About

Professor Bo Zhang is a Professor of Chemistry specializing in electrochemistry, microscopy, and sensors. His research group focuses on advancing the understanding and application of electrochemical processes through innovative imaging techniques and sensor development. The group members work on projects related to electrochemical imaging, chemical interfaces, optical microscopy, and surface nano- and microbubbles, reflecting a broad interest in the microscopic and nanoscale aspects of electrochemical phenomena. Professor Zhang's work integrates electrochemistry with advanced microscopy methods to explore and characterize chemical interfaces and electrochemical reactions at high spatial resolution.

Research topics

• Computer Science
• Telecommunications
• Engineering
• Electrical engineering
• Control engineering
• Automotive engineering
• Economics
• Reliability engineering
• Mathematics
• Mathematical optimization

Selected publications

• Safe Trajectory Gradient Flow Control of a Grid-Interfacing Inverter

  ArXiv.org · 2026-01-15

  articleOpen accessSenior author

  Grid-interfacing inverters serve as the interface between renewable energy resources and the electric power grid, offering fast, programmable control capabilities. However, their operation is constrained by hardware limitations, such as bounds on the current magnitude. Existing control methods for these systems often neglect these constraints during controller design and instead rely on ad hoc limiters, which can introduce instability or degrade performance. In this work, we present a control framework that directly incorporates constraints into the control of a voltage-source inverter. We propose a safe trajectory gradient flow controller, which applies the safe gradient flow method to a rolling horizon trajectory optimization problem to ensure that the states remain within a safe set defined by the constraints while directing the trajectory towards an optimal equilibrium point of a nonlinear program. Simulation results demonstrate that our approach can drive the outputs of a simulated inverter system to optimal values and maintain state constraints, even when using a limited number of optimization steps per control cycle.

  PublisherOA PDF

• Safe Trajectory Gradient Flow Control of a Grid-Interfacing Inverter

  arXiv (Cornell University) · 2026-01-15

  preprintOpen accessSenior author

  Grid-interfacing inverters serve as the interface between renewable energy resources and the electric power grid, offering fast, programmable control capabilities. However, their operation is constrained by hardware limitations, such as bounds on the current magnitude. Existing control methods for these systems often neglect these constraints during controller design and instead rely on ad hoc limiters, which can introduce instability or degrade performance. In this work, we present a control framework that directly incorporates constraints into the control of a voltage-source inverter. We propose a safe trajectory gradient flow controller, which applies the safe gradient flow method to a rolling horizon trajectory optimization problem to ensure that the states remain within a safe set defined by the constraints while directing the trajectory towards an optimal equilibrium point of a nonlinear program. Simulation results demonstrate that our approach can drive the outputs of a simulated inverter system to optimal values and maintain state constraints, even when using a limited number of optimization steps per control cycle.

  PublisherDOI

• Pricing Uncertainties With General Parametric Distributions in Power Systems

  IEEE Transactions on Industrial Informatics · 2026-02-09

  articleSenior author

  With the increasing uncertainty brought by renewable energy sources, it is crucial to price uncertainty and quantify the impact on operation costs. For certain types of uncertainty, such as those following jointly Gaussian distributions, the price of uncertainty has been defined. However, the definition of prices for uncertainties following more general parametric forms of distributions is not clear. To fill the gap, this article proposes a uniform price definition of uncertainties with general parametric distributions and a practical calculation method. First, the price definition for uncertainty is proposed based on the marginal pricing principle, which can be derived from the Lagrangian function of the chance-constrained optimal power flow (CCOPF) problem. Next, the scenario-based method is used to analytically transform CCOPF into a deterministic form. The prices of uncertainties in each scenario can be calculated from the Lagrangian function of the scenario-based model. Then, the relationship between the distribution parameters and the uncertainties in scenarios is revealed. We compute the prices of distribution parameters through the prices of uncertainties in multiple scenarios and their relationship. Finally, this article demonstrates the entire calculation procedure using the correlated non-Gaussian uncertainties as typical examples. The performance of proposed method is verified in the PJM 5-bus, IEEE 30-bus, and 118-bus test systems. It shows that the proposed method can accurately compute the price of uncertainty, ensuring nonnegative market surplus, and keeping the constraint violation probability within the tolerance.

