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2014
In this paper the sub-problems associated with the economical operation of a power system can be reduced. But the electrical marketing of a system is so complicated. The congestion management is the recent problem facing today much in order to provide cons tant supply of power to the consumers in an reliable manner. So we are trying to remove the congestion in the transmission line by means of generation rescheduling with the cost involved in the rescheduling process will be minimized. In this paper the adaptive bacterial foraging algorithm with Nelder Mead is used for optimizing the congestion cost. The outputs and results are compared with the optimization techniques like genetic algorithm (GA), Particle Swarm Optimization (PSO) and simple bacterial foraging algorithm. The numerical expression for representing the performance of the optimization techniques are done by means of six generating units having standard IEEE 30 bus system. This technique can be reformulated in future for...
The impact of restructuring in the field of communication sector has brought an evolutionary change in power sector too. This revolutionary idea has brought about competition in this sector with an aim of reduction in the electricity price. The competitive environment not only benefits the utilities and customers however it kindles some of the technical issues, typical one being the transmission congestion. It is considered to be tenacious since it admonish system security and may result in inflation of electricity prices effecting in feeble market condition. The explication to the dispute of congestion has been furnished in this paper. To minimize the congestion cost, an effective multi objective approach is proposed to endorse generator rescheduling and FACTS technology using a metaheurisitc optimization algorithm, symbiotic organic search algorithm. The choice of most sensitive generators to reschedule real and reactive power is realized using real power transmission congestion distribution factor
International Journal of Simulation: Systems, Science & Technology, 2016
This paper presents a comparative analysis of Bacterial Foraging Optimization Algorithm (BFOA) and Evolutionary Programming (EP) in determining the locations and amount of load to be shed in power systems for optimal load shedding. Load shedding is done by removing a certain amount of loads at appointed locations of a bus system. By doing so, the stability of the system can be improved, as well as the total power losses. The objective functions of total power losses and voltage stability index values are used in determining the optimal load shedding in that particular system. In this research, the technique is implemented into IEEE 30-bus bus system. Simulations of BFAO proved that a better result can be obtained than EP when compared to the base case values of total power losses and voltage stability index values of that particular bus system. Results obtained from BFOA are also compared with Evolutionary Programing to determine the performance.
IEEE Transactions on Power Systems, 2008
This paper presents a framework to achieve an optimal power flow solution in a decentralized bilateral multitransaction-based market. An independent optimal dispatch solution has been used for each market. The interior point (IP)-based optimization technique has been used for finding a global economic optimal solution of the whole system. In this method, all the participants try to maximize their own profits with the help of system information announced by the operator. In the present work, a parallel algorithm has been used to find out a global optimum solution in decentralized market model. The study has been carried out on a modified IEEE-30 bus system. The results show that the suggested decentralized approach can provide a better optimal solution. The obtained results show the effectiveness of IP optimization-based optimal generator schedule and congestion management in the decentralized market.
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.
AIP Conference Proceedings, 2021
In the deregulated environment, the transmission grids are used optimally. This utilization of the transmission system makes some lines congested due to the capacity constraints of the line. Congestion becomes a barrier of power trading and it affects the security of the power system. Congestion Management (CM) acts as a major issue that threatens the system security and it is a most difficult task for the system operators. This paper tries to introduce a novel optimization based CM model with advanced soft computing technique. An algorithm is introduced in this paper to deal with CM, which obviously optimize the generating power of added generators with the bus system. This manages the congestion with minimum rescheduling cost. The proposed optimization algorithm termed as Whale Optimization algorithm (WOA) involves in the management of congestion optimally. Subsequently, the experimentation is performed in the test bus system of 118 bus systems. The effectiveness of proposed model is compared with the conventional methods, with respect to cost and convergence.
