Accurate and computationally tractable dynamic models of inverter-based resources (IBRs) are necessary for control design and performance evaluation of these devices in the present grid. This paper discusses the data-driven approach to obtain a nonlinear data-driven dynamic model of an IBR. Specifically, a Hammerstein–Wiener (HW) model of a three-phase inverter is obtained using the open-source dynoNet application to obtain the parameters of the model. To demonstrate the application of the model, a model reference control (MRC) strategy is proposed for the inverter to achieve the grid-forming objective of reference voltage tracking. The high fidelity simulation results from the test in MATLAB®/Simulink® environment demonstrate the good performance of the HW model and its efficacy in tracking the reference voltage through MRC strategy.

Unlike synchronous machines, whose fault response is dictated by their physical design, the fault current of type- 3 wind turbine (WT) generators differs due to their rotor terminal voltage being controlled by a grid-connected back-to-back converter. The IEEE 2800 Standard requires inverter-based resources to inject negative-sequence current $(I_{2})$ during unbalanced faults. To ensure compatibility with protection systems, the allowable angle of $I_{2}$ is restricted. However, there is a more relaxed $I_{2}$ angle requirement for type-3 WTs due to rotor-side converter control challenges. The effects of this relaxation on protection systems have not been fully explored. This work investigates the fault dynamics of type-3 WTs using electromagnetic transient analysis, benchmarking them against synchronous machines and an IEEE 2800-compliant solar photo voltaic model during various shunt fault scenarios. The analysis provides new insights into the interplay between $I_{2}$ dynamics and traditional protection systems, even in light of existing IEEE 2800 requirements.

This paper presents a convolutional neural network (CNN) developed to identify voltage events in photovoltaic (PV) inverters. The CNN is trained on synthetic data generated using the IEEE 13-bus distribution feeder model and evaluated on field-measured data collected from Energy Northwest's Horn Rapids Solar, Storage, and Training (HRSST) facility. The study focuses on two common voltage events: faults and voltage sags. The CNN is configured to analyze voltage and current waveforms from three-phase PV systems, demonstrating excellent accuracy during training. Field data from the HRSST facility is employed to assess its real-world performance, where the CNN achieves perfect identification of faults and voltage sags in a sample of nine events. This work highlights the potential of the proposed method to enhance PV protection schemes, providing a robust foundation for improved voltage event detection and grid reliability.

Many of the challenges associated with interconnection of Distributed Energy Resources (DERs) are related to the type of interconnection transformer. DER aggregation models developed for distribution networks with single and three phase photovoltaic (PV) systems are required to consider the type of interconnection transformers in the modeling. In this work, a machine-learning (ML) based aggregation model for PVs considering the configurations of interconnection transformer is presented. The developed method is also applicable for unbalanced distribution networks in the presence of faults.

The integration of distributed energy resources (DERs) in distribution networks has become a pivotal strategy for achieving decarbonization, enhancing grid resilience, and optimizing grid efficiency. Remote monitoring and control operations of such resources rely on a network of sensors and communication infrastructure, exposing the system to potential cyber threats. Therefore, as the deployment of DERs increases, ensuring secure monitoring and control becomes an imperative challenge. In this paper, a cybersecurity assessment framework is introduced, alongside several pertinent operational scenarios that researchers can leverage for DER-rich distribution grids. The feeder model is accurately represented within ePHASORSIM, a real-time simulation tool, allowing users to conduct simulations of cyber-attacks within a real-world environment and to analyze power distribution operations under vulnerable conditions. Furthermore, we discuss several practical sets of grid parameters to identify critical levels of DERs and evaluate various scenarios that simulate cyber threats on sensitive DERs. The modified IEEE 123-bus model is used as the test case for demonstrating the proposed scenarios. The findings from this study provide valuable insights into the vulnerabilities and potential consequences of cyber attacks on DERs, allowing for better mitigation strategies and improved cyber resilience in future distribution networks.

Data-driven models of power system inverter-based resources are desired to run simulations faster than with detailed electromagnetic transient models, to hide proprietary design details, to support control system design applications, and to aggregate the effects of distributed energy resources. This paper applies a customized Hammerstein Wiener framework to train block diagram models from thousands of electromagnetic transient simulations or experimental test records. The block diagram models integrate with larger grid simulations as voltage-controlled current sources or current-controlled voltage sources for several simulators. Guidelines for block architecture and training are presented. Three-phase balanced, three-phase unbalanced, and single-phase examples all achieve an acceptable root mean square error of no more than 0.05 per-unit.

An implementation of stable droop voltage response for distributed energy resources (DER) is described. The key point is to use a new “autonomously adjusting reference voltage” as described in IEEE Standard 1547-2018. Examples include battery voltage response, a photovoltaic inverter at the end of a long secondary circuit, and 180 photovoltaic inverters on a distribution lateral. Default inverter settings are proposed for DER that would mitigate voltage fluctuations, with or without communications.

