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Microgrid management using hybrid inverter fuzzy-based control

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Abstract

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.

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Abbreviations

I ph :

Photocurrent

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

PN:

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

MPPT:

Maximum power point tracking

P&O:

Perturb and observe

PV:

Photovoltaic

SOC:

State of charge

ESS:

Energy storage system

PM:

Power management

MG:

Microgrid

FL:

Fuzzy logic

DC:

Direct current

AC:

Alternative current

DG:

Diesel generator

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Author information

Authors and Affiliations

  1. Departement of Electrical Engineering, USTHB, Algiers, Algeria

    Mustapha Habib & Ahmed Amine Ladjici

  2. Optics and Precision Mechanics Institute, Ferhat Abbas University, Cite Maabouda (ex. Travaux), 19000, Setif, Algeria

    Abdelghani Harrag

  3. CCNS Laboratory, Electronics Department, Faculty of Technology, Ferhat Abbas University, Campus Maabouda, 19000, Setif, Algeria

    Abdelghani Harrag

Authors
  1. Mustapha Habib
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  2. Ahmed Amine Ladjici
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  3. Abdelghani Harrag
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Correspondence to Abdelghani Harrag.

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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

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