Publications

Journal Papers

T. Tapia, Z. Liang, C. Konstantinou, and Y. Dvorkin, “Electricity Market-Clearing with Extreme Events,” in IEEE Transactions on Energy Markets; Policy and Regulation. [online]
Q. Li *, Z. Liang *, A. Bernstein, and Y. Dvorkin, “Revealing Decision Conservativeness Through Inverse Distributionally Robust Optimization,” IEEE Control Systems Letters, vol.8, pp.1018-1023, 2024. (*equal contribution). [online]
S. Khanal, C. Graf, Z. Liang, Y. Dvorkin, and B. Ünel, “Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study,” IEEE Transactions on Energy Markets, Policy and Regulation, 2024. [online]
R. Ferrando, L. Pagnier, R. Mieth, Z. Liang, Y. Dvorkin, D. Bienstock, and M. Chertkov, “Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study,” IEEE Transactions on Energy Markets, Policy, and Regulation, vol. 2, no. 1, pp. 40-51, 2024. [online]
Z. Liang, R. Mieth, and Y. Dvorkin. “Inertia Pricing in Stochastic Electricity Markets,” IEEE Transactions on Power Systems, vol. 38, no. 3, pp. 2071-2084 2022. [Online]
Z. Liang, R. Mieth, and Y. Dvorkin. “Operation-Adversarial Scenario Generation,” Electric Power Systems Research, vol. 212, pp.108451, 2022. [Online]
Z. Liang, Z. Song, et al., “Three-stage Scheduling Scheme for Hybrid Energy Storage Systems to Track Scheduled Feed-in PV Power,” Solar Energy, vol. 188, pp. 1054-1087, 2019. [Online]
Z. Liang, Z. Song, et al., “Optimal Configuration of Liquid Metal Battery Energy Storage System in Photovoltaic and Hydrogen Coupled Microgrid,” Automation of Electric Power Systems, vol. 42, pp. 64-69, 2018. [Online]

Conference Papers

Z. Liang*, Q. Li*, A. Liu, and Y. Dvorkin, “Prescribing Decision Conservativeness in Two-Stage Power Markets: A Distributionally Robust End-to-End Approach”. in Proceedings of the 59th Annual Conference on Information Science and Systems (CISS). (*equal contribution). [online]
Z. Liang, Q. Li, J. Comden, A. Bernstein and Y. Dvorkin, “Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation,” in Proceedings of 2024 IEEE 63rd Conference on Decision and Control (CDC), Milan, Italy, 2024. [online]
Z. Liang and Y. Dvorkin, “Data-Driven Inverse Optimization for Marginal Offer Price Recovery in Electricity Markets,” in Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy). June 2023, Orlando, U.S. [online]
Z. Liang, R. Mieth, and Y. Dvorkin. “Operation-Adversarial Scenario Generation,” in Proceedings of 2022 Power Systems Computation Conference (PSCC), July 2022, Porto, Portugal. [Online]
Z. Liang and S. Grijalva, “Considering Battery Degradation in Energy Storage System Design for Multi-Services Scenarios,” in Proceedings of 2020 IEEE PES General Meeting (PESGM), August 2020, Virtual. [Online]
Z. Liang, Z. Song, et al.,“Optimal Scheduling Scheme and Battery configuration for Microgrids with Dual Battery Energy Storage Systems,” in Proceedings of 54th IEEE Industry Applications Society Annual Meeting, September 2019, Baltimore, U.S. [Online]
Z. Liang, Z. Song, et al., “Residual Capacity Estimation of Valve-regulated Lead-acid (VRLA) Batteries for Second-use,” in Proceedings of 2019 IEEE PES Innovative Smart Grid Technologies Asia (ISGT-Asia), May 2019, Chengdu, China. [Online]
Z. Liang, Z. Song, et al.,“Design and Comparison of Scheduling Schemes for Grid-Connected Hybrid PV-Hydrogen-Battery Microgrid,” in Proceedings of 6th IEEE International Conference on Smart Energy Grid Engineering (SEGE), August 2018, Oshawa, Canada. [Online]

Technical Reports

Z. Liang, R. Mieth, Y. Dvorkin, and M. A. Ortega-Vazquez, “Program on Technology Innovation: Weather-Driven Flexibility Reserve Dimensioning,” Electric Power Research Institute (EPRI) Technical Report, No. 3002024638, 2022. [online]