The potentials of vehicle-grid integration on peak shaving of a community considering random behavior of aggregated vehicles
Yalun Li,
Kun Wang,
Chaojie Xu,
Yu Wu,
Liguo Li,
Yuejiu Zheng,
Shichun Yang,
Hewu Wang,
Minggao Ouyang
Affiliations
Yalun Li
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China; School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Kun Wang
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China; School of Transportation Science and Engineering, Beihang University, Beijing 100191, China; School of Beijing, Beihang University, Beijing 100191, China
Chaojie Xu
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China; School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Yu Wu
School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
Liguo Li
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Yuejiu Zheng
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; Corresponding authors.
Shichun Yang
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China; School of Beijing, Beihang University, Beijing 100191, China
Hewu Wang
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Minggao Ouyang
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China; Corresponding authors.
With large-scale electric vehicles (EVs) promoted and connected to the power grid, the uncontrolled charging of EVs enlarges the peak-valley range of load in the distribution grid. To alleviate the peak-valley range and enhance the stability of the distribution grid, vehicle-grid integration (VGI) is proposed as an economic and potential solution. However, the impact of disorderly charging and the potential of VGI considering random user behavior requires clarification. This paper established a mixed-integer linear programming model with user behavior simulated by the Monte Carlo algorithm. The travel and charging behavior of EVs are provided by Monte Carlo simulation with characteristic parameters from statistical data of urban vehicle travel data. A digital model describing the VGI charging boundary is built to restrict the transition from uncontrolled charging to VGI. Through analysis of the global optimization results, the comparison of disorderly charging with VGI under different scenarios is provided to illustrate the effectiveness of avoiding load uplift and reducing load peak-valley range. In a typical residential community with 100 EVs per 1000 people, disorderly charging increases the peak load by 17.1%, while VGI, with a participation ratio of 30%, reduces the load range by 74.8%. This study clearly demonstrates the effectiveness of VGI and guides the implementation of VGI in the rapid growth of EVs.
