Model-free predictive allocation control of large-scale pumped-storage clusters for active grid balancing via cutting-plane method
DOI:
https://doi.org/10.4108/ew.14126Keywords:
Hybrid power system, model predictive control, Lyapunov theory, optimization, cooperative controlAbstract
INTRODUCTION: Pumped-storage hydropower plants (PSHPs) are indispensable for maintaining grid balancing amid high-penetration renewable energy integration, yet optimizing their predictive control is severely hindered by the scale of interconnected units and the resulting decision-making complexity.
OBJECTIVES: This paper aims to develop a computationally efficient and operationally scalable assignment predictive control framework tailored for modern PSHP fleets.
METHODS: To alleviate the computational burden and enhance real-time responsiveness, a model-free assignment predictive control scheme incorporating a cutting-based method is proposed to effectively streamline the solution space while guaranteeing grid stabilization.
RESULTS: Furthermore, the framework is extended to multi-PSHP clusters, establishing a cooperative model-free predictive architecture that maximizes collective balancing capacity across diverse geographical regions. Numerical simulations validate the proposed method's efficacy.
CONCLUSION: The results indicate that the proposed cooperative framework provides a practical and highly scalable solution for active grid balancing and renewable integration in large-scale PSHP systems.
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[1] Jiang L, Wang C, Shang X, Zhang Z. Two-step event-triggered data driven model predictive control for trajectory tracking of unmanned surface vessel under environmental disturbances. IEEE Trans Autom Sci Eng. 2025;22:16801-13.
[2] Zheng H, Li J, Tian Z, Liu C, Wu W. Hybrid physics-learning model based predictive control for trajectory tracking of unmanned surface vehicles. IEEE Trans Intell Transp Syst. 2024;25(9):11522-33.
[3] Lyu G, Peng Z, Wang J. Safety-certified parallel model predictive control of autonomous surface vehicles via neurodynamic optimization. IEEE/CAA J Autom Sin. 2025;12(10):2056-66.
[4] Jia Z, Lu H, Chen H, Zhang W. Robust distributed cooperative rendezvous control for heterogeneous marine vehicles using model predictive control. IEEE Trans Veh Technol. 2024;73(8):11002-13.
[5] Li H, Li X. Distributed fixed-time consensus of discrete-time heterogeneous multi-agent systems via predictive mechanism and Lyapunov approach. IEEE Trans Circuits Syst II. 2024;71(1):321-5.
[6] Li H, Li X. Finite-time predictive consensus for discrete-time heterogeneous multi-agent systems over switching digraphs. IEEE Trans Circuits Syst II. 2023;70(6):2136-40.
[7] Huang Y, Liu G-P, Yu Y, Hu W. Data-driven distributed predictive tracking control for heterogeneous nonlinear multiagent systems with communication delays. IEEE Trans Autom Control. 2024;69(7):4786-92.
[8] Luo W, Lu P, Du C, Liu H. Cooperative output tracking control of heterogeneous multi-agent systems with random communication constraints: an observer-based predictive control approach. IEEE Trans Circuits Syst II. 2022;69(3):1139-43.
[9] Huang D, Li H, Li X. Formation of generic uavs-usvs system under distributed model predictive control scheme. IEEE Trans Circuits Syst II. 2020;67(12):3123-7.
[10] Sun Z, Wu B, Wang D, Chen J. Event-triggered model predictive control of spacecraft formation. IEEE Trans Autom Sci Eng. 2025;22:7696-7711.
[11] Chen Q, Jin Y, Wang T, Wang Y, Yan T, Long Y. UAV formation control under communication constraints based on distributed model predictive control. IEEE Access. 2022;10:126494-507.
[12] Wang Z, Diao W, Li W, Xu B. Formation tracking control of auvs subject to disturbances via distributed adaptive sliding model predictive control. IEEE Trans Veh Technol. 2025;74(7):10067-80.
[13] Vašak M, Banjac A, Hure N, Novak H, Marušić D, Lešić V. Modular hierarchical model predictive control for coordinated and holistic energy management of buildings. IEEE Trans Energy Convers. 2021;36(4):2670-82.
[14] Agharazi H, Prica M, Mondal A, Fu Q, Bhavaraju V, May RD, Fok K, Dise S, Swierc A, Loparo KA. Installation and testing of a two-level model predictive control building energy management system. IEEE Trans Control Syst Technol. 2024;32(2):326-39.
[15] Song D, Chang Q, Zheng S, Yang S, Yang J, Joo YH. Adaptive model predictive control for yaw system of variable-speed wind turbines. J Mod Power Syst Clean Energy. 2021;9(1):219-24.
[16] Liu X, Suo Y, Zhang Z, Song X, Zhou J. A new model predictive current control strategy for hybrid energy storage system considering the soc of the supercapacitor. IEEE J Emerg Sel Top Power Electron. 2023;11(1):325-38.
[17] Chazarra M, Pérez-Díaz JI, García-González J. Optimal joint energy and secondary regulation reserve hourly scheduling of variable speed pumped storage hydropower plants. IEEE Trans Power Syst. 2018;33(1):103-15.
[18] Liu Y, Wu L, Yang Y, Chen Y, Baldick R, Bo R. Secured reserve scheduling of pumped-storage hydropower plants in iso day-ahead market. IEEE Trans Power Syst. 2021;36(6):5722-33.
[19] Zhang S, Xiang Y, Liu J, Liu J, Yang J, Zhao X, Jawad S, Wang J. A regulating capacity determination method for pumped storage hydropower to restrain pv generation fluctuations. CSEE J Power Energy Syst. 2022;8(1):304-16.
[20] Christe A, Faulstich A, Vasiladiotis M, Steinmann P. World’s first fully rated direct ac/ac mmc for variable-speed pumped-storage hydropower plants. IEEE Trans Ind Electron. 2023;70(7):6898-907.
[21] Tiwari R, Nilsen R, Mo O, Nysveen A. Control methods for operation of pumped storage plants with full-size back-to-back converter fed synchronous machines. IEEE Trans Ind Appl. 2023;59(6):6792-803.
[22] Chen C, Li H, Baldwin MW, Wen Y, Zeng C, Zhu L, Qiu W, Liu Y. Online inertia estimation: a pumped storage hydropower turn-off traces based-method in large interconnection systems. IEEE Trans Ind Inform. 2025;21(12):9712-23.
[23] Grimm G, Messina MJ, Tuna SE, Teel AR. Nominally robust model predictive control with state constraints. IEEE Trans Autom Control. 2007;52(10):1856-70.
[24] Han H, Qiao J. Nonlinear model-predictive control for industrial processes: an application to wastewater treatment process. IEEE Trans Ind Electron. 2014;61(4):1970-82.
[25] Li D, De Schutter B. Distributed model-free adaptive predictive control for urban traffic networks. IEEE Trans Control Syst Technol. 2022;30(1):180-92.
[26] Santos TB, Oliani I, Figueiredo R, Albieiro D, Pelizari A, Sguarezi Filho AJ. Robust finite control set model predictive current control for induction motor using deadbeat approach in stationary frame. IEEE Access. 2023;11:13067-78.
[27] Wu W, Qiu L, Rodriguez J, Liu X, Ma J, Fang Y. Data-driven finite control-set model predictive control for modular multilevel converter. IEEE J Emerg Sel Top Power Electron. 2023;11(1):523-31.
[28] Lin X, Liu J, Liu Z, Gao Y, Peretti L, Wu L. Model-free current predictive control for pmsms with ultralocal model employing fixed-time observer and extremum-seeking method. IEEE Trans Power Electron. 2025;40(8):10682-93.
[29] Wei Y, Young H, Ke D, Wang F, Qi H, Rodríguez J. Model-free predictive control using sinusoidal generalized universal model for pmsm drives. IEEE Trans Ind Electron. 2024;71(11):13720-31.
[30] Zhao P, Ma J, Liu X, Qiu L, Liu C, Zhang Z, Fang Y. An online neural network approximator-based model-free predictive control approach for power converters. IEEE Trans Power Electron. 2025;40(10):15757-67.
[31] Su J, Wang H, Li P, Yang G. Dimension-reduced direct speed control based on line-constrained explicit model predictive control for ipmsm. IEEE Trans Ind Electron. 2025;72(12):14963-8.
[32] Vinyals O, Fortunato M, Jaitly N. Pointer networks. Advances in neural information processing systems, 2015, 28.
[33] Bello I, Pham H, Le Q V, et al. Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940, 2016.
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Copyright (c) 2026 Kaiqiang Li, Liande Liu, Dongdong Zhang, Yang Dong, Ben Ao, Leixin Li, Biao Wang, Zhe Wang, Wei Zhang, Jingxin Yan, Zhong Du, Zhiqiang Pang
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