Optimal intelligent strategic
            <scp>LMP</scp>
            Solution and Effect of
            <scp>DG</scp>
            in Deregulated System for Congestion Management

Prashant, .; Siddiqui, Anwar Shahzad; Saxena, Abhinav

doi:10.1002/2050-7038.13040

Cited by 6 publications

(1 citation statement)

References 40 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the sensitivity of any overloaded sections due to power injection at any bus is often regarded as consistent with the earlier frameworks [2]. Similarly, the locational marginal pricing (LMP) approach is used to optimally place and scale distributed power production for power pools in energy sector markets [3]. The LMP is also being used for zonal congestion management which focuses on real and practical congestion delivery variables [4].…”

Section: Introductionmentioning

confidence: 91%

Effective Transmission Congestion Management via Optimal DG Capacity Using Hybrid Swarm Optimization for Contemporary Power System Operations

et al. 2022

Self Cite

View full text Add to dashboard Cite

Managing transmission congestion had been a major problem with growing competition in the power networks. 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. 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. The performance of the proposed methodology of HSO and PSO has been tested successfully on standard IEEE-30 & IEEE-57 bus configurations in a MATLAB environment with the application of MATPOWER power system package.

show abstract

Section: Introductionmentioning

confidence: 91%