Yu Zhang Research

Open to postdoctoral opportunities · Spring 2027

Control Science · Energy Systems

Control for energy systems. Designed to converge.

I develop optimization and distributed control methods for reliable, resilient hybrid AC/DC microgrids. Ph.D. researcher at HUST and visiting researcher at NTU Singapore.

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9
Journal papers
7
Conference papers
18
Conference Presentations
Yu Zhang portrait

Research focus

Fast. Safe. Resilient. Optimization · Distributed control · AI for energy

Education

Built across power, control, and intelligent systems.

  1. Visiting Scholar/Student

    Nanyang Technological University, School of Electrical and Electronic Engineering.

    Advisors: Prof. Hung Dinh Nguyen and Prof. Changyun Wen

  2. Ph.D. Control Science & Engineering

    Huazhong University of Science and Technology, School of Artificial Intelligence and Automation.

    Advisor: Prof. Yan-Wu Wang

  3. B.S. Electrical Engineering

    Taiyuan University of Technology, College of Electrical and Power Engineering.

    Advisor: Prof. Xiao-Ming Chang
An original aerial visualization of interconnected renewable-rich microgrids with wind, solar, battery storage, electric-vehicle charging, and distributed power flows

Problem Space

Energy systems are becoming distributed. Their control must become smarter.

My research targets the operational layer of microgrids: fast convergence, constraint handling, networked implementation, and robustness under limited communication or malicious data.

Research System

Four directions. One connected control stack.

01

Economic Dispatch

Smooth reconstruction penalty functions and distributed predefined-time dispatch.

02

Cooperative Control

Voltage, frequency, and global power sharing in hybrid AC/DC microgrids.

03

Power Flow Optimization

Multi-objective phase-angle control and voltage-safe load sharing.

04

Resilient Control

DoS and FDI attack compensation for single and networked DC microgrids.

Selected Output

Research, peer reviewed.

Work spanning control, optimization, power and energy systems. Citation counts are from Web of Science Core Collection and Google Scholar, checked 17 August 2026; coverage differs by database.

Working Papers

What I am building next.

A physics-informed learning framework that connects renewable-rich grid dynamics, mechanism discovery, and intelligent energy storage control.

A renewable-rich heterogeneous power network flows through physical mechanism discovery into reinforcement-learning control of a battery and supercapacitor hybrid energy storage system
01 · Model Heterogeneous network physics Model renewable-rich grid dynamics with an inertial Kuramoto network.
Research in progress

Physics-informed reinforcement learning for renewable-rich power systems

Combining interpretable grid dynamics with data-driven decision-making to improve stability, efficiency, and control performance.

  1. 01 · Model Build the physics layer Develop an inertial Kuramoto model for a heterogeneous, renewable-rich power network.
  2. 02 · Discover Reveal the mechanism Identify synchronization, stability, and energy-flow mechanisms that govern system performance.
  3. 03 · Control Learn better decisions Construct a physics-informed reinforcement learning policy for coordinated hybrid energy storage.

Skills

From proof to code to hardware.

Predefined-time control Distributed optimization Reinforcement learning PPO algorithms Reward scaling design Physics-informed reinforcement learning Decision transformers Python MATLAB / Simulink Power electronics modeling PCB design High-precision industrial controller development Modern research automation workflow

Projects & Honors

Research support. Recognized work.

Hosted

HUST Innovation Research Institute Technology Innovation Fund

Optimal frequency control strategy of microgrid based on distributed energy storage, 2024.09-2025.09.

Hubei Key R&D Program

Coordinated Control and Optimization for Smart Microgrids Based on Energy Storage Clusters

Multi-timescale cooperative control of energy storage under emergency scenarios, 2025.8-2027.8.

NSFC Key Project

Regional Energy Internet

Flow optimization and collaborative control for distributed regional energy networks, 2023.01-2027.12.

Conference Atlas

Research travels. Life comes with it.

18 conference records 18 field notes 2021-2026
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Field Notes

Across conferences, collaborations, and the moments between.

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Complete Trajectory

Attended conferences and academic events

    Contact

    Let us build energy systems that respond in time.

    Open to postdoctoral research collaborations starting Spring 2027.

    N2505927K@e.ntu.edu.sg yuzhang_hust@hust.edu.cn Wuhan, China / Singapore