盛裕杰, 郭庆来, 薛屹洵, 王嘉炜, 常馨月. 信息-物理-社会视角下的电力-交通耦合网络建模与协同优化[J]. 电力系统自动化, 2024, 48(7): 62-85.
引用本文: 盛裕杰, 郭庆来, 薛屹洵, 王嘉炜, 常馨月. 信息-物理-社会视角下的电力-交通耦合网络建模与协同优化[J]. 电力系统自动化, 2024, 48(7): 62-85.
SHENG Yujie, GUO Qinglai, XUE Yixun, WANG Jiawei, CHANG Xinyue. Collaborative Modeling and Optimization of Power-Transportation Coupling Network from Cyber-Physical-Social Perspective[J]. Automation of Electric Power Systems, 2024, 48(7): 62-85.
Citation: SHENG Yujie, GUO Qinglai, XUE Yixun, WANG Jiawei, CHANG Xinyue. Collaborative Modeling and Optimization of Power-Transportation Coupling Network from Cyber-Physical-Social Perspective[J]. Automation of Electric Power Systems, 2024, 48(7): 62-85.

信息-物理-社会视角下的电力-交通耦合网络建模与协同优化

Collaborative Modeling and Optimization of Power-Transportation Coupling Network from Cyber-Physical-Social Perspective

  • 摘要: 近年来,电动汽车(EV)、快速充电设施的高速增长将电力系统和交通系统这两个复杂的基础设施网络紧密耦合。EV具备充电时间和充电位置的灵活性,对于新型电力系统正是理想的移动储能资源,可提供规模巨大的时间、空间灵活调节能力。然而,宏观的电力-交通耦合背后是海量EV用户在各类信息引导下做出的微观社会化决策,构成了复杂的信息-物理-社会系统。从这一视角对电力-交通耦合网络建模分析与协同优化的相关研究进行了梳理。首先,对两网耦合的基本场景和关键挑战进行了介绍;接着,以社会层与物理层为重点归纳了融合微观车主决策与宏观网络动态的EV群体出行-充电行为建模方法;然后,进一步纳入信息层,总结了多类定价主体与EV间的策略交互与协同优化;最后,对电力-交通耦合网络建模分析与协同优化相关方向的研究进行展望。

     

    Abstract: In recent years, the rapid growth of electric vehicles(EVs) and fast charging facilities has two closely coupled complex infrastructure networks, which is the power system and transportation system. With flexibility in charging time and location, EVs become ideal mobile energy storage resources for new power systems, which provide massive spatial and temporal flexible regulation capabilities. However, behind the macro power-transportation coupling is the micro social decision-making of numerous EV users under the guidance of the multi-source information, which forms a complex cyber-physical-social system. The relevant research on collaborative modeling and optimization of power-transportation coupling network from such perspective is reviewed. Firstly, the basic scenarios and key challenges of power-transportation network are introduced. Subsequently, focusing on social and physical layers, the modeling of EV traveling-charging behavior is reviewed, which integrates the micro EV user decisionmaking and macro network dynamics. Then, further incorporating the cyber layer, the strategy interaction and collaborative optimization among multiple pricing entities with EV drivers are summarized. Finally, prospects are made for the research on modeling and the optimization of power-transportation coupling network.

     

/

返回文章
返回