ReliAble Online LeaRning in Power ConverTErs to Unlock Flexibility from Motor AppliCaTions

forthcoming

Electric motor assets such as pumps, fans, and compressors—responsible for over half of global electricity use—represent a vast untapped flexibility resource capable of balancing entire EU renewable energy variability without any added cost. Yet, unlocking this potential presents unprecedented challenges: vast, noisy and diverse data generated by motors is difficult to process in real-time and further restricted by strict privacy regulations, making existing centralized, semi-manual approaches to flexibility quantification and aggregation fundamentally unsuitable. ARTEFACT proposes a paradigm shift to a fully decentralized framework that harnesses advanced edge-computing in modern power electronic converters to enable motors to self-quantify and aggregate their flexibilities at scale. By rethinking fundamentals of machine learning and system identification, we introduce several key breakthroughs: 1) Reinventing ideas from experimental design theory to filter high-value signals from noise and enable efficient real-time learning in resource-constrained edge devices; 2) Combining physics-informed, offline-trained Bayesian priors with the novel concept of temporal posterior fusion, to robustly learn knowledge across various motor mission profiles and preventing catastrophic forgetting; 3) Developing new probabilistic flexibility envelopes to quantify flexibility with formal uncertainty bounds, while ensuring operational safety; 4) Establishing a fully decentralized aggregation mechanism that will enable millions of motors to self-organize and provide grid services with minimal central intervention, eliminating reliance on historical datasets and centralized processing, and aligning with strict data-privacy regulations. By unlocking gigawatts of distributed flexibility, ARTEFACT will reduce EU dependence on fossil-fuel plants, avoid investments in grid infrastructure and batteries, yielding billions in annual savings while accelerating the transition to renewable energy.