电力系统保护与控制2024，Vol.52Issue(13)：78-89,12.DOI:10.19783/j.cnki.pspc.231146
计及最恶劣场景概率和供需灵活性的综合能源系统分布鲁棒低碳优化调度
Distributed robust low-carbon optimal scheduling of an integrated energy system considering worst-case scenario probability and flexibility of supply and demand
摘要
Abstract
As the penetration of renewable energy increases,its uncertainty poses great challenges to the economics and robustness of integrated energy systems.To promote renewable energy consumption and reduce carbon emissions,a data-driven distributionally robust optimization(DRO)scheduling strategy is proposed.First,a flexible supply and demand response model consisting of an organic Rankine cycle(ORC),hydrogen fuel cell and electric vehicle is constructed,and a stepped carbon trading mechanism is introduced to constrain the carbon emissions of the system.Secondly,in order to obtain the probability distribution of the scene in the worst case,a comprehensive norm is used to constrain the probability distribution confidence set of the wind power output scene.Then,a two-stage robust optimization model is established with the goal of minimizing the total cost of integrated energy system(IES)operation in the worst scenario probability distribution,and the model is iteratively analyzed by a column and constraint generation(CCG)algorithm.Finally,the simulation results show the effectiveness of the proposed model and solution method.It also analyzes the influence of the ladder carbon trading mechanism and the supply and demand flexible response model in improving the system flexibility and low-carbon economy.关键词
综合能源系统/供需灵活性/阶梯碳交易/数据驱动/分布鲁棒优化Key words
integrated energy system/supply-demand flexibility/ladder carbon trading/data-driven/distributionally robust optimization引用本文复制引用
王蓬蓬,宋运忠..计及最恶劣场景概率和供需灵活性的综合能源系统分布鲁棒低碳优化调度[J].电力系统保护与控制,2024,52(13):78-89,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.61340041 and No.61374079). 国家自然科学基金项目资助(61340041,61374079) （No.61340041 and No.61374079）
河南省自然科学基金项目资助(182300410112) （182300410112）
