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Enhanced coyote optimizer-based cascaded load frequency controllers in multi-area power systems with renewable

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Abstract

Recently, the renewable energy has been occupied a lot of attention around the world since it presents cheap and sustainable energy. Consequently, its presence in power systems becomes a fact that had to deal with. Hence, load frequency control (LFC) in multi-area power systems that constitute photovoltaic (PV) and thermal plant sources is proposed. Two forms of competing cascaded controllers, namely proportional integral–proportional integral (PI–PI) and proportional–derivative with filter-PI (PDn-PI), are investigated, and their performances are compared with traditional PI and PIDn controller. An enhanced coyote optimization algorithm (ECOA) is proposed for finding the optimal tuned parameters of the proposed controllers. Furthermore, the uncertainty is considered under the variation of system parameters by ± 40%. The performance of the proposed competing controllers is tested under dynamic load change that is applied individually in each area. These controllers are applied on two dissimilar test cases with various sets of disturbances. The obtained results are compared with various reported techniques. The simulated comparisons declare the great efficiency with high superiority robustness of the proposed cascaded PDn-PI based on ECOA for handling the LFC in multi-area power systems.

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Abbreviations

l :

Subscript denotes area (l = 1, 2)

R :

Droop characteristics of governor speed

B :

Frequency bias factor

Kg, T g :

Gain and time constant of governor for thermal plant

K t, T t :

Gain and time constant of turbine for thermal plant

k p, T p :

Gain and time constant of thermal plant

K r, T r :

Gain and time constant of re-heater

P R :

MW capacity for thermal plant

P L :

Nominal loading for thermal plant

a, b, c, d :

Parameters of the PV unit

∆P Dl :

Changes in the demand powers

∆P TIE :

Change in tie-line power (p.u.)

T 12 :

Coefficient of synchronization, subscripts ‘1’ and ‘2’ indicate each power system area

∆f l :

Frequency deviation (Hz)

K p, K i and K d :

Proportional, integral and derivative gains of PID controller, respectively

J :

Fitness function

t sim :

Time range of simulation

N :

Population size

N p :

Number of packs

N c :

Number of coyotes

soc :

The social condition

cth:

Coyote

pth:

Pack of coyote

tth:

The instant of time

D :

Number of the designed parameters

jth:

Decision variable

lb j and ub j :

Lower and upper bounds of the decision variable

r j, e 1, e 2 and rnd j :

Real random numbers within range [0, 1]

P e :

Coyote probability for leaving its pack

δ 1 :

Alpha coyote influence

δ 2 :

Pack influence

cr 1 :

The cultural difference from a random coyote of the pack to the alpha coyote (alpha)

cr 2 :

The cultural difference from a random coyote to the cultural tendency of the pack

pup:

The birthed new coyotes

r 1 and r 2 :

Random chosen coyotes from the pth pack

j 1 and j 2 :

Random dimensions of the problem

P s :

Scatter probability

P a :

Association probability

R j :

Random generated number within the decision variables bounds

LFC:

Load frequency control

ACEl :

Area control error

MPPT:

Maximum power point tracking

ITAE:

Integral time-multiplied absolute value of the error (∆f1, ∆f2, ∆PTIE,)

IAE:

Integral of absolute error (∆f1, ∆f2, ∆PTIE,)

ISE:

Integral of square error (∆f1, ∆f2, ∆PTIE,)

ITSE:

Integral of time multiply square error (∆f1, ∆f2, ∆PTIE,)

PV:

Photovoltaic

PI–PI:

Proportional integral–proportional integral

PDn-PI:

Proportional–derivative with filter-PI

ECOA:

Enhanced coyote optimization algorithm

ADRC:

Active disturbance rejection control

2DOF PID:

Two degrees-of-freedom PID

SFS:

Stochastic fractal search algorithm

CCGT:

Combined cycle gas turbine

PLL:

Phase-locked loop

PDF-PI:

Proportional–derivative with filter putted in cascaded with proportional–integral controller

EVs:

Electric vehicles

SMO:

Spider monkey optimization algorithm

GA:

Genetic algorithm

MFO:

Mouth-flame optimization algorithm

MWOA:

Modified whale optimization algorithm

MPC:

Model predictive control

SSA:

Salp swarm algorithm

SSSC:

Static synchronous series compensator

VPL:

Volleyball premier league algorithm

References

  1. Ibraheem Kumar P, Kothari DP (2005) Recent philosophies of automatic generation control strategies in power systems. IEEE Trans Power Syst 20(1):346–357

