arXiv:2608.15076v4 Announce Type: replace Abstract: The transition toward a renewable-dominated power system increases the flexibility needed to balance net-load variations. Industrial loads account for over 40% of the world's electricity consumption and can shift large amounts of demand through automated production scheduling. However, making industrial processes a dependable flexibility resource for power systems remains challenging because industrial process constraints are non-convex, equipment parameters are often private, and device-level models of thousands of facilities are too detailed for aggregator and power-system optimization. This dissertation builds computationally efficient methods for modeling and optimizing industrial flexibility for power-system interaction. Specifically, this dissertation uses smart meters as the external measurement interface and maps industrial process constraints into a concise flexibility space for power-system operation. The resulting chain of mechanism modeling, parameter identification, constraint dimension reduction, and optimal decision-making makes industrial-load participation computationally tractable while retaining precise process models for final execution.