[Could not find the bibliography file(s)]

– Document in progress –

#### Introduction

Wind farm layout optimization has been a subject of research for alm

ost 15 years.

Recently, with the increase of focus on

offshore wind energy, where positioning the turbine is based on fewer types of factors, it has raised in interest dramatically.

#### Optimization Methods

##### Genetic Algorithm

Genetic algorithm seems to be the most popular approach so far.

It was first introduced by Mosetti et al. in 1994 [?], and followed by many others [?], [?], [?], [?], [?], [?], [?], [?], [?], [?], [?], and [?].

##### Heuristic Methodology

Aytun Ozturk et al. [?], and Elkinton et al. [?] used a greedy heuristic methodology.

##### Swarm Optimization

Wan et al. [?] advocate the use of swarm optimization as more efficient than genetic algorithms.

##### Pattern Search

Du Pont and Cagan [?] use a pattern search algorithm with stochastic injections to avoid converging to local minima.

#### Cost Functions

##### Financial Balance

##### Foundation

##### Electrical Grid

#### Wake Models

#### Test Cases

(1974) An Estimate of the Interaction of Windmills in Widespread Arrays, National Aeronautical Establishment (Canada), Ottawa, Canada: National Aeronautical Establishment (Canada), url

(1975) An estimate of the interaction of a limited array of windmills, NASA STI/Recon Technical Report N 77, p. 13539, url

(1977) The spacing of wind turbines in large arrays, Energy Conversion 16, p. 169-171, url

(1980) The performance of arrays of wind turbines, Journal of Wind Engineering and Industrial Aerodynamics 5(3), p. 403-430

To assess the amount of power available in the wind, interactions between wind turbines in an array, or cluster, have been studied using a number of experimental techniques, and methods of mathematical analysis. These studies have included wake measurements behind wind turbine rotors and wind tunnel tests of model clusters, together with analyses using wake mixing and boundary-layer theories. The results from these studies are reviewed and compared and it is shown that there is reasonable agreement between the estimates for the power loss due to interactive effects in a cluster generating 1000 MW (about 25% is lost, if the rotors are spaced 10 diameters apart). Estimates of the output from larger arrays show some variation and there are conflicting views on the effects of certain parameters — such as rotor height. Other topics requiring further study — such as the influence of machine design — are also identified and discussed., url

(1987) Optimal spacing of wind turbines in a wind energy power plant, Solar Energy 39(6), p. 467-471

A nonlinear optimization problem is formulated to determine the optimal spacing between wind turbines to maximize instantaneous power from a one-line array of machines to be placed in a line parallel to constant wind direction. A specific example is then taken to illustrate the difference between optimal spacing and equidistant spacing., url

(1988) The design and construction of 25MW wind farm, Journal of Wind Engineering and Industrial Aerodynamics 27(1-3), p. 319-325

The recent success of the California Wind Farms has demonstrated the feasibility of generating large quantities of electricity from the wind. It has brought a growing demand from developers and utilities for larger machines to improve land utilisation and simplify installation and maintenance. In 1985, the James Howden Group installed a 25MW wind farm in the Altamont Pass Area of California. The wind farm mainly comprises 75 Howden 330kW turbines and one Howden 750kW machine. The main pre-requisite for a successful wind farm is an adequate wind resource. In addition, the siting of the individual turbines has to be considered to maximise the energy capture. The paper will describe the methods used to assess the overall wind resource and to decide on the final turbine locations. The paper will also review the development of Howden Wind Turbines to suit the methods of construction and maintenance applicable to wind farms, and to suit operation in various wind climates around the world., url

(1994) A Method for the Aerodynamically Optimal Design of Wind Parks, 15th Wind Energy Conference, Wind Energy Conversion 1993, p. 231-238, London: Mechanical Engineering Publications Ltd, url

(1994) Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm, Journal of Wind Engineering and Industrial Aerodynamics 51(1), p. 105-116

In this paper a novel approach to the optimization of large windfarms is presented. The wind turbine distribution at a given site is optimized in order to extract the maximum energy for the minimum installation costs. The optimization is made by associating a windfarm simulation model based on wake superposition with a genetic search code. The purpose of the paper is to prove the feasibility of the method by analyzing the results obtained in some simple applications. As a test case, a square site subdivided into 100 square cells as possible turbine locations has been taken, and the optimization is applied to the number and position of the turbines for three wind cases: single direction, constant intensity with variable direction, and variable intensity with variable direction., url

(1999) Wind and Solar Power Systems, CRC Press LLC, Boca Raton, Florida 33431: CRC Press LLC

The total electricity demand in 1997 in the United States of America was three trillion kWh, with the market value of $210 billion. The worldwide demand was 12 trillion kWh in 1997, and is projected to reach 19 trillion kWh in 2015. This constitutes the worldwide average annual growth of 2.6 percent. The growth rate in the developing countries is projected to be approximately 5 percent, almost twice the world average. Most of the present demand in the world is met by fossil and nuclear power plants. A small part is met by renewable energy technologies, such as the wind, solar, biomass, geothermal and the ocean. Among the renewable power sources, wind and solar have experienced a remarkably rapid growth in the past 10 years. Both are pollution free sources of abundant power. Additionally, they generate power near the load centers, hence eliminate the need of running high voltage transmission lines through rural and urban landscapes. Since the early 1980s, the wind technology capital costs have declined by 80 percent, operation and maintenance costs have dropped by 80 percent and availability factors of grid-connected plants have risen to 95 percent. These factors have jointly contributed to the decline of the wind electricity cost by 70 percent to 5 to 7 cents per kWh. The grid-connected wind plant can generate electricity at cost under 5 cents per kWh. The goal of ongoing research programs funded by the U.S. Department of Energy and the National Renewable Energy Laboratory is to bring the wind power cost below 4 cents per kWh by the year 2000. This cost is highly competitive with the energy cost of the conventional power technologies. For these reasons, wind power plants are now supplying economical clean power in many parts of the world. In the U.S.A., several research partners of the NREL are negotiating with U.S. electrical utilities to install additional 4,200 MW of wind capacity with capital investment of about $2 billion during the next several years. This amounts to the capital cost of $476 per kW, which is comparable with the conventional power plant costs. A recent study by the Electric Power Research Institute projected that by the year 2005, wind will produce the cheapest electricity available from any source. The EPRI estimates that the wind energy can grow from less than 1 percent in 1997 to as much as 10 percent of this country’s electrical energy demand by 2020. On the other hand, the cost of solar photovoltaic electricity is still high in the neighborhood of 15 to 25 cents per kWh. With the consumer cost of electrical utility power ranging from 10 to 15 cents per kWh nationwide, photovoltaics cannot economically compete directly with the utility power as yet, except in remote markets where the utility power is not available and the transmission line costs would be prohibitive. Many developing countries have large areas falling in this category. With ongoing research in the photovoltaic (pv) technologies around the world, the pv energy cost is expected to fall to 12 to 15 cents per kWh or less in the next several years as the learning curves and the economy of scale come into play. The research programs funded by DOE/NREL have the goal of bringing down the pv energy cost below 12 cents per kWh by 2000. After the restructuring of the U.S. electrical utilities, as mandated by the Energy Policy Act (EPAct) of 1992, the industry leaders expect the power generation business, both conventional and renewable, to become more profitable in the long run. The reasoning is that the generation business will be stripped of regulated price and opened to competition among electricity producers and resellers. The transmission and distribution business, on the other hand, would still be regulated. The American experience indicates that the free business generates more profits than the regulated business. Such is the experience in the U.K. and Chile, where the electrical power industry had been structured similar to the EPAct of 1992 in the U.S.A. As for the wind and pv electricity producers, they can now sell power freely to the end users through truly open access to the transmission lines. For this reason, they are likely to benefit as much as other producers of electricity. Another benefit in their favor is that the cost of the renewable energy would be falling as the technology advances, whereas the cost of the electricity from the conventional power plants would rise with inflation. The difference in their trends would make the wind and pv power even more advantageous in the future., url

(2001) Optimum Siting of Wind Turbine Generators, IEEE Transactions on Energy Conversion 16(1), p. 8-13, IEEE

This paper investigates optimum siting of wind turbine generators from the viewpoint of site and wind turbine generator selection. The methodology of analysis is based on the accurate assessment of wind power potential of various sites. The analytical computations of annual and monthly capacity factors are done using theWeibull statistical model using cubic mean cube root of wind speeds. As many as fifty- four potential wind sites, with and without wind turbine installations, geographically distributed in different states of India are used for the Siting Analysis. As an outcome of this analysis several definitive conclusions of archival nature have been arrived at and are presented in the paper. If this analysis is done at the planning and development stages of installation of wind power stations, will enable the wind power developer or the power utilities to make a judicious choice of potential site and wind turbine generator system from the available potential sites and wind turbine generators respectively., url

(2001) Short-cut design of wind farms, Energy Policy 29(7), p. 567-578

The problem of designing wind parks has been addressed in terms of maximizing the economic benefits of such an investment. An appropriate mathematical model for wind turbines was used, taking into account their construction characteristics and operational performance. The regional wind field characteristics for a wide range of sites have been appropriately analyzed and a model involving significant physical parameters has been developed. The design problem was formulated as a mathematical programming problem, and solved using appropriate mathematical programming techniques. The optimization covered a wide range of site characteristics and four types of commercially available wind turbines. The methodology introduced a short-cut design empirical equation for the determination of the optimum number of wind turbines. The model is appropriate for regional planning purposes. Variation of unit cost of wind farm area introduced an additional degree of freedom to the problem and more efficient designs could be obtained., url

