Optimal Allocation Of FACTS Devices Research Articles

Optimal Allocation of FACTS Devices Using Kinetic Gas Molecular Optimization and Cuckoo Search Algorithm

Optimal Allocation of FACTS Devices Using Kinetic Gas Molecular Optimization and Cuckoo Search Algorithm

Optimal Allocation of FACTS Devices in Restructured Power Systems Integrated Wind Generation

This paper proposes an approach to optimally allocate flexible AC transmission system (FACTS) devices in market-based restructured power systems based on Expected Benefit (EB) and installation cost of FACTS devices (FDs). The aims of the approach are minimizing device investment cost, and the locational marginal pricing (LMP) differences between buses to maximize revenue. This revenue is defined as the difference between EB with and without FACTS installation. The expected cost includes operating cost not only under normal condition but also under contingencies along with their associated probabilities to occur. The proposed method accurately evaluates the annual cost and benefits obtainable by FACTS devices installation by formulating a large-scale optimization problem that contains power flow analyses for a large number of system states representing annual power system operations. The objectives for normal state are maximizing social welfare by minimizing LMPs differences between buses and minimizing of generations re-scheduling, while the objectives in case of contingency are maximizing social welfare as well as minimizing compensations paid for generations re-scheduling and load shedding cost. The overall problem is solved using both Particle Swarm optimization (PSO) for attaining optimal FACTS devices setting and allocation as main problem and optimal power flow to calculate the operating costs and benefits as sub optimization problem. The effectiveness of the proposed approach is demonstrated on modified IEEE 14-bus test system and IEEE 118-bus test system.

Optimal placement and sizing of multi-type FACTS devices in power systems using metaheuristic optimisation techniques: An updated review

Abstract The growth of demand, the need for economic efficiency and optimal utilisation of electric power networks and the high cost of construction of new power networks result in inevitable challenges, such as overloading and excessive power transfer along transmission lines, high losses, voltage instability, low power quality, reliability problems and voltage profile problems. To manage the power transmission system, the recently developed Flexible AC Transmission System (FACTS) can be used for electric transmission networks since it plays an important role in enhancing the static and dynamic performance of power systems. However, location, type and capacity of FACTS devices should be optimised to maximise the resulting benefits. In this paper, different types of FACTS devices are discussed along with their modelling and functions. In addition, the proposed and the compared techniques and approaches in the existing research works, such as analytic approaches, arithmetic programming methods, meta-heuristic optimisation techniques and hybrid methods, are discussed. Analytic approaches have insufficient computation accuracy in determining optimal allocation of FACTS devices and arithmetic programming approaches are often inefficient in managing constrained optimisation problems. However, meta-heuristic approaches are stochastic, population-based optimisation algorithms that are highly efficient in dealing with a multimodal, highly constrained, multi-objective and discrete system. Meta-heuristic techniques are the most commonly used methodologies to determine the optimal allocation of FACTS devices. Furthermore, the utilisation of analytic methods or classical optimisation approaches with meta-heuristic optimisation techniques plays an important role in reducing the search space of the proposed meta-heuristic optimisation technique. In the present paper, an overall review of 50 recent research work studies, including proposed and compared approaches and techniques, objective functions, approaches, the utilised FACTS devices, constraints, contingency conditions and all the analysed and simulated parameters, is provided and discussed in details. In addition, a more weighted discussion of the proposed methods based on meta-heuristic optimisation techniques is provided.

Loadability enhancement with FACTS devices using gravitational search algorithm

Abstract In the present work, GSA (gravitational search algorithm) based optimization algorithm is applied for the optimal allocation of FACTS devices in transmission system. IEEE 30 & IEEE 57 test bus systems are taken as standards. Both active and reactive loading of the power system is considered and the effect of FACTS devices on the power transfer capacity of the individual generator is investigated. The proposed approach of planning of reactive power sources with the FACTS devices is compared with other globally accepted techniques like GA (Genetic Algorithm), Differential Evolution (DE), and PSO (Particle Swarm Optimization). From the results obtained, it is observed that incorporating FACTS devices, loadability of the power system increases considerably and each generator present in the system is being able to dispatch significant amount of active power under different increasing loading conditions where the steam flow rate is maintained corresponding to the base active loading condition. The active power loss & operating cost also reduces by significant margin with FACTS devices at each loading condition and GSA based planning approach of reactive power sources with FACTS devices found to be the best among all the methods discussed in terms of reducing active power loss and total operating cost of the system under all active and reactive loading situations.