Advances in Intelligent Systems and Computing, 2018
The practitioners and researchers have received considerable attention solving complex optimization problems with meta-heuristic algorithms during the past decade. Many of these algorithms are inspired by various phenomena of nature. One of the promising solutions for secure and continuous power flow in the transmission line is rescheduling-based congestion management approach, but the base problem is rescheduling cost. To solve the congestion with minimized rescheduling cost, a new population-based algorithm, the Lion Algorithm (LA), is introduced in this paper. The basic motivation for development of this optimization algorithm is based on special lifestyle of lions and their cooperation characteristics. Based on some benchmark, Lion Algorithm (LA) is compared with the existing conventional algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), artificial bee colony (ABC) and firefly (FF) by analysing the convergence, cost and congestion. In IEEE 30 bus system, experimental investigation is carried out and the obtained results by the proposed algorithm Lion Algorithm (LA) in comparison with the other algorithms used in this paper.
With appearance of deregulated electricity markets, line congestions in transmission systems is considered as critical problem for power system which may prevent dispatching all of the contracted power transactions. Many studies have been carried out to express techniques for congestion management (CM). In this paper real and reactive power rescheduling of generators approach is utilized. This method includes two steps. First one is optimum selection of generators on the basis of sensitivities of generator to power flow on congested line/lines. Next step is optimal rescheduling of generators and identification of capacitors required to provide real and reactive power support for CM. Optimization process in this paper is carried out using big bang-big crunch (BB-BC) algorithm which is improved by particle swarm optimization (PSO) method as hybrid BB-BC (HBB-BC) optimization for the first time. Effectiveness of proposed method has been tested on IEEE 30-bus system, the 39-bus New England system, and 75-bus Indian system and compared with the other proposed methodologies.
In this paper proposes an efficient technique to solve Economic load dispatch with optimal power flow problem. The optimal power flow problem consists the real and reactive power flow problems. The real power problem is the traditional economic dispatch considering the minimum fuel cost. Modified bacterial foraging algorithm (MBFA) method is used for solving the non-linear optimization problems. The optimization technique inspired by the foraging behavior of the E.Coli bacteria. Simulation results for IEEE-30 bus network are presented to show the effectiveness of the proposed method. Our proposed approach satisfactorily finds global optimal solution within a smaller number of iteration. In this proposed MBFA-OPF, Newton-Raphson method and MBFA algorithm have been used for power flow and economic dispatch respectively. The solutions obtained are quite encouraging and useful in the present de-regulated environment.
IEEE Access
Managing transmission congestion had been a major problem with growing competition in the power networks. Accordingly, competitiveness emerges through the network's reconfiguration and the proliferation of secondary facilities. Congestion of transmission lines is a critical issue, and their regulation poses a technical challenge as the power system is deregulated. Therefore, the present research illustrates a multi-objective strategy for reaching the optimal capabilities of distributed generators (DG) like wind power plants and geothermal power-producing plants to alleviate congestion throughout the transmission network. Goals such as congestion management during power delivery, power loss reduction, power flow improvement with the enhancement of voltage profile, and investment expenditure minimization are considered to boost the network's technological and economic reliability. The congestion management is achieved using the locational marginal price (LMP) and calculation of transmission congestion cost (TCC) for the optimal location of DG. After identification of congested lines, DG is optimally sized by particle swarm optimization (PSO) and a newly proposed technique that combines the features of modified IL-SHADE and PSO called hybrid swarm optimization (HSO) which employs linear population size reduction technique which improves its performance greatly by reducing the population size by elimination of least fit individuals at every generation giving far better results than those obtained with PSO. In addition, optimal rescheduling of generations from generators has been done to fulfill the load demand resulting in alleviation of congested lines thereby enhancing the performance of the network under investigation. Furthermore, the performance of the proposed methodology of HSO and PSO has been tested successfully on standard benchmark IEEE-30 & IEEE-57 bus configurations in a MATLAB environment with the application of MATPOWER power system package.
