AI 中文总结
研究人工智能驱动下数据中心部署致电力压力增大，企业采用太阳能发电利弊不明的问题，提出滚动时域优化框架协调相关运行，揭示太阳能集成虽提升能源自给率和减排，但限制吸收电网低成本低碳电力能力，凸显个体与系统脱碳目标矛盾。
AI 中文摘要
人工智能的指数级增长正在加速数据中心（DC）的部署，给电力基础设施带来前所未有的压力。对此，主要IT企业越来越多地采用现场太阳能发电来减少对电网的依赖并实现可持续发展目标。然而，该策略的实际影响仍不明确。虽然数据中心是能够进行时间负载转移的灵活资产，但将其锚定在自发电上可能会无意中限制其电网响应能力。为评估这些权衡，我们提出了一个滚动时域优化（RHO）框架，用于协调独立数据中心的作业调度、电网交互和现场太阳能发电。我们的研究结果揭示了一个关键悖论：尽管太阳能集成提高了能源自给率并减少了数据中心的总体排放，但它本质上限制了设施从电网吸收低成本、低碳电力的能力。这意味着企业个体可持续发展目标与系统级电网脱碳之间存在根本矛盾。
英文摘要
The exponential growth of AI is accelerating the deployment of data centers (DCs), placing unprecedented strain on power infrastructures. In response, major IT corporations are increasingly adopting on-site solar generation to reduce grid dependence and meet sustainability targets. However, the true impacts of this strategy remain ambiguous. While DCs are flexible assets capable of temporal load-shifting, anchoring them to self-generated power may inadvertently constrain their grid responsiveness. To evaluate these trade-offs, we propose a receding-horizon optimization (RHO) framework coordinating job scheduling, grid interactions, and on-site solar generation for a stand-alone DC. Our findings reveal a critical paradox: although solar integration increases energy self-sufficiency and reduces overall DC emissions, it inherently limits the facility's capacity to absorb low-cost, low-carbon electricity from the grid. This implies a fundamental tension between individual corporate sustainability goals and system-wide grid decarbonization.
