摘要
提升可再生能源在能源供给中的比例成为实现低碳经济的重要举措之一。为减少碳排放量并降低用电成本,提出了一种基于深度强化学习的微电网低碳经济优化调度模型。首先,介绍了碳排放流理论并基于此构建了碳计量模型以及阶梯碳价模型;其次,将低碳经济优化问题转换为一个马尔科夫决策;最后,利用深度强化学习对该多目标优化问题求解。实验结果表明,所提方法通过控制发电机组的出力以及负荷的转移,有效地提升了系统经济性并降低了碳排放量。
Enhancing the proportion of renewable energy sources in energy supply becomes a significant initiative to realize a low-carbon economy.A model based on deep reinforcement learning(DRL) for optimal allocation of low-carbon economy in microgrid is proposed to mitigate carbon emission and decrease electricity cost.Firstly,carbon emission flow theory is introduced on which a carbon measurement model and a stepped carbon price model are constructed.Secondly,the low-carbon economy optimization problem is converted into a Markov decision.Finally,the multi-objective optimization issue can be addressed utilizing DRL.The experimental results demonstrate that the proposed approach is effective in boosting system economy and mitigating carbon emissions by regulating the capacity of generating units and shifting the load.
作者
冯文韬
李龙胜
曾愚
潘可佳
张子闻
景致远
FENG Wentao;LI Longsheng;ZENG Yu;PAN Kejia;ZHANG Ziwen;JING Zhiyuan(State Grid Sichuan Information and Communication Company,Chengdu 610041,Sichuan,China;School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,Sichuan,China)
出处
《四川电力技术》
2023年第6期75-82,共8页
Sichuan Electric Power Technology
基金
国网四川省电力公司科技项目(B7194723R001)。
关键词
碳排放流理论
阶梯碳价模型
深度强化学习
carbon emission flow theory
stepped carbon price model
deep reinforcement learning