(2001) Siting of Large Wind Farm and Optimum Wind Turbine-Site Matching, Acta Energiae Solaris Sinica 2001, Issue 4, 0254-0096, p. 6, url

(2002) A Viscous Three-Dimensional Differential/Actuator-Disk Method for the Aerodynamic Analysis of Wind Farms, Journal of Solar Energy Engineering 124(4), p. 345, American Society of Mechanical Engineers, url

(2002) On some of the design aspects of wind energy conversion systems, Energy Conversion and Management 43(16), p. 2175-2187

In the overall process of utilizing wind power, two essential components of technical data, i.e. one related to the engineering or performance characteristics of commercially available wind turbine generators, and the other related to the availability of wind resources, are needed. The performance of wind energy conversion systems (WECs) depends upon subsystems like wind turbine (aerodynamic), gears (mechanical), and generator (electrical). The availability of wind resources is governed by the climatic conditions of the region, for which the wind survey is extremely important to exploit wind energy. In this paper, design aspects, such as factors affecting wind power, siting requirements for WECs, problems related with grid connections, classification of wind electric generation schemes, criteria for selection of equipment for WECs, choice of generators, three basic design philosophies, main considerations in wind turbine design, choice between two and three blade rotors, weight and size considerations and environmental aspects related with WECs have been presented., url

(2003) Recommendations for Spacing in Wind Farms, EWEC 2003, Madrid, Spain, 17 June 2003(June), p. 2-7, Madrid: 2003, EWEC, pdf

(2004) Heuristic methods for wind energy conversion system positioning, Electric Power Systems Research 70(3), p. 179-185

This paper considers the problem of determining the locations of wind generators in a wind farm consisting of many generators. The objective is to find a generator placement that maximizes profit, which is the product of the cost efficiency of the generators and the total power output from the wind farm. Generator placement is significant because if generator A is located close to generator B and is located downwind of generatorBthen the power output of generatorAis reduced by an amount that varies with the distance between the generators. The problem can be formulated using mathematical programming but to solve the problem one cannot employ traditional optimization methods. Therefore, a greedy improvement heuristic methodology is developed and described in detail. The effectiveness of the proposed heuristic is demonstrated on a suite of test problems. These results indicate that the proposed method represents an effective solution strategy for this problem., url

(2004) Optimization of Electrical System for a Large DC Offshore Wind Farm by Genetic Algorithm, Nordic Workshop on Power and Industrial Electronics 2004 - 037, p. 8

This paper proposes an optimization platform based on Genetic Algorithm, where the main components of the electrical systemof a wind farm and key technical specifications are used as input parameters and the topology of the electrical system is to be optimized for a minimum cost and high reliability. A method to encode and decode an electrical system is studied. The reliability evaluation for a given network is also investigated. Genetic Algorithm is implemented to find the optimum network design for a large DC wind farm. It is concluded that different topologies may cause very different cost and reliability, and the Genetic Algorithm is capable of finding the optimum solution., url

(2004) Optimization of energy generation in wind farm through fuzzy control

Wind farms are energy generation systems being autonomous and controllable generating units. At current technological status, each set turbine-generator-power converter should be considered as a unit. These units work as an individual system for variable speed systems. Variable speed wind systems demand for an appropriate control of operating point at different wind regimens as well they can operate at different rotational speed and, consequently, at different system efficiency. Control characteristics of each machine are based on associated power converter that implements control on power flow to the grid. In the paper, authors discuss the design of the controller for the wind farm. The approach is based on the development of a fuzzy controller for the entire farm. So an appropriate structure and rules of inference allow at optimizing the operation characteristics of the farm. The paper demonstrates the demanding for a whole wind farm controller and discusses some results carried out in the development of a fuzzy controller that optimizes total generated power of a wind farm, persecuting the goal of a good quality generated power., url

(2004) Wind Farm Planning at the Gulf of Suez 1387(November), p. 1-97

The Wind Atlas for Egypt project is an element in a national effort to provide the best possible basis for planning of future environmentally sustainable development and utilization of wind energy resources and technology in Egypt. The present report compiles the data, information and recommendations available for planning of wind farm projects in the Gulf of Suez.

(2005) Offshore wind farm layout optimization (OWFLO) project: an introduction, Offshore Wind

Optimizing the layout of an offshore wind farm presents a significant engineering challenge. Most of the optimization literature to date has focused on land-based wind farms, rather than on offshore farms. The conventional method used to lay out a wind farm combines a turbine cost model and a wake model in conjunction with an optimization routine. In offshore environments, however, factors such as operation and maintenance (O&M) and availability also play significant roles in the design of a wind farm. To better account for these and the other critical factors that distinguish offshore wind farms from their onshore counterparts, the Offshore Wind Farm Layout Optimization (OWFLO) project was launched in 2004. The objective is to develop an analysis tool that unites offshore turbine micrositing criteria with efficient optimization algorithms. The project combines wake and component cost models, but also includes O&M, availability, and electrical interconnection models. When integrated within an appropriate optimization routine, these “sub-models” will work together to better reflect the real-world conditions and constraints unique to individual offshore sites. The OWFLO project will consider several optimization algorithms—including heuristic and genetic methods—to minimize the cost of energy while maximizing the energy production of the wind farm. This paper summarizes the results from the first year of this on-going project. The development of the component models and analysis software is discussed in detail and some initial results are compared with existing wind farms. A summary of the current and future phases of the project is also presented., pdf

(2005) Placement of wind turbines using genetic algorithms, Renewable Energy 30(2), p. 259-270

A genetic algorithm approach is employed to obtain optimal placement of wind turbines for maximum production capacity while limiting the number of turbines installed and the acreage of land occupied by each wind farm. Specifically, three cases are considered—(a) unidirectional uniform wind, (b) uniform wind with variable direction, and (c) non-uniform wind with variable direction. In Case (a), 600 individuals are initially distributed over 20 subpopulations and evolve over 3000 generations. Case (b) has 600 individuals spread over 20 subpopulations initially and evolves for 3000 generations. Case (c) starts with 600 individuals spread over 20 subpopulations and evolves for 2500 generations. In addition to optimal configurations, results include fitness, total power output, efficiency of power output and number of turbines for each configuration. Disagreement with the results of an earlier study is observed and a possible explanation is provided., url

(2005) Wind farm optimization, Proceedings of the 40th Annual Conference of the Operations Research Society, Wellington, 2005. University of Auckland, Dept. Engineering Science, Citeseer

This paper will formulate integer programs to determine the optimal positions of wind turbines within a wind farm. The formulations were based on variations of the vertex packing problem. Three formulations are presented, which seek to maximize power generated in accordance with constraints based on the number of turbines, turbine proximity, and turbine interference. These were in the form of budget, clique, and edge constraints. Results were promising, with turbines exhibiting a tendency to concentrate in areas of high elevation and avoid situations where downstream interference would be significant., pdf

(2006) An improved mixed integer programming model for wind farm layout optimisation, ANNUAL CONFERENCE- OPERATIONAL RESEARCH SOCIETY OF NEW ZEALAND; 143-152 Operational Research Society of New Zealand; ORSNZ'06 41st:; ANNUAL CONFERENCE, Operational Research Society of New Zealand; ORSNZ'06, p. 143-151, ORSNZ

Mixed integer programming (MIP) formulations have the potential to provide a powerful new tool when applied to the field of wind farm layout optimisation. These models seek to determine the optimal positions of wind turbines within a new wind farm, subject to constraints on turbine proximity and turbine interference. This paper outlines several methods that improves the efficiency with which these MIP models can be solved, which make use of criteria designed to reduce the size of the problem, a more effective branching strategy, a stronger model formulation, and dynamic constraint generation. The size of the problem is reduced by relating the capital cost of the turbine to a minimum payback period. This approach ensures that the model only includes locations in which the value of the electricity generated by the turbine would exceed its capital cost within the minimum required payback period. A branching strategy is implemented that prioritizes branching on decision variables corresponding to turbine positions that generate relatively high power when compared to other positions within the wind farm. The MIP formulation is strengthened by the addition of a simple family of mixed integer rounding inequalities associated with turbine interference. These improvements led to moderate reductions in branch and bound gap and solution time., pdf

(2006) Modeling the Trade-Offs in Offshore Wind Energy Micrositing, Proc. WINDPOWER 2006 Conference and Exhibition

The layout of an offshore wind farm is a complex problem involving many trade-offs. To achieve the most energy at the least cost, these trade-offs must be understood. Energy production increases with turbine spacing, as do electrical costs and losses. Energy production also increases with distance from shore, but so do O&M, foundation, transmission, and installation costs. Determining which of these factors dominate requires a thorough understanding of the physics behind these trade-offs, can lead to the optimal layout, and helps lower the cost of energy from these farms. This paper details the results of ongoing research carried out at the University of Massachusetts. The Offshore Wind Farm Layout Optimization (OWFLO) project investigates these trade-offs in order to better understand the micrositing process for offshore wind energy. A software tool has been developed that estimates the cost of energy for an offshore farm. This tool is comprised of mathematical models of the major cost and energy components of the farm: turbine, support structure, and foundation costs, energy production and losses, availability, etc... These models were used to examine the impacts on the cost and energy trade-offs of modifications in the layout of a theoretical offshore wind farm. The results of an initial examination of these trade-offs are discussed and examples of recent modeling results are compared with data from existing offshore wind farms., url

(2006) Offshore Wind Farm Layout Optimization (OWFLO) Project: Preliminary Results, Renewable Energy, p. 1-9

