Research Interests

  • Power & Energy Systems: Renewable energy grid integration, power system operations, microgrids, electricity market, distributed energy resources, grid resilience

  • Renewable Energy: Renewable and load forecasting, wind and solar resource assessment, wind farm design, mobile marine energy

  • Complex Networks: Multi-layer networks, power network, transportation network, network resilience

  • Energy Management: Integrated energy systems, transactive energy, building-to-grid, vehicle-to-grid, energy storage, battery power and thermal management

  • Big Data Analytics: Data-driven design, energy analytics, machine learning, deep learning

  • Multidisciplinary Design Optimization: Surrogate modeling, optimization, uncertainty quantification, probabilistic design, sensitivity analysis

  • Complex Engineered Systems: Cyber-physical systems, energy system modeling, design, and optimization

Codes (DOES Lab GitHub)

  • SolarNet: A sky image-based deep convolutional neural network for intra-hour solar forecasting.

  • M3 Forecasting: The Machine Learning-based Multi-Model (M3) forecasting framework is able to generate both short-term deterministic and probabilistic forecasts.

  • QLearningForecast: This is a package that implements forecasting model dynamic selection via Q-learning.

  • OpenSolar: OpenSolar is developed to enhance the openness and accessibility of publicly available solar datasets. (Python version)


  • DRD (Dallas repeated driving cycle dataset) (Download): A repeated driving cycle dataset generated in the Dallas area, aiming to simulate a daily commuting route and serves as a base for further vehicle energy management study.

  • AMI Load Dataset (Download): This dataset contains UTD campus load data with 13 buildings, together with 20 weather and calendar features, spanning from 01/01/2014 to 12/31/2015 with an hourly resolution.

Research Sponsors

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Current Research Projects

  • Nuclear-Renewable-Storage Digital Twin: Enhancing Design, Dispatch, and Cyber Response of Integrated Energy Systems (2021 - 2024, funded by INL LDRD/DOE)

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As high-fidelity physics model can be used to inform cost-effective and reliable operations of integrated energy systems (IES), their highly complex and non-linear natures often make it computationally prohibitive for real-time implementation. The overarching objective of this project is to develop a set of deep reinforcement learning (DRL)-based strategies to enable real-time decision-making of physics-informed IES operation. We will develop an integrated IES representation using existing high-fidelity physics models developed by INL and apply it to a Nuclear-Renewable-Storage Integrated Energy Systems (N-R-S IES).

  • Learning on Graphs for Resilience Decision-Support in Real-World Networks (2021 - 2025, funded by DOD ONR)

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This project is to develop a unified “learning on graphs” framework that enables generation of robust, scalable, and generalizable combinatorial policies over graphs, to support real-time decision-making for recovery of disrupted real-world networks. The following integrative research activities will be performed: 1) abstraction of topological descriptors of graphs; 2) integrated Artificial Intelligence (AI) framework with graph convolutional networks, reinforcement learning and topological inputs/outputs, to generate network reconfiguration and restoration policies; and 3) training and evaluation setup/process on grid resiliency problems.


  1. *Jacob, R. A., Paul, S., Li, W., Chowdhury, S., Gel, Y. R. and Zhang, J., Reconfiguring Unbalanced Distribution Networks using Reinforcement Learning over Graphs, IEEE Texas Power and Energy Conference (TPEC), College Station, TX, Feb 28 - March 1, 2022.

  • Deep Learning-based Reliability and Resilience Enhancement of Future Navy Ships and Their Integration into Power Networks under Extreme Event (2020 - 2023, funded by DOD ONR YIP), UTD News

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This project is to (i) study the energy management and fault detection of the Navy power ship systems, by utilizing deep learning-based techniques to track the demand changes with real-time interactions, and (ii) study the future potential Navy power ship systems and their interactions with typical coastal city electric network, to enhance the reliability and resilience of both the Navy ships and the coastline power grids in the presence of natural hazards, cyber/physical attacks, or any possible contingency.


