Single Line Outage Contingencies Research Articles

Operation of coupled power-gas networks with hydrogen refueling stations, responsive demands and storage systems: A novel stochastic framework

Because of factors such as low natural gas prices, low greenhouse gas emission and high efficiency has increased the penetration of natural gas-fired generators in electric power systems. Large gas consumption of gas-fired generators has caused intense interdependence of electricity and gas systems. Most often, the operational planning of power and gas systems is conducted through a co-optimisation model in which the total cost of electricity and gas systems is minimised and the constraints of both systems are considered. On the other hand, due to environmental concerns, the usage of fuel cell vehicles (FCVs) is increasing. Hydrogen refueling stations (HRSs) are needed for refueling FCVs. This paper puts forward a stochastic model for co-optimisation of wind-integrated power and gas systems, hosting HRSs, electric storage systems (ESS's) and responsive electricity and gas demands. The effect of ESS's, responsive demands, contingencies, wind uncertainties and demand response on operation of power-gas nexus is scrutinized. An incentive-based demand response program has been used for both electricity and gas demands. The case study is the Belgian gas network connected to the a 24-bus power system. The findings indicate that responsive electric demands reduce electric system cost by 9.4 %, while responsive gas demands reduce the gas network cost and total operation cost by 1 %. The contingency analysis on the power-gas nexus shows that single line outage contingencies in power system does not result in any demand shed and does not increase the operation cost of either electricity network or gas network.

A unified optimal power flow modeling for VSC-HVDC converter: a novel methodology for optimal installation based on average loadability index

PurposeThis paper aims to propose a novel methodology for optimal voltage source converter (VSC) station installation in hybrid alternating current (AC)/direct current (DC) transmission networks.Design/methodology/approachIn this analysis, a unified power flow model has been developed for the optimal power flow (OPF) problem for VSC-based high voltage direct current (VSC-HVDC) transmission network and solved using a particle swarm optimization (PSO) algorithm. The impact of the HVDC converter under abnormal conditions considering N-1 line outage contingency is analyzed against the congestion relief of the overall transmission network. The average loadability index is used as a severity indicator and minimized along with overall transmission line losses by replacing each AC line with an HVDC line independently.FindingsThe developed unified OPF (UOPF) model converged successfully with (PSO) algorithm. The OPF problem has satisfied the defined operational constraints of the power system, and comparative results are obtained for objective function with different HVDC test configurations represented in the paper. In addition, the impact of VSC converter location is determined on objective function value.Originality/valueA novel methodology has been developed for the optimal installation of the converter station for the point-to-point configuration of HVDC transmission. The developed unified OPF model and methodology for selecting the AC bus for converter installation has effectively reduced congestion in transmission lines under single line outage contingency.

A parallel computing approach for integrated security assessment of power system

Abstract Fast and accurate assessment of integrated security of present day complex and highly interconnected power system within time frame is a challenging task. The traditional methods used for integrated security assessment are fast but approximate. On the other hand, full simulation methods provide more accurate security assessment, but require large computation time. This paper proposes the use of parallel computing for fast and accurate assessment of integrated security of power system by running full Newton Raphson (NR) load flow program with sparsity technique to compute real power flow or MW performance index ( MWPI ), and voltage performance index ( VPI ), and continuation power flow program with sparsity technique to compute voltage stability index ( VSI ) for all possible single line outage contingencies. The proposed work has been carried out using the Matlab Parallel Computing Toolbox Software (PCTS). To implement parallel computing, the job scheduler, a part of PCTS, divides a computationally intensive job into various tasks and distributes these tasks to its assigned workers for evaluation. Effectiveness of the proposed approach has been demonstrated on IEEE 30-bus system and a practical NREB 246-bus Indian power system. As the proposed method utilizes NR load flow and continuation load flow programs, the accurate post contingency operating status of a power system is also available, which can be used for appropriate corrective actions planning to bring the power system back into secure state. The proposed approach is quite accurate and fast and hence can be implemented for integrated security assessment of practical power systems in smart grid environment also.

