11 research outputs found

    Despacho multiobjetivo en sistemas de energía eléctrica en áreas múltiples mediante el método de satisfacción difusa

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    Los modelos matemáticos para solventar el problema de despacho económico a nivel de generación clásicamente se encuentran conducente a minimizar el costo operativo global del sistema, satisfaciendo el abastecimiento de la demanda en varios espacios de tiempo. En el momento actual, los sistemas de potencia reúnen una serie de generadores de fuentes de energía convencionales y renovables lo cual a más de una producción de energía económica ayuda en la mitigación de los gases de efecto invernadero. Por lo expuesto, el problema del despacho económico de generación puede ampliar su aplicación para el abastecimiento de la demanda en diferentes regiones y adicional se puede incorporar varias funciones objetivo cuyo resultado optimizará los recursos energéticos menorando los costos, obteniendo superioridades de tipo económico y técnico. Por esta razón, el presente documento plantea un modelo de optimización que determinará la potencia horaria de las distintas unidades de generación ubicadas en diferentes áreas del sistema eléctrico, que permita la minimización de las dos funciones objetivo, cumpliendo las restricciones técnicas, cuyo modelo obedece a un problema de programación no lineal que será resuelto aplicando GAMS.The mathematical models to solve the problem of economic dispatch at the generation level are classically conducive to minimizing the overall operating cost of the system, satisfying the supply of demand in various periods of time. At the present time, power systems bring together a series of generators from conventional and renewable energy sources which, in addition to economic energy production, helps in the mitigation of greenhouse gases. Therefore, the problem of the economic dispatch of generation can broaden its application to supply demand in different regions and, additionally, several objective functions can be incorporated, the result of which will optimize energy resources reducing costs, obtaining economic and technical superiorities. For this reason, this document proposes an optimization model that will determine the hourly power of the different generation units located in different areas of the electrical system, which allows the minimization of the two objective functions, complying with the technical restrictions, whose model obeys a nonlinear programming problem that will be solved by applying GAMS

    Using Prior Knowledge and Learning from Experience in Estimation of Distribution Algorithms

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    Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. One of the primary advantages of EDAs over many other stochastic optimization techniques is that after each run they leave behind a sequence of probabilistic models describing useful decompositions of the problem. This sequence of models can be seen as a roadmap of how the EDA solves the problem. While this roadmap holds a great deal of information about the problem, until recently this information has largely been ignored. My thesis is that it is possible to exploit this information to speed up problem solving in EDAs in a principled way. The main contribution of this dissertation will be to show that there are multiple ways to exploit this problem-specific knowledge. Most importantly, it can be done in a principled way such that these methods lead to substantial speedups without requiring parameter tuning or hand-inspection of models

    Evolutionary Algorithms with Mixed Strategy

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    Advances in Evolutionary Algorithms

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    With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

    Optimisation of wind turbine blade structures using a genetic algorithm

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    The current diminution of fossil-fuel reserves, stricter environmental guidelines and the world’s ever-growing energy needs have directed to the deployment of alternative renewable energy sources. Among the many renewable energies, wind energy is one of the most promising and the fastest growing installed alternative-energy production technology. In order to meet the production goals in the next few decades, both significant increases in wind turbine installations and operability are required, while maintaining a profitable and competitive energy cost. As the size of the wind turbine rotor increases, the structural performance and durability requirements tend to become more challenging. In this sense, solving the wind turbine design problem is an optimization problem where an optimal solution is to be found under a set of design constraints and a specific target. Seen the world evolution towards the renewable energies and the beginning of an implementation of a local wind industry in Quebec, it becomes imperative to follow the international trends in this industry. Therefore, it is necessary to supply the designers a suitable decision tool for the study and design of optimal wind turbine blades. The developed design tool is an open source code named winDesign which is capable to perform structural analysis and design of composite blades for wind turbines under various configurations in order to accelerate the preliminary design phase. The proposed tool is capable to perform a Pareto optimization where optimal decisions need to be taken in the presence of trade-offs between two conflicting objectives: the annual energy production and the weight of the blade. For a given external blade shape, winDesign is able to determine an optimal composite layup, chord and twist distributions which either minimizes blade mass or maximizes the annual energy production while simultaneously satisfying design constraints. The newly proposed graphical tool incorporates two novel VCH and KGA techniques and is validated with numerical simulation on both mono-objective and multi-objective optimization problems

    Evolutionary mechanism design using agent-based models

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    Real-Coded ECGA for Economic Dispatch

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    In this paper, we propose a new approach that consists of the extended compact genetic algorithm (ECGA) and split-ondemand (SoD), an adaptive discretization technique, to economic dispatch (ED) problems with nonsmooth cost functions. ECGA is designed for handling problems with decision variables of the discrete type, while the decision variables of ED problems are oftentimes real numbers. Thus, in order to employ ECGA to tackle ED problems, SoD is utilized for discretizing the continuous decision variables and works as the interface between ECGA and the ED problem. Furthermore, ED problems in practice are usually hard for traditional mathematical programming methodologies because of the equality and inequality constraints. Hence, in addition to integrating ECGA and SoD, in this study, we devise a repair operator specifically for making the infeasible solutions to satisfy the equality constraint. To examine the performance and effectiveness, we apply the proposed framework to two different-sized ED problems with nonsmooth cost function considering the valve-point effects. The experimental results are compared to those obtained by various evolutionary algorithms and demonstrate that handling ED problems with the proposed framework is a promising research direction
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