面向大规模电动车群体的智能充电管理与优化技术研究
结题报告
批准号:
62001412
项目类别:
青年科学基金项目
资助金额:
24.0 万元
负责人:
唐晓莹
学科分类:
通信网络
结题年份:
2023
批准年份:
2020
项目状态:
已结题
项目参与者:
唐晓莹
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中文摘要
现今,环保、经济等因素不断推动电动车的大力发展。然而,电动车群体用户“充电难”的问题仍是制约电动汽车产业发展的重大难题。与此同时,大规模电动车的投入使用也给智慧城市中的智能电网和智能交通网络的高效与稳定带来挑战。为解决这些挑战,本项目将从电动车群体充电与路径协同规划、充电站/桩最优充电调度方案与充电价格制定三个关键问题进行研究。具体来说,本项目拟采用图论、动态规划、模型预测控制、强化学习对电动车群体充电与路径规划问题进行建模和优化算法设计;拟通过采用凸优化理论和在线调度方法对充电站/桩的充电调度进行建模分析,并设计最优调度优化算法;拟通过采用博弈论、联邦学习来制定多充电站合作与竞争不同模式下的最优定价机制,以提高充电站/桩的整体利用率和经济利益,并保障城市智能电网的高效性与稳定性。本项目将对智慧城市大规模电动车群体智能充电管理与优化综合策略商业化的发展提供重要的理论和技术支持。
英文摘要
Nowadays environmental awareness and the rising fuel cost have stimulated an increasing interest in electrical vehicles (EVs). However, the EV charging problem is the main obstacle which restricts the development of the EV industry. Meanwhile, the deployment of large-scale EVs will pose great challenges to the stability and efficiency of smart grids and transportation systems in a smart city. To address these challenges, in this project, we study three important yet interconnected problems, i.e., jointly routing and charging optimization of large-scale EVs, optimal charging scheduling and pricing for charging stations/points. On one hand, we will apply stochastic programming, model predictive control and reinforcement learning to investigate the jointly routing and charging optimization of large-scale EVs under different application scenarios. On the other hand, we will apply convex optimization theory and online scheduling method to study the optimal charging scheduling algorithm for charging stations/points to improve the profit. In addition, we will also apply game theory and federated learning to achieve an efficient pricing scheme for charging stations/points by considering the competition and cooperation between the charging stations/points, in order to improve the stability and efficiency of charging stations/points and smart grids. The successful completion of this project will contribute to the future deployment of optimal charging and intelligent management for large scale EVs into smart city in a new and effective way.
期刊论文列表
专著列表
科研奖励列表
会议论文列表
专利列表
DOI:10.1109/TII.2023.3245633
发表时间:2023
期刊:IEEE Transactions on Industrial Informatics
影响因子:12.3
作者:Chenxi Sun;Tongxin Li;Xiaoying Tang
通讯作者:Xiaoying Tang
DOI:10.1109/tsg.2021.3078238
发表时间:2021-11
期刊:IEEE Transactions on Smart Grid
影响因子:9.6
作者:Ankun Yu;Xiaoying Tang;Y. Zhang;Jianwei Huang
通讯作者:Ankun Yu;Xiaoying Tang;Y. Zhang;Jianwei Huang
DOI:10.1109/tpwrs.2023.3303176
发表时间:2024-03
期刊:IEEE Transactions on Power Systems
影响因子:6.6
作者:Huanxin Liao;Chao Yang;Huisheng Gao;Wenxuan Liu;Huanhai Xin;Xiaoying Tang;Junhua Zhao
通讯作者:Huanxin Liao;Chao Yang;Huisheng Gao;Wenxuan Liu;Huanhai Xin;Xiaoying Tang;Junhua Zhao
DOI:10.1109/tnse.2023.3334476
发表时间:2024-03
期刊:IEEE Transactions on Network Science and Engineering
影响因子:6.6
作者:Hongyi Wu;Xiaoying Tang;Y. Zhang;Lin Gao
通讯作者:Hongyi Wu;Xiaoying Tang;Y. Zhang;Lin Gao
DOI:10.1109/jiot.2023.3334027
发表时间:2024-03
期刊:IEEE Internet of Things Journal
影响因子:10.6
作者:Jie Liu;Shuoyao Wang;Xiaoying Tang
通讯作者:Jie Liu;Shuoyao Wang;Xiaoying Tang
基于联邦学习的复合类型电动车充电场 站网络优化研究
  • 批准号:
    --
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2025
  • 负责人:
    唐晓莹
  • 依托单位:
国内基金
海外基金