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1. Tutorial on Dynamics and Control of Grid-Connected Power Electronics and Renewable Generation

   https://doi.org/10.1109/CDC49753.2023.10383826  

   Groß, Dominic (December 2023, IEEE Conference on Decision and Control)

   Electrical power systems are transitioning from fuel-based generation to renewable generation and transmission interfaced by power electronics. This transition challenges standard power system modeling, analysis, and control paradigms across timescales from milliseconds to seasons. This tutorial focuses on frequency stability on timescales of milliseconds to seconds. We first review basic results for grid-following (GFL) and grid-forming (GFM) control of voltage source converters (VSCs), typical renewable generation, and high voltage direct current (HVdc) transmission. In this context, it becomes apparent that GFL and GFM control functions are needed to operate emerging power systems. However, combining GFL resources, GFM resources, and legacy generation on the same system results in highly complex dynamics that are a significant obstacle to stability analysis. The remainder of the tutorial provides an overview of recent developments in universal GFM controls that bridge the gap between GFL and GFM control and provide a pathway to a coherent control and analysis framework accounting for power generation, power conversion, and power transmission. 

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2. Wide-area Measurement System-based Low Frequency Oscillation Damping Control through Reinforcement Learning

   https://doi.org/10.1109/TSG.2020.3008364  

   Hashmy, Yousuf; Yu, Zhe; Shi, Di; Weng, Yang (July 2020, IEEE Transactions on Smart Grid)

   Ensuring the stability of power systems is gaining more attention today than ever before due to the rapid growth of uncertainties in load and increased renewable energy penetration. Lately, wide-area measurement system (WAMS)-based centralized controlling techniques are offering flexibility and more robust control to keep the system stable. WAMS-based controlling techniques, however, face pressing challenges of irregular delays in long-distance communication channels and subsequent responses of equipment to control actions. This paper presents an innovative control strategy for damping down low-frequency oscillations in transmission systems. The method uses a reinforcement learning technique to overcome the challenges of communication delays and other non-linearity in wide-area damping control. It models the traditional problem of oscillation damping control as a novel faster exploration-based deep deterministic policy gradient (DDPG-S). An effective reward function is designed to capture necessary features of oscillations enabling timely damping of such oscillations, even under various kinds of uncertainties. A detailed analysis and a systematically designed numerical validation are presented to prove feasibility, scalability, interpretability, and comparative performance of the modelled low-frequency oscillation damping controller. The benefit of the technique is that stability is ensured even when uncertainties of load and generation are on the rise. 

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3. Virtual Inertia Emulation using Commercial Off-The-Shelf Inverters

   https://doi.org/10.1109/ECCE.2018.8557592  

   Tamrakar, Ujjwol; dos Reis, Fernando B.; Luna, Andre; Shrestha, Dipesh; Fourney, Robert; Tonkoski, Reinaldo (September 2018, 2018 IEEE Energy Conversion Congress and Exposition (ECCE))

   Virtual inertia based control of renewable energy sources (RESs) helps to enhance the frequency stability of power systems. In this paper, a Control Area Network (CAN) communication-based method is demonstrated to emulate virtual inertia using commercial off-the-shelf inverters. This allows the currently installed systems to be retrofitted with virtual inertia in a cost-effective manner which would allow for higher RES penetration in power systems. The proof-of-concept is demonstrated using a Xantrex XW6048 hybrid inverter/charger and OPAL-RT real-time digital simulator. Results show that CAN-based communication can be an effective way to reduce frequency variations in the power system. 

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4. Emphasizing Electro-Thermal Dynamics for Design, Modeling, and Control of Microgrids

   https://doi.org/10.1109/ACCESS.2025.3593387  

   Ouedraogo, Asmaou S; Docimo, Donald J (July 2025, IEEE Access)

   This article studies the design, modeling, and control of microgrid systems with the inclusion of internal electro-thermal dynamics. Microgrids play a vital role in integrating renewable energy and enabling distributed energy systems. However, their complexity arising from diverse and dynamic components necessitates advanced control strategies. While existing works often utilize model-based controllers, the focus is primarily on electrical dynamics, with limited attention to the thermal behavior of components. The intricate interplay between electrical and thermal power terms heavily impacts component behavior, exemplified by the dependency of photovoltaic (PV) module electricity production on temperature. This article addresses the limited studies on electro-thermal microgrid dynamics through three contributions. First, a candidate microgrid design is developed to utilize electro-thermal knowledge, incorporating active cooling for PVs. Second, a graph-based modeling methodology is expanded to represent microgrid component- and system-level dynamics. Third, a hierarchical control framework is developed to define controllers for microgrids using the graphical model. Controllers produced from the framework enable management of electro-thermal behavior while adhering to battery charge limits. Case studies utilizing realistic environmental data are explored to evaluate the performance of the proposed system. Results indicate design and model-based control that integrate electro-thermal dynamics provide improvements to energy generation and performance even under nonideal conditions. 

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5. Leveraging Predictions in Power System Frequency Control: An Adaptive Approach

   https://doi.org/10.1109/CDC49753.2023.10383969  

   Cui, Wenqi; Shi, Guanya; Shi, Yuanyuan; Zhang, Baosen (December 2023, 2023 62nd IEEE Conference on Decision and Control (CDC))

   Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations. In recent years, a number of advanced controllers have been designed to optimize frequency control. These controllers, however, almost always assume that the net load in the system remains constant over a sufficiently long time. Given the intermittent and uncertain nature of renewable resources, it is becoming important to explicitly consider net load that is time-varying. This paper proposes an adaptive approach to frequency control in power systems with significant time-varying net load. We leverage the advances in short-term load forecasting, where the net load in the system can be accurately predicted using weather and other features. We integrate these predictions into the design of adaptive controllers, which can be seamlessly combined with most existing controllers including conventional droop control and emerging neural network-based controllers. We prove that the overall control architecture achieves frequency restoration decentralizedly. Case studies verify that the proposed method improves both transient and frequency-restoration performances compared to existing approaches. 

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