Planning of multi-type FACTS devices in restructured power systems with wind generation

Many electrical power systems are changing from a vertically integrated entity to a deregulated, open-market environment. This paper proposes an approach to optimally allocate multi-type flexible AC transmission system (FACTS) devices in restructured power systems with wind generation. The objective of the approach is to maximize the present value of long-term profit. Many factors like load variation, wind generation variation, generator capacity limit, line flow limit, voltage regulation, dispatchable load limits, generation rescheduling cost, load shedding cost, and multilateral power contracts are considered in problem formulation. The proposed method accurately evaluates the annual costs and benefits obtainable by FACTS devices in formulating the large-scale optimization problem under both normal condition and possible contingencies. The overall problem is solved using both Particle Swarm Optimization (PSO) for attaining optimal FACTS devices allocation as main problem and optimal power flow as sub optimization problem. The efficacy of the proposed approach is demonstrated for modified IEEE 14-bus test system and IEEE 118-bus test system.     

Assessment of techno-economic contribution of FACTS devices to power system operation

In contemporary power system studies, the optimal allocation and utilization of Flexible AC Transmission System (FACTS) devices are important issues primarily due to their cost. In this study four types of FACTS devices (Static VAr compensator (SVC), Thyristor-Controlled Series Capacitor (TCSC), Thyristor-Controlled Voltage Regulator (TCVR), and Thyristor-Controlled Phase Shifting Transformer (TCPST)) are optimally placed in a multi-machine power system to reduce the overall costs of power generation. The placement methodology considers simultaneously the cost of generated active and reactive powers and cost of selected FACTS devices for a range of operating conditions. The optimal power flow (OPF) and genetic algorithm (GA) based optimization procedure are employed to solve the allocation task. The net present value (NPV) method is used to assess the economic value of the proposed methodology. In addition to net reduction in generation cost allocated FACTS devices increased power transfer across the network and improved damping of electromechanical oscillations.     

A new dynamic control strategy for power transmission congestion management using series compensation

The growth of electricity market due to increase in demand and infrastructure made the power system more complex. Managing the transmission congestion is one of the main challenges faced by the utilities. To relieve from the bottlenecks, Flexible AC Transmission Systems (FACTS) and Distributed FACTS (D-FACTS) devices can be used in controlling the transmission line power flows. The real power flow control is realized by varying the transmission line impedance. The power flow in transmission line should satisfy inequality constraints to maintain the system in normal state. To achieve this, an algorithm is developed to control the FACTS/D-FACTS devices connected to all the transmission lines of n-bus system. The significant changes required in line impedance which will be deployed by FACTS devices are decided by the algorithm. In this paper, a 5 bus system and a 14 bus system with FACTS devices in all the transmission lines is considered. The transmission lines of the 5 and 14 bus systems are made to be overloaded in different combinations by choosing appropriate loading conditions. The control algorithm is tested on all the overloaded conditions to overcome the congestion. The FACTS devices controlled by the algorithm removes the overloading effect and improve the reliability of the network.     

State-variable control of shunt FACTS devices using phasor measurements

This paper addresses the problem of state-variable stabilizing control of power system using shunt FACTS devices. This stabilizing control is activated in the transient state of a power system and is supplementary with respect to the main steady-state control of a FACTS device. Stabilizing control laws have been derived for a non-linear multi-machine system model using direct Lyapunov method with the aim to maximize the rate of energy dissipation during power swings and therefore maximization of damping. The proposed control strategy is executed by a non-linear multi-loop controller with rotor angles and speed deviations of synchronous generators used as the input signals. The input signals, obtained from a phasor measurement system, are necessary only from a small area around the controlled shunt FACTS device. Validity of the proposed state-variable control has been confirmed by computer simulation for a small multi-machine test system.     

Minimization of voltage sag induced financial losses in distribution systems using FACTS devices

Voltage sag can have significant economic consequences for different types of industries. Flexible AC Transmission System (FACTS) is originally developed for transmission networks but similar ideas are now starting to be applied in distribution systems. FACTS devices have become popular as a cost effective solution for the protection of sensitive loads from voltage sag. This paper presents the modeling of FACTS devices to minimize the voltage sag induced financial losses. The overall system financial losses due to voltage sag could be significantly reduced depending on the type of FACTS devices used. The short circuit analysis approach is used to incorporate the effect of these devices on financial losses. Voltage sag produced by balanced and unbalanced short circuits is analyzed by means of an analytical approach using system impedance matrix (Z_Bus) which incorporates FACTS devices. Two types of FACTS devices, which are most often used in practical applications, are considered in this study: Distribution Static Compensator (D-STATCOM) and Static VAR Compensator (SVC). Case studies based on a real Indian distribution system are used to illustrate the modeling method and the effectiveness of these devices in minimization of financial losses.     

