Publications

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This paper uses co-located wind and photovoltaic generation, along with battery energy storage, as a single plant and introduces a method to provide a flexible synthetic inertia (SI) response based on plant-wide settings. The proposed controller accounts for variable resources and correctly adjusts device responses when an inverter-based resource (IBR) may become unavailable to provide a consistent plant level SI response. The flexible SI response is shown to adequately replace the lost synchronous inertial response from equivalent conventional generation when IBR penetration is approximately 25% in a small power system. Furthermore, it is shown that a high gain SI response provided by the combined IBR plant can reduce the rate of change of frequency magnitude over 50% from the equivalently rated conventional generation response.

Primary frequency control in power systems is being challenged by the large-scale integration of inverter-based resources (IBRs) because they do not typically respond to frequency fluctuations. This paper suggests introducing new reserve products into the electricity market that provide incentive for IBRs to contribute to primary frequency control in ways that take advantage of their fast-acting capabilities. In addition to a Primary Frequency Response (PFR) reserve product, which accommodates standard droop control, we suggest introducing a Fast Frequency Response (FFR) reserve product, a reserve product for Virtual Inertia (VI), which is also known as synthetic inertia, and an inertia product. We adopt a reserve requirement that guarantees sufficient primary frequency response reserve to adequately arrest frequency decline in response to a large generator outage within a certain margin. We place this reserve requirement into a real-time co-optimization problem, derive prices for each product and analyze the incentives provided to IBRs.

The paper proposes an implementation of Graph Neural Networks (GNNs) for distribution power system Traveling Wave (TW) - based protection schemes. Simulated faults on the IEEE 34 system are processed by using the Karrenbauer Transform and the Stationary Wavelet Transform (SWT), and the energy of the resulting signals is calculated using the Parseval's Energy Theorem. This data is used to train Graph Convolutional Networks (GCNs) to perform fault zone location. Several levels of measurement noise are considered for comparison. The results show outstanding performance, more than 90% for the most developed models, and outline a fast, reliable, asynchronous and distributed protection scheme for distribution level networks.

This paper studies the differences in a synthetic inertia controller of using two different feedback measurements: (i) an estimate of the rate of change of frequency from local voltage measurements, and (ii) a remote machine acceleration from a generator nearby to the actuator. The device that provides the synthetic inertia action is a converter interfaced generator (CIG). The paper carries out analysis in the frequency domain, using Bode plots, to show that synthetic inertia control using frequency estimates is more prone to instabilities than for the case where a machine speed is used. The paper then proposes a controller (or a filter) to mitigate these effects. In addition, the paper shows the effects that a delay of the machine speed signal of the nearby generator has on the synthetic inertia control of the system and how a controller is also needed in this case. Finally, the paper shows the difference in performance of a synthetic inertia controller when using these different measurement signals with simulations in time domain a electromagnetic transient program platform.

This paper presents a novel approach for fault location and classification based on combining mathematical morphology (MM) with Random Forests (RF). The MM stage of the method is used to pre-process voltage and current data. Signal vector norms on the output signals of the MM stage are then used as the input features for a RF machine learning classifier and regressor. The data used as input for the proposed approach comprises only a window of 50 µs before and after the fault is detected. The proposed method is tested with noisy data from a small simulated system. These results show 100% accuracy for the classification task and prediction errors with an average of ~13 m in the fault location task.

A novel method for fault classification and location is presented in this paper. This method is divided into an initial signal processing stage that is followed by a machine learning stage. The initial stage analyzes voltages and currents with a window-based approach based on the dynamic mode decomposition (DMD) and then applies signal norms to the resulting DMD data. The outputs for the signal norms are used as features for a random-forests for classifying the type of fault in the system as well as for fault location purposes. The method was tested on a small distribution system where it showed an accuracy of 100% in fault classification and a mean error of ~ 30 m when predicting the fault location.

This paper uses clusters of solar photovoltaic units, wind turbines, and battery energy storage systems as a single controllable plant and proposes a method to enable adaptive plant wide droop control. Each of these clusters is defined as a Flexpower plant. The proposed control is presented with multiple configurations that enable the same overall droop characteristic to the Flexpower plant, but use each of the resource technologies in a different manner. One of the control configurations considers the availability of the resources for wind and solar units, as well as the state of charge of energy storage units, when distributing droop action to each unit that comprise the Flexpower plant. The proposed approaches were tested in a small power system where it is shown that the Flexpower plant can provide frequency regulation to the system in a variety of ways depending on which of the proposed control configurations was selected.

