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An SOA-RBFNN approach for the system modelling of optimal energy management in grid-connected smart grid system

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Abstract

A hybrid method for energy management on grid-connected MG system is proposed under this manuscript. Grid-connected MG system takes photovoltaic (PV), wind turbine (WT), battery. The proposed system is an integration of seagull optimization algorithm (SOA) and the radial basic functional neural network (RBFNN), thus it is named SOA-RBFNN. Here, in the grid-connected microgrid configuration, the necessary load demand is always monitored with RBFNN methodology. SOA optimizes the perfect match of the MG taking into account the predictable load requirement. The fuel cost, together with the power variation per hour of the electric grid, the operation and maintenance cost of microgrid system linked with grid, is described. The proposed model runs on the MATLAB/Simulink workstation and efficiency is investigated using existing techniques as AGO-RNN and MBFA-ANN. Statistical analysis, elapsed time, modeling metrics, and determination of optimal sample size for adjustment and validation of proposed and existing technique are evaluated. The efficiency values on the 100, 200, 500, and 1000 trails are 99.7673%, 99.7609%, 99.9099%, and 99.9373%.

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Data availability

Data sharing does not apply to this article as no new data has been created or analyzed in this study.

Abbreviations

SOA:

Seagull optimization algorithm

RBFNN:

Radial basis functional neural network

MG:

Microgrid

GA:

Genetic algorithm

EDE:

Enhanced differential evolution

ACO:

Ant colony optimization

BF:

Bacteria foraging

GWO:

Grey wolf optimization

PSO:

Particle swarm optimization

GSA:

Gravitational search algorithm

HSA:

Harmony search algorithm

DSM:

Demand side management

ANFIS:

Adaptive neuro fuzzy inference system

PI:

Proportional-integral

DRPs:

Demand response programs

LPSP:

Loss of power supply probability

DA:

Dragonfly algorithm

MBFA-ANN:

Modified bacterial foraging algorithm-Artificial neural network

AGO-RNN:

Adaptive grasshopper optimization-recurrent neural network

ANFASO:

Adaptive neuro fuzzy and salp swarm optimization

\(S\) :

Input signal that has selected features

\(p^{actual}\) and \(p^{forecast}\) :

Real and forecasted wind speed values with solar radiation

M:

Count of days in deliberation

a :

RBFNN input vector

\(a\) :

Input

\(\beta\) :

Mean

\(\alpha\) :

Standard deviation

\(Error(x)\) :

Error function that engages active and reactive power management

β :

Mean distribution at Gaussian distribution

\(\mathop {C_{s} }\limits^{ \to }\) :

Search agent position cannot collides with another search agent

\(\mathop {P_{s} }\limits^{ \to }\) :

Current search agent position

\(x\) :

Current iteration

\(A\) :

Search agent movement behavior on following search space

\(\mathop {M_{s} }\limits^{ \to }\) :

Search agents’ positions \(\mathop {P_{s} }\limits^{ \to }\) towards best search agent \(\mathop {P_{bs} }\limits^{ \to }\)

\(f_{c}\) :

Frequency control

\(\mathop {D_{s} }\limits^{ \to }\) :

Distance amid the optimal fit search agent and search agent

\(r\) :

Radius of each spiral turn

\(k\) :

Random number of \(\left[ {0 \le k \le 2\pi } \right]\) range

\(u\) with \(\upsilon\) :

Steady to determine the spiral shape

\(e\) :

Natural logarithm base

\(\mathop {P_{s} }\limits^{ \to } \left( x \right)\) :

Optimal solution

\(\Gamma_{Sh}^{{}}\) :

Hourly electricity consumption against set of shiftable appliances

\(\Gamma_{Ns}^{{}}\) :

Hourly electricity consumption against set of non-shiftable appliances

\(LOT(a)\) :

Length of operational time for every appliance in home u

\(\eta_{r - pv}\) :

Efficiency of reference module

\(\eta_{PC}\) :

Power condition efficiency

\(N_{T}\) :

Temperature coefficient efficiency of photovoltaic panel

\(T_{C}\) :

Cell temperature (°C)

\(T_{ref}\) :

Cell temperature in reference conditions

\(T_{A}\) :

Ambient air temperature

\(NOCT\) :

Nominal cell operating temperature

\(WS\) :

Wind speed

\(WS_{CI}\) :

Wind cut-in speed

\(\,WS_{R}\) :

Rated wind speed

\(WS_{CO}\) :

Wind cut-off speed

\(P_{r\,}\) :

Power rate

\(C_{p}\) :

Power coefficient

\(\Gamma_{Sh}\) and \(\Gamma_{Ns}\) :

Shiftable and non-shiftable energy demand devices

\(\delta (T)\) :

Total cost per hour and day

\(\phi (T)\) :

Import electricity in time t

\(EP(T)\) :

Electricity price in time slot

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This research did not receive any specific Grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India

    Karthikumar Kuppusamy, Karuppiah Marimuthu, Udhayaraj Natarajan & Krishnakumar Sekar

  2. Department of Electrical and Electronics Engineering, College of Engineering, Guindy, Anna University, Chennai, Tamil Nadu, India

    Senthil Kumar Vairakannu

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  1. Karthikumar Kuppusamy
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  2. Senthil Kumar Vairakannu
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  3. Karuppiah Marimuthu
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  4. Udhayaraj Natarajan
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  5. Krishnakumar Sekar
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Correspondence to Karthikumar Kuppusamy.

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Kuppusamy, K., Vairakannu, S.K., Marimuthu, K. et al. An SOA-RBFNN approach for the system modelling of optimal energy management in grid-connected smart grid system. Artif Intell Rev 56, 4171–4196 (2023). https://doi.org/10.1007/s10462-022-10261-x

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