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Effect of Electric Vehicles and Renewable Sources on Frequency Regulation in Hybrid Power System Using QOAOA Optimized Type-2 Fuzzy Fractional Controller

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Abstract

In the modern era, the demand for an interconnected power system, which can integrate electric vehicles and renewable energy sources is increasing day by day. The primary goal of this demand is to make a sustainable and green power source, which can be fulfilled by utilizing Electric vehicles and renewable energy sources. However, there are some demerits of this technology, its lesser system inertia and hence it is not sufficient to respond to the required load capabilities. Further, the approach of electric vehicles with moving batteries can enable higher performance and resolve the issue. In this current analysis, the vehicles to grid idea (V2G) technique has been discussed, which can react as automatic generation control (AGC) in a three-area deregulated environment including hydro, thermal, and gas turbine units. In this type of power grid, all the sources such as Solar, wind, geothermal, and DEG power is also incorporated. To grip various ambiguities, the current study proposed a cascade combination of Interval type-2 fuzzy and Fractional Order Proportional-integral-derivative (FOPIDN) controllers. Further, the current work proposed a modified quasi-opposition Arithmetic Optimization Algorithm (QOAOA) to tune the scaling factor and membership function of an interval type-2 fuzzy FOPIDN controller. To discuss the significance of the anticipated controller, the estimated outputs have been compared with previously reported controllers and optimization techniques. The effect of constant and variations in DG, the penetration level of (PEVs) in various operating modes, subjected to step and random load disturbances have been focused on. Finally, to validate the outcomes of the proposed controller, a real-time (RT) hardware-in-the-loop (HIL) simulation has been adopted by using OPAL-RT.

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Abbreviations

N 1 i,N 2 i :

Governor dead-band constant of ith area

T g1 i,T GH i :

Thermal and Hydro governor time constant

T t1 i,T r1 i :

Thermal turbine and reheat time constant

K r1 i :

Reheat turbine gain parameter

c g i,b g i :

Valve position constants of gas power system

X G i,Y G i :

Speed governor time constant of gas power system

T CR i,T F i :

Fuel system parameters

T CD i :

Compressor discharge time constant

T R i,T RH i :

Hydro power transient droop compensation parameters

T w i :

Hydro turbine time constant

Δf i :

Change in frequency of ith area

ΔP tie :

Deviation in tie-line power

ΔP t i, ΔP g i, ΔP h i :

Deviation in power output of thermal, gas and hydro-power system

ICA:

Imperialist competitive algorithm

PSO:

Particle swarm optimization

GA:

Genetic algorithm

QOEO:

Quasi-opposition based equilibrium optimizer

HSMC:

Hybrid sliding mode control

MFO:

Moth flame optimization

WOA:

Whale optimization algorithm

BELBIC:

Brain emotional learning based intelligent

FIS:

Fuzzy inference system

GOA:

Grasshopper optimization algorithm

SCA:

Sign cosign algorithm

AOA:

Arithmetic optimization algorithm

QOHHO:

Quasi-opposition Harris Hawks optimization

EVS :

Electric vehicles

PEVS :

Plug-in electric vehicles

RFB:

Redox flow battery

HES:

Hybrid energy storage

SMES:

Superconducting-magnetic-energy-storage system

IES:

Inertia cumulative system

GRC:

Generation-rate constraint

RES:

Renewable energy sources

IEA:

International-energy-agency

BD:

Boiler dynamics

PHEVs:

Plug-in hybrid electric vehicles

GDB:

Governor-dead-band

INEC:

Inertia control

ISE:

Integral square error

DOF:

Degree of freedom

RTS:

Real-time simulator

I:

Integral

PI:

Proportion integral

PD:

Proportional derivative

PID:

Proportional integral derivative

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Authors and Affiliations

  1. Department of Electrical Engineering, NIT, Patna, Bihar, 800005, India

    Mrinal Ranjan & Ravi Shankar

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  1. Mrinal Ranjan
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Correspondence to Mrinal Ranjan.

Appendix

Appendix

Values of System Parameters

Thermal: N1i = 0.8, N2i = − 0.064, Tg1i = 0.08, Tg2i = 0.08, Tr1i = 10, Kr1i = 0.5, Tt1i = 0.3; BD: K3 = 0.92, K1 = 0.095, K2 = 0.85, Kib = 0.03, Trb = 69, Td = 1, Tf = 10; Hydro: TGHi = 0.2, TRHi = 28.75, TRi = 5, Twi = 1; Gas: Cgi = 1, bi = 0.049 XGi =0.6, YGi = 1.1, TCri = 0.01, TFi = 0.239, TCDi = 0.2; Power system parameter: Kps1 = 120, Rth = 2.4, Kps2 = Kps3 = 120, B1 = B2 = B3 = 0.545.

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Ranjan, M., Shankar, R. Effect of Electric Vehicles and Renewable Sources on Frequency Regulation in Hybrid Power System Using QOAOA Optimized Type-2 Fuzzy Fractional Controller. Int. J. Fuzzy Syst. 26, 825–848 (2024). https://doi.org/10.1007/s40815-023-01638-3

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