电网技术2024，Vol.48Issue(5)：1873-1883,中插10-中插13,15.DOI:10.13335/j.1000-3673.pst.2023.1036
基于乐观行动-评判深度强化学习的含氢综合能源系统低碳经济调度
Low-carbon Economic Scheduling of Hydrogen Integrated Energy System Based on Optimistic Actor-critic Deep Reinforcement Learning
摘要
Abstract
Under the background of"dual carbon",the hydrogen integrated energy system with the hydrogen energy as the energy carrier is an important support for the low-carbon transformation of China's energy industry.To ensure the supply and efficient utilization of the hydrogen energy in the hydrogen integrated energy system,this article proposes an operational mode of the integrated energy system which makes hydrogen energy from electricity and gas,which realizes the comprehensive utilization of the hydrogen energy.Based on the carbon capture devices and the integrated demand response,a carbon reduction mechanism with the complementarity between energy sources and loads is established to fully tap the potential of carbon reduction in the system,further improving the consumption rate of renewable energy and the low-carbon level of the system.In addition,in order to achieve a rapid response to the random fluctuations of the energy source and load in the integrated energy system containing hydrogen,an optimistic action-critic deep reinforcement learning method is proposed for the offline training and the online optimization of the scheduling model in the system,which efficiently achieves the low-carbon economic online optimization scheduling of the integrated energy system containing hydrogen energy.Finally,the superiority of the proposed method is verified through a responding case.关键词
含氢综合能源系统/互补降碳/低碳经济调度/深度强化学习/氢能综合利用Key words
hydrogen integrated energy system/complementary carbon reduction/low-carbon economic scheduling/deep reinforcement learning/comprehensive utilization of hydrogen分类
信息技术与安全科学引用本文复制引用
孙惠娟,段伟男,陈俐,刘金祥,彭春华..基于乐观行动-评判深度强化学习的含氢综合能源系统低碳经济调度[J].电网技术,2024,48(5):1873-1883,中插10-中插13,15.基金项目
国家自然科学基金项目(52267007,52167009).Project Supported by the National Natural Science Foundation of China(52267007,52167009). （52267007,52167009）
