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.