C2G-BENCH: A Cyber-Physical Evaluation Benchmark for Hierarchical Reinforcement Learning in Grid-Interactive Hyperscale Data Centers

The rapid growth of generative AI is reshaping the energy footprint of modern computing infrastructure. Hyperscale AI data centers now operate at power levels comparable to large industrial facilities, creating new challenges for grid reliability, energy procurement, and real time demand flexibility. At the same time, their large controllable loads, cooling systems, and on site storage make them potential participants in grid services such as frequency regulation. However, existing evaluation environments do not capture the coupled decision making required for grid interactive operation. We introduce C2G-BENCH, a cyber-physical benchmark for hierarchical reinforcement learning in hyperscale data centers. C2G-BENCH simulates a real-world scenario, where a 250 MW-class hyperscale data center participates in grid frequency regulation while serving realistic AI workload demand under weather-driven ambient conditions. The benchmark explicitly links 15-minute market level orchestration with 5-second physical control, enabling agents to jointly reason over regulation commitments, workload flexibility, cooling, battery dispatch, and facility safety. C2G-BENCH achieves this through two coupled Gymnasium environments: C2GMacroEnv, where a high level controller bids into the regulation market, and C2GFastEnv, where a low level controller actuates the Dynamic Voltage and Frequency Scaling (DVFS), Coolant Distribution Unit (CDU) pump speed, HVAC effort, and Battery dispatch (BESS). The simulator models a 250 MW class two zone facility using seven modular physics engines covering workload, thermal, electrical, battery, grid signal, weather, and market dynamics, driven by real workload and weather traces. We benchmark a family of rule-based, LLM-based & RL-based controllers for both the macro and fast environments, and evaluate them on four stress-test environments in multiple grid markets. Baseline results show that active low level control substantially improves tracking quality: a rule-based macro controller paired with a RL-based low level controller reduces the tracking RMSE from 2,737 kW to 229 kW compared with a macro only configuration, while maintaining full throughput and zero thermal violations. These results demonstrate that C2G-BENCH exposes meaningful cross layer tradeoffs and provides a reusable benchmark for studying safe, hierarchical control of grid interactive AI data centers.