A microgrid with solar, storage, and responsive load resources has been implemented and tested on an urban academic campus. Through modeling and simulation, a consensus transactive energy mechanism has been implemented, with each resource participating as a virtual battery. Most owners of large buildings do not have the information and expertise to develop and validate suitable models of their buildings using available tools. To mitigate this adoption barrier, a data-driven building model has been implemented and validated. It uses 5-minute weather data, 10-second feeder data, 3-second revenue meter data, energy audit information, and a load reduction test conducted by the building owner. The model tracks daily and seasonal variations, along with changes in grid voltage and feeder load.

In order to conduct system dynamic studies, it is necessary to have dynamic models of both inverter and plant levels. Detailed and aggregated modeling approaches are two essential options. The detailed modeling method involves capturing the dynamic characteristics of each individual device (e.g., wind turbine or PV array), as well as their interconnections. However, as the scale of the IBR plant increases, the complexity and computation time required for detailed modeling also increase. On the other hand, aggregated modeling offers a more efficient way of representing large-scale IBRs in power system dynamic studies. This approach involves aggregating a large number of wind turbines, PV arrays, inverters, and/or plant controllers into one or a smaller number of equivalent models. In order to analyze the impact of a high-level IBR penetration in power systems, it is important to develop accurate and computationally efficient models for both the detailed and aggregated methods.

Globally, the number of electric vehicles (EVs) continues to increase. This on-going trend poses challenges for power distribution systems. The inclusion of electric vehicle supply equipment (EVSE) should be compatible with a changing energy system, whose structure is becoming increasingly distributed. The impact of future EVs should be analyzed and considered in the system planning and upgrades. In this study, impact analysis of future EVs is conducted for a utility company of U.S. West Coast. Real data of distribution feeders is converted into the GridLAB-D model for running power flow analysis. An estimation of the additional loading of EVs in 2050 is provided by the utility company. Simple mitigation methods are tested to improve voltage profiles.

Extreme weather events, many of which are climate-change related, are occurring with increasing frequency and intensity and causing catastrophic outages. One of the major modern-day concerns of utilities is dealing with such extreme outages and consequently, its repercussions on the lives of people in society and social aspects. Traditionally, operators have been using rolling blackouts as a contingency plan to serve critical loads during such events when electricity supply is scarce. However, such blackouts practices are executed on a last-minute mandatory basis, depriving customers having low-capacity high-priority loads (i.e. refrigeration, water, telecommunication, etc) that should also be serviced if at all possible. Historically, policymakers have often adopted quota-based regulatory actions or rationing for other commodities (such as gasoline, butter, sugar) to handle scarcity situations. Motivated from such quota-based systems, our contribution presented in this work is an alternative transactive rationing mechanism that would provide some minimum level of service to all of the customers and serve the critical loads using market-based control during such scarcity-based contingencies. This is in contrast to the state-of-art TE mechanisms that allocate resources to customers solely based on their willingness-to-pay. The effectiveness of the proposed mechanism is demonstrated through simulation-based evaluation on two real-life use cases having feeder-level and microgrid-level configurations respectively. Simulation results clearly demonstrate the capability of the rationing scheme to serve customers' high-priority loads through prolonged outages even during extreme scarcity scenarios.

This research project will address the most pressing uncertainties in modeling and measuring the electric power grid effects of geomagnetic disturbances (GMDs) and the E3 portion of nuclear electromagnetic pulse (EMP). The primary goal is to help decision-makers in the electric power sector have the knowledge and tools they need to most effectively mitigate GMD effects on our nation's electric grid, with a secondary focus on EMP. Comprehensive modeling, model assessment through sensitivity analysis, and validation with field measurement data will be the primary tasks undertaken. The goal will be a more widespread adoption of these modeling approaches, namely characterizing the uncertainty associated with these models and how that uncertainty will affect decision-making by industry. This document summarizes industry requirements for better decision-making tools, informed by feedback from an industry advisory board. These requirements help guide planning and execution of the project's remaining tasks.

The control and protection functions of inverter-based resources (IBR) have raised concerns with bulk system reliability. Most of the current interest lies with solar photovoltaic generation but increasing amounts of storage would pose the same risks. Newer North American Electric Reliability Corporation (NERC) guidelines call for electromagnetic transient (EMT) studies of IBR and recommend that transmission operators collect distributed energy resource (DER) data to support such modeling. IEEE Standard P2800.1 is defining tests for model parameterization, so good model data should become available from inverter vendors. (EMT studies also apply to large power transformer reliability, and transformer vendors can provide EMT models.) Utilities don’t currently have the rest of the bulk system represented for EMT studies at large scale. An International Electrotechnical Commission (IEC) standard Common Information Model (CIM) provides a way of supporting these detailed models from physical asset data, e.g., conductors, towers, transformer data sheets, control block diagrams, while avoiding software vendor lock-in. CIM-for-EMT, with proposed schema extensions and open-source converters, provides a way to exchange EMT data between organizations and tools. This project leverages Office of Electricity (OE) funding of CIM-for-EMT code base through the GridAPPS-DTM project, and of GridPACKTM (parallelized transmission solver), for interoperability testing in CIM-for-EMT. The project also leverages partner PGSTech investments in EMTP® interoperability with CIM.