Research We Do

Our research consists of four Five Areas to ensure a deep, swift, secure, and human-centered energy transition to meet the 2030/2050 climate goals:

Focus Area 1: Harnessing Generative AI for Energy Transition

Our research focuses on leveraging cutting-edge generative AI to drive the transition toward a sustainable, low-carbon future by addressing the intricate challenges within modern energy systems. Recognizing the global demand for resilient and efficient energy solutions, we explore two pioneering applications of generative AI that support the broader energy transformation, with a particular emphasis on Texas’s unique energy landscape.

AOI 1 involves the use of large language models (LLMs) to enhance energy load forecasting by incorporating unstructured socio-demographic information about energy consumers. This approach enables grid operators to improve the accuracy of demand predictions, especially among diverse demographic groups, to achieve better resource allocation and reduced risk of outages. For Texas, where the grid is frequently challenged by extreme demand fluctuations, this LLM-driven model provides a strategic advantage in maintaining grid stability and energy efficiency.

AOI 2 is on AI-based generative agents that simulate consumer energy behaviors at scale. These agents, trained with real-world human-sourced data, model the diverse consumption patterns of individuals and communities and provide a realistic foundation for designing interventions that encourage sustainable practices. By predicting how people might respond to various energy initiatives, these simulations support targeted outreach and policymaking efforts that foster low-carbon lifestyle adoption. This approach is particularly valuable in Texas, where values of individualism and energy independence are deeply embedded.

The ultimate goal for our first focus area is to push the boundaries of AI-driven energy solutions.

Select Publications

1. Dingwen Pan, Weilong Chen, Jian Shi, Chenye Wu, Dan Wang, Choong Seon Hong, and Zhu Han, “Cooperation and Decision-Making of LLM Agents in Bayesian-Informed Infinitely Repeated Games”, 2025 59th Annual Conference on Information Sciences and Systems (CISS).

Focus Area 2: Deep Decarbonization of Power Systems Incorporating the Carbon Perspective

Electric power systems are the backbone of the global energy system and account for 25% of worldwide greenhouse gas (GHG) emissions. With the rapid trend of electrifying final energy use, a low-carbon electric power system will also enable the clean electrification of transportation, buildings, and industry sectors to support full economy-wide decarbonization.

Our research aims to establish a carbon-oriented framework to facilitate power systems' effective decarbonization and deliver reliable, secure, clean, and cost-effective energy services.

Motivated by the tremendous role decarbonization will be playing in the future energy system, we take a different route than the traditional literature, which is primarily scoped from the electricity and energy perspective concentrating on one piece of technological solutions. Instead, we focus on developing a carbon perspective to analyze and exploit the "life cycle" of carbon emissions embodied in the electric energy value chain.

As shown in the figure above, we divide the carbon flows through the multi-energy power system into three stages: i) carbon allowance initiation and allocation, ii) carbon allowance exchange and pricing, and iii) carbon allowance circulation, based on which interdisciplinary research, e.g., optimization, game theory, and machine learning, can be performed to collectively accelerate the carbon neutral transition of the energy sector.

Select Publications

1. Jian Shi, Dan Wang, Chenye Wu, and Zhu Han, "Deep Decarbonization of Multi-Energy Systems: A Carbon-Oriented Framework with Cross-Disciplinary Technologies", in IEEE Carbon Neutrality Newsletter, 2022.

2. Yijie Yang, Dan Wang, Jian Shi, Chenye Wu, and Zhu Han, "Uncertainty-aware Day-ahead Datacenter Workload Planning with Load-following Small Modular Reactors", the 4th Workshop on Sustainable Computer Systems (HotCarbon'25).

3. Yijie Yang, Jian Shi, Dan Wang, Chenye Wu, Zhu Han, "Carbon-Aware Scheduling of Thermostatically Controlled Loads: A Bilevel DRCC Approach", in IEEE Transactions on Smart Grid, 2025.

4. Yijie Yang, Jian Shi, Dan Wang, Chenye Wu, and Zhu Han, "Net-Zero Scheduling of Multi-energy Building Energy Systems: A Learning-based Robust Optimization Approach with Statistical Guarantees", in IEEE Transactions on Sustainable Energy, 2024.

