Search

This paper proposes an alternative methodology to optimally design an electrical distribution system in a real urban area. A medium voltage (MV) network polygon is extracted from the web page of a distribution company (GIS platform), which serves as a comparison pattern. A redesign of the medium voltage network is made to the extracted polygon. Taking as a premise that it is a new urbanization that needs to be provided with electric power, with all the technical requirements imposed by the regulating entity, good power quality and at minimum cost. To achieve this goal, two mathematical optimization models are used. The methodology begins by delimiting the project area in a geo-referenced manner; in this area 36 possible sites are located where the distribution transformers that will provide electricity to the new urbanization can be placed. The first optimization model determines how many transformers will be installed, by means of a minimization objective function, the constraints of the model are: transformer capacity (KVA) and coverage (number of subscribers connected to the low voltage network of each transformer). Once the number of transformers to be installed and their respective geo-referenced optimal locations are determined, the model is run. The second heuristic optimization model, based on graph theory, calculates the mine spanning tree for the connection of the medium voltage electrical network, thus interconnecting the transformers of the project with the minimum distance, thus optimizing the construction cost. These optimization models are implemented and solved with Matlab and LpSolve software. The topology found with the two proposed models is electrically evaluated by running power flows with CYMEDIST software. Finally, the proposed model is evaluated by comparing the electrical parameters obtained with the electrical parameters of the polygon extracted from the GIS system of the distribution company. [ABSTRACT FROM AUTHOR]

In the past few decades structural optimization through metaheuristics has gain recognition in the scientific community; non the less, to guarantee good results we require a good selection of metaheuristic's parameters. In this paper we propose a multi-chromosome genetic algorithm with self-adaptive parameters, to optimize steel trusses in a threedimensional space. The design variable are the sections assign to each truss element of the structure. The optimization objective is the minimize the weight of the structure, considering the displacement y maximum stress as constrains. The propose algorithm was applied to the optimization of two trusses, obtaining designs that had a 35% less weight than the initial designs and comparable to results obtained in other papers. However, the adaptation of the parameters allows a more robust optimization process when analyzing different types of trusses and eliminates the initial runs of the optimization algorithm required to calibrate the initial parameters. [ABSTRACT FROM AUTHOR]

The uphill time-trial is a cycling race in which there is at least one mountain pass or a combination of low and high gradient sections. Usually, climbing cyclists achieve good results in uphill time-trials; however, in addition to the cyclist's ability, the race strategy is also important for achieving good results. As part of the race strategy, the selection of the type of bicycle is important. The time-trial bicycles usually reduce aerodynamic drag while the traditional road bicycles are lighter. Taking into account the road gradient profile of the race, as part of the bicycle selection strategy, the cyclist can change the type of bicycle during the race to take advantage of each one in specific sections of the route. This paper presents a methodology for planning the bicycle change strategy for some ideal routes with simplified road gradient profiles. An optimization problem is stated to minimize the race time and to find the location on the route where the bicycle change must be done. The methodology is applied to three simplified road profiles to define if the bicycle change strategy is beneficial when different cyclist' power output levels are analyzed. [ABSTRACT FROM AUTHOR]