Renewable energy has become a viable solution for reducing the harmful effects that fossil fuels have on our environment, prompting utilities to replace traditional synchronous generators (SG) with more inverter-based devices that can provide clean energy. One of the biggest challenges utilities are facing is that by replacing SG, there is a reduction in the systems' mechanical inertia, making them vulnerable to frequency instability. Grid-forming inverters (GFMI) have the ability to create and regulate their own voltage reference in a manner that helps stabilize system frequency. As an emerging technology, there is a need for understanding their dynamic behavior when subjected to abrupt changes. This paper evaluates the performance of a GFMI when subjected to voltage phase jump conditions. Experimental results are presented for the GFMI subjected to both balanced and unbalanced voltage phase jump events in both P/Q and V/f modes.
DC microgrids envisioned with high bandwidth communications may well expand their application range by considering autonomous strategies as resiliency contingencies. In most cases, these strategies are based on the droop control method, seeking low voltage regulation and proportional load sharing. Control challenges arise when coordinating the output of multiple DC microgrids composed of several Distributed Energy Resources. This paper proposes an autonomous control strategy for transactional converters when multiple DC microgrids are connected through a common bus. The control seeks to match the external bus voltage with the internal bus voltage balancing power. Three case scenarios are considered: standalone operation of each DC microgrid, excess generation, and generation deficit in one DC microgrid. Results using Sandia National Laboratories Secure Scalable Microgrid Simulink library, and models developed in MATLAB are compared.
Due to the increased penetration in Distributed Energy Resources (DERs), especially in Photovoltaic (PV) systems, voltage and frequency regulation has become a topic of interest. Utilities have been requesting DER voltage and frequency support for almost two decades. Their request was addressed by standards such as the IEEE Std 1547-2018. With the continuous improvements in inverters' ability to control their output voltage, power, and frequency, a group of advanced techniques to support the grid is now required by the interconnection standard. These techniques are known as Grid Support Functions (GSF), and they allow the inverter to provide voltage and frequency support to the grid as well as the ability to ride-through abnormal events. Understanding how a GSF behaves is challenging, especially when multiple GSFs are combined to help the utility to control the system voltage and frequency. This paper evaluates the effects of GSF's on the IEEE Std 1547.1-2020 Unintentional Islanding Test 5B by comparing simulation results from a developed PV inverter model and experimental results from a Power Hardware-in-the-Loop platform.
In this paper, the development of a mathematical model for islanding detection method based on the concept of a digital twin is presented. The model estimates the grid impedance seen by a distributed energy resource. The proposed algorithm has characteristics of passive and active islanding detection methods. Using a discrete state-space representation of a dq0 axis power system as equality constraints, a digital twin is optimized to match the power system of interest. The concept is to use the estimated grid impedance as the parameter to identify the difference between normal operation and islanding scenarios. Selecting arbitrary initial values, the digital twin approximates the response of the actual system and therefore a value for the system impedance. Results indicate that the proposed method has the potential to estimate the grid impedance at the point of common coupling.
Grid support functionalities from advanced PV inverters are increasingly being utilized to help regulate grid conditions and enable high PV penetration levels. To ensure a high degree of reliability, it is paramount that protective devices respond properly to a variety of fault conditions. However, while the fault response of PV inverters operating at unity power factor has been well documented, less work has been done to characterize the fault contributions and impacts of advanced inverters with grid support enabled under conditions like voltage sags and phase angle jumps. To address this knowledge gap, this paper presents experimental results of a three-phase photovoltaic inverter's response during and after a fault to investigate how PV systems behave under fault conditions when operating with and without a grid support functionality (autonomous Volt-Var) enabled. Simulations were then conducted to quantify the potential impact of the experimental findings on protection systems. It was observed that fault current magnitudes across several protective devices were impacted by non-unity power factor operating conditions, suggesting that protection settings may need to be studied and updated whenever grid support functions are enabled or modified.
In order to address the recent inclement weather-related energy events, electricity production is experiencing an important transition from conventional fossil fuel based resources to the use of Distributed Energy Resources (DER), providing clean and renewable energy. These DERs make use of power electronic based devices that perform the energy conversion process required to interface with the utility grids. For the particular cases where DC/AC conversion is required, grid-forming inverters (GFMI) are gaining popularity over their grid-following (GFLI) counterpart. This is due to the fact that GFMI do not require a dedicated Phase Locked Loop (PLL) to synchronize with the grid. The absence of a PLL allows GFMI to operate in stand-alone (off-grid) mode when needed. Nowadays, inverter manufacturers are already offering several products with grid-forming capabilities. However, modeling the dynamics of commercially available GFMI under heavy loads or faults scenarios has become a critical task not only for stability studies, but also for coordination and protection schemes in power grids (or microgrids) that are experiencing a steady growth in their levels of DERs. Based upon experimental low-impedance fault results performed on a commercially available GFMI, this paper presents a modeling effort to replicate the dynamics of such inverters under these abnormal scenarios. The proposed modeling approach relies on modifying previously developed GFMI models, by adding the proper dynamics, to match the current and voltage transient behavior under low-impedance fault scenarios. For the first inverter tested, a modified CERTS GFMI model provides matching transient dynamics under faults scenarios with respect to the experimental results from the commercially available inverter.
