Placement Of Thyristor Controlled Series Compensator Research Articles

Multiobjective optimal TCSC placement using multiobjective grey wolf optimizer for power losses reduction

This study investigates the application of the multiobjective grey wolf optimizer (MOGWO) for optimal placement of thyristor-controlled series compensator (TCSC) to minimize power loss in power systems. Two conflicting objectives are considered: (1) minimizing real and reactive power loss, and (2) minimizing real power loss and TCSC capital cost. The Pareto-optimal method is employed to generate the Pareto front for these objectives. The fuzzy set technique is used to identify the optimal trade-off solution, while the technique for order preference by similarity to the ideal solution suggests multiple optimal solutions catering to diverse utility preferences. Simulations on an IEEE 30 bus test system demonstrate the effectiveness of TCSC placement for power loss minimization using MOGWO. The superiority of MOGWO is confirmed by comparing its results with those obtained from a multiobjective particle swarm optimization algorithm. These findings can assist power system utilities in identifying optimal TCSC locations to maximize their performance.

Risk Mitigation & Profit Improvement of a Wind-Fuel Cell Hybrid System With TCSC Placement

The incorporation of renewable energy into the existing electrical system is vital in a competitive electrical system. The unpredictable nature of renewable sources is the main obstacle to energy source integration. Since wind energy is unpredictable, integrating it into an existing thermal system requires some additional operating procedures to maintain the economic and functioning sustainability of the system. In a competitive power network, renewable energy uncertainty creates an imbalance cost (IC) which directly affects the system economy. This study investigates system generation costs, voltage profiles, and electric losses in a deregulated power market incorporating wind farms (WF) & fuel cells (FC). The fuel cell has been used here as a reserve generating unit to mitigate the deficit of power in the renewable incorporated system. To check the efficacy of the presented method, two locations in India are chosen at random. To assess the imbalance cost caused by the discrepancy between forecasted (FWS) and actual wind speeds (AWS), several charge rates (i.e. surplus and deficit) were established. The electrical system has been restructured, so consumers are continually looking for efficient and stable economic power which is only possible by reducing the system risk. This paper outlines a strategy for the optimal operation of a Thyristor-Controlled Series Compensator (TCSC) and fuel cell in a wind-integrated system to maximize system profit and minimize the system risk. In this work, different algorithms like Sequential Quadratic Programming (SQP), Artificial Bee Colony Algorithms (ABC), and Moth Flame Optimization Algorithms (MFO) are used to analyze the economic and functional risk of the system. Additionally, it explains how the fuel cell system is employed to offset the wind farm integration’s deviation in the real-time power market. Value-at-Risk (VaR) and conditional Value-at-Risk (cVaR) have been used for risk analysis. A modified IEEE 14-bus test system is considered to validate the entire study whereas any small, large as well as hybrid systems can be considered to perform this methodology.

Improved harmony search optimization-based TCSC placement for loss minimization

Placement of thyristor-controlled series compensator (TCSC) devices at appropriate lines reduces the net transmission loss (NTL) through injecting suitable series voltage in the transmission lines. The classical approaches for placing TCSCs in the power network may not provide optimal solution and face intricacies in solving the problem with multifarious constraints and vehemently place all the allotted TCSCs in the network. This paper presents a method employing improved harmony search optimization (MHSO), an evolutionary algorithm, for solving TCSC problem (TCSCP) and places the vital number of SVCs from the allotted ones. This paper presents the solution of TCSCP problem of 14, 30 and 57 bus systems and compares the performances in various aspects with existing TCSCP methods.

Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm

Electricity demand has been growing due to the increase in the world population and higher energy usage per capita as compared to the past. As a result, various methods have been proposed to increase the efficiency of power systems in terms of mitigating congestion and minimizing power losses. Power grids operating limitations result in congestion that specifies the final capacity of the system, which decreases the conventional power capabilities between coverage areas. Flexible AC Transmission Systems (FACTS) can help to decrease flows in heavily loaded lines and lead to lines loadability improvements and cost reduction. In this paper, total power loss reduction and line congestion improvement are assessed by determining the optimal locations and compensation rates of Thyristor-Controlled Series Compensator (TCSC) devices using the Multi-Objective Genetic Algorithm (MOGA). The results of applying the proposed method on the IEEE 30-bus test system confirmed the efficiency of the proposed procedure. In addition, to check the performance, applicability, and effectiveness of the proposed method, different heuristic algorithms, such as the multi-objective Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm, and Mixed-Integer Non-Linear Program (MINLP) technique, are used for comparison. The obtained results show the accuracy and fast convergence of the proposed method over the other heuristic techniques.

Optimal Placement and Sizing of TCSC for Improving the Voltage and Economic Indices of System with Stochastic Load Model

In this paper, an efficient method is proposed for optimal allocation and sizing of Thyristor Controlled Series Compensator (TCSC) to improve the technical and economic indices of a power network with deterministic and stochastic load models. First, the compensator allocation is done in the transmission system with the deterministic load model. After calculating the technical and economic indices of the network in the presence of a deterministic load model, the proposed method is applied to the system with a stochastic load model. The two-point estimation method is used for simulating the stochastic conditions. The indices of voltage deviation and economics of the system are optimized for selecting the optimal location and size of TCSCs. The economic index comprises loss cost, cost of the produced active power of generators and also the costs of installation, operation and maintenance of TCSCs. The multi-objective particle swarm optimization (MOPSO) is utilized to optimize the objective functions. After the multi-objective optimization, the fuzzy decision method is employed to extract one of the Pareto-optimal solutions as the best compromise one. For evaluating the proposed method, comprehensive simulations have been performed on the IEEE 39-bus network by using MATLAB/Matpower software. The simulation results clearly prove the remarkable performance of the proposed method in improving the technical and economic indices of the system.

Transmission Congestion Management and Techno Economical Analysis using Placement of TCSC

Congestion management is a major problem in deregulated power system. Congestion leads to increase in transmission line loading, increases losses and reducing transmission efficiency. It also leads to increase in congestion cost and thus affects technical as well as economical parameters. Research work is carried out under single line critical contingency condition. Critical contingency is found out by overall performance index (OPI). Optimal Power Flow (OPF) is carried out and objective is to maximize social welfare. Thyristor controlled series compensator is used to improve power flow, thus it reduces congestion and improves techno-economical parameters. Optimal location of Thyristor controlled series compensator (TCSC) is found out by LMP difference method.

Bi-Objective Optimization Model for Optimal Placement of Thyristor-Controlled Series Compensator Devices

Exposure to extreme weather conditions increases power systems’ vulnerability in front of high impact, low probability contingency occurrence. In the post-restructuring years, due to the increasing demand for energy, competition between electricity market players and increasing penetration of renewable resources, the provision of effective resiliency-based approaches has received more attention. In this paper, as the major contribution to current literature, a novel approach is proposed for resiliency improvement in a way that enables power system planners to manage several resilience metrics efficiently in a bi-objective optimization planning model simultaneously. For demonstration purposes, the proposed method is applied for optimal placement of the thyristor controlled series compensator (TCSC). Improvement of all considered resilience metrics regardless of their amount in a multi-criteria decision-making framework is novel in comparison to the other previous TCSC placement approaches. Without loss of generality, the developed resiliency improvement approach is applicable in any power system planning and operation problem. The simulation results on IEEE 30-bus and 118-bus test systems confirm the practicality and effectiveness of the developed approach. Simulation results show that by considering resilience metrics, the performance index, importance of curtailed consumers, congestion management cost, number of curtailed consumers, and amount of load loss are improved by 0.63%, 43.52%, 65.19%, 85.93%, and 85.94%, respectively.

