An evolutive Algorithm (EA) for wind farm optimal overall design is presented. The algorithm objective is to optimize the profits given an investment on a wind farm. Net Present Value (NPV) will be used as a figure of...
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An evolutive Algorithm (EA) for wind farm optimal overall design is presented. The algorithm objective is to optimize the profits given an investment on a wind farm. Net Present Value (NPV) will be used as a figure of the revenue in the proposed method. To estimate the NPV is necessary to calculate the initial capital investment and net cash flow throughout the wind farm life cycle. The maximization of the NPV means the minimization of the investment and the maximization of the net cash flows (to maximise the generation of energy and minimise the power losses). Both terms depend mainly on the number and type of wind turbines, the tower height and geographical position, electrical layout, among others. Besides, other auxiliary costs must be to keep in mind to calculate the initial investment such as the cost of auxiliary roads or tower foundations. The difficulty of the problem is mainly due to the fact that there is neither analytic function to model the wind farm costs nor analytic function to model net generation. The complexity of this problem arises not only from a technical point of view, due to strong links between its variables, but also from a purely mathematical point of view. The problem consists of both discrete and continuous variables, being therefore an integer-mixed type problem. The problem exhibits manifold optimal solutions (convexity), some variables have a range of non allowed values (solutions space not simply connected) and others are integers. This fact makes the problem non-derivable, preventing the use of classical analytical optimization techniques. (C) 2010 Elsevier Ltd. All rights reserved.
The optimum wind farm configuration problem is discussed in this paper and an evolutive algorithm to optimize the wind farm layout is proposed. The algorithm's optimization process is based on a global wind farm c...
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The optimum wind farm configuration problem is discussed in this paper and an evolutive algorithm to optimize the wind farm layout is proposed. The algorithm's optimization process is based on a global wind farm cost model using the initial investment and the present value of the yearly net cash flow during the entire wind-farm life span. The proposed algorithm calculates the yearly income due to the sale of the net generated energy taking into account the individual wind turbine loss of production due to wake decay effects and it can deal with areas or terrains with non-uniform load-bearing capacity soil and different roughness length for every wind direction or restrictions such as forbidden areas or limitations in the number of wind turbines or the investment. The results are first favorably compared with those previously published and a second collection of test cases is used to proof the performance and suitability of the proposed evolutive algorithm to find the optimum wind farm configuration. (C) 2010 Elsevier Ltd. All rights reserved.
Optimal parameter model finding is usually a crucial task in engineering applications of classification and modelling. The exponential cost of linear search on a parameter grid of a given precision rules it out in all...
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Optimal parameter model finding is usually a crucial task in engineering applications of classification and modelling. The exponential cost of linear search on a parameter grid of a given precision rules it out in all but the simplest problems and random algorithms such as uniform design or the covariance matrix adaptation-evolution strategy (CMA-ES) are usually applied. In this work we shall present two focused grid search (FGS) alternatives in which one repeatedly zooms into more concentrated sets of discrete grid points in the parameter search space. The first one, deterministic FGS (DFGS), is much faster than standard search although still too costly in problems with a large number of parameters. The second one, annealed FGS (AFGS), is a random version of DFGS where a fixed fraction of grid points is randomly selected and examined. As we shall numerically see over several classification problems for multilayer perceptrons and support vector machines, DFGS and AFGS are competitive with respect to CMA-ES, one of the most successful evolutive black-box optimizers. The choice of a concrete technique may thus rest in other facts, and the simplicity and basically parameter-free nature of both DFGS and AFGS may make them worthwile alternatives to the thorough theoretical and experimental background of CMA-ES. (C) 2009 Elsevier B.V. All rights reserved.
In this paper we propose a hybrid method, combining heuristics and local search, to solve flow shop scheduling problems under uncertainty. This method is compared with a genetic algorithm from the literature, enhanced...
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ISBN:
(纸本)9783540730521
In this paper we propose a hybrid method, combining heuristics and local search, to solve flow shop scheduling problems under uncertainty. This method is compared with a genetic algorithm from the literature, enhanced with three new multi-objective functions. Both single objective and multi-objective approaches are taken for two optimisation goals: minimisation of completion time and fulfilment of due date constraints. We present results for newly generated examples that illustrate the effectiveness of each method.
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