Congestion Management Problem Research Articles

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.

Multi-objective transmission congestion management considering demand response programs and generation rescheduling

With the implementation of restructuring and deregulation in the electricity industry, efficient and economic operations of power systems have become remarkably significant. Meanwhile, congestion in transmission lines is one of the major problems in power system operation. Therefore, in this paper, the multi-objective particle swarm optimization (MOPSO) method has been used for transmission congestion management considering demand response programs (DRPs) and generation rescheduling. Total operation/DR cost, emission and increasing the loading of transmission lines are the objective functions of this problem. Using DRPs increases the operator power of choice with regard to the participation of small consumers in reducing the demand. The method already mentioned has been tested on two test systems (IEEE 30-bus and IEEE 118-bus test systems). The results of the evaluation in the form of different scenarios show that DRPs reduce the power system transmission lines congestion, allowing the use of the transmission lines with less loading capacity. Also, in some instances, without using DRPs, solving the transmission congestion management problem is impossible.

Application of firefly algorithm for congestion management problem in the deregulated electricity market

Application of firefly algorithm for congestion management problem in the deregulated electricity market

Application of firefly algorithm for congestion management problem in the deregulated electricity market

In the deregulation of electricity market, the transmission congestion management (CM) has become extremely important in order to ensure security and reliability of the system. This paper proposes a method to manage congestion by optimal rescheduling of the active powers of generators based on firefly algorithm (FA). However, all generators in the system need not take part in CM. Thus, in this article, generators are selected based on the magnitude of generator sensitivities to the congested line. In this paper, the proposed FA is tested on standard IEEE 30 bus, 118 bus systems and a practical Indian utility 62 bus system for the solution of CM problem. The results of these test systems provide minimum rescheduling cost and are compared with that of CPSO, PSO-TVIW, PSO-TVAC, VEPSO and PSO-ITVAC methods. Results prove that FA is indeed capable of getting a high quality solution for the CM problem.

A novel quasi-oppositional chaotic antlion optimizer for global optimization

This paper proposes a novel quasi-oppositional chaotic antlion optimizer (ALO) (QOCALO) for solving global optimization problems. ALO is a population based algorithm motivated by the unique hunting behavior of antlions in nature and exhibits strong influence in solving global and engineering optimization problems. In the proposed QOCALO algorithm of the present work, the initial population is generated using the quasi-opposition based learning (QOBL) and the concept of QOBL based generation jumping is utilized inside the main searching strategy of the proposed algorithm. Utilization of QOBL ensures better convergence speed of the proposed algorithm and it also provides better exploration of the search space. Alongside the QOBL, a chaotic local search (CLS) is also incorporated in the proposed QOCALO algorithm. The CLS guides local search around the global best solution that provides better exploitation of the search space. Thus, a better trade-off between exploration and exploitation holds for the proposed algorithm which makes it robust. It is observed that the proposed algorithm offers better results than the original ALO in terms of solution quality and convergence speed. The proposed QOCALO algorithm is implemented and tested, successfully, on nineteen mathematical benchmark test functions of varying complexities and the experimental results are compared to those offered by the basic ALO and some other recently developed nature inspired algorithms. The efficacy of the proposed algorithm is further utilized to solve three real world engineering optimization problems viz. (a) the placement and sizing problem of distributed generators in radial distribution networks, (b) the congestion management problem in power transmission system and (c) the optimal design of pressure vessel.

Congestion mitigation using UPFC

Transmission congestion is one of the most challenging issues in operation of deregulated electricity markets of today. This study investigates into benefit of unified power flow controller (UPFC) as a viable solution to the problem of congestion management. A comprehensive load flow model for the UPFC is presented which has the capability to control active and reactive powers and voltage magnitude simultaneously. This study discusses about two different approaches for determination of suitable location of UPFC. One of them is sensitivity-based approach and the other is pricing-based method. Reduction in total system losses has been taken as objective in sensitivity-based approach. Furthermore, sensitivity of total system losses with respect to control parameters of UPFC have been evaluated using two different methods. A pricing-based approach which makes use of locational marginal price difference and congestion rent for determination of suitable location of UPFC. Minimisation of cost of rescheduling subjected to system operating constraints is the objective to be met for congestion management. Interior point method, a mathematical tool for non-linear optimisation, is used to obtain the solution of above-mentioned problem.

