发电技术2024，Vol.45Issue(4)：651-665,15.DOI:10.12096/j.2096-4528.pgt.24021
基于信息间隙决策理论的含碳捕集-电转气综合能源系统优化调度
Optimization and Scheduling of Integrated Energy Systems With Carbon Capture and Storage-Power to Gas Based on Information Gap Decision Theory
摘要
Abstract
[Objectives]The main issue in the current rational optimization of integrated energy systems is to adopt technological means to improve energy conversion efficiency,reduce system energy waste and regional environmental pollution,in order to scientifically coordinate the optimization goals of economic,stability,and low-carbon operation of the integrated energy system.To this end,a low-carbon optimization strategy for a carbon capture and storage(CCS)two-stage power to gas(P2G)integrated energy system based on scenario generation and information gap decision theory(IGDT)was proposed.[Methods]At the technical level,by finely modeling the two-stage conversion from power to gas,the efficiency of hydrogen energy utilization was improved,and a combined heating and power(CHP)-CCS-P2G coupling model was established.At the market mechanism level,a tiered carbon trading model was introduced to reduce CO2 emissions in the system.Finally,based on the IGDT,an optimization scheduling model was constructed for different risk preferences.[Results]Taking a typical integrated energy system as an example,the simulation results show that the proposed model can improve the wind and solar energy consumption rate,achieve low-carbon,economic,and stable operation of the system.[Conclusions]This optimization strategy can effectively help decision-makers develop scheduling plans under risk avoidance and risk pursuit strategies based on their risk preferences,achieving a balance between system uncertainty and economy.关键词
综合能源系统/场景生成/信息间隙决策理论(IGDT)/碳捕集与封存(CCS)/电转气(P2G)/阶梯型碳交易Key words
integrated energy system/scenario generation/information gap decision theory(IGDT)/carbon capture and storage(CCS)/power to gas(P2G)/stepped carbon trading分类
能源科技引用本文复制引用
赵振宇,包格日乐图,李炘薪..基于信息间隙决策理论的含碳捕集-电转气综合能源系统优化调度[J].发电技术,2024,45(4):651-665,15.基金项目
北京市自然科学基金项目(8232013). Project Supported by the Nature Science Foundation of Beijing(8232013). （8232013）