    Article  Google Scholar 

  2. Rakhshani E, Rouzbehi K, Sadeh S (2009) A new combined model for simulation of mutual effects between LFC and AVR loops. In: Proceedings of the Asia–Pacific power and energy engineering conference, Wuhan, China, pp 1–5

  3. Parmar KS, Majhi S, Kothari D (2010) Multi-area load frequency control in a power system using optimal output feedback method. In: Proceedings of the 2010 joint international conference on power electronics, drives and energy systems (PEDES) & 2010 Power India, New Delhi, India, pp 1–5

  4. Parmar KS, Majhi S, Kothari D (2012) Load frequency control of a realistic power system with multi-source power generation. Int J Electr Power Energy Syst 42:426–433

    Article  Google Scholar 

  5. Saikia LC, Nanda J, Mishra S (2011) Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system. Int J Electric Power Energy Syst 33(3):394–401

    Article  Google Scholar 

  6. Bhatt P, Roy R, Ghoshal SP (2010) GA/particle swarm intelligence based optimization of two specific varieties of controller devices applied to two-area multi-units automatic generation control. Int J Electr Power Energy Syst 32(4):299–310

    Article  Google Scholar 

  7. Rahmani M, Sadati N (2012) Hierarchical optimal robust load-frequency control for power systems. IET Gener Transm Distrib 6:303–312

    Article  Google Scholar 

  8. Panda S, Mohanty B, Hota PK (2013) Hybrid BFOAPSO algorithm for automatic generation control of linear and non-linear interconnected power systems. Appl Soft Comput 13(12):4718–4730

    Article  Google Scholar 

  9. Taher SA, Fini MH, Aliabadi SF (2014) Fractional order PID controller design for LFC in electric power systems using imperialist competitive algorithm. Ain Shams Eng J 5(1):121–135

    Article  Google Scholar 

  10. Arya Y (2019) A new optimized fuzzy FOPI-FOPD controller for automatic generation control of electric power systems. J Franklin Inst 356(11):5611–5629

    Article  Google Scholar 

  11. Debbarma S, Saikia LC, Sinha N (2014) Automatic generation control using two degree of freedom fractional order PID controller. Int J Electr Power Energy Syst 58:120–129. https://doi.org/10.1016/j.ijepes.2014.01.011

    Article  Google Scholar 

  12. Mohapatra T, Dey AK, Sahu BK (2020) Implementation of Quasi Oppositional SSA based two-degree-of freedom fractional order PID controller for AGC with diverse source of generations. IET Gener Transm Distrib

  13. Dash P, Saikia LC, Sinha N (2016) Flower pollination algorithm optimized PI–PD cascade controller in automatic generation control of a multi-area power system. Int J Electr Power Energy Syst 82:19–28

    Article  Google Scholar 

  14. Chintu JMR, Sahu RK (2019) Design and implementation of ADE based cascade PD–PI controller for AGC of multi-area power system. In: Applications of robotics in industry using advanced mechanisms, pp 46–58. https://doi.org/10.1007/978-3-030-30271-9_5

  15. Prakash A, Kumar K, Parida SK (2020) PIDF (1 + FOD) controller for load frequency control with SSSC and AC–DC tie-line in deregulated environment. IET Gener Transm Distrib 14:2751

    Article  Google Scholar 

  16. Miaomiao MA, Xiangjie LIU, Chunyu Z (2017) LFC for multi-area interconnected power system concerning wind turbines based on DMPC. IET Gener Transm Distrib 11(10):2689–2696

    Article  Google Scholar 

  17. Zhao M, Zhang J, Ren K (2018) Load frequency control of interconnected power system with wind power based on active disturbance rejection control. In: IEEE 3rd advanced information technology, electronic and automation control conference (IAEAC), pp 1085–1091

  18. Saha D, Saikia LC (2017) Impact of phase-locked loop on system dynamics of a CCGT incorporated diverse source system employed with AC/DC interconnection. J Renew Sustain Energy 9(4):045506

    Article  Google Scholar 

  19. Alhelou HH, Hamedani-Golshan ME, Heydarian-Forushani E, Al-Sumaiti AS, Siano P (2018) Decentralized fractional order control scheme for LFC of deregulated nonlinear power systems in presence of EVs and RER. In: IEEE international conference on smart energy systems and technologies (SEST), pp 1–6

  20. Tripathy D, Sahu BK, Dev Choudhury NB, Dawn S (2018) Spider Monkey optimization based cascade controller for LFC of a hybrid power system. Int J Comput Intell IoT 2(4):1–7

