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Session 13: Coordinated Optimization of Power-Transportation Coupled Networks: Theory and Applications

“电力-交通耦合网协同优化:理论与应用”

Session 13

Coordinated Optimization of Power-Transportation Coupled Networks: Theory and Applications
“电力-交通耦合网协同优化:理论与应用”

The integration of power and transportation systems has become a critical focus in modern urban infrastructure development, driven by the increasing interdependence between energy supply and mobility demands. This session will bring together researchers, practitioners, and policymakers to discuss cutting-edge theoretical frameworks, advanced modeling techniques, and practical applications that improves the efficiency of power-transportaiton coupled networks.
Topics of interest include but are not limited to:

  • 1、Modeling and Simulation: Developing accurate and efficient models of power-transportation coupled networks to simulate EV charging behaviors, traffic flow distribution, and their impacts on the power grid.
  • 2、Coordinated Optimization: Designing models and algorithms for coordinated optimization of power and transportation systems, balancing economic efficiency, reliability, and environmental benefits.
  • 3、Resilience Enhancement: Investigating resilience assessment mdoels and methods for power-transportation coupled networks to improve system resilience, enhancing its ability to withstand extreme events and uncertainties.
  • 4、Electricity Pricing Optimization: Exploring electricity pricing mechanisms tailored to power-transportation coupled networks, designing rational pricing strategies to guide orderly EV charging and facilitate renewable energy integration.
  • 5、Market Mechanisms: Designing effective market mechanisms to incentivize EV users to participate in grid regulation, promoting renewable energy utilization and low-carbon transformation in transportation systems.
  • 6、Applications and Case Studies: Examining the application prospects of power-transportation coordinated optimization in smart cities and vehicle-to-grid (V2G) networks, sharing successful case studies and lessons learned.

Chair: Dr. Changxu Jiang, Fuzhou University, China

Dr. Changxu Jiang is a IEEE Member, and graduate supervisor. He dedicates his career to interdisciplinary research in power-transportation coupling network, power system resilience, and reinforcement learning (AI). His research leadership is evidenced by principal investigator roles in multiple competitive grants including the National Natural Science Foundation of China (NSFC) Youth Program, Fujian Provincial Natural Science Foundation Project, and Fuzhou University Startup Fund. As a China Scholarship Council awardee, Dr. Jiang completed a one-year visiting fellowship at Cardiff University, strengthening international academic collaboration.
His scholarly output includes 20+ SCI/EI-indexed publications and 20+ authorized invention patents, demonstrating consistent contributions to smart grid innovation and AI-driven energy solutions.

Co-chair: Dr. Tao Qian, Southeast University, China

Tao Qian received the B.S. and Ph.D. degrees in electrical engineering from Xi’an Jiaotong University, Xi’an, China, in 2017 and 2022, respectively. He was a Visiting Scholar with the School of Engineering, Cardiff University, U.K., from 2020 to 2021. He is currently a Lecturer with Southeast University, Nanjing, China. His current research interests include the coordinated power and transportation systems, market mechanism design, and DRL methods.

 

Co-chair: Dr. Zhenjia Lin, The Hong Kong Polytechnic University, Hong Kong, China

Dr. Zhenjia Lin obtained his B.S. and Ph.D. degrees in Electrical Engineering from the South China University of Technology in Guangzhou, China. He was a Research Assistant at the Technical University of Denmark, Denmark, from 2019 to 2020, and at Tsinghua University, China, from 2021-2022. In 2022, He joined the Hong Kong Polytechnic University and served as a Research Associate at the Department of Electrical and Electronic Engineering, and then as a Postdoctoral Fellow at the Department of Building Environment and Energy Engineering. Currently, he is a Research Assistant Professor, focusing on data-driven analytics, uncertainty optimization, and their applications in power and energy systems, as well as renewable energy integration.

Call for Papers Timeline / 征稿时间

  • Submission of Full Paper: March 10th, 2025
    投稿截止日: 2025年3月10日 

  • Notification Deadline: March 30th, 2025
    通知书发送: 2025年3月30日 

  • Registration Deadline: April 10th, 2025
    注册截止日期: 2025年4月10日