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1. Improved primary frequency response through deep reinforcement learning

   T. Thacker; H. Pulgar-Painemal (October 2022, North American Power Symposium)

   Mostafa Sahraei-Ardakani; Mingxi Liu (Ed.)

   This paper explores the application of deep reinforcement learning (DRL) to create a coordinating mechanism between synchronous generators (SGs) and distributed energy resources (DERs) for improved primary frequency regulation. Renewable energy sources, such as wind and solar, may be used to aid in frequency regulation of the grid. Without proper coordination between the sources, however, the participation only results in a delay of SG governor response and frequency deviation. The proposed DRL application uses a deep deterministic policy gradient (DDPG) agent to create a generalized coordinating signal for DERs. The coordinating signal communicates the degree of distributed participation to the SG governor, resolving delayed governor response and reducing system rate of change of frequency (ROCOF). The validity of the coordinating signal is presented with a single-machine finite bus system. The use of DRL for signal creation is explored in an under-frequency event. While further exploration is needed for validation in large systems, the development of this concept shows promising results towards increased power grid stabilization. 

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2. Enhancing Frequency Response Using Deep Reinforcement Learning-Controlled Battery Energy Storage Systems

   https://doi.org/10.1109/NAPS66256.2025.11272197  

   Hasan, Abu Shouaib; Fan, Rui; Wu, Di (October 2025, IEEE)

   As the power grid transitions from traditional synchronous generation to inverter‑based resources, operators face growing concerns over reduced system inertia and the resulting rapid, severe frequency declines following major disturbances. Such declines can trigger instability, unnecessary under‑frequency load shedding (UFLS), or even widespread blackouts. This work proposes a reinforcement‑learning‑based control scheme for battery energy storage systems (BESS) to deliver fast frequency support, stabilize system dynamics, and prevent unwarranted UFLS operations. The approach employs a surrogate‑gradient evolution‑strategy algorithm for continuous BESS control, leveraging parallel computation to accelerate training and improve policy search efficiency. Time‑domain simulations on a modified IEEE 39‑bus system demonstrate that the proposed method reliably supports system frequency, mitigates rapid frequency drops, and avoids unnecessary load shedding, thereby enhancing overall system stability and reliability. 

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3. A Meta-Strategy Framework With Deep Reinforcement Learning for Damping Interarea Oscillation via High-Voltage DC Transmission

   https://doi.org/10.1109/TII.2025.3594123  

   Gao, Wei; Fan, Rui; Huang, Qiuhua; Li, Yushuai; Gao, David Wenzhong (November 2025, IEEE Transactions on Industrial Informatics)

   Inter‑area oscillations pose significant threats to power‑system security and stability, requiring rapid damping to prevent potential system failure. Our previous work introduced a deep reinforcement learning (DRL) strategy built on the power oscillation damping (POD) control architecture using high‑voltage direct current (HVDC) transmissions, which effectively generated POD signals from wide‑area information to modulate transferred power and damp inter‑area oscillations. However, its performance degrades under significant structural or operating‑condition changes, revealing a general adaptability limitation in DRL‑based control schemes. To address this issue, we propose a novel meta evolution strategy (MES) framework that integrates meta‑strategy optimization (MSO) with evolutionary‑strategy (ES)‑based DRL. The ES algorithm uses fewer, easier‑to‑tune hyperparameters than typical DRL methods and rapidly identifies optimal policies by exploring the policy parameter space through parallel computation, while MSO introduces a latent variable that projects power‑system parameters under varying operating conditions to enable fast, efficient adaptation to unforeseen scenarios. A modified IEEE 39‑bus system with HVDC transmission is studied to validate the performance and effectiveness of the proposed meta‑strategy in damping inter‑area oscillations. 

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4. Model Predictive Frequency Control of Low Inertia Microgrids

   Tamrakar, Ujjwol; Copp, David; Hansen, Timothy; Tonkoski, Reinaldo (January 2019, The 28th International Symposium on Industrial Electronics (ISIE))

   In isolated power systems with low rotational inertia, fast-frequency control strategies are required to maintain frequency stability. Furthermore, with limited resources in such isolated systems, the deployed control strategies have to provide the flexibility to handle operational constraints so the controller is optimal from a technical as well as an economical point-ofview. In this paper, a model predictive control (MPC) approach is proposed to maintain the frequency stability of these low inertia power systems, such as microgrids. Given a predictive model of the system, MPC computes control actions by recursively solving a finite-horizon, online optimization problem that satisfies peak power output and ramp-rate constraints. MATLAB/Simulink based simulations show the effectiveness of the controller to reduce frequency deviations and the rate-of-change-of-frequency (ROCOF) of the system. By proper selection of controller parameters, desired performance can be achieved while respecting the physical constraints on inverter peak power and/or ramp-rates. 

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5. Implementation of CVR in Distribution Networks by Optimal Coordination of BESS and PV Inverters Using Arithmetic Optimization Algorithm

   https://doi.org/10.1109/NAPS52732.2021.9654489  

   Ahanch, Mojtaba; McCann, Roy (November 2021, IEEE Green Technologies Conference (GreenTech))

   This article proposes a new framework for the substation demand reduction and power loss minimization in distribution networks by implementing conservation voltage reduction (CVR) strategy. The proposed framework coordinates Battery Energy Storage Systems (BESS), Smart PV inverters and voltage control devices -including OLTC and voltage regulators- so that the substation demand and network power loss are reduced while the service voltage range meets the IEEE 1547 standard (120-114 V). The suggested CVR strategy is applied to the IEEE 34-bus case study system consisting of two PV generations and BESS. The smart PV inverters are controlled based on the combined Volt/VArVolt/Watt (VVW) characteristics scheme. Also, BESS is charged and discharged with regard to the time and peaks have control modes, respectively. The Arithmetic Optimization Algorithm (AOA) is implemented in MATLAB scripts for solving the optimization problem. Power flow studies are carried out using OpenDSS software. Results reveal that the new framework can achieve higher substation demand reduction considering the concurrent control of PVs and BESS. 

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