1. 国网四川省电力公司信息通信公司
2. 电子科技大学机械与电气工程学院
纸质出版:2023
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冯文韬, 李龙胜, 曾愚, 等. 基于深度强化学习的微电网源-荷低碳调度优化研究[J]. 四川电力技术, 2023,(6):75-82.
冯文韬, 李龙胜, 曾愚, et al. 基于深度强化学习的微电网源-荷低碳调度优化研究[J]. 2023, (6): 75-82.
冯文韬, 李龙胜, 曾愚, 等. 基于深度强化学习的微电网源-荷低碳调度优化研究[J]. 四川电力技术, 2023,(6):75-82. DOI: 10.16527/j.issn.1003-6954.20230611.
冯文韬, 李龙胜, 曾愚, et al. 基于深度强化学习的微电网源-荷低碳调度优化研究[J]. 2023, (6): 75-82. DOI: 10.16527/j.issn.1003-6954.20230611.
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。
可再生能源制零碳天然气助力我国实现碳中和[J]. 宋鹏飞;张超;侯建国;王秀林.现代化工,2023(07)
碳中和背景下可再生能源发电产业研究[J]. 储兆诤;张晓悦;王涛.能源与节能,2023(05)
深度强化学习在配电网优化运行中的应用[J]. 胡维昊;曹迪;黄琦;张斌;李思辰;陈哲.电力系统自动化,2023(14)
基于改进碳排放流理论的电力系统动态低碳调度方法[J]. 陈家兴;王春玲;刘春明.中国电力,2023(03)
面向碳达峰碳中和目标的我国电力系统发展研判[J]. 李晖;刘栋;姚丹阳.中国电机工程学报,2021(18)
考虑阶梯式碳交易的电-气-热综合能源系统低碳经济调度[J]. 崔杨;曾鹏;仲悟之;崔文利;赵钰婷.电力自动化设备,2021(03)
基于深度强化学习的综合能源系统动态经济调度[J]. 杨挺;赵黎媛;刘亚闯;冯少康;盆海波.电力系统自动化,2021(05)
考虑LCA能源链与碳交易机制的综合能源系统低碳经济运行及能效分析[J]. 王泽森;石岩;唐艳梅;门向阳;曹军;王海风.中国电机工程学报,2019(06)
电力系统碳排放流的计算方法初探[J]. 周天睿;康重庆;徐乾耀;陈启鑫.电力系统自动化,2012(11)
电力系统碳排放流分析理论初探[J]. 周天睿;康重庆;徐乾耀;陈启鑫.电力系统自动化,2012(07)
Measuring environmental performance of power dispatch influenced by low-carbon approaches[J]. Jin Jingliang;Wen Qinglan;Zhao Liya;Zhou Chaoyang;Guo Xiaojun.Renewable Energy,2023
Modeling and operation optimization of hydrogen-based integrated energy system with refined power-to-gas and carbon-capture-storage technologies under carbon trading[J]. Wu Qunli;Li Chunxiang.Energy,2023
Low-carbon optimal learning scheduling of the power system based on carbon capture system and carbon emission flow theory[J]. Li Jifeng;He Xingtang;Li Weidong;Zhang Mingze;Wu Jun.Electric Power Systems Research,2023
Energy optimization of multi-carrier energy systems to achieve a low carbon community[J]. Nasiri Tohid;Moeini-Aghtaie Moein;Foroughi Mehdi;Azimi Meisam.Journal of Cleaner Production,2023
Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system[J]. Alabi Tobi Michael;Lawrence Nathan P.;Lu Lin;Yang Zaiyue;Bhushan Gopaluni R..Applied Energy,2023
Model-free dynamic management strategy for low-carbon home energy based on deep reinforcement learning accommodating stochastic environments[J]. Hou Hui;Ge Xiangdi;Chen Yue;Tang Jinrui;Hou Tingting;Fang Rengcun.Energy & Buildings,2023
Optimal scheduling of a wind energy dominated distribution network via a deep reinforcement learning approach[J]. Zhu Jiaoyiling;Hu Weihao;Xu Xiao;Liu Haoming;Pan Li;Fan Haoyang;Zhang Zhenyuan;Chen Zhe.Renewable Energy,2022
Low-carbon economic dispatch of electricity-gas systems[J]. Xiang Yue;Wu Gang;Shen Xiaodong;Ma Yuhang;Gou Jing;Xu Weiting;Liu Junyong.Energy,2021
Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning.[J]. Zhiqiang Wan;Hepeng Li;Haibo He;Danil V. Prokhorov.IEEE Trans. Smart Grid,2019
Optimal dispatch strategy for integrated energy systems with CCHP and wind power[J]. Guoqing Li;Rufeng Zhang;Tao Jiang;Houhe Chen;Linquan Bai;Hantao Cui;Xiaojing Li.Applied Energy,2017
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