Changhong Zhao, PhD, Assistant Professor

Google Scholar

PhD students

- Bohang Fang (BS: UESTC, Chengdu), 2021 -

- Heng Liang (BE: Nanjing University), 2021 -

- Yujin Huang (BE: Tsinghua University), 2023 -

Postdocs

- Kaiping Qu, 2/2024 - present (with Yue Chen)

- Wanjun Huang, 11/2021 - 8/2022. Beihang University.

- Wei Lin, 8/2021 - 9/2022. University of Hong Kong, postdoc.

- Sidun Fang, 5/2020 - 8/2021. Chongqing University.

Other students

- Runjie Zhang, 8/2023 - present, MPhil student.

- Xinyi Chen, 11/2022 - 11/2023, RA, PhD student at Southeast University.

- Zexin Sun, 5/2022 - 8/2022, RA, PhD student at Boston University.

- Jinyan Su, 12/2021 - 5/2022, undergraduate. PhD student, Cornell University.

- Xiaojie Li, 9/2021 - 12/2021, undergraduate. PhD student, NTU, Singapore.

- Zhenyi Yuan, 6/2021 - 9/2021, RA, PhD student at UC San Diego.

- Tong Wu, 12/2019 - 5/2021, PhD coadvised with Angela Zhang. UCF (Florida).

- Chenxu Wang, 9/2019 - 7/2021, MSc/RA. PhD student, CityU Hong Kong.

- Xinran Liu, 9/2019 - 5/2020, MSc project. China Southern/Guangxi Power Grid.

Research projects

RGC General Research Fund: Joint optimization of distribution network topology and nonlinear power flow, 1/2025 - 12/2027.

- We consider a combinatorial optimization problem to find a minimum-cost topology of a networked system, where the cost of each feasible topology is defined by an underlying continuous optimization of network resource allocation and flows. We focus on such a problem, called optimal topology and power flow (OTPF), in power distribution networks, which jointly optimizes the on/off status of switches on power lines and the generations, loads, and power flows, for more reliable, economical, and sustainable operation. The essential tradeoff between scalability (by which we mean a mild increase of computational burden with the network size) and optimality (a lower cost without violating physical and operational constraints) makes it hard to improve both attributes of an algorithm to solve OTPF, especially considering the practical three-phase unbalanced nonlinear alternating-current (AC) power flow models, the uncertainties of renewable generations and loads, and the key reliability indices, e.g., voltage stability, that do not have closed-form expressions. We will tackle these challenges to develop OTPF solution algorithms with improved scalability and optimality. The proposed research is planned as three tasks: (1) Develop a topology-informed switch opening and exchange algorithm based on convex relaxation to AC power flow, to solve deterministic OTPF problems with provable suboptimality bounds. (2) Extend the algorithm in (1) with a topology-informed scenario clustering method, to solve stochastic and robust OTPF problems under uncertainties of renewable generations and loads, with improved computational efficiency. (3) Merge deep-learning-based prediction of voltage stability indices into the topology-informed algorithms, to solve OTPF problems considering voltage stability enhancement. We shall validate scalability and optimality of the proposed algorithms through software simulations of practical power distribution network models with up to 11,000 nodes.

RGC General Research Fund: Optimizing fast frequency response of distributed energy resources under distribution network constraints, 1/2023 - 12/2025.

- The increasing shares of highly variable and low-inertia wind and solar generation are threatening power system stability. Alleviating this threat calls for active participation of distributed energy resources (DERs, such as controllable loads, batteries, and solar photovoltaics) in power system dynamics and control, particularly the fast frequency response including inertial response and primary frequency control. However, the fact that these DERs are integrated into distribution networks makes it difficult to analyze and optimize the interactions between DER control and frequency dynamics--because the former is restricted by complicated physical and operational constraints of distribution networks, while the latter arises from the bulk transmission network that connects distribution networks with synchronous generators. In this project, we aim to develop a systematic framework for DERs to provide fast frequency response to the transmission network, under realistic nonlinear alternating-current (AC) power flow models and voltage safety limits of distribution networks. Backed by rigorous mathematical analyses and accurate software simulations, the proposed framework will be distinguished from related work by the following features: (1) At the distribution level, the capabilities of DERs to provide fast frequency reserve can be quantified under realistic nonlinear distribution network models. This shall make our framework more reliable than existing efforts based on simplified (e.g., linearized) models. (2) At the transmission level, the power exchanges at substations can be optimized to respect the safety limits of the rate of change of frequency (RoCoF), frequency overshoot, settling time, and steady-state error, while achieving a desired trade-off between these metrics. Compared to other studies that considered similar criteria, our framework will include the limits on fast frequency reserves exerted by distribution network constraints. (3) The proposed framework will be extended to incorporate different volt/var control strategies of DERs. Analytical and experimental studies will reveal the impact of these strategies on fast frequency reserve and response.

RGC Early Career Award: Optimizing multiphase power flow via exact convex relaxation and distributed feedback design, 1/2021 - 12/2023.

- Optimal power flow (OPF) is a class of optimization problems that are fundamental to power system operations. Solving for the global optimum of OPF is important in reducing operational cost and emissions, yet it is also a hard task because of the nonconvexity of OPF. Moreover, the revolutionary transformation of power systems is gravely challenging OPF solution methods today in terms of computational efficiency and scalability, especially at the distribution-network level where tens of millions of distributed energy resources (DERs) are installed and coming into operation. The drastic growth of variable renewable generation, mainly solar photovoltaic (PV), requires faster OPF algorithms to cope with rapidly changing problem conditions caused by intermittent power supplies. Concurrently, OPF problem sizes are proliferating with the massive deployment of controllable DERs, such as smart appliances and buildings, electric vehicles, energy storage devices, and PV inverters, which calls for more scalable OPF algorithms. This project aims to develop a theoretical and algorithmic framework to overcome the challenges above. Specifically, our goal is to solve for the global optimum of nonconvex OPF in a scalable and computationally efficient manner, in distribution networks featuring radial topology, unbalanced multiple phases, and wye- and delta-connected power sources and loads. To achieve this goal, we shall develop a convex semidefinite relaxation technique for this kind of OPF problems and derive analytic conditions that can be checked a priori to ensure exactness of the proposed relaxation. We shall further design a distributed feedback-based algorithm that can be proved to converge to the global optimum of the relaxed problem (which is also the global optimum of the nonconvex OPF under the exactness conditions we derived). Through software simulations in realistic distribution network models, the proposed relaxation and algorithm will be numerically validated in terms of global optimality, convergence speed, and capability of tracking the global optimum of time-varying OPF problems. The project team is capable of and confident in accomplishing the proposed mission with solid expertise and rich experience in developing breakthrough theories, advanced algorithms, and realistic simulations for convex relaxation of OPF, feedback-based optimization, and distributed control of power systems.