Deep Reinforcement Learning for Real-Time Green Energy Integration in Data Centers

2025-07-23

AI Summary

To address the challenge of real-time integration of green energy in e-commerce data centers, this paper proposes a deep reinforcement learning (DRL)-based multi-source collaborative energy management framework. The framework jointly optimizes real-time renewable energy forecasting, dynamic workload scheduling, and battery energy storage (BES) charge/discharge control. A composite reward function is designed to simultaneously optimize economic cost, energy efficiency, and carbon emissions, enabling adaptive coordination among the utility grid, photovoltaic/wind generation, and BES. Ablation studies validate the effectiveness of the model architecture and key hyperparameters. Experimental results demonstrate that, compared to baseline methods, the proposed approach reduces energy cost by 38%, cuts carbon emissions by 45%, improves energy efficiency (reflected by an 82% reduction in Power Usage Effectiveness, PUE), lowers SLA violation rate to 1.5%, and achieves a cumulative reward of 950—collectively indicating substantial performance gains across all metrics.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper explores the implementation of a Deep Reinforcement Learning (DRL)-optimized energy management system for e-commerce data centers, aimed at enhancing energy efficiency, cost-effectiveness, and environmental sustainability. The proposed system leverages DRL algorithms to dynamically manage the integration of renewable energy sources, energy storage, and grid power, adapting to fluctuating energy availability in real time. The study demonstrates that the DRL-optimized system achieves a 38% reduction in energy costs, significantly outperforming traditional Reinforcement Learning (RL) methods (28%) and heuristic approaches (22%). Additionally, it maintains a low SLA violation rate of 1.5%, compared to 3.0% for RL and 4.8% for heuristic methods. The DRL-optimized approach also results in an 82% improvement in energy efficiency, surpassing other methods, and a 45% reduction in carbon emissions, making it the most environmentally friendly solution. The system's cumulative reward of 950 reflects its superior performance in balancing multiple objectives. Through rigorous testing and ablation studies, the paper validates the effectiveness of the DRL model's architecture and parameters, offering a robust solution for energy management in data centers. The findings highlight the potential of DRL in advancing energy optimization strategies and addressing sustainability challenges.

Problem

• Optimizing energy management in e-commerce data centers
• Reducing energy costs and carbon emissions effectively
• Balancing renewable energy integration with grid power

Innovation

• DRL optimizes real-time green energy integration
• Dynamic management of renewable and grid power
• Achieves high energy efficiency and low emissions