IEEE Access
Independent System Operators have difficulty in fulfilling all contractual power transactions in a competitive energy market due to transmission network congestion. As a result, applications of generator rescheduling become one of the antidotes in alleviating this difficulty in the consequence of ever-increasing numerous power transactions. The goal of this research is to lower the cost of active and reactive power of the generators by reducing the deviation of rescheduled active and reactive power from scheduled values. The inclusion of reactive power rescheduling and voltage stability in this paper is innovative, as compare to other existing methodologies solely examine active power rescheduling. This paper made the following contributions: formulated a multi-objective function for congestion control in an electric transmission network. Furthermore, formulated the generator sensitivity factors to identify overloaded lines and which generators will be involved in congestion management. Developed a particle swarm optimization (PSO) algorithm to solve the multi-objective function of the transmission congestion management system. In addition, the developed PSO method for CM approach was validated on three IEEE standard test system networks (14, 30, and 118). The simulation results prove that reduces active and reactive power, lowering the cost of generator rescheduling, and demonstrating the usefulness of developed PSO method for transmission network congestion. Furthermore, voltage stability and voltage profile improvements demonstrate the performance effectiveness of the PSO algorithm used in this work. INDEX TERMS Congestion management, generator rescheduling, particle swarm optimization, sensitivity factors, and voltage stability. NOMENCLATURE Cost of rescheduling active power by the partaking generator in congestion management Cost of rescheduling reactive power by the partaking generator in congestion management ∆ Generator's active power adjustments ∆ Generator's reactive power adjustments Maximum voltage stability indicator Penalty factor Generator's maximum nominal apparent power Cost of generating reactive power by the generator Profit rate of reactive power generation Number of buses Active power produced at bus Reactive power produced at bus Active power demand at bus Reactive power demand at bus | | < Bus complex voltage | | < Bus complex voltage | | < Bus and mutual admittance Bus and impedance angle Minimum active power generation Maximum active power generation Minimum reactive power generation Maximum reactive power generation ∆ Change in minimum active power generation This article has been accepted for publication in IEEE Access.
International Journal of Computer Applications
This paper proposes an optimal congestion management approach under hybrid electricity market using Self organizing hierarchical particle swarm optimization with Time Varying Acceleration Coefficients (SPSO-TVAC). The aim of the proposed work is to minimize deviations from preferred transaction schedules and hence the congestion cost under hybrid electricity market. The values of Transmission Congestion Distribution factors (TCDFs) are used to select redispatch of generators. Generator reactive power support is considered to lower the congestion cost. Numerical results on IEEE 57 bus system is presented for illustration purpose and the results are compared with Particle swarm optimization (PSO) in terms of solution quality. The comprehensive experimental results prove that the SPSO-TVAC is one among the challenging optimization methods which is indeed capable of obtaining higher quality solutions for the proposed problem.
Computers & Mathematics with Applications, 2010
This paper proposes an optimal congestion management approach in a deregulated electricity market using particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC). Initially, the values of generator sensitivity are used to select redispatched generators. PSO-TVAC is used to determine the minimum redispatch cost. Test results on IEEE 30-bus and 118-bus systems indicate that the PSO-TVAC approach could provide a lower rescheduling cost solution compared to classical particle swarm optimization and particle swarm optimization with time-varying inertia weight.
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
In today's scenario there is a challenge for power companies to meet the expected demand load due to continuous increases in load demand causes unpredictable failure in the components of power system including transmission line, generator, transformer and various other equipment this leads to over loading in the power system and the line become congested if this failure not removed on time the system reaches to emergency state, therefore we are using various techniques to control or manage these situations these methods includes Generator Rescheduling(GR),Load Shedding, Particle Swarm Optimizer(PSO), Grey Wolf Optimization (GWO),Harmony search algorithm etc. Optimal load shedding is effective control action for congestion management. The various algorithm applied on IEEE 30 bus system.
