The proliferation of large-scale AI and data-intensive applications has driven the development of Computing Power Networks (CPN). It is a key paradigm for delivering ubiquitous, on-demand computational services with high efficiency. However, CPNs face dual challenges in service computing. Immense energy consumption threatens sustainable operations. And the integration with power grids also features high penetration of intermittent Renewable Energy Sources (RES), complicating task scheduling while ensuring Quality of Service (QoS). To address these issues, this paper proposes a novel Two-Stage Co-Optimization (TSCO) framework. It synergistically coordinates CPN task scheduling and power system dispatch, aiming to optimize service performance while achieving low-carbon operations. The framework decomposes the complex, large-scale problem into a day-ahead stochastic unit commitment stage and a real-time operational stage. The former is solved using Benders decomposition for computational tractability, while in the latter, economic dispatch of generation assets is coupled with an adaptive CPN task scheduling managed by a deep reinforcement learning agent. It makes carbon-aware decisions by responding to dynamic grid conditions, including real-time electricity prices and marginal carbon intensity. Extensive simulations demonstrate that the TSCO outperforms baseline approaches significantly. It reduces carbon emissions by 16.2% and operational costs by 12.7%, while decreasing RES curtailment by over $60\%$, maintaining a task success rate of 98.5%, and minimizing average task tardiness to 12.3s. This work advances cross-domain service optimization in CPNs.

翻译：大规模人工智能与数据密集型应用的激增推动了算力网络（CPN）的发展。该网络是实现高效、按需、泛在计算服务的关键范式。然而，算力网络在服务计算方面面临双重挑战。巨大的能源消耗威胁着其可持续运营。同时，与电网的融合伴随着间歇性可再生能源（RES）的高渗透率，这在确保服务质量（QoS）的同时，使得任务调度变得复杂。为解决这些问题，本文提出了一种新颖的两阶段协同优化（TSCO）框架。该框架协同协调算力网络任务调度与电力系统调度，旨在优化服务性能的同时实现低碳运营。该框架将复杂的大规模问题分解为日前随机机组组合阶段和实时运行阶段。前者采用Benders分解法求解以保证计算可行性；在后者中，发电资产的经济调度与一个由深度强化学习智能体管理的自适应算力网络任务调度相耦合。该框架通过响应动态电网条件（包括实时电价和边际碳强度）做出碳感知决策。大量仿真实验表明，TSCO框架显著优于基线方法。它能减少16.2%的碳排放和12.7%的运营成本，同时将可再生能源弃电率降低超过60%，并保持98.5%的任务成功率，将平均任务延迟时间最小化至12.3秒。本研究推动了算力网络中跨域服务优化的进展。