Gravitational search assisted algorithm for TCSC placement for congestion control in deregulated power system

The congestion is a vital concern for the independent system operator in the deregulated energy market because it entails additional cost and poses a threat to power system security. To relieve congestion, this paper presents optimal power flow (OPF) and available transfer capability (ATC) based methods for allocating thyristor controlled series compensator (TCSC) using the congestion rent contribution approach based on location marginal price. The dc power transfer distribution factors are utilized to compute ATC in bilateral transactions environment. The gravitational search assisted algorithm is employed to manage congestion in a pool electricity market. Its performance is compared with OPF based on other heuristics. The congestion management is investigated for normal and contingency due to line outage and abrupt load variation. The efficacy of both the methods is investigated on IEEE 30-bus and IEEE 57-bus test systems.

A two step approach for improvement of economic profit and emission with congestion management in hybrid competitive power market

Integration of renewable energy with conventional energy sources is very challenging due to the uncertain nature of renewable energy, which creates imbalance and congestion in the system. Moreover, every market participant/player in deregulated power market wants to achieve the economic benefits within critical system constraints like congestion. Therefore, this paper presents a simple, efficient and reliable two-step optimization strategy to solve the congestion problem along with maximization of system profit, minimization of system generation cost, congestion cost and emission by optimal placement & numbers of Thyristor Controlled Series Compensator (TCSC) along with wind generators in a deregulated power market. Two new indices i.e. ‘Congestion Detection Index (CDI)’ and ‘Index of Total Congestion Cost (ITCC)’ have been proposed towards faster identification and reduction of system congestion effectively. The pool & bilateral deregulated market model has been considered for this study. The maximum system profit has been considerably achieved after optimal placement of wind generator and TCSC(s) in the system. The effectiveness of the proposed approach has been analyzed and tested in modified IEEE 14 bus and modified IEEE 118 bus test system. The proposed approach has been compared with the existing related approach for congestion mitigation, and better results have been achieved from the proposed approach.

Optimal placement of TCSC for congestion management in deregulated power system using firefly algorithm

In today's competitive electricity market, managing transmission congestion is a major challenging issue due to operational constraints. Flexible AC transmission system (FACTS) device can be a choice to control the power flow in the congested transmission lines. This paper explores the use of thyristor controlled series compensator (TCSC) for power flow control in congested network using firefly algorithm (FA). The optimal location of TCSC is identified based on real power performance index and reduction of total system reactive power loss methods. Further in the congestion management (CM) problem, single line outage analysis is also performed. FA is used to determine the minimum total cost which includes production cost and TCSC cost. Results of five bus, IEEE 14 and IEEE 30 bus test systems indicate that FA provides minimum cost compared to the previous literature. The efficiency of the proposed FA for obtaining the high quality solution is also established.

Optimal placement of TCSC and SVC for reactive power planning using Whale optimization algorithm

Abstract In the present work, Whale optimization algorithm (WOA), Differential evolution (DE), Grey wolf optimization (GWO), Quasi-opposition based Differential Evolution (QODE) and Quasi-opposition based Grey wolf optimization (QOGWO) algorithm has been applied for the solution of reactive power planning with FACTS devices i.e., Thyristor controlled series compensator (TCSC) and Static Var compensator (SVC). WOA is a recently developed nature-inspired meta-heuristic algorithm based on hunting behaviour of Humpback Whales; DE is a stochastic real-parameter optimization technique comprising of genetic parameters namely - mutation & cross-over; and GWO is a nature-inspired meta-heuristic algorithm based on hunting behaviour of Grey wolf. Standard IEEE 30 and IEEE 57 bus test system has been adopted for the testing purposes. Location of TCSC has been determined by the power flow analysis method and location of SVC has been determined by the voltage collapse proximity indication (VCPI) method. Further, WOA, GWO, DE, QODE and QOGWO algorithms have been applied to find the optimal setting of all control variables including TCSC, the series type and SVC, the shunt kind of FACTS device in the test system which minimizes active power loss and system operating cost while maintaining voltage profile within permissible limit. The superiority of the proposed WOA technique has been illustrated by comparing the results obtained with all other techniques discussed in the present problem. ANOVA test has also been conducted to show the statistical analysis between different techniques. The proposed approach shows lesser number of iterations which does not gets trapped in the local minima and offers promising convergence characteristics.

