COMPARITIVE STUDY OF PARTICLE SWARM OPTIMISATION & GREY WOLF OPTIMIZATION IN ECONOMIC LOAD DISPATCH

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COMPARITIVE STUDY OF PARTICLE SWARM OPTIMISATION & GREY WOLF OPTIMIZATION IN ECONOMIC LOAD DISPATCH PROBLEM

1ME Scholar, Electrical Department, MBM Engineering College, Jodhpur, INDIA 2Professor, Electrical Department, MBM Engineering College, Jodhpur, INDIA 3,4Ph.D Scholar, Electrical Department, MBM Engineering College, Jodhpur, INDIA ***

Abstract - In present scenario, the scarcity of energy resources, the increase in electricity production costs and the concern for the environment requires optimal economic transmission. In fact, the distance between the power plant and the load is not equal and there is no similar fuel cost function. Therefore, the load must be distributed between the different power plants in a way that results in the lowest energy production costs, in order to provide cheaper electricity. The current economic shipping problem (ED) has averynon-linearobjectivefunctionandhasstrictsubstitution and inequality constraints. Use of Particle Swarm Optimization (PSO) and GWO to distribute active power between power plants that meet system requirements and minimize the cost of electricity generation. The accuracy and speed of convergence of these methods is analyzed. The use of PSO and GWO has solved the economic burden distribution problem in electric power bus systems. PSO and GWO are implemented in 14bus and 30bus systems.

Key Words: Economic Load Dispatch, PSO, Power Systems, Optimization Techniques, Grey wolf optimization GWO

1. INTRODUCTION

In the conventional methods, it is difficult to solve the optimaleconomicproblemifthe loadchanged.Itneedsto computetheeconomicloaddispatcheachtimewhichusesa longtimeineachofcomputationloops.

It is a computational process where the total required generation is distribution among the generation units in operation, by minimizing the selected cost criterion, and subjectsittoloadandoperationalconstraintsaswell.

Foraninterconnectedsystem,itisnecessarytominimizethe expenses.Theeconomicloaddispatchisusedtodefinethe production level of each plant, so that the total cost of generation and transmission is minimum for a prescribed scheduleofload.

2. SIMULATED RESULTS

Theobjectiveofeconomicloaddispatchistominimizethe overallcostofgeneration.Forsolvingthiswell-knownnontraditional PSO optimization is chosen, and 14 bus and

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G5 14 11.79

VOLTAGEATBUS 14BUSSYSTEM 14BUSSYSTEM b1 1.06 1.06 b2 1.043 1.041 b3 1.23 1.023 b4 1.01 1.012 b5 1.01 1.01 b6 1.07 1.071 b7 1.02 1.021 b8 1.01 1.01 b9 1.047 1.048 b10 1.064 1.065 b11 1.082 1.082 b12 1.06 1.061 b13 1.071 1.071 b14 1.0512 1.0516

GeneratorCost 700 700

1.2

COMPARISON BETWEEN PSO AND GWO FOR 30 BUS SYSTEMS

Table -2:ComparisonbetweenPSOANDGWOfor 30bussystem Algorithm PSO 30 BUS SYSTEM GWO 30 BUS SYSTEM

FuelCost(rs/hr.) 775.11 756.0183 Emission(kg/hr) 344.1790 343.0071

GeneratorPower G1 200 200 G2 21.046 20.029 G3 19.24 15.032 G4 13.62 10.42 G5 10.35 10.03 G6 12 12

VOLTAGEATBUS 30BUSSYSTEM 30BUSSYSTEM b1 1.06 1.06 b2 1.04 1.04 b3 1.025 1.024 b4 1.0167 1.0163 b5 1.01 1.1 b6 1.0147 1.0143 b7 1.005 1.004

b8 1.01 1.01 b9 1.0529 1.0523 b10 1.046 1.043 b11 1.082 1.082 b12 1.059 1.0599 b13 1.071 1.071 b14 1.045 1.044 b15 1.04 1.03 b16 1.0471 1.046 b17 1.041 1.040 b18 1.03 1.02 b19 1.027 1.02 b20 1.0316 1.0311 b21 1.034 1.033 b22 1.035 1.034 b23 1.0293 1.0294 b24 1.023 1.023 b25 1.02 1.01 b26 1.0024 1.0022 b27 1.026 1.026 b28 1.012 1.012 b29 1.0066 1.0065 b30 0.9952 0.9951 TransmissionLoss 12.90 12.83 GeneratorCost 824 810

1.3 IEEE 14 BUS System Graphs For PSO and GWO

Fig.1(a ) : PSOfuelcostandemission

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Fig.1(b) : GWOfuelcostandemission

Fig.3(a) : PSOVoltageateachbus

Fig.3(b) : GWOVoltageateachbus

Fig.2(a) : PSOGeneratorpower

Fig.4(a) : PSOTransmissionloss

Fig.2(b) : GWOGeneratorpower

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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

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1.4 IEEE 30 BUS SYSTEM GRAPHS FOR PSO AND GWO

Fig.4(b) : GWOTransmissionloss

Fig.6(a) : IEEE30-PSOFuelcostandEmission

Fig.5(a) : PSOGeneratorcost

Fig.6(b) : IEEE30-GWOFuelcostandEmission

Fig.5(b) : GWOGeneratorcost

Fig.7(a) : IEEE30-PSOGeneratorPower

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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

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Fig.7(b) :IEEE30-PSOGeneratorPower

Fig.9(a) : IEEE30-PSOTransmissionloss

Fig.9(b) : IEEE30-GWOTransmissionloss

Fig.8(a) : IEEE30-PSOVoltageatEachBus

Fig.8(b) : IEEE30-GWOVoltageatEachBus

Fig.10(a) : IEEE30-PSOGeneratorcost

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dispatch of Nigerian power system considering valve point loading effect through interior point method” Electrical&ElectronicEngineeringDepartment,Federal Polytechnic,Ede,NigeriaApril22

[7] ManishParihar,M.K.Bhaskar"ReviewofPowerSystem Blackout" International Journal of Research and InnovationinAppliedScience-IJRIASvol.3issue6June 2018, pp.08-13 URL: https://www.rsisinternational.org/journals/ijrias/Digit alLibrary/Vol.3&Issue6/08-13.pdf.

[8] Emmanuel Gbenga DADA and Shafi'i Muhammad ABDULHAMID“ApplicationofGreyWolfOptimization Algorithm:RecentTrends,Issues,andPossibleHorizons” GUJSci35(2):485-504(2022)11May2021

Fig.10(b) : IEEE30-PSOGeneratorcost

3. CONCLUSION

This work adopts the best meta-heuristic optimization techniques to solve the problem of balance between economyandemissions.TheresultsarebetterthanPSOs.As the complexity of the system increases, the integration progressestoimprovement.Therefore,thesolutionforhigh -endsystemscanbeobtainedinashortertimecomparedto conventionalmethods.

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