Hao Zhu

Young-ho Cho, Min-Seung Ko, Hao Zhu

Achieving a sustainable electricity infrastructure requires the explicit integration of carbon emissions into power system modeling and optimization. However, existing open-source test cases for power system research lack generator-level carbon profiling, preventing the benchmark of carbon-aware operational strategies. To address this gap, this work introduces PGLib-CO2, an open-source extension to the PGLib-OPF test case library. The proposed PGLib-CO2 enriches standard grid test cases with CO2 and CO2-equivalent emission intensity factors to achieve realistic, generator-level carbon profiling with an expanded list of fuel types. Using the standardized data, PGLib-CO2 allows us to enhance the algorithms for computing key carbon emission metrics. We first utilize the differentiable programming paradigm for computing LMCE by treating the OPF-based grid dispatch as a differentiable layer. This method provides a rigorous marginal sensitivity for general convex cost functions, eliminating the need of using a small incremental change in numerical perturbation. Moreover, to accelerate the real-time LMCE computation, we develop an MPP-based approach that shifts the optimization burden to offline phase of identifying the OPF critical regions. Since each critical region is characterized by a pre-computed affine dispatch function, the online phase reduces to identifying the region followed by efficiently evaluating the region-specific LMCE values. Numerical evaluations on IEEE test systems demonstrate that the differentiable LMCE computation attains the precise sensitivity information, and the MPP-based approach retrieves the LMCE signals faster than the direct optimization approach. By bridging high-fidelity data with advanced parametric computation, PGLib-CO2 provides a reproducible and computationally efficient foundation for future research in sustainable power system operations.

Yifei Zeng, Yanqin Jiang, Siyu Zhu et al.

Recent progress in pre-trained diffusion models and 3D generation have spurred interest in 4D content creation. However, achieving high-fidelity 4D generation with spatial-temporal consistency remains a challenge. In this work, we propose STAG4D, a novel framework that combines pre-trained diffusion models with dynamic 3D Gaussian splatting for high-fidelity 4D generation. Drawing inspiration from 3D generation techniques, we utilize a multi-view diffusion model to initialize multi-view images anchoring on the input video frames, where the video can be either real-world captured or generated by a video diffusion model. To ensure the temporal consistency of the multi-view sequence initialization, we introduce a simple yet effective fusion strategy to leverage the first frame as a temporal anchor in the self-attention computation. With the almost consistent multi-view sequences, we then apply the score distillation sampling to optimize the 4D Gaussian point cloud. The 4D Gaussian spatting is specially crafted for the generation task, where an adaptive densification strategy is proposed to mitigate the unstable Gaussian gradient for robust optimization. Notably, the proposed pipeline does not require any pre-training or fine-tuning of diffusion networks, offering a more accessible and practical solution for the 4D generation task. Extensive experiments demonstrate that our method outperforms prior 4D generation works in rendering quality, spatial-temporal consistency, and generation robustness, setting a new state-of-the-art for 4D generation from diverse inputs, including text, image, and video.