Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization

Dutta, Susanta; Singh, S. P.

doi:10.1109/tpwrs.2008.922647

Cited by 277 publications

(143 citation statements)

References 21 publications

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“…Whereas genetic algorithms discard the weakest chromosomes immediately, but PSO keeps on over time and influences the search space. PSO has proved in yielding promising results in generation rescheduling for congestion management [5] [6].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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The Phenomenal Alleviation of Transmission Congestion by Optimally Placed Multiple Distributed Generators Using PSO

Muthulakshmi¹,

Sasiraja²,

Kumar³

2016

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In the current electricity paradigm, the rapid elevation of demands in industrial sector and the process of restructuring are the main causes for the overuse of transmission systems. Hence, the evolution of novel technology is the ultimate need to avoid the damages in the available transmission systems. An appreciable volume of renewable energy sources is used to produce electric power, after the implementation of deregulation in power system. Even though, they are intended to improve the reliability of power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand and the sudden failures of vital equipment cause transmission congestion in one or some transmission lines. Generation rescheduling and load shedding can be used to alleviate congestion, but some cases require quite few improved methods. With the extensive application of Distributed Generation (DG), congestion management is also performed by the optimal placement of DGs. Therefore, this research employs a Line Flow Sensitivity Factor (LFSF) and Particle Swarm Optimization (PSO) for the determination of optimal location and size of multiple DG units, respectively. This proposed problem is formulated to minimize the total system losses and real power flow performance index. This approach is experimented in modified IEEE-30 bus test system. The results of N-1 contingency analysis with DG units prove the competence of this proposed approach, since the total numbers of congested lines get reduced from 15 to 2. Hence, the results show that the proposed approach is robust and simple in alleviating transmission congestion by the optimal placement and sizing of multiple DG units. K. Muthulakshmi et al. 1678

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…However, it does not take care about each load. Particle Swarm Optimization (PSO) is used to minimize the rescheduling cost of active power [5]. However, the effect of reactive power of generators and voltage stability constraints are ignored.…”

Section: Introductionmentioning

confidence: 99%

The Phenomenal Alleviation of Transmission Congestion by Optimally Placed Multiple Distributed Generators Using PSO

Muthulakshmi¹,

Sasiraja²,

Kumar³

2016

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“…Congestion management in open access ensuring voltage stability is proposed by the authors of (Yesturatnam & Thukaram, 2007). Congestion management based on optimum generation rescheduling and load shedding schemes are reported in (Talukdar et al, 2005;Dutta & Singh, 2008). Multi objective PSO for congestion management is presented in (Hazra & Sinha, 2007).…”

Section: Literature Surveymentioning

confidence: 99%

Application of Differential Evolution for Congestion Management in Power System

Balaraman¹,

Kamaraj²

2010

MAS

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In the emerging restructured power system, the congestion management (CM) has become extremely important in order to ensure the security and reliability of the system. This paper proposes an algorithm for congestion management in a pool based electricity market based on differential evolution (DE). The aim of the proposed work is to minimize deviations from preferred transaction schedules and hence the congestion cost. Numerical results on test system namely IEEE 30 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 DE is one among the challenging optimization methods which is indeed capable of obtaining higher quality solutions for the proposed problem.

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“…Ashwani Kumar proposed a zonal sensitivity-based optimum real and reactive power generation rescheduling method for congestion management [2]. Another technique for optimum selection of generators to be rescheduled is demonstrated in [3], which is based on generator sensitivities to the power flow on congested lines. Congestion management techniques in different deregulated electricity markets are estimated in [4].…”

Section: Introductionmentioning

confidence: 99%

A Congestion Management Approach Using Probabilistic Power Flow Considering Direct Electricity Purchase

Wang¹,

Jiang²

2015

Journal of Electrical Engineering and Technology

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-In a deregulated electricity market, congestion of the transmission lines is a major problem the independent system operator (ISO) would face. Rescheduling of generators is one of the most practiced techniques to alleviate the congestion. However, not all generators in the system operate deterministically and independently, especially wind power generators (WTGs). Therefore, a novel optimal rescheduling model for congestion management that accounts for the uncertain and correlated power sources and loads is proposed. A probabilistic power flow (PPF) model based on 2m+1 point estimate method (PEM) is used to simulate the performance of uncertain and correlated input random variables. In addition, the impact of direct electricity purchase contracts on the congestion management has also been studied. This paper uses artificial bee colony (ABC) algorithm to solve the complex optimization problem. The proposed algorithm is tested on modified IEEE 30-bus system and IEEE 57-bus system to demonstrate the impacts of the uncertainties and correlations of the input random variables and the direct electricity purchase contracts on the congestion management. Both pool and nodal pricing model are also discussed.

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Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization

Cited by 277 publications

References 21 publications

The Phenomenal Alleviation of Transmission Congestion by Optimally Placed Multiple Distributed Generators Using PSO

The Phenomenal Alleviation of Transmission Congestion by Optimally Placed Multiple Distributed Generators Using PSO

Application of Differential Evolution for Congestion Management in Power System

A Congestion Management Approach Using Probabilistic Power Flow Considering Direct Electricity Purchase

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