Microgrid management using hybrid inverter fuzzy-based control


Microgrid systems are becoming a very promising solution to meet the power demand growth especially in remote areas where diesel generators (DG) are commonly used as a main energy source. Photovoltaic (PV) systems are commonly used as a sustainable energy source to economize DG fuel. Due to the intermittent and fluctuating behavior of PV generators, energy storage systems (ESS) such as electrochemical battery are suggested. PV and ESS are usually connected using one inverter/charger called hybrid inverter. The power management is crucial to optimize the fuel consumption and operate efficiently ESS. Additionally, in an off-grid operation, the microgrid frequency becomes sensible due to the slow dynamic of DG which requires an additional control tool to improve the frequency regulation. This paper proposes a new power management based on Mamdani fuzzy logic. The proposed controller considers the targets mentioned above by only controlling the hybrid inverter. Simulation results prove that fuzzy-based controller reduces the DG fuel consumption by more than 12% compared to classical hysteresis management control. Moreover, the proposed controller performs efficiently regarding the conventional frequency regulation, which is widely used in microgrid control.

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

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
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31


I ph :


I :

Diode saturation current

q :

Coulomb constant (1.602 × 10−19 C)

K :

Boltzmann’s constant (1.38 × 10−23 J/K)

T :

Cell temperature


P–N junction ideality factor

R s :

Intrinsic series resistance

R sh :

Intrinsic parallel resistance

S :

Real solar radiation

S ref :

Solar radiation in standard test conditions (1000 w/m2)

T ref :

Cell absolute temperature in standard test conditions

I ph-ref :

Photocurrent in standard test conditions

C T :

Temperature coefficient

I s-ref :

Diode saturation current in standard test conditions

E g :

Band-gap energy of the cell semiconductor

E :

Battery no-load voltage

E o :

Battery constant voltage

k :

Polarization voltage

Q :

Battery capacity

A :

Exponential zone amplitude

B :

Exponential zone time constant inverse

E full :

Fully charged voltage

E exp :

Voltage at the end of exponential zone

Q exp :

Charge at the end of exponential zone

E nom :

Voltage at the end of nominal zone

Q nom :

Charge at the end of nominal zone

F min :

Minimum allowed frequency value

F r :

Regulation frequency value

F max :

Maximum allowed frequency value

T sm :

Governor time constant

T d :

Engine time constant

R :

Frequency drop

V f :

Excitation voltage of the synchronous machine


Maximum power point tracking


Perturb and observe




State of charge


Energy storage system


Power management




Fuzzy logic


Direct current


Alternative current


Diesel generator


  1. 1.

    Zhang D, Shah N, Papageorgiou LG (2013) Efficient energy consumption and operation management in a smart building with microgrid. Energy Convers Manag 74:209–222

    Article  Google Scholar 

  2. 2.

    Rossi I, Banta L, Cuneo A, Ferrari ML, Traverso AN, Traverso A (2016) Real-time management solutions for a smart polygeneration microgrid. Energy Convers Manag 112:11–20

    Article  Google Scholar 

  3. 3.

    Zhang L, Gari N, Hmurcik LV (2014) Energy management in a microgrid with distributed energy resources. Energy Convers Manag 78:297–305

    Article  Google Scholar 

  4. 4.

    Hernández ACL, Meng L, Aldana NLD, Graells M, Quintero JCV, Guerrero JM (2017) Online energy management systems for microgrids: experimental validation and assessment framework. IEEE Trans Power Electron 33:2201–2215

    Google Scholar 

  5. 5.

    Hernández ACL, Aldana NLD, Savaghebi M, Quintero JCV, Guerrero JM (2016) Optimal Power Scheduling for an islanded hybrid microgrid. In: Proceedings of 2016 8th international power electronics and motion control conference—ECCE Asia (IPEMC 2016-ECCE Asia)

  6. 6.

    Parvizi-Mosaed M, Farmani F, Anvari-Moghaddam A (2013) Optimal energy management of a micro-grid with renewable energy resources and demand response. J Renew Sustain Energy 5:053148

    Article  Google Scholar 

  7. 7.

    Piagi P, Lasseter RH (2006) Autonomous control of microgrids. In: IEEE PES meeting, Montreal, June 2006

  8. 8.

    Ma Y, Yang P, Wang Y, Zhou S, He P (2014) Frequency control of Islanded microgrid based on wind-PV-diesel-battery hybrid energy sources. In: 17th international conference on electrical machines and systems (ICEMS), Hangzhou, China, 22–25 Oct 2014

  9. 9.

    Habib M, Ladjici AA, Khoucha F (2015) Frequency control in off-grid hybrid diesel/PV/battery power system. In: 4th international conference on electrical engineering. University of Boumerdes, Algeria, 13–15 Dec 2015

  10. 10.

    Oureilidis KO, Bakirtzis EA, Demoulias CS (2016) Frequency-based control of islanded microgrid with renewable energy sources and energy storage. J Mod Power Syst Clean Energy 4(1):54–62

    Article  Google Scholar 

  11. 11.