  1. *Senemmar, S. and Zhang, J., Non-intrusive Load Monitoring in MVDC Shipboard Power Systems using Wavelet-Convolutional Neural Networks, IEEE Texas Power and Energy Conference (TPEC), College Station, TX, Feb 28 - March 1, 2022.

  2. *Badakhshan, S., *Senemmar, S. and Zhang, J., Dynamic Feasibility Assessment of Ship-to-Grid Interconnection by DC-Link, IEEE Texas Power and Energy Conference (TPEC), College Station, TX, Feb 28 - March 1, 2022.

  3. *Dabbaghjamanesh, M., *Senemmar, S. and Zhang, J., Resilient Distribution Networks Considering Mobile Marine Microgrids: A Synergistic Network Approach, IEEE Transactions on Industrial Informatics, Vol. 17, Issue 8, 2021, pp. 5742-5750. (PDF)

  4. *Jacob, R. A., *Senemar, S. and Zhang, J., Fault Diagnostics in Shipboard Power Systems using Graph Neural Networks, IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Virtual, August 22-25, 2021.

  5. *Senemar, S. and Zhang, J., Deep Learning-based Fault Detection, Classification, and Locating in Shipboard Power Systems, IEEE Electric Ship Technologies Symposium, Virtual, August 3-6, 2021.

  • Co-located Wind Farm and Hydrogen Plant Energy System Study (2021 - 2022, funded by WindSTAR (NSF IUCRC))

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This project will perform a planning & operational study on a co-located wind farm and hydrogen energy system. Integrated energy systems (IES) of this genre need to be designed considering both the financial and technical constraints to demonstrate their feasibility rather than relying on additional incentives or favoring policies. REopt, an open source tool developed by NREL, will be adopted and improved to perform the study.


  1. *Li, H., *Rahman, J. and Zhang, J., Optimal Planning of Co-Located Wind Energy and Hydrogen Plants: A Techno-Economic Analysis, The Science of Making Torque from Wind (TORQUE 2022), Delft, Netherlands, June 1-3, 2022.

  • Machine Learning-based Real-time Optimal Switchings of Reconfigurable Microgrids (2021 - 2023, funded by U.S. Army ERDC)

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This project is to explore how machine learning could be leveraged to solve the optimal switching of reconfigurable microgrids in real time under both normal and extreme operations (e.g., grid contingencies, natural disasters, and cyber/physical attacks). A learn-to-reconfiguration (L2R) methodology will be developed to learn the relationship between microgrid state and optimal reconfiguration decisions with an end-to-end manner based on state-of-the-art deep learning models.


  1. *Rahman, J., *Jacob, R. A., Paul, S., Chowdhury, S. and Zhang, J., Reinforcement Learning Enabled Microgrid Network Reconfiguration Under Disruptive Events, IEEE Kansas Power and Energy Conference (KPEC), Manhattan, Kansas, April 25 - 26, 2022.

  2. *Dabbaghjamanesh, M. and Zhang, J., Deep Learning-based Real-time Switching of Reconfigurable Microgrids, The Eleventh Conference on Innovative Smart Grid Technologies (ISGT 2020), Washington D.C., February 17-20, 2020.

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The project is designing novel algorithms to create probabilistic solar power forecasts and automate their integration into power system operations. Adaptive reserves will dynamically adjust reserve levels conditional on meteorological and power system states. Risk-parity dispatch will be developed to produce optimal dispatch strategies by cost-weighting solar generation scenarios on forecast uncertainty. This project will test the integration of probabilistic solar forecasts into the Electric Reliability Council of Texas’ real-time operation environment through automated reserve and dispatch tools that can increase economic efficiency and improve system reliability.


