Automatic Generation Control of Multi-area System Incorporating Renewable Unit and Energy Storage by Bat Algorithm

  • Subhranshu Sekhar Pati
  • Aurobindo BeheraEmail author
  • Tapas Kumar Panigrahi
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


The proposed work demonstrates the automatic generation control (AGC) of two area systems. Both areas contain conventional plants such as thermal–hydro–diesel, as well as renewable solar PV and geothermal units with proper physical constraints. Energy storage device named ultra-capacitor (UC) has also been incorporated in the system to support the stability and damp out the oscillations. Proportional–integral–derivative with filter (PIDN) optimized through bat algorithm is used, and the response is compared with other integer-order controllers. The system is tested under three distinct cases [i.e., SLP of 1% in area-1 (Case I), 1% area-2 (Case II), and 1% in area-1, 2 (Case III)]. The analysis points out the superior performance of PIDF to that of other controllers. The impact of geothermal plant and energy storage on system performance is investigated, and it can be inferred that the inclusion of such devices would enhance the performance of the model to a great extent.


Automatic generation control (AGC) Bat algorithm Integral square error (ISE) Renewable sources 


  1. 1.
    Elgerd OI (1983) Electric energy systems theory: an introduction, 2nd edn. Tata McGraw-Hill, New DelhiGoogle Scholar
  2. 2.
    Nanda J, Mishra S, Saikia LC (2009) Maiden application of bacterial foraging based optimization technique in multiarea automatic generation control. IEEE Trans Power Syst 24(2):602–609CrossRefGoogle Scholar
  3. 3.
    Gozde H, Taplamacioglu MC (2011) Automatic generation control application with craziness based particle swarm optimization in a thermal power system. Int J Electr Power Energy Syst 33(1):8–16Google Scholar
  4. 4.
    Bhatt P, Roy R, Ghoshal SP (2010) GA/particle swarm intelligence based optimization of two specific varieties of controller devices applied to two area multiunits automatic generation control. Int J Electr Power Energy Syst 32(4):299–310CrossRefGoogle Scholar
  5. 5.
    Saikia LC, Nanda J, Mishra S (2011) Performance comparison of several classical controllers in AGC for the multiarea interconnected thermal system. Int J Electr Power Energy Syst 33(3):394–401CrossRefGoogle Scholar
  6. 6.
    Hossain MS, Madlool NA, Rahim NA, Selvaraj J, Pandey AK, Khan AF (2016) Role of smart grid in renewable energy: an overview. Renew Sustain Energy Rev 60:1168–1184CrossRefGoogle Scholar
  7. 7.
    Morsali J, Zare K, High MT (2016) Performance comparison of TCSC with TCPS and SSSC controllers in AGC of the realistic interconnected multisource power system. Ain Shams Eng J 7(1):143–158Google Scholar
  8. 8.
    Tasnin W, Saikia LC (2018) Performance comparison of several energy storage devices in deregulated AGC of a multi-area system incorporating geothermal power plant. IET Renew Power Gen 12(7):761–772CrossRefGoogle Scholar
  9. 9.
    Javad M, Kazem Z, Mehrdad TH (2017) Applying fractional order PID to design TCSC based damping controller in coordination with automatic generation control of interconnected multisource power system. Eng Sci Technol Int J 20(1):1–17Google Scholar
  10. 10.
    Sahu RK, Panda S, Rout UK (2013) DE optimized parallel 2DOF PID controller for load frequency control of power system with governor dead band nonlinearity. Int J Electr Power Energy Syst 49:19–33Google Scholar
  11. 11.
    Sekhar GC, Sahu RK, Baliarsingh AK, Panda S (2016) Load frequency control of power system under a deregulated environment using optimal firefly algorithm. Int J Electr Power Energy Syst 74:195–211CrossRefGoogle Scholar
  12. 12.
    Barisal A, Panigrahi T, Mishra S (2017) A hybrid PSO-LEVY flight algorithm based fuzzy PID controller for automatic generation control of multi area power systems: fuzzy based hybrid PSO for automatic generation control. Int J Power Energy Convers 6:42–63Google Scholar
  13. 13.
    Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, pp 65–74Google Scholar
  14. 14.
    Biswal S, Barisal AK, Behera A, Prakash T (2013) Optimal power dispatch using BAT algorithm. In: 2013 IEEE international conference on energy-efficient technologies for sustainability (ICEETS), pp 1018–1023Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Subhranshu Sekhar Pati
    • 1
  • Aurobindo Behera
    • 2
    Email author
  • Tapas Kumar Panigrahi
    • 3
  1. 1.International Institute of Information TechnologyBhubaneswarIndia
  2. 2.Cambridge Institute of TechnologyRanchiIndia
  3. 3.Parala Maharaja Engineering CollegeBerhampurIndia

Personalised recommendations