Publications

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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