OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination

TL;DR

OpenG2G is a versatile simulation platform that models AI datacenter-grid coordination, enabling analysis of control strategies and AI deployment impacts on grid stability and datacenter flexibility.

Contribution

It introduces a modular, extensible simulation platform for evaluating AI datacenter-grid coordination strategies using real AI workload data and high-fidelity grid models.

Findings

OpenG2G can simulate various control paradigms for datacenter-grid coordination.

The platform helps quantify how AI deployment choices affect grid stability.

OpenG2G demonstrates its utility through realistic scenarios and workloads.

Abstract

AI's growing compute demand and new datacenter buildouts present major capacity and reliability challenges for the electricity grid, leading to multi-year interconnection delays for new datacenters and bottlenecking AI growth. To ease this strain, datacenters increasingly offer rapid power flexibility in response to grid signals, where the datacenter can increase or decrease its power consumption by adapting its workload in real time. In order to understand the impact of large datacenters on the grid and to facilitate the design of effective coordination strategies, we build OpenG2G, a simulation platform for AI datacenter-grid runtime coordination. We show that OpenG2G is capable of answering a wide range of coordination questions by allowing users to implement and compare various control paradigms (including classic, optimization, and learning-based controllers), and quantify how AI model and deployment choices affect datacenter flexibility and coordination outcomes. This versatility is enabled by OpenG2G's modular and extensible architecture: a datacenter backend driven by real measurements of production-grade AI services, a grid backend built on high-fidelity grid simulators, and a generic controller interface that closes the loop between them. We describe the design of OpenG2G and demonstrate its usefulness through realistic grid scenarios and AI workloads.