Optimal placement of UPFC for maximizing system loadability and minimizing active power losses in system stability margins by NSGA-II

Wartana, I Made Hedy; Agustini, Ni Putu

doi:10.1109/iceei.2011.6021665

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…In the area of energy, the NSGA-II algorithm has been widely used in the reactive power planning problem [15,16,17,18,19], the optimal power flow problem [20,21,22] and the GEP problem [6,23].…”

Section: Methodsmentioning

confidence: 99%

A multi-step multi-objective generation expansion planning model-A case study in Mexico

Palácios¹,

Valdés²,

Batres³

2018

SGCE

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Planning in the energy sector implies multiple and conflicting objectives. Multi-objective models allow the analysis of the inter-relationships and trade-off solutions to be obtained. This paper presents a mixed integer linear model for multi-step multi-objective generation expansion planning (MMGEP). The MMGEP problem is defined as the problem of determining the answers to the following questions: What types of generation technologies are to be added to the grid? What is the capacity of each new generation plant? Where will the plant be located? When will the plant be located? The MMGEP objectives are to minimize the global cost of the system, minimize the environmental impact and maximize the social profits. The proposed model is based on a real power system in Mexico for the planning period between 2017 and 2037. The problem was solved using the NSGA-II algorithm.

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Section: Methodsmentioning

confidence: 99%

A multi-step multi-objective generation expansion planning model-A case study in Mexico

Palácios¹,

Valdés²,

Batres³

2018

SGCE

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“…Overview of PSO In a PSO system [17], [18], each particle in search space is defined by the following elements [19]: x i k is the value of particle i at generation k. The update of particle i in the search space is defined by (11); p best is the best value found by the particle i until generation k; v i k+1 is the velocity of particle i at generation k. The update of velocity during the search procedure is presented by (12); g best is the best particle found in the group until generation k…”

Section: Methodology Developmentmentioning

confidence: 99%

“…From the previous works [8], [7], [9], [10], [11], it can be concluded that most the problem of optimal location of FACTS controllers using the evolutionary optimization algorithm is generally formulated as a mono-objective optimization problem. Unfortunately, the formulation of FACTS location problem as a mono-objective optimization is not quite practical.…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of a Shunt FACTS Controller in Java-Bali 24-Bus Indonesian System by Evolutionary Optimization Technique

Wartana

Agustini

2013

2013 1st International Conference on Artificial Intelligence, Modelling and Simulation

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One type of Evolutionary Optimization Technique namely Particle Swarm Optimization (PSO) have been proposed in this paper to obtain the optimal placement a Shunt Flexible AC Transmission System (FACTS) Controller i.e. Static VAr Compensator (SVC) in the network. An Optimal Power Flow (OPF) problem with mixed integer programming has been formulated for simultaneously optimizing multiobjectives optimization problem viz., enhancing the power system loadability, minimizing the active power loss of transmission line, and by considering installation cost of the controller whereas maintaining the system security and stability margins, e.g., small signal stability, fast voltage stability index, and line stability factor in their acceptable margins. The effectiveness of the proposed methodology has been investigated on a practical Java-Bali 24-bus Indonesia system. Results demonstrate that the static and dynamic performances of the power system can be effectively enhanced by the optimal allocation of the SVC in the network.

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“…In the literature, 11,14 to get optimal power flow, immune algorithm is used for optimal placement of UPFC. The authors in previous studies 11,[15][16][17][18][19][20][21][22][23][24][25][26][27] have used static parameters such as voltage stability index, load factor, and numerical methods to try locating UPFC. In Chang et al, 15 the placement of multi UPFCs is investigated using a numerical method based on power flow equations, and the results of the effect of each UPFC is presented.…”

Section: Introductionmentioning

confidence: 99%

Optimal placement method of multi UPFCs to damp power system oscillations

motiebirjandi

Fateh

2017

Int Trans Electr Energ Syst

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Summary This paper proposed the placement of multi unified power flow controllers (UPFCs) using the system critical modes and residue factor to increase damping of the electromechanical oscillations in power system. For this purpose, the critical modes are used to determine the number of UPFCs and their optimal locations are determined using residue factor. Also, the dynamic model of UPFC, which is consists of a voltage source and a current source is used for small signal stability analysis. To control the parameters of this model, supplementary controller is designed and applied. Detection of the input signal for this controller is based on the participation factor, which is a simple and effective method. Finally, the proposed method is investigated on IEEE 39‐bus system and the results are presented.

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Optimal placement of UPFC for maximizing system loadability and minimizing active power losses in system stability margins by NSGA-II

Cited by 13 publications

References 15 publications

A multi-step multi-objective generation expansion planning model-A case study in Mexico

A multi-step multi-objective generation expansion planning model-A case study in Mexico

Optimal Placement of a Shunt FACTS Controller in Java-Bali 24-Bus Indonesian System by Evolutionary Optimization Technique

Optimal placement method of multi UPFCs to damp power system oscillations

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