During the last decade, utility companies around the world have experienced a significant increase in the occurrences of either planned or unplanned blackouts, and microgrids have emerged as a viable solution to improve grid resiliency and robustness. Recently, power converters with grid-forming capabilities have attracted interest from researchers and utilities as keystone devices enabling modern microgrid architectures. Therefore, proper and thorough testing of Grid-Forming Inverters (GFMIs) is crucial to understand their dynamics and limitations before they are deployed. The use of closed-loop real-time Power Hardware-in-the-Loop (PHIL) simulations will facilitate the testing of GFMIs using a digital twin of the power system under various contingency scenarios within a controlled environment. So far, lower to medium scale commercially available GFMIs are difficult to interface into PHIL simulations because of their lack of a synchronization mechanism that allows a smooth and stable interconnection with a voltage source such as a power amplifier. Under this scenario, the use of the well-known Ideal Transformer Method to create a PHIL setup can lead to catastrophic damages of the GFMI. This paper addresses a simple but novel method to interface commercially available GFMIs into a PHIL testbed. Experimental results showed that the proposed method is stable and accurate under standalone operation with abrupt (step) load-changing dynamics, followed by the corresponding steady state behavior. Such results were validated against the dynamics of the GFMI connected to a linear load bank.
IEEE Journal of Emerging and Selected Topics in Power Electronics
Augustine, Sijo; Reno, Matthew J.; Brahma, Sukumar M.; Lavrova, Olga
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.
Sikeridis, Dimitrios; Bidram, Ali; Devetsikiotis, Michael; Reno, Matthew J.
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.
Siratarnsophon, Piyapath; Hernandez, Miguel; Peppanen, Jouni; Deboever, Jeremiah; Rylander, Matthew; Reno, Matthew J.
Distribution system modelling and analysis with growing penetration of distributed energy resources (DERs) requires more detailed and accurate distribution load modelling. Smart meters, DER monitors, and other distribution system sensors provide a new level of visibility to distribution system loads and DERs. However, there is a limited understanding of how to efficiently leverage the new information in distribution system load modelling. This study presents the assessment of 11 methods to leverage the emerging information for improved distribution system active and reactive power load modelling. The accuracy of these load modelling methods is assessed both at the primary and the secondary distribution levels by analysing over 2.7 billion data points of results of feeder node voltages and element phase currents obtained by performing annual quasi-static time series simulations on EPRI's Ckt5 feeder model.
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.
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).
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.
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.
Distribution system analysis requires yearlong quasi-static time-series (QSTS) simulations to accurately capture the variability introduced by high penetrations of distributed energy resources (DER) such as residential and commercial-scale photovoltaic (PV) installations. Numerous methods are available that significantly reduce the computational time needed for QSTS simulations while maintaining accuracy. However, analyzing the results remains a challenge; a typical QSTS simulation generates millions of data points that contain critical information about the circuit and its components. This paper provides examples of visualization methods to facilitate the analysis of QSTS results and to highlight various characteristics of circuits with high variability.
The Ideal Transformer Method (ITM) and the Damping Impedance Method (DIM) are the most widely used techniques for connecting power equipment to a Power-Hardware-in-the-Loop (PHIL) real-time simulation. Both methods have been studied for their stability and accuracy in PHIL simulations, but neither have been analyzed when the hardware is providing grid-support services with volt-var, frequency-watt, and fixed power factor functions. In this work, we experimentally validate the two methods of connecting a physical PV inverter to a PHIL system and evaluate them for dynamic stability and accuracy when operating with grid-support functions. It was found that the DIM Low Pass Lead Filter (LPF LD) method was the best under unity and negative power factor conditions, but the ITM LPF LD method was preferred under positive power factor conditions.
Spectral clustering is applied to the problem of phase identification of electric customers to investigate the data needs (resolution and accuracy) of advanced metering infrastructure (AMI). More accurate models are required to accurately interconnect high penetrations of PV/DER and for optimal electric grid operations. This paper demonstrates the effects of different data collection implementations and common errors in AMI datasets on the phase identification task. This includes measurement intervals, data resolution, collection periods, time synchronization issues, noisy measurements, biased meters, and mislabeled phases. High quality AMI data is a critical consideration to model correction and accurate hosting capacity analyses.