Grid technologies connected via power electronic converter (PEC) interfaces increasingly include the grid support functions for voltage and frequency support defined by the IEEE 1547-2018 standard. The shift towards converter-based generation necessitates accurate PEC models for assessing system dynamics that were previously ignored in conventional power systems. In this paper, a method for assessing photovoltaic (PV) inverter dynamics using a data-driven technique with power hardware-in-the-loop is presented. The data-driven modeling technique uses various probing signals to estimate commercial off-the-shelf (COTS) inverter dynamics. The MATLAB system identification toolbox is used to develop a dynamic COTS inverter model from the perturbed grid voltage (i.e., probing signal) and measured current injected to the grid by the inverter. The goodness-of-fit of COTS inverter dynamics in Volt-VAr support mode under each probing signal is compared. The results show that the logarithmic square-chirp probing signal adequately excites the COTS inverter in Volt-VAr mode to fit a data-driven dynamic model.

The Cramér-Rao Lower Bound (CRLB) is used as a classical benchmark to assess estimators. Online algorithms for estimating modal properties from ambient data, i.e., mode meters, can benefit from accurate estimates of forced oscillations. The CRLB provides insight into how well forced oscillation parameters, e.g., frequency and amplitude, can be estimated. Previous works have solved the lower bound under a single-channel PMU measurement; thus, this paper extends works further to study CRLB under two-channel PMU measurements. The goal is to study how correlated/uncorrelated noise can affect estimation accuracy. Interestingly, these studies shows that correlated noise can decrease the CRLB in some cases. This paper derives the CRLB for the two-channel case and discusses factors that affect the bound.

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This paper presents a preliminary investigation on controlling the existing high voltage dc (HVDC) links connecting the North American western interconnection (WI) to the other interconnections, to provide damping to inter-area oscillations. The control scheme is meant to damp inter-area modes of oscillation in the WI by using wide area synchrophasor feedback. A custom model is developed in General Electric's PSLF software for the wide area damping control scheme, and simulations are analyzed on a validated full 22,000 bus WI model. Results indicate that implementing the proposed control technique to the existing HVDC links in the WI can significantly improve the damping of the inter-area modes of the system.

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This paper presents a new method for detecting power quality disturbances, such as faults. The method is based on the dynamic mode decomposition (DMD)-a data-driven method to estimate linear dynamics whose eigenvalues and eigenvectors approximate those of the Koopman operator. The proposed method uses the real part of the main eigenvalue estimated by the DMD as the key indicator that a power quality event has occurred. The paper shows how the proposed method can be used to detect events using current and voltage signals to distinguish different faults. Because the proposed method is window-based, the effect that the window size has on the performance of the approach is analyzed. In addition, a study on the effect that noise has on the proposed approach is presented.

As a result of the increase in penetration of inverter-based generation such as wind and solar, the dynamics of the grid are being modified. These modifications may threaten the stability of the power system since the dynamics of these devices are completely different from those of rotating generators. Protection schemes need to evolve with the changes in the grid to successfully deliver their objectives of maintaining safe and reliable grid operations. This paper explores the theory of traveling waves and how they can be used to enable fast protection mechanisms. It surveys a list of signal processing methods to extract information on power system signals following a disturbance. The paper also presents a literature review of traveling wave-based protection methods at the transmission and distribution levels of the grid and for AC and DC configurations. The paper then discusses simulations tools to help design and implement protection schemes. A discussion of the anticipated evolution of protection mechanisms with the challenges facing the grid is also presented.

As renewable energy sources are becoming more dominant in electric grids, particularly in micro grids, new approaches for designing, operating, and controlling these systems are required. The integration of renewable energy devices such as photovoltaics and wind turbines require system design considerations to mitigate potential power quality issues caused by highly variable generation. Power system simulations play an important role in understanding stability and performance of electrical power systems. This paper discusses the modeling of the Global Laboratory for Energy Asset Management and Manufacturing (GLEAMM) micro grid integrated with the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) test site, providing a dynamic simulation model for power flow and transient stability analysis. A description of the system as well as the dynamic models is presented.

