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Allocation of multi-type FACTS devices using multi-objective genetic algorithm approach for power system reinforcement

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Abstract

This paper presents a novel approach to find optimum locations and capacity of flexible alternating current transmission systems (FACTS) devices in a power system using a multi-objective optimization function. Thyristor controlled series compensator (TCSC) and static var compensator (SVC) are the utilized FACTS devices. Our objectives are: active power loss reduction, new introduced FACTS devices cost reduction, voltage deviation reduction, and increase on the robustness of the security margin against voltage collapse. The operational and controlling constraints, as well as load constraints, are considered in the optimum allocation. A multi-objective genetic algorithm (MOGA) is used to approach the Pareto-optimal front (non-dominated) solutions. In addition, the estimated annual load profile has been utilized in a sequential quadratic programming (SQP) optimization sub-problem to the optimum siting and sizing of FACTS devices. IEEE 14-bus Network is selected to validate the performance and effectiveness of the proposed method.

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Abbreviations

A :

Power plant installation cost in ($/kW)

B :

Refundable investment rate in (percent)

B SVCj :

Susceptance of jth SVC in (pu)

\({C_{{\rm M var \_SVC}_i }}\) :

Cost of one Mvar related to ith SVC in ($/Mvar)

\({C_{{\rm M var \_TCSC}_j }}\) :

Cost of one Mvar related to jth TCSC in ($/Mvar)

dv i :

Maximum voltage violation tolerance (percent)

DM:

Decision Maker

f1, f2, f3:

Problem objective functions

J L :

A set contains all load buses

K e :

Active power cost in ($/kWh)

nfacts, nplant:

Life times of FACTS devices and power plants, respectively in (year)

\({P_{{\rm loss}_i } (x,u,z)}\) :

Active power loss of ith load level from system annual load curve in (kW)

Ppeak (x, u, z):

Peak point power generation in year of study in (kW)

\({P_{\rm injf}^{\rm TCSC}, Q_{\rm injf}^{\rm TCSC}}\) :

Injected active and reactive power at bus f in (pu)

\({P_{\rm injt}^{\rm TCSC}, Q_{\rm injt}^{\rm TCSC}}\) :

Injected active and reactive power at bus t in (pu)

P0, Q0:

Prescribed real and reactive loads at rated (normal) voltage in (pu)

p f , q f , p t , q t :

Constants that reflect the load-voltage characteristics at buses f and t

P m :

Mutation rate \({\in }\) [0, 1]

\({S_j^{\rm initial}, S_j^{\rm limit}}\) :

Demands related to load bus j at initial and limit (critical) states (MVA)

\({S_{{\rm SVC}_i }}\) :

Complex power of ith SVC in (MVA)

\({S_{{\rm TCSC}_j }}\) :

Complex power of jth TCSC in (MVA)

u :

Control variables vector

v i :

Voltage of bus i in (pu)

\({v_i^{\rm ideal}}\) :

Ideal voltage of bus i in (pu)

x :

State variables vector

X c :

Magnitude of X TCSC in (pu)

X TCSCi :

Reactance of ith TCSC in (pu)

z :

Vector containing amount and type of FACTS devices

χ :

Set of feasible solutions

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  1. School of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Islamic Republic of Iran

    M. Gitizadeh

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Gitizadeh, M. Allocation of multi-type FACTS devices using multi-objective genetic algorithm approach for power system reinforcement. Electr Eng 92, 227–237 (2010). https://doi.org/10.1007/s00202-010-0179-x

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  • DOI: https://doi.org/10.1007/s00202-010-0179-x

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