Vulnerability Analysis for Data Driven Pricing Schemes
This addresses security risks in electricity market design for power system operators, but is incremental as it builds on existing adversarial machine learning concepts.
The paper tackles the vulnerability of data-driven electricity pricing schemes to adversarial manipulation by malicious users, concluding that such schemes are susceptible to strategic disguising behaviors.
Data analytics and machine learning techniques are being rapidly adopted into the power system, including power system control as well as electricity market design. In this paper, from an adversarial machine learning point of view, we examine the vulnerability of data-driven electricity market design. More precisely, we follow the idea that consumer's load profile should uniquely determine its electricity rate, which yields a clustering oriented pricing scheme. We first identify the strategic behaviors of malicious users by defining a notion of disguising. Based on this notion, we characterize the sensitivity zones to evaluate the percentage of malicious users in each cluster. Based on a thorough cost benefit analysis, we conclude with the vulnerability analysis.