Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning
arXiv:2606.30316v1 Announce Type: new Abstract: This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers. We introduce a reproducible fixed-day simulation framework with synthetic wind and price signals and delayed completion feedback, designed to be extensible toward more complex scenarios. As a controlled benchmarking basis, we then focus on the minimal case with one wind turbine and one co-located data center. In this setting, pure Reinforcement Learning exhibits a prono
The increasing energy demands of AI and high-performance computing, coupled with the instability of traditional energy grids, are driving urgent research into optimizing data center operations with renewable energy sources like wind.
This research explores a practical method for integrating renewable energy with data centers, addressing the critical energy bottleneck facing the AI compute industry and enabling more sustainable and resilient infrastructure.
The ability to dynamically manage and shift data center workloads in response to renewable energy availability could fundamentally alter how and where large-scale compute is deployed, prioritizing energy efficiency and grid stability.
- · Data Center Operators
- · Renewable Energy Sector
- · AI/HPC Providers
- · Grid Management Software Companies
- · Fossil Fuel Dependent Data Centers
- · Traditional Energy Utilities (without adaptation)
Online workload shifting becomes a standard practice for data centers co-located with intermittent renewable energy sources.
The economic viability of deploying data centers in remote, windy locations increases significantly, leading to new infrastructure hubs.
National energy policies for AI compute infrastructure begin to heavily incentivize co-location with renewables and advanced energy management systems.
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