Optimal power flow with FACTS devices by hybrid TS/SA approach

In this paper, a hybrid tabu search and simulated annealing (TS/SA) approach is proposed to minimize the generator fuel cost in optimal power flow (OPF) control with flexible AC transmission systems (FACTS) devices. The problem is decomposed into the optimal setting of FACTS parameters subproblem that is searched by the hybrid TS/SA approach and the OPF with fixed FACTS parameters subproblem that is solved by the quadratic programming (QP). Two types of FACTS devices are used: thyristor-controlled series capacitor (TCSC) and thyristor-controlled phase shifting (TCPS). Test results on the modified IEEE 30 bus system indicates that the proposed hybrid TS/SA approach can obtain better solutions and require less CPU times than genetic algorithm (GA), SA, or TS alone.     

Loadability enhancement with FACTS devices using gravitational search algorithm

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.     

Voltage stability enhancement using VSC-OPF including wind farms based on Genetic algorithm

This paper describes the impact of wind generation to enhance the voltage stability of power system in optimal power flow problem. In this work, the Voltage Stability Constrained Optimal Power Flow (VSCOPF) algorithm is used, which combines the economical aspects and the voltage stability control of power system networks. A voltage stability index called L-index has been utilized to identify the most sensitive node prone to voltage collapse. Here Improved Genetic Algorithm (IGA) with mixed form of representation has been used for multi-objective formulation. Real power setting and voltage magnitudes are represented as floating point numbers and transformer tap settings and capacitors as integers. For effecting genetic processing, crossover and mutation operator, which can be directly deal with floating point number and integers, are used. The IGA has been implemented on IEEE 30 bus system to study the impact of wind farm on voltage stability. The obtained simulation shows the effectiveness of Improved Genetic algorithm on voltage stability of the power systems.     

Online fuzzy voltage collapse risk quantification

Many voltage stability indicators have been proposed in the past for the voltage collapse assessment. Almost all of them are determined through quite complex analytical tools; therefore, it is difficult for system operators to give them a physical meaning. In order to perform a simple and reliable evaluation of the security margins, it is necessary to make a synthesis of the information given by the various indices. The present work proposes an Artificial Intelligence-based tool for the evaluation of the voltage security. In particular, a Fuzzy Inference Engine is developed and optimized by two different approaches (Neural Networks and Genetic Algorithms). Starting from the state estimation, a given set of mathematical indices is computed to represent a snapshot of the current electric system operating point. The numerical values are then translated into a set of symbolic and linguistic quantities that are manipulated through a set of logical connectives and Inference Methods provided by the mathematical logic. As a result, the Fuzzy Logic gives a MW measure of the distance from the collapse limit, a metric usually appreciated by system operators.     

Comparison of performances of several FACTS devices using Cuckoo search algorithm optimized 2DOF controllers in multi-area AGC

This paper presents automatic generation control (AGC) of three unequal area thermal systems with single reheat turbine and appropriate generation rate constraints (GRC) in each area. A two degree of freedom (2DOF) controller called 2DOF-integral plus double derivative (2DOF-IDD) is proposed for the first time in AGC as secondary controller. Secondary controller gains and other parameters are optimized simultaneously using a more recent evolutionary computational technique called Cuckoo Search algorithm (CS). The system dynamic responses for various 2DOF controllers such as 2DOF-PI, 2DOF-PID, and 2DOF-DD are compared. Investigations reveal that responses with 2DOF-IDD are better than others. Performance of several FACTS devices such as Static synchronous series compensator (SSSC), Thyristor controlled series capacitor (TCSC), Thyristor controlled phase shifter (TCPS), and Interline power flow controller (IPFC) in presence of 2DOF-IDD controller are compared and found that the dynamic responses with IPFC are better than others. For the first time in AGC, a case study is performed with placement of IPFC and observed that IPFC present in all three areas of the system performs better. Sensitivity analysis reveals that the CS optimized 2DOF-IDD controller parameters obtained in presence of IPFC in all three areas at nominal condition of loading and size of step load perturbation (SLP) are robust and need not be reset with wide changes in system loading and SLP. Also, the comparison of convergence curve of various algorithms reveals that CS algorithm converges much faster than others.     

A unified management of congestions due to voltage instability and thermal overload

This paper proposes two approaches for a unified management of congestions due to voltage instability and thermal overload in a deregulated environment. Both techniques aim to remove, in some optimal manner, voltage and thermal congestions stemming from base case or post-contingency states, by a simultaneous handling of operating and security constraints with respect to several contingencies. The objective of the first approach is to adjust the market-based power injections (generator output and possibly load consumption) at the least cost while the second one aims at curtailing power transactions in a transparent and non-discriminatory way. These techniques rely on sensitivities which pinpoint the best remedial actions against congestions owing to voltage instability and thermal overload. Numerical results with both approaches are provided on a realistic 80-bus system model.     

