Enhancing power grid resilience to cyber-physical attacks using distributed retail electricity markets

TL;DR

The paper tackles cyber-physical threats to power grids by introducing a hierarchical, market-based framework that coordinates distributed energy resources at the distribution level. A primary market (PM) and multiple secondary markets (SMs) run in tandem, with a commitment-score metric $C_j \in [0,1]$ guiding reliable DER responses, all optimized in a distributed, privacy-preserving way via NST-PAC. Attacks are detected through anomalies in PCC injections, after which the PMO perturbs objective-function coefficients (e.g., $\Delta_{\alpha}, \Delta_{\beta}, \Delta_{\xi}$) to re-dispatch local resources and minimize imports from the transmission grid. Numerical experiments on a modified IEEE-123 feeder show the method can mitigation both small and large attacks, at the cost of higher local operation costs but with reduced dependency on centralized generation, illustrating practical resilience gains and considerations for social welfare tradeoffs.

Abstract

We propose using a hierarchical retail market structure to alert and dispatch resources to mitigate cyber-physical attacks on a distribution grid. We simulate attacks where a number of generation nodes in a distribution grid are attacked. We show that the market is able to successfully meet the shortfall between demand and supply by utilizing the flexibility of remaining resources while minimizing any extra power that needs to be imported from the main transmission grid. This includes utilizing upward flexibility or reserves of remaining online generators and some curtailment or shifting of flexible loads, which results in higher costs. Using price signals and market-based coordination, the grid operator can achieve its objectives without direct control over distributed energy resources and is able to accurately compensate prosumers for the grid support they provide.

Figures (9)

• Figure 1: Hierarchical market co-located with the distribution grid, showing a primary feeder network based on the IEEE-123 node test case. This figure has been adapted from Nair2022AEdge.
• Figure 2: Market inputs and outputs, adapted from Nair2022AEdge.
• Figure 3: Timeline of SM and PM operation.
• Figure 4: Timeline of attack detection and mitigation.
• Figure 5: Communication among market participants during both nominal and attack scenarios.