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Enhancement of power management in micro grid system using adaptive ALO technique

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Abstract

Micro grids are drawing in more consideration since they can mitigate the worry of primary transmission systems, reduce feeder losses, enhance system power quality (PQ), and power management. Power management in MG is challengeable utilizing energy storage and generation components like PV, diesel generator, micro-hydro generator, battery bank with a bidirectional inverter. In this paper, the power management of the MG is investigated with the proposed droop controller. The droop controller is intended to get the optimal energy management of MG. The proposed method is the combination of Ant Lion Optimization (ALO) with a Bat algorithm to enhance the power management of MG. The ALO calculation is a nature-inspired algorithm. It imitates the chasing system of ant lions in nature. The Bat algorithm is utilized to refresh the insect lion position of the ALO algorithm. The droop control is sharing the power in the generation side depends upon the load demand of the grid side through the control action of the real and reactive power. The goal of the paper is used to enhance the real and reactive power of MG. With the assistance of the proposed technique, the real and reactive power is enhanced and the voltage is controlled. Based on the droop characteristics, power management is accomplished continuously by way of the upgraded proposed approach. The proposed strategy is executed in the MATLAB/Simulink platform and also analysis of the PV power, wind power, power demand, real power, reactive power, voltage, current, demand values, and grid power. The upgrading proposed framework is compared to current techniques such as CSO and PSO algorithms. Hence, the overall performance of the proposed approach will be effective.

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Abbreviations

\(V_{m}\) :

The maximum voltage of V

\(\omega\) :

The angular frequency

\(I_{m}\) :

The maximum current

\(V_{\alpha }\) and \(V_{\beta }\) :

The \(\alpha - \beta\) coordinate system

\(P_{ref}\) :

The reference real power

\(Q_{ref}\) :

The reference reactive power

P and Q :

The contrasted and the values of \(P_{ref}\) and \(Q_{ref}\)

E :

The referred voltage amplitude

\(\omega^{*}\) :

The nominal frequency

\(E^{*}\) :

The nominal voltage amplitude

\(p\) and \(q\) :

The calculated real and reactive power

\(p^{*}\) and \(q^{*}\) :

The preferred value of real and reactive power

\(s\) :

The real power coefficient

\(t\) :

The reactive power coefficient

\(\Delta \omega_{{\rm max}}\) :

The maximum allowed voltage amplitude droop

\(P_{{\rm max}}\) :

The maximum allowed real power

\(\Delta E_{{\rm max}}\) :

The maximum allowed voltage frequency

\(Q_{{\rm max}}\) :

The maximum allowed reactive power

\(K_{p}\) :

The proportional constant

\(K_{i}\) :

The integration constant

\(E(t)\) :

The error value

\(E_{p} (t)\) :

The error value in real power or active power

\(E_{q} (t)\) :

The error value in the reactive power or active power

\(N_{A}^{t}\) :

The random walk around the ant lion chosen by the roulette wheel at \(t\text{th}\) iteration

\(N_{E}^{t}\) :

The random walk around the elite at tth iteration

\(ANT_{i}^{t}\) :

The position of ith ant at tth iteration

\(G^{t}\) :

The minimum of all variables at tth iteration

\(H^{t}\) :

The vector admitting the maximum of all variables at tth iteration

Fk :

The pulse frequency

Pk :

The pulse rate

Bk :

The loudness parameters

\(\Delta E_{p}\) :

Represent the change in error value of real power

\(\Delta E_{q}\) :

Represent the change in error value of reactive power

RE:

Renewable energy

DG:

Distributed generation

MG:

Micro grid

AE:

Alternative energy

RES:

Renewable energy sources

SOC:

State of charge

WT:

Wind turbine

PV:

Photovoltaic cell

FC:

Fuel cell

PQ:

Power quality

SMC:

Sliding mode control

ALO:

Ant Lion Optimization algorithm

PSO:

Particle Swarm Optimization algorithm

ANNs:

Artificial neural networks

FLC:

Fuzzy logic control

BDPCs:

Bi-directional power converters

CSO:

Cuckoo-Search Optimization algorithm

FA:

Firefly algorithm

MO:

Multi-objective

MOPM:

Multi-objective power management

VSI:

Voltage source inverter

PWM:

Pulse width modulation

AC:

Alternating current

DC:

Direct current

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Authors and Affiliations

  1. School of Electrical Engineering, Vellore Institute of Technology, Katpadi, Vellore, 632014, Tamil Nadu, India

    N. Sridhar & M. Kowsalya

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  1. N. Sridhar
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  2. M. Kowsalya
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Correspondence to N. Sridhar.

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Sridhar, N., Kowsalya, M. Enhancement of power management in micro grid system using adaptive ALO technique. J Ambient Intell Human Comput 12, 2163–2182 (2021). https://doi.org/10.1007/s12652-020-02313-3

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  • DOI: https://doi.org/10.1007/s12652-020-02313-3

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