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This paper presents model formulations for generators that have the ability to use multiple fuels and to switch between them if necessary. These models are used to generate different scenarios of fuel switching penetration from a test power system. With these scenarios, for a severe disruption in the fuel supply to multiple generators, the paper analyzes the effect that fuel switching has on the resilience of the power system. Load not served is used as the proxy metric to evaluate power system resilience. The paper shows that the presence of generators with fuel switching capabilities considerably reduces the amount and duration of the load shed by the system facing the fuel disruption.

The state of California is leading the nation with respect to solar energy and storage. The California Energy Commission has mandated that starting in 2020 all new homes must be solar powered. In 2010 the California state legislature adopted an energy storage mandate AB 2514. This required California's three largest utilities to contract for an additiona11.3 GW of energy storage by 2020, coming online by 2024. Therefore, there is keen interest in the potential advantages of deploying solar combined with energy storage. This paper formulates the optimization problem to identify the maximum potential revenue from pairing storage with solar and participating in the California Independent System Operator (CAISO) day ahead market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes were analyzed to identify trends and opportunities for the deployment of solar plus storage.

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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.

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Low frequency electromechanical oscillations can pose a threat to the stability of power systems if not properly addressed. This paper proposes a novel methodology to damp these inter-area oscillations using loads, the demand side of the system. In the proposed methodology, loads are assigned to an aggregated cluster whose demand is modulated for oscillation damping. The load cluster control action is obtained from an optimal output feedback control (OOFC) strategy. The paper presents an extension to the regular OOFC formulation by imposing a constraint on the sum of the rows in the optimal gain matrix. This constraint is useful when the feedback signals are generator speeds. In this case, the sum of the rows of the optimal gain matrix is the droop gain of each load actuator. Time-domain simulations of a large-scale power system are used to demonstrate the efficacy of the proposed control algorithm. Two different cases are considered: a power imbalance and a line fault. The simulation results show that the proposed controllers successfully damp inter-area oscillations under different operating conditions and with different clustering for the events considered. In addition, the simulations illustrate the benefit of the proposed extension to the OOFC that enable load to provide a combination of droop control and small signal stability augmentation.

Forced oscillations in power systems are of particular interest when they interact and reinforce inter-area oscillations. This paper determines how a previously proposed inter-area damping controller mitigates forced oscillations. The damping controller modulates active power on the Pacific DC Intertie (PDCI) based on phasor measurement units (PMU) frequency measurements. The primary goal of the controller is to improve the small signal stability of the north south B mode in the North American Western Interconnection (WI). The paper presents small signal stability analysis in a reduced order system, time-domain simulations of a detailed representation of the WI and actual system test results to demonstrate that the PDCI damping controller provides effective damping to forced oscillations in the frequency range below 1 Hz.

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As penetration of converter interfaced generators (CIGs) increases, the need for CIG frequency control participation increases. Traditionally, research in this area has been performed using positive sequence simulation software, which provides voltage magnitude and phase measurements, but not point-on-wave (POW) measurements. This means that the effect of frequency estimation algorithms cannot be accurately modeled, especially when the voltage waveform is distorted by faults or load connection events. This report serves as a user manual for an electromagnetic transient simulation testbed, which allows for accurate modeling of frequency estimation and control techniques.

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This paper discusses how to design an inter-area oscillations damping controller using a frequency-shaped optimal output feedback control approach. This control approach was chosen because inter-area oscillations occur at a particular frequency range, from 0.2 to 1 Hz, which is the interval the control action must be prioritized. This paper shows that using only the filter for the system states can sufficiently damp the system modes. In addition, the paper shows that the filter for the input can be adjusted to provide primary frequency regulation to the system with no effect to the desired damping control action. Time domain simulations of a power system with a set of controllable power injection devices are presented to show the effectiveness of the designed controller.

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This paper analyzes how two Kalman Filter (KF) based frequency estimation algorithms react to phase steps. It is demonstrated that phase steps are interpreted as sharp changes in frequency. The paper studies whether the location of the phase step, within the sinusoidal waveform, has any effect on the frequency estimate. Because phase steps are not the product of a permanent change in the underlying frequency, the paper proposes an algorithm to correct frequency estimates deemed erroneous. The algorithm makes use of the residual of the KF to determine when an estimate is incorrect and to trigger a corrective action in which the frequency estimate is replaced by an average of the previous values that were considered accurate. Using synthesized and simulated data with distortions the paper shows the effectiveness of the correction algorithm in fixing frequency estimates.