A general approach for optimal allocation of FACTS devices using equivalent impedance models of VSCs

Summary Flexible AC transmission system (FACTS) devices such as static synchronous series compensators, static synchronous compensators, and unified power flow controllers which use voltage source convertors (VSCs) can noticeably improve different characteristics of power systems. Therefore, finding their optimal allocation is a vital issue. To find the optimized placement of different FACTS devices, it is necessary to calculate the effects of such devices in the whole system. Sensitivity analysis has been recently proposed as a fast and reliable method to find the effects of voltage source convertors on system variables. In this paper, a novel approach is introduced to optimize the allocation of different FACTS devices based on the equivalent impedance model obtained from sensitivity analysis. This method requires neither the exact modeling of voltage source convertors nor more than one load flow, so it is much faster than conventional methods. Due to its simplicity and fastness, it can cover all possible locations, continuous sizing, type, and number of compensators. Therefore, its performance and accuracy are considerably high. The proposed method based on a novel objective function, which includes voltage profile, transmission capability, and stability, is applied to a typical 6-bus and the 30-bus IEEE test systems. Furthermore, genetic algorithm is implemented to solve the optimization problem. The test results illustrate the effectiveness of the introduced method. Copyright © 2014 John Wiley & Sons, Ltd.

Optimal Allocation of FACTS Devices in Distribution Networks Using Imperialist Competitive Algorithm

FACTS devices are used for controlling the voltage, stability, power flow and security of transmission lines. Imperialist Competitive is a recently developed optimization technique, used widely in power systems. This paper presents an approach to finding the optimal location and size of FACTS devices in a distribution network using the Imperialist Competitive technique. IEEE 30-bus system is used as a case study. The results show the advantages of the Imperialist Competitive technique over the conventional approaches.

An Optimal Allocation of FACTS Devices for Maximizing Loadability with Hybrid Cording EPSO

This paper proposes an optimal allocation of FACTS devices for maximizing Loadability with hybrid cording EPSO (Evolutionary Particle Swarm Optimization). Loadability shows the maximum load that can be supplied in power network without the violation of thermal, voltage limits, etc. It is important to study it due to the system planning and operating. An allocation of FACTS devices is one of the attractive ways to increase Loadability. However, it is not easy to determine the optimal location and the optimal variable output of FACTS devices. The former is expressed in discrete number while the latter is represented in continuous one. As a result, the problem to be solved results in a nonlinear mixed integer problem that is very hard to solve. In addition, the use of MCS (Monte Carlo Simulation) to consider the uncertainty such as variable generations leads an increase in computational time. This paper proposes a hybrid cording EPSO that is combination of discrete and continuous EPSO to reduce the computational time and to improve accuracy. The proposed method is successfully applied to a sample system.

Optimal Allocation of FACTS Devices by using multi-Objective Optimal Power Flow and Genetic Algorithms

The introduction of the flexible AC transmission system (FACTS) in the power system reduces the losses, reduces the cost of the generation, improves the stability and also improves the load capability of the system. Some application of the Flexible AC transmission system (FACTS) technologies to existing high voltage power system has proves the use of FACTS technology may be a cost effective option for power delivery system enhancement. Amongst various power electronic devices unified power flow controller (UPFC) may be considered to be a capable of regulating the power flow and minimizing the power loss simultaneously. Since for the cost effective application of the FACTS technology a proper selection of the number and the placement of these devices is required. The main aim of this paper is to propose the methodology based on the genetic algorithm, able to identify the optimal number and the location of the UPFC devices in an assigned power system network for maximizing system capabilities. In order to validate the usefulness of the approach suggested here is , a case study using a IEEE 30-bus power system is presented and discussed.

Optimal Allocation of Facts Devices: Classical Versus Metaheuristc Approaches

The application of optimization techniques to allocate and size FACTS devices in a power system is still under study. Currently, there is no widely accepted method since many researchers claim their methods to be better than others. This paper compares the effectiveness of classical and metaheuristic optimization approaches in a simple hut realistic case study of optimal allocation of FACTS (Flexible AC Transmission System) devices, considering steady state and economic criteria, Concepts and details about the optimization process that tend to be overlooked in the literature are discussed together with some considerations about statistical analyses in the case of metaheuristic approaches.

Optimal Allocation of FACTS Devices by Using Multi-Objective Optimal Power Flow and Genetic Algorithms

The increases in power flows and environmental constraints are forcing electricity utilities to install new equipment to enhance network operation. Some application of Flexible AC Transmission System (FACTS) technologies to existing high-voltage power systems has proved the use of FACTS technology may be a cost-effective option for power delivery system enhancements. Amongst various power electronic devices, the unified power flow controller (UPFC) device has captured the interest of researchers for its capability of regulating the power flow and minimizing the power losses simultaneously. Since for a cost-effective application of FACTS technology a proper selection of the number and placement of these devices is required, the scope of this paper is to propose a methodology, based on a genetic algorithm, able to identify the optimal number and location of UPFC devices in an assigned power system network for maximizing system capabilities, social welfare and to satisfy contractual requirements in an open market power.In order to validate the usefulness of the approach suggested herein, a case study using a IEEE 30-bus power system is presented and discussed.