Publications

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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.

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.

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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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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.

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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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