Abstract
In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.
Highlights
In electrical power systems, some problems correspond to the high values of energy losses [1]
The Chu and Beasley genetic algorithm (CBGA) is in charge of determining the nodes regarding the D-STACOM location, and the second-order cone programming (SOCP) model solves the optimal multi-period power flow problem to establish the optimal D-STATCOM sizes; even though this methodology is efficient to solve the problem, it has three main difficulties: (i) the SOCP only works with pure-radial distribution networks; (ii) the SOCP only works with the minimization of the energy losses in the networks; and (iii) the processing times of the methodology can increase significantly as a function of the number of nodes in the distribution system
For the solution of the Mixed Integer Non-linear Programming (MINLP) (Equations (1)–(8)) model associated with the location and optimal dimensioning of D-STATCOM in electrical distribution networks proposed in Section 2, it suggests a master–slave optimization methodology [2]
Summary
Some problems correspond to the high values of energy losses [1]. This article proposes the installation and optimal sizing of D-STATCOM in distribution systems to reduce annual operating costs associated with energy losses. The authors in [16] propose an optimization method based on a multi-target particle swarm algorithm; this allows finding the optimal location and dimensioning of D-STATCOM in distribution systems. This method takes into account the possibility of reconfiguring the network for different demand scenarios. The authors of [18] propose a heuristic method based on power and voltage loss indicators to optimally locate and size D-STATCOM in radial networks to reduce energy losses.
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