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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]

Introduction-- A mathematical model for flexible electrical loads and energy storage system scheduling is presented in this paper, leveraged by the hourly rates according to resolution CREG 015/2018 and analyzed at the residential sector in the context of new demand curves resulting from measures against the COVID-19 pandemic. Objectives-- Three mathematical optimization models are proposed, aiming to find the lowest billing cost for the end-user through flexible load characteristics and seeking optimization through mixed-integer linear programming models. Methodology-- For the calculation of hourly rates, the mathematical formulation described in resolution CREG 015 is implemented and hourly rates are obtained in three different levels following a logic similar to the demand response schemes called Time of Use. Results-- The results obtained show that a demand response program with hourly rates effectively introduces an economic incentive during the evaluated period, through the possibility of modifying consumption patterns that reduce the payment of the electricity bill. Conclusions-- The proposed model of energy demand through scheduling selected flexible loads model and the opportunity to use the energy stored in electric vehicles generates economic benefits for the user by minimizing the cost of energy during the teleworking period in COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Mathematical Model of a Series-Parallel (SP) photovoltaic (PV) generator represents each module through an equivalent electrical circuit denominated single diode model, this model has associated a nonlinear equation system that describes the electrical behavior of SP generator. This paper presents a solution of this system using optimization methods widely using: Trust Region Dogleg and Particle swarm optimization (PSO) for solving the electrical model of PV generator operating under homogeneous or non-homogeneous conditions, changing the number of submodules and shading pattern. It has been made simulations about generators composed by 3 and 15 series submodules, operating under different partial shading conditions. Between the implemented methods, Trust Region Dogleg show a better performance than other methods, with 2 and 14 times less computation time than the reference method and PSO, respectively, and a RMSE equal or 50% lower than PSO. [ABSTRACT FROM AUTHOR]

Proper management of projects has become an element of vital importance for the solution to problems of society. In this context, there are two main objectives to be solved: the construction of project plans which may comply with the constraints of the problem and the optimization in terms of cost and time of those plans. To this end, all activities of project must be organized in such way that the constraints related to the precedence among them and the availability of renewable and non-renewable resources at each instant of time are met. The aim of this paper is to present an Estimation of Distribution Algorithm (EDA), which incorporates the handling of the constraints in the probabilistic model, for the construction of optimal or quasi-optimal project plans. For the validation of the algorithm, authors used both the PSPLib database repository for the development of the scheduling research and the databases of projects of the Repository for Research in Project Management, University of Infomatic Sciences. [ABSTRACT FROM AUTHOR]

With the simulation and optimization of a manufacturing system, the input parameters are sought to obtain the best performance of the system, considering in many cases a single objective or measure of performance. However, many decisions involve multiple criteria, usually in conflict. Currently, companies are required to improve their profitability as well as to reduce their impact on the environment and use energy rationally. In this paper, the wood products manufacturing process of a small company was studied to determine redesign alternatives, with simultaneous economic and environmental criteria. A simulation model of the system was built, which was validated and verified, and an optimization model for it in which two objectives were combined in a single function, incorporating in it the preferences of the decision makers with respect to each criterion. Results expose that combining both criteria it is possible to achieve substantial improvements in the benefit, at the same time that generating less emissions than if only the economic objective were pursued. It is concluded that simulation and optimization combining both criteria can improve economic-environmental performance in wood manufacturing systems, even in small companies. [ABSTRACT FROM AUTHOR]