The Ideal Transformer Method (ITM) and the Damping Impedance Method (DIM) are the most widely used techniques for connecting power equipment to a Power-Hardware-in-the-Loop (PHIL) real-time simulation. Both methods have been studied for their stability and accuracy in PHIL simulations, but neither have been analyzed when the hardware is providing grid-support services with volt-var, frequency-watt, and fixed power factor functions. In this work, we experimentally validate the two methods of connecting a physical PV inverter to a PHIL system and evaluate them for dynamic stability and accuracy when operating with grid-support functions. It was found that the DIM Low Pass Lead Filter (LPF LD) method was the best under unity and negative power factor conditions, but the ITM LPF LD method was preferred under positive power factor conditions.
This paper discusses common types of errors that are frequently present in utility distribution system models and which can significantly influence distribution planning and operational assessments that rely on the model accuracy. Based on Google Earth imagery and analysis of correlation coefficients, this paper also illustrates some common error types and demonstrates methods to correct the errors. Error types include misla-beled interconnections between customers and service transformers, three-phase customers labeled as single-phase, unmarked transformers, and customers lacking coordinates. Identifying and correcting for these errors is critical for accurate distribution planning and operational assessments, such as load flow and hosting capacity analysis.
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.
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.
Distribution system analysis requires yearlong quasi-static time-series (QSTS) simulations to accurately capture the variability introduced by high penetrations of distributed energy resources (DER) such as residential and commercial-scale photovoltaic (PV) installations. Numerous methods are available that significantly reduce the computational time needed for QSTS simulations while maintaining accuracy. However, analyzing the results remains a challenge; a typical QSTS simulation generates millions of data points that contain critical information about the circuit and its components. This paper provides examples of visualization methods to facilitate the analysis of QSTS results and to highlight various characteristics of circuits with high variability.
Quasi-static time-series (QSTS) analysis of distribution systems can provide critical information about the potential impacts of high penetrations of distributed and renewable resources, like solar photovoltaic systems. However, running high-resolution yearlong QSTS simulations of large distribution feeders can be prohibitively burdensome due to long computation times. Temporal parallelization of QSTS simulations is one possible solution to overcome this obstacle. QSTS simulations can be divided into multiple sections, e.g. into four equal parts of the year, and solved simultaneously with parallel computing. The challenge is that each time the simulation is divided, error is introduced. This paper presents various initialization methods for reducing the error associated with temporal parallelization of QSTS simulations and characterizes performance across multiple distribution circuits and several different computers with varying architectures.
Quasi-static time-series (QSTS) analysis of distribution systems can provide critical information about the potential impacts of high penetrations of distributed and renewable resources, like solar photovoltaic systems. However, running high-resolution yearlong QSTS simulations of large distribution feeders can be prohibitively burdensome due to long computation times. Temporal parallelization of QSTS simulations is one possible solution to overcome this obstacle. QSTS simulations can be divided into multiple sections, e.g. into four equal parts of the year, and solved simultaneously with parallel computing. The challenge is that each time the simulation is divided, error is introduced. This paper presents various initialization methods for reducing the error associated with temporal parallelization of QSTS simulations and characterizes performance across multiple distribution circuits and several different computers with varying architectures.
Historically, photovoltaic inverters have been grid-following controlled, but with increasing penetrations of inverter-based generation on the grid, grid-forming inverters (GFMI) are gaining interest. GFMIs can also be used in microgrids that require the ability to interact and operate with the grid (grid-tied), or to operate autonomously (islanded) while supplying their corresponding loads. This approach can substantially improve the response of the grid to severe contingencies such as hurricanes, or to high load demands. During islanded conditions, GFMIs play an important role on dictating the system's voltage and frequency the same way as synchronous generators do in large interconnected systems. For this reason, it is important to understand the behavior of such grid-forming inverters under fault scenarios. This paper focuses on testing different commercially available grid-forming inverters under fault conditions.
Modern power grids include a variety of renewable Distributed Energy Resources (DERs) as a strategy to comply with new environmental and renewable portfolio standards (RPSs) imposed by state and federal agencies. Typically, DERs include the use of power electronic (PE) interfaces to interactwith the power grid. Recently this interaction has not only been focused on supplying maximum available energy, but also on supporting the power grid under abnormal conditions such as low voltage/frequency conditions or non-unity power factor. Over the last few years, grid-following inverters (GFLIs) have proven their value while providing these ancillary grid-support services either at residential or utility scale. However, the use of grid-forming inverters (GFMIs) is gaining momentum as the penetration-level of DERs increases and system inertia decreases. Under abnormal operating conditions, GFMIs tend to better preserve grid stability due to their intrinsic ability to balance loadswithout the aid of coordination controls. In order to gain and propose fundamental insights into the interfacing of GFMIs to real time simulation, this paper analyzes the dynamics of two different GFMI simulation models in terms of stability and load changes using a Power Hardware-in-the-Loop (PHIL) simulation testbed.