Analysis of FACTS devices on Security Constrained Unit Commitment problem

Abstract This paper focuses on solving Security Constrained Unit Commitment (SCUC) problem using ABC algorithm incorporating FACTS devices. The objective of the SCUC problem is to obtain the minimum operating cost simultaneously maintaining the security of the system. The SCUC problem is decomposed into Unit Commitment (UC), the master problem and Security-Constrained Economic Dispatch (SCED) as the sub-problem. The existing generation constraints, such as hourly power demand, system reserves, and minimum up/down time limits, ramp up/down limits are included in the SCUC problem formulation. The ability of FACTS devices to control the power flow through designated routes in transmission lines and thereby reducing the overloading of lines are studied. The solution of SCUC problem is also analyzed during a single line outage contingency. The SCUC is carried out incorporating FACTS devices such as SVC, TCSC, STATCOM, SSSC, UPFC and IPFC. The modeling of the FACTS devices within the power system network and finding a suitable location are discussed. The SCUC has been solved and validated on an IEEE 118-bus test system and a practical South Indian 86 bus utility.

Static synchronous series compensator and static var compensator interaction on voltage stability limit enhancement and active power loss minimization through differential evolution algorithm

This paper proposes an application of differential evolution (DE) algorithm based extended voltage stability margin and minimization of real power loss incorporating static synchronous series compensator (SSSC) and static var compensator (SVC) devices. A new circuit element based model of SSSC and variable susceptance model of SVC are utilized to control the line power flows and bus voltage magnitudes for voltage stability limit improvement. The line stability index is used to assess the voltage stability of a power system. Voltage profile improvement values, real power loss minimization values and the location and size of SSSC and SVC devices were optimized by differential evolution algorithm. The results are obtained from the IEEE-30 Bus test case system and were analyzed with the voltage and real power loss under normal loading, critical loading and single line outage contingency conditions. Key words: Differential evolution algorithm (DEA), voltage stability, static synchronous series compensator (SSSC), static var compensator (SVC), line stability index, FACTS devices, load flow.

OPF based reactive power planning and voltage stability limit improvement under single line outage contingency condition through evolutionary algorithms

Reactive power planning is vital for maintaining the voltage stability of power systems and evolutionary algorithms are highly useful for achieving this task. This paper compares the effectiveness of the differential evolution (DE) and evolutionary programming (EP) algorithms in optimizing the reactive power planning of power systems under line outage contingency conditions. DE is efficient in exploration through the search space of the problem, while EP is simple and easy to implement. The low cost but fast response thyristor-controlled series capacitor (TCSC) flexible alternating current transmission system (FACTS) device is incorporated to control the power flows. The optimal settings of the control variables of the generator voltages, transformer tap settings, and location and parameter settings of the TCSC are considered for reactive power planning and the resultant reactive power reserves. The effectiveness of the proposed work is tested on the IEEE-30 Bus test system under the most critical line outage condition.

Shuffled Frog Leaping Algorithm based Voltage Stability Limit Improvement and Loss Minimization Incorporating FACTS Devices under Stressed Conditions

day power system networks are having high risks of voltage instability problems and several network blackouts have been reported. This phenomenon tends to occur from lack of reactive power supports under heavily stressed operating conditions caused by increased load demand and the fast developing deregulation of power systems across the world. This paper proposes an application of Shuffled Frog Leaping Algorithm (SFLA) based extended voltage stability margin and minimization of loss by incorporating TCSC and SVC (variable susceptance model) devices. The line stability index (LQP) is used to assess the voltage stability of a power system. The location and size of Series connected and Shunt connected FACTS devices are optimized by shuffled frog leaping algorithm. The results are obtained from the IEEE-30 bus test case system under critical loading and single line outage contingency conditions

ANN based integrated security assessment of power system using parallel computing

This paper presents the application of cascade neural network (CANN) based approach for integrated security (voltage and line flow security) assessment. The developed cascade neural network is a combination of one screening module and two ranking modules, which are Levenberg–Marquardt Algorithm based neural networks (LMANNs). All the single line outage contingency cases are applied to the screening module, which is 3-layered feed-forward ANN having two outputs. The screening module is trained to classify them either in critical contingency class or in non-critical contingency class from the viewpoint of voltage/line loading. The screened critical contingencies are passed to the corresponding ranking modules, which are developed simultaneously by using parallel computing. Parallel computing deals with the development of programs where multiple concurrent processes cooperate in the fulfillment of a common task. For contingency screening and ranking, two performance indices: one based on voltage security of power system (VPI) and other based on line flow (MWPI) are used. Effectiveness of the proposed cascade neural network based approach has been demonstrated by applying it for contingency selection and ranking at different loading conditions for IEEE 30-bus and a practical 75-bus Indian system. The results obtained clearly indicate the superiority of the proposed approach in terms of speedup in training time of neural networks as compared to the case when the two ranking neural networks were developed sequentially to estimate VPI and MWPI.

