An Efficient Simulated Annealing Algorithm for Network Reconfiguration in Large-Scale Distribution Systems

Jeon, Oc-Yeub; Lee, Myeong Soo; Shin, Jae Hoon

doi:10.1109/mper.2002.4312424

Cited by 76 publications

(76 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Step 5: Update the bus voltage using Equation (10), where no_load V is the no-load voltage at each bus, that is:…”

Section: Power Flow Algorithmmentioning

confidence: 99%

“…Civanlar et al [9] proposed a branch-exchange method to minimize the number of switching operations; however, this approach is not systematic and can only reduce power loss. Jeon et al [10] presented a simulated annealing algorithm for network reconfiguration; this algorithm was easy to code but required considerable computation time in large-scale systems. Venkatesh and Ranjan [11] proposed an approach that used an evolutionary programming with fuzzy adaptation as a solution technique; however, as a system grew larger, this method became increasingly complex.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm

Huang

Chen

et al. 2015

Energies

View full text Add to dashboard Cite

This study aimed to minimize energy losses in traditional distribution networks and microgrids through a network reconfiguration and phase balancing approach. To address this problem, an algorithm composed of a multi-objective function and operation constraints is proposed. Network connection matrices based on graph theory and the backward/forward sweep method are used to analyze power flow. A minimizing energy loss approach is developed for network reconfiguration and phase balancing, and the particle swarm optimization (PSO) algorithm is adopted to solve this optimal combination problem. The proposed approach is tested on the IEEE 37-bus test system and the first outdoor microgrid test bed established by the Institute of Nuclear Energy Research (INER) in Taiwan. Simulation results demonstrate that the proposed two-stage approach can be applied in network reconfiguration to minimize energy loss.

show abstract

“…Step 5: Update the bus voltage using Equation (10), where no_load V is the no-load voltage at each bus, that is:…”

Section: Power Flow Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm

Huang

Chen

et al. 2015

Energies

View full text Add to dashboard Cite

show abstract

“…Over the recent years, reconfiguration has been addressed through stochastic, that is, metaheuristic methods, such are the methods based on simulated annealing algorithms [7], fuzzy logics [5], tabu search [12], ant colony [11], and genetic algorithms [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Distribution Network Reconfiguration Considering Power Losses and Outages Costs Using Genetic Algorithm

Nuhanovic¹,

Hivziefendić²,

Hadžimehmedović³

2013

Journal of Electrical Engineering

View full text Add to dashboard Cite

This paper discusses the problem of finding the optimal network topological configuration by changing the feeder status. The reconfiguration problem is considered as a multiobjective problem aiming to minimize power losses and total interruptions costs subject to the system constraints: the network radiality voltage limits and feeder capability limits. Due to its complexity, the metaheuristic methods can be applied to solve the problem and often the choice is genetic algorithm. NSGA II is used to solve the multiobjective optimization problem in order to get Pareto optimal set with possible solutions. The proposed method has been tested on real 35 kV distribution network. The numerical results are presented to illustrate the feasibility of the proposed genetic algorithm.

show abstract

“…Simulated annealing methods are developed to solve the network reconfiguration problem in [10] and [11]. Genetic algorithm considering multiobjective [12], a fuzzy mutated genetic algorithm [13] and refined genetic algorithm [14] for optimal network configuration are published in the literature.…”

Section: Introductionmentioning

confidence: 99%

Radial Distribution Network Reconfiguration for Loss Reduction and Load Balancing using Plant Growth Simulation Algorithm

Rao¹,

Sivanagaraju²,

Nagar³

2010

ijeei

View full text Add to dashboard Cite

Network reconfiguration is a combinatorial optimization problem because it accounts various operational constraints in distribution systems. Plant Growth Simulation Algorithm has emerged as a useful optimization tool for handling nonlinear programming problems. In this paper, Plant Growth Simulation Algorithm has been proposed with a view to enhance speed and robustness. This method has been applied successfully on some benchmark mathematical problems. The solution for loss reduction through network reconfiguration involves a guiding search over the relevant configurations. The branch load balancing index and system load balancing index are defined for formulation of objective function of load balancing problem. Most of the methods published in the literature require external parameters such as barrier factors, crossover rate etc. The main advantage of the method presented here does not require external parameters. The proposed method is tested to reconfigure 69-node radial distribution system for loss minimization and load balancing.

show abstract

An Efficient Simulated Annealing Algorithm for Network Reconfiguration in Large-Scale Distribution Systems

Cited by 76 publications

References 14 publications

A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm

A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm

Distribution Network Reconfiguration Considering Power Losses and Outages Costs Using Genetic Algorithm

Radial Distribution Network Reconfiguration for Loss Reduction and Load Balancing using Plant Growth Simulation Algorithm

Contact Info

Product

Resources

About