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This paper describes the design and implementation of a proof-of-concept Pacific dc Intertie (PDCI) wide area damping controller and includes system test results on the North American Western Interconnection (WI). To damp inter-area oscillations, the controller modulates the power transfer of the PDCI, a ±500 kV dc transmission line in the WI. The control system utilizes real-time phasor measurement unit (PMU) feedback to construct a commanded power signal which is added to the scheduled power flow for the PDCI. After years of design, simulations, and development, this controller has been implemented in hardware and successfully tested in both open and closed-loop operation. The most important design specifications were safe, reliable performance, no degradation of any system modes in any circumstances, and improve damping to the controllable modes in the WI. The main finding is that the controller adds significant damping to the modes of the WI and does not adversely affect the system response in any of the test cases. The primary contribution of this paper, to the state of the art research, is the design methods and test results of the first North American real-time control system that uses wide area PMU feedback.

This paper explores the revenue potential for electric storage resources (ESRs), also referred to as electrical energy storage, in the Southwest Power Pool Integrated Marketplace. In particular, opportunities in the day-ahead market with the energy and frequency regulation products are considered. The revenue maximization problem is formulated as a linear program model, where an ESR seeks to maximize its revenue through the available revenue streams. The ESR has perfect foresight of historical prices and determines the optimal policy accordingly. A case study using FY2018 data shows that frequency regulation services are the most lucrative for revenue potential. This paper also explores different methods of using area control error data to infer the regulation control signal and the consequent effect on the optimization. Finally, the paper conducts a sensitivity analysis of ESR payback period to energy capacity and power rating.

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This report presents a complete listing, as of May 2019, of the damping controller (DCON) project accomplishments including a project overview, project innovations, awards, patent application, journal papers, conference papers, project reports, and project presentations. The purpose of the DCON is to mitigate inter-area oscillations in the WI by active improvement of oscillatory mode damping using phasor measurement unit (PMU) feedback to modulate power flow in the PDCI. The DCON project is the result of a collaboration between Sandia National Laboratories (SNL), Montana Technological University (MTU), Bonneville Power Administration (BPA), and the Department of Energy Office of Electricity (DOE-OE).

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This paper analyzes the effect of wind turbine integration (WT) on the inter-area oscillation mode of a test two-area power system. The paper uses a root-locus based design method to propose a pair of controllers to provide damping to the inter-area mode of the system. The controllers are selected from the best combination of feedback signal and WT control action. One of the controllers uses the active power control part of the WT while the other uses the reactive power part. The paper analyzes the impact that increases on the transmission line connecting the WT to the system have on the controllers' performance. Time domain simulations are provided to evaluate the effectiveness of the controllers under different conditions.

This paper focuses on a transmission system with a high penetration of converter-interfaced generators participating in its primary frequency regulation. In particular, the effects on system stability of widespread misconfiguration of frequency regulation schemes are considered. Failures in three separate primary frequency control schemes are analyzed by means of time domain simulations where control action was inverted by, for example, negating controller gain. The results indicate that in all cases the frequency response of the system is greatly deteriorated and, in multiple scenarios, the system loses synchronism. It is also shown that including limits to the control action can mitigate the deleterious effects of inverted control configurations.

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This study describes the implementation of a tool to estimate latencies and data dropouts in communication networks transferring synchrophasor data defined by the C37.118 standard. The tool assigns a time tag to synchrophasor packets at the time it receives them according to a global positioning system clock and with this information is able to determine the time those packets took to reach the tool. The tool is able to connect simultaneously to multiple phasor measurement units (PMUs) sending packets at different reporting rates with different transport protocols such as user datagram protocol or transmission control protocol. The tool is capable of redistributing every packet it receives to a different device while recording the exact time this information is re-sent into the network. The results of measuring delays from a PMU using this tool are presented and compared with those of a conventional network analyzer. The results show that the tool presented in this paper measures delays more accurately and precisely than the conventional network analyzer.

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This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.

A wide-area controller to damp inter-area oscillations in the North American Western Interconnection (WI) by modulating power transfers in a HVDC link is used in this paper to investigate the effects that latencies in its feedback signals have on its performance. This controller uses two feedback measurements to perform its control action. The analysis show that the stabilizing effect of the controller in transient stability and small signal stability is compromised as the feedback measurements experience higher delays. The results show that one of the feedback signals can tolerate more delay than the other. The analysis was performed with Bode plots and time domain simulations on a reduced order model of the WI from which a linear version was obtained.

