a diverse group of communities and their electric utilities. The resulting case studies span geographies and utility regulatory structures and represent a range of threats. They also vary in terms of population density and size. This report summarizes our approach and the findings gleaned from these conversations. All the communities and utilities we interviewed see increased interest in and commitment of resources for energy-related resilience. The risks and consequences these communities and utilities faced in the past, face now, and will face in the future drove them to improve engagement, advance processes, further decision-making, and in many cases invest in projects. While no process used by communities and utilities was the same, the different processes used by communities and utilities allowed each one to make progress in its own way. Several approaches are emerging that can provide good models for other communities and utilities with an interest in improving resilience.
Broderick, Robert J.; Reno, Matthew J.; Lave, Matthew S.; Azzolini, Joseph A.; Blakely, Logan; Galtieri, Jason G.; Mather, Barry M.; Weekley, Andrew W.; Hunsberger, Randolph H.; Chamana, Manohar C.; Li, Qinmiao L.; Zhang, Wenqi Z.; Latif, Aadil L.; Zhu, Xiangqi Z.; Grijalva, Santiago G.; Zhang, Xiaochen Z.; Deboever, Jeremiah D.; Qureshi, Muhammad U.; Therrien, Francis T.; Lacroix, Jean-Sebastien L.; Li, Feng L.; Belletête, Marc B.; Hébert, Guillaume H.; Montenegro, Davis M.; Dugan, Roger D.
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.
The Energy Surety Design Methodology (ESDM) provides a systematic approach for engineers and researchers to create a preliminary electric grid design, thus establishing a means to preserve and quickly restore customer-specified critical loads. Over a decade ago, Sandia National Laboratories (Sandia) defined Energy Surety for applications with energy systems to include elements of reliability, security, safety, cost, and environmental impact. Since then, Sandia has employed design concepts of energy surety for over 20 military installations and their interaction with utility systems, including the Smart Power Infrastructure Demonstration for Energy Reliability and Security (SPIDERS) Joint Capability Technology Demonstration (JCTD) project. In recent years, resilience has also been added as a key element of energy surety. This methodology document includes both process recommendations and technical guidance, with references to useful tools and analytic approaches at each step of the process.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Accurately representing the local solar variability at distribution timescales (30-seconds and shorter) is essential to modeling the impact of solar photovoltaics (PV) on distribution feeders. Previous works have examined variability at single locations, but this may not be useful to an operator whose distribution feeder is in a different climate region. In this work, we compare high-frequency variability from 8 locations in the United States. We define a variability metric for quantifying variability and use this metric to quantify and compare the variability at each of the 8 locations. We also explore the relationship between high-frequency and low-frequency (hourly) variability to see if widely-available low-frequency data (e.g., satellite data) may be used to determine variability climate zones. The end goal is to provide high-frequency solar inputs with climatologically representative solar variability for use in distribution studies.
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.
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.
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.
The Task 1 objective is to “expedite the PV interconnection process by revising the screening process in California”. The goal of this task is to develop a data-driven, validated approach to determining feeder limits that can simplify interconnection processes and lead to greater PV adoption across the California distribution system.
This task describes R&D activities to establish methods to cost-effectively achieve very high PV penetration scenarios (well beyond 100% of peak load at the feeder level) by leveraging distributed inverters to increase situational awareness and provide local voltage support. The grid performance and reliability objectives (SI vision) of this proposal were selected to enhance feeder planning models that can better characterize and quantify the electric power system including the secondary system servicing customers. The Communication objective was selected to demonstrate how visibility and control of behind-the-meter systems and distributed storage at large scale can be optimized to address system reliability and variability impacts, and to maximize the value of solar in high PV penetration scenarios. The goal of this project was to achieve enhanced grid operation and optimized PV penetration utilizing highly distributed sensor data via three subtasks 1.1-1.3.
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.
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.
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.
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.
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.
The purpose of the report is to describe the findings from the analysis of 100 Small Generation Interconnection Procedure (SGIP) studies and describe the methodology used to develop the database. The database was used to identify the most likely impacts and mitigation costs associated with PV system interconnections. A total of 100 SGIP reports performed by 3 utilities and one regional transmission operator (RTO) were analyzed. Each record within the database represents an itemized SGIP report and includes information about the generation facility, interconnection topology, electrical power system characteristics, identified adverse system impacts, mitigation options, and costs associated with interconnection the generation facility. The analysis identified several key findings: * 44% of generation facilities that entered the SGIP study process had no adverse impact on the electrical power system. * Interconnection topologies were strongly correlated to the presence/absence of adverse system impacts. * Protection impacts were the most common adverse system impact. * 50% of SGIP studies identified total connection costs of less than $689,431. * 50% of SGIP studies identified total connection costs per MW of less than $133,833
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.
Data from of a highly instrumented residential feeder in Ota City, Japan was used to determine 1 second load variability for the aggregation of 50, 100, 250, and 500 homes. The load variability is categorized by binning the data into seasons, weekdays vs. weekends, and time of day to create artificial sub-15-minute variability estimates for modeling dynamic load profiles. An autoregressive, AR(1) function along with a high pass filter was used to simulate the high resolution variability. The simulated data were validated against the original 1-second measured data.
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.
With an increasing number of Distributed Generation (DG) being connected on the distribution system, a method for simplifying the complexity of the distribution system to an equivalent representation of the feeder is advantageous for streamlining the interconnection study process. The general characteristics of the system can be retained while reducing the modeling effort required. This report presents a method of simplifying feeders to only specified buses-of-interest. These buses-of-interest can be potential PV interconnection locations or buses where engineers want to verify a certain power quality. The equations and methodology are presented with mathematical proofs of the equivalence of the circuit reduction method. An example 15-bus feeder is shown with the parameters and intermediate example reduction steps to simplify the circuit to 4 buses. The reduced feeder is simulated using PowerWorld Simulator to validate that those buses operate with the same characteristics as the original circuit. Validation of the method is also performed for snapshot and time-series simulations with variable load and solar energy output data to validate the equivalent performance of the reduced circuit with the interconnection of PV.
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.