    Google Scholar 

  21. Behera A, Panigrahi TK, Ray PK, Sahoo AK (2019) A novel cascaded PID controller for automatic generation control analysis with renewable sources. IEEE/CAA J Autom Sin 6(6):1438–1451

    Article  Google Scholar 

  22. Prakash A, Murali S, Shankar R, Bhushan R (2019) HVDC tie-link modeling for restructured AGC using a novel fractional order cascade controller. Electr Power Syst Res 170:244–258

    Article  Google Scholar 

  23. Abd-Elazim SM, Ali ES (2018) Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm. Neural Comput Appl 30:607–616

    Article  Google Scholar 

  24. Sharma M, Bansal RK, Prakash S, Dhundhara S (2018) Frequency regulation in PV integrated power system using MFO tuned PIDF controller. In: IEEE 8th power India international conference (PIICON), pp 1–6

  25. Khadanga RK, Kumar A, Panda S (2019) A novel modified whale optimization algorithm for load frequency controller design of a two-area power system composing of PV grid and thermal generator. Neural Comput Appl 32:1–12

    Google Scholar 

  26. Zeng GQ, Xie XQ, Chen MR (2017) An adaptive model predictive load frequency control method for multi-area interconnected power systems with photovoltaic generations. Energies 10(11):1840

    Article  Google Scholar 

  27. Pierezan J, Coelho LDS (2018) Coyote optimization algorithm: a new metaheuristic for global optimization problems. In: IEEE congress on evolutionary computation (CEC), pp 1–8

  28. Güvenç U, Kaymaz E (2019) Economic dispatch integrated wind power using coyote optimization algorithm. In: 2019 7th international Istanbul smart grids and cities congress and fair (ICSG), pp 179–183

  29. Fathy A, Al-Dhaifallah M, Rezk H (2019) Recent coyote algorithm-based energy management strategy for enhancing fuel economy of hybrid FC/Battery/SC system. IEEE Access 7:179409–179419

    Article  Google Scholar 

  30. Qais MH, Hasanien HM, Alghuwainem S, Nouh AS (2019) Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules. Energy 187:116001

    Article  Google Scholar 

  31. Chin VJ, Salam Z (2019) Coyote optimization algorithm for the parameter extraction of photovoltaic cells. Sol Energy 194:656–670

    Article  Google Scholar 

  32. Abdelwaness M, Abaza A, El-Sehiemy RA, Nabil M, Rezk H (2020) Parameter estimation of electric power transformers using coyote optimization algorithm with experimental verification. IEEE Access 8:50036

    Article  Google Scholar 

  33. Abaza A, El-Sehiemy RA, Abdelrazek AS (2019) Optimal parameter estimation of solid oxide fuel cells model using coyote optimization algorithm. In: International conference on recent advances in engineering mathematics & physics, Cairo University, Springer

  34. Santy T, Natesan R (2015) Load frequency control of a two area system consisting of a grid connected PV system and diesel generator. Int J Emerg Technol Comput Electron 13(1):456–461

    Google Scholar 

  35. Vilanova R, Visioli A (2012) PID control in the third millennium: lessons learned and new approaches. Springer, London, pp 237–253

    Book  Google Scholar 

  36. Shabani H, Vahidi B, Ebrahimpour M (2012) A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems. ISA Trans 52:88–95

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, Electrical Engineering Department, Menoufiya University, Shebin El-Kom, Egypt

    Adel A. Abou El-Ela

  2. Faculty of Engineering, Electrical Engineering Department, Kafrelsheikh University, Kafrelsheikh, Egypt

    Ragab A. El-Sehiemy

  3. Faculty of Engineering, Electrical Engineering Department, Suez University, Suez, Egypt

    Abdullah M. Shaheen

  4. Ministry of Electricity and Renewable Energy, Middle Delta Electricity Production Company, (MDEPC), El-Buhayira, Egypt

    Abd El-Gelil Diab

Authors
  1. Adel A. Abou El-Ela
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  2. Ragab A. El-Sehiemy
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  3. Abdullah M. Shaheen
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  4. Abd El-Gelil Diab
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Correspondence to Ragab A. El-Sehiemy.

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Abou El-Ela, A.A., El-Sehiemy, R.A., Shaheen, A.M. et al. Enhanced coyote optimizer-based cascaded load frequency controllers in multi-area power systems with renewable. Neural Comput & Applic 33, 8459–8477 (2021). https://doi.org/10.1007/s00521-020-05599-8

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