Optimizing the layout of an offshore wind farm presents a significant engineering challenge. Most of the optimization literature to date has focused on land-based wind farms, rather than on offshore farms. Typically, energy production is the metric by which a candidate layout is evaluated. The Offshore Wind Farm Layout Optimization (OWFLO) project instead uses the levelized production cost as the metric in order to account for the significant roles factors such as support structure cost and operation and maintenance (O&M) play in the design of an offshore wind farm. The objective of the project is to pinpoint the major economic hurdles present for offshore wind farm developers by creating an analysis tool that unites offshore turbine micrositing criteria with efficient optimization algorithms. This tool will then be used to evaluate the effects of factors such as distance from shore and water depth on the economic feasibility of offshore wind energy. The project combines an energy production model—taking into account wake effects, electrical line losses, and turbine availability—with offshore wind farm component cost models. The components modeled include the rotor-nacelle assembly, support structure, electrical interconnection, as well as O&M, installation, and decommissioning costs. The models account for the key cost drivers, which include turbine size and rating, water depth, distance from shore, soil type, and wind and wave conditions. When integrated within an appropriate optimization routine, these component models work together to better reflect the real-world conditions and constraints unique to individual offshore sites. The OWFLO project considers several optimization algorithms—including heuristic and genetic methods—to minimize the cost of energy while maximizing the energy production of the wind farm. Particular attention has been paid to the results of recent European studies, including the ENDOW and DOWEC projects. This paper summarizes the initial results from this project. A comparison of model results and data from the Middelgrunden offshore wind farm is presented. The overall energy and cost of energy estimations compare well with the real data, but further improvements to the models are planned. A summary of the on-going and future phases of the project is also presented. Nomenclature, pdf

(2006) Wind Turbine Design Cost and Scaling Model Wind Turbine Design Cost and Scaling Model(December)

... System (NEMS) runs. The second purpose of this work was to provide a baseline tool for evaluating the impact of machine design and growth on cost for proposed offshore wind turbine systems. 3 Page 8. To prepare this ..., pdf

(2007) An Analytical Framework for Offshore Wind Farm Layout Optimization, Wind Engineering 31(1), p. 17-31

A method is developed for using the levelized cost of energy as the objective function for offshore wind farm layout optimization problems. The method converts the cost of energy into a function of turbine position only. To accomplish this, wind speed data are first characterized by direction sector. Continuous functions are then fitted to the Weibull parameters for each direction sector. The wind direction probability density function and the turbine power curve are also transformed into continuous functions. For each turbine in the farm, the continuous function that describes the Weibull scale parameter can be scaled to reflect wake losses from other turbines. The function may also be adjusted according to the variation in wind speed with fetch. The annual energy production of the farm is thus modeled as a function only of the turbine positions. When combined with wind farm cost estimates, the levelized cost of energy is still only a function of turbine position and can then be used as an objective function within a variety of optimization algorithms, url

(2007) An evolutive algorithm for wind farm optimal design, Neurocomputing 70(16-18), p. 2651-2658

An evolutive algorithm for the optimal design of wind farms is presented. The algorithm objective is to optimize the profits given an investment on a wind farm. Net present value will be used as a figure of the revenue. To work out this figure, several economic factors such as the initial capital investment, the discount rate, the value of the generated energy and the number of the years spanned by the investment are considered. All this factors depends on the preliminary design of the wind park (number, type, tower height and layout situation of wind generators), which are the variables to set., url

(2007) Distributed Genetic Algorithm for Optimization of Wind Farm Annual Profits, 2007 International Conference on Intelligent Systems Applications to Power Systems, p. 1-6, Ieee

In this paper, a distributed genetic algorithm is adopted to search the optimal number and locations of wind turbines in large wind farms. The objective of this optimal process is to find a solution that maximizes the annual profit obtained from a wind farm. It is well known that traditional genetic algorithms are time consuming and the quality of final solution is not very well. For improving the performance of finding the optimal solution in large search space, the distributed genetic algorithm provides a powerful strategy for searching the global optimal by dividing large population into multiple small subpopulations that occasionally exchange some individuals. Test results show that the distributed genetic algorithm well demonstrates its effectiveness on solution quality and execution time., url

(2007) Offshore wind farm layout optimization, url

(2007) Optimal, reliability-based turbine placement in offshore wind turbine parks, Civil Engineering and Environmental Systems 24(2), p. 99-109, Taylor & Francis

Offshore wind turbines for electricity production placed in wind farms are expected to be of one of the major future contributors for sustainable energy production. In this article, some of the problems associated with optimal planning and design of wind turbine parks are addressed. The number of wind turbines in a park is usually restricted to be placed within a fixed, limited geographical area. Behind a wind turbine, a wake is formed where the mean wind speed decreases and the turbulence intensity increases. The distance between the turbines is among other things dependent on the recovery of wind energy behind the neighboring turbines and the increased wind load. Models for the mean wind speed and turbulence intensity in wind turbine parks are considered with emphasis on modeling the spatial correlation. Representative limit state equations for structural failure of wind turbine towers are formulated. The probability of failure is determined taking into account that wind turbines are parked for wind speeds larger than 25 m/s resulting in reduced wind loads. An illustrative example is presented where illustrative models for the spatial correlation is taken into account. Offshore wind turbines for electricity production placed in wind farms are expected to be of one of the major future contributors for sustainable energy production. In this article, some of the problems associated with optimal planning and design of wind turbine parks are addressed. The number of wind turbines in a park is usually restricted to be placed within a fixed, limited geographical area. Behind a wind turbine, a wake is formed where the mean wind speed decreases and the turbulence intensity increases. The distance between the turbines is among other things dependent on the recovery of wind energy behind the neighboring turbines and the increased wind load. Models for the mean wind speed and turbulence intensity in wind turbine parks are considered with emphasis on modeling the spatial correlation. Representative limit state equations for structural failure of wind turbine towers are formulated. The probability of failure is determined taking into account that wind turbines are parked for wind speeds larger than 25 m/s resulting in reduced wind loads. An illustrative example is presented where illustrative models for the spatial correlation is taken into account., url

(2007) Optimization algorithms for offshore wind farm micrositing, WINDPOWER 2007 Conference and Exhibition, Los Angeles, CA, p. 1-23, Los Angeles: WINDPOWER 2007

In the United States, offshore wind energy is poised to facilitate substantial growth in wind energy production. Unlike most onshore projects, this growth hasthe potential to occur in close proximity to large load centers(New York,Boston, Houston, for example). In order for offshore wind to be able to compete with other energy generating technologies, however, further reductionsin the cost of energy are required. Making optimal use of current technology is one simple approach to this problem. As part of a larger projectfocused on offshore wind farm analysis and optimization, thisresearch examinesthe use of optimization algorithmsfor wind farm micrositing. The paperstarts with a discussion offive different types of optimization algorithms: gradient search, heuristic, pattern search,simulated annealing, and evolutionary algorithms. The relevance of each algorithm to wind turbine micrositing is evaluated by considering two separate objectives: minimization ofthe levelized production cost and maximization of the energy production. Two algorithms, the genetic and greedy heuristic, are further evaluated forthe specific case of offshore wind farm design through the use of design simulations. In these simulations, a fullset ofsite conditions are considered, including as water depth,soil conditions, wind climate, and distance from shore. In addition, comparisons are made with previousstudies in the literature. Finally, these algorithms are employed to optimize the layout of a potential, real-world offshore wind farm near Hull, Massachusetts. This process, results, and lessons learned are discussed., pdf

(2008) Algorithms for Offshore Wind Farm Layout Optimization, Wind Engineering 32(1), p. 67-84

Offshore wind energy is positioned to facilitate substantial growth in wind energy production, but further reductions in the cost of energy will strengthen its ability to compete directly with other energy generating technologies. One simple solution is the optimal use of current technologies. To this end, this study investigates the use of optimization algorithms for offshore wind farm micrositing. First, a discussion is given of five different types of optimization algorithms: gradient search, heuristic, pattern search, simulated annealing, and evolutionary algorithms. The relevance of each algorithm to wind turbine micrositing is then evaluated by considering two separate objectives: minimization of the levelized production cost and maximization of the energy production. The genetic and greedy heuristic algorithms are further evaluated through the use of design simulations. Finally, these algorithms are employed to optimize the layout of a potential, real-world offshore wind farm near Hull, Massachusetts., url

(2008) Mixed Integer Programming Models for Wind Farm Design, pdf

(2008) Optimal choice of wind turbine generator based on Monte-Carlo method, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, p. 2487-2491, IEEE

Wind power generation of wind farm has relation to wind energy resources, wind generation unit parameter and its crew, etc. The goal of optimizing the design for wind turbine generator in wind farm should be to utilize wind energy as efficiently as possible and to reduce production cost of wind power generation. Therefore, the paper investigates optimal choice of wind generation unit type matching wind farm site, which takes wind power generation capacity factor and generation cost as optimization target. Probabilistic method used to evaluate the economic indices of wind turbine generators, such as wind power generation and capacity factor, is more reasonable due to wind energy randomness and component failure uncertainty. Results obtained from several case studies are presented and discussed., url

(2008) Optimal design of an offshore wind farm layout, 2008 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, p. 1470-1474, IEEE

The cost of offshore wind farms are about 30-60% higher than onshore wind farms of same capacity. The cost and efficiency of offshore wind farms are determined by variety of factors which include the type of electrical system (AC or DC), transmission length, transmission voltage, rated power, wind turbine type and the farm topology, and wind speed. Since the design variable space is large for such problems, computational optimization is necessary to find an optimal solution. We discuss the development of cost, loss and reliability models and the application of geometric program for optimization of the layout and configuration of an offshore wind farm. Preliminary calculations show that HVAC systems work better for short distances of the farm from the shore and the HVDC system performed better for longer distances., url

