Multi-Objective Optimisation of the Benchmark Wind Farm Layout Problem

Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve the maximum power output and minimum wind farm cost. This may be accomplished by applying single or multi-objective optimisation techniques. In this paper, we apply a single objective hill-climbing algorithm (HCA) and three multi-objective evolutionary algorithms (NSGA-II, SPEA2 and PESA-II) to a well-known benchmark optimisation problem proposed by Mosetti et al., which includes three different wind scenarios. We achieved better results by applying single- and multi-objective algorithms. Furthermore, we showed that the best performing multi-objective algorithm was NSGA-II. Finally, an extensive comparison of the results of past publications is made.

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Multi-Objective Wind Farm Layout Optimization Considering Energy Generation and Noise Propagation With NSGA-II

Journal of Mechanical Design ◽

10.1115/1.4027847 ◽

2014 ◽

Vol 136 (9) ◽

Cited By ~ 17

Author(s):

Wing Yin Kwong ◽

Peter Yun Zhang ◽

David Romero ◽

Joaquin Moran ◽

Michael Morgenroth ◽

...

Keyword(s):

Wind Farm ◽

Wind Farms ◽

Energy Generation ◽

Health Impact ◽

Layout Optimization ◽

Nsga Ii ◽

Design Constraint ◽

Wind Farm Layout ◽

Multi Objective ◽

Wind Farm Layout Optimization

Recently, the environmental impact of wind farms has been receiving increasing attention. As land is more extensively exploited for onshore wind farms, they are more likely to be in proximity with human dwellings, increasing the likelihood of a negative health impact. Noise generation and propagation remain an important concern for wind farm's stakeholders, as compliance with mandatory noise limits is an integral part of the permitting process. In contrast to previous work that included noise only as a design constraint, this work presents continuous-location models for layout optimization that take noise and energy as objective functions, in order to fully characterize the design and performance spaces of the wind farm layout optimization (WFLOP) problem. Based on Jensen's wake model and ISO-9613-2 noise calculations, single- and multi-objective genetic algorithms (GAs) are used to solve the optimization problem. Results from this bi-objective optimization model illustrate the trade-off between energy generation and noise production by identifying several key parts of Pareto frontiers. In particular, it was observed that different regions of a Pareto front correspond to markedly different turbine layouts. The implications of noise regulation policy—in terms of the actual noise limit—on the design of wind farms are discussed, particularly in relation to the entire spectrum of design options.

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Constrained Multi-Objective Wind Farm Layout Optimization: Introducing a Novel Constraint Handling Approach Based on Constraint Programming

Volume 2A: 41st Design Automation Conference ◽

10.1115/detc2015-47535 ◽

2015 ◽

Cited By ~ 1

Author(s):

Sami Yamani Douzi Sorkhabi ◽

David A. Romero ◽

J. Christopher Beck ◽

Cristina H. Amon

Keyword(s):

Land Use ◽

Constraint Programming ◽

Wind Farm ◽

Layout Optimization ◽

Constraint Handling ◽

Objective Functions ◽

Nsga Ii ◽

Wind Farm Layout ◽

Multi Objective ◽

Wind Farm Layout Optimization

Recently, land has been exploited extensively for onshore wind farms and turbines are frequently located in proximity to human dwellings, natural habitats, and infrastructure. This proximity has made land use constraints and noise generation and propagation matters of increasing concern for all stakeholders. Hence, wind farm layout optimization approaches should be able to consider and address these concerns. In this study, we perform a constrained multi-objective wind farm layout optimization considering energy and noise as objective functions, and considering land use constraints arising from landowner participation, environmental setbacks and proximity to existing infrastructure. The optimization problem is solved with the NSGA-II algorithm, a multi-objective, continuous variable Genetic Algorithm. A novel hybrid constraint handling tool that uses penalty functions together with Constraint Programming algorithms is introduced. This constraint handling tool performs a combination of local and global searches to find feasible solutions. After verifying the performance of the proposed constraint handling approach with a suite of test functions, it is used together with NSGA-II to optimize a set of wind farm layout optimization test cases with different number of turbines and under different levels of land availability (constraint severity). The optimization results illustrate the potential of the new constraint handling approach to outperform existing constraint handling approaches, leading to better solutions with fewer evaluations of the objective functions and constraints.

