Causative Cyberattacks on Online Learning-based Automated Demand Response Systems

TL;DR

This paper investigates vulnerabilities in AI-driven Automated Demand Response systems, demonstrating how cyberattacks can manipulate incentives, data, and customer responses, potentially disrupting electricity grid support services.

Contribution

It introduces a data-driven attack strategy on AI-based DR systems, highlighting security risks and potential impacts on demand response operations.

Findings

Cyberattacks can alter real-time DR incentives.

Malicious tampering affects DR event data accuracy.

Customer responses to incentives can be manipulated.

Abstract

Power utilities are adopting Automated Demand Response (ADR) to replace the costly fuel-fired generators and to preempt congestion during peak electricity demand. Similarly, third-party Demand Response (DR) aggregators are leveraging controllable small-scale electrical loads to provide on-demand grid support services to the utilities. Some aggregators and utilities have started employing Artificial Intelligence (AI) to learn the energy usage patterns of electricity consumers and use this knowledge to design optimal DR incentives. Such AI frameworks use open communication channels between the utility/aggregator and the DR customers, which are vulnerable to \textit{causative} data integrity cyberattacks. This paper explores vulnerabilities of AI-based DR learning and designs a data-driven attack strategy informed by DR data collected from the New York University (NYU) campus buildings. The case study demonstrates the feasibility and effects of maliciously tampering with (i) real-time DR incentives, (ii) DR event data sent to DR customers, and (iii) responses of DR customers to the DR incentives.