Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning

• URL: http://arxiv.org/abs/2606.30316v1
• Date: Mon, 29 Jun 2026 13:59:33 GMT
• Title: Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning
• Abstract summary: Reinforcement Learning is an online controller for curtailment-aware workload shifting in high-performance computing data centers.<n>We introduce a fixed-day simulation framework with synthetic wind and price signals and delayed completion feedback.<n>We evaluate two complementary countermeasures: optimization-based Learning and potential-based Reward Shaping.
• Score: 39.799207552858114
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 pronounced credit-assignment problem and tends to underuse free wind energy early in the day. We therefore evaluate two complementary countermeasures: optimization-based Imitation Learning and potential-based Reward Shaping. Across multi-seed training and a 200-day test set, Proximal Policy Optimization (PPO) and a Soft Actor-Critic (SAC) variant with an additional on-policy update routine achieve strong empirical performance among learned policies, and both Imitation Learning and Reward Shaping provide improvements in relevant configurations. A performance gap to the optimizer remains, which is expected: the optimizer plans offline with full-day foresight, whereas Reinforcement Learning must decide online from current observations without future realizations. The benchmark and ablation results provide a transparent basis for extending the approach toward richer multi-site and continuous-time scenarios.

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