Open AccessOptimal shared mobility planning for electric vehicles in the distribution network
Author(s) -
Fan Vivienne Hui,
Wang Shu,
Meng Ke,
Dong Zhao Yang
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6900
Subject(s) - computer science , interdependence , mathematical optimization , operations research , work (physics) , network planning and design , fuzzy logic , multi objective optimization , component (thermodynamics) , service (business) , engineering , computer network , business , mechanical engineering , physics , mathematics , marketing , artificial intelligence , machine learning , political science , law , thermodynamics
Shared electric vehicle (EV) is an emerging component across interdependent power and transportation networks that affect economic, environmental and societal benefits. Studies on how to collaboratively enable the optimal planning of these interacted networks is less explored. In this work, a joint distribution network expansion planning framework integrated with a shared EV charging station is proposed. One of the objectives is to minimise overall investment cost considering operational waiting cost of shared EV charging service and power loss, while the other aims to maximise the utilisation of charging infrastructures. To overcome the difficulties in solving the multi‐objective mixed‐integer non‐linear problem, Tchebycheff decomposition based evolutionary algorithm is modified to find the Pareto front showing the trade‐offs between goals above. Besides, stochastic traffic assignment model is used to obtain traffic distribution and travel demand. The final optimal solution is decided by the fuzzy satisficing method. The performance of the proposed approach is evaluated on a 54‐node test system. Sensitivity analysis is performed to assess the impact of key parameters from the perspectives of transportation and distribution network sectors. The numerical results demonstrate the capability and feasibility of the proposed method.