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Improved-GWO designed FO based type-II fuzzy controller for frequency awareness of an AC microgrid under plug in electric vehicle

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Abstract

The paper proposes to use an improved Grey Wolf Optimization (GWO) optimized Fractional Order (FO) based type-II fuzzy controller for frequency regulation of an AC microgrid in presence of plug-in electric vehicles. The AC microgrid comprises various renewable-based energy sources such as wind turbine generator (WTG), photovoltaic cell (PV), microturbine (MT), aqua electrolyzer based fuel cell (FC) and diesel engine generator (DEG) along with various storage devices (ESD) like battery energy storage (BES), flywheel energy storage (FES) and electric vehicles (EV). The uncertain nature of solar, wind technologies and load demand makes the system more complicated and introduces the frequency oscillations in the system. It is highly needed to establish power balance among generation by designing an appropriate controller for frequency regulation. This paper proposes a robust fractional order type-II fuzzy PID (FO-T2-FPID) where the parameters of the FO-T2-FPID controllers are optimized by suggesting an improved GWO (I-GWO) algorithm. To demonstrate the capability of proposed I-GWO algorithm few comparative analyses have been performed over particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithm. It is also observed that for the same FO-T2-FPID controller structure, the percentage improvement in the objective function (ITAE) under load uncertainty by I-GWO compared to GWO and PSO are 29.74% and 65.94% respectively. Finally, it is conferred that proposed I-GWO optimized FO-T2-FPID controller significantly improves microgrid dynamic performance under various operational conditions.

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References

  • Alharbi T, Bhattacharya K, Kazerani M (2019) Planning and operation of isolated microgrids based on repurposed electric vehicle batteries. IEEE Trans Ind Inf 15(7):4319–4331

    Article  Google Scholar 

  • Ali ES, Abd-Elazim SM (2011) Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Int J Electr Power Energy Syst 33(3):633–638

    Article  Google Scholar 

  • Arghandeh R, Pipattanasomporn M, Rahman S (2012) Flywheel energy storage systems for ride-through applications in a facility microgrid. IEEE Trans Smart Grid 3(4):1955–1962

    Article  Google Scholar 

  • Bagal HA, Soltanabad YN, Dadjuo M, Wakil K, Ghadimi N (2018) Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory. Sol Energy 169:343

    Article  Google Scholar 

  • Bahmani-Firouzi B, Azizipanah-Abarghooee R (2014) Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm. Int J Electr Power Energy Syst 56:42–54

    Article  Google Scholar 

  • Bevrani H, François B, Ise T (2017) Microgrid dynamics and control. Wiley, New York

    Book  Google Scholar 

  • Chen M-R, Zeng G-Q, Dai Y-X, Kang-Di L, Bi D-Q (2019) Fractional-order model predictive frequency control of an islanded microgrid. Energies 12(1):84

    Article  Google Scholar 

  • Enrico S, Kai D (2016) Fractional calculus: models and numerical methods, vol 5. World Scientific, Singapore

    Google Scholar 

  • Gao W, Darvishan A, Toghani M, Mohammadi M, Abedinia O, Ghadimi N (2019) Different states of multi-block based forecast engine for price and load prediction. Int J Electr Power Energy Syst 104:423–435

    Article  Google Scholar 

  • Ghadimi N, Akbarimajd A, Shayeghi H, Abedinia O (2018) Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy 161:130–142

    Article  Google Scholar 

  • Hemmati M, Abapour M, Mohammadi-Ivatloo B (2020) Optimal scheduling of smart microgrid in presence of battery swapping station of electrical vehicles. In Electric vehicles in energy systems. Springer, Cham, pp 249–267

    Google Scholar 

  • Kennedy RP, Ravindra MK (1984) Seismic fragilities for nuclear power plant risk studies. Nucl Eng Des 79(1):47–68

    Article  Google Scholar 

  • Khodaei H, Hajiali M, Darvishan A, Sepehr M, Ghadimi N (2018) Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Appl Therm Eng 137:395–405

    Article  Google Scholar 

  • Kishor N, Saini RP, Singh SP (2007) A review on hydropower plant models and control. Renew Sustain Energy Rev 11(5):776–796

    Article  Google Scholar 

  • Mehri S, Shafie-Khah M, Siano P, Moallem M, Mokhtari M, Catalão JPS (2017) Contribution of tidal power generation system for damping inter-area oscillation. Energy Convers Manag 132:136–146

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Mishra D, Sahu PC, Prusty RC (2019) Tidal energy integrated robust frequency control of an islanded AC microgrid with improved-MFO tuned tilt controller. Int J Innov Technol Explor Eng

  • Mohamed EA, Mitani Y (2019) Load frequency control enhancement of islanded micro-grid considering high wind power penetration using superconducting magnetic energy storage and optimal controller. Wind Eng 43(6):609–624

    Article  Google Scholar 

  • Mohammadi S, Soleymani S, Mozafari B (2014) Scenario-based stochastic operation management of microgrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices. Int J Electr Power Energy Syst 54:525–535

