Backend
Joint Electricity Price and Load Forecasting for Data Center Battery Dispatch Using Polynomial Diffusion Models
arXiv cs.SY0 views
arXiv:2611.08470v1 Announce Type: new Abstract: The rapid growth of large-scale data centers (DCs) is increasing electricity demand, market volatility, and operating costs. Battery energy storage systems (BESSs) can mitigate these challenges by shifting electricity purchases from high-price to low-price periods. This paper proposes a real-time BESS dispatch framework using polynomial diffusion models (PDMs) to forecast both electricity prices and DC loads. Leveraging the closed-form conditional expectations of PDMs, the forecasts are integrated into a dynamic programming (DP) framework to minimize electricity procurement costs. Across the evaluated BESS configurations, the proposed approach achieves 2.63 times the cost savings of a rule-based (RB) strategy.
Source：https://arxiv.org/abs/2611.08470PreviousEstimating Distribution Grid Upgrade Costs based on County-level Building Electrification ProjectionsNextA Hybrid Framework for Explainable Aviation Risk: Causal Chain Extraction from Safety Reports with Physics-Based Failure Quantification and Interventional Analysis
Related posts
A Refusal Gate for Free Infrastructure: Eight Traits That Should Block Your Deploy
0dev.to
roadrunner: el servidor php de alto rendimiento
0dev.to
Cap the Context, Not the Review: A Byte-Budgeted Evidence Packet for OSS Patches
0dev.to
osFoundry 的 Loop 和 Graph 是什麼，什麼時候該用哪一個？
0dev.to
Building a Real-Time B2B Hiring-Intent & Lead Enrichment Pipeline with MCP, Claude Desktop, and n8n
0dev.to
The Take-Home Where the Candidate Must Break Their Own PR
0dev.to