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AI + OR for Energy Management

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My research interests are the complexities of intertwining technological, environmental, and societal factors within energy systems, aiming to forge pathways towards sustainability and resilience. By pioneering sophisticated methodologies and frameworks, I confront the multifaceted challenges inherent in water-energy-carbon nexus management, multi-energy management, cybersecurity, social computing modeling, and distributionally robust optimization.

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  • AI + Operation Research: When AI and OR are combined, it allows for more intelligent, data-driven, and adaptive approaches to solving complex real-world problems. AI + OR is applied across various domains, including logistics, supply chain management, finance, healthcare, energy, and more, to improve efficiency, effectiveness, and decision quality. For example, the integration of distributionally robust Q-learning exemplifies the synergy between AI and OR in solving complex real-world problems. Unlike conventional Reinforcement Learning, it leverages advanced mathematical techniques to address robustness challenges and adapt to diverse real-world conditions. This algorithm transforms complex learning problems into tractable ones, showcasing the power of mathematical sophistication. With multi-level Monte-Carlo sampling, it attains precise approximations, demonstrating its effectiveness despite mathematical complexity. In domains like logistics, supply chain management, finance, healthcare, and energy, the fusion of AI and OR empowers intelligent, data-driven, and adaptive solutions, enhancing efficiency, effectiveness, and decision quality.

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  • Water-Energy-Carbon Nexus: I tackle the intricate optimization of interconnected water, energy, and carbon resources. This involves advanced modeling to capture the nuanced dynamics among these critical resources, considering regional disparities, policy frameworks, and climate change implications. My goal is to devise adaptive strategies that are robust against future uncertainties, employing cutting-edge optimization and simulation techniques. The challenge is multifaceted, encompassing technical innovation as well as the strategic navigation of regulatory and stakeholder landscapes to realize sustainable resource management.

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  • Multi-Energy Management: My work in multi-energy management focuses on the cohesive integration and optimization of diverse energy carriers. This demands a deep understanding of the physical and operational intricacies of various energy sources, necessitating the development of innovative mathematical models and algorithms. These models are adept at managing the stochastic nature of energy supply and demand, the interdependencies between systems, and the integration of decentralized energy resources. Incorporating real-time data and predictive analytics, I aim to dynamically manage these complex systems, highlighting the sophisticated integration of distributed resources and demand response mechanisms.

  • Cybersecurity in Energy Systems: In the realm of energy systems' cybersecurity, I spearhead the development of advanced defense mechanisms that utilize artificial intelligence, machine learning, and blockchain technologies. My research is not just about defending against cyber threats but ensuring the resilience of critical infrastructures against evolving cyber challenges. This area requires a blend of technical prowess and strategic foresight, aiming to pre-empt and neutralize potential cyber threats through innovative security strategies that can adapt to future adversarial tactics.

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  • Social Computing Modelling in Energy Systems: My incorporation of social computing modeling into energy systems research represents a novel approach to understanding and influencing energy consumption behaviors. This interdisciplinary venture combines behavioral science, big data analytics, and user-centric design to leverage social networks, gamification, and participatory sensing. The primary challenge here is the accurate modeling of unpredictable human behaviors and crafting interventions that effectively promote energy efficiency and sustainability across diverse communities.

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  • Distributionally Robust Optimization: My work with Distributionally Robust Optimization seeks to create optimization models resilient to worst-case scenarios of uncertain parameters, a critical aspect of decision-making under uncertainty. This effort involves forging new theoretical frameworks and computational algorithms capable of dealing with ambiguous and incomplete data. Drawing on statistics, machine learning, and operations research, I aim to equip energy planners and operators with tools for optimal performance and resilience, even under significant future uncertainties like renewable energy availability, market volatility, and climate change impacts.

FIRST AUTHORED PUBLICATIONS                                                                                                              

 

  1. P. Zhao, C. Gu, D. Huo, Y. Shen and I. Hernando-Gil, "Two-Stage Distributionally Robust Optimization for Energy Hub Systems," in IEEE Transactions on Industrial Informatics, vol. 16, no. 5, pp. 3460-3469, May 2020. (IF=12.3)

  2. P. Zhao, Z. Cao, D. D. Zeng, C. Gu, Z. Wang, Y. Xiang, M. Qadrdan, X. Chen, X. Yan, and S. Li, "Cyber-Resilient Multi-Energy Management for Complex Systems," IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 2144-2159, 2022, doi: 10.1109/TII.2021.3097760. (IF=12.3)

  3. P. Zhao, C. Gu, Z. Cao, Y. Shen, F. Teng, X. Chen, C. Wu, D. Huo, X. Xu and S. Li, , "Data-Driven Multi-Energy Investment and Management under Earthquakes," in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2020.3043086. (IF=12.3)

  4. P. Zhao, S. Li, P. J. H. Hu, C. Gu, S. Lu, S. Ding, Z. Cao, D. Xie, and Y. Xiang, "Blockchain-Based Water-Energy Transactive Management With Spatial-Temporal Uncertainties," IEEE Transactions on Smart Grid, vol. 14, no. 4, pp. 2903-2920, 2023, doi: 10.1109/TSG.2022.3230693. (IF=9.6)

  5. P. Zhao, S. Li, P. J. H. Hu, C. Gu, Z. Cao, and Y. Xiang, "Managing Water-Energy-Carbon Nexus for Urban Areas With Ambiguous Moment Information," IEEE Transactions on Power Systems, vol. 38, no. 5, pp. 4432-4446, 2023, doi: 10.1109/TPWRS.2022.3214189. (IF=6.6)