    Article  Google Scholar 

  • Pan I, Das S (2015) Fractional order AGC for distributed energy resources using robust optimization. IEEE Trans Smart Grid 7(5):2175–2186

    Article  Google Scholar 

  • Panda S, Yegireddy NK (2013) Automatic generation control of multi-area power system using multi-objective non-dominated sorting genetic algorithm-II. Int J Electr Power Energy Syst 53:54–63

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Podlubny I, Dorcak L, Kostial I (1997) On fractional derivatives, fractional-order dynamic systems and PI/sup/spl lambda//D/sup/spl mu//-controllers. In: Proceedings of the 36th IEEE conference on decision and control (vol. 5, pp 4985–4990). IEEE

  • Porwal M, Parmar G, Bhatt R (2018) Robustness analysis of GWO/PID approach in control of ball hoop system with ITAE objective function. Int J Comput Sci Eng 6(8):218–222

    Google Scholar 

  • Regulagadda P, Dincer I, Naterer GF (2010) Exergy analysis of a thermal power plant with measured boiler and turbine losses. Appl Therm Eng 30(8–9):970–976

    Article  Google Scholar 

  • Rout UK, Sahu RK, Panda S (2013) Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system. Ain Shams Eng J 4(3):409–421

    Article  Google Scholar 

  • Saeedi M, Moradi M, Hosseini M, Emamifar A, Ghadimi N (2019) Robust optimization based optimal chiller loading under cooling demand uncertainty. Appl Therm Eng 148:1081–1091

    Article  Google Scholar 

  • Sahu PC, Prusty RC (2019) Robust frequency control of an islanded AC micro grid using BDA optimized 3DOF controller under plug in electric vehicle. Int J Eng Adv Technol

  • Sahu RK, Panda S, Sekhar GC (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 

  • Sahu PC, Prusty RC, Panda S (2017) ALO optimized NCTF controller in multi area AGC system integrated with WECS based DFIG system. In: 2017 International conference on circuit, power and computing technologies (ICCPCT) (pp 1–6). IEEE

  • Sahu PC, Mishra S, Prusty RC, Panda S (2018) Improved-salp swarm optimized type-II fuzzy controller in load frequency control of multi area islanded AC microgrid. Sustain Energy Grids Netw 16:380–392

    Article  Google Scholar 

  • Sahu PC, Prusty RC, Panda S (2019a) Stability analysis in RECS-integrated multi-area AGC system with SOS algorithm based fuzzy controller. In Computational intelligence in data mining. Springer, Singapore, pp 225–235

    Google Scholar 

  • Sahu PC, Prusty RC, Panda S (2019b) Approaching hybridized GWO-SCA based type-II fuzzy controller in AGC of diverse energy source multi area power system. J King Saud Univ Eng Sci 32:186

    Google Scholar 

  • Sedghi L, Fakharian A (2017) Voltage and frequency control of an islanded microgrid through robust control method and fuzzy droop technique. In: 2017 5th Iranian joint congress on fuzzy and intelligent systems (CFIS) (pp 110–115). IEEE

  • Sivalingam R, Chinnamuthu S, Dash SS (2017) A modified whale optimization algorithm-based adaptive fuzzy logic PID controller for load frequency control of autonomous power generation systems. Automatika 58(4):410–421

    Article  Google Scholar 

  • Yin C, Wu H, Locment F, Sechilariu M (2017) Energy management of DC microgrid based on photovoltaic combined with diesel generator and supercapacitor. Energy Convers Manag 132:14–27

    Article  Google Scholar 

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Correspondence to Prakash Chandra Sahu.

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Appendix

Appendix

D = Coefficient of damping = 0012 (pu/Hz); M = Constant for Inertia = 0.2 (pu/s);TFC = Time constant (TC) of fuel cell (FC) = 4 s; KFC = Gain of FC = 1/5; TBES = TC of battery = 0.1 s; KBES = Gain of battery = 1/300; TFES = TC of flywheel energy storage(FES) = 0.1 s; KFES = Gain of FES = 1/100; Td = TC of Diesel engine generator(DEG) = 2 s; TMT = TC of micro-turbine (MT) = 2 s; KMT = Gain of MT = 1; TWTG = TC of wind turbine generator (WTG) = 1.5 s; KWTG = Gain of WTG = 1; TPV = TC of photo voltaic (PV) cell = 1.8 s;KPV = Gain of PV = 1; TEV = TC of Electric Vehicle (EV) = 0.2 s; KEV = Gain of EV = 1/100; TAE = TC of aqua electrolyser (AE) = 0.5 s; KAE = Gain of AE = 1/25; R = System regulation = 2.4 Hz/Mw

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Sahu, P.C., Prusty, R.C. & Panda, S. Improved-GWO designed FO based type-II fuzzy controller for frequency awareness of an AC microgrid under plug in electric vehicle. J Ambient Intell Human Comput 12, 1879–1896 (2021). https://doi.org/10.1007/s12652-020-02260-z

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