A wide-area controller to damp inter-area oscillations in the North American Western Interconnection (WI) by modulating power transfers in a HVDC link is used in this paper to investigate the effects that latencies in its feedback signals have on its performance. This controller uses two feedback measurements to perform its control action. The analysis show that the stabilizing effect of the controller in transient stability and small signal stability is compromised as the feedback measurements experience higher delays. The results show that one of the feedback signals can tolerate more delay than the other. The analysis was performed with Bode plots and time domain simulations on a reduced order model of the WI from which a linear version was obtained.

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This paper discusses the optimal output feedback control problem of linear time-invariant systems with additional restrictions on the structure of the optimal feedback control gain. These restrictions include setting individual elements of the optimal gain matrix to zero and making the sum of certain rows of the gain matrix equal to desired values. The paper proposes a method that modifies the standard quadratic cost function to include soft constraints ensuring the satisfaction of these restrictions on the structure of the optimal gain. Necessary conditions for optimality with these soft constraints are derived, and an algorithm to solve the resulting optimal output feedback control problem is given. Finally, a power systems example is presented to illustrate the usefulness of proposed approach.

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This paper discusses the broad use of rotational kinetic energy stored in wind turbine rotors to supply services to the electrical power grid. The grid services are discussed in terms of zero-net-energy, which do not require a reduction in power output via pitch control (spill), but neither do they preclude doing so. The services discussed include zero-net-energy regulation, transient and small signal stability, and other frequency management services. The delivery of this energy requires a trade-off between the frequency and amplitude of power modulation and is limited, in some cases, by equipment ratings and the unresearched long-term mechanical effects on the turbine. As wind displaces synchronous generation, the grid's inertial storage is being reduced, but the amount of accessible kinetic energy in a wind turbine at rated speed is approximately 6 times greater than that of a generator with only a 0.12% loss in efficiency and 75 times greater at 10% loss. The potential flexibility of the wind's kinetic storage is also high. However, the true cost of providing grid services using wind turbines, which includes a potential increase in operations and maintenance costs, have not been compared to the value of the services themselves.

This report documents the use of wind turbine inertial energy for the supply of two specific electric power grid services; system balancing and real power modulation to improve grid stability. Each service is developed to require zero net energy consumption. Grid stability was accomplished by modulating the real power output of the wind turbine at a frequency and phase associated with wide-area modes. System balancing was conducted using a grid frequency signal that was high-pass filtered to ensure zero net energy. Both services used Phasor Measurement Units (PMUs) as their primary source of system data in a feedforward control (for system balancing) and feedback control (for system stability).

This paper presents simulation results of a control scheme for damping inter-area oscillations using high-voltage DC (HVDC) power modulation. The control system utilizes realtime synchrophasor feedback to construct a supplemental commanded power signal for the Pacific DC Intertie (PDCI) in the North American Western Interconnection (WI). A prototype of this controller has been implemented in hardware and, after multiple years of development, successfully tested in both open and closed-loop operation. This paper presents simulation results of the WI during multiple severe contingencies with the damping controller in both open and closed-loop. The primary results are that the controller adds significant damping to the controllable modes of the WI and that it does not adversely affect the system response in any of the simulated cases. Furthermore, the simulations show that a feedback signal composed of the frequency difference between points of measurement near the Washington-Oregon border and the California-Oregon border can be employed with similar results to a feedback signal constructed from measurements taken near the Washington-Oregon border and southern California. This is an important consideration because it allowed the control system to be designed without relying upon cross-system measurements, which would have introduced significant additional delay.

Distributed control compensation based on local and remote sensor feedback can improve small-signal stability in large distributed systems, such as electric power systems. Long distance remote measurements, however, are potentially subject to relatively long and uncertain network latencies. In this work, the issue of asymmetrical network latencies is considered for an active damping application in a two-area electric power system. The combined effects of latency and gain are evaluated in time domain simulation and in analysis using root-locus and the maximum singular value of the input sensitivity function. The results aid in quantifying the effects of network latencies and gain on system stability and disturbance rejection.

Distributed control compensation based on local and remote sensor feedback can improve small-signal stability in large distributed systems, such as electric power systems. Long distance remote measurements, however, are potentially subject to relatively long and uncertain network latencies. In this work, the issue of asymmetrical network latencies is considered for an active damping application in a two-area electric power system. The combined effects of latency and gain are evaluated in time domain simulation and in analysis using root-locus and the maximum singular value of the input sensitivity function. The results aid in quantifying the effects of network latencies and gain on system stability and disturbance rejection.