Application of FACTS devices for congestion management in restructured power systems

2006 ◽  
Author(s):  
H. Barati ◽  
M. Ehsan ◽  
M. Fotuhi-Firuzabad
Keyword(s):  
Power Systems ◽  
Congestion Management ◽  
Facts Devices

scholarly journals Optimal TCSC placement for congestion management in deregulated power systems using antlion optimization algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 77
Author(s):  
Majid Moazzami ◽  
Hossein Shahinzadeh ◽  
Gevork B. Gharehpetian ◽  
Abolfazl Shafiei
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Congestion Management ◽  
Test System ◽  
Production Costs ◽  
High Price ◽  
Facts Devices ◽  
Antlion Optimization ◽  
Deregulated Power Systems

Congestion management is one of the important issues in the deregulated power systems. There are several methods to eliminate congestion. Utilizing FACTS devices is an appropriate option for large-scale and quick control of flows of transmission lines. FACTS devices such as Thyristor Controlled Series Capacitor (TCSC) can help to mitigate the transmitting flow of power in the congested lines, which leads to an increase in the network loading ability as well as reduction of both losses and production costs. Due to the considerably high price of FACTS devices, it is important to determine their optimum location on the network. Accordingly, in this paper, the Antlion optimization algorithm (ALO) has been employed to conduct a congestion management analysis to determine the optimal location for the installation of TCSC, which is simulated on an IEEE 14-bus test system subject to satisfy the constraints of the market environment.

scholarly journals Clonal evolutionary particle swarm optimization for congestion management and compensation scheme in power system

2019 ◽  
Vol 16 (2) ◽  
pp. 591
Author(s):  
N. Z. Mohd Ali ◽  
I. Musirin ◽  
H. Mohamad
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Stability Index ◽  
Congestion Management ◽  
Compensation Scheme ◽  
Voltage Profile ◽  
Swarm Optimization ◽  
Facts Devices

This paper presents computational intelligence-based technique for congestion management and compensation scheme in power systems. Firstly, a new model termed as Integrated Multilayer Artificial Neural Networks (IMLANNs) is developed to predict congested line and voltage stability index separately. Consequently, a new optimization technique termed as Clonal Evolutionary Particle Swarm Optimization (CEPSO) was developed. CEPSO is initially used to optimize the location and sizing of FACTS devices for compensation scheme. In this study, Static VAR Compensator (SVC) and Thyristor Control Static Compensator (TCSC) are the two chosen Flexible AC Transmission System (FACTS) devices used in this compensation scheme. Comparative studies have been conducted between the proposed CEPSO and traditional Particle Swarm Optimization (PSO). Results obtained by the developed IMLANNs demonstrated high accuracy with respect to the targeted output. Consequently, the proposed CEPSO implemented for single objective in single unit of SVC and TCSC has resulted superior results as compared to the traditional PSO in terms of achieving loss reduction and voltage profile improvement.

Congestion Management in Restructured Power Systems with Economic and Technical Considerations

2019 ◽  
Vol 1 (1) ◽  
pp. 41-46
Author(s):  
Manikandan R ◽  
Kavya P ◽  
Shalini R
Keyword(s):  
Power Systems ◽  
Developed Countries ◽  
Congestion Management ◽  
Power Demand ◽  
Independent System ◽  
Deregulated Power System ◽  
Independent System Operator ◽  
System Operator ◽  
Management Techniques ◽  
Technical Considerations

In this paper, restructuring of monopolistic power systems is inevitable in this day and age to cope up with the radical growth of power demand. In developed countries restructuring is already in place while developing countries are getting accustomed to it. Above and beyond the benefits to customers in terms of economy and quality, there are several challenges prevailing particularly in transmission while exercising deregulation. The foremost challenging task of Independent System Operator (ISO) is managing the transmission line congestion in a deregulated power system. In most of the congestion management techniques, only the economic aspects are considered. The minimum voltage derivation for electronic industries and acceptable voltage derivation for high power applications are considered with suitable weighting factors in the objective function.

Optimal Placement of FACTS Devices for Voltage Stability Improvement with Economic Consideration Using Firefly Algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 5-11
Author(s):  
S. Rajasekaran ◽  
S. Muralidharan
Keyword(s):  
Power Systems ◽  
Voltage Stability ◽  
Firefly Algorithm ◽  
Transmission System ◽  
Operating Conditions ◽  
Optimal Placement ◽  
Voltage Collapse ◽  
Facts Devices ◽  
The Cost ◽  
Bus System

Background: Increasing power demand forces the power systems to operate at their maximum operating conditions. This leads the power system into voltage instability and causes voltage collapse. To avoid this problem, FACTS devices have been used in power systems to increase system stability with much reduced economical ratings. To achieve this, the FACTS devices must be placed in exact location. This paper presents Firefly Algorithm (FA) based optimization method to locate these devices of exact rating and least cost in the transmission system. Methods: Thyristor Controlled Series Capacitor (TCSC) and Static Var Compensator (SVC) are the FACTS devices used in the proposed methodology to enhance the voltage stability of power systems. Considering two objectives of enhancing the voltage stability of the transmission system and minimizing the cost of the FACTS devices, the optimal ratings and cost were identified for the devices under consideration using Firefly algorithm as an optimization tool. Also, a model study had been done with four different cases such as normal case, line outage case, generator outage case and overloading case (140%) for IEEE 14,30,57 and 118 bus systems. Results: The optimal locations to install SVC and TCSC in IEEE 14, 30, 57 and 118 bus systems were evaluated with minimal L-indices and cost using the proposed Firefly algorithm. From the results, it could be inferred that the cost of installing TCSC in IEEE bus system is slightly higher than SVC.For showing the superiority of Firefly algorithm, the results were compared with the already published research finding where this problem was solved using Genetic algorithm and Particle Swarm Optimization. It was revealed that the proposed firefly algorithm gives better optimum solution in minimizing the L-index values for IEEE 30 Bus system. Conclusion: The optimal placement, rating and cost of installation of TCSC and SVC in standard IEEE bus systems which enhanced the voltage stability were evaluated in this work. The need of the FACTS devices was also tested during the abnormal cases such as line outage case, generator outage case and overloading case (140%) with the proposed Firefly algorithm. Outputs reveal that the recognized placement of SVC and TCSC reduces the probability of voltage collapse and cost of the devices in the transmission lines. The capability of Firefly algorithm was also ensured by comparing its results with the results of other algorithms.

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

Sign in / Sign up

Export Citation Format

Share Document