(2008) Optimal placement of wind turbines in a wind park using Monte Carlo simulation, Renewable Energy 33(7), p. 1455-1460

In the present study, a novel procedure is introduced for the optimal placement and arrangement of wind turbines in a wind park. In this approach a statistical and mathematical method is used, which is called ‘Monte Carlo simulation method’. The optimization is made by the mean of maximum energy production and minimum cost installation criteria. As a test case, a square site is subdivided into 100 square cells that can be possible turbine locations and as a result, the program presents us the optimal arrangement of the wind turbines in the wind park, based on the Monte Carlo simulation method. The results of this study are compared to the results of previous studies that handle the same issue., url

(2008) Optimización Global de Parques Eólicos Mediante Algoritmos Evolutivos, p. 278

Dada la complejidad estructural del problema, tanto desde el punto de vista técnico -económico como desde el puramente matemático, y como una primera aproximación a su solución, el problema de la optimización global del parque eólico se ha dividido en dos subproblemas: Optimización de la localización (o emplazamiento) individual de las turbinas eólicas Optimización de la configuración de la instalación eléctrica del parque Esta subdivisión conduce a una simplificación que se deriva del desacoplamiento de los problemas, y puede justificarse en base a la repercusión económica de cada uno de ellos. Desde un punto de vista puramente económico, el primero de los problemas, el de la localización individual de los aerogeneradores en el parque, es el más significativo, ya que es responsable de entre los dos tercios y las tres cuartas partes de la inversión total necesaria y es el que más directamente afecta a la producción anual de energía eléctrica, es decir, al posterior retorno de la inversión [16]. El segundo problema, el del diseño de la infraestructura eléctrica del parque eólico, es muy similar al del diseño de una nueva red radial [28-31] y tiene menor significación económica, en cuanto a inversión inicial se refiere, aunque también incide en la producción neta anual del parque ya que las pérdidas que se produzcan en la instalación interior del parque será energía eléctrica producida que no estará disponible para su inyección en la red. Ambos problemas se analizan por medio de funciones económicas que permiten el análisis y la comparación de los diferentes componentes de costes relacionados con el diseño y la operación de un parque eólico. Como es lógico, esta división del problema conduce a soluciones subóptimas, pero bastante próximas al óptimo global, como habrá ocasión de comprobar más adelante. Posteriormente se aborda el problema de la optimización global del parque, analizando de forma conjunta e integrada los problemas de emplazamiento individual de las turbinas y de diseño de la infraestructura eléctrica del parque y su interacción, utilizando el mismo tipo de funciones económicas. Tras este breve capítulo introductorio, el resto del trabajo se ha estructurado en cinco capítulos. En el que sigue se hace una revisión de la bibliografía relativa al problema y se formula el objetivo y alcance del trabajo. En el capítulo tercero se plantea el subproblema de la optimización de la localización individual de los aerogeneradores en el parque y se analizan y critican las soluciones obtenidas en una serie de casos con diferentes configuraciones geográficas, estructuras de viento, etcétera. El cuarto capítulo estudia el subproblema de la optimización de la configuración de la instalación eléctrica del parque. En el capítulo siguiente se aborda el problema de la optimización global del parque eólico y se analizan y critican las soluciones obtenidas en una serie de casos. El sexto y último capítulo recoge las principales conclusiones del trabajo así como algunas orientaciones de mejora para trabajos futuros. El trabajo concluye con las referencias bibliográficas relevantes sobre el tema., url

(2008) Optimization of Offshore Wind Farm Layouts(August), p. 1-34

This study aims to determine the optimal layout of the wind turbines inside an offshore wind farm when taking into account the wake effects. The wake model used is an N.O. Jensen model [12]. For this purpose I use a simulated annealing algorithm adapted to this specific problem, where three types of local search operations, namely remove, move and add, are performed recursively with every turbine until the sys- tem converges. Two different objective functions are considered: the Annual Energy Production and the Profit, which lead to different optimizations. A detailed analysis has been made to help determining the best objective func- tion to choose in each case. Many tests were performed, proving the effectiveness of the program and yielding intuitive results in specially prepared test cases, e.q. in a wind rose with a very strong main wind direction the output layout of the program was a row of turbines facing the wind. The program is also able to increase in a 0.95% the power output of the Danish offshore wind farm Horns Rev, when comparing it with the present layout. A comparison between the installed capacity per km2 and the efficiency of the wind farm was carried out. It was found that for very large wind farms (in the order of a hundred) there is less potential for optimizing the layouts. A sensitivity analysis was performed and the program’s output was seen to be little sensitive to changes in the wind rose. The optimized layout yielded better results than a grid layout if the wind rose was rotated or even changed. The main reason was found to be the tendency of turbines to move towards the perimeter of the wind farm. Using these results, the visual impact was taken into account and a re- arrangement of turbines was proposed for the HR wind farm. A grid-alike layout is adapted following the general trends of the optimized layout, allow- ing a good compromise between visual effects and an optimized layout. The Profit function is also tested, and it is seen that it performs well but that the running times are prohibitive. Some ways around this problem are discussed and compared., pdf

(2008) Optimization of the Regional Spatial Distribution of Wind Power Plants to Minimize the Variability of Wind Energy Input into Power Supply Systems, Journal of Applied Meteorology and Climatology 47(12), p. 3099-3116

In contrast to conventional power generation, wind energy is not a controllable resource because of its stochastic nature, and the cumulative energy input of several wind power plants into the electric grid may cause undesired fluctuations in the power system. To mitigate this effect, the authors propose a procedure to calculate the optimal allocation of wind power plants over an extended territory to obtain a low temporal variability without penalizing too much the overall wind energy input into the power system. The procedure has been tested over Corsica (France), the fourth largest island in the Mediterranean Basin. The regional power supply system of Corsica could be sensitive to large fluctuations in power generation like wind power swings caused by the wind intermittency. The proposed methodology is based on the analysis of wind measurements from 10 anemometric stations located along the shoreline of the island, where most of the population resides, in a reasonably even distribution. First the territory of Corsica has been preliminarily subdivided into three anemological regions through a cluster analysis of the wind data, and the optimal spatial distribution of wind power plants among these regions has been calculated. Subsequently, the 10 areas around each station have been considered independent anemological regions, and the procedure to calculate the optimal distribution of wind power plants has been further refined to evaluate the improvements related to this more resolved spatial scale of analysis. © 2008 American Meteorological Society., url

(2008) Optimizing the Layout of Offshore Wind Energy Systems, Marine Technology Society Journal 42(2), p. 19-27, Marine Technology Society

Offshore wind energy technology is a reality in Europe and is poised to make a significant contribution to the U.S. energy supply in the near future as well. The layout of an offshore wind farm is a complex problem involving many trade-offs. For example, energy production increases with turbine spacing, as do electrical costs and losses. Energy production also increases with distance from shore, but so do O&M (operations and maintenance), foundation, transmission, and installation costs. Determining which of these factors dominates requires a thorough understanding of the physics behind these trade-offs, can lead to the optimal layout, and helps lower the cost of energy from these farms. This paper presents the results of a study carried out to investigate these trade-offs and to develop a method for optimizing the wind farm layout during the micrositing phase of an offshore wind energy system design. It presents a method for analyzing the cost of energy from offshore wind farms as well as a summary of the development of an offshore wind farm layout optimization tool. In addition to an initial validation of the optimization tool, an example of the use of this tool for the design of an offshore wind farm in Hull, Massachusetts, is also given., url

(2008) Whither the wind blows : wind flow modelling and wind farm layout optimisation, p. 92, url, url

(2009) A new tool for wind farm optimal design, 2009 IEEE Bucharest PowerTech, p. 1-7, Ieee

An Evolutive Algorithm (EA) for wind farm optimal 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 maximize the generation of energy and minimize the power losses). Both terms depend mainly of 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 complexity of the problem is mainly due to the fact that there is not analytic function to model the wind farm costs and most of the main variables are linked., url

(2009) A simple generic wind farm cost model tailored for wind farm optimization

The TOPFARM project addresses optimization of wind farm topology and control strategy based on aeroelastic modeling of loads as well as of power production. Crucial factors in this connection are the overall (ambient) wind climate at the wind farm site, the characteristics, position of the the wind turbine individual wind turbines, the wind turbine control/operation strategy for wind turbines interacting through wakes, and possible a priory defined constraints imposed on the wind farm topology and control. To achieve an optimal economic output from a wind farm during its lifetime, the optimal balance between capital costs, operation and maintenance costs, fatigue lifetime consumption and power production output is to be assessed on a rational background. This is formed by aeroelastic simulations of the loads as well as of the power production for each turbine in a wind farm, taking into account the effects of wind turbine wakes on the individual wind turbine inflow characteristics. In an optimization context, the synthesis of all required sub-models is performed by formulating an object (or penalty) function for the optimization problem. In the framework of TOPFARM this penalty function is formulated in economical terms, whereby the need of cost models for all costs depending on wind farm production and loading arises. The present report deals with a generic wind farm cost model complex with this characteristic. The main components of the cost model complex are wind farm financial costs and wind farm operating costs, each of which in turn consists of a number of separate sub-models

(2009) Efficient hybrid distributed genetic algorithms for wind turbine positioning in large wind farms, 2009 IEEE International Symposium on Industrial Electronics(ISlE), p. 2196-2201, Ieee