Managing multi-line power congestion by using Hybrid Nelder–Mead – Fuzzy Adaptive Particle Swarm Optimization (HNM-FAPSO)

This paper presents a Hybrid Nelder–Mead – Fuzzy Adaptive Particle Swarm Optimization (HNM-FAPSO) for a Multi-Line Congestion Management (MLCM) problem. MLCM is a nonlinear optimization problem in deregulated power system. The generators which are sensitive to the congested lines are selected based on the novel Apparent Power Sensitivity Factor (APSF) and are rescheduled by using the HNM-FAPSO algorithm. The objective of hybridizing the Nelder–Mead (NM), Fuzzy adaptive Particle Swarm Optimization (FAPSO) is to blend their unique advantages as well as their efficacy. The NM method is a very efficient local search procedure and is used to initialize the population for Particle Swarm Optimization (PSO) which is used to search global best value. But PSO has a problem of premature convergence due to linear variation of its internal parameter i.e. inertia weight. To overcome this, a fuzzy inference system is used to dynamically update the inertia weight. The feasibility and performance of the HNM-FAPSO algorithm is demonstrated on the standard IEEE 30-bus system and IEEE 118-bus system. A comparison of simulation results and statistical analysis reveal optimization efficacy of the HNM-FAPSO algorithm over other algorithms like FAPSO, NM, Simple Bacterial Foraging (SBF), adaptive bacterial foraging algorithm with Nelder–Mead (ABFNM), Genetic Algorithm (GA) and PSO available in literature. The main outcome of the proposed algorithm in solving MLCM is reduction in congestion cost, rescheduled real power, power loss and computational time.

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

In today’s competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

A new multi-objective solution approach to solve transmission congestion management problem of energy markets

Transmission congestion management plays a key role in deregulated energy markets. To correctly model and solve this problem, power system voltage and transient stability limits should be considered to avoid obtaining a vulnerable power system with low stability margins. Congestion management is modeled as a multi-objective optimization problem in this paper. The proposed scheme includes the cost of congestion management, voltage stability margin and transient stability margin as its multiple competing objectives. Moreover, a new effective Multi-objective Mathematical Programming (MMP) solution approach based on normalized normal constraint (NNC) method is presented to solve the multi-objective optimization problem of the congestion management, which can generate a well-distributed and efficient Pareto frontier. The proposed congestion management model and MMP solution approach are implemented on the New-England’s test system and the obtained results are compared with the results of several other congestion management methods. These comparisons verify the superiority of the proposed approach.

Stochastic congestion management considering power system uncertainties: a chance-constrained programming approach

Considering system uncertainties in developing power systems, algorithms such as congestion management (CM) are vital in power system analysis and studies. This paper proposes a new model for power system CM by considering power system uncertainties based on chance-constrain programming (CCP). In the proposed approach, transmission constraints are taken into account by stochastic, instead of deterministic, models. The proposed approach considers network uncertainties with a specic level of probability in the optimization process, and then an analytical approach is used to solve the new model of stochastic congestion management. In this approach, the stochastic optimization problem is transformed into an equivalent deterministic problem. Moreover, an efficient numerical approach based on a real-coded genetic algorithm and Monte Carlo technique is proposed to solve the CCP-based congestion management problem in order to make a comparison to the analytical approach. The effectiveness of the proposed approach is evaluated by applying the method to the IEEE 30-bus test system. The results show that the proposed CCP model and the analytical solving approach outperform the existing models.

