摘要
碳捕集和可再生能源利用已经成为减少碳排放的重要措施。但捕集到的二氧化碳利用方式不明确,弃风弃光现象频发,限制了上述2种措施的减排效果。文章通过耦合电转气(power to gas,P2G)技术和碳捕集系统(carbon capture system,CCS),将其扩展到园区级电-热-气综合能源系统中,建立了一种高比例可再生能源渗透水平下的经济低碳多目标优化模型,在多情景下模拟分析了该综合能源系统在某工业园区的运行效果。结果表明,该耦合能源系统排放的大部分CO2可以被有效捕获并送往P2G装置用于合成燃气,为利用CO2提供了新的思路,同时显著提高了系统对可再生能源的消纳能力。对P2G设备容量的敏感性分析表明,单纯增加P2G容量虽然能减少弃能率,但会增加碳排放,因此对P2G的容量规划应当综合考虑可再生能源可用量与碳排放之间的关系。
Carbon capture and renewable energy utilization have become effective measures to reduce carbon emission.However,the utilization of captured carbon dioxide is not clear and the curtailment of wind and solar power is serious,which limits the reduction effects.This paper couples the power to gas(P2G)technology and carbon capture system(CCS),and extends it into the park-level integrated energy system including electricity,heat and gas,and proposes an economic and low carbon scheduling model under the high renewable energy penetration level.Through the coupling of CCS and P2G,most of the CO2 emitted by the system can be captured and sent to synthesize gas,which provides a new idea for the reutilization of CO2 rather than storage.In addition,the simulation results also show that the presence of CCS and P2G significantly improves the system’s ability to receive renewable energy.Sensitivity analysis of P2G capacity shows that increasing P2G capacity can reduce the energy curtailment rate,but cause more carbon emissions.Therefore,the capacity planning of P2G should comprehensively consider the relationship between renewable energy availability and carbon emissions.
作者
张兴平
张又中
ZHANG Xingping;ZHANG Youzhong(School of Economics and Management,North China Electric Power University,Beijing 102206,China;Beijing Key Laboratory of New Energy and Low-Carbon Development(North China Electric Power University),Beijing 102206,China)
出处
《电力建设》
北大核心
2020年第12期92-101,共10页
Electric Power Construction
基金
国家自然科学基金项目(72074075)。
关键词
综合能源系统
电转气(P2G)
碳捕集系统(CCS)
多目标优化
改进熵权法
integrated energy system
power to gas(P2G)
carbon capture system(CCS)
multi-objective optimization
improved entropy weight method