Hybridization of Metaheuristic Algorithms for Optimal Location and Capacity in Radial Distribution System

Samala, Rajesh Kumar; Kotapuri, Mercy Rosalina

doi:10.1109/icpedc47771.2019.9036661

Cited by 1 publication

(2 citation statements)

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“…In MATLAB, gammaincinv (giniv) is the inverse incomplete gamma function, which computes the opposite of the incomplete gamma function concerning the integration limit x as gammaincinv(x, a). For the case under study, x = 0.1 and a [0, 1] where Equation ( 13) explains a. a = tr t max (13) where t max is the maximum number of iterations, and tr is a number that initializes equal to the maximum number of iterations but decreases by one unit as the iteration concludes. On the other hand, it is defined that t max can vary depending on the size of the system, which implies that the variation of a will change in the steps determined by t max .…”

Section: Vortex Search Algorithm (Vsa)mentioning

confidence: 99%

“…These algorithms use advantages that vary according to the implemented numerical optimization methods, which are adaptable algorithms to any problem. This variety allows finding a space solution that reduces losses [12] and costs of the distributed generation [13], which is because the algorithm provides the information necessary to determine the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Integration of Photovoltaic Sources in Distribution Networks for Daily Energy Losses Minimization Using the Vortex Search Algorithm

et al. 2021

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This paper deals with the optimal siting and sizing problem of photovoltaic (PV) generators in electrical distribution networks considering daily load and generation profiles. It proposes the discrete-continuous version of the vortex search algorithm (DCVSA) to locate and size the PV sources where the discrete part of the codification defines the nodes. Renewable generators are installed in these nodes, and the continuous section determines their optimal sizes. In addition, through the successive approximation power flow method, the objective function of the optimization model is obtained. This objective function is related to the minimization of the daily energy losses. This method allows determining the power losses in each period for each renewable generation input provided by the DCVSA (i.e., location and sizing of the PV sources). Numerical validations in the IEEE 33- and IEEE 69-bus systems demonstrate that: (i) the proposed DCVSA finds the optimal global solution for both test feeders when the location and size of the PV generators are explored, considering the peak load scenario. (ii) In the case of the daily operative scenario, the total reduction of energy losses for both test feeders are 23.3643% and 24.3863%, respectively; and (iii) the DCVSA presents a better numerical performance regarding the objective function value when compared with the BONMIN solver in the GAMS software, which demonstrates the effectiveness and robustness of the proposed master-slave optimization algorithm.

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Section: Vortex Search Algorithm (Vsa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%