Separable scheduling unleashes the deployment flexibility of mobile emergency resources by dispatching carriers and functional modules separately yet in a coordinated manner, offering a promising avenue to enhance power system resilience. However, this flexibility induces a distinct carrier-supported module routing structure, where non-self-mobile modules must be routed through compatible carrier movements. The resulting carrier-module spatio-temporal coupling makes exact and tractable optimization challenging. This letter identifies this structure and develops a novel exact bi-layer time-space network formulation as a mixed-integer linear program. The proposed formulation represents carrier and module trajectories as interacting network flows and enforces their support relations through explicit arc-level coupling. Compared with the prior logic-based model, the proposed formulation preserves exactness while improving modeling flexibility by eliminating mandatory post-arrival dwelling. Numerical studies validate its correctness and demonstrate substantial computational advantages.
The rapid expansion of offshore wind energy is central to the European Union's climate-neutrality targets, with High Voltage Direct Current-connected offshore wind power plants (HVDC-OWPPs) becoming increasingly important for integrating gigawatt-scale renewable generation over l…
Motion planning algorithms compute control sequences that drive autonomous robots to goal regions while avoiding unsafe states. Existing methods, from sampling-based planning to deep reinforcement learning, typically provide task-completion guarantees only with respect to a nomin…
This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Awa…
This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and…
This paper introduces a novel multivariate Transformer \emph{StateFormer} that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of…
We introduce trajectory-regularized stochastic optimal control (TRSOC), which augments standard stochastic optimal control (SOC) with a Kullback--Leibler (KL) divergence between controlled and reference trajectory distributions. Using Girsanov's theorem, the trajectory KL reduces…