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Genetic Algorithm-Based Load Frequency Control of a Grid-Connected Microgrid in Presence of Electric Vehicles

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Sustainable Energy and Technological Advancements

Part of the book series: Advances in Sustainability Science and Technology ((ASST))

Abstract

The frequency stability of the power system is becoming more serious with the increase in renewable energy penetration. Due to fast response and vehicle to grid capability, electric vehicles (EVs) are involved in improving the frequency stability of the power system. In this paper, hybrid generation consists of thermal power generation involving solar photovoltaic (PV), wind turbine generators (WTGs), geothermal power plant (GTPP) renewable generations and electric vehicle aggregators. An improvement in stability is achieved with different number of electric vehicles. Genetic algorithm (GA) with integral time absolute error (ITAE) performance indices as objective function is used to obtain the optimality of the controllers. The effects on transient behaviour of the microgrid is probed with PI, PID and GAPID controllers. Susceptibility analysis is executed on GAPID controller to prove the robustness under uncertainty conditions with both SLP and RLP.

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References

  1. Elgerd OI (1982) Electric energy systems theory, an introduction, 2nd edn. McGraw Hill Book Company, New York

    Google Scholar 

  2. Kundur P, Balu NJ, Lauby MG (1994) Power system stability and control. McGraw-Hill, New York

    Google Scholar 

  3. Mu C, Liu W, Xu W (2018) Hierarchically adaptive frequency control for an EV-integrated smart grid with renewable energy. IEEE Trans Ind Inform 14(9):4254–4263

    Article  Google Scholar 

  4. Pandey SK, Mohanty SR, Kishor N (2013) A literature survey on load-frequency control for conventional and distribution generation power systems. Renew Sustain Energy Rev 25:318–334

    Article  Google Scholar 

  5. Aldeen M, Trinh H (1994) Load-frequency control of ınterconnected power systems via constrained feedback control schemes. Comput Electr Eng 20(I):71–88

    Google Scholar 

  6. Saxena S, Hote YV (2013) Load frequency control in power systems via internal model control scheme and model-order reduction. IEEE Trans Power Syst 28(3):2749–2757

    Article  Google Scholar 

  7. Tasnin W, Saikia LC (2018) Comparative performance of different energy storage devices in AGC of multi-source system including geothermal power plant. J Renew Sustain Energy 10(2):1–11

    Article  Google Scholar 

  8. Tasnin W, Saikia LC, Raju M (2017) Deregulated AGC of multi-area system incorporating dish-Stirling solar thermal and geothermal power plants using fractional order cascade controller. Int J Electr Power Energy Syst 101:1–11

    Google Scholar 

  9. Koley I, Bhowmik PS, Datta A (2017) Load frequency control in a hybrid thermal-wind-photovoltaic power generation system. In: 2017 4th international conference on power, control embedded systems, ICPCES 2017, pp 1–5

    Google Scholar 

  10. Datta A, Bhattacharya G, Mukherjee D, Saha H (2016) Modelling and simulation-based performance study of a transformer less single-stage grid-connected photovoltaic system in Indian ambient conditions. Int J Ambient Energy 37(2):172–183

    Article  Google Scholar 

  11. Wu S, Wang Y, Cheng S (2013) Extreme learning machine-based wind speed estimation and sensor less control for wind turbine power generation system. Neurocomputing 102:163–175

    Article  Google Scholar 

  12. Khayyer P, Özgüner Ü (2014) Decentralized control of large-scale storage-based renewable energy systems. IEEE Trans Smart Grid 5(3):1300–1307

    Article  Google Scholar 

  13. Veronica AJ, Kumar NS, Gonzalez Longatt F (2020) Design of load frequency control for a microgrid using D-partition method. Int J Emerg Electr Power Syst 21(1):1–11

    Google Scholar 

  14. Pham TN, Trinh H, Van Hien L, Wong KP (2016) Integration of electric vehicles for load frequency output feedback H∞ control of smart grids. IET Gener Transm Distrib 10(13):3341–3352

    Article  Google Scholar 

  15. Vachirasricirikul S, Ngamroo I (2014) Robust LFC in a smart grid with wind power penetration by coordinated V2G control and frequency controller. IEEE Trans Smart Grid 5(1):371–380

    Article  Google Scholar 

  16. Nguyen, Zhang HC, Mahmud MA (2014) Smart charging and discharging of electric vehicles to support grid with high penetration of renewable energy. IFAC Proc 47(3):8604–8609

    Google Scholar 

  17. Jia H et al (2018) Coordinated control for EV aggregators and power plants in frequency regulation considering time-varying delays. Appl Energy 210(2017):1363–1376

    Article  Google Scholar 

  18. Ramakrishna KSS, Bhatti TS (2007) Sampled-data automatic load frequency control of a single area power system with multi-source power generation. Electr Power Comp Syst 35(8):955–980

    Article  Google Scholar 

  19. Inthiyaz S, Nalli R, Rakesh, Subbarao TK, Ahammad SH, Rajesh V (2021) GA based PID controller: design and optimization. In: 2021 6th ınternational conference on ınventive computation technologies (ICICT), pp 285–289

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of EEE, Gayatri Vidya Parishad College of Engineering and Technology, Autonomous, Vishakhapatnam, India

    Krosuru Anuradhika & Puja Dash

Authors
  1. Krosuru Anuradhika
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  2. Puja Dash
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology Meghalaya, Shillong, India

    Gayadhar Panda

  2. School of Electrical and Electronic Engineering, Newcastle University, Singapore, Singapore

    R. T. Naayagi

  3. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India

    Sukumar Mishra

Appendix

Appendix

Kggeo = 0.05, Ttgeo = 0.1, Kg = 0.1, Tt = 0.3, Kpv = 1, Tpv = 1.8, Kpw = 1.3, Tpw = 1, Ki = 1.47, Ctp = 0.59, Tev = 0.35, B1 = 0.6, B2 = 0.4.

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Anuradhika, K., Dash, P. (2022). Genetic Algorithm-Based Load Frequency Control of a Grid-Connected Microgrid in Presence of Electric Vehicles. In: Panda, G., Naayagi, R.T., Mishra, S. (eds) Sustainable Energy and Technological Advancements. Advances in Sustainability Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-16-9033-4_33

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  • DOI: https://doi.org/10.1007/978-981-16-9033-4_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9032-7

  • Online ISBN: 978-981-16-9033-4

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