AI & ML
Market Efficiency and Capacity Investment under Flexible Price-Making Loads: A Computational Study
arXiv eess.SY0 views
arXiv:2610.41196v1 Announce Type: new Abstract: Large flexible loads such as data centers can shift consumption across locations and hours to lower their bills. We model a price-making consumer leading the system operator in a Stackelberg game and compare its shifts with a cost-minimizing planner's over a simulated RTS-GMLC year. Strategic temporal and spatial shifting capture 75% and 93% of the planner's savings, forgoing the rest mostly by withholding flexibility to protect favorable prices. Scarce capacity and abundant renewables raise this share for temporal shifting; scarce transmission lowers it for spatial shifting. Temporal and spatial flexibility substitute for thermal and transmission capacity, respectively, and complement renewables.
Source:https://arxiv.org/abs/2610.41196PreviousOptimal climate policy in incomplete electricity marketsNextEMT Assessment of SCR and X/R Effects on Current-Priority-Dependent LVRT Response of a Grid-Following BESS
Related posts
There’s a New Way to Shop With AI. Text Alexa
0CNET News

13 waterproof jacket deals to save you from the November drizzle — up to 40% off leading brands
0TechRadar
In Vivo CRISPR Base Editing of <i>ANGPTL3</i> for Durable Lipid Lowering
0Nature News

Shure’s popular podcasting and streaming mic is almost $50 off
0The Verge

BoopFace: an MIT-licensed SVG robot avatar Web Component for AI agents
0dev.to

Written, translated into eleven languages, and seen by nobody
0dev.to