Optimal reconfiguration and renewable distributed generation allocation in electric distribution systems

Sambaiah, Kola Sampangi; Jayabarathi, T.

doi:10.1080/01430750.2019.1583604

Cited by 77 publications

(60 citation statements)

References 40 publications

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“…The result of IBSA is identical to that of PSO and CGA [10]. Compared to FA, HTELA [13], SFS [11] and SSA [15], power loss reduction of IBSA is respectively 2.58%, 0.49%, 0.01% and 0.02% higher and the minimum voltage amplitude of IBSA is 0.0067 and 0.0003 higher than that of the HTELA [13] and SSA [15] methods. The current of branches of the system before and after reconfiguration is shown in Figure 6 (a).…”

mentioning

confidence: 64%

“…The result of IBSA is identical to that of PSO, CGA [10] and better than that of FA. Compared with HTELA [13], GWO-PSO [12], SSA [15] and SFS [11] power loss reduction of IBSA is 0.21% lower but the minimum voltage amplitude is 0.0034 higher than that of the aforementioned methods. The current of branches of the system before and after reconfiguration is shown in Figure 5 (a).…”

mentioning

confidence: 87%

“…However, network reconfiguration is a nonlinear and discrete problem that needs to have effective solving methods. For finding optimal network configuration, there are a lot of solving methods consisting of methods based on mathematical approaches [1][2][3][4] methods based on experience of operating the power system [5][6][7] and methods developed from metaheuristic algorithms [8][9][10][11][12][13][14][15][16]. By using the methods based on mathematical approaches, description of network reconfiguration problem and solving process are complicated because the network reconfiguration problem has to be linearized.…”

Section: Introductionmentioning

confidence: 99%

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Optimal distribution network configuration using improved backtracking search algorithm

Nguyen

2020

TELKOMNIKA

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Optimal network configuration is one of the effective approaches for power loss reduction of the distribution network. This paper shows a network reconfiguration method using improved backtracking search algorithm (IBSA). Wherein, IBSA is improved in the process of generating randomly the initialization population. The network reconfiguration method based on IBSA is used to find the optimal network configuration for the 33-node and 69-node systems. The results are compared to the original backtracking search algorithm (BSA), particle swarm optimization (PSO), firefly algorithm (FA) and previous approaches. From the compared results, IBSA can determine the optimal network configuration with higher success rate than BSA, PSO, FA and lower power loss than other previous approaches. As a result, IBSA is an effective approach for finding the optimal network configuration.

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confidence: 64%

mentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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Optimal distribution network configuration using improved backtracking search algorithm

Nguyen

2020

TELKOMNIKA

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“…(18) the leader (first salp) only updates its site with respect to the food source ( . is the coefficient of exploration and exploitation, which can be determined according to the following equation [17]: (19) Where l is the present iteration, and L is the maximum, number, of iterations. By utilization of newton's law of motion to update the follower's site by the following equation [17]:…”

Section: Salp Swarm Algorithmmentioning

confidence: 99%

Optimum Simultaneous Distributed Generation Units Insertion and Distribution Network Reconfiguration Using Salp Swarm Algorithm

.Mashal

Al-Rubayi²,

Kdair³

2020

ETJ

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Contemporary researches offer that most researchers have concentrated on either network reconfiguration or Distributed Generation (DG) units insertion for boosting the performance of the distribution system (DS). However, very few researchers have been studied optimum simultaneous distributed generation units insertion and distribution networks reconfiguration (OSDGIR). In this paper, the stochastic meta-heuristic technique belong to swarm intelligence algorithms is proposed. Salp Swarm Algorithm (SSA) is inspired by the behavior of salps when navigating and foraging in the depth of the ocean. It utilized in solving OSDGIR. The objective function is to reduce power loss and voltage deviation in the Distribution System. The SSA is carried out on two different systems: IEEE 33-bus and local Iraqi radial (AL-Fuhood distribution network). Three cases are implemented; only reconfiguration, only DG units insertion, and OSDGIR. Promising results were obtained, where that power loss reduced by 93.1% and recovery voltage index enhanced by 5.4% for the test system and by 78.77% reduction in power loss and 8.2% improvement in recovery voltage for AL-Fuhood distribution network after applying OSDGIR using SSA. Finally, SSA proved effectiveness after an increase in test system loads by different levels in terms of reduced power loss and voltage deviation comparison with other methods

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“…In [ 34 ], an improved Elitist–Jaya (IEJAYA) algorithm is proposed for minimizing the real power losses and loadability enhancement via joint optimal location and rating of DGs and reconfiguration of distribution system. In [ 35 ], Salp Swarm Algorithm (SSA) is implemented for minimizing the losses and voltage deviation in solving the simultaneous OADG and ONR problem together in the distribution system.…”

Section: Introductionmentioning

confidence: 99%

A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience

Janamala

2021

SN Appl. Sci.

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A new meta-heuristic Pathfinder Algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications.

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Optimal reconfiguration and renewable distributed generation allocation in electric distribution systems

Cited by 77 publications

References 40 publications

Optimal distribution network configuration using improved backtracking search algorithm

Optimal distribution network configuration using improved backtracking search algorithm

Optimum Simultaneous Distributed Generation Units Insertion and Distribution Network Reconfiguration Using Salp Swarm Algorithm

A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience

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