Title Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMs
Authors Montoya O.D. , Chamorro H.R. , ALVARADO BARRIOS, LÁZARO, Gil-González W. , Orozco-Henao C.
External publication No
Means Appl. Sci.-Basel
Scope Article
Nature Científica
JCR Quartile 2
SJR Quartile 1
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104175856&doi=10.3390%2fapp11083353&partnerID=40&md5=f18a0e35666562e364bfa47129e00024
Publication date 01/01/2021
ISI 000644005100001
Scopus Id 2-s2.0-85104175856
DOI 10.3390/app11083353
Abstract This paper proposes a new hybrid master–slave optimization approach to address the problem of the optimal placement and sizing of distribution static compensators (D-STATCOMs) in electrical distribution grids. The optimal location of the D-STATCOMs is identified by implement-ing the classical and well-known Chu and Beasley genetic algorithm, which employs an integer codification to select the nodes where these will be installed. To determine the optimal sizes of the D-STATCOMs, a second-order cone programming reformulation of the optimal power flow problem is employed with the aim of minimizing the total costs of the daily energy losses. The objective function considered in this study is the minimization of the annual operative costs associated with energy losses and installation investments in D-STATCOMs. This objective function is subject to classical power balance constraints and device capabilities, which generates a mixed-integer non-linear programming model that is solved with the proposed genetic-convex strategy. Numerical validations in the 33-node test feeder with radial configuration show the proposed genetic-convex model’s effectiveness to minimize the annual operative costs of the grid when compared with the optimization solvers available in GAMS software. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords Annual operational cost minimization; Chu and Beasley genetic algorithm (CBGA); Daily active and reactive demand curves; Distribution static compensators (D-STATCOMs); Radial distribution networks; Re
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