Publications

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

With this work, we aim to speed up simulation and reduce computational complexity of Converter Dominated Power System (CDPS) within an acceptable accuracy.

Diesel hybrid power systems including inverter-based generation have faster and more stochastic dynamics than traditional systems. It is necessary to develop accurate models of the system components to ensure the stability of these systems and proper controller design. The parameters of the diesel generators in hybrid power systems, such as the inertia constant, are time-varying, requiring online parameter estimation techniques. This paper presents a simplified linear model developed to represent the frequency dynamics of the detailed diesel generator system and validated the model using a moving horizon estimation (MHE) approach. The proposed optimization-based MHE algorithm is employed to accurately provide an estimation of multiple parameters of a simplified diesel generator model. The proposed method extracts the parameters minimizing a cost function with a given set of constraints on the parameters. A non-intrusive square wave excitation signal generated by step changes in load is used to perturb the system with minimal impacts on power system operation. MHE estimates the parameters based on the power and frequency from the diesel generator system measured using the phase-locked loop (PLL) and provides reasonable estimates of unknown parameters. The estimated parameters are further verified by using them back in the simplified model and comparing them with the PLL measurements to represent the frequency dynamics of the diesel genset system.

With the advancement in power electronics technology and grid standards, traditional converters are being supplemented with the new IEEE 1547-2018 standard based grid support functions (GSFs) to support power system voltage and frequency. Inverter dynamics in power systems vary with different modes of operation, thus new modeling methods for proper system planning, operation, and dispatch are required. This work presents a data-driven approach for partitioned dynamic modeling of inverters to speed up simulation time and reduce computational complexity while ensuring acceptable accuracy. The proposed method was tested for a smart inverter with voltage support (Volt-VAr function) on a two-bus system considering dynamic residential loads, and the results showed a four-time speedup in simulation time compared to the use of the detailed model with acceptable levels of accuracy.

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Microgrid parameter estimation is essential to enable optimal voltage and frequency control using distributed energy resources (DER). Microgrid parameters vary through time, e.g., when generation is re-dispatched/committed, during microgrid reconfiguration. Furthermore, sensor measurements are noisy and preservation of the fast dynamics measurements is required, which is difficult to achieve with a lowpass filter. In this paper, a moving horizon estimation (MHE) approach is applied to estimate microgrid parameters for voltage and frequency support. The proposed approach estimates the states i.e., frequency, rate of change of frequency, grid voltage and current, and system parameters i.e., inertia, damping, and equivalent impedance. The MHE is formulated as an optimization problem using data over a fixed past horizon and solved online such that the sum of the square of measurement noise and process noise is minimized. Results showed that the proposed approach was able to estimate microgrid states, parameters, and disturbances within 5% for most values, which is sufficient to use in microgrid voltage and frequency control.

In this work, a model predictive dispatch framework is proposed to utilize Energy Storage Systems (ESSs) for voltage regulation in distribution systems. The objective is to utilize ESS resources to assist with voltage regulation while reducing the utilization of legacy devices such as on-load tap changers (OLTCs), capacitor banks, etc. The proposed framework is part of a two-stage solution where a secondary layer computes the ESS dispatch every 5-min based on 1-hr generation and load forecasts while a primary layer would handle the real-time uncertainties. In this paper, the secondary layer to dispatch the ESS is formulated. Simulation results show that dispatching ESSs by providing active and reactive support can minimize the OLTC movement in distribution networks thus increasing the lifetime of legacy mechanical devices.

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Increasing installation of renewable energy resources makes the power system inertia a time-varying quantity. Furthermore, converter-dominated grids have different dynamics compared to conventional grids and therefore estimates of the inertia constant using existing dynamic power system models are unsuitable. In this paper, a novel inertia estimation technique based on convolutional neural networks that use local frequency measurements is proposed. The model uses a non-intrusive excitation signal to perturb the system and measure frequency using a phase-locked loop. The estimated inertia constants, within 10% of actual values, have an accuracy of 97.35% and root mean square error of 0.2309. Furthermore, the model evaluated on unknown frequency measurements during the testing phase estimated the inertia constant with a root mean square error of 0.1763. The proposed model-free approach can estimate the inertia constant with just local frequency measurements and can be applied over traditional inertia estimation methods that do not incorporate the dynamic impact of renewable energy sources.