  6. P. Zhao, C. Gu, Z. Cao, D. Xie, F. Teng, J. Li, X. Chen, C. Wu, D. Yu, X. Xu and S. Li "A Cyber-Secured Operation for Water-Energy Nexus," in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.3043757. (IF=6.6)

  7. P. Zhao, C. Gu, Z. Hu, X. Zhang, X. Chen, I. Hernando-Gil and Y. Ding, "Economic-Effective Multi-Energy Management with Voltage Regulation Networked with Energy Hubs," in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.3025861. (IF=6.6)

  8. P. Zhao, X. Lu, C. Gu, Q. Ai, H. Liu, Z. Cao, Y. Bian and S. Li. " Volt-VAR-Pressure Optimization of Integrated Energy Systems with Hydrogen Injection," in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.3028530. (IF=6.6)

  9. P. Zhao, C. Gu, Q. Ai, Y. Xiang, T. Ding and S. Li. " Water-Energy Nexus: A Mean-Risk Distributionally Robust Co-Optimization of District Integrated Energy Systems," in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.3038076. (IF=6.6)

  10. P. Zhao, C. Gu, Z. Hu, D. XIE, I. Hernando-Gil and Y. Shen, "Distributionally Robust Hydrogen Optimization with Ensured Security and Multi-Energy Couplings," in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.3005991. (IF=6.6)

  11. P. Zhao, C. Gu and D. Huo, "Two-Stage Coordinated Risk Mitigation Strategy for Integrated Electricity and Gas Systems under Malicious False Data Injections," in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.2986455. (IF=6.6)

  12. P. Zhao, S. Li, Z. Cao, P. J.-H. Hu, D. D. Zeng, D. Xie, Y. Shen, J. Li, and T. Luo, "A Social Computing Method for Energy Safety," Journal of Safety Science and Resilience, 2024/01/12/ 2024, doi: https://doi.org/10.1016/j.jnlssr.2023.12.001. (IF=5.4)

  13. P. Zhao, S. Li, Z. Cao, P. J. H. Hu, C. Gu, X. Yan, D. Huo, T. Luo, and Z. Wang, "Socially Governed Energy Hub Trading Enabled by Blockchain-Based Transactions," IEEE Transactions on Computational Social Systems, pp. 1-16, 2023, doi: 10.1109/TCSS.2023.3308608. (IF=5)

  14. P. Zhao, Y. Ding, C. Gu, H. Liu, Y. Bian and S. Li, "Cyber-Resilience Enhancement and Protection for Uneconomic Power Dispatch under Cyber-Attacks," in IEEE Transactions on Power Delivery, doi: 10.1109/TPWRD.2020.3038065. (IF=4.4)

  15. P. Zhao, S. Li, PJ. Hu, Z. Cao, C. Gu, D. Xie, D. Zeng, "Coordinated Cyber Security Enhancement for Grid-Transportation Systems With Social Engagement," in IEEE Transactions on Emerging Topics in Computational Intelligence, 2022, doi: 10.1109/TETCI.2022.3209306 (IF=5.3)

  16. P. Zhao, S. Li, PJ. Hu, Z. Cao, C. Gu, X. Yan, D. Huo, I. Hernando-Gil, "Two-Stage Co-Optimization for Utility-Social Systems With Social-Aware P2P Trading," in IEEE Transactions on Computational Social Systems, 2022, doi: 10.1109/TCSS.2022.3200032 (IF=5)

  17. Zhao, P., Gu, C., Cao, Z., Xiang, Y., Yan, X. and Huo, D., 2020, September. A two-stage data-driven multi-energy management considering demand response. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers (pp. 588-595).

  18. S. Li, P. Zhao, C. Gu, S. Bu, J. Li, and S. Cheng, "Integrating Incentive Factors in the Optimization for Bidirectional Charging of Electric Vehicles," IEEE Transactions on Power Systems, pp. 1-12, 2023, doi: 10.1109/TPWRS.2023.3308233. (IF=6.6) (Co-first author)

  19. S. Li, P. Zhao, C. Gu, S. Bu, J. Li, and S. Cheng, "Modeling and Mitigating the Cycle Aging Cost of Vehicle Batteries in Energy Transportation Nexus," IEEE Transactions on Smart Grid, pp. 1-1, 2023, doi: 10.1109/TSG.2023.3296328. (IF=9.6) (Co-first author)

  20. S. Li, P. Zhao, C. Gu, S. Bu, X. Pei, X. Zeng, J. Li, and S. Cheng, "Hybrid Power System Topology and Energy Management Scheme Design for Hydrogen-Powered Aircraft," IEEE Transactions on Smart Grid, pp. 1-1, 2023, doi: 10.1109/TSG.2023.3292088. (IF=9.6) (Co-first author)

  21. P. Zhao, C. Gu, Y. Xiang, X. Zhang, Y. Shen and S. Li, "Reactive Power Optimization in Integrated Electricity and Gas Systems," in IEEE Systems Journal, doi: 10.1109/JSYST.2020.2992583.  (IF=4.4)

  22. P. Zhao, H. Wu, C. Gu, and I. H. Gil, "Optimal Home Energy Management under Hybrid PV-Storage Uncertainty: A Distributionally Robust Chance-Constrained Approach," IET Renewable Power Generation, vol. 13, no. 11, pp. 1911-1919, 19 8 2019. (IF=3.0)

  23. P. Zhao, I. Hernando-Gil, and H. Wu, "Optimal Energy Operation and Scalability Assessment of Microgrids for Residential Services," in 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 12-15 June 2018 2018, pp. 1-6, doi: 10.1109/EEEIC.2018.8493765.

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