Diesel generators (gensets) are often the lowest-cost electric generation for reliable supply in remote microgrids. The development of converter-dominated diesel-backed microgrids requires accurate dynamic modeling to ensure power quality and system stability. Dynamic response derived using original genset system models often does not match those observed in field experiments. This paper presents the experimental system identification of a frequency dynamics model for a 400 kVA diesel genset. The genset is perturbed via active power load changes and a linearized dynamics model is fit based on power and frequency measurements using moving horizon estimation (MHE). The method is first simulated using a detailed genset model developed in MATLAB/Simulink. The simulation model is then validated against the frequency response obtained from a real 400 kVA genset system at the Power System Integration (PSI) Lab at the University of Alaska Fairbanks (UAF). The simulation and experimental results had model errors of 3.17% and 11.65%, respectively. The resulting genset model can then be used in microgrid frequency dynamic studies, such as for the integration of renewable energy sources.
A microgrid is characterized by a high R/X ratio, making the voltage more sensitive to active power changes unlike in bulk power systems where voltage is mostly regulated by reactive power. Because of its sensitivity to active power, control approach should incorporate active power as well. Thus, the voltage control approach for microgrids is very different from conventional power systems. The energy costs associated with these power are different. Furthermore, because of diverse generation sources and different components such as distributed energy resources, energy storage systems, etc, model-based control approaches might not perform very well. This paper proposes a reinforcement learning-based voltage support framework for a microgrid where an agent learns control policy by interacting with the microgrid without requiring a mathematical model of the system. A MATLAB/Simulink simulation study on a test system from Cordova, Alaska shows that there is a large reduction in voltage deviation (about 2.5-4.5 times). This reduction in voltage deviation can improve the power quality of the microgrid: ensuring a reliable supply, longer equipment lifespan, and stable user operations.
Low-inertia microgrids experience significant frequency deviations compared to bulk-power systems. In such microgrids, energy storage systems (ESSs) can be a viable option to provide fast-frequency support to keep frequency variations within allowable bounds. A model predictive control (MPC)-based strategy is one of the efficient control strategies to enable fast-frequency support through ESSs. MPC provides the capability to explicitly incorporate physical constraints of the microgrid and the ESS into the control formulation while allowing signifi-cant operational flexibility. MPC allows near-optimal control by optimizing the system over a rolling horizon based on a predictive model of the system. However, the effectiveness of MPC relies on the accuracy of this predictive model. This paper proposes a data-driven system identification (SI) based approach to obtain an accurate yet computationally tractable predictive model for frequency support in microgrids. The proposed data-driven MPC is compared with the conventional MPC that utilizes a simplified transfer-function-based predictive model of the system. Results show that the data-driven MPC offers a better quality of service in terms of lower frequency deviations and rate-of-change of frequency (ROCOF).
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 Laboratories (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 hardware demonstration of “resilience nodes” concept.
For the model-based control of low-voltage microgrids, state and parameter information are required. Different optimal estimation techniques can be employed for this purpose. However, these estimation techniques require knowledge of noise covariances (process and measurement noise). Incorrect values of noise covariances can deteriorate the estimator performance, which in turn can reduce the overall controller performance. This paper presents a method to identify noise covariances for voltage dynamics estimation in a microgrid. The method is based on the autocovariance least squares technique. A simulation study of a simplified 100 kVA, 208 V microgrid system in MATLAB/Simulink validates the method. Results show that estimation accuracy is close to the actual value for Gaussian noise, and non-Gaussian noise has a slightly larger error.
Dynamic state and parameter estimation in current and future power systems are critical for advanced monitoring, control, and protection. There are numerous methods to perform dynamic state and parameter estimation; this paper compares the accuracy and computational time of four methods (i.e., Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and moving horizon estimation (MHE)) designed to estimate the states and parameters for frequency dynamics of a power system. A simulation study was conducted using Matlab/Simulink by introducing Gaussian and non-Gaussian noise in the measurements. Results under Gaussian noise showed similar accuracy performance for all filters. EKF and UKF presented convergence or numerical instability issues due to incorrect initial guesses of parameters. MHE did not present convergence issues, however, required comparatively higher computation time. Nonetheless, the MHE could still be implemented in real-time for state and parameter estimation of power system. The impact of non-Gaussian noise on the methods was inconclusive and will require further study.
2022 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2022
Guruwacharya, Nischal; Bhandari, Harish; Subedi, Sunil; Vasquez-Plaza, Jesus D.; Stoel, Matthew L.; Tamrakar, Ujjwol; Wilches-Bernal, Felipe; Andrade, Fabio; Hansen, Timothy M.; Tonkoski, Reinaldo
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.
Large-scale deployment of energy storage systems is a pivotal step toward achieving the clean energy goals of the future. An accurate and publicly accessible database on energy storage projects can help accelerate deployment by providing valuable information and characteristic data to different stakeholders. The U.S. Department of Energy's Global Energy Storage Database (GESDB) aims at providing high-quality and accurate data on energy storage projects around the globe. This paper first provides an overview of the GESDB, briefly describing its features and overall usage. This is followed by a detailed description of the procedure used to validate the database. In doing so, the paper aims at improving the usability of the website while enhancing its value to the community. Furthermore, the presented validation procedure makes the underlying assumptions transparent to the public so that data misinterpretation can be minimized/avoided.
Subedi, Sunil; Guruwacharya, Nischal; Tamrakar, Ujjwol; Cicilio, Phylicia; Fourney, Robert; Rekabdarkolaee, Hossein M.; Tonkoski, Reinaldo; Hansen, Timothy M.
With this work, we aim to speed up simulation and reduce computational complexity of Converter Dominated Power System (CDPS) within an acceptable accuracy.
Recent developments in the renewable energy sector have seen an unprecedented growth in residential photovoltaic (PV) installations. However, high PV penetration levels often lead to overvoltage problems in low-voltage (LV) distribution feeders. Smart inverter control such as active power curtailment (APC)-based overvoltage control can be implemented to overcome these challenges. The APC technique utilizes a constant droop-based approach which curtails power rigidly, which can lead to significant energy curtailment in the LV distribution feeders. In this paper, different variations of the APC technique with linear, quadratic, and exponential droops have been analyzed from the point-of-view of energy curtailment for a LV distribution network in North America. Further, a combinatorial approach using various droop-based APC methods in conjunction with adaptive dynamic programming (ADP) as a supplementary control scheme has also been proposed. The proposed approach minimizes energy curtailment in the LV distribution network by adjusting the droop gains. Simulation results depict that ADP in conjunction with exponential droop reduces the energy curtailment to approximately 50% compared to using the standard linear droop.
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.
The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.
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.
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.
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.