# Online Event-Triggered Switching for Frequency Control in Power Grids with Variable Inertia

arXiv: https://arxiv.org/abs/2408.15436 · HTML: https://mlsanity.com/paper/2408.15436/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Reinforcement Learning for Optimal Primary Frequency Control: A Lyapunov Approach](https://mlsanity.com/paper/2009.05654/) (2020)
- [Online switching control with stability and regret guarantees](https://mlsanity.com/paper/2301.08445/) (2023)
- [Design and Stability of Load-Side Primary Frequency Control in Power Systems](https://mlsanity.com/paper/1305.0585/) (2013)
- [Impact of Low Rotational Inertia on Power System Stability and Operation](https://mlsanity.com/paper/1312.6435/) (2013)
- [Structured Neural-PI Control with End-to-End Stability and Output Tracking Guarantees](https://mlsanity.com/paper/2305.17777/) (2023)
- [Reinforcement Learning for Distributed Transient Frequency Control with Stability and Safety Guarantees](https://mlsanity.com/paper/2207.03329/) (2022)
- [Optimal load-side control for frequency regulation in smart grids](https://mlsanity.com/paper/1410.2931/) (2014)
- [Optimal Placement of Virtual Inertia in Power Grids](https://mlsanity.com/paper/1510.01497/) (2015)
- [Stability-Constrained Learning for Frequency Regulation in Power Grids with Variable Inertia](https://mlsanity.com/paper/2405.20489/) (2024)
- [Robust Decentralized Secondary Frequency Control in Power Systems: Merits and Trade-Offs](https://mlsanity.com/paper/1711.07332/) (2017)
- [A Performance and Stability Analysis of Low-inertia Power Grids with Stochastic System Inertia](https://mlsanity.com/paper/1903.00635/) (2019)
- [Frequency Stability Using MPC-based Inverter Power Control in Low-Inertia Power Systems](https://mlsanity.com/paper/1909.12810/) (2019)
- [Generalized Model of VSC-based Energy Storage Systems for Transient Stability Analysis](https://mlsanity.com/paper/1509.05290/) (2015)
- [MPC-Based Fast Frequency Control of Voltage Source Converters in Low-Inertia Power Systems](https://mlsanity.com/paper/2004.02442/) (2020)
- [Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach](https://mlsanity.com/paper/2205.00411/) (2022)

## Built on by (most cited first, 3 of 3)

- [Analytical Lyapunov Function Discovery: An RL-based Generative Approach](https://mlsanity.com/paper/2502.02014/) (2025)
- [Wide-Area Feedback Control for Renewables-Heavy Power Systems: A Comparative Study of Reinforcement Learning and Lyapunov-Based Design](https://mlsanity.com/paper/2511.12911/) (2025)
- [Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret](https://mlsanity.com/paper/2607.26370/) (2026)

## Abstract (arXiv)

The increasing integration of renewable energy resources into power grids has led to time-varying system inertia and consequent degradation in frequency dynamics. A promising solution to alleviate performance degradation is using power electronics interfaced energy resources, such as renewable generators and battery energy storage for primary frequency control, by adjusting their power output set-points in response to frequency deviations. However, designing a frequency controller under time-varying inertia is challenging. Specifically, the stability or optimality of controllers designed for time-invariant systems can be compromised once applied to a time-varying system. We model the frequency dynamics under time-varying inertia as a nonlinear switching system, where the frequency dynamics under each mode are described by the nonlinear swing equations and different modes represent different inertia levels. We identify a key controller structure, named Neural Proportional-Integral (Neural-PI) controller, that guarantees exponential input-to-state stability for each mode. To further improve performance, we present an online event-triggered switching algorithm to select the most suitable controller from a set of Neural-PI controllers, each optimized for specific inertia levels. Simulations on the IEEE 39-bus system validate the effectiveness of the proposed online switching control method with stability guarantees and optimized performance for frequency control under time-varying inertia.