5. Wenqian Jiang, Qiushi Huang, Jian Shi, Chenye Wu, Dan Wang, and Zhu Han, “Federated Shift-Invariant Dictionary Learning Enabled Distributed User Profiling”, in IEEE Transactions on Power Systems, 2023.

Focus Area 3: Maritime Energy Nexus for Electrification, Decarbonization, and Resilience

Our research aims to develop a Smart Maritime Energy Nexus (SMEN) framework, to advance the synergies among different technical, operational, policy-level, and stakeholder engagement measures that can be implemented by maritime ports and vessels, improve the resilience of the maritime transportation system, and accelerate its electrification and decarbonization. SMEN includes a holistic set of algorithms ranging from long-term capacity building and policymaking, to daily/hourly-ahead scheduling and market operation. Our work relies on the following three key operational measures:

Port Microgrid

When implemented within the port, a microgrid provides an integrated means to manage the high penetration level of distributed energy resources (DERs), such as renewable energy resources, energy storage devices, and controllable loads.
Port microgrid opens up a variety of opportunities for technology integration, capacity expansion, and sustainability enhancement.
The electrified shipboard power system can also be seen as a remote microgrid that is highly efficient, self-manageable, and sustainable, and can be plugged into the port energy infrastructure via OPS.

Onshore Power Supply

OPS, also known as cold ironing, allows a ship to be “plugged” into the port electricity system.
In this way, the ship can completely shut down its auxiliary diesel-burning engine and utilize the onshore electricity, a much cleaner alternative, to support its energy demand at berth without disruption to onboard services.
According to the U.S. environmental protection agency (EPA), the benefits of OPS deployment contribute to 60%-80% reductions of emissions of CO2 and other air pollutants from ships at different ports globally.

Zero/low-Carbon Vessel Fleet

Carbon-neutral fuels are critical to ease the transition of the maritime energy mix.
Cost and maturity of technology: Electric energy storage, renewable energy, ammonia from electrolysis/natural gas, hydrogen, carbon capture and storage, bio-methanol, bio-LNG, bio-marine gas oil
The key is to design cost-effective and robust ship technologies and fuel strategies for existing vessels and newbuilds complying with the environmental regulations and stakeholder demands.

Select Publications

Book Chapters:

1. Jian Shi, Gino Lim, and Anahita Molavi, “Optimization for Power Systems in Maritime Environment”, accepted for publication, in Encyclopedia of Optimization, 2023.

Journal Publications:

1. Chengji Liang, Weiwei Sun, Jian Shi*, Kailai Wang, Yue Zhang, and Gino Lim, "Decarbonizing Maritime Transport through Green Fuel-Powered Vessel Retrofitting: A Game-Theoretic Approach", in Journal of Marine Science and Engineering, 2024.

2. Yue Zhang, Chengji Liang, Jian Shi*, Gino J. Lim, and Yiwei Wu, “Optimal Port Microgrid Scheduling Incorporating Onshore Power Supply and Berth Allocation Under Uncertainty”, in Applied Energy, vol 313, 2022.

3. Lifen Wang, Chengji Liang, Jian Shi*, Anahita Molavi, Gino J. Lim, and Yue Zhang, “A Bilevel Hybrid Economic Approach for Optimal Deployment of Onshore Power Supply”, in Applied Energy, vol. 292, 2021.

4. Wanlu Zhu, Jian Shi*, Pengfei Zhi, Lei Fan, and Gino J. Lim, “Distributed Reconfiguration of a Hybrid Shipboard Power System”, in IEEE Transactions on Power Systems, vol. 36, no. 1, pp. 4-16, Jan. 2021.

5. Anahita Molavi, Gino J. Lim, and Jian Shi*, “Stimulating Sustainable Energy at Maritime Ports by Hybrid Economic Incentives: A Bilevel Optimization Approach”, in Applied Energy, vol. 272, 2020.

6. Anahita Molavi, Jian Shi*, Yiwei Wu, and Gino J. Lim, “Enabling Smart Ports Through the Integration of Microgrids: A Two-Stage Stochastic Programming Approach”, in Applied Energy, vol. 258, 2020.

