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

Ibrahim, Sarmad; Alwash, Shamam; Ahmed, Ammar

doi:10.22266/ijies2020.0229.35

Cited by 11 publications

(8 citation statements)

References 26 publications

(46 reference statements)

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“…Additionally, in the presence of distributed generations (DGs), electricity network automation has been addressed based on the PNR and optimal operation of dispatchable and non-dispatchable DGs [14]. Numerous research have also been conducted using several objective features and optimization approaches such as Mixed-Integer Linear Programming [15], equilibrium optimizer [16], hybrid of analytical approach and particle swarm optimization [17,18], Water Cycle Algorithm [19] and Chaos Disturbed Beetle Antennae Search [20].…”

Section: Introductionmentioning

confidence: 99%

Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm

et al. 2022

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In this paper, the electricity network automation based on Power Network Reconfiguration (PNR) is implemented to improve the operational reliability of distribution systems using jellyfish search algorithm. For this purpose, system average interruption frequency index (SAIFI), system average interruption unavailability index (SAIUI) and total energy not supplied (TENS) are critical measures. In this paper, a new optimization technique of jellyfish search (JFS) algorithm is employed for distribution network reconfiguration for reliability improvement. It is concerned with the moving patterns of jellyfish. They are divided into three categories. The jellyfish could flow towards the ocean current or between its own swarm. Meanwhile, whenever the food supply is adequate, the jellyfishes are attracted to its location. It is formulated considering the three reliability indices of SAIFI, SAIUI and TENS, simultaneously in a multi-objective model based on the weight factors. The proposed methodology based on the JFS optimizer is implemented on an IEEE 33-node distribution network. According to the numerical results, the SAIFI, SAIUI and TENS is improved by 36.44%, 34.11% and 33.35%, compared to the initial condition. For comparison purposes, tuna swarm optimizer and tunicate swarm algorithm, besides the JFS algorithm, are implemented as well. The simulation results declare the significant outperformance of the JFS algorithm compared to TUNA and TSA in terms of the obtained improvements and the regarding convergence properties.

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Section: Introductionmentioning

confidence: 99%

Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm

et al. 2022

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“…To minimize overall power losses and improve voltage and frequency profiles, the authors concentrated on determining the best placement and size for a photovoltaic distributed generation using particle swarm optimization and the genetic algorithm [11]. The DS's voltage stability index and network reconfiguration were both improved simultaneously by utilizing a modified water cycle algorithm in order to reduce system power losses while taking into account all operational restrictions [12].…”

Section: Introductionmentioning

confidence: 99%

Optimal DG Integration in Power Distribution Systems Using Coronavirus Herd Immunity Optimizer

2022

IJIES

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In this study, the problem of finding an optimal location and size of a distributed generator (DG) in distribution systems with considering operational distribution system constraints is proposed with the objective of maximizing DG hosting capacity (MDHC), reducing system loss, and improving voltage stability index (VSI). The proposed objective function is formulated as a multi-objective mixed-integer nonlinear optimization in order to solve it simultaneously. To solve this problem, the coronavirus herd immunity optimizer (CHIO), a bio-inspired metaheuristic optimization method, is herein proposed to simultaneously tackle a discrete and continuous DG integration problem in distribution systems. Extensive simulations on an IEEE 69-node system with different load levels and DG numbers are performed using MATLAB software to evaluate the efficacy of the proposed method. The simulation results demonstrate that the proposed method efficiently improves overall distribution system performance when compared to different DG numbers and load levels. Furthermore, the CHIO optimization method shows encouraging results and almost obtains the best results in all proposed cases when compared with well-known metaheuristic optimization methods such as genetic algorithm (GA), the hunger games search (HGS), the chaotic neural network algorithm (CNNA), and the water cycle algorithm (WCA). The CHIO can successfully offer a notable solution for the DG integration problem, and the obtained results, for example in case 1, revealed outperforming the CHIO compared to other methods in terms of the MDHC (i.e., 99.999 %), voltage profile improvement (i.e., the minimum voltage magnitude of 0.9696 p.u), VSI improvement (i.e., 29.16 %), and system loss reduction (i.e., 66.95 %) compared with the base case, respectively.

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“…In [9], the multi-goal ENRP combined with DGP for decreasing power loss and operating costs is considered by the sine-cosine algorithm. In [10], water cycle algorithm is applied to determine the optimal network configuration for reducing power losses and rising voltage stability index. In [11], the hybrid of exchange market and wild goats algorithms is successful used for the multi-goal ENRP with member goals of power loss and reliability indexes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Metaheuristic Method for Multi-Goal Electric Distribution Network Reconfiguration

2022

IJIES

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This paper demonstrates an artificial ecosystem optimization (AEO) for the multi-goal electric network reconfiguration (ENR) problem (ENRP). The membership goal functions of the ENRP comprise power loss reduction, voltage deviation reduction, reduction of load unbalance index among branches and reduction of number of switch operations. To show the advantages of AEO for the multi-goal ENRP, the multi-goal ENR approaches relied on particle swarm optimization (PSO), genetic algorithm (GA) and cuckoo search algorithm (CSA) are applied to contrast with the AEO method. The result comparisons among methods on the 33-node distribution network show that the AEO method can archive the optimal solution with the higher success rate and the lower average value and standard deviation of the fitness values than GA and PSO. In addition, the result comparisons with other previous approaches also show the reliability of the proposed AEO method for the single and multi-goal function ENRP. Therefore, the AEO can be an effective and useful approach for the ENRP to optimize the single and the multi-goal functions.

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

Cited by 11 publications

References 26 publications

Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm

Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm

Optimal DG Integration in Power Distribution Systems Using Coronavirus Herd Immunity Optimizer

A Novel Metaheuristic Method for Multi-Goal Electric Distribution Network Reconfiguration

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