Historically, photovoltaic inverters have been grid-following controlled, but with increasing penetrations of inverter-based generation on the grid, grid-forming inverters (GFMI) are gaining interest. GFMIs can also be used in microgrids that require the ability to interact and operate with the grid (grid-tied), or to operate autonomously (islanded) while supplying their corresponding loads. This approach can substantially improve the response of the grid to severe contingencies such as hurricanes, or to high load demands. During islanded conditions, GFMIs play an important role on dictating the system's voltage and frequency the same way as synchronous generators do in large interconnected systems. For this reason, it is important to understand the behavior of such grid-forming inverters under fault scenarios. This paper focuses on testing different commercially available grid-forming inverters under fault conditions.
The grid of the future will integrate various distributed energy resources (DERs), microgrids, and other new technologies that will revolutionize our energy delivery systems. These technologies, as well as proposed grid-support functions, require inverter-based systems to achieve incorporation into the overall system(s). However, the presence of inverters and other power electronics changes the behavior of the grid and renders many traditional tools and algorithms less effective. An inverter is typically designed to limit its own current output to avoid overloading. This can result in both voltage collapse at the inverter output and limited energy being delivered during a fault so that protective relays cannot respond properly. To avoid sustained faults and unnecessary loss of service, it is proposed that either supercapacitor or flywheel energy storage be utilized to energize faults upon overload of the inverter to achieve fault current correction. This paper will discuss these challenges for inverter-based system fault detection, explore fault current correction strategies, and provide MATLAB/Simulink simulation results comparing the effectiveness of each strategy.
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.
Pecenak, Zachary K.; Disfani, Vahid R.; Reno, Matthew J.; Kleissl, Jan
The proliferation of distributed generation on distribution feeders triggers a large number of integration and planning studies. Further, the complexity of distribution feeder models, short simulation time steps, and long simulation horizons rapidly render studies computational burdensome. To mend this issue, we propose a methodology for reducing the number of nodes, loads, generators, line, and transformers of p-phase distribution feeders with unbalanced loads and generation, non-symmetric wire impedance, mutual coupling, shunt capacitance, and changes in voltage and phase. The methodology is derived on a constant power load assumption and employs a Gaussian elimination inversion technique to design the reduced feeder. Compared to previous work by the authors, the inversion reduction takes half the time and voltage errors after reduction are reduced by an order of magnitude. Using a snapshot simulation the reduction is tested on six additional publicly available feeders with a maximum voltage error 0.0075 p.u. regardless of feeder size or complexity, and typical errors on the order of 1 × 10 -4 p.u. For a day long quasi-static time series simulation on the UCSD A feeder, errors are shown to increase with changes in loading when a large number of buses removed, but shows less variation for less than 85% of buses removed.
This is a review of existing microgrid design tool capabilities, such as the Microgrid Design Tool (MDT), LANL PNNL NRECA Optimal Resilience Model (LPNORM), Distributed Energy Resource-Customer Adoption Model (DER-CAM), Renewable Energy Optimization (REopt), and the Hybrid Optimization Model for Multiple Energy Resources (HOMER). Additionally, other simulation and analysis tools which may provide fundamental support will be examined. These will include GridLAB-DTM, OpenDSS, and the hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). Their applicability to networked microgrid operations will be evaluated, and strengths and gaps of existing tools will be identified. This review will help to determine which elements of the proposed optimal design and operations (OD&D) tool should be formulated from first principles, and which elements should be integrated from past DOE investments.
Understanding the impact of distributed photovoltaic (PV) resources on various elements of the distribution feeder is imperative for their cost effective integration. A year-long quasi-static time series (QSTS) simulation at 1-second granularity is often necessary to fully study these impacts. However, the significant computational burden associated with running QSTS simulations is a major challenge to their adoption. In this paper, we propose a fast scalable QSTS simulation algorithm that is based on a linear sensitivity model for estimating voltage-related PV impact metrics of a three-phase unbalanced, nonradial distribution system with various discrete step control elements including tap changing transformers and capacitor banks. The algorithm relies on computing voltage sensitivities while taking into account all the effects of discrete controllable elements in the circuit. Consequently, the proposed sensitivity model can accurately estimate the state of controllers at each time step and the number of control actions throughout the year. For the test case of a real distribution feeder with 2969 buses (5469 nodes), 6 load/PV time series power profiles, and 9 voltage regulating elements including controller delays, the proposed algorithm demonstrates a dramatic time reduction, more than 180 times faster than traditional QSTS techniques.
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.
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.