Optimal placement of thyristor controlled series compensators for mitigation of congestion under critical contingency condition

In unbundled electricity market managing transmission congestion is a real challenge for independent system operator. This paper presents placement of thyristor controlled series compensators (TCSC) at optimal location for maintaining voltage stability and mitigating congestion under single line contingency condition. Optimal location is found out by Line voltage stability index calculation. Congested lines are found out under contingency condition (single line outage). Critical contingencies are found out by finding performance index. Effect of placement of TCSC under critical contingency can be observed. TCSC is a member of Flexible AC Transmission Systems devices can reduce power loss in overloaded transmission lines, have ability to increase loadability, reduce system loss and maintains stability of power systems. Work is carried out on IEEE 14 bus test system using MATLAB.

Placement of Dg, Cb, and Tcsc in radial distribution system for power loss minimization using back-tracking search algorithm

The back-tracking search algorithm (BSA) is a new heuristic algorithm. BSA has two especially important properties: it is not sensitive to the initial value and has a single control parameter. This study presents the BSA-based optimal sizing and placement of distributed generations (DGs), capacitor banks (CBs), and thyristor-controlled series compensator (TCSC) in a radial distribution system (RDS). These elements are integrated separately and simultaneously in RDS. The objective function is power loss. The BSA is executed on IEEE 33 bus RDS. The obtained results are compared to a genetic algorithm (GA) and other algorithms in the literature. The results demonstrate that the BSA is more efficient and has the potential to find optimal solutions with less power loss. In this paper, optimal placement and sizing of DGs, TCSC, and CBs in a RDS is solved simultaneously using BSA for the first time.

Hybrid technique for optimal location and cost sizing of thyristor controlled series compensator to upgrade voltage stability

In this study, a hybrid technique is employed for ameliorating the voltage stability of test system with thyristor controlled series compensator (TCSC) installation. Improved gravitational search algorithm is used to first set the optimal location of TCSC to generate optimal power flow solution under normal and contingency condition while firefly algorithm is utilised to improve voltage stability and TCSC cost sizing, respectively. The proposed technique is implemented in MATLAB working platform and the power flow security and voltage stability is evaluated with IEEE 6, 14 and 30 bus transmission systems. The optimal TCSC placement is governed by new proposed index of apparent power flow index. The performance of the proposed method is deduced to be superior when results are compared with traditional gravitational search algorithm (GSA) and other hybrid methods of fuzzy-GSA and radial basis function neural network techniques.

Improvement of economic profit by optimal allocation of TCSC & UPFC with wind power generators in double auction competitive power market

This paper presents a simple and reliable optimization approach to optimally allocate the Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controller (UPFC) with wind generator under deregulated power system. The proposed approach is based on step by step variation in control parameters of TCSC and UPFC devices. Results have been determined for all possible locations, compensation level and reactance of TCSC & UPFC, reactive power injection or absorption, maximization of social welfare, maximization of profit with minimization of objective function. The double auction bidding model has been incorporated in this paper. The impacts on the locational marginal pricing and system voltage have been also investigated in this work. The effectiveness of the proposed approach for optimal placement of TCSC and UPFC has been tested and analyzed on modified IEEE 14-bus and modified IEEE 118-bus systems.

Impact of TCSC Installation on ATC in a System Incorporating Wind and Hydro Generations

This paper examines the impact of installing Thyristor Controlled Series Compensator (TCSC) on Available Transfer Capability (ATC) in a system with wind and hydro generations. Wind resource is modelled as an active power generation, wind power being decided by average wind velocity values over 24 hour duration. Hydro power output is represented as a polynomial in two variables, water discharge and volume of water in the reservoir. The variation in ATC is first observed with varying wind and hydro generations over 24hours and then a steady state model for TCSC is used to bring out its effect on ATC. The ATC values with TCSC are obtained by varying the location of the device through different lines. The site which gives the maximum values for ATC is chosen as the optimal placement of TCSC. Modified IEEE 24 bus reliability test system (RTS) is used for the study.

