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Comparison of various electricity market pricing strategies to reduce generation cost of a microgrid system using hybrid WOA-SCA

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Abstract

This paper aims to minimize the generation cost of a low voltage (LV) grid-connected microgrid system using a novel hybrid whale optimization algorithm (WOA)- Sine cosine algorithm (SCA) optimization technique. Three different methods of electricity market prices are formulated in turns and the generation cost is evaluated for each method to sort out the best among them which yields the least generation cost of the same microgrid system. Both active and passive participation of grid are studied to acknowledge its importance. Numerical results show that the time of usage (TOU) method of electricity market pricing proved to be the most convenient and economic method for cost analysis of the system. The generation cost was at its maximum when the electricity price was a constant value. Also during the TOU method of electricity pricing, a 15% surge in the price was realized when the grid was passively participating in supplying power to the system. Figures and statistical data aids the fact that proposed hybrid WOASCA outperformed WOA in consistently providing a robust and superior quality result.

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Abbreviations

LV:

Low Voltage

WOA:

Whale Optimization Algorirthm

SCA:

Sine Cosine Algorithm

TOU:

Time Of Usage

WOASCA:

Whale Optimization Algorirthm- Sine Cosine Algorithm

MG:

Microgrid

DG:

Distributed Generation

DER:

Distributed Energy Resources

RES:

Renewable Energy Sources

AFSA:

Artificial Fish Swarm Algorithm

AIMD:

Additive Inverse Multiplicative Decrease

CuSA:

Cuckoo Search Algorithm

BSA:

Backtracking Search Algorithm

WT:

Wind Turbine

PV:

Photo Voltaic System

FC:

Fuel Cell

CSA:

Crow Search Algorithm

DE:

Differential Evolution

PSO:

Particle Swarm Optimization

GWO:

Grey Wolf Optimizer

NSGA:

Non-Dominated Sorted Genetic Algorithm

DNO:

Distribution Network Operator

BESS:

Battery Energy Storage System

EVB:

Electric Vehicle Batteries

DRM:

Demand Response Management

PC-DRM:

Power Company Learning Selection Demand Response Management

EED:

Economic Emission Dispatch

EMS:

Energy Management Strategy

KHA:

Krill Herd Algorithm

t:

Index representing Time

g:

Index representing generators

F:

Fuel cost coefficient

P:

Active power

I:

Binary index representing ON/OFF status

SU:

Start up

SD:

Shut down

Cgrid :

Cost of the grid power exchanged throughout the day

FP:

Fixed price

ME:

Microgrid expense

GE:

Grid expense

tax:

Taxable charge

PbuyPsell:

Power bought/sold to microgrid

Pgrid :

Active power related to the grid

Max Min:

Maximum and minimum values

ONT/OFFT:

Designated on and off time for the generators

Ton/off :

Numbers of successive on and off time of the generator

G:

Distance between whales

Y:

Whale

Yp :

Prey

A, A, C, l:

WOA parameters

b:

Constant for defining the shape of logarithmic spiral

iter, Max_iter:

Iteration, maximum iteration

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, IIT (ISM), Dhanbad, India

    Bishwajit Dey & Biplab Bhattacharyya

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  1. Bishwajit Dey
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  2. Biplab Bhattacharyya
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Correspondence to Bishwajit Dey.

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Appendix

Appendix

1.1 Realization of the proposed algorithm on the benchmark functions

Any metaheuristic algorithm is inherently stochastic in obtaining the optimal solution for the given problem, which means that the performance varies over different runs. To comment on the suitability and effectiveness of the proposed algorithm, it has been undergone for realization on the set of certain benchmark functions. Here, for the proposed WOASCA method, the authors have used a set of 23 benchmark functions that are mostly used by various researchers [32]. Table

Table 5 Unimodal benchmark function
Full size table

5,

Table 6 Multimodal Benchmark function
Full size table

6 and

Table 7 Fixed dimensional multimodal Benchmark function
Full size table

7 lists the formula, dimensions and variable limits for unimodal, multimodal and fixed dimensional multimodal benchmark functions respectively. All of these functions were evaluated using WOA, SCA and proposed hybrid WOASCA for 30 individual trials. The best values, worst values, their average and standard deviation after 30 runs are listed in Table

Table 8 Statistical Analysis of the values of benchmark functions
Full size table

8. Figure 

Fig. 10
figure 10figure 10figure 10figure 10figure 10

Benchmark functions from F1-F23 a 3D Function plot, c Convergence characteristics with the proposed algorithms c Box plot considering values in Table 5

Full size image

10 displays the 2D Function plot, convergence characteristics with the proposed algorithms and box plot after 30 runs of the 23 benchmark functions for all the 23 benchmark functions.

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Dey, B., Bhattacharyya, B. Comparison of various electricity market pricing strategies to reduce generation cost of a microgrid system using hybrid WOA-SCA. Evol. Intel. 15, 1587–1604 (2022). https://doi.org/10.1007/s12065-021-00569-y

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