Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?

2026-07-07

AI Summary

This study investigates the vulnerability of industrial demand response to adversarial manipulations of electricity price forecasts and their subsequent impact on scheduling decisions and economic performance. By constructing adversarial attacks tailored to electricity price prediction models, the authors generate perturbed price signals and integrate them into the scheduling optimization framework of energy-intensive production processes. Notably, this work is the first to incorporate the sensitivity of scheduling models into the design of adversarial attacks, revealing that attack efficacy depends not only on perturbation magnitude but more critically on perturbation direction. Experimental results demonstrate that even small, imperceptible adversarial perturbations can induce economic losses of approximately 10%, underscoring the necessity for fine-grained risk assessment of cybersecurity threats on the demand side.

Abstract

Adversarial attacks are crafted data manipulations that aim to deteriorate the outcomes of prediction or decision-making algorithms. In the energy systems literature, adversarial attacks have been studied with a focus on problems regarding the electricity grid. Such problems include forecasting and grid state estimation, where adversarial attacks are also known as false data injection attacks. Only few studies have analyzed the potential impact that adversarial attacks have on the demand side. We analyze how manipulated price forecasts impact the decision-making in industrial demand response. To this end, we design adversarial attacks that aim to deteriorate the output of electricity price forecasting models and solve scheduling optimization problems of energy-intensive production processes using the distorted price forecasts. We make use of a generalized process model to investigate the vulnerability to adversarial attacks for a range of production scheduling problems with different levels of process flexibility. We find that adversarial attacks can erode the profits gained from demand response. However, when perturbations are limited in extent (so that they are hard to detect by the human user), demand response preserves about 90\% of its financial advantage compared to steady-state process operation. Further, we find that the impact of adversarial attacks on demand response does not only depend on the magnitude of the perturbations but rather on the orientation of the adversarial perturbations. Therefore, we argue that attack analyses should explicitly incorporate the sensitivities of scheduling optimization models into the attack design to enable more rigorous assessments of decision-making under adversarial attacks.

Problem

• Demand Response
• Adversarial Attacks
• Cyber Vulnerability
• Price Forecasting
• Industrial Scheduling

Innovation

• adversarial attacks
• demand response
• price forecasting
• scheduling optimization
• process flexibility