The laboratory testing of quantum computing (QC) and quantum-inspired computing (QIC) technologies will involve multiple experimental platforms to evaluate their effectiveness against traditional computing methods. One key experiment will focus on benchmarking accuracy, processing time, and the ability to handle complex problem sizes. This will be conducted across different quantum architectures, including gate-based QC systems and quantum annealing approaches. Additionally, hybrid quantum-classical moThe laboratory validation of AI technologies in Transmission & Distribution (T&D) systems within the GRAVITEQA project focuses on demonstrating the potential of AI-based solutions to enhance the reliability, observability, and efficiency of power grids. This involves the development, testing, and validation of AI technologies for various applications, including improved monitoring, control, and predictive maintenance of T&D systems.
The validation process includes rigorous testing in controlled laboratory environments to assess the performance and accuracy of AI algorithms under different scenarios, ensuring their effectiveness in real-world applications. This validation aims to confirm the potential of AI-based solutions to drive significant improvements in the operation and management of power grids, contributing to the evolution of smarter, more resilient energy infrastructure.
dels will be tested to determine their efficiency in solving large-scale optimization problems commonly found in energy system applications. These experiments will use standardized industry datasets to ensure consistency in evaluation and applicability to real-world scenarios.
The validation process begins with testing trustworthy AI algorithms in a controlled lab setting using FPGA (Field Programmable Gate Array) units. These AI models will be integrated into a reference design architecture for edge AI in smart grids, ensuring efficient real-time data processing and decision-making. Various optimization algorithms will be deployed to analyze their impact on grid reliability, stability, and fault detection. These tests will help quantify the performance of AI-enabled strategies in managing distributed energy resources, load forecasting, and demand-response mechanisms. The use of FPGA units ensures that the AI models can operate with low latency, making them suitable for real-time grid management applications.
To test AI algorithms in a controlled environment using FPGA units by focusing on optimizing smart grid operations and validating AI-driven grid management strategies using real-world datasets.
