arXiv (All)
open access
Communication Delay Robust Control of BESS for AI Training Load Smoothing
AI training loads can exhibit rapid power fluctuations because their power demand differs significantly between computational and communication phases, creating challenging ramp rates at the data center point of common coupling (PCC). Integrating battery energy storage systems (BESS) in data centers
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Distributed Secondary Frequency Control and SoC Balance for Droop-Controlled BESSs with A Unified SoC Relative Variation Rate#1013264
- A Marginal Cost Consensus Scheme with Reset Mechanism for Distributed Economic Dispatch in BESSs#1013277
- Towards Input-Convex Neural Network Modeling for Battery Optimization in Power Systems#1130436
- A Stochastic Mean-CVaR Framework for BESS Multi-Market Bidding Strategies#1012537
- Battery-Swapping Station Operation Under Forecast Uncertainty: A Scenario-Based Stochastic MPC Framework#625371
Record · ID 1037225
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive