Load Frequency Control Strategy of Interconnected Power System Based on Tube DMPC
DOI:
https://doi.org/10.13052/dgaej2156-3306.39311Keywords:
Robust model predictive control, load frequency control, uncertain parametersAbstract
Solar thermal power generation shares technical characteristics with traditional thermal power generation. This enables rapid adjustment of turbine generator output to meet the demands of the power grid load for frequency modulation. However, fluctuations in light intensity lead to variations in interconnected power system parameters, posing challenges for load frequency control (LFC). In this study, we propose a Robust Distributed Model Predictive Control (RDMPC) method. This method achieves system trajectory tracking by solving the nominal system optimization problem. It also flexibly adjusts the weights of different Tube models to determine the optimal control law using the standard Tube online combination with various gain values. Additionally, we incorporate the states of adjacent areas into the feedback control law to achieve effective coordination between these areas. Using MATLAB/Simulink, we simulated the power system in two areas. Compared to standard Tube DMPC, our proposed algorithm effectively mitigates the impact of light intensity, enhances adjustment speed, reduces frequency fluctuation, and demonstrates superior control effectiveness.
Downloads
References
Mohsen Babaei, Mohsen Hadian. Learning-based Fractional Order PID Controller for Load Frequency Control of Distributed Energy Resources Including PV and Wind Turbine Generator[J]. Distributed Generation & Alternative Energy Journal, 2022,37(6):1755–1772.
Yatin Sharma, Lalit Chandra Saikia, Automatic generation control of a multi-area ST – Thermal power system using Grey Wolf Optimizer algorithm based classical controllers [J]. International Journal of Electrical Power & Energy Systems, 2015(73): 853–862.
Nong Huiyun. Distributed Model Predictive Control with Application to Load Frequency Control System[D], 2015(in Chinese).
Zhang Yi, Chang Pengfei. Load Frequency Control of Multi-area Interconnected Power System with Renewable Energy[J]. Industrial Control Computer, 2020, 33(10):47–49 (in Chinese).
Liao Xiaobing; Liu Kaipei; Qin Liang, et al., Cooperative DMPC-Based Load Frequency Control of AC/DC Interconnected Power System with Solar Thermal Power Plant [C]. 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Kota Kinabalu, Malaysia, 2018:341–346.
Zhang Yi, Liu Xiangjie. Robust distributed model predictive control for load frequency control of uncertain power systems[J]. Control Theory & Applications, 2016, 33(5):621–630 (in Chinese).
Liu Xiangjie, Wang Ce, Kong Xiaobin, Zhang Yi, Wang, Weisheng and Lee K. Y., Tube-based Distributed MPC for Load Frequency Control of Power System with High Wind Power Penetration[J], IEEE Transactions on Power Systems, 2023:1–12.
T. Krishnaiah, Dr. S. Srinivasa Rao, Dr. K. Madhu Murthy, Solar Stirling Dish Power Generation Atlas of India[J]. Distributed Generation & Alternative Energy Journal, 2009, 24(2):35–50.
Dr. Sriram Somasundaram, Dr. Kevin Drost, Mr. Daryl R. Brown, Diurnal Thermal Energy Storage for Cogeneration Applications[J]. Distributed Generation & Alternative Energy Journal, 1997, 12(2):35–50.
Mayne D.Q., Kerrigan E.C., Falugi P. Robust model predictive control: advantages and disadvantages of tube-based methods[J]. IFAC Proceedings Volumes, 2011, 44(1): 191–196.
Riverso S., Ferrari-Trecate G. Plug-and-Play distributed model predictive control with coupling attenuation[J]. Optimal Control Applications & Methods, 2015, 3(36): 292–305.
Oshnoei A., Kheradmandi M., Muyeen S.M. Robust control scheme for distributed battery energy storage systems in load frequency control[J]. IEEE Transactions on Power Systems, 2020, 35(6): 4781–4791.
Riverso S, Farina M, Ferrari-Trecate G. Plug-and-play decentralized model predictive control for linear systems[J]. IEEE Transactions on Automatic Control, 2013, 10(58):2608–2614.
Langson W., Chryssochoos I., Rakoviæ S.V., et al. Robust model predictive control using tubes[J]. Automatica, 2004, 40(1): 125–133.
Rakovic S V, Kerrigan E C, Kouramas K I, and Mayne D Q. Invariant Approximations of the Minimal Robust Positively Invariant Set[J]. IEEE Transactions on Automatic Control, 2005, 3(50):406–410.
Dashkovskiy S, Ruffer B S, Wirth F R, An ISS small gain theorem for general networks[J], Mathematics of Control, Signals, and Systems, 2007, 19(2):93–122.