Focus Area 4: Community-based Energy Microgrids/Hubs and Advance Energy Burden Sharing

Our Research Focus:

1. Systematically evaluate the energy/climate burdens imposed on traditionally underrepresented and underserved communities.

2. Design affordable, accessible, secure, resilient, and equitable energy facilities and infrastructure, such as community-owned buildings and multi-service centers, for community members in need.

3. Create community-centered engineering solutions with quantifiable enhancements and empowerment to societal welfare.

4. Extreme heat and heat-mitigation for vulnerable populations (e.g., children and the elderly).

Select Publications

1. Behnam Sabzi, Jian Shi, Gino Lim, Farzane Ezzati, and Kailai Wang, "Energy equity-centered planning of community microgrids", in Sustainable Cities and Society, 2025.

1. Weilong Chen, Xinru Liu, Xinran Zhang, Jian Shi, Han Yang, Zhu Han, and Yanru Zhang, "A Socio-Aware Diffusion Model for Residential Electricity Consumption Data Generation", in IEEE Transactions on Smart Grid, 2025.

2. Jieyu Lei, Shibin Gao, Xiaoguang Wei, Jian Shi, Tao Huang, Nikos D. Hatziargyriou, and C. Y. Chung Yang, "A Shareholding-based Resource Sharing Mechanism for Promoting Energy Equity in Peer-to-Peer Energy Trading", in IEEE Transactions on Power Systems, 2022.

3. Gaoyuan Xu, Jian Shi, Jiaman Wu, Chenbei Lu, Chenye Wu, Dan Wang, Zhu Han, “An optimal solutions-guided deep reinforcement learning approach for online energy storage control”, in Applied Energy, 2024.

4. Jiaman Wu, Chenbei Lu, Chenye Wu, Jian Shi, Dan Wang, and Zhu Han, “A Cluster-based Appliance-level-of-use Demand Response Program Design”, in Applied Energy, 2024.

Focused Area 5: Smart Grid Cyber-Physical Security

Selected Journal Publications

1. Jieyu Lei, Shibin Gao, Jian Shi, Xiaoguang Wei, and Mohammad Shahidehpour, “A Data-Driven Approach for Quantifying and Evaluating Overloading Dependencies Among Power System Branches under Load Redistribution Attacks”, in IEEE Transactions on Smart Grid, 2023.

2. Yigu Liu, Shibin Gao, Jian Shi, Xiaoguang Wei, Xingpeng Li, and Zhu Han, “Critical Branch Identification Based on False Data Injection Flow Under Load Redistribution Attacks”, in IEEE Transactions on Industrial Informatics, 2023.

3. Jieyu Lei, Shibin Gao, Jian Shi, Xiaoguang Wei, and Mohammad Shahidehpour, and Tao Huang, “A Reasoning Approach Based on Pattern Graph for Analyzing the Risk of Power Outage Propagation”, in IEEE Transactions on Power Systems, 2023.

4. Shibin Gao, Jieyu Lei, Jian Shi, Xiaoguang Wei, Ming Dong, and Zhu Han, “Assessment of Overloading Associations Among Transmission Lines Under False Data Injection Attacks”, in IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1570-1581, 2022.

5. Jieyu Lei, Shibin Gao, Jian Shi, Xiaoguang Wei, Ming Dong, Wenshuang Wang, and Zhu Han, “A Reinforcement Learning Approach for Defending Against Multi-Scenario Load Redistribution Attacks”, in IEEE Transactions on Smart Grid, vol. 13, no. 5, pp. 3711-3722, Sept. 2022.

6. Yigu Liu, Shibin Gao, Jian Shi, Xiaoguang Wei, Zhu Han, and Tao Huang, “Pre-overload-Graph-based Vulnerable Correlation Identification under Load Redistribution Attacks”, in IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 5216-5226, Nov. 2020.

7. Yigu Liu, Shibin Gao, Jian Shi, Xiaoguang Wei, and Zhu Han, “Sequential-Mining-Based Vulnerable Branches Identification under Continuous Load Redistribution Attacks”, in IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 5151-5160, Nov. 2020.

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