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Research on Dynamic Hierarchical Control Strategy of AGC in Complex Power Grid with Penetration Effect of Wind Power

  • Research Article-Electrical Engineering
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Abstract

The system inertia and relative reserve capacity will decrease due to the increasing penetration of wind power, which adds difficulty for the traditional automatic generation control (AGC). In addition, the participation of wind power into the AGC system will cause the control strategy more complex. Therefore, a dynamic hierarchical method is proposed for targeted control effect of frequency regulation. Firstly, an AGC system model of multi-area interconnected grid with participation of wind power is analyzed. Then, the effect of the participation of wind power into AGC with different penetrations is analyzed. Due to the effect of wind power on AGC slowing down when the penetration of that becomes high, a two-layer control architecture with centralized model predictive control of upper layer and regional proportional integral derivative controller of lower layer is designed. Because the complex control strategy will result in a large number of system resource being occupied, simplification is necessary when the requirement of frequency regulation is not ample due to the reasons such as the small load fluctuation. Therefore, a dynamic hierarchical control strategy is designed. Two scenarios of step disturbance and random load disturbance are analyzed, which verified the feasibility and effectiveness of the proposed method.

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References

  1. Mohandes, B.; Moursi, M.S.E.; Hatziargyriou, N., et al.: A review of power system flexibility with high penetration of renewables. IEEE Trans. Power Syst. 34(4), 3140–3155 (2019)

    Article  Google Scholar 

  2. Yang, J.B.; Liu, Q.Y.; Li, X., et al.: Overview of wind power in china: status and future. Sustainability 9, 1454 (2017)

    Article  Google Scholar 

  3. Attya, A.B.; Dominguez-Garcia, J.L.; Anaya-Lara, O.: A review on frequency support provision by wind power plants: current and future challenges. Renew. Sustain. Energy Rev. 81(2), 2071–2087 (2018)

    Article  Google Scholar 

  4. Chen, X.C.; Lin, J.; Wan, C., et al.: A unified frequency-domain model for automatic generation control assessment under wind power uncertainty. IEEE Trans. Smart Grid 10(3), 2936–2947 (2019)

    Article  Google Scholar 

  5. Sharma, M.; Dhundhara, S.; Arya, Y., et al.: Frequency excursion mitigation strategy using a novel COA optimised fuzzy controller in wind integrated power systems. IET Renew. Power Gener. 14(19), 4071–4085 (2020)

    Article  Google Scholar 

  6. Sharma, G.; Panwar, A.; Arya, Y., et al.: Integrating layered recurrent ANN with robust control strategy for diverse operating conditions of AGC of the power system. IET Gener. Transm. Distrib. 14(18), 3886–3895 (2020)

    Article  Google Scholar 

  7. Ye, L.; Zhang, C.H.; Xue, H., et al.: Study of assessment on capability of wind power accommodation in regional power grids. Renew. Energy 133(4), 647–662 (2018)

    Google Scholar 

  8. Wei, Z.; Fang, K.L.: Controlling active power of wind farms to participate in load frequency control of power systems. IET Gener. Transm. Distrib. 11(9), 2194–2203 (2017)

    Article  Google Scholar 

  9. Hakimuddin, N.; Nasiruddin, I.; Bhatti, T.S., et al.: Optimal automatic generation control with hydro, thermal, gas, and wind power plants in 2-area interconnected power system. Electr. Power Compon. Syst. 11, 1–14 (2020)

    Google Scholar 

  10. Sun, M.; Min, Y.; Chen, L., et al.: Optimal auxiliary frequency control of wind turbine generators and coordination with synchronous generators. CSEE J. Power Energy Syst. 7(1), 78–85 (2021)

    Google Scholar 

  11. Yang, P.; He, B.; Wang, B., et al.: Coordinated control of rotor kinetic energy and pitch angle for large-scale doubly fed induction generators participating in system primary frequency regulation. IET Renew. Power Gener. 5, 1–12 (2021)

    Google Scholar 

  12. Daraz, A.; Malik, S.A.; Haq, I.U., et al.: Modified PID controller for automatic generation control of multi-source interconnected power system using fitness dependent optimizer algorithm. PLoS ONE 11, 1–31 (2020)

    Google Scholar 

  13. Nayak, J.R.; Shaw, B.; Sahu, B.K.: Implementation of hybrid SSA-SA based three-degree-of-freedom fractional order PID controller for AGC of a two area power system integrated with small hydro plants. IET Gener. Transm. Distrib. 14(13), 2430–2440 (2020)

    Article  Google Scholar 

  14. Gbadega, P.A.; Saha, A.K.: Load frequency control of a two-area power system with a stand-alone micro-grid based on adaptive model predictive control. IEEE J. Emerg. Sel. Topics Power Electron. 9, 7253–7263 (2020)

    Article  Google Scholar 

  15. Mcnamara, P.; Milano, F.: Model predictive control based AGC for multi-terminal HVDC-connected AC grids. IEEE Trans. Power Syst. 33, 1036–1048 (2017)

    Article  Google Scholar 

  16. Zhang, Y.F.; Cortés, J.: Model predictive control for transient frequency regulation of power networks. Automatica 123, 109335 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  17. Hilliard, T.; Swan, L.: Methodology to determine the impact of simplified building models on model-predictive-control morning start optimization performance. Sci. Technol. Built Environ. 24(7), 779–792 (2018)