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Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms

Expert Systems with Applications ◽

10.1016/j.eswa.2014.04.044 ◽

2014 ◽

Vol 41 (15) ◽

pp. 6585-6595 ◽

Cited By ~ 51

Author(s):

Francisco G. Montoya ◽

Francisco Manzano-Agugliaro ◽

Sergio López-Márquez ◽

Quetzalcoatl Hernández-Escobedo ◽

Consolación Gil

Keyword(s):

Evolutionary Algorithms ◽

Wind Turbine ◽

Wind Farm ◽

Wind Farm Layout ◽

Multi Objective ◽

Selection For

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Multi-Objective Energy-Noise Wind Farm Layout Optimization Under Land Use Constraints

Volume 6B: Energy ◽

10.1115/imece2014-37063 ◽

2014 ◽

Author(s):

Sami Yamani Douzi Sorkhabi ◽

David A. Romero ◽

Gary Kai Yan ◽

Michelle Dao Gu ◽

Joaquin Moran ◽

...

Keyword(s):

Land Use ◽

Transmission Lines ◽

Wind Farm ◽

Wind Farms ◽

Continuous Variable ◽

Layout Optimization ◽

Variable Model ◽

Nsga Ii ◽

Multi Objective ◽

Regulatory Constraints

Recently, the environmental impact of wind farms has been receiving increasing attention. As land is more extensively exploited for onshore wind farms, they are more likely to be in proximity with human dwellings, infrastructure (e.g. roads, transmission lines) and environmental features (e.g. rivers, lakes, forests). As a result of regulatory constraints, this proximity causes significant portions of the wind farm terrain to become unusable for turbine placement. In this work, we present a constrained, continuous-variable model for layout optimization that takes noise and energy as objective functions, based on Jensen’s wake model and ISO-9613-2 noise calculations. A multi-objective genetic algorithm (NSGA-II) is used to solve the optimization problem, considering a set of land use constraints, which are handled with static and dynamic penalty functions. A set of test cases with different number of turbines and percentages of land availability are solved. Results from this bi-objective optimization model illustrate how the severity of the land use constraints affects the trade-off between energy generation and noise production.

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Design Optimisation of the Feeding System of a Novel Counter-Gravity Casting Process

Metals ◽

10.3390/met8100817 ◽

2018 ◽

Vol 8 (10) ◽

pp. 817 ◽

Cited By ~ 5

Author(s):

Michail Papanikolaou ◽

Emanuele Pagone ◽

Konstantinos Georgarakis ◽

Keith Rogers ◽

Mark Jolly ◽

...

Keyword(s):

Casting Process ◽

Shrinkage Porosity ◽

Cfd Simulations ◽

Nsga Ii ◽

Gravity Casting ◽

Multi Objective ◽

Process Characteristics ◽

Computational Fluid Dynamics Cfd ◽

Optimum Solution ◽

Optimisation Techniques

The appropriate design of feeders in a rigging system is critical for ensuring efficient compensation for solidification shrinkage, thus eliminating (shrinkage-related) porosity and contributing to the production of superior quality castings. In this study, a multi-objective optimisation framework combined with Computational Fluid Dynamics (CFD) simulations has been introduced to investigate the effect of the feeders’ geometry on shrinkage porosity aiming to optimise casting quality and yield for a novel counter-gravity casting process (CRIMSON). The weighted sum technique was employed to convert this multi-objective optimisation problem to a single objective one. Moreover, an evolutionary multi-objective optimisation algorithm (NSGA-II) has been applied to estimate the trade-off between the objective functions and support decision makers on selecting the optimum solution based on the desired properties of the final casting product and the process characteristics. This study is one of the first attempts to combine CFD simulations with multi-objective optimisation techniques in counter-gravity casting. The obtained results indicate the benefits of applying multi-objective optimisation techniques to casting processes.

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Product form evolutionary design system construction based on neural network model and multi-objective optimization

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201439 ◽

2020 ◽

Vol 39 (5) ◽

pp. 7977-7991

Author(s):

Yixiang Wu

Keyword(s):

Functional Relation ◽

Product Form ◽

Design System ◽

Evolutionary Design ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Form Design ◽

