Testing and Experimentation Facility for AI Decision Support Systems for Energy Solutions

Asthe integration and deployment of Artificial Intelligence (AI) solutions within the energy sector deepens, a framework for testing of AI solutions becomes more critical toward realizing trustworthy, sustainable, and resilient energy systems. This paper presents the objectives, methodologies, and anticipated outcomes of the EnergyGuard project, a large-scale Testing and Experimentation Facility (TEF) for AI energy solutions. EnergyGuard seeks to establish an open, scalable, and environmentally sustainable infrastructure that enables innovators to validate and accelerate the market entry of trustworthy AI solutions across diverse energy scenarios. The project leverages a combination of digital twins of energy facilities, state-of-the-art high-performance computing, and experimentation environments spanning the entire energy value chain-from renewable energy sources and microgrids to transmission networks and hydrogen technologies. Furthermore, EnergyGuard introduces a unified AI risk assessment framework, fostering enhanced cybersecurity and compliance with emerging AI regulatory landscapes. The TEF aims not only to bridge the gap between AI research and commercial deployment but also to support long-term sustainable operations through a self-sufficient business model, providing subscription-based access to integrated testing assets and professional support.