Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization

被引:0
|
作者
Bao, Qiong [1 ]
Gao, Minghao [1 ]
Chen, Jianming [1 ]
Tan, Xu [1 ]
机构
[1] Southeast Univ, Sch Transportat, Nanjing 211189, Peoples R China
基金
中国国家自然科学基金;
关键词
electric vehicles; charging demand; charging parking lots; position and size planning; fuzzy bi-objective optimization; 90-10;
D O I
10.3390/math12193143
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.
引用
收藏
页数:21
相关论文
共 50 条
  • [31] Multi-objective optimal allocation of DG and EV charging station based on space-time characteristics and demand response
    Liu L.
    Wu T.
    Chen X.
    Zheng W.
    Xu Q.
    Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2021, 41 (11): : 48 - 56
  • [32] Bi-objective Optimization for Multi-modal Transportation Routing Planning Problem Based on Pareto Optimality
    Sun, Yan
    Lang, Maoxiang
    JOURNAL OF INDUSTRIAL ENGINEERING AND MANAGEMENT-JIEM, 2015, 8 (04): : 1195 - 1217
  • [33] A risk-based maintenance planning in process industry using a bi-objective robust optimization model
    Alipour, Zohreh
    Monfared, Mohammadali Saniee
    Monabbati, Sayyed Ehsan
    Computers and Chemical Engineering, 2025, 194
  • [34] Multifunctional applications of batteries within fast-charging stations based on EV demand-prediction of the users' behaviour
    Gjelaj, Marjan
    Arias, Nataly Banol
    Traeholt, Chresten
    Hashemi, Seyedmostafa
    JOURNAL OF ENGINEERING-JOE, 2019, (18): : 4869 - 4873
  • [35] Real-Time Algorithm Based Intelligent EV Parking Lot Charging Management Strategy Providing PLL Type Demand Response Program
    Sengor, Ibrahim
    Guner, Sitki
    Erdinc, Ozan
    IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2021, 12 (02) : 1256 - 1264
  • [36] Optimization of Charging Station Capacity Based on Energy Storage Scheduling and Bi-Level Planning Model
    Wang, Wenwen
    Liu, Yan
    Fan, Xinglong
    Zhang, Zhengmei
    WORLD ELECTRIC VEHICLE JOURNAL, 2024, 15 (08):
  • [37] Hierarchical framework for demand prediction and iterative optimization of EV charging network infrastructure under uncertainty with cost and quality-of-service consideration
    Tungom, Chia E.
    Niu, Ben
    Wang, Hong
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 237
  • [38] MPC-based co-optimization of an integrated PV-EV-Hydrogen station to reduce network loss and meet EV charging demand
    Zhang, Jiamei
    Sun, Kai
    Li, Canbing
    Yang, Hanyu
    Zhou, Bin
    Hou, Xiaochao
    Ge, Rui
    ETRANSPORTATION, 2023, 15
  • [39] Bi-Objective Fuzzy Optimization Model for Multiproject and Multi-item Investment Based on the Perspective of Real Options
    Yu, Jing
    Xu, Bin
    Shi, Yong
    2010 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2010), VOL 2, 2010, : 765 - 769
  • [40] Credibility-based fuzzy mathematical programming for bi-objective capacitated partial facility interdiction with fortification and demand outsourcing model
    Azadeh, A.
    Kokabi, R.
    Hallaj, D.
    SCIENTIA IRANICA, 2017, 24 (02) : 778 - 793