Dynamic operations of electric power switches in microgrid mode allows for distributed photovoltaic (PV) systems to support a critical load and enable the transfer of electrical power to non-critical loads. Instead of relying on an expensive system that includes a constant generation source (e.g. fossil fuel based generators), this work assess the potential balance of load and PV generation to properly charge a critical load battery while also supporting non-critical loads during the day. This work assumes that the battery is sized to only support the critical load and that the PV at the critical load is undersized. To compensate for the limited power capacity, a battery charging algorithm predicts and defines battery demand throughout the day; a particle swarm optimization (PSO) scheme connects and disconnects switch sections inside a distribution system with the objective of minimizing the difference between load and generation. The PSO reconfiguration scheme allows for continuous operations of a critical load as well as inclusion of non-critical loads.
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.
With inverter-based distributed energy resources (DERs) becoming more prevalent in grid-connected or islanded distribution feeders, a better understanding of the performance of these devices is needed. Increasing the amount of inverter-based generation, and therefore reducing conventional generation, i.e. rotating machines and synchronous generators, decreases generation sources with well-known characteristic responses for unbalanced and transient fault conditions. This paper experimentally tests the performance of commercial grid-forming inverters under fault and unbalanced conditions and provides a comparison between grid-forming inverters and their grid-following counterparts.
The benefits and risks associated with Volt-Var Curve (VVC) control for management of voltages in electric feeders with distributed, roof-top photovoltaic (PV) can be defined using a stochastic hosting capacity analysis methodology. Although past work showed that a PV inverter's reactive power can improve grid voltages for large PV installations, this study adds to the past research by evaluating the control method's impact (both good and bad) when deployed throughout the feeder within small, distributed PV systems. The stochastic hosting capacity simulation effort iterated through hundreds of load and PV generation scenarios and various control types. The simulations also tested the impact of VVCs with tampered settings to understand the potential risks associated with a cyber-attack on all of the PV inverters scattered throughout a feeder. The simulation effort found that the VVC can have an insignificant role in managing the voltage when deployed in distributed roof-top PV inverters. This type of integration strategy will result in little to no harm when subjected to a successful cyber-attack that alters the VVC settings.
An overall capacity assessment and an analysis of the system's X/R ratios for six actual distribution feeders was conducted to characterize the voltage response to various levels of distributed Electric Vehicle Supply Equipment (EVSE). The evaluation identified the capacity of the system at which a voltage violation occurred. This included a review of the uncontrolled and controlled cases to quantify the value of injecting reactive power as the grid voltage decreases. The evaluation found that the implementation of a Volt-Var curve with a global voltage reference provided a notable increase in capacity. A local reference voltage, measured at the point of common coupling, did not increase the capacity of every feeder in the experiment. The review of the X/R line properties using a Principal Component Analysis (PCA) identified groups within the six feeders that corresponded with each system's voltage response rate. This suggests the X/R ratios provide a direct prediction of the feeder's ability to avoid voltage violations while charging EVs.
Grid operators are now considering using distributed energy resources (DERs) to provide distribution voltage regulation rather than installing costly voltage regulation hardware. DER devices include multiple adjustable reactive power control functions, so grid operators have the difficult decision of selecting the best operating mode and settings for the DER. In this work, we develop a novel state estimation-based particle swarm optimization (PSO) for distribution voltage regulation using DER-reactive power setpoints and establish a methodology to validate and compare it against alternative DER control technologies (volt-VAR (VV), extremum seeking control (ESC)) in increasingly higher fidelity environments. Distribution system real-time simulations with virtualized and power hardware-in-the-loop (PHIL)-interfaced DER equipment were run to evaluate the implementations and select the best voltage regulation technique. Each method improved the distribution system voltage profile; VV did not reach the global optimum but the PSO and ESC methods optimized the reactive power contributions of multiple DER devices to approach the optimal solution.
This paper describes the design of a very high power density inverter drive module using aggressive high-frequency design methods and multi-objective optimization tools. This work is part of a larger effort to develop electric drive designs with >97% efficiency, power densities of 100 kW/L for the power electronics, and with predicted reliable operation to 300, 000 miles. The approach taken in this work is to develop designs that utilize wide band gap devices (SiC or GaN) and ceramic capacitors to enable high-frequency switching and a compact integrated design. The multi-objective optimization is employed to select key parameters for the design.
Reducing the risk of cyber-attacks that affect the confidentiality, integrity, and availability of distributed Photovoltaic (PV) inverters requires the implementation of an Intrusion Detection System (IDS) at the grid-edge. Often, IDSs use signature or behavior-based analytics to identify potentially harmful anomalies. In this work, the two approaches are deployed and tested on a small, single-board computer; the computer is setup to monitor and detect malevolent traffic in-between an aggregator and a single PV inverter. The Snort, signature-based, analysis tool detected three of the five attack scenarios. The behavior-based analysis, which used an Adaptive Resonance Theory Artificial Neural Network, successfully identified four out of the five attacks. Each of the approaches ran on the single-board computer and decreased the chances of an undetected breach in the PV inverters control system.