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A novel hybrid algorithm for rescheduling-based congestion management scheme in power system

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Abstract

Secure and continuous power flow in the transmission line is one of the critical issues that must be rectified. In fact, rescheduling-based congestion management is considered to be one of the promising solutions for this aspect. Still, the model faces issues on the basis of rescheduling costs. More research works have been addressed so far to solve the problems of congestion management. Optimization algorithms also play a vital role in solving this problem. Under this scenario, this paper introduces a new rescheduling-based congestion management model that incorporates a new algorithm, refractor update-based ROA (RU-ROA) that optimizes the generating power of added generators with the bus system. The proposed RU-ROA algorithm is the hybridization of two algorithms, namely rider optimization algorithm (ROA) and water wave optimization (WWO), that aims to manage the congestion with the reduced cost of rescheduling. Further, the proposed model compares its performance over other conventional models like particle swarm optimization, FireFly, grey wolf optimization, traditional ROA and traditional WWO-based rescheduling strategy with respect to cost analysis and convergence analysis, and proves the efficiency of proposed work over others.

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Abbreviations

GSF:

Generator sensitivity factor

GLCM:

Generation and load shedding cost minimization

TLBO:

Teaching–learning-based optimization

WWO:

Water wave optimization

ALO:

Ant lion optimizer

ABC:

Artificial bee colony

RU-ROA:

Refractor update-based ROA

PSODAC:

PSO with distributed acceleration constant

FF:

FireFly

MOPSO:

Multi-objective particle swarm optimization

GWO:

Grey wolf optimization

ROA:

Rider optimization algorithm

PSHU:

Pumped storage hydro unit

DRPs:

Demand response programs

LSM:

Load served maximization

BSF:

Bus sensitivity factor

RED:

Relative electrical distance

PSO:

Particle swarm optimization

SOs:

System operators

CM:

Congestion management

GENCOs:

Generating companies

P p :

Generation quantity in MW

ΩPp :

Previously generated power quantity

COtotal :

Total cost to modify the active power output ($/N)

P DE :

Total power demand

P LO :

Transmission loss

Lqp, L0p and L00 :

Loss coefficients

\( \left( {V_{i}^{{\rm min} } ,V_{i}^{{\rm max} } } \right) \) :

Voltage limits

\( \partial_{i}^{{\rm min} } ,\partial_{i}^{{\rm max} } \) :

Angle limits

Pp(n − 1):

Generated active power at (n − 1)

Pp(n):

Power generated at the current hour

\( M_{ij}^{{\rm max} } \) :

Limit of maximum power flow in MVA

ΔPp :

Change in active power

E constraints :

Penalty cost forced on violating the constraints

E profile :

Penalty cost forced on violating the voltage profile

\( V_{p}^{{\rm min} } \) :

Generator’s minimum voltage

\( V_{p}^{{\rm max} } \) :

Generator’s maximum voltage

RD:

Number of riders that are equal to \( \bar{U} \)

Z :

Coordinate count

BI:

Bypass rider

FL:

Follower

OT:

Overtakers

AK:

Attackers

\( [X1, XRD/4] \) :

Range of bypass riders position

\( [XRD/4+1, XRD/2] \) :

Range of followers position

\( [XRD/2+1, X3RD/4] \) :

Range of overtakers position

\( [X3RD/4+1, XRD] \) :

Range of attackers position

θ 1 :

Position angle of ith vehicles

φ :

Coordinate angle

GEi :

ith rider vehicle’s gear

aci :

Accelerator of ith the rider

\( X_{{\rm MX}}^{i} \) :

Maximum value in ith rider position

\( X_{{\rm MI}}^{i} \) :

Minimum value in ith rider position

T OFF :

Total allowed time to reach the target, or the total iteration to find the target location

\( O_{{\rm max} }^{i} \) :

Maximum speed of rider i’s

|GE|:

Number of gears

TAT :

Position of targets

δ :

Arbitrary number that ranges between 0 and 1

η :

Random number ranges among 1 to RD

ξ :

Number ranges among 1 and RD

c :

Coordinate selector

X TA :

Position of the leading rider

TA:

Leading rider’s index

\( T_{i,c}^{t} \) :

Rider i’s steering angle in cth coordinate

\( d_{i}^{t} \) :

Distance to be traveled by rider i

\( \upsilon_{i}^{t} \) :

Velocity of ith rider

T OFF :

Off-time

\( O_{{\rm max} }^{i} \) :

Maximum speed of ith rider’s vehicle

\( {\text{GE}}_{i}^{t} \) :

ith rider vehicle’s of gear

\( {\rm ac}_{i}^{t} \) :

ith rider vehicle’s of accelerator

\( {\rm BR}_{i}^{t} \) :

ith rider vehicle’s of brake

\( O_{i}^{\rm GE} \) :

Speed limit of gear of ith the rider’s vehicle

t :

Current time

\( {\rm PR}_{\rm ON}^{t} \) :

On-time probability

Xt(i, c):

Position of ith rider in cth coordinate

\( {\rm DR}_{t}^{I} \left( i \right) \) :

Direction indicator of ith rider at t

\( {\rm SU}_{t}^{\rm RD} \left( i \right) \) :

Relative success rate at t

rt(i):

Success rate of ith rider at t

XTA(TA, j):

Location of the leading rider in jth coordinate

l(i, j):

Distance vector (normalized)

μ :

Mean

XTA(TA, j):

Leading rider’s position

\( T_{i,j}^{t} \) :

ith rider’s steering angle in jth coordinate

\( d_{i}^{t} \) :

Distance that to be traveled by the rider i

X :

Solution space

FT(x):

Fitness

HT:

Height

λ :

Wavelength

\( {\text{LN}}\left( {\bar{d}} \right) \) :

Length of \( \bar{d} \)

FTmin :

Maximum fitness value among the current population

FTmax :

Minimum fitness value among the current population

α :

Coefficient of wavelength minimization

ε :

Minimal positive number to avoid division-by-zero

x*:

Best solution achieved so far

GA(μ,σ):

Gaussian random number with σ and μ

σ :

Standard deviation

μ :

Mean

RA:

Random

\( \bar{d} \) :

Solitary wave

β :

Breaking coefficient

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Authors and Affiliations

  1. Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, Haryana, India

    Jyoti Srivastava & Naresh Kumar Yadav

  2. KIET Group of Institutions, Muradnagar, Ghaziabad, U.P., India

    Jyoti Srivastava & Arvind Kumar Sharma

Authors
  1. Jyoti Srivastava
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  2. Naresh Kumar Yadav
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  3. Arvind Kumar Sharma
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Correspondence to Jyoti Srivastava.

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Srivastava, J., Yadav, N.K. & Sharma, A.K. A novel hybrid algorithm for rescheduling-based congestion management scheme in power system. Electr Eng 102, 1993–2010 (2020). https://doi.org/10.1007/s00202-020-00985-w

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