Sensitivity and Particle Swarm Optimization-based Congestion Management

Pandya, Kartik S.; Joshi, S. K.

doi:10.1080/15325008.2012.749555

Cited by 24 publications

(15 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order show the effectiveness of the proposed strategy, the derived results have been compared with various methods discussed in the literature in Table . From the table, it is very clear that the CM cost obtained by the proposed method is only 1390.7 $/h, which is very less than those of Pandya and Joshi, Hazra and Sinha, and Saravanabalaji et al It is noted that the total real power reschedules for the congestion alleviation obtained by the proposed method are 46.1 MW, which is higher than that obtained in other techniques. This is due to the participation of loads in the congestion management.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…However, these methods fail to include the contribution of reactive power in congestion alleviation, ie, the reactive power sensitivities were not considered in selecting participating nodes. Pandya et al introduced real and reactive power sensitivity factors for selection of participating nodes. In this, the congestion management cost which includes both active and reactive power rescheduling cost was minimized.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel approach for congestion management using improved differential evolution algorithm

Saravanabalaji¹,

Ramar

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This article proposes a new methodology for Congestion Management (CM) in a deregulated power system based on Generator Rescheduling/Load curtailment. This work utilizes 4 different sensitivity factors derived from the modified Jacobian matrix, used to identify the participating generator and loads for CM effectively. Also, a novel strategy of combining sensitivity factors and bidding costs is employed for selecting the control variables namely generator real power rescheduling, generator voltage adjustment, and load curtailment at selected busses. Next, an Improved Differential Evolution Algorithm (IDE) has been recommended to solve CM with the objective of minimizing Congestion Management Cost (CMC). The standard IEEE 30‐bus system and IEEE 118‐bus system are used to evaluate the proposed scheme, and based on the obtained results, it is shown that the proposed method is superior to the other CM methods reported in the literature.

show abstract

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel approach for congestion management using improved differential evolution algorithm

Saravanabalaji¹,

Ramar

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…With an eye on estimating the number of generators needed in congestion administration, Pandya and Joshi (2013) have proficiently proposed dynamic and reactive power generator sensitivity features of the generators to the jam-packed lines. Thereafter, for the purpose of cutting back the variances of rescheduled values of dynamic power and reactive power of generators from scheduled values, a PSO based algorithm is initiated in respect of the voltage constancy upgrade and voltage profile growth benchmark.…”

Section: Recent Research Workmentioning

confidence: 99%

Minimal constraint based cuckoo search algorithm for removing transmission congestion and rescheduling the generator units

Chidambararaj

Chitra

2019

IJBIDM

View full text Add to dashboard Cite

In the paper, a minimal constraint based cuckoo search (CS) algorithm is proposed for solving transmission congestion problem by considering both increase and decrease in generation power. Thus, the proposed algorithm is used to optimise the real power changes of generator while transmission congestion occurred. Then, the power loss, generator sensitivity factor and congestion management cost of the system is evaluated by the proposed algorithm according to the transmission congestion. The proposed method is implemented in MATLAB working platform and their congestion management performance is analysed. The performance of the proposed method is compared with the other existing methods such as fuzzy adaptive bacterial foraging (FABF), simple bacterial foraging (SBF), particle swarm optimisation (PSO), and artificial neural network (ANN)-CS respectively. The congestion management cost is reduced up to 26.169%. Through the analysis of comparison, it is shown that the proposed technique is better and outperforms other existing techniques in terms of congestion management measures.

show abstract

“…Recently, the advancements in computation technology like parallel computation have stimulated many recent researchers to focus on the application of artificial intelligent (AI) techniques for CM problems in restructured power systems. Meta-heuristics algorithms like genetic algorithms (GA) [13,14], Particle Swarm Optimization (PSO) [15,16], etc., are applied to find the optimal settings of real power values which result in minimum congestion cost. The application of Differential Evolution (DE) for CM is discussed in [17,18].…”

Section: Introductionmentioning

confidence: 99%

Optimal generator rescheduling with distributed slack bus model for congestion management using improved teaching learning based optimization algorithm

Suganthi¹,

Devaraj

Thilagar

et al. 2018

Sādhanā

View full text Add to dashboard Cite

This article proposes a new strategy for Congestion Management (CM) through Generation Rescheduling (GR) with Distributed Slack Bus (DSB) model using Improved Teaching Learning Based Optimization (ITLBO) algorithm. Most of the previous research mainly focuses on rescheduling the existing generators for CM and least concerned about the consequence of this rescheduling on the slack bus. As a result, the slack bus is supposed to carry the entire residual effect of all other generators which leads to increased Congestion Management Cost (CMC). This work proposes a distributed slack bus model for reducing the excess burden on the slack bus and a twofold contribution is made to develop the proposed technique. First, is the selection of the participating generator for CM by means of Incremental Generator Sensitivity Factor (IGSF), which adopts the generator sensitivity as well as the bidding cost. Next, CM is formulated as an optimization problem with the objective function involving congestion management cost and solved using ITLBO algorithm, which incorporates the self-motivated learning concept with the basic TLBO operators. For evaluation, the standard IEEE 30-bus and IEEE 118-bus systems are used and the obtained results are compared with the other CM systems reported through literature.

show abstract

Sensitivity and Particle Swarm Optimization-based Congestion Management

Cited by 24 publications

References 24 publications

A novel approach for congestion management using improved differential evolution algorithm

A novel approach for congestion management using improved differential evolution algorithm

Minimal constraint based cuckoo search algorithm for removing transmission congestion and rescheduling the generator units

Optimal generator rescheduling with distributed slack bus model for congestion management using improved teaching learning based optimization algorithm

Contact Info

Product

Resources

About