# Mitigating Load-Altering Attacks Against Power Grids Using Cyber-Resilient Economic Dispatch

arXiv: https://arxiv.org/abs/2206.13129 · HTML: https://mlsanity.com/paper/2206.13129/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2022.

## Built on by (most cited first, 2 of 2)

- [Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning](https://mlsanity.com/paper/2311.17941/) (2023)
- [Efficient Sampling for Data-Driven Frequency Stability Constraint via Forward-Mode Automatic Differentiation](https://mlsanity.com/paper/2407.15045/) (2024)

## Abstract (arXiv)

Large-scale Load-Altering Attacks (LAAs) against Internet-of-Things (IoT) enabled high-wattage electrical appliances (e.g., wifi-enabled air-conditioners, electric vehicles, etc.) pose a serious threat to power systems' security and stability. In this work, a Cyber-Resilient Economic Dispatch (CRED) framework is presented to mitigate the destabilizing effect of LAAs while minimizing the overall operational cost by dynamically optimizing the frequency droop control gains of Inverter-Based Resources (IBRs). The system frequency dynamics incorporating both LAAs and the IBR droop control are modeled. The system stability constraints are explicitly derived based on parametric sensitivities. To incorporate them into the CRED model and minimize the error of the sensitivity analysis, a recursive linearization method is further proposed. A distributionally robust approach is applied to account for the uncertainty associated with the LAA detection/parameter estimation. The overall performance of the proposed CRED model is demonstrated through simulations in a modified IEEE reliability test system.