  PublisherDOI

• Experimental Study on Rheological Parameters and Rapid Flow on Slopes of Dam Break Non-Newtonian Fluid

  Sustainable civil infrastructures · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Strategic and Fair Aggregator Interactions in Energy Markets: Multi-Agent Dynamics and Quasiconcave Games

  IEEE Transactions on Energy Markets Policy and Regulation · 2025-06-23

  articleSenior author

  The introduction of aggregator structures has proven effective in bringing fairness to energy resource allocation by negotiating for more resources and economic surplus on behalf of users. This paper extends the fair energy resource allocation problem to a multi-agent setting, focusing on interactions among multiple aggregators in an electricity market. We consider a setting where aggregators submit quantity-only bids as in a noncooperative Cournot game. Unlike classical Cournot models, where firms optimize only for profit, our framework incorporates a bi-level decision process, in which each aggregator determines its total purchase while simultaneously optimizing the internal allocation among its users based on fairness-efficiency trade-off objectives and constraints. We prove that the strategic optimization problems faced by the aggregators form a quasi-concave game, ensuring the existence of a Nash equilibrium. This resolves complexities related to market price dependencies on total purchases and balancing fairness and efficiency in energy allocation. In addition, we design simulations to characterize the equilibrium points of the induced game, demonstrating how aggregators stabilize market outcomes, ensure fair resource distribution, and optimize user surplus. Our findings offer a robust framework for understanding strategic interactions among aggregators, contributing to more efficient and equitable energy markets.

  PublisherDOI

• Geometry of the Feasible Output Regions of Grid-Interfacing Inverters With Current Limits

  IEEE Control Systems Letters · 2025-01-01

  articleSenior author

  Many resources in the grid connect to power grids via programmable grid-interfacing inverters that can provide grid services and offer greater control flexibility and faster response times compared to synchronous generators. However, the current through the inverter needs to be limited to protect the semiconductor components. Existing controllers are designed using somewhat ad hoc methods, for example, by adding current limiters to preexisting control loops, which can lead to stability issues or overly conservative operations. In this paper, we study the geometry of the feasible output region of a current-limited inverter. We show that under a commonly used model, the feasible region is convex. We provide an explicit characterization of this region, which allows us to efficiently find the optimal operating points of the inverter. We demonstrate how knowing the feasible set and its convexity allows us to improve upon existing grid-forming inverters such that their steady-state currents always remain within the current magnitude limit, whereas standard grid-forming controllers can lead to instabilities and violations.

  PublisherDOI

• Safe Control of Grid-Interfacing Inverters with Current Magnitude Limits

  Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · 2025-01-01 · 1 citations

  articleOpen accessSenior author

  Grid-interfacing inverters allow renewable resources to be connected to the electric grid and offer fast and programmable control responses. However, inverters are subject to significant physical constraints. One such constraint is a current magnitude limit required to protect semiconductor devices. While many current limiting methods are available, they can often unpredictably alter the behavior of the inverter control during overcurrent events leading to instability or poor performance. In this paper, we present a safety filter approach to limit the current magnitude of inverters controlled as voltage sources. The safety filter problem is formulated with a control barrier function constraint that encodes the current magnitude limit. To ensure feasibility of the problem, we prove the existence of a safe linear controller for a specified reference. This approach allows for the desired voltage source behavior to be minimally altered while safely limiting the current output.

  PublisherOA PDFDOI

• Research on a DBSCAN-IForest Optimisation-Based Anomaly Detection Algorithm for Underwater Terrain Data

  Water · 2025-02-21 · 12 citations

  articleOpen access

  The accurate acquisition of underwater topographic data is crucial for the representation of river morphology and early warning of water hazards. Owing to the complexity of the underwater environment, there are inevitably outliers in monitoring data, which objectively reduce the accuracy of the data; therefore, anomalous data detection and processing are key in effectively using data. To address anomaly detection in underwater terrain data, this paper presents an optimised DBSCAN-IForest algorithm model, which adopts a distributed computation strategy. First, the K-distance graph and Kd-tree methods are combined to determine the key computational parameters of the DBSCAN algorithm, and the DBSCAN algorithm is applied to perform preliminary cluster screening of underwater terrain data. The isolated forest algorithm is subsequently used to carry out refined secondary detection of outliers in multiple subclusters that were initially screened. Finally, the algorithm performance is verified through example calculations using a dataset of about 8500 underwater topographic points collected from the Yellow River Basin, which includes both elevation and spatial distribution attributes; the results show that compared with other methods, the algorithm has greater efficiency in outlier detection, with a detection rate of up to 93.75%, and the parameter settings are more scientifically sound and reasonable. This research provides a promising framework for anomaly detection in underwater terrain data.

