Congestion Management Using Multi-Objective Glowworm Swarm Optimization Algorithm

Salkuti, Surender Reddy; Kim, Seong-Cheol

doi:10.1007/s42835-019-00206-w

Cited by 22 publications

(16 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This fuel cost curve can be represented as a quadratic convex or a set of piecewise-linear functions. Here, the fuel cost curve is modeled as a convex quadratic function [20], [21]…”

Section: B Objective Functions 1) Minimization Of the Total Fuel Cost (F C )mentioning

confidence: 99%

Multiobjective Optimal Power Flow Using a Semidefinite Programming-Based Model

Davoodi

Babaei

Mohammadi‐Ivatloo

et al. 2021

IEEE Systems Journal

View full text Add to dashboard Cite

show abstract

“…This fuel cost curve can be represented as a quadratic convex or a set of piecewise-linear functions. Here, the fuel cost curve is modeled as a convex quadratic function [20], [21]…”

Section: B Objective Functions 1) Minimization Of the Total Fuel Cost (F C )mentioning

confidence: 99%

Multiobjective Optimal Power Flow Using a Semidefinite Programming-Based Model

Davoodi

Babaei

Mohammadi‐Ivatloo

et al. 2021

IEEE Systems Journal

View full text Add to dashboard Cite

show abstract

“…Table III shows the optimum ratings and locations of the TCSC as well as the corresponding MSL, expected real power loss, the FACTS establishment cost and the BCS. In case of SLR, the TCSC are installed in line number 25 between buses (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), with rating of -0.72 pu, based on the BCS. In this case the MSL is 125%, the power losses is 6.46 MW and the TCSC establishment cost is US$ 02.67×10 6 .…”

Section: ) Scenario Twomentioning

confidence: 99%

“…The capability of FACTS controller in improving the network performance depends mostly on their sizes and locations [8]. Therefore, the proper installation of a FACTS controller at different locations in the network will give different results in terms of network loadability increase [9], loss reduction [6,10], static voltage stability enhancement [11], voltage profile improvement [12], available transfer capability improvement [13] and total generation fuel cost reduction [14] and Congestion management [15]. From the literature survey, it is found that many researchers have suggested different methods to find optimum allocations of FACTS controller for optimization of one or two objectives with or without the presence of renewable energy.…”

Section: Introductionmentioning

confidence: 99%

Allocation of FACTS Devices Using a Probabilistic Multi-Objective Approach Incorporating Various Sources of Uncertainty and Dynamic Line Rating

et al. 2020

View full text Add to dashboard Cite

Flexible AC transmission system (FACTS) controller play an essential role in increasing the penetration level of renewable energy resources owing to their ability in continuously controlling the active and reactive power flow in the network. This paper presents a probabilistic multi-objective optimization approach to obtain the optimal sizes and locations of static var compensators (SVCs) and thyristor-controlled series capacitors (TCSCs) in a power transmission network with high penetration level of wind generation. The objective of the problem is to maximize the system loadability while minimizing the network power losses and the installation cost of the FACTS controllers. In this study, the uncertainties associated with wind power generation and the correlated load demand are considered. The uncertainties are handled in this work using the points estimation method. Moreover, the dynamic line ratings (DLRs) of the transmission lines are considered in this work. In this case, the maximum transmission capacity of transmission lines is estimated dynamically according to the weather conditions. Considering the DLRs or transmission lines is expected to avoid unrealistic congestion in the network, and hence, improve its loadability. The optimization problem is solved using the multi-objective teaching-learning based optimization (MO-TLBO) algorithm to find the best locations and ratings for the FACTS controllers. Additionally, a technique based on the fuzzy decisionmaking approach is employed to extract one of the Pareto optimal solutions as the best compromise. The proposed approach is applied on the modified IEEE 30-bus system. The numerical results demonstrate the effectiveness of the proposed approach and show that the maximum loadability limit of the study system increases when considering the DLR. This limit can be enhanced to 123.0% without FACTS controller and 137.0 % ,130 % and 132.0% by SVC, TCSC and (SVC-TCSC) respectively.

show abstract

“…Integrating an adaptive ANN besides using a modified PSO approach was developed as a real-time hybrid optimization algorithm. In 2019, Surender Reddy Salkuti and Seong-Cheol Kim [2], presented a new CM algorithm in an OPF model in the background of restructured power markets. The developed CM issue was devised by taking into consideration the 2 objective models.…”

Section: Meta-heuristic Algorithm In Congestion Managementmentioning

confidence: 99%

“…An enhancement in power demand, unforeseen failures of generation, limitation on unscheduled power flow in lines, the structure of new lines, transmission lines tripping otherwise malfunctions of additional types of equipment are a few possible reasons for congestion. Considering this, reactive power sustains demand re-adjustment, generation rescheduling, et cetera are some choices which possibly will be developed to control the congestion issue [2].…”

Section: Introductionmentioning

confidence: 99%

A Review on Congestion Management Methodologies and its Applications

Raisi¹

2020

JCMPS

View full text Add to dashboard Cite

Since electric power industries are moving toward reconstruction of the power system, numerous challenges and key problems occur like congestion, operation, pricing, and management. In a power system, infringement of line loading and/or bus voltage limits because of different transactions during any time is called congestion. In the system, congestion might happen uneconomical operation and/or blackouts, a failure of the interconnected system and a disturbance. In the power system, Congestion Management plays a significant function in the operation of secure, economical, and stable operation in promising competitive electricity markets. This article surveys the conventional congestion management techniques for distribution networks using the meta-heuristic approaches in the current research literature. Moreover, it has to turn out to be a significant task in ever-developing challenges and factors that are forcing the development of novel methods. Also, this review discusses the congestion management techniques with diverse system applications are explained. In congestion management, the problems encountered with the roles and tasks of the methodologies are discussed. Several techniques and their compensation level are explained.

show abstract

Congestion Management Using Multi-Objective Glowworm Swarm Optimization Algorithm

Cited by 22 publications

References 33 publications

Multiobjective Optimal Power Flow Using a Semidefinite Programming-Based Model

Multiobjective Optimal Power Flow Using a Semidefinite Programming-Based Model

Allocation of FACTS Devices Using a Probabilistic Multi-Objective Approach Incorporating Various Sources of Uncertainty and Dynamic Line Rating

A Review on Congestion Management Methodologies and its Applications

Contact Info

Product

Resources

About