This paper investigates the design of low-level probing signals for accurate estimation of inertia and damping constants in microgrids. Increasing utilization of renewable energy sources and their different dynamics has created unknowns in time-varying system inertia and damping constants. Thus, it is difficult to know these parameters at any given time in converter-dominated microgrids. This paper describes the design characteristics, considerations, methodology, and accuracy level of different probing signals in determining unknown parameters of a system. The main goal of this paper is to find an effective probing signal with a simple implementation and minimal impacts on power system operation. The test-case model in this paper analyzes nonintrusive excitation signals to perturb a power system model (i.e., square wave, multisine wave, filtered white Gaussian noise, and pseudo-random binary sequence). A moving horizon estimation (MHE)-based approach is then implemented in an energy storage system (ESS) in MATLAB/Simulink for estimation of inertia and damping constants of a system based on frequency measurements from a local phase-locked-loop (PLL). The accuracy of parameter estimates alters depending on the chosen probing signal; when estimating inertia and damping constants using MHE with the different probing signals, square waves yielded the lowest error.

For the optimal performance of a microgrid where line resistance to reactance (R/X) ratio is high, one must consider the coupling between voltage and frequency. Furthermore, microgrid operational costs are time-varying. Thus, the voltage and frequency support controller must be flexible enough to handle technical and economic constraints. In this paper, the model predictive control (MPC) approach is proposed for voltage and frequency support considering the coupling between voltage and frequency dynamics. With the predictive model of the system, the finite horizon optimization problem is solved online and a control signal is calculated such that defined cost is minimized. By proper choice of MPC parameters, desired performance based on the availability of resources and market incentives can be achieved.

A significant amount of converter-based generation, such as wind and photovoltaic, is being integrated into thebulk electric power grid to fulfill the future electric demand. Such converter-based distributed energy resources (DERs) will be providing multiple grid support functions (GSFs) to supportvoltage and frequency control of the power system. In thispaper, we present the development of a MA /Simulink-based simulation model to study power system dynamics whenDERs are equipped with GSFs. The simulation model of aninverter with GSFs is validated through comparisons against thecharacteristic curves for each function of the IEEE 1547-2018standard. The normalized root-mean-square-error (NRMSE) wascalculated to be less than 2%. The developed model is then used ina sample power systems dynamics study under various operatingconditions. Results show the exnected resnonse of inverfers withGSFs, properly supporting the grid voltage and frequency andmaintaining the value within an acceptable range.

Low voltage microgrid systems are characterized by high sensitivity to both active and reactive power for voltage support. Also, the operational conditions of microgrids connected to active distribution systems are time-varying. Thus, the ideal controller to provide voltage support must be flexible enough to handle technical and operational constraints. This paper proposes a model predictive control (MPC) approach to provide dynamic voltage support using energy storage systems. This approach uses a simplified predictive model of the system along with operational constraints to solve an online finite-horizon optimization problem. Control signals are then computed such that the defined cost function is minimized. By proper selection of MPC weighting parameters, the quality of service provided can be adjusted to achieve the desired performance. A simulation study in Matlab/Simulink validates the proposed approach for a simplified version of a 100 kVA, 208 V microgrid using typical parameters. Results show that performance of the voltage support can be adjusted depending on the choice of weight and constraints of the controller.

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The displacement of rotational generation and the consequent reduction in system inertia is expected to have major stability and reliability impacts on modern power systems. Fast-frequency support strategies using energy storage systems (ESSs) can be deployed to maintain the inertial response of the system, but information regarding the inertial response of the system is critical for the effective implementation of such control strategies. In this paper, a moving horizon estimation (MHE)-based approach for online estimation of inertia constant of low inertia microgrids is presented. Based on the frequency measurements obtained in response to a non-intrusive excitation signal from an ESS, the inertia constant was estimated using local measurements from the ESS's phase-locked loop. The proposed MHE formulation was first tested in a linearized power system model, followed by tests in a modified microgrid benchmark from Cordova, Alaska. Even under moderate measurement noise, the technique was able to estimate the inertia constant of the system well within ±20% of the true value. Estimates provided by the proposed method could be utilized for applications such as fast-frequency support, adaptive protection schemes, and planning and procurement of spinning reserves.

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Fast-frequency control strategies have been proposed in the literature to maintain inertial response of electric generation and help with the frequency regulation of the system. However, it is challenging to deploy such strategies when the inertia constant of the system is unknown and time-varying. In this paper, we present a data-driven system identification approach for an energy storage system (ESS) operator to identify the inertial response of the system (and consequently the inertia constant). The method is first tested and validated with a simulated genset model using small changes in the system load as the excitation signal and measuring the corresponding change in frequency. The validated method is then used to experimentally identify the inertia constant of a genset. The inertia constant of the simulated genset model was estimated with an error of less than 5% which provides a reasonable estimate for the ESS operator to properly tune the parameters of a fast-frequency controller.