An efficient hybrid distributed genetic algorithm is proposed to determine the proper number and locations of wind turbines in large wind farms. The objective of this optimal process is to find a solution that maximizes the annual profit obtained from a wind farm. It is well-known that genetic algorithms are good for global searches, but are weak for local searches. To improve the performance of finding the optimal solution in a large search space, the hybrid methodology combines a distributed genetic algorithm and steepest ascent hill-climbing local search algorithms. The hill-climbing algorithm provides a powerful strategy for searching the local optimal solution by exploring the neighborhood of the current state. In this paper, the hill-climbing algorithm is further enhanced by a heuristic method to reduce the execution time for finding the optimal value. Test results show that this proposed hybrid distributed genetic algorithm adequately demonstrates its effectiveness in solution quality and execution time., url

(2009) GA and PSO Applied to Wind Energy Optimization, XV Congreso Argentino de Ciencias de la Computación, p. 10

In this article we analyze two kinds of metaheuristic algorithms applied to wind farm optimization. The basic idea is to utilize CHC (a sort of GA) and GPSO (a sort of PSO) algorithms to obtain an acceptable configuration of wind turbines in the wind farm that maximizes the total output energy and minimize the number of wind turbines used. The energy produced depends of the farm geometry, wind conditions and the terrain where it is settled. In this work we will analyze three study farm scenarios with different wind speeds and we will apply both algorithms to analyze the performance of the algorithms and the behavior of the computed wind farm designs., url

(2009) Linear Wind Farm Layout Optimization through Computational Intelligence, MICAI 2009: Advances in Artificial Intelligence Lecture Notes in Computer Science Volume 5845, Aguirre Arturo-Hernández, Borja Raúl-Monroy, Reyes-Garciá Carlos-Alberto (ed.), p. 692-703, Guanajuato, México: Springer Berlin Heidelberg

The optimal positioning of wind turbines, even in one dimension, is a problem with no analytical solution. This article describes the application of computational intelligence techniques to solve this problem. A systematic analysis of the optimal positioning of wind turbines on a straight line, on flat terrain, and considering wake effects has been conducted using both simulated annealing and genetic algorithms. Free parameters were the number of wind turbines, the distances between wind turbines and wind turbine hub heights. Climate and terrain characteristics were varied, like incoming wind speed, wind direction, air density, and surface roughness length, producing different patterns of positioning. Analytical functions were used to model wake effects quantifying the reduction in speed after the wind passes through a wind turbine. Conclusions relevant to the placement of wind turbines for several cases are presented., url

(2009) Modeling and simulation of optimal wind turbine configurations in wind farms, 2009 World Non-Grid-Connected Wind Power and Energy Conference, p. 1-5, Ieee

Wake effect is a key factor leading to the efficiency decrease of the whole wind farm. For different configurations of wind turbines, the accurate prediction of wake effect between wind turbines is vital to the calculation of wind farm cost and benefit. To describe the wake effect better in the wind turbine optimization, a new non-linear wake expansion of the wake model was introduced into the calculation of flat or slightly undulating topography and offshore wind farms. Combining the cost-benefit model of wind farm projects and considering multiple factors and the benefit evaluation model of increasing the number of wind turbines in a wind farm, and then a more complete and effective mathematical model was developed to decide the optimal configuration of wind turbines. Finally, a program using a genetic algorithm was made based on the above models. According to the economical efficiency analysis and comparison of the different wind turbine placement schemes for some wind farm projects to the earlier studies, the optimal number of the wind turbines and the corresponding placement scheme were determined and shows some differences from the earlier study. ©2009 IEEE., url

(2009) Optimal micro-siting of wind turbines by genetic algorithms based on improved wind and turbine models, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference(3), p. 5092-5096, Ieee

Micro-siting of wind turbines is a key technology for wind farm configuration. In this paper, Weibull function is used to describe the probability of wind speed distribution and turbine speed-power curve is employed to estimate turbine power generation. The improved wind and turbine models are formulated into an optimal control framework in terms of minimizing the cost per unit energy of the wind farm, which is solved by a binary-encoded genetic algorithm. Simulation results indicate that the proposed method could provide better performance and represents a more realistic and effective strategy for optimal micro-siting of the wind farm., url

(2009) Optimal Siting of Wind Turbines Using Real-Coded Genetic Algorithms, EWEC 2009(010)

In this paper, a real-coded genetic algorithm is utilized to optimize wind turbine placement in a wind farm. The location of each wind turbine could be freely adjusted within a predefined cell in order to maximize the generated energy. Simulations are carried out to demonstrate that the present study could improve wind farm efficiency and extract more energy., url, pdf

(2009) Simulated Annealing for Optimization of Wind Farm Annual Profit, Logistics and Industrial Informatics, 2009. LINDI 2009. 2nd International 0(2), p. 1–5, IEEE

In this paper, a simulated annealing is designed and applied to compute the optimal placement of wind turbines in a wind farm. The objetive of this process is to find a combination of aero-generators that maximizes the annual profit obtained from a wind farm, measured as the generated power in one year. Simulated annealing (SA) is a metaheuristic for global optimization that we will use here to search in the large landscape of possible solutions. Maximizing the generated power depends on the distribution of the aero-generators and the geometry of the wind farm. In this article we analyze several case studies according to different wind conditions. We will show optimal solutions and prove that they are better concerning the present best result in the literature., url

(2009) Solving the Turbine Positioning Problem for Large Offshore Wind Farms by Simulated Annealing, Wind Engineering 33(3), p. 287-297

The current paper is concerned with determining the optimal layout of the turbines inside large offshore wind farms by means of an optimization algorithm. We call this the Turbine Positioning Problem. To achieve this goal a simulated annealing algorithm has been devised, where three types of local search operations are performed recursively until the system converges. The effectiveness of the proposed algorithm is demonstrated on a suite of real life test cases, including Horns Rev offshore wind farm. The results are verified using a commercial wind resource software indicating that this method represents an effective strategy for the wind turbine positioning problem. The findings enable the comparison of the optimized and the grid layouts and the study of the wake differences between these configurations. It is seen that for very large offshore wind farms the difference in wake losses is negligible while, as the wind farm’s size reduces, the differences start becoming significant. A sensitivity analysis is also performed showing that greater density of turbines in the perimeter of the optimized wind farm reduces the wake losses even if the wind climate changes., url

(2009) Study on computational grids in placement of wind turbines using genetic algorithm, 2009 World Non-Grid-Connected Wind Power and Energy Conference 2, p. 1-4, IEEE

To optimize the placement of wind turbines using a genetic algorithm for the fixed size of wind farm, the appropriate computational grids are the basis of the succeeding work. The optimized scheme was tightly restricted by the rationality and accuracy of computational grids. In this paper, based on the consideration of actual wind and wake characteristics of wind turbines, the (a) shape of the grids, (b) arranging the direction of the grids, and (c) the density of the grids were introduced to study the effect of computation grids on the optimization results. Furthermore, the grids' division method in the scheme's optimization of wind turbines placement under different conditions was discussed to increase the power capacity of the wind farm to obtain the maximum benefit of the investment., url

(2010) A novel method for optimal placing wind turbines in a wind farm using particle swarm optimization (PSO), 2010 Conference Proceedings IPEC, p. 134-139, IEEE

In this paper, for the first time a particle swarm optimization (PSO) method is utilized to optimize placing of wind turbines in a wind park. The location of each wind turbine could be freely adjusted within a predefined cell in order to maximize the generated energy. Simulated results and graphs are carried out to prove that the present study is improved wind farm efficiency and extract more electrical power in respect of the total costs., url

(2010) An extended pattern search approach to wind farm layout optimization, ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2010, p. 1-10

An extended pattern search approach is presented for optimizing the placement of wind turbines on a wind farm. The algorithm will develop a two-dimensional layout for a given number of turbines, employing an objective function that minimizes costs while maximizing the total power production of the farm. The farm cost is developed using an established simplified model that is a function of the number of turbines. The power development of established simplified wake model, which accounts the farm is estimated using an for the aerodynamic effects of turbine blades on downstream wind speed, to which the power output is directly proportional. The interaction of the turbulent wakes developed by turbines in close proximity largely determines the power capability of the farm. As pattern search algorithms are deterministic, multiple extensions are presented to aid escaping local optima by infusing stochastic characteristics This stochasticity improves the algorithm’s performance, yielding better results than purely deterministic search methods. Three test cases are presented: a) constant, unidirectional wind, b) constant, multidirectional wind, and c) varying, multidirectional wind. Resulting layouts developed by this extended pattern search algorithm develop more power than previously explored algorithms with functions. In addition, the same evaluation models and objective the algorithm’s layouts motivate a heuristic that yields the best layouts found to date.