  PublisherDOI

• Voltage Regulation in Distribution Systems with Data Center Loads

  ArXiv.org · 2025-07-08

  preprintOpen accessSenior author

  Recent boom in foundation models and AI computing have raised growing concerns on the power and energy trajectories of large-scale data centers. This paper focuses on the voltage issues caused by volatile and intensity of data center power demand, which also aligns with recent observations of more frequent voltage disturbances in power grids. To address these data center integration challenges, we propose a dynamic voltage control scheme by harnessing data center's load regulation capabilities. By taking local voltage measurements and adjusting power injections at each data center buses through the dynamic voltage and frequency scaling (DVFS) scheme, we are able to maintain safe voltage magnitude in a distributed fashion with higher data center computing load. Simulations using real large language model (LLM) inference load validate the effectiveness of our proposed mechanism. Both the LLM power data and proposed control scheme are open sourced.

  PublisherOA PDFDOI

• Adaptive Pricing for Optimal Coordination in Networked Energy Systems with Nonsmooth Cost Functions

  ArXiv.org · 2025-04-01

  preprintOpen accessSenior author

  Incentive-based coordination mechanisms for distributed energy consumption have shown promise in aligning individual user objectives with social welfare, especially under privacy constraints. Our prior work proposed a two-timescale adaptive pricing framework, where users respond to prices by minimizing their local cost, and the system operator iteratively updates the prices based on aggregate user responses. A key assumption was that the system cost need to smoothly depend on the aggregate of the user demands. In this paper, we relax this assumption by considering the more realistic model of where the cost are determined by solving a DCOPF problem with constraints. We present a generalization of the pricing update rule that leverages the generalized gradients of the system cost function, which may be nonsmooth due to the structure of DCOPF. We prove that the resulting dynamic system converges to a unique equilibrium, which solves the social welfare optimization problem. Our theoretical results provide guarantees on convergence and stability using tools from nonsmooth analysis and Lyapunov theory. Numerical simulations on networked energy systems illustrate the effectiveness and robustness of the proposed scheme.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Learning and Optimizing Power Systems: A Geometric Approach

  NSF · $225k · 2018–2022

• Collaborative Research: Data-driven Power Systems Control with Stability Guarantees

  NSF · $100k · 2022–2025

• CAREER: Optimal Control of Energy Systems via Structured Neural Networks: A Convex Approach

  NSF · $500k · 2020–2026

• EAGER: Congestion Mitigation via Better Parking: New Fundamental Models and A Living Lab

  NSF · $218k · 2016–2019

• Enhanced Power System Stability using Fast, Distributed Power Electronics Control

  NSF · $400k · 2019–2023

Frequent coauthors

• Yuanyuan Shi

  31 shared

• Yize Chen

  31 shared

• Wenqi Cui

  24 shared

• Daniel S. Kirschen

  University of Washington

  24 shared

• David Tse

  21 shared

• Ram Rajagopal

  21 shared

• Bolun Xu

  17 shared

• Lillian J. Ratliff

  University of Washington

  16 shared

Labs

• Bo Zhang Electrochemistry GroupPI

Education

• Ph.D., Electrochemistry

  University of California, Berkeley

  2007

• M.S., Electrochemistry

  University of California, Berkeley

  2004

• B.S., Chemistry

  University of Science and Technology of China

  2002

Awards & honors

• 2020 American Chemical Society (ACS) Electrochemistry Award
• Sloan Research Fellowship
• Royce Murray Young Investigator Award from the Society for E…
• Top 40 under 40 by the Analytical Scientist (2014)