Optimal placement of TCSC using controllability Gramian to damp power system oscillations

Summary The increase of the scale and complexity of interconnected power systems leads to power system oscillations. Therefore, the electric network needs to be improved to be more stable. One type of flexible AC transmission system devices, namely, the thyristor-controlled series compensator (TCSC), can be placed in the transmission line to increase stability margins and dampen power oscillations by means of adjusting the impedance of the compensated line. The efficiency and robustness of TCSC controller depend on its optimal location in network. Hence, finding the optimal placement for TCSC has become a key issue. In this paper, a novel method based on a two-step approach is proposed to find the optimal location for TCSC. First, the energetic approach based on the combination of the controllability Gramian with the balanced realization reduction technique is applied to search feasible locations, and then the transient stability analysis is performed to compare and determine the optimal location through various test cases. The effectiveness of the proposed method has been demonstrated on the IEEE 39-bus England network. Copyright © 2015 John Wiley & Sons, Ltd.

An Efficient Approach for Optimal Allocation and Parameters Determination of TCSC With Investment Cost Recovery Under Competitive Power Market

This paper proposes an investment cost recovery based efficient and reliable optimization approach to optimal allocation of thyristor controlled series compensator (TCSC) in double auction power market. The optimization technique has been used with an objective to maximizing the social welfare and minimizing the device investment cost by suitable location and rating of single TCSC in the system. The effectiveness of proposed approach for location of TCSC has been compared with some existing methods of single TCSC placement, in terms of its impact on social welfare, TCSC investment recovery and optimal generation as well as load patterns. The results have been obtained on 5-bus system, modified IEEE 14-bus system and 246-bus Indian practical Northern Regional Power Grid (NRPG) system.

Congestion Management Using Optimal Placement of TCSC in Deregulated Power System

This paper presents a congestion management methodology in deregulated power systems by making use of optimal placement of thyristor controlled series compensators (TCSCs) in transmission network. The problem formulation consists of maximization of social welfare function subject to real and reactive power balance, constant power factor of consumers, transmission congestion (line loading), generators' capability curve constraints, and bounds on variables. The social welfare function, which customarily comprises of demand benefits minus generation costs, has been modified to include the effect of usage cost of TCSCs. The proposed congestion management methodology takes into account number of potential scenarios of market conditions. The location of TCSCs in power system is decided by making use of integer variables; hence formulation of proposed problem takes the form of Mixed Integer Nonlinear Programming (MINLP) problem. The effectiveness of the proposed methodology is tested on a 5-bus and IEEE 14-bus systems and results obtained are compared with those obtained by existing approaches. It can be concluded that congestion management results obtained with proposed methodology are better than those obtained by existing approaches.

Optimal placement and parameter setting of SVC and TCSC using PSO to mitigate small signal stability problem

This paper aims to select the optimal location and setting parameters of SVC (Static Var Compensator) and TCSC (Thyristor Controlled Series Compensator) controllers using PSO (Particle Swarm Optimization) to mitigate small signal oscillations in a multimachine power system. Though Power System Stabilizers (PSSs) associated with generators are mandatory requirements for damping of oscillations in the power system, its performance still gets affected by changes in network configurations, load variations, etc. Hence installations of FACTS devices have been suggested in this paper to achieve appreciable damping of system oscillations. However the performance of FACTS devices highly depends upon its parameters and suitable location in the power network. In this paper the PSO based technique is used to investigate this problem in order to improve the small signal stability. An attempt has also been made to compare the performance of the TCSC controller with SVC in mitigating the small signal stability problem. To show the validity of the proposed techniques, simulations are carried out in a multimachine system for two common contingencies, e.g., load increase and transmission line outage. The results of small signal stability analysis have been represented employing eigenvalue as well as time domain response. It has been observed that the TCSC controller is more effective than SVC even during higher loading in mitigating the small signal stability problem.