Skip to main content

Advertisement

SpringerLink
Log in

Research on the Predictive Optimal PID Plus Second Order Derivative Method for AGC of Power System with High Penetration of Photovoltaic and Wind Power

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

Because of the uncertainty of the external environment, high penetration of renewable energy such as wind power and solar energy in the modern power system renders the traditional automatic generation control (AGC) methods more challenging. An improved AGC method named predictive optimal proportional integral differential plus second order derivative (PO-PID + DD) for multi-area interconnected grid is proposed in this paper to reduce the negative impacts of the uncertainty which is caused by the high penetration of renewable energy. Firstly, the mathematical model of the AGC system of multi-area power grid with penetration of photovoltaic (PV) and wind power is built. Then, PO-PID + DD controller is presented to improve the system robustness with respect to system uncertainties. In order to obtain the predictive sequence of the integral system output, the characteristic of the controller is included in the system model. Thus, according to the predictive sequence and designed objective function, the input of the controller can be readjusted to obtain the optimal effect of AGC. An IEEE 39-bus system is introduced as an example to testify the feasibility and effectiveness of the proposed method. The simulation results indicate that the system controlled by the proposed controller has desired dynamic performances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Noruzi A, Banki T, Abedinia O et al (2015) A new method for probabilistic assessments in power systems, combining monte carlo and stochastic-algebraic methods. Complexity 21(2):100–110

    Article  MathSciNet  Google Scholar 

  2. Altıntas E, Salor O, Buyukdagli U (2016) Correlation between multiple electric arc furnace operations and unscheduled power flows in the interconnection lines at the eastern cross border of ENTSO-E. IEEE Trans Ind Appl 52(4):3508–3517

    Article  Google Scholar 

  3. Ghadimi N, Akbarimajd A, Shayeghi H et al (2018) A new prediction model based on multi-block forecast engine in smart grid. J Ambient Intell Human Comput 9(6):1873–1888

    Article  Google Scholar 

  4. Abedinia O, Bekravi M, Ghadimi N (2017) Intelligent controller based wide-area control in power system. Int J Uncertain Fuzziness Knowl Based Syst 25(01):1–30

    Article  Google Scholar 

  5. Arya Y, Kumar N (2017) Optimal control strategy-based AGC of electrical power systems: a comparative performance analysis. Optim Control Appl Methods 38(6):982–992

    Article  MathSciNet  MATH  Google Scholar 

  6. Bevrani H, Hiyama T (2008) Robust decentralized PI based LFC design for time delay Power systems. Energy Convers Manag 49(2):193–204

    Article  Google Scholar 

  7. Tan W (2010) Unified tuning of PID load frequency controller for power systems via IMC. IEEE Trans Power Syst 25(1):341–350

    Article  Google Scholar 

  8. Zheng Y, Zhou J, Xu Y et al (2017) 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

    Article  Google Scholar 

  9. Shiroei M, Toulabi MR, Ranjbar AM (2013) Robust multivariable predictive based load frequency control considering generation rate constraint. Int J Electr Power Energy Syst 46:405–413

    Article  Google Scholar 

  10. Prasad S, Purwar S, Kishor N (2017) Non-linear sliding mode load frequency control in multi-area power system. Control Eng Pract 61:81–92

    Article  Google Scholar 

  11. Abdelaziz AY, Ali ES (2015) Cuckoo search algorithm based load frequency controller design for nonlinear interconnected power system. Int J Electr Power Energy Syst 73:632–643

    Article  Google Scholar 

  12. Sahu RK, Panda S, Rout UK (2013) DE optimized parallel 2-DOF PID controller for load frequency control of power system with governor dead-band nonlinearity. Int J Electr Power Energy Syst 49:19–33

    Article  Google Scholar 

  13. Chandra SL, Nanda J, Mishra S (2011) Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system. Int J Electr Power Energy Syst 33:394–401

    Article  Google Scholar 

  14. Raju M, Saikia LC, Sinha N (2016) Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller. Int J Electr Power Energy Syst 80:52–63

    Article  Google Scholar 

  15. Sahu RK, Panda S, Sekhar GTC (2015) A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems. Int J Electr Power Energy Syst 64:880–893

    Article  Google Scholar 

  16. Prakash S, Sinha SK (2014) Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system. Appl Soft Comput 23:152–164

    Article  Google Scholar 

  17. Guha D, Roy PK, Banerjee S (2017) Multi-verse optimisation: a novel method for solution of load frequency control problem in power system. IET Gener Transm Distrib 11(14):3601–3611

    Article  Google Scholar 

  18. Hasanien HM (2018) Whale optimisation algorithm for automatic generation control of interconnected modern power systems including renewable energy sources. IET Gener Transm Distrib 12(3):607–614

    Article  Google Scholar 

  19. Ganger D, Zhang J, Vittal V (2018) Forecast-based anticipatory frequency control in power systems. IEEE Trans Power Syst 33(1):1004–1012

    Article  Google Scholar 

  20. Ghadimi N (2014) Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization. Front Energy 8(4):412–425

    Article  Google Scholar 

  21. Xu Y, Li F, Jin Z et al (2016) Dynamic gain-tuning control (DGTC) approach for AGC with effects of wind power. IEEE Trans Power Syst 31(5):3339–3348

    Article  Google Scholar 

  22. Rahman A, Saikia LC, Sinha N (2017) Automatic generation control of an interconnected two-area hybrid thermal system considering dish-stirling solar thermal and wind turbine system. Renew Energy 105:41–54

    Article  Google Scholar 

  23. Xi Y, Li D, Lin S (2013) Model predictive control—status and challenges. Acta Autom Sin 39(3):222–236

    Article  MathSciNet  Google Scholar 

  24. Sahib MA (2015) A novel optimal PID plus second order derivative controller for AVR system. Eng Sci Technol 18(2):194–206

    Google Scholar 

  25. Ghadimi N, Akbarimajd A, Shayeghi H et al (2017) Application of a new hybrid forecast engine with feature selection algorithm in a power system. Int J Ambient Energy 12:1–10

    Article  Google Scholar 

  26. Eskandari Nasab M, Maleksaeedi I, Mohammadi M et al (2014) A new multiobjective allocator of capacitor banks and distributed generations using a new investigated differential evolution. Complexity 19(5):40–54

    Article  MathSciNet  Google Scholar 

  27. Soliman M, Malik OP, Westwick DT (2011) Multiple model predictive control for wind turbines with doubly fed induction generators. IEEE Trans Sustain Energy 2(3):215–225

    Article  Google Scholar 

  28. Abedinia O, Amjady N, Ghadimi N (2018) Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Comput Intell 34(1):241–260

    Article  MathSciNet  Google Scholar 

  29. Singh VP, Kishor N, Samuel P (2016) Communication time delay estimation for load frequency control in two-area power system. Ad Hoc Netw 41:69–85

    Article  Google Scholar 

  30. Aziz A, Oo AT, Stojcevski A (2018) Analysis of frequency sensitive wind plant penetration effect on load frequency control of hybrid power system. Int J Electr Power Energy Syst 99:603–617

    Article  Google Scholar 

Download references

Acknowledgements

The research team members thank for the support given by the National Natural Science Foundation of China (Grant no. 51309094), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (Grant no. [2014]1685).

Author information

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, People’s Republic of China

    Xilin Zhao, Zhenyu Lin, Bo Fu & Li He

  2. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China

    Chaoshun Li

Authors
  1. Xilin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenyu Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chaoshun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Fu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, X., Lin, Z., 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, 1075–1086 (2019). https://doi.org/10.1007/s42835-019-00113-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-019-00113-0

Keywords

Search

Navigation