[1]DENG Cuiyan,QI Xiaogang.Low-carbon green energy methods for 5G photovoltaic station based on the deep actor-critic strategy[J].CAAI Transactions on Intelligent Systems,2025,20(5):1198-1206.[doi:10.11992/tis.202501024]
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Low-carbon green energy methods for 5G photovoltaic station based on the deep actor-critic strategy

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 5 Page number: 1198-1206 Column: 学术论文—智能系统 Public date: 2025-09-05
Title:
Low-carbon green energy methods for 5G photovoltaic station based on the deep actor-critic strategy
Author(s):
DENG Cuiyan1;  QI Xiaogang2
1. Jinzhong College of Information, Jinzhong 030800, China;
2. School of Mathematics and Statistics, Xidian University, Xi’an 710071, China
Keywords:
5G low-carbon green energy; photovoltaic; DAC energy-saving strategy; reinforcement learning; power-saving strategy; agent; power grid; battery
CLC:
TP311.13
DOI:
10.11992/tis.202501024
Abstract:
The large-scale application of 5G ultra-broadband technology and IoT devices has significantly increased energy consumption, operational costs, and CO2 emissions. For example, a single 5G base station can consume approximately 2,000 W, incurring monthly electricity costs of about 1,000 yuan. With the development of the photovoltaic industry, integrating clean energy sources into high-power 5G networks has emerged as a promising approach to support low-carbon development. This paper proposes a low-carbon energy management method for 5G networks based on a deep actor-critic (DAC) strategy, which replaces conventional grid power with photovoltaic energy. An integrated energy storage model combining photovoltaic systems, battery storage, and grid connectivity is developed. A DAC-based optimization strategy is then applied to maximize carbon emission reductions and improve the efficiency of green energy supply. To support this strategy, a 5G network power supply agent is constructed using the A3C algorithm in conjunction with a deep Q-network, enabling dynamic energy optimization. By optimizing reward functions, the proposed method achieves high energy efficiency. Simulation results demonstrate that, across different seasons, the proposed method outperforms both Q-learning and the deep Q-network algorithms in improving network energy efficiency, thereby advancing low-carbon and green energy objectives.
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