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
The Book Made by Sunlight and Seaweed
0dev.to AI
Meta-Optimized Continual Adaptation for deep-sea exploration habitat design for extreme data sparsity scenarios
0dev.to AI
Cheapest LLM for 77-intent banking classification: GPT-6.1 Terra at $0.000727 a success
0dev.to AI
Best Humanizer MCP Servers in 2026: Claude, ChatGPT, Cursor and Windsurf
0dev.to AI

Sources: major investors are increasingly skeptical Anthropic's IPO can achieve a $1.5T valuation after a market selloff; one says it could be closer to $1T (Wall Street Journal)
0Techmeme

Thinking Machines Lab debuts Inkling 2 Small, a MoE model with 594B total and 17B active parameters, and plans to release weights next week after safety testing (Thinking Machines Lab)
0Techmeme