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

2026-05-06

AI Summary

The surging electricity demand from AI data centers intensifies stress on grid capacity and reliability, thereby constraining AI development and underscoring the urgent need for source-grid coordination. To address this challenge, this work proposes OpenG2G—the first modular and scalable simulation platform designed specifically for co-optimizing AI data centers and power grids. OpenG2G integrates real-world production-grade AI service workloads, a high-fidelity power grid simulator, and a generic controller interface, enabling closed-loop evaluation of diverse control strategies, including classical, optimization-based, and learning-driven approaches. Experimental results under realistic grid conditions and AI workloads demonstrate that OpenG2G effectively quantifies the impact of various coordination strategies on both grid stability and data center performance, establishing a robust foundation for future joint optimization of AI infrastructure and power systems.

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.

Problem

• AI datacenter
• grid coordination
• power flexibility
• interconnection delays
• runtime coordination

Innovation

• simulation platform
• datacenter-grid coordination
• power flexibility
• modular architecture
• AI workload management