Optimal Power Flow based Congestion Management Methods for Competitive Electricity Markets

Saini, Ashish; Saxena, A. K.

doi:10.7763/ijcee.2010.v2.116

Cited by 15 publications

(8 citation statements)

References 12 publications

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“…As a result, congestion management is one of the key functions in the power system. Various congestion management schemes suitable for the different power systems have been reported in [1]- [5]. Sensitivity-based optimum generations rescheduling and/or load shedding schemes to get better alleviation of transmission lines are reported in [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

A Constriction Factor Based Particle Swarm Optimization for Congestion Management in Transmission Systems

Naik¹,

Raju²,

Rao³

2018

IJEEI

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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.

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Section: Introductionmentioning

confidence: 99%

A Constriction Factor Based Particle Swarm Optimization for Congestion Management in Transmission Systems

Naik¹,

Raju²,

Rao³

2018

IJEEI

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“…For regulators, grid reference price forecast results can help to improve the monitoring capability of electricity market operation and discover and resolve the problems in the How to cite this paper: Gao, G., Lo market. Also, government can formulate related policies by electricity prices and guide electricity market development [3].…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Electricity Price Forecasting Scheme for Power Market

Gao¹,

Lo²,

Lu³

et al. 2016

WJET

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Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model's parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1 st to July7 th 2010.

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“…In the literature, many methods have been reported for determining a secure operating point [1][2][3][4][5][6][7]. All these methods use optimization technique, which are complicated and timeconsuming from a computation point of view, especially for large systems.…”

Section: Introductionmentioning

confidence: 99%

Overload alleviation in electric power system using fuzzy logic

Pandiarajan¹,

Babulal²

2011

2011 International Conference on Computer, Communication and Electrical Technology (ICCCET)

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The electric power system is a complex system, whose operating condition may not remain at a constant value. This necessitates the power system operator be alert to keep the system performance under normal condition. The various contingencies like, large load variations, outage of components (transmission lines, transformers, generators, etc.) are more common. Any of these conditions causes overloads and system parameters to exceed the limits thus resulting in an insecure system. Hence the operator has to maintain the security level by proper analysis and reschedule the system accordingly. The most practiced techniques for overload alleviation is generator rescheduling and/or load shedding. The conventional method is to solve an optimal power flow problem to find out the rescheduling for overload alleviation. But this will not give the desired speed of solution. This paper considers the application of fuzzy logic to dispatch the generator to relieve the overload in power system. A fuzzy rule based controller is developed which reschedules the real power generation. The system parameters such as overload factor (OF), generation shift sensitivity factor (GSSF) and sensitivity of vulnerability index of generation system (SVIGS) are given to a fuzzy inference system (FIS) as inputs. The output from the FIS gives the quantity of generation to be rescheduled. The proposed approach is illustrated with 39 bus New England system. The validity of the proposed method is done using MATPOWER.Index Terms-fuzzy logic control, generator rescheduling, overload alleviation, sensitivity factors and vulnerability index.

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Optimal Power Flow based Congestion Management Methods for Competitive Electricity Markets

Cited by 15 publications

References 12 publications

A Constriction Factor Based Particle Swarm Optimization for Congestion Management in Transmission Systems

A Constriction Factor Based Particle Swarm Optimization for Congestion Management in Transmission Systems

A Short-Term Electricity Price Forecasting Scheme for Power Market

Overload alleviation in electric power system using fuzzy logic

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