Transmission congestion and voltage profile management coordination in competitive electricity markets

This paper describes a generalized active/reactive iterative coordination process between GENCOs and the Independent System Operator (ISO) for active (transmission congestion) and reactive (voltage profile) management in the day-ahead market. GENCOs apply priced-based unit commitment without transmission and voltage security constraints, schedule their units and submit their initial bids to the ISO. The ISO executes congestion and voltage profile management for eliminating transmission and voltage profile violations. If violations are not eliminated, the ISO minimizes the transmission and voltage profile violations and sends a signal via the Internet to GENCOs. GENCOs reschedule their units taking into account the ISO signals and submit modified bids to the ISO. The voltage problem is addressed and a linear model is formulated and used in the proposed method. The voltage problem is formulated as a linear programming with a block-angular structure and Dantzig-Wolfe decomposition is applied to generate several smaller problems for a faster and easier solution of large-scale power systems. Two 36 unit GENCOs are used to demonstrate the performance of the proposed generalized active/reactive coordination algorithm.     

Optimal power flow using differential evolution algorithm

This paper presents an evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs differential evolution (DE) algorithm for optimal settings of OPF control variables. The proposed approach is examined and tested on the standard IEEE 30-bus test system with different objective functions that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. In addition, non-smooth piecewise quadratic cost function has been considered. The simulation results of the proposed approach are compared to those reported in the literature. The results demonstrate the potential of the proposed approach and show its effectiveness and robustness to solve the OPF problem for the systems considered.     

Optimal power flow using differential evolution algorithm

This paper presents an efficient and reliable evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs differential evolution algorithm for optimal settings of OPF problem control variables. The proposed approach is examined and tested on the standard IEEE 30-bus test system with different objectives that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. The proposed approach results are compared with the results reported in the literature. The results show the effectiveness and robustness of the proposed approach.     

基因控制遗传算法在变压器铁心截面优化设计中的应用

分析了铁心最小级片宽下限的选择依据,给出了叠厚的递推公式,提出了采用基因控制遗传算法进行铁心截面的优化设计.     

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.     

遗传算法在干式电力变压器系列优化设计中的应用研究

在传统遗传算法的基础上,对算法进行了改进,使之适用于干式变压器系列优化设计.在SC9-50/10系列变压器的系列优化设计上应用结果表明,这种算法效果是比较理想的.     

Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach

This research develops a weighted evolving fuzzy neural network for monthly electricity demand forecasting in Taiwan. This study modifies the evolving fuzzy neural network framework (EFuNN framework) by adopting a weighted factor to calculate the importance of each factor among the different rules. In addition, an exponential transfer function (exp(−D)) is employed to transfer the distance of any two factors to the value of similarity among different rules, thus a different rule clustering method is developed accordingly. Seven factors identified by the Taiwan Power Company will affect the power consumption in Taiwan. These seven factors will be inputted into the WEFuNN to forecast the electricity demand of the future. The historical data will be used to train the WEFuNN. After training, the trained model will forecast the future electricity demands. Finally, the WEFuNN model is compared with other approaches, which are proposed in the literature. The experimental results reveal that the MAPE for WEFuNN model is 6.43% which is better than the MAPE value for other approaches. Thus, the WEFuNN model is more accurate in forecasting the monthly electricity demand than the other approaches. In summary, the WEFuNN model can be practically applied as an electricity demand forecasting tool in Taiwan.     

A GA approach to compare ORPF objective functions including Secondary Voltage Regulation

In the past, with vertically integrate utilities, Optimal Reactive Power Flow (ORPF) procedures were designed to minimize power system losses, keeping the voltage profile in an acceptable range. Nowadays, in the market environment, a new formulation of the ORPF, aimed at the system security maximization, is necessary. In particular, as congestions and overloads are usually taken into account by the energy market rules, voltage security could become the main goal of ORPFs. Voltage problems are caused by the increase in power transfer among areas of interconnected systems, by the lack of reactive power support and by the increasing limitations of transmission networks. In this paper, an analysis of several ORPF Objective Functions (OFs) is reported, where the goal considered is the network security maximization. To solve the optimization problem for the considered OFs, a Genetic Algorithm (GA) approach has been adopted, together with a particular formulation of the Power Flow (PF) taking into account the Secondary Voltage Regulation (SVR). Tests are performed on a detailed model of the Italian power system, comparing the results in terms of system losses, reactive power required, loadability limits and voltage collapse indices. Furthermore, the improvements achievable with the adoption of the SVR in the new market environment have been investigated.     

Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization

This paper presents a newly developed teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal reactive power dispatch (ORPD) problem by minimizing real power loss, voltage deviation and voltage stability index. To accelerate the convergence speed and to improve solution quality quasi-opposition based learning (QOBL) concept is incorporated in original TLBO algorithm. The proposed TLBO and quasi-oppositional TLBO (QOTLBO) approaches are implemented on standard IEEE 30-bus and IEEE 118-bus test systems. Results demonstrate superiority in terms of solution quality of the proposed QOTLBO approach over original TLBO and other optimization techniques and confirm its potential to solve the ORPD problem.