  1. *Feng, C., Zhang, J., Zhang, W. and Hodge, B.-M., Convolutional Neural Networks for Intra-hour Solar Forecasting Based on Sky Image Sequences, Applied Energy, Vol. 310, 2022, pp. 118438. (PDF)

  2. *Feng, C. and Zhang, J., SolarNet: A Sky Image-based Deep Convolutional Neural Network for Intra-hour Solar Forecasting, Solar Energy, Vol. 204, 2020, pp. 71-78. (PDF)

  3. *Li, B., *Feng, C. and Zhang, J., Multi-Timescale Simulation of Non-Spinning Reserve in Wholesale Electricity Markets, 2021 IEEE Green Technologies Conference (GreenTech), Virtual, April 7-9, 2021.

  4. *Feng, C., *Sun, M., Zhang, J., Doubleday, K., Hodge, B.-M. and Du, P., A Data-driven Method for Adaptive Reserve Requirements Estimation via Probabilistic Net Load Forecasting, IEEE Power & Energy Society General Meeting, Montreal, Canada, August 2-6, 2020.

  5. *Feng, C., Yang, D., Hodge, B.-M. and Zhang, J., OpenSolar: Promoting the Openness and Accessibility of Diverse Public Solar Datasets, Solar Energy, Vol. 188, 2019, pp. 1369-1379. (PDF)

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This project is advancing the state-of-the-art in solar forecasting technologies by developing short-term and day-ahead probabilistic solar power prediction capabilities. The proposed technology will be based on the big-data-driven, transformative IBM Watt-Sun platform, which will be driven by parallel computation-based scalable and fast data curation technology and multi-expert machine learning based model blending. The integration of validated probabilistic solar forecasts into the scheduling operations of both the Midcontinent and California Independent System Operators will be tested, via efficient and dynamic procurement of ramp product and regulation. Integration of advanced visualization of ramping events and associated alerts into their energy management systems and control room operations will also be researched and validated.


  1. *Li, B., Feng, C., Siebenschuh, C., Zhang, R., Spyrou, E., Krishnan, Hobbs, B. and Zhang, J., Sizing Ramping Reserve Using Probabilistic Solar Forecasts: A Data-Driven Method, Applied Energy, Vol. 313, 2022, pp. 118812. (PDF)

  2. *Li, B. and Zhang, J., A Review on the Integration of Probabilistic Solar Forecasting in Power Systems, Solar Energy, Vol. 210, 2020, pp. 68-86. (Invited Paper) (PDF)

  3. *Li, B., Zhang, J. and Hobbs, B., A Copula Enhanced Convolution for Uncertainty Aggregation, The Eleventh Conference on Innovative Smart Grid Technologies (ISGT 2020), Washington D.C., February 17-20, 2020.

  4. *Cui, M. and Zhang, J., Estimating Ramping Requirements With Solar-Friendly Flexible Ramping Product in Multi-Timescale Power System Operations, Applied Energy, Vol. 225, 2018, pp. 27-41. (PDF)

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Nuclear and renewable energy sources are important to consider in the U.S. economy's evolution because both are clean, non-carbon-emitting energy sources. Advanced nuclear-renewable hybrid energy systems (N-R HESs) composed of nuclear and renewable energy sources, industrial energy users, and energy storage systems are being evaluated for their economic benefit and technical feasibility. N-R HESs have been proposed as a technology that can generate very low-carbon, dispatchable electricity and provide very low-carbon energy to industry at a lower cost than many other options. Beyond classic energy-shifting services, N-R HESs may be able to provide a suite of services at finer time-scales to promote a safer and more reliable integration of renewable energy resources. The overarching objective of this project is to develop a multi-timescale N-R HESs operations framework to provide different types of grid products.


  1. *Rahman, J., *Jacob, R. A. and Zhang, J., Harnessing Operational Flexibility from Power to Hydrogen in A Grid-Tied Integrated Energy System, ASME 2022 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Paper No. IDETC2022-89621, St. Louis, Missouri, August 14-17, 2022.

  2. Wu, J., Chen, X., Zhang, J. and Wang, P., Optimizing Intentional Islanding Design Strategies for Enhanced Failure Resilience of Power Systems, ASME 2022 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Paper No. IDETC2022-89619, St. Louis, Missouri, August 14-17, 2022.

