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Modified Salp Swarm Algorithm-Optimized Fractional-Order Adaptive Fuzzy PID Controller for Frequency Regulation of Hybrid Power System with Electric Vehicle

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Abstract

A considerable no. of intermittent renewable sources such as PV generation and wind energy when integrated to the conventional grid technology causes serious issues in the power systems like frequency instability. So a more balancing controller is desired for a stable and reliable operation of the power system. Bidirectional power control of the EV aggregator is making itself a wise choice for distributed energy storage to scale down the frequency and power fluctuation. In this work, an intelligent load frequency controller using a fractional-order adaptive fuzzy PID controller with filter (FOAFPIDF) for hybrid power system with electric vehicle (EV) based on modified salp swarm algorithm (MSSA) technique is proposed. The effectiveness of MSSA technique is compared with original salp swarm algorithm as well as moth flame optimization , grey wolf optimization , particle swarm optimization and sine cosine algorithm techniques for benchmark test functions using statistical analysis. The effectiveness of the suggested load frequency control strategy by the use of electric vehicle as well as with other energy storing elements such as the superconducting magnetic energy storage component, flywheel energy storage system and ultra-capacitor along with their inherent rate constraint nonlinearity is validated by numerical simulations conducted on the studied test system. It is demonstrated that the proposed controller provides a better control action to suppress the frequency fluctuations as compared to PID controller. The robustness of the controller is also investigated against variation of system parameters and random load changes.

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Abbreviations

n :

No. of variables

F :

Food source

l :

Current iteration

L :

Maximum no. of iteration

K PV :

Gain of photovoltaic system

T PV :

Time constant of photovoltaic system

K WTG :

Gain of WTG

T WTG :

Time constant of WTG

K AE :

Gain of aqua electrolyser

T AE :

Time constant of aqua electrolyser

K FC :

Gain of FC

T FC :

Time constant of FC

K DEG :

Gain of DEG

T DEG :

Time constant of DEG

K FESS :

Gain of FESS

T FESS :

Time constant of FESS

K UC :

Gain of ultra-capacitor

T UC :

Time constant of ultra-capacitor

\(K_{SMES}\) :

Gain of BESS

\(K_{SMES}\) :

Time constant of BESS

\(K_{EV}\) :

Gain of EV

\(T_{EV}\) :

Time constant of EV

V W :

Wind speed

\(\varphi\) :

Solar radiation

D :

Damping constant

M :

Inertia constant

∆f :

Frequency deviation

u :

Control signal

P PV :

Photovoltaic power output

P WTG :

Wind turbine generator output

P L :

Load demand

P FESS :

FESS power output

P SMES :

SMES power output

P EV :

Electric vehicle power output

P UC :

Ultra-capacitor power output

PU:

Per unit

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Authors and Affiliations

  1. Department of Electrical Engineering, VSSUT, Burla, Odisha, 768018, India

    Debidasi Mohanty & Sidhartha Panda

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  1. Debidasi Mohanty
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  2. Sidhartha Panda
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Correspondence to Sidhartha Panda.

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Mohanty, D., Panda, S. Modified Salp Swarm Algorithm-Optimized Fractional-Order Adaptive Fuzzy PID Controller for Frequency Regulation of Hybrid Power System with Electric Vehicle. J Control Autom Electr Syst 32, 416–438 (2021). https://doi.org/10.1007/s40313-020-00683-9

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  • DOI: https://doi.org/10.1007/s40313-020-00683-9

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