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<p>Data centers are a rapidly growing class of energy-intensive facilities whose increasing integration of variable renewable generation introduces pronounced short-term fluctuations and persistent seasonal mismatches between electricity supply and cooling demand. This study proposes a coordinated long–short-term scheduling framework for electro–hydrogen–cooling (EHC) systems that integrates photovoltaic generation, hydrogen energy storage, and a seasonal cold-storage strategy that harvests and stores cold during winter for use in summer, thereby reducing peak summer cooling demand. To handle the extended temporal scale and large decision space efficiently, we develop a Simplified Time-Horizon Compression (STHC) method that combines time-domain compression with deterministic quantitative reduction (DQR), cutting computational burden by 36%. Uncertainties in renewable output and electricity prices are managed using a multi-stage robust optimization (MSRO) formulation augmented with CVaR-based risk constraints, which enables a controlled trade-off between expected cost and tail-risk across four seasonal stages. Case studies show that the proposed MSRO–CVaR framework lowers total annualized system cost by 13.6% and achieves a renewable energy utilization rate of 39.3% relative to a deterministic optimization baseline, while improving resilience to uncertainty. The proposed methodology therefore offers a transferable decision-support tool for cost-effective, low-carbon operation of data centers and other multi-energy systems that require long-duration storage and seasonal load shifting.</p>

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Coordinated short- and long-term optimization of electricity-hydrogen-cooling energy stations in data centers

  • 期刊论文

  • 发表日期：

    2026年5月26日

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  • Chunxia Jia,
  • Xinyu Pan,
  • Hongpeng Liu,
  • Qing Wang

摘要

Data centers are a rapidly growing class of energy-intensive facilities whose increasing integration of variable renewable generation introduces pronounced short-term fluctuations and persistent seasonal mismatches between electricity supply and cooling demand. This study proposes a coordinated long–short-term scheduling framework for electro–hydrogen–cooling (EHC) systems that integrates photovoltaic generation, hydrogen energy storage, and a seasonal cold-storage strategy that harvests and stores cold during winter for use in summer, thereby reducing peak summer cooling demand. To handle the extended temporal scale and large decision space efficiently, we develop a Simplified Time-Horizon Compression (STHC) method that combines time-domain compression with deterministic quantitative reduction (DQR), cutting computational burden by 36%. Uncertainties in renewable output and electricity prices are managed using a multi-stage robust optimization (MSRO) formulation augmented with CVaR-based risk constraints, which enables a controlled trade-off between expected cost and tail-risk across four seasonal stages. Case studies show that the proposed MSRO–CVaR framework lowers total annualized system cost by 13.6% and achieves a renewable energy utilization rate of 39.3% relative to a deterministic optimization baseline, while improving resilience to uncertainty. The proposed methodology therefore offers a transferable decision-support tool for cost-effective, low-carbon operation of data centers and other multi-energy systems that require long-duration storage and seasonal load shifting.