AI-powered microgrids: vulnerabilities to terrorism
- Project team:
“Engineering Digital Futures” program by the Helmholtz Association
- Start date:
- End date:
- Project partners:
Markus Götz & James Kahn (Steinbuch Centre for Computing, KIT)
- Research group:
The identification of vulnerabilities in critical infrastructures is essential in order to prevent their exploitation by terrorist actors. Digitization and localization of electrical grids give rise to new technical applications, promising a more efficient and resilient energy management. One such technology is microgrids, and the use of artificial intelligence (AI) in their decision logic is rapidly growing. New technological innovations and assemblages tend to bring about unintended consequences and add to the uncertainty in managing complex technical systems. Increased complexity may imply new vulnerabilities, especially in view of the interconnected nature of critical infrastructures and their latent potential for cascade effects. The technical design and socially embedded operation of AI-powered microgrids may render them vulnerable to malicious attacks.
Consequently, the focus of this core-funded project, which aims to foster networking across different units of KIT, is a three-step vulnerability assessment: It first evaluates what advantages are expected from microgrids with regard to energy supply and its resilience. To get a grasp on the technical design and necessary social organization around its operation, the project develops a description of microgrids as sociotechnical entities. The project subsequently reviews the state of AI in microgrids, which developmental trajectories can be identified, and what are the potential vulnerabilities that AI introduces into the system? Finally, the project aims to answer the questions of how social actors maintain a resilient state of operation for AI-powered microgrids and which actors would likely be affected by feasible terrorist attacks on AI-powered microgrids. In the end, the project shall propound a sociotechnical model for assessing the vulnerability of AI-powered microgrids. The model is intended to be potentially applicable to other sociotechnical systems in critical infrastructures as well.