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

In today’s competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool-based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population-based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

An Improved Differential Evolution Algorithm for Congestion Management considering Voltage Stability

In deregulated electricity market, Congestion Management (CM) is one of the most significant issues in order to maintain the system in secure state and to get the reliable system operation. While addressing Congestion Management voltage stability should also be taken into account. This paper elucidates an Improved Differential Evolution (IDE) algorithm to alleviate Congestion in transmission line by rescheduling of generators while considering voltage stability. Differential Evolution (DE) is one of the heuristic, population based algorithm which is well suited for solving complex and non-linear optimization problems. A Double Best Mutation Operator (DBMO) is proposed to improve DE algorithm's convergence rate. In order to validate suitability of the suggested approach, it has been evaluated on the IEEE-30 bus test system on both base case loading as well as 10% increased load. The test system has been also examined under critical line outages. The results and discussions clearly depicts the effectiveness of the projected approach in solving Congestion Management Problem.

Hybrid harmony search algorithm and fuzzy mechanism for solving congestion management problem in an electricity market

A hybrid technique for solving the congestion management problem in an electricity market based on harmony search algorithm (HAS) and Fuzzy mechanism is presented in this article. This algorithm does not require initial value setting for the variables and does not require differential gradients, thus it can consider discontinuous functions as well as continuous functions. The HAS is a recently developed powerful evolutionary algorithm, inspired by the improvisation process of musicians, for solving single/multiobjective optimization problems. In the proposed technique, each musician plays a note for finding a best harmony all together. Transmission pricing and congestion management are the key elements of a competitive electricity market based on direct access. They also focus of much of the debate concerning alternative approaches to the market design and the implementation of a common carrier electricity system. This article focuses on the tradeoffs between simplicity and economic efficiency in meeting the objectives of a transmission pricing and congestion management scheme. The effectiveness of the proposed technique is applied on 30 and 118 bus IEEE standard power system in comparison with CPSO, PSO‐TVAC, and PSO‐TVIW. The numerical results demonstrate that the proposed technique is better and superior than other compared methods. © 2015 Wiley Periodicals, Inc. Complexity 21: 90–98, 2016

Optimal Utilization of Renewable Energy Sources for Congestion Management

This article analyse the optimal utilization of Renewable Energy Sources (RES) to manage congestion under power system restructured environment. The proposed congestion management problem is formulated to minimize the rescheduling cost of renewable and conventional generators to alleviate congestion subject to operational, line overloading, seasonal and time constraints. The rescheduling has been done by generator selection based on the proposed Apparent power Congestion Index. Particle swarm optimization with time varying acceleration coefficient minimizes the deviation of rescheduled generator power output from scheduled values. The seasonal and time constraints considered in this paper, is the pioneering work in congestion management involving RES. The validation of the proposed method has been studied on IEEE 30-bus system and the obtained results certify the advantages of RES.

IJIREEICE - Electrical, Electronics, Instrumentation and Control

Congestion management is one of the important techniques to relieve congestion in transmission system. The usage of transmission line crosses the limits, then congestion occurs. Application of FACTS controllers is as a solution to the problem of congestion management. In the literature Genetic Algorithm (GA) based Optimum Power Flow (OPF) technique is used to determine the optimal location of Static VAR Compensator (SVC). But in this paper Unified-Power-Flow Controller (UPFC) is modeled and located in the system to relieve congestion, also compared with the SVC. This method was applied on IEEE 30-bus test system and a program was developed in MATLAB software environment.

Multi Objective Genetic Algorithm for Congestion Management in Deregulated Power System Using Generator Rescheduling and Facts Devices

The problem of congestion management is more pronounced in deregulated environment as the participants of the energy market are market oriented rather than socially responsible-as exhibited by the government operated bundled system. Customers would like to purchase the electricity from the cheapest available sources. The seller in energy market would like to derive more benefit out of their investments, engages with contracts that may lead to overloading of the transmission elements of the power system. An Independent System Operator (ISO) who has no vested interest in the energy market, coordinates the trades and make sure that the interconnected power system always operates in a secure state at a minimum cost by meeting the all the load requirements and losses. In this proposed study, Congestion is mitigated by Generator Rescheduling and implementation of FACTS devices. Minimization of rescheduling costs of the generator and minimization of the cost of deploying FACTS devices are taken as the objectives of the given multi-objective optimization problem. Non-dominated sorting genetic algorithm II is used to solve this problem by implementing the series FACTS device namely TCSC and shunt FACTS device namely SVC. The proposed algorithm is tested on IEEE 30 bus system.