    Datta MS, Yona T, Funabashi A, Chul-Hwan Kim T (2011) A frequency-control approach by photovoltaic generator in a PV–diesel hybrid power system. IEEE Trans Energy Convers 26:559–571

    Article  Google Scholar 

  12. 12.

    Singh S, Singh SK, Chanana S, Singh YP (2014) Frequency regulation of an isolated hybrid power system with battery energy storage system. In: Power and energy systems conference: towards sustainable energy, 2014

  13. 13.

    Mi Y, Fu Y, Zhao JB, Wang P (2013) The novel frequency control method for PV-diesel hybrid system. In: 10th IEEE international conference on control and automation (ICCA), 12–14 June 2013

  14. 14.

    Tiar M, Betka A, Drid S, Abdeddaim S, Becherif M, Tabandjat A (2017) Optimal energy control of a PV-fuel cell hybrid system. Int J Hydrog Energy 42(2):1456–1465

    Article  Google Scholar 

  15. 15.

    Han Y, Zhang G, Li Q, You Z, Chen W, Liu H (2018) Hierarchical energy management for PV/hydrogen/battery island DC microgrid. Int J Hydrog Energy. https://doi.org/10.1016/j.ijhydene.2018.08.135

    Article  Google Scholar 

  16. 16.

    Ou K, Yuan WW, Choi M, Yang S, Jung S, Kim YB (2018) Optimized power management based on adaptive-PMP algorithm for a stationary PEM fuel cell/battery hybrid system. J Hydrog Energy 43:15433–15444

    Article  Google Scholar 

  17. 17.

    Tabanjat A, Becherif M, Hissel D, Ramadan HS (2018) Energy management hypothesis for hybrid power system of H2/WT/PV/GMT via AI techniques. Int J Hydrog Energy 43(6):3527–3541

    Article  Google Scholar 

  18. 18.

    Hosseini H, Tousi B, Razmjooy N (2014) Application of fuzzy subtractive clustering for optimal transient performance of automatic generation control in restructured power system. J Intell Fuzzy Syst 26(3):1155–1166

    Article  Google Scholar 

  19. 19.

    Razmjooy N, Mehdi R, Noradin G (2017) Imperialist competitive algorithm-based optimization of neuro-fuzzy system parameters for automatic red-eye removal. Int J Fuzzy Syst 19(4):1144–1156

    Article  Google Scholar 

  20. 20.

    Payman M, Razmjooy N, Mousavi BS (2014) Robust potato color image segmentation using adaptive fuzzy inference system. Iran J Fuzzy Syst 11(6):47–65

    MathSciNet  Google Scholar 

  21. 21.

    Habib M, Khoucha F (2014) On line energy management strategies for fuel cell/battery electric vehicle: from rules logic to fuzzy logic strategy. In: 2nd international conference on electrical engineering and control applications, Constantine, Algeria, 18–20 Nov 2014

  22. 22.

    Kermadi M, Berkouk A (2017) Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: comparative study. Renew Sustain Energy Rev 69:369–386

    Article  Google Scholar 

  23. 23.

    Harrag A, Messalti S (2018) Adaptive GA-based reconfiguration of photovoltaic array combating partial shading conditions. Neural Comput & Applic 30(4):1145–1170

    Article  Google Scholar 

  24. 24.

    Kyriakarakos G, Dounis AI, Arvanitis KG, Papadakis G (2012) A fuzzy logic energy management system for polygeneration microgrids. Renew Energy 41:315–327

    Article  Google Scholar 

  25. 25.

    Datta M, Senjyu T, Yona A, Funabashi T (2011) A fuzzy based method for leveling output power fluctuations of photovoltaic-diesel hybrid power system. Renew Energy 36:1693–1703

    Article  Google Scholar 

  26. 26.

    Roumila Z, Rekioua D, Rekioua T (2017) Energy management based fuzzy logic controller of hybrid system wind/photovoltaic/diesel with storage battery. Int J Hydrog Energy 42(30):19525–19535

    Article  Google Scholar 

  27. 27.

    Papathanassiou SA, Papadopoulos MP (2001) Dynamic characteristics of autonomous wind-diesel systems. Renew Energy 23:293–311

    Article  Google Scholar 

  28. 28.

    Tremblay O, Dessaint LA, Dekkiche A (2007) A generic battery model for the dynamic simulation of hybrid electric vehicles. In: Proceedings of the 2007 IEEE vehicle power and propulsion conference, pp 284–289

  29. 29.

    Long B, Jeong TW, Lee JD, Jung YC, Chong KT (2015) Energy management of a hybrid AC–DC micro-grid based on a battery testing system. Energies 8:1181–1194

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Abdelghani Harrag.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Verify currency and authenticity via CrossMark

Cite this article

Habib, M., Ladjici, A.A. & Harrag, A. Microgrid management using hybrid inverter fuzzy-based control. Neural Comput & Applic 32, 9093–9111 (2020). https://doi.org/10.1007/s00521-019-04420-5

Download citation


  • Diesel generator
  • DG
  • Photovoltaic
  • PV
  • Electrochemical battery
  • Power management
  • Mamdani fuzzy logic