Converter-based generators are increasingly replacing classical synchronous generation, resulting in significant challenges to the operation and planning of modern power systems. Power electronics (PE)-based equipment, along with non-linear PE-driven loads, introduce time-varying characteristics and fast switching behavior that increases the complexity of the power system model. Faster control actions are needed to overcome the fast switching dynamics to ensure the reliability and stability of future power systems. Thus, this requires advanced and detailed simulation methods and tools with highly accurate equivalent models to embody the relatively slower electromechanical to faster electromagnetic transient (EMT) phenomena. Conventional transient stability analysis using positive-sequence simulators has become inadequate for representing converter-dominated power systems, while EMT simulators suffer from the high computational burden. This review paper presents accelerated EMT simulation methods and tools that are categorized and discussed in three topics: system equivalents, simulation methods, and accelerating tools. Dynamic system equivalent techniques are discussed to model small to large interconnected external systems of the grid network. Moreover, a systematic review is made for existing EMT simulation methods, along with advanced co-simulation methods, for addressing simulation speed and accuracy issues in large power system networks. Emerging hardware-based simulation tools are reviewed that reduce the computational burden and increase the simulation efficiency of the power system model. Challenges and trends in EMT simulation are also presented and concluded by providing perspectives on this research topic.

Power electronic converter (PEC)–based resources are growing ubiquitously in power systems and there is a vital necessity for precise dynamic models to comprehend their dynamics to different events and control strategies. Inaccurate modeling can lead to instability, higher costs, and reliability issues. Anticipating the increase in PECs in the near future, detailed modeling becomes computationally and mathematically complex, requiring extensive computing power and knowledge of vendor-specific PECs. To overcome these challenges, data-driven machine learning/artificial intelligence (ML/AI) approaches are widely used, tracking the dynamic responses of PECs operating in various modes with limited knowledge. These models find applications in protection, stability, fault diagnosis, optimization, control and monitoring, and power quality. While the literature on power systems often emphasizes the advantages of data-driven modeling, an in-depth look at the limitations, challenges, and opportunities related to converter-dominated grids is still lacking. The purpose of this survey is to conduct a comprehensive review of ML/AI methodologies in PECs and investigate their applications in power systems. The article introduces various PEC types, their roles, and modeling approaches. It then provides an in-depth overview of how ML/AI can be applied to PECs in power systems. Finally, the survey highlights gaps in the field's knowledge and suggests potential directions for future research.

Electric distribution networks increasingly host distributed energy resources based on power electronic converter (PEC) toward active distribution networks (ADN). Despite advances in computational capabilities, electromagnetic transient models are limited in scalability because of their reliance on exact data about the distribution system and each of its components. Similarly, the use of the DER_A model, which is intended to examine the combined dynamic behavior of many DERs, is limited by the difficulty in parameterization. There is a need for improved dynamic models of DERs for use in large power system simulations for stability analysis. This paper proposes an aggregate model‐free, data‐driven approach for deriving a dynamic partitioned model (DPM) of ADNs. Detailed residential distribution feeders were first developed, including PEC‐based DERs and composite load models (CMLDs), from which the aggregated DPM was derived. The performance was evaluated through various case studies and validated against the detailed ADN model and state‐of‐the‐art DER_A model with CMLD. The data‐driven DPM achieved a of over 90%, accurately representing the aggregated dynamic behavior of ADNs. Furthermore, the DPM significantly accelerated the simulation process with a computational speedup of 68 times compared to the detailed ADN and a 3.5 times speedup compared to the DER_A CMLD model.

To address grid variability caused by renewable energy integration and to maintain grid reliability and resilience, hydropower must quickly adjust its power generation over short time periods. This changing energy generation landscape requires advance technology integration and adaptive parameter optimization for hydropower systems via digital twin effort. However, this is difficult owing to the lack of characterization and modeling for the nonlinear nature of hydroturbines. To solve this issue, this paper first formulates a six-coefficient Kaplan hydroturbine model and then proposes a parametric optimization tuning framework based on the Nelder–Mead algorithm for adaptive dynamic learning of the six-coefficients so as to build models that describe the turbine. To assess the performance of the proposed optimal parametric tuning technique, operational data from a real-world Kaplan hydroturbine unit are collected and used to model the relationship between the gate opening and the generated power production. The findings show that the proposed technique can effectively and adaptively learn the unknown dynamics of the Kaplan hydroturbine while optimally tune the unknown coefficients to match the generated power output from the real hydroturbine unit with an inaccuracy of less than 5%. The method can be used to provides optimal tuning of parameters critical for controller design, operational optimization and daily maintenance for hydroturbines in general.

Grid-supportive loads (GSLs) are power electronics-based end-use loads that can provide frequency response and other grid services by autonomously adjusting their output power using local grid measurements. This report provides details on GSL hardware design options and their associated costs and benefits for multiple devices, including EV chargers, refrigerators, and heat pumps. We consider mechanisms to incentivize adoption of GSLs at scale and estimate the quantity of frequency response that can be procured with GSLs.