The paper introduces Decision‑Focused Learning via Neural Optimization Layers (BS‑DFL) for battery‑assisted grid‑import smoothing in AI data centers. It formulates a risk‑aware battery energy storage system scheduling problem that balances reserve commitments, demand‑responsive dispatch, operating costs, grid‑ramp penalties, and conditional value‑at‑risk. By training a constraint‑aware neural optimization layer directly on the scheduling objective, the method eliminates the need for optimal‑solution labels and enables fast, solver‑free rolling‑horizon scheduling, achieving a 71.70% reduction in minute‑to‑minute grid‑import changes and an 18.01% cost savings in a 10,000‑minute simulation.
By Xinda Zheng, Canchen Jiang, Frits de Nijs, Edward Lam, Markus Wagner
arXiv:2605. 02965v2 Announce Type: replace Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers.
By Yang Fu, Peng Qin, Liming Chen, Zihao Zhang, Hao Yu, Yifei Wang
arXiv:2606. 00561v1 Announce Type: cross Abstract: Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to deploy on constrained hardware, and opaque to operators.
By Aleksandra Dmitruka, Karlis Freivalds
arXiv:2505. 05203v3 Announce Type: replace-cross Abstract: With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces growing challenges in maintaining economic efficiency, security, and robustness.
By Wangkun Xu, Zhongda Chu, Fei Teng
arXiv:2606. 30316v1 Announce Type: new Abstract: This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers.
By Jan Stenner, Alexander Kilian, Sebastian Peitz, Hermann de Meer
arXiv:2609.37065v1 Announce Type: new
Abstract: Decision-making under uncertainty often relies on predicted parameters, yet accurate prediction does not necessarily lead to good operational decisions...
By Jiahui Feng, Dafang Zhao, Zheng Chen, Zhengmao Li, Lingwei Zhu
This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers. We introduce a reproducible fixed-day simulation framework with synthetic wind and price signals and delayed completion feedback, designed to be extensible toward more complex scenarios.
arXiv:2606. 10600v1 Announce Type: cross Abstract: Radio frequency wireless power transfer (RF-WPT) is an enabling technology for supporting uninterrupted communications in future Internet of Things systems by reducing the need for battery replacement and mitigating battery-waste-related issues.
By Amirhossein Azarbahram, Osmel M. Rosabal, David Ernesto Ruiz-Guirola, Melike Erol-Kantarci, Kaibin Huang, Onel L. A. L\'opez
The paper presents an LLM-based predictive scheduling system that forecasts execution time and energy consumption from source code, aiming to improve data center sustainability. By integrating these predictions into a real-time GPU allocation algorithm, the system reduces both energy use and queuing delays. In a collaboration with a data center, the approach achieved a 32% drop in energy consumption and a 30% reduction in waiting time.
By Hanzhao Wang, Jingxuan Wu, Yumeng Li, Yu Pan, Guanting Chen
Artificial intelligence (AI) is increasingly central to power and energy systems, supporting modeling, forecasting, optimization, and control. Yet most existing works emphasize specialized applications and offer little reusable material for newcomers or interdisciplinary learners, who increasingly rely on large language models rather than building their own.
arXiv:2604. 25848v2 Announce Type: replace Abstract: We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times.
By An Nguyen, Hoang Nguyen, Phuong Le, Hung Pham, Cuong Do, Laurent El Ghaoui
arXiv:2607. 12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions.
By Takumi Shioda, Kohei Terashima, Tatsuo Nagai