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1.西安交通大学电气工程学院,陕西省西安市 710049
2.国家电网有限公司西北分部,陕西省西安市 710048
丁涛(1986—),男,通信作者,博士,教授,博士生导师,东南大学电气工程学院2005级校友,主要研究方向:电力系统经济运行、电力市场、能源经济。E-mail:tding15@xjtu.edu.cn
贾文皓(1999—),男,博士研究生,主要研究方向:综合能源系统。E-mail:jiawenhao@stu.xjtu.edu.cn
黄雨涵(2000—),女,硕士研究生,主要研究方向:电力市场。E-mail:hyh2018@stu.xjtu.edu.cn
收稿:2023-06-19,
修回:2023-08-21,
网络出版:2023-11-03,
纸质出版:2023-12-10
移动端阅览
丁涛,贾文皓,黄雨涵等.基于分布鲁棒机会约束的移动氢能系统制-储-运氢协同优化[J].电力系统自动化,2023,47(23):1-11.
DING Tao,JIA Wenhao,HUANG Yuhan,et al.Collaborative Optimization for Hydrogen Generation, Storage, and Transportation in Mobile Hydrogen Energy Systems Based on Distributionally Robust Chance Constraint[J].Automation of Electric Power Systems,2023,47(23):1-11.
丁涛,贾文皓,黄雨涵等.基于分布鲁棒机会约束的移动氢能系统制-储-运氢协同优化[J].电力系统自动化,2023,47(23):1-11. DOI: 10.7500/AEPS20230619003.
DING Tao,JIA Wenhao,HUANG Yuhan,et al.Collaborative Optimization for Hydrogen Generation, Storage, and Transportation in Mobile Hydrogen Energy Systems Based on Distributionally Robust Chance Constraint[J].Automation of Electric Power Systems,2023,47(23):1-11. DOI: 10.7500/AEPS20230619003.
氢能是中国能源低碳化转型的重要载体之一。移动氢能系统利用富余新能源制氢后充入储氢罐存储,然后通过氢气长管拖车将氢产品运输至各加氢站。文中提出一种基于分布鲁棒机会约束的移动氢能系统制-储-运氢协同优化方法。首先,构建移动氢能系统新能源制氢储氢与长管拖车运氢协同优化模型,并采用一种“虚拟节点插入”方法对氢气长管拖车路径规划模型进行等价化简。其次,采用数据驱动的分布鲁棒机会约束处理加氢站氢气需求的不确定性,构建基于Wasserstein距离的移动氢能系统分布鲁棒模型,并基于条件风险价值近似将所提分布鲁棒模型转化为混合整数线性规划问题。最后,以中国西安市交通网为基础构造移动氢能系统算例,验证了所提模型和方法的有效性。
Hydrogen energy is one of the important carriers for the low-carbon transition of China’s energy industry. Mobile hydrogen energy systems (MHESs) utilize surplus renewable energy to produce hydrogen
which is injected into hydrogen storage tanks for storage and then transported to hydrogen refueling stations (HRSs) via hydrogen tube trailers (HTs). This paper proposes a collaborative optimization method for hydrogen generation
storage
and transportation in MHESs based on distributionally robust chance constraint (DRCC). First
a collaborative optimization model for renewable energy to hydrogen
hydrogen storage
and hydrogen transportation is established
where a “dummy node insertion” method is adopted to equivalently simplify the route planning model of HTs. Then
a data-driven DRCC approach is introduced to deal with the uncertainty of hydrogen demand of each HRS. A Wasserstein distance-based distributionally robust model is constructed for MHESs
which is further transformed into a mixed-integer linear programming based conditional value-at-risk approximation. Finally
case studies are conducted on a typical MHES based on traffic network and simulation results of Xi’an in China
which verifies the validity of the proposed model and method.
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