Particle Swarm Optimization- Artificial Neural Network For Power System Load Flow

Load flow study is done to determine the power system static states (voltage magnitudes and voltage angles) at each bus to find the steady state working condition of a power system. It is important and most frequently carried out study performed by power utilities for power system planning, optimization, operation and control. In this paper a Particle Swarm Optimization Neural Network (PSO-ANN) is proposed to solve load flow problem under different loading/ contingency conditions for computing bus voltage magnitudes and angles of the power system. A multilayered feed-forward neural network is trained by using PSO technique. The results show the effectiveness of the proposed PSO-ANN based approach for solving power flow problem having different loading conditions and single-line outage contingencies in IEEE 14 bus system

Real Power Loss and Voltage Stability Limit Optimization Incorporating TCSC and SVC through DE Algorithm under Different Operating Conditions of a Power System

Modern day power system networks are having high ri sks of voltage instability problems and several network blackouts have been reported. This phenomenon tends to occur from lack of reactive pow er supports in heavily stressed operating conditions c aused by increased load demand and the fast develop ing deregulation of power systems across the world. Thi s paper proposes an application of Differential Evo lution (DE) Algorithm based extended voltage stability mar gin and minimization of loss by incorporating TCSC and SVC (variable susceptance model) devices. The line stability index (LQP) is used to assess the voltage stability of a power system. The location and size of Series connected and Shunt connected FACTS devices were optimized by DE algorithm. The results are obtained from the IEEE-30 bus test case system under critic al loading and single line outage contingency conditio ns.

Neural Network-Based Approach for ATC Estimation Using Distributed Computing

In the competitive electric power market allowing open access transmission environment, the knowledge of available transfer capability (ATC) is very important for optimum utilization of existing transmission facility. ATC information conveys how much power can be transmitted through the power network over and above already committed usage without violation of system security limits. This paper presents a Levenberg-Marquardt algorithm neural network (LMANN)-based approach for fast and accurate estimation of system ATC. System ATC has been estimated for both varying load condition as well as for single line outage contingency condition by employing distributed computing. Principal component analysis (PCA) has been applied for effective input feature selection. Contingency clusters are formed such that each cluster contains almost similar ATC values. For each contingency clusters separate LMANNs have been developed. All the proposed LMANNs have been trained and tested under distributed computing environment and a considerable speed up in the training is obtained. The proposed approach has been examined on 75-bus Indian power system and IEEE 300-bus system and found significantly efficient.

Fast voltage contingency analysis of power systems using model trees

This paper presents a model tree (MT) based approach for fast voltage contingency ranking, of most severe contingencies for online applications in energy management systems. To measure the severity of contingencies, a bus voltage violation based scalar performance index is used. Model trees are developed to estimate the real value of voltage performance index (VPI) and rank the single line outage contingencies, according to their severity. Full AC power flow is performed, to generate the training and testing patterns for the proposed MT under each contingency. The effectiveness of the proposed approach is tested on IEEE test systems. Once trained, the MT method gives fast and accurate ranking of contingencies for unknown load patterns, hence it is suitable for online applications in energy management systems.

Degree of insecurity estimation in a power system using functional link neural network

AbstractSecure operation of power system has always been a challenge to system operators. With increasing interconnection and growing load demand, a power system, sometimes, may go into the insecure operation especially after severe contingencies. It is important to develop a technique to quantify the degree of insecurity in both planning and operational stages. This paper presents such an algorithm.The distance (Euclidean norm) in parameter space between any insecure point and closest point on feasible (secure) hypersurface is used as a measure of insecurity. Artificial Neural Network (ANN) based Functional Link Network (FLN) is proposed because ANN is very popular for complex problems. The various FLN models are evolved to estimate the degree of insecurity. The sample study is performed on an IEEE 30‐bus test system for different loading patterns and single line outage contingencies. The investigations reveal that the proposed FLN accurately estimates the degree of insecurity with least computational time compared to the existing methods.