温室气体
碳纤维
气候变化
环境科学
高效能源利用
全球变暖
能量(信号处理)
环境经济学
自然资源经济学
计算机科学
工程类
生态学
经济
统计
电气工程
复合数
生物
数学
算法
作者
Zhonglian Zhang,Xiaohui Yang,Yang Li,Zhaojun Wang,Zhisheng Huang,Xiaopeng Wang,Linghao Mei
出处
期刊:Energy
[Elsevier]
日期:2023-11-01
卷期号:283: 129188-129188
被引量:2
标识
DOI:10.1016/j.energy.2023.129188
摘要
How to achieve the "double carbon" goal in energy systems has been the concern of governments. Integrated energy system (IES) is affected by climate change during his operation, in order to study the impact of climate change on IES and achieve the "double carbon" goal in energy systems, this paper proposes an integrated machine learning(IML) to forecast the long-term load, then investigates IES costs and carbon emissions in relation to climate, followed by the establishment of carbon peak energy system(CPES) and carbon neutral energy system(CNES), finally the honey badger algorithm is used to optimize the configuration of CPES and CNES. The results show that: IML can accurately make load forecasts. Under climate change, changes in load reduce the cost and carbon emissions of IES, and changes in equipment efficiency increase the cost and carbon emissions of IES. When both are considered, the cost and carbon emissions of IES increase by 1.18% and 0.92% per decade respectively. The costs of CPES and CNES increase by 0.93% and 1% respectively for every 10 years earlier than the year of achievement. To meet China's "double carbon" goal, CPES and CNES need to increase their costs by 1.97% and 2% respectively.
科研通智能强力驱动
Strongly Powered by AbleSci AI