Product Form Design ◽

Single Objective

The product form evolutionary design based on multi-objective optimization can satisfy the complex emotional needs of consumers for product form, but most relevant literatures mainly focus on single-objective optimization or convert multiple-objective optimization into the single objective by weighting method. In order to explore the optimal product form design, we propose a hybrid product form design method based on back propagation neural networks (BP-NN) and non-dominated sorting genetic algorithm-II (NSGA-II) algorithms from the perspective of multi-objective optimization. First, the product form is deconstructed and encoded by morphological analysis method, and then the semantic difference method is used to enable consumers to evaluate product samples under a series of perceptual image vocabularies. Then, the nonlinear complex functional relation between the consumers’ perceptual image and the morphological elements is fitted with the BP-NN. Finally, the trained BP-NN is embedded into the NSGA-II multi-objective evolutionary algorithm to derive the Pareto optimal solution. Based on the hybrid BP-NN and NSGA-II algorithms, a multi-objective optimization based product form evolutionary design system is developed with the electric motorcycle as a case. The system is proved to be feasible and effective, providing theoretical reference and method guidance for the multi-image product form design.

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Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms

Energies ◽

10.3390/en14144185 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4185

Author(s):

Nicolas Kirchner-Bossi ◽

Fernando Porté-Agel

Keyword(s):

Degrees Of Freedom ◽

Wind Farm ◽

Efficient Solutions ◽

Nsga Ii ◽

Multi Objective ◽

Cost Of Energy ◽

Wake Effects ◽

Wake Model ◽

Total Freedom ◽

The Cost

In recent years, wind farm layout optimization (WFLO) has been extendedly developed to address the minimization of turbine wake effects in a wind farm. Considering that increasing the degrees of freedom in the decision space can lead to more efficient solutions in an optimization problem, in this work the WFLO problem that grants total freedom to the wind farm area shape is addressed for the first time. We apply multi-objective optimization with the power output (PO) and the electricity cable length (CL) as objective functions in Horns Rev I (Denmark) via 13 different genetic algorithms: a traditionally used algorithm, a newly developed algorithm, and 11 hybridizations resulted from the two. Turbine wakes and their interactions in the wind farm are computed through the in-house Gaussian wake model. Results show that several of the new algorithms outperform NSGA-II. Length-unconstrained layouts provide up to 5.9% PO improvements against the baseline. When limited to 20 km long, the obtained layouts provide up to 2.4% PO increase and 62% CL decrease. These improvements are respectively 10 and 3 times bigger than previous results obtained with the fixed area. When deriving a localized utility function, the cost of energy is reduced up to 2.7% against the baseline.

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An Extended Pattern Search Approach to Wind Farm Layout Optimization

Journal of Mechanical Design ◽

10.1115/1.4006997 ◽

2012 ◽

Vol 134 (8) ◽

Cited By ~ 42

Author(s):

Bryony L. Du Pont ◽

Jonathan Cagan

Keyword(s):

Wind Speed ◽

Wind Farm ◽

Search Algorithm ◽

Pattern Search ◽

Global Evaluation ◽

Layout Problem ◽

Pattern Search Algorithm ◽

Wind Farm Layout ◽

Search Approach ◽

Extended Pattern Search

An extended pattern search approach is presented for the optimization of the placement of wind turbines on a wind farm. Problem-specific extensions infuse stochastic characteristics into the deterministic pattern search, inhibiting convergence on local optima and yielding better results than pattern search alone. The optimal layout for a wind farm is considered here to be one that maximizes the power generation of the farm while minimizing the farm cost. To estimate the power output, an established wake model is used to account for the aerodynamic effects of turbine blades on downstream wind speed, as the oncoming wind speed for any turbine is proportional to the amount of power the turbine can produce. As turbines on a wind farm are in close proximity, the interaction of turbulent wakes developed by the turbines can have a significant effect on the power development capability of the farm. The farm cost is estimated using an accepted simplified model that is a function of the number of turbines. The algorithm develops a two-dimensional layout for a given number of turbines, performing local turbine movement while applying global evaluation. Three test cases are presented: (a) constant, unidirectional wind, (b) constant, multidirectional wind, and (c) varying, multidirectional wind. The purpose of this work is to explore the ability of an extended pattern search (EPS) algorithm to solve the wind farm layout problem, as EPS has been shown to be particularly effective in solving multimodal layout problems. It is also intended to show that the inclusion of extensions into the algorithm can better inform the search than algorithms that have been previously presented in the literature. Resulting layouts created by this extended pattern search algorithm develop more power than previously explored algorithms using the same evaluation models and objective functions. In addition, the algorithm’s resulting layouts motivate a heuristic that aids in the manual development of the best layout found to date. The results of this work validate the application of an extended pattern search algorithm to the wind farm layout problem, and that its performance is enhanced by the use of problem-specific extensions that aid in developing results that are superior to those developed by previous algorithms.

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