    Article  Google Scholar 

  18. Ali, H.H.; Kassem, A.M.; Al-Dhaifallah, M., et al.: Multi-verse optimizer for model predictive load frequency control of hybrid multi-interconnected plants comprising renewable energy. IEEE Access 7(99), 114623–114642 (2020)

    Article  Google Scholar 

  19. Zhang, F.; Fu, A.H.; Ding, L., et al.: MPC based control strategy for battery energy storage station in a grid with high photovoltaic power penetration. Int. J. Electr. Power Energy Syst. 115, 105448 (2020)

    Article  Google Scholar 

  20. Zheng, Y.; Zhou, J.Z.; Xu, Y.H., et al.: A distributed model predictive control based load frequency control scheme for multi-area interconnected power system using discrete-time Laguerre functions. ISA Trans. 68, 127–140 (2017)

    Article  Google Scholar 

  21. Liu, X.J.; Zhang, Y.; Kwang, Y.L.: Robust distributed MPC for load frequency control of uncertain power systems. Control Eng. Pract. 56, 136–147 (2016)

    Article  Google Scholar 

  22. Liu, X.J.; Yi, Z.; Lee, K.Y.: Coordinated distributed MPC for load frequency control of power system with wind farms. IEEE Trans. Ind. Electron. 64(6), 5140–5150 (2017)

    Article  Google Scholar 

  23. Uyioghosa, I.E.; Saha, A.K.: A comparative analysis of different MPC controllers for load frequency control for interconnected power system. In: 2020 International SAUPEC/ROBMECH/PRASA Conference, Cape Town, South Africa, pp. 1–6 (2020)

  24. Wang, D.; Glavic, M.; Wehenkel, W.: Comparison of centralized, distributed and hierarchical model predictive control schemes for electromechanical oscillations damping in large-scale power systems. Electr. Power Energy Syst. 58, 32–41 (2014)

    Article  Google Scholar 

  25. Xia, C.; Liu, H.J.: Bi-level model predictive control for optimal Coordination of multi-area automatic generation control units under wind power integration. Processes 7(10), 669 (2019)

    Article  Google Scholar 

  26. Zhang, B.; Chen, J.; Wu, W.: A hierarchical model predictive control method of active power for accommodating large-scale wind power integration. Autom. Electr. Power Syst. 38(9), 6–14 (2014)

    Google Scholar 

  27. Taher, A.M.; Hasanien, H.M.; Ginidi, A.R., et al.: Hierarchical model predictive control for performance enhancement of autonomous microgrids. Ain Shams Eng. J. 12, 1867–1881 (2021)

    Article  Google Scholar 

  28. Liu, W.P.; Liu, Y.T.: Hierarchical model predictive control of wind farm with energy storage system for frequency regulation during black-start. Int. J. Electr. Power Energy Syst. 2, 119–130 (2020)

    Google Scholar 

  29. Vasudevan, K.R.; Ramachandaramurthy, V.K.; Venugopal, G., et al.: Hierarchical frequency control framework for a remote microgrid with pico hydel energy storage and wind turbine. Int. J. Electr. Power Energy Syst. 127, 106666 (2021)

    Article  Google Scholar 

  30. Ye, L.; Zhang, C.H.; Tang, Y., et al.: Hierarchical model predictive control strategy based on dynamic active power dispatch for wind power cluster integration. IEEE Trans. Power Syst. 34(6), 4617–4629 (2019)

    Article  Google Scholar 

  31. Berkel, F.; Gorges, D.; Liu, S.: Load-frequency control, economic dispatch and unit commitment in smart microgrids based on hierarchical model predictive control. IEEE 12, 1–8 (2013)

    Google Scholar 

  32. Lin, Z.W.; Chen, Z.Y.; Qu, C.Z., et al.: A hierarchical clustering-based optimization strategy for active power dispatch of large-scale wind farm. Int. J. Electr. Power Energy Syst. 121, 106155 (2020)

    Article  Google Scholar 

  33. Zhao, X.L.; Lin, Z.Y.; Fu, B., et al.: Research on the predictive optimal PID plus second order derivative method for AGC of power system with high penetration of photovoltaic and wind power. J. Electr. Eng. Technol. 14(3), 1075–1086 (2019)

    Article  Google Scholar 

  34. Zhao, X.L.; Lin, Z.Y.; Fu, B., et al.: Research on automatic generation control with wind power participation based on predictive optimal 2-degree-of-freedom PID strategy for multi-area interconnected power system. Energies 11, 3325 (2018)

    Article  Google Scholar 

  35. Zhao, X.L.; Lin, Z.Y.; Fu, B., et al.: Research on frequency control method for micro-grid with a hybrid approach of FFR-OPPT and pitch angle of wind turbine. Int. J. Electr. Power Energy Syst. 127(4), 106670 (2021)

    Article  Google Scholar 

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Funding

The article was funded by National Natural Science Foundation of China (61603127).

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Authors and Affiliations

  1. Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, 430068, China

    Xilin Zhao, Chufeng Gong, Sili Gong & Bo Fu

Authors
  1. Xilin Zhao
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  2. Chufeng Gong
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  3. Sili Gong
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  4. Bo Fu
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Correspondence to Xilin Zhao.

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Zhao, X., Gong, C., Gong, S. et al. Research on Dynamic Hierarchical Control Strategy of AGC in Complex Power Grid with Penetration Effect of Wind Power. Arab J Sci Eng 48, 6307–6319 (2023). https://doi.org/10.1007/s13369-022-07358-4

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  • DOI: https://doi.org/10.1007/s13369-022-07358-4

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