Congestion management solution under secure bilateral transactions in hybrid electricity market for hydro-thermal combination

• A generation rescheduling based congestion management approach has been proposed for hybrid (pool + bilateral) market. • The secure bilateral transactions between GENCOS and DISCOS have been modeled by obtaining GD matrix. • The hydro units with least start up time have been modeled for congestion management which reduces the cost of congestion. • An optimal power flow modeling helps in keeping the voltage and MVA values within limits after congestion management. The deregulation of power system has created an environment of competitiveness among different market players and the transmission lines are forced to operate near to their thermal or stability limits. It is a challenge with System Operators (SO) to ensure a secure and reliable transmission of power under these conditions. This paper proposes a rescheduling based congestion management strategy in hybrid (pool + bilateral) electricity market structure for a combination of hydro and thermal units. The proposed congestion management problem has been formulated as mixed integer nonlinear programming (MINLP) problem with an objective to minimize the congestion management cost by suitably rescheduling the hydro and thermal units based on their up and down generation cost bids. The hydro units having lowest operational cost and fast startup time have been used to alleviate the congestion by considering non-concave piecewise linear performance curves for them. The secure bilateral transactions have been ensured while rescheduling of the generators for alleviating the congestion. The performance of the proposed model has been demonstrated by solving the congestion management problem on modified IEEE-24 bus system.

Congestion Management Considering Optimal Placement of Distributed Generator in Deregulated Power System Networks

This article presents an effective methodology for congestion management in deregulated power system networks considering optimal placement of a distributed generator. The novelty of this method is that the optimal placement of a distributed generator in a deregulated power system is decided on the basis of bus impedance matrix (Zbus ) based contribution factors. The Zbus -based contribution factors are independent of slack bus location, which complies with the prevailing competitive environment. The congestion management problem formulation comprises the maximization of social welfare function subject to power balance and transmission congestion constraints. The maximization of the social welfare function causes maximization of consumer benefits and minimization of supplier generation cost and distributed generators. The proposed methodology has been simulated on an IEEE 30-bus system, and comparisons of results are presented with and without distributed generators. The results show that the proposed approach gives significant improvement in social welfare and decreases congestion rent with distributed generator placement.

Multi-Objective Differential Evolution for Voltage Security Constrained Optimal Power Flow in Deregulated Power Systems

Abstract Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal front obtained from MODE is compared with reference pareto front and the best compromise solution for all the cases are obtained from fuzzy decision making strategy. The performance measures of proposed MODE in two test systems are calculated using suitable performance metrices. The simulation results show that the proposed approach provides considerable improvement in the congestion management by generation rescheduling and load shedding while enhancing the voltage stability in deregulated power system.

NSGAII-based Congestion Management in a Pool-based Electricity Market Incorporating Voltage and Transient Stability

Congestion of transmission lines is a key challenge for the system operator in the open access regime and is usually managed by rescheduling generators and demands. The changes in schedule due to congestion management may cause stability margins to shrink to unacceptable levels. Stability issues must, therefore, be considered during congestion management. This article presents a multi-objective formulation of the congestion management problem in a pool-based electricity market with the competing objective functions of minimizing congestion management cost and maximizing voltage and transient stability margins. The non-dominated sorting genetic algorithm II is employed to achieve the best trade off among the three objectives. A fuzzy decision maker is used to extract the best-compromise non-dominated solution. The proposed method has been tested on the New England 39-bus system, and results are compared with those of other reported multi-objective mathematical programming based approaches. The proposed non-dominated sorting genetic algorithm II based congestion management algorithm outperforms the previously reported method. The quality of the results establishes the efficacy of the proposed approach in solving the multi-objective congestion management problem.