(2010) CHC and SA Applied To The Distribution Of Wind Turbines on Irregular Fields, XVI Congreso Argentino de Ciencias de la Computación, p. 72-81

In this article we analyze two kinds of metaheuristic algorithms and random search algorithm applied to distribution of wind turbines in a wind farm . The basic idea is to utilize CHC (a sort of GA) and Simulated Annealing algorithms to obtain an acceptable configuration of wind turbines in the wind farm that maximizes the total output energy and minimize the number of wind turbines used. The energy produced depends of the farm geometry, wind conditions and the terrain where it is settled. In this work, the terrain is irregular and we will analyze two study farm scenarios with a real wind distribution of Comodoro Rivadavia city in Argentina and we will apply both algorithms to analyze the performance of the algorithms and the behavior of the computed wind farm designs., url

(2010) CHC and SA applied to wind energy optimization using real data, IEEE Congress on Evolutionary Computation, p. 1-8, IEEE

In this article we analyze different metaheuristic algorithms applied to wind farm optimization. The basic idea is to utilize CHC (a sort of GA) and Simulated Annealing to obtain an acceptable configuration of wind turbines in the wind farm. The goal is to maximize the total output energy and minimize the number of wind turbines used. The energy produced depends of the farm geometry, wind conditions, and the terrain where it is settled. After analize some case studies we face a real wind distribution taken from Comodoro Rivadavia in Argentina. We study four scenarios, three of them having a constant west wind and the last one with the mentioned real wind distribution. We conclude that our methods outperform existing ones, as well as they produce actually useful results for real wind farms., url

(2010) Computational intelligence techniques for placement of wind turbines: A brief plan of research in Saudi Arabian perspective, 2010 IEEE International Energy Conference, p. 519-523, IEEE

Placement of wind turbines (WTP) in a wind farm is a complex optimization problem, consisting of a number of design objectives and constraints. Although computational intelligence techniques have been applied to solve different versions of this problem, use of these techniques has been very limited to date. In this paper, we identify a number of computational intelligence techniques that have not been fully explored, or not explored at all, to efficiently solve the WTP problem. A research plan to utilize computational intelligence techniques to wind farm design layout in the context of Saudi Arabia has been briefly discussed., url

(2010) Considerations for siting offshore wind farms

(2010) Design of wind farm layout for maximum wind energy capture, Renewable Energy 35(3), p. 685-694, Elsevier Ltd

Wind is one of the most promising sources of alternative energy. The construction of wind farms is destined to grow in the U.S., possibly twenty-fold by the year 2030. To maximize the wind energy capture, this paper presents a model for wind turbine placement based on the wind distribution. The model considerswake loss, which can be calculated based on wind turbine locations, and wind direction. Since the turbine layout design is a constrained optimization problem, for ease of solving it, the constraints are transformed into a second objective function. Then a multi-objective evolutionary strategy algorithm is developed to solve the transformed bi-criteria optimization problem, which maximizes the expected energy output, as well as minimizes the constraint violations. The presented model is illustrated with examples as well as an industrial application., url

(2010) Equilateral-triangle mesh for optimal micrositing of wind farms, p. 187-195, World Scientific and Engineering Academy and Society (WSEAS), url

(2010) Global Optimization of Wind Farms Using Evolutive Algorithms, Springer Berlin Heidelberg, Lingfeng Wang, Chanan Singh, Andrew Kusiak (ed.), p. 53-104, Berlin, Heidelberg: Springer Berlin Heidelberg

The design of a facility for wind power generation is a complex and multidisciplinary problem. The complexity of the problem derives mainly from the many interrelated variables and constraints or restrictions involved. Thus, the solution is usually obtained by heuristics after several cycles of trial and error, and it is heavily based on previous experience of the team planner. Evolutionary algorithms are efficient optimization techniques to tackle the problem of global optimization for wind farms by considering the turbines layout and electrical and civil infrastructure as a whole. The algorithm should evaluate each potential solution based on their economic returns over the entire period production of the wind farm, providing economic and financial information useful for prospective developers. Therefore, the algorithm needs to be driven by a thorough cost wind farm model that considers both the initial costs of acquisition and installation of equipment (initial investment) and the yearly cash flow. This cash flow is calculated as the difference between the incomes due to the energy selling and the ordinary maintenance and operation costs, along the whole lifespan of the wind farm. A final cost for the installation decommissioning and a residual value, after the facility production period, should also be considered. The content of this chapter is organized into five main sections. After an initial introductory section, the problem of wind farm design and planning is formulated. Then there is a brief section on the basics of evolutionary algorithms. Having discussed the problem and the optimization technique, the next section is devoted to integral wind farm optimization through evolutionary algorithms. This section includes a comparison with published works and a collection of new cases to test this new tool performance. The chapter ends with conclusions and references., url

(2010) Maximum yield from symmetrical wind farm layouts, DEWEK 18/11/2010, 10th German Wind Energy Conference. GL Garrad Hassan Deutschland GmbH, Oldenburg, Germany, p. 3-6, pdf

(2010) New approach on optimization in placement of wind turbines within wind farm by genetic algorithms, Renewable Energy 35(7), p. 1559-1564, Elsevier Ltd

In the present study, the placement of wind turbines in wind farm has been resolved with a new coding and also a novel objective function in Genetic algorithm approach. In comparison to previous works, the results have been noticeably improved. The presented objective function, with its adjustable coefficients, provides more control on the cost, power, and efficiency of wind farm in comparison with earlier objective functions. Furthermore, in earlier jobs it was required to consider some subpopulations as well as individuals. However, there is no need to use the subpopulations in recent research by applying new coding approach in solving this problem. Therefore, running genetic algorithm only once for each case is sufficient. In this approach, three cases are considered (a) unidirectional uniform wind, (b) uniformwind with variable direction, and (c) non-uniform wind with variable direction. In Case (a), 10 individuals evolve over 150 generations. Case (b) has 20 individuals evolve for 150 generations. Case (c) starts with 20 individuals evolve for 100 generations. In addition to optimal configurations, results include fitness, total power output, efficiency of output power, number of turbines and objective function coefficients for each configuration., url

(2010) Offshore windfarm layout optimization, 2010 9th International Conference on Environment and Electrical Engineering, p. 542-545, Ieee

The paper investigates the appliance of an optimization routine to establish best offshore windfarm layout, in terms of chosen cost function. An introduction to windfarm layout problem is given. Wake effect, turbine spacing and in- farm wind distribution are covered. Sample Matlab program results are provided and commented on., url

(2010) On Aerodynamic Optimization of Wind Farm Layout, Pamm 10(1), p. 539-540

This paper presents a method for determination of optimum positions of single wind turbines within the wind farms installed on arbitrary configured terrains, in order to achieve their maximum production effectiveness., url

(2010) Optimal Micro-siting of Wind Farms by Particle Swarm Optimization, Advances in Swarm Intelligence 6145, Ying Tan, Yuhui Shi, Kay Chen Tan (ed.), p. 198-205, Berlin, Heidelberg: Springer Berlin Heidelberg

This paper proposes a novel approach to optimal placement of wind turbines in the continuous space of a wind farm. The control objective is to maximize the power produced by a farm with a fixed number of turbines while guaranteeing the distance between turbines no less than the allowed minimal distance for turbine operation safety. The problem of wind farm micro-siting with space constraints is formulated to a constrained optimization problem and solved by a particle swarm optimization (PSO) algorithm based on penalty functions. Simulation results demonstrate that the PSO approach is more suitable and effec- tive for micro-siting than the classical binary-coded genetic algorithms., url

(2010) Optimal placement of wind turbines: A Monte Carlo approach with large historical data set, 2010 IEEE International Conference on Electro/Information Technology, p. 1-5, IEEE

Numerous technical issues arise with the close spacing of multiple wind turbines in a wind farm, particularly one with a severely limited spatial footprint. One of the most important factors under consideration is the wake effect. Since the energy losses due to wakes can significantly decrease the energy production and lead to fluctuations in the output power of a wind farm it is desired to determine optimal positions for installing multiple wind turbines. In the current study, an algorithm that determines an optimal positioning of multiple wind mills in a small footprint wind park under multiple wake effects is introduced. This approach is a mathematical method which explores the various possible positioning combinations via a Monte Carlo-like random search methodology and finds the best choice which maximizes the objective. Matlab (©Mathworks) is used to numerically generate the algorithm and obtain an optimal solution. The case study considered for implementing this algorithm is the Minnesota State University 2-year grant project for installation and testing of four small wind turbine systems on campus. Statistical data from the Weather Analysis Laboratory for Teaching and Educational Resources (WALTER) on campus, consisting of wind speed and direction data over a period of one year is considered to determine the annual power generated., url

(2010) Optimal positioning of wind turbines on Gökçeada using multi-objective genetic algorithm, Wind Energy 13(4), p. 297-306

This paper addresses the problem of optimal placement of wind turbines in a farm on Gokçeada Island located at the north-east of Aegean Sea bearing full potential of wind energy generation. A multi-objective genetic algorithm approach is employed to obtain optimal placement of wind turbines by maximizing the power production capacity while constraining the budget of installed turbines. Considering the speed and direction history, wind with constant intensity from a single direction is used during optimization. This study is based on wake deficit model mainly because of its simplicity, accuracy and fast calculation time. The individuals of the Pareto optimal solution set are evaluated with respect to various criteria, and the best configurations are presented. In addition to best placement layouts, results include objective function values, total power output, cost and number of turbines for each configuration., url

(2010) Optimization of the Layout of Large Wind Farms using a Genetic Algorithm

In this study, a code ‘Wind Farm Optimization using a Genetic Algorithm’ (referred as WFOG) is developed in MATLAB for optimizing the placement of wind turbines in large wind farms to minimize the cost per unit power produced from the wind farm. A genetic algorithm is employed for the optimization. WFOG is validated using the results from previous studies. The grid spacing (distance between two nodes where a wind turbine can be placed) is reduced to 140 wind turbine rotor diameter as compared to 5 rotor diameter in previous studies. Results are obtained for three different wind regimes: Constant wind speed and fixed wind direction, constant wind speed and variable wind direction, and variable wind speed and variable wind direction. Cost per unit power is reduced by 11.7 % for Case 1, 11.8 % for Case 2, and 15.9 % for Case 3 for results obtained using WFOG. The advantages/benefits of a refined grid spacing of 140 rotor diameter (1 m) are evident and are discussed., url

