摘要
考虑到碳捕集利用与封存(CCUS)是对电转气(P2G)过程中二氧化碳(CO_(2))来源的细化,文中提出对综合能源系统(IES)进行电-氢-氧-甲烷-二氧化碳和虚拟二氧化碳等P2G过程的定性定量建模,以及对风-光-电动汽车-负荷等随机源荷进行时空聚类以深度挖掘IES氢源碳源潜力的CCUS设备容量规划模型。以阶梯正负碳交易、弃风弃光惩罚和年设备投资成本等的年综合成本最低为目标,以碳捕集、碳存储和甲烷化等CCUS设备功率容量限值为全局变量对电气平衡和能量耦合进行二次约束,经过混合整数线性化处理后利用Cplex求解。算例分析表明,源荷时空聚类可在典型日场景缩减时保留IES廉价碳源氢源特征,合理规划后的CCUS设备可兼具弃风消纳和碳减排功能,使P2G制取的天然气更具市场竞争力。
Considering that carbon capture utilization and storage (CCUS) is a refinement of carbon dioxide (CO_2) sources in the power-to-gas (P2G) process,a qualitative and quantitative modeling of P2G processes such as electricity-hydrogen-oxygen-methane-CO_(2) and virtual CO_(2) for integrated energy systems (IES) is proposed,and a CCUS equipment capacity planning model that conducts spatiotemporal clustering of random sources and loads such as wind-light-electric vehicle-load to deeply explore the potential of IES hydrogen sources and carbon sources is proposed.The goal is to minimize the annual comprehensive cost by considering positive and negative carbon trading,penalties for wind and solar curtailment,and annual equipment investment costs.The power capacity limits of CCUS equipment such as carbon capture,carbon storage,and methanation are used as global variables to impose quadratic constraints on electrical balance and energy coupling.After mixed integer linearization,the problem is solved using Cplex.The case analysis shows that source load spatiotemporal clustering can retain the characteristics of IES low-cost carbon and hydrogen sources when typical daily scenes are reduced.Reasonable planning of CCUS equipment can combine wind curtailment and carbon reduction functions,making the natural gas produced by P2G more competitive in the market.
作者
陈仕龙
李崇绪
CHEN Shilong;LI Chongxu(Kunming University of Science and Technology,Kunming 650500,China)
出处
《现代电子技术》
北大核心
2024年第6期109-118,共10页
Modern Electronics Technique
基金
国家自然科学基金项目(52067009)。
关键词
碳捕集利用与封存
综合能源系统
P2G
场景聚类
容量规划
源荷聚类
carbon capture utilization and storage
integrated energy system
P2G
scene clustering
capacity planning
source load clustering