Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence

TL;DR

This paper presents a distributed AI approach to analyze complex cyber-physical systems like power grids, ICT, and energy markets without relying on domain-specific knowledge, enabling detection of risks and vulnerabilities.

Contribution

It introduces a novel distributed AI method for analyzing interdependent CPS domains independently, identifying emergent risks without pre-configured domain knowledge.

Findings

Effective detection of risk situations in CPS

Identification of exploitable loopholes in energy markets

Demonstrated analysis without domain-specific pre-knowledge

Abstract

Modern cyber-physical systems (CPS), such as our energy infrastructure, are becoming increasingly complex: An ever-higher share of Artificial Intelligence (AI)-based technologies use the Information and Communication Technology (ICT) facet of energy systems for operation optimization, cost efficiency, and to reach CO2 goals worldwide. At the same time, markets with increased flexibility and ever shorter trade horizons enable the multi-stakeholder situation that is emerging in this setting. These systems still form critical infrastructures that need to perform with highest reliability. However, today's CPS are becoming too complex to be analyzed in the traditional monolithic approach, where each domain, e.g., power grid and ICT as well as the energy market, are considered as separate entities while ignoring dependencies and side-effects. To achieve an overall analysis, we introduce the concept for an application of distributed artificial intelligence as a self-adaptive analysis tool that is able to analyze the dependencies between domains in CPS by attacking them. It eschews pre-configured domain knowledge, instead exploring the CPS domains for emergent risk situations and exploitable loopholes in codices, with a focus on rational market actors that exploit the system while still following the market rules.