This project will develop a novel geometric deep learning model supported with topological data analysis tools for robust decision-making and efficient knowledge transfer for disruptions with time-dominant characteristics, particularly focusing on predicting the distribution network evolution and operational decisions (by leveraging distributed energy resources and clean energy resources) for resilience under disruptive events such as natural disasters and adversarial attacks. Envisioned gains in generalizability, robustness and learning efficiency via knowledge-transfer (across events and operation timescales) will be demonstrated by studying response and recovery operation problems in power grids.

The overall objective of this project is to comprehensively model, design, and evaluate the use of advanced nuclear reactors in future nuclear-powered ships with Integrated Power System, to enhance the efficiency, reliability, and resilience of shipboard energy distribution systems. The novelty of the proposed approach lies in (i) integrated thermal-electric modeling of advanced nuclear-powered shipboard energy system, (ii) novel solutions for total-ship energy management to improve the energy efficiency and prevent catastrophic failures in electric propulsion and other vital systems, and (iii) the integration of advanced nuclear-powered ships with the terrestrial power grid to enhance the power network resilience during disruptive events.

Papers:

1. *Badakhshan, S., *Rahman, J. and Zhang, J., Black Start of Coastline Power Networks From Grid-forming Ship-to-Grid Services, IEEE Transactions on Smart Grid, 2023. (in press) (PDF)

2. *Badakhshan, S., *Senemmar, S. and Zhang, J., Dynamic Modeling and Reliable Operation of All-Electric Ships with Small Modular Reactors and Battery Energy Systems, IEEE Electric Ship Technologies Symposium (ESTS), Old Town Alexandria, VA, August 1 - 4, 2023. (PDF)

3. *Senemmar, S., *Badakhshan, S. and Zhang, J., Dynamic Modeling and Simulation of Thermal-Electrical Energy Systems in MVDC All-Electric Ships with Small Modular Reactors, IEEE Electric Ship Technologies Symposium (ESTS), Old Town Alexandria, VA, August 1 - 4, 2023. (PDF)

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).

Papers:

1. Poudel, B., Gautam, M., Li, B., Huang, J. and Zhang, J., Design, Modeling and Simulation of Nuclear-Powered Integrated Energy Systems With Cascaded Heating Applications, Journal of Renewable and Sustainable Energy, Vol. 15, Issue 5, 2023, 054103. (Featured Article and Scilight) (PDF)

2. *Jacob, R. A. and Zhang, J., Modeling and Control of Nuclear-Renewable Integrated Energy Systems: Dynamic System Model for Green Electricity and Hydrogen Production, Journal of Renewable and Sustainable Energy, Vol. 15, 2023, 046302. (Featured Article and Scilight) (PDF)

3. *Rahman, J., *Jacob, R. A. and Zhang, J., Multi-timescale Power System Operations for Electrolytic Hydrogen Generation in Integrated Nuclear-Renewable Energy Systems. (under review)

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.

Papers:

1. *Jacob, R. A., Paul, S., Chowdhury, S., Gel, Y. R. and Zhang, J., Real-Time Outage Management in Active Distribution Networks Using Reinforcement Learning over Graphs. (under review)

2. Chen, Y., *Jacob, R. A., Gel, Y. R., Zhang, J. and Poor, H. V., Learning Power Grid Outages with Higher-Order Topological Neural Networks, IEEE Transactions on Power Systems, 2023. (in press) (PDF) (JSM Best Student Paper Award)

3. *Badakhshan, S., *Jacob, R. A., Li. B. and Zhang, J., Reinforcement Learning for Intentional Islanding in Resilient Power Transmission Systems, IEEE Texas Power and Energy Conference (TPEC), College Station, TX, Feb 13 - 14, 2023. (PDF)

4. *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.

5. *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.

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.

Papers:

1. *Senemmar, S. and Zhang, J., Convolutional Wavelet Neural Network Based Non-intrusive Load Monitoring for Next Generation Shipboard Power Systems. (under review)

2. *Badakhshan, S., *Rahman, J. and Zhang, J., Black Start of Coastline Power Networks From Grid-forming Ship-to-Grid Services, IEEE Transactions on Smart Grid, 2023. (in press) (PDF)

3. *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.

4. *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.

5. *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)

6. *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.

7. *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.

This project will develop a cost-effective and reliable condition monitoring system for wind turbines, leveraging high-resolution voltage and current signals. By analyzing these signals in real time using machine/deep learning models, faults in both electrical and mechanical systems can be detected, enabling proactive maintenance, improving reliability, and increasing energy production while reducing costs.

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.

Papers:

1. *Rahman, J., *Jacob, R. A. and Zhang, J., Multi-timescale Power System Operations for Electrolytic Hydrogen Generation in Integrated Nuclear-Renewable Energy Systems. (under review)

2. *Jacob, R. A. and Zhang, J., Modeling and Control of Nuclear-Renewable Integrated Energy Systems: Dynamic System Model for Green Electricity and Hydrogen Production, Journal of Renewable and Sustainable Energy, Vol. 15, 2023, 046302. (Featured Article and Scilight) (PDF)

3. *Rahman, J. and Zhang, J., Multi-timescale Operations of Nuclear-Renewable Hybrid Energy Systems for Reserve and Thermal Products Provision, Journal of Renewable and Sustainable Energy, Vol. 15, 2023, 025901. (Featured Article and Scilight) (PDF)

4. Wu, J., Chen, X., *Badakhshan, S., Zhang, J. and Wang, P., Spectral Graph Clustering for Intentional Islanding Operations in Resilient Hybrid Energy Systems, IEEE Transactions on Industrial Informatics, Vol. 19, Issue 4, 2023, pp. 5956-5964. (PDF)

5. *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.

6. *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.

7. 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.

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.

Papers:

1. *He, L. and Zhang, J., Energy Trading in Local Electricity Markets with Behind-The-Meter Solar and Energy Storage, IEEE Transactions on Energy Markets, Policy and Regulation, Vol. 1, Issue 2, 2023, pp. 107-117. (PDF)

2. *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)

3. *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)

4. *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)

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.

Papers:

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)