A Novel Hierarchical Co-Optimization Framework for Coordinated Task Scheduling and Power Dispatch in Computing Power Networks

2025-08-05

AI Summary

To address the high energy consumption, substantial carbon emissions, and grid instability induced by high renewable energy source (RES) penetration in Computing-Power Networks (CPNs), this paper proposes a two-stage computing–power coordinated optimization framework. At the day-ahead stage, a stochastic unit commitment model integrated with Benders decomposition enables low-carbon planning; at the real-time stage, economic dispatch is synergized with deep reinforcement learning to achieve carbon-aware task scheduling. The framework innovatively bridges cross-temporal and cross-spatial decision-making layers, enabling dynamic responses to time-varying electricity prices and marginal carbon intensity signals. Evaluated on the IEEE 30-bus system, the approach reduces operational cost and carbon emissions significantly compared to baseline methods, decreases wind and solar curtailment by over 60%, and maintains stringent computing-power service quality (QoS) requirements.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Combinatorial Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The proliferation of large-scale artificial intelligence and data-intensive applications has spurred the development of Computing Power Networks (CPNs), which promise to deliver ubiquitous and on-demand computational resources. However, the immense energy consumption of these networks poses a significant sustainability challenge. Simultaneously, power grids are grappling with the instability introduced by the high penetration of intermittent renewable energy sources (RES). This paper addresses these dual challenges through a novel Two-Stage Co-Optimization (TSCO) framework that synergistically manages power system dispatch and CPN task scheduling to achieve low-carbon operations. The framework decomposes the complex, large-scale problem into a day-ahead stochastic unit commitment (SUC) 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 (DRL) agent. This agent makes intelligent, carbon-aware decisions by responding to dynamic grid conditions, including real-time electricity prices and marginal carbon intensity. Through extensive simulations on an IEEE 30-bus system integrated with a CPN, the TSCO framework is shown to significantly outperform baseline approaches. Results demonstrate that the proposed framework reduces total carbon emissions and operational costs, while simultaneously decreasing RES curtailment by more than 60% and maintaining stringent Quality of Service (QoS) for computational tasks.

Problem

• Optimize task scheduling and power dispatch in Computing Power Networks
• Reduce carbon emissions and operational costs in power grids
• Decrease renewable energy curtailment while maintaining service quality

Innovation

• Two-stage co-optimization for power-task synergy
• Benders decomposition for day-ahead scheduling
• DRL agent for carbon-aware real-time dispatch