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An intelligent management of power flow in the smart grid system using hybrid NPO-ATLA approach

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Abstract

In this manuscript, an intelligent hybrid approach is proposed to manage the power flow (PF) in the smart grid (SG) system. The proposed approach is the combined execution of Nomadic People Optimizer (NPO) algorithm and artificial transgender longicorn algorithm (ATLA), hence it is named NPO-ATLA approach. The Renewable energy system consists of photovoltaic (PV), wind turbine (WT), battery and grid. The major aim of this work is to control the power flow in the hybrid renewable energy sources (HRES) depending on parameter variation of source and load side and satisfies the load demand of the system. The voltage source inverter (VSI) control signals are generated through the NPO approach based upon the variation of power transfer amid the source and load side. ATLA is utilized to recognize the control signals of the system against the variation of active with reactive power. The proposed approach is carried out in MATLAB, then the performance is compared with various existing approaches.

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Data availability statement

Data sharing does not apply to this article because no new data has been developed or investigated in this manuscript.

Abbreviations

\(P_{t}^{HRES}\) :

Whole power generated from HRES

\(P_{t}\;^{battery}\) :

Battery power at time \(t\)

\(v_{dc}\) :

Voltage at dc link

\(P_{t}^{PV}\) :

Solar cell output power

\(R^{f}\) :

Module de-rating factor

\(g_{stc}\) :

Solar radiation at standard test conditions [W/m2]

\(t^{c}\) :

Cell temperature

\(t^{a}\) :

Air temperature

\(k\), \(c\) :

Shape, scale index of Weibull distribution

\(v\) :

Wind speed (WS) in wind unit hub height

\(v_{in}\) :

Cut in WS

\(C_{mx}\) :

Nominal battery capacity

\(P_{G}^{Max}\) :

Maximal power generated using HES

\(f_{\omega }\) :

Angular frequency

\(v_{s}\) :

Grid voltage

\(F\) :

Input of filter

\(P_{Gi}\) :

As active power dispatched into transmission bus number \(i\,\,(MW)\)

\(V_{j}\) :

Bus voltage in bus \(j\)

\(Y_{ij}\) :

Admittance in between bus \(i\) and \(j\)

\(Q_{G\,i}\) :

Reactive power dispatch as transmission bus number \(i\,\,(MVar)\)

\(Q_{ij}\) :

Reactive power flow at feeder associating bus \(i\) along \(j\,\,(MVar)\)

\(r\) :

Random value amid 0 and 1

\(x_{0}\) and \(y_{0}\) :

Origin point of the coordinates

\(\theta\) :

At the point of angle value

\(A_{C}\) :

Average distance amid the clan area for normal families

\(k_{p}^{{}} k_{i}^{{}}\) :

Gain parameters

\(Y_{j}^{t}\) :

Position of male

\(p_{j}^{t}\) :

Male longicorn that moves step length

\(\delta\) :

Amount of step control equal to 1

\(L(\alpha )\) :

Unsystematic walk those step size \(v\) move after Lévy distribution

SG:

Smart grid

ATLA:

Artificial transgender longicorn algorithm

WT:

Wind turbine

MPC:

Model predictive control

GSA:

Gravitational search algorithm

AWO:

Adaptive whale optimization algorithm

\(P_{t}^{PV}\) :

PV power at time \(t\)

\(P_{t}^{WT}\) :

WT power at time \(t\)

\(c^{dc}\) :

Dc link capacitance

\(P^{ra}\) :

Solar cell rated power

\(g\) :

Solar radiation

\(b\) :

Temperature coefficient

\(t^{stc}\) :

Solar panel cell temperature at standard test condition

\(NOCT\) :

Normal operating cell temperature (NOCT) of a solar panel

\(P_{ra}\) :

Rated power of single WT

\(v_{N}\) :

Rated WS

\(v_{out}\) :

Cut out WS

\(DOD\) :

Depth of discharge in battery

\(P_{con}^{Max}\) :

Maximum power of the inverter

\(r_{s}\), \(l_{s}\) :

Lumped resistance and inductance

\(i_{a}\), \(i_{b}\), \(i_{c}\) and \(v_{a}\), \(v_{b}\), \(v_{c}\) :

Three-phase current and voltage

\(\tau\) :

Time constant

\(V_{i}\) :

Bus voltage in bus \(i\)

\(\delta\), \(\theta\) :

Load including admittance angle

\(P_{i}^{d}\) :

Overall active power demand in bus \(i\,\,(MW)\)

\(Q_{i}^{d}\) :

Overall reactive power demand in bus \(i\,\,(MVar)\)

\(u_{b}\), \(l_{b}\) :

Upper and lower bound

\(e(y)\) :

Error function of the system

\(r_{1}\) and \(r_{2}\) :

Perimeter of the circle and random coordinate

\(\overrightarrow {{X_{i}^{New} }} and\,\overrightarrow {{X_{i}^{old} }}\) :

Current families of new and old position

\(\overrightarrow {{\sigma_{C} }}\) and \(\overrightarrow {{X_{C}^{i} }}\) :

Normal families and the position of leader

\(t\) :

Current iteration number

\(Y_{best}^{t}\) :

Position of female

\(\oplus\) :

Point-point multiplicand operator

\(m\) :

Longicorn population size

PF:

Power flow

NPO:

Nomadic people optimizer

PV:

Photovoltaic

VSI:

Voltage source inverter

GA:

Genetic algorithm

ANN:

Artificial neural network

ANFIS:

Adaptive Neuro-Fuzzy Interference System

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  1. Department of Electrical and Electronics Engineering, The National Institute of Engineering, Mysuru, Karnataka, India

    Anil Kumar Dsouza, Ananthapadmanabha Thammaiah & Likith Kumar M. Venkatesh

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  1. Anil Kumar Dsouza
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  2. Ananthapadmanabha Thammaiah
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  3. Likith Kumar M. Venkatesh
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Correspondence to Anil Kumar Dsouza.

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Dsouza, A., Thammaiah, A. & Venkatesh, L.K.M. An intelligent management of power flow in the smart grid system using hybrid NPO-ATLA approach. Artif Intell Rev 55, 6461–6503 (2022). https://doi.org/10.1007/s10462-022-10158-9

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