arXiv subjects

Jiebao Zhang

Publications and source records attributed to Jiebao Zhang.

5 recordsLinked to original sources

arXiv2026

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling

The rapid proliferation of Electric Vehicles (EVs) introduces significant spatio-temporal uncertainties into power grids, while Vehicle-to-Grid (V2G) technology offers critical flexibility through bidirectional power flow. However, integrating large-scale EVs into the Optimal Power Flow framework presents substantial challenges due to computational bottlenecks arising from solver complexity and coupled spatio-temporal constraints. Existing Reinforcement Learning (RL) methods often struggle to balance strict constraint satisfaction with scalability in highly dynamic EV fleet environments. To address these challenges, this paper proposes a Hierarchical Policy for Constrained Reinforcement Learning (HPC-RL) framework for spatially and temporally coupled V2G scheduling. The framework adopts a two-layer architecture: the upper level utilizes a RL algorithm based on the Generalized Reduced Gradient method to strictly enforce spatial grid-level hard constraints; the lower level implements a novel dynamic boundary strategy to compute real-time feasible charging power bounds for individual EVs, thereby ensuring the satisfaction of temporal charging demands. This integrated design not only enables the simultaneous handling of spatially and temporally coupled constraints during the RL optimization process but also significantly enhances generalization capabilities for large-scale fleets through hierarchical decoupling. Extensive experiments on IEEE 14, 30, and modified 141-bus systems demonstrate that HPC-RL outperforms Model Predictive Control and state-of-the-art safe RL baselines across all metrics. The proposed method achieves near-optimal scheduling strategies and drastically reduces online computation time in large-scale scenarios from hours to minutes, while maintaining a near-zero constraint violation rate and nearly 100\% charging demand satisfaction.

cs.CE↗

arXiv2026

A Predict-then-Schedule framework for Power Distribution Networks with AI Data Centers

The surge of GPU-intensive workloads in artificial intelligence (AI) data centers drives massive energy demands, leading to soaring costs and significant stress on local power distribution networks. Coordinating delay-tolerant workload scheduling with power grid conditions via precise workload prediction can mitigate these issues. However, a critical gap remains in conventional approaches, i.e., minimizing prediction error does not necessarily lead to minimized downstream operational loss. Hence, this paper proposes an end-to-end Predict-Then-Schedule (PTS) framework that integrates upstream workload prediction with downstream scheduling optimization. By leveraging differentiable convex optimization, the PTS framework maps input features directly to optimal scheduling and enables gradient-based training. Furthermore, to respect the data center's capacity, a workload over-shifted loss combining electricity cost with a penalty for load-shedding is introduced to evaluate scheduling quality. Experiments demonstrate that the proposed framework significantly reduces operational cost and enhances system security compared to the conventional two-stage baseline.

cs.CE↗

arXiv2026

Unsupervised Learning for AC Optimal Power Flow with Fast Physics-Aware Layer

Learning to solve the Alternating Current Optimal Power Flow (AC-OPF) problem by neural networks (NNs) is a promising approach in real-time applications. Existing methods to ensure the physical feasibility of NN outputs embed a power flow (PF) solver within networks. However, the gradient through the PF solver, namely, implicit differentiation, needs manual Jacobian derivation and the solution of linear systems, which is computationally prohibitive and hinders integration with modern automatic differentiation (AD) frameworks. To address these challenges, we propose FPL-OPF, a novel unsupervised learning framework that incorporates a Fast Physics-aware Layer for AC-OPF problems. FPL-OPF embeds a fast PF iterative solver within the NN and takes solely the last few or even the final iterations into the AD graph. This design ensures high computational efficiency for both the forward and backward passes, circumventing complex custom backward implementations. Theoretically, we rigorously prove that the gradient from this design serves as a high-fidelity surrogate of the true implicit gradient under mild conditions. Extensive experiments demonstrate that FPL-OPF achieves significant speedups over state-of-the-art unsupervised learning approaches, while maintaining near-zero constraint violations and competitive optimality. Our code is available at https://github.com/wowotou1998/fpl-opf

cs.CE↗

arXiv2023

LP-BFGS attack: An adversarial attack based on the Hessian with limited pixels

Deep neural networks are vulnerable to adversarial attacks. Most $L_{0}$-norm based white-box attacks craft perturbations by the gradient of models to the input. Since the computation cost and memory limitation of calculating the Hessian matrix, the application of Hessian or approximate Hessian in white-box attacks is gradually shelved. In this work, we note that the sparsity requirement on perturbations naturally lends itself to the usage of Hessian information. We study the attack performance and computation cost of the attack method based on the Hessian with a limited number of perturbation pixels. Specifically, we propose the Limited Pixel BFGS (LP-BFGS) attack method by incorporating the perturbation pixel selection strategy and the BFGS algorithm. Pixels with top-k attribution scores calculated by the Integrated Gradient method are regarded as optimization variables of the LP-BFGS attack. Experimental results across different networks and datasets demonstrate that our approach has comparable attack ability with reasonable computation in different numbers of perturbation pixels compared with existing solutions.

cs.CR↗

arXiv2022

Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information

A counter-intuitive property of convolutional neural networks (CNNs) is their inherent susceptibility to adversarial examples, which severely hinders the application of CNNs in security-critical fields. Adversarial examples are similar to original examples but contain malicious perturbations. Adversarial training is a simple and effective defense method to improve the robustness of CNNs to adversarial examples. The mechanisms behind adversarial examples and adversarial training are worth exploring. Therefore, this work investigates similarities and differences between normally trained CNNs (NT-CNNs) and adversarially trained CNNs (AT-CNNs) in information extraction from the mutual information perspective. We show that 1) whether NT-CNNs or AT-CNNs, for original and adversarial examples, the trends towards mutual information are almost similar throughout training; 2) compared with normal training, adversarial training is more difficult and the amount of information that AT-CNNs extract from the input is less; 3) the CNNs trained with different methods have different preferences for certain types of information; NT-CNNs tend to extract texture-based information from the input, while AT-CNNs prefer to shape-based information. The reason why adversarial examples mislead CNNs may be that they contain more texture-based information about other classes. Furthermore, we also analyze the mutual information estimators used in this work and find that they outline the geometric properties of the middle layer's output.

cs.LG↗