Distribution network reconfiguration using a genetic algorithm with varying population size

Abdelaziz, Morad

doi:10.1016/j.epsr.2016.08.026

Cited by 108 publications

(53 citation statements)

References 10 publications

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“…These cases, including the base case, are categorized as follows: Base case: The distribution system is without considering the network reconfiguration and the DG installation. Case 1: It is similar to conventional methods in which network reconfiguration is only considered [4].…”

Section: Test System Descriptionmentioning

confidence: 99%

“…These researchers have believed that other methods such as capacitor placement and cable size upgrading might add more additional cost burdens to the distribution system utilities. In [4][5][6][7], the system losses and voltage profile are improved by formulating the network reconfiguration problem as a mixed-integer nonlinear optimization problem using various metaheuristic algorithms with considering operational constraints. Due to the rapid growth of the integration of the distributed generations (DGs) into the distribution system, the use of the network reconfiguration technique without considering the presence of the DGs is no longer applicable.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Network Reconfiguration and DG Integration in Power Distribution Systems Using Enhanced Water Cycle Algorithm

Ibrahim¹,

Alwash²,

Ahmed³

2020

IJIES

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This paper presents an Enhanced Water Cycle Algorithm (EWCA) to optimize the network reconfiguration and distributed generation (DG) integration simultaneously for minimizing system power losses and improving voltage stability index (VSI) in the distribution system while considering all operational constraints. For validation, the performance of the proposed method is compared with other methods, which utilized well-known meta-heuristic algorithms. Different cases for network reconfiguration and DG integration are carried out in order to evaluate the performance of the proposed method. The effectiveness of the proposed method is assessed using the IEEE 69-node radial distribution system. According to the simulation results obtained, the proposed method in which the simultaneous optimal network reconfiguration and the DG size and location are implemented can provide a remarkable solution in terms of power loss reduction and voltage profile improvement. The proposed method also proved its superiority compared with other existing methods in terms of power loss reduction (i.e., 84.2% loss reduction compared with the base case).

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Section: Test System Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Network Reconfiguration and DG Integration in Power Distribution Systems Using Enhanced Water Cycle Algorithm

Ibrahim¹,

Alwash²,

Ahmed³

2020

IJIES

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“…The proposed algorithm introduces several improvements related to the generation of the initial set of possible solutions as well as crossover and mutation steps in the genetic algorithm. Although genetic algorithms are often used in the optimal reconfiguration of a distribution networks [15][16][17][18][19][20][21][22][23][24], most of the approaches [16][17][18][19][20][21][22][23] don't provide an effective means of creating an initial population, as well as effective operators to implement a crossover and mutation process over the set of population individuals. Due to this, during the evolution process, a large number of generated individuals is often rejected and power flow calculations are often conducted for unfeasible individuals (network topologies), that don't provide the radial network topology or include the isolated parts of the network.…”

Section: Introductionmentioning

confidence: 99%

Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

et al. 2020

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This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often applicable only to the small scale distribution networks. Unlike these approaches, the algorithm described in this paper brings a number of uniqueness and improvements that allow its application to the distribution networks of real size with a high degree of topology complexity. The optimal distribution network reconfiguration is formulated for the two different objective functions: minimization of total power/energy losses and minimization of network loading index. In doing so, the algorithm maintains the radial structure of the distribution network through the entire process and assures the fulfilment of various physical and operational network constraints. With a few minor modifications in the heuristic part of the algorithm, it can be adapted to the problem of determining the distribution network optimal structure in order to equalize the network voltage profile. The proposed algorithm was applied to a variety of standard distribution network test cases, and the results show the high quality and accuracy of the proposed approach, together with a remarkably short execution time.

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“…However, both the studies did not consider renewable DG and reconfiguration. Researchers have also used other optimization techniques such as simulated annealing, artificial immune algorithm, vaccine‐enhanced artificial immune system, modified plant growth simulation algorithm, PSO, GA, runner root algorithm, harmony search algorithm, artificial bee colony algorithm, hybrid Harmony search and particle artificial bee colony algorithm, interval analysis, stochastic MILP, Ant Colony Optimization, hybrid receding horizon control and scenario analysis, nondominated sorting GA, fuzzy mutated GA, tabu search, Benders decomposition approach, and evolutionary programming . Summary of the reviewed literature is presented in Tables and .…”

Section: Introductionmentioning

confidence: 99%

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

Hesaroor

Das

2019

Int Trans Electr Energ Syst

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Summary Renewable energy sources are becoming popular day by day for distributed generation as technology is becoming cheaper and modular. Power loss reduction is a powerful incentive for a distribution system operator to promote distributed generation. This paper presents a method to minimize annual energy losses through the integration of nondispatchable renewable distributed generation (DG) units and network reconfiguration under varying load demand condition. The optimal location and DG power level are calculated, and then these data are used to determine DG plant size taking into account the nonavailability of renewable energy at certain times. A network reconfiguration strategy is proposed, which takes into account both the time‐varying load and the renewable energy source such that annual energy loss is minimum. The proposed methodology is applied to 33‐node and 118‐node test distribution system with different scenarios. Results show a substantial reduction in energy loss. A cost‐benefit analysis is also carried out.

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Distribution network reconfiguration using a genetic algorithm with varying population size

Cited by 108 publications

References 10 publications

Optimal Network Reconfiguration and DG Integration in Power Distribution Systems Using Enhanced Water Cycle Algorithm

Optimal Network Reconfiguration and DG Integration in Power Distribution Systems Using Enhanced Water Cycle Algorithm

Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

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