  3. *Jacob, R. A., *Rahman, J. and Zhang, J., Dynamic Modeling and Simulation of Integrated Energy Systems with Nuclear, Renewable, and District Heating, The 53rd North American Power Symposium, College Station, Texas, November 14-16, 2021.

  4. Li, D., Wu, J., Zhang, J. and Wang, P., Co-Design Optimization of a Combined Heat and Power Hybrid Energy System, ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Paper No. IDETC2021-71304, Virtual, August 17-20, 2021.

  5. *Rahman, J. and Zhang, J., Optimization of Nuclear-Renewable Hybrid Energy System Operation in Forward Electricity Market, 2021 IEEE Green Technologies Conference (GreenTech), Virtual, April 7-9, 2021.

  • Peer-to-Peer Energy Sharing and Demand Response

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Transactive energy utilizes the flexibility of various generation/load resources to maintain a dynamic balance of supply and demand, which features real-time, autonomous, and decentralized decision making. Within a community sharing market, the neighbors, consisting of multiple agents, i.e., prosumers and consumers, are allowed to fairly exchange energy through a sharing platform.


  1. *He, L., *Liu, Y. and Zhang, J., An Occupancy-Informed Customized Price Design for Consumers: A Stackelberg Game Approach, IEEE Transactions on Smart Grid, Vol. 13, Issue 3, 2022, pp. 1988-1999. (PDF)

  2. *He, L., *Liu, Y. and Zhang, J., Peer-to-Peer Energy Sharing with Battery Storage: Energy Pawn in The Smart Grid, Applied Energy, Vol. 297, 2021, pp. 117129. (PDF)

  3. *He, L. and Zhang, J., A Community Sharing Market with PV and Energy Storage: An Adaptive Bidding-based Double-side Auction Mechanism, IEEE Transactions on Smart Grid, Vol. 12, Issue 3, 2021, pp. 2450-2461. (PDF)

  • Battery Thermal Management Systems for Electric Vehicles

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Thermal management plays a significant role in the life, performance, safety, and cost of the lithium-ion battery modules in electric vehicles. Battery thermal management systems (BTMS) aim to improve the temperature uniformity among all battery cells, and to prevent battery cells from very high temperature which may likely cause their explosion. This project is to develop a hybrid approach by combining flat heat pipe, phase change material, and air cooling through modeling, experiments, and optimization.


  1. *Liu, Y. and Zhang, J., A Repeated Commuting Driving Cycle Dataset with Application to Short-term Vehicle Velocity Forecasting, Journal of Autonomous Vehicles and Systems, Vol. 1, Issue 3, 2021, pp. 031002. (PDF)

  2. *Liu, Y. and Zhang, J., Electric Vehicle Battery Thermal and Cabin Climate Management Based on Model Predictive Control, Journal of Mechanical Design, Vol. 143, Issue 3, 2021, pp. 031705. (PDF)

  3. *Liu, Y. and Zhang, J., Self-adapting J-type Air-based Battery Thermal Management System via Model Predictive Control, Applied Energy, Vol. 263, 2020, pp. 114640. (PDF)

  4. *Liu, Y. and Zhang, J., Design A J-type Air-based Battery Thermal Management System through Surrogate-based Optimization, Applied Energy, Vol. 252, 2019, pp. 113426. (PDF)

  5. *Wang, X., *Liu, Y., Sun, W., Song, X. and Zhang, J., Multidisciplinary and Multifidelity Design Optimization of Electric Vehicle Battery Thermal Management System, Journal of Mechanical Design, Vol. 140, Issue 9, 2018, pp. 094501 (8 pages). (PDF)

  6. *Wang, X., *Li, M., *Liu, Y., Sun, W., Song, X. and Zhang, J., Surrogate based Multidisciplinary Design Optimization of Lithium-ion Battery Thermal Management System in Electric Vehicles, Structural and Multidisciplinary Optimization, Vol. 56, Issue 6, 2017, pp. 1555-1570. (PDF)

Past Research Projects