US 12,255,453 B2
Method for aggregating group of electric vehicles based on electric vehicle flexibility, electronic device, and storage medium
Qinglai Guo, Beijing (CN); Mengjie Liu, Beijing (CN); and Yujie Sheng, Beijing (CN)
Assigned to Tsinghua University, Beijing (CN)
Filed by Tsinghua University, Beijing (CN)
Filed on Dec. 15, 2021, as Appl. No. 17/551,404.
Claims priority of application No. 202111253252.1 (CN), filed on Oct. 27, 2021.
Prior Publication US 2023/0127845 A1, Apr. 27, 2023
Int. Cl. H02J 3/00 (2006.01); B60L 53/66 (2019.01); G06Q 30/0283 (2023.01); H02J 7/00 (2006.01)
CPC H02J 3/003 (2020.01) [B60L 53/66 (2019.02); G06Q 30/0283 (2013.01); H02J 7/0014 (2013.01); H02J 7/0048 (2020.01); B60L 2260/54 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for distribution network optimization, comprising:
obtaining a prediction result on a fast charging demand of each electric vehicle (EV) of a group of EVs based on a preset EV trip chain model;
determining a distribution area of each substation involved in an activity range of the EVs as an aggregation area, wherein a number of aggregation areas is equal to a number of substations within the activity range of the EVs;
establishing an area-based aggregation model based on the fast charging demand of each EV, in which the aggregation model includes energy balance equations, energy constraints, power constraints, time coupling constraints and spatial coupling constraints;
obtaining an EV fast charging load power curve and an EV energy curve of each aggregation area based on the area-based aggregation model;
determining upper and lower limits of multiple EV fast charging load power curves as a power flexibility range for the EV fast charging load power in the aggregation area, and determining upper and lower limits of multiple EV energy curves as an energy flexibility range for the EV energy in the aggregation area; and
providing the power flexibility range and the energy flexibility range for a distribution network node corresponding to a substation in each aggregation area, and dispatching the group of EVs in a power grid by using multi-period optimal power flow optimization in combination with the power flexibility range and the energy flexibility range.