Congestion management is one of the major tasks performed by system operators (SOs) to ensure the operation of transmission system within operating limits. To permit smooth and quality flow of power the problem of congestion has to be solved. The congestion in the transmission line will be removed by generation rescheduling with the cost involved in the rescheduling process should be minimized. T he literature, classical optimization techniques were applied to solve this problem. The main drawbacks of the classical optimization techniques are higher computation time requirement, nondifferentiable characteristics of objective function and inferior quality of solutions.
— In the competitive power market, congestion in the transmission line has grown as a serious problem which threats the power system security and reliability. Congested condition leads to the increased congestion cost and it is not wise to allow the situation to persist. In this work, real and reactive power rescheduling based congestion management is proposed to relieve the transmission congestion. For optimal rescheduling of the real and reactive power, the recently introduced nature black hole algorithm (BHA) is implemented. The objective of the present work is to minimize the total transmission congestion cost while adjusting the generation pattern to relieve congestion. The work uses the generator sensitivity indices for identifying the more influencing generators. The validation of the suggested method has been studied on modified IEEE 57 bus test system with two congestion cases of bilateral and multilateral transactions and the obtained numerical results are compared with other metaheuristic algorithms like particle swarm optimization (PSO) and big bang big crunch (BB-BC) algorithm. Keywords—black hole algorithm, deregulated power system, generator power rescheduling, real and reactive power sensitivity, Bilateral / Multilateral transactions.
In the competitive electricity market, it is not always possible to discharge all of the contracted power transactions due to congestion in the transmission lines. In most cases, Independent System Operator tries to remove congestion by rescheduling output power of the generators. In this research paper, active and reactive power generator sensitivity factors of the generators to the congested lines have been used to determine the number of generators participating in congestion management. Secondly, Particle Swarm Optimization (PSO) based algorithm has been suggested to minimize the deviations of rescheduled values of active and reactive powers of generators from scheduled values, considering voltage stability enhancement and voltage profile improvement criteria. Thus, rescheduling cost of active power and reactive power were minimized by PSO. The proposed algorithm has been tested on IEEE 30 bus system and results obtained have been compared with previous literature in terms of so...
International Journal of Swarm Intelligence Research, 2010
This paper proposes the Hybrid Particle Swarm Optimization (HPSO) method for solving congestion management problems in a pool based electricity market. Congestion may occur due to lack of coordination between generation and transmission utilities or as a result of unexpected contingencies. In the proposed method, the control strategies to limit line loading to the security limits are by means of minimum adjustments in generations from the initial market clearing values. Embedding Evolutionary Programming (EP) technique in Particle Swarm Optimization (PSO) algorithm improves the global searching capability of PSO and also prevents the premature convergence in local minima. A number of functional operating constraints, such as branch flow limits and load bus voltage magnitude limits are included as penalties in the fitness function. Numerical results on three test systems namely modified IEEE 14 Bus, IEEE 30 Bus and IEEE 118 Bus systems are presented and the results are compared with ...
International Journal on Electrical Engineering and Informatics, 2018
This paper proposes an application of constriction factor based particle swarm optimization technique for solving the congestion management problem in transmission networks. Congestion management is one of the vital tasks performed by the power system engineers to ensure the operation of transmission network within operating limits. Nowadays, congestion management in electric power trade is extremely significant which can enforce a barrier to the electrical energy trade. The congestion management technique is based on optimal power flow which is proposed to relieve congestion in transmission lines. It's been observed in the recent studies that alleviation of congestion in the transmission network is a serious problem in the power system operation. To maintain power system security by keeping all equipment in the system within the limit is the main objective of optimal power flow. The proposed constriction factor based particle swarm optimization combines the original particle swarm optimization algorithm with a constriction factor. It ensures convergence of particle swarm optimization algorithm to obtain optimal power flow solutions. The IEEE 30-bus system is tested by the proposed algorithm.
Electric Power Components and Systems, 2015
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