(2010) Optimization of Wind Farm Layout, FME Transactions 38, p. 107-114

This paper presents a method for determination of optimum positions of single wind turbines within the wind farms installed on arbitrary configured terrains, in order to achieve their maximum production effectiveness. This method is based on use of the genetic algorithm as optimization technique. The wind turbine aerodynamic calculation is unsteady, based on the blade modeled as a vortex lattice and a free-wake type airflow behind the blade. Optimization method is developed for two different fitness functions. Both functions use the total energy obtained from the farm as one of the key variables. The second also involves the total investments in a single wind turbine, so the optimization process can also include the total number of turbines as an additional variable. The method has been tested on several different terrain configurations, with special attention paid to the overall algorithm performance improvements by selecting certain genetic algorithm parameters., pdf

(2010) Optimization of wind farm turbines layout using an evolutive algorithm, Renewable Energy 35(8), p. 1671-1681

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., url

(2010) Optimizing the Unrestricted Placement of Turbines of Differing Rotor Diameters in a Wind Farm for Maximum Power Generation, Proceedings of the ASME 2010 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, p. 775-790, ASME, url

(2010) Optimizing wind farm layout – more bang for the buck using mixed integer linear programming, p. 111

Designing a wind farm layout is as of today mainly performed manually. Many different factors affecting the revenue of a wind farm and a large amount of information must be considered, making a close to optimum layout a hard problem to solve manually. In this thesis, mixed integer linear programming models capable of optimizing many common layout problems are developed. We have formulated two optimization models, the Wind Farm Production Optimization model and the Wind Farm Infrastructure Optimization model. In the Production model, wind turbines are positioned with respect to a minimum separation distance. Furthermore, production losses due to wake effects between wind turbines are accounted for. The model also superpositions sound pressures in nearby areas for different wind directions and is also capable of maximizing profit instead of maximizing the production. The Infrastructure model connects the wind turbines by roads and cables, where the latter can include choosing cable dimensions and calculating cable power losses relating to the current. The model can also optimize the positioning of the transformer station. The two models are capable of optimizing the problems stated at a reasonable computation time. The Production model was compared to the commercial WindPRO 2.6 Optimize module in verification examples housing 20 to 30 wind turbines. For the problem of maximizing the total production in the given geographic areas, the Production model of this thesis managed to find locations for many more wind turbines than Optimize, yielding about 40% higher total production. When restricted to allow only as many wind turbines as the software Optimize was able to place, the Production model still performed equal or better. Our tests show that the field of mixed integer linear programming and the mathematical models of this thesis possess a great potential to aid in the process of increasing the return of wind farms. These tools are versatile and can be adopted to many different scenarios. They need further development to be able to handle really large wind farm projects but are still, as of today, capable of delivering more bang for the buck in wind farm layout design.

(2010) Particle Swarm Optimization (PSO) algorithm for wind farm optimal design, International Journal of Management Science and Engineering Management 5(1), p. 53-58, Taylor & Francis

Abstract In this paper, a PSO algorithm for optimal design of wind farm is presented. In particular, we solve the problem of minimizing the cost per unit of power produced in a wind farm. This cost depends on the number of turbines placed in a wind farm and the total power produced. In order to solve this problem, the algorithm PSO is used and in particular the gbest model. In this problem we take into account the number of turbines which are placed in a wind farm, the power produced by each one of them and the velocity of the wind which affects their function. The gbest model of the PSO algorithm is used for a wind farm which consists of 10, 20 and 30 wind turbines. Abstract In this paper, a PSO algorithm for optimal design of wind farm is presented. In particular, we solve the problem of minimizing the cost per unit of power produced in a wind farm. This cost depends on the number of turbines placed in a wind farm and the total power produced. In order to solve this problem, the algorithm PSO is used and in particular the gbest model. In this problem we take into account the number of turbines which are placed in a wind farm, the power produced by each one of them and the velocity of the wind which affects their function. The gbest model of the PSO algorithm is used for a wind farm which consists of 10, 20 and 30 wind turbines., url

(2010) Particle swarm optimization based on Gaussian mutation and its application to wind farm micro-siting, 49th IEEE Conference on Decision and Control (CDC), p. 2227-2232, IEEE

In this paper, a particle swarm optimization algorithm with Gaussian mutations, denoted by GPSO, is proposed to solve constrained optimization problems. Two Gaussian mutation operators are employed to search the promising regions for better solutions. One operator is for the region between the personal best position and the global best one. The other operator is for the region around the global best position. The Gaussian mutations help the population jump out of local optima and find better solutions with more probability. The feasibility-based method compares the performance of different particles. Evaluated by three typical optimization problems, GPSO is more accurate, robust and efficient for locating global optima. The GPSO method is applied to a wind-farm micro-siting problem. Simulation results demonstrate that the power generation of the wind farm is further improved while the execution time is substantially reduced., url

(2010) Study on Optimization of Wind Farm Micro-Layout, 2010 Asia-Pacific Power and Energy Engineering Conference, p. 1-4, IEEE

In the case of wind energy resources identified, wind farm micro-layout mainly involves the arrangement of the wind turbines including to define the number of the turbines and the space between them. In this paper, such impact factors on the wind farm micro-layout as topography, geomorphology, turbulence, wake and economic features of the wind farm are analyzed, and the model for optimization of the wind farm micro-layout is established to maximize the economic benefits in power generation of the wind farm. Also, a case study shows that this model is applicable., url

(2010) The Wind Farm Layout Optimization Problem

An important phase of a wind farm design is solving the Wind Farm Layout Optimization Problem (WFLOP), which consists in optimally positioning the turbines within the wind farm so that the wake effects are minimized and therefore the expected power production maximized. Although this problem has been receiving increasing attention from the scientific community, the existing approaches do not completely respond to the needs of a wind farm developer, mainly because they do not address construction and logistical issues. This paper describes the WFLOP, gives an overview on the existing work, and discusses the challenges that may be overcome by future research., pdf

(2010) TOPFARM-next generation design tool for optimization of wind farm topology and operation... background, vision and challenges, Torque 2010

This paper describes the background and vision of the TOPFARM project, which aims at rational economical optimization of wind farm layout and control strategy, in the sense that the optimal between capital balance maintenance costs, costs, operation and fatigue lifetime consumption and power production output is pursued. Consequently, and contrary to common practice, both production and load aspects are thus included in the optimization procedure. A key factor in the successful achievement of the project goal is computational speed, which is a challenge that inevitable has to be meet on all levels ranging from the wind farm flow field modelling over aeroelastic simulation to the optimization procedure. Examples of specific derived challenges, following from this request, are identified and described, and solutions to overcome qualitatively sketched., url, url

(2010) Wind farm calculation and optimization with FarmFlow(October)

(2010) Wind Farm Optimal Design Including Risk, Modern Electric Power Systems (MEPS), 2010 Proceedings of the International Symposium

(2010) Wind turbines type and number choice using combinatorial optimization, Renewable Energy 35(9), p. 1887-1894, Elsevier Ltd

The paper addresses the problem associated with the optimal wind park design. A combinatorial opti- mization model for wind turbines type and number choice and placement considering the given wind conditions and wind park area is developed. The wind park investment costs and the total power relation as function of wind turbines number and type are used as optimization criteria. The optimization problem is formulated as a single criterion mixed-integer nonlinear discrete combinatorial task. The different wind park conditions are introduced into optimization tasks formulation as variables relations and restrictions. Two basic wind directions cases are taken into consideration – uniform and predomi- nant wind directions for two wind park area shapes – square and rectangular. The developed wind park design approach was tested numerically by solving of different optimization tasks formulations based on wind turbines real parameters data. The numerical testing shows the applicability of the developed optimization approach. Using it will help to find mathematically reasoned wind turbines choice as contradiction to the heuristic approaches., url

(2011) A fast algorithm based on the submodular property for optimization of wind turbine positioning, Renewable Energy 36(11), p. 2951-2958

In the design of a wind farm, the placement of turbines is an important factor that affects the efficiency and profit, but automatic placement of turbines is still a challenging problem. This study reveals the “submodular” property of the wind turbine positioning problem based on Jensenwake model. Based on this property, a “lazy greedy” algorithm is used to optimize the placement. This method can obtain solutions with theoretical guarantee of quality. It can also estimate the lower bound of the optimal value of the objective function. This method is tested on three types of wind scenarios. Compared to previous research, this algorithm takes much less time, and always gains a better solution. To enlarge the application scope, the wake model is extended to the large scale complex terrain in this study. The present algorithm and some other algorithms are tested in the simulation of the complex terrain. The experimental results demonstrate the present method’s superior performance., url

(2011) A review of recent evolutionary computation-based techniques in wind turbines layout optimization problems, Central European Journal of Computer Science 1(1), p. 101-107, url

(2011) Adequacy study of wind farms considering reliability and wake effect of WTGs, 2011 IEEE Power and Energy Society General Meeting, p. 1-7, IEEE

The wake effect of a wind turbine generator (WTG) may affect output of a wind farm. Wake effect depends on wind speed, and the size and location of WTGs in a wind farm. When one or more WTGs fail in a wind farm, the wind speed distribution in the farm will change, which may lead to different wind power output from the farm. This paper proposes a Monte Carlo simulation technique is used to evaluate adequacy of a wind farm with considering the reliability and location of WTGs, wind speed and the wake effect of WTGs. A quadratic interpolation method is used to study the optimal distribution of WTG in a wind farm. Reliability of a wind farm with and without the optimal WTG distribution is investigated by using the proposed technique. The results show that, the optimal layout of a wind farm with considering wake effect should be considered in wind farm adequacy studies., url

(2011) An Improved Evolutive Algorithm for Large Offshore Wind Farm Optimum Turbines Layout, IEEE Trondheim PowerTech, p. 1-6

A tool for optimal wind turbines location in large offshore wind farms is proposed. The algorithm objective is to optimize the profits given an investment on an offshore wind farm. Net Present Value (NPV) is 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 during the offshore wind farm life cycle. The problem is an integer- mixed type problem, exhibits manifold optimal solutions (convexity) and some variables have a range of non allowed values (solutions space not simply connected). This fact makes the problem non-derivable, preventing the use of classical analytical optimization techniques. The proposed optimization algorithm is an improved evolutionary algorithm, for which crossover and mutation operations, specifics to the problem of micro positioning, have been developed.

(2011) Application of Powell's optimisation method for the optimal number of wind turbines in a wind farm, IET Science, Measurement & Technology 5(3), p. 77

The goal of every wind farm designer is the production of the maximum possible power and minimising the installation cost. The cost can be significantly reduced using the minimum required number of wind turbines for specific power production, occupying at the same time the least possible acreage of land. In this work, Powell’s optimisation method has been used for the estimation of the optimal number of wind turbines and the total produced power in a wind farm. The method’s results are compared with those of earlier studies that have followed other approaches, presenting its effectiveness. The proposed method can be useful in the studies of wind farm designers as a supportive tool for the estimation of the optimal number of wind turbines in a wind farm., url

(2011) Characterizing the Influence of Land Configuration on the Optimal Wind Farm Performance, Proceedings of ASME 2011 International Design Engineering Technical Conferences (IDETC) and Computers and Information in Engineering Conference (CIE) IDETC/CIE 2011 August 28-31, 2011, Washington, DC, USA(315), p. 1-12, Washington: ASME 2011

ToThe development of large scale wind farms that can produce energy at a cost comparable to that of other conventional energy resources presents signiﬁcant challenges to today’s wind energy industry. The consideration of the key design and environmental factors that inﬂuence the performance of a wind farm is a crucial part of the solution to this challenge. In this paper, we develop a methodology to account for the conﬁguration of the farm land (length-to-breadth ratio and North-South-East-West orientation) within the scope of wind farm optimization. This approach appropriately captures the correlation between the (i) land conﬁguration, (ii) the farm layout, and (iii) the selection of turbines-types. Simultaneous optimization of the farm layout and turbine selection is performed to minimize the Cost of Energy (COE), for a set of sample land conﬁgurations. The optimized COE and farm efﬁciency are then represented as functions of the land aspect ratio and the land orientation. To this end, we apply a recently developed response surface method known as the Reliability-Based Hybrid Functions. The overall wind farm design methodology is applied to design a 25MW farm in North Dakota. This case study provides helpful insights into the inﬂuence of the land conﬁguration on the optimum farm performance that can be obtained for a particular site., pdf

(2011) Genetic Optimal Micrositing of Wind Farms by Equilateral-Triangle Mesh, InTech(1994)

The micrositing problem designs the layout, i.e. the number of turbines and specific location of each one, for a given farm based on the information of the weather, terrain and landscape of the farm. It aims to capture the wind energy of a farm more effectively while satisfying the constrains on economical, social and environmental issues. The micrositing process, a challenging subject involving fluid dynamics and decision making, plays a crucial role in wind farmplanning (Conover & Davis, 1994). In engineering practice, designers usually calculate the wind flow of a given wind farm by commercial software, and empirically determine the installation positions of turbines based on the flow field. The flow field usually does not include the influence of turbines on the deflexion of the original air flow, i.e. wake effects. However, as the wake effects are complicated and strongly coupled, they play a crucial role in wind farm micrositing. In academia, the micrositing problem with the consideration of turbine wake effects were studied for relative flat terrains. Patel (1999) suggested that wind turbines should be placed in rows 8 ∼ 12 rotor diameters apart in the windward direction, and 1.5 ∼ 3 rotor diameters apart in the crosswind direction. As wind profiles were not considered, the solution was still an “empirical” one. Mosetti et al. (1994), for the first time, applied genetic algorithms (GA) to solve the problem of wind farm micrositing in a systematical manner. Grady et al. (2005) improved Mosetti’s work in terms of programming and computing, and obtained more reasonable results. Wan et al. (2009) introduced the Weibull function to describe the probability ofwind speed distributions and employed turbine speed-power curves to estimate turbine power generation. In Mosetti et al. (1994) and Grady et al. (2005), a square wind farm of 2km × 2km was partitioned into 10 × 10 squares and the turbines could only be installed in the center of suitable small squares. The square mesh (SM) simplified and reformulated the micrositing problem into a discrete-time optimization one, which could be tackled by a binary-coded GA. Although SM is the most simple and instinctive choice, it is worthwhile investigating alternative, and probably better meshing methods. In this paper, a novel equilateral-triangle mesh (ETM) is presented, which proves to be a more suitable method in terms of wind farm production and energy efficiency. The remainder of this paper is organized as follows. Section 2 introduces the methodology of the optimal micrositing problem. Section 3 carries out computational simulations and analyzes the results. Section 4makes concluding remarks., url, pdf

(2011) Optimal Evolutionary Wind Turbine Placement in Wind Farms Considering New Models of Shape, Orography and Wind Speed Simulation, 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Torremolinos-Málaga, Spain, June 8-10, 2011, Proceedings, Part I 6691, Joan Cabestany, Ignacio Rojas, Gonzalo Joya (ed.), p. 25-32, Berlin, Heidelberg: Springer Berlin Heidelberg

In this paper we present a novel evolutionary algorithm for optimal positioning of wind turbines in wind farms. We consider a realistic model for the wind farm, which includes orography, shape of the wind farm, simulation of the wind speed and direction, and costs of installation, connection and road construction among wind turbines. Several experiments show that the proposed evolutionary approach obtains very good solutions which maximize power production, and takes into account the different constraints of the problem., url

(2011) Optimal Placement of the Wind Generators in the Medium Voltage Power Grid, 2011 Sixth International Symposium on Parallel Computing in Electrical Engineering, p. 42-45, IEEE

The minimization of power losses in the medium voltage (MV) grid requires adjustment of network of power sources. This problem is particularly important for renewable energy sources, for example for the farms of wind generators. Their placement and nominal power should be selected according to the configuration of the network and the largest loads. The implementation of the genetic algorithm (GA) to the presented problem is discussed in this paper. The formulation of the GA algorithm and its performance for different numbers of power sources is analyzed. The optimal placements of wind generators were computed for some case problems., url

(2011) Optimal study design for Wind farm in Arwad Island, Energy Procedia 6, p. 721-735

This research purpose is to design a wind Farm for Arwad Island to stand a necessity for electricity energy, and we are set to perform a complete study for the situation of the Island (climatic – geographic – population) and its electricity requirement. And as we see in consequence we suggest seven choices for this design in addition to offshore choice, when we can, to stand clear of negative facets and positive aspects for every choice to determine what seems to be the best. After we go into a feasibility study to pick a product hour kilo watt price for best option. Toward the end we study probability of producing electricity from solar energy (pv system) to confuted disability economy this choice for many reasons. Finally to perform between production electricity classic ways, wind energy and solar energy photovoltaic., url

(2011) Optimal Wind Turbine Placement via Randomized Optimization Techniques, Power Systems Computation Conference (PSCC), Stockholm, Sweden 41(0), p. 1-8

The main contribution of this paper is the development of a novel hybrid methodology so as to achieve optimal wind turbine turbine placement on a selected wind farm site. Earlier approaches are either hampered by the high number of decision variables, or they are restricted to heuristic methods, and hence do not provide any optimality guarantees. The proposed scheme merges genetic algorithm tools with Markov Chain Monte Carlo methods, in an attempt to identify optimal conﬁgurations. The performance of the developed method is demonstrated via an extensive simulation study, and is compared with the outcome of standard approaches based on genetic algorithms., pdf

(2011) Optimization of Wind Farm Micro Sitting Based on Genetic Algorithm, Advanced Materials Research 347-353, p. 3545-3550

In order to increase wind energy utilization efficiency by the optimization of the wind farm micro sitting, a method which could calculate the wind farm velocity is proposed by consideration of multi turbines wake loss and superposition. Based on the given velocity data of a wind farm, the maximal annual energy production is set as the optimal objective and the ordinates of wind turbines would be the optimal variables, micro sittings of the wind farm turbines are optimized by genetic algorithm. Layout calculation result of the optimal method is quite similar to that of other successful search method, but higher efficiency is reached, and the micro sitting layout is agreement with the regular plum-type layout. Annual energy productions are also calculated under the condition of different wind turbine number. Results show annual energy production increases with the wind turbine number increased, but the increasing trend is lower and lower. The research could provide a reference to wind farm micro-sitting., url

(2011) Optimization of Wind Turbine Placement Using a Viral Based Optimization Algorithm, Procedia Computer Science 6, p. 469-474

In the present research, a new viral based optimization algorithm is used to find the optimal solution to wind turbine placement problems considering constant wind speed and unidirectional uniform wind. In this study, a MATLAB based program is developed to search the optimal number and position of wind turbines in large wind farms with the objective of minimizing the total cost per unit of power produced from the wind park. The results obtained by the proposed algorithm are compared with results from previous studies., url

(2011) Optimizing the Layout of 1000 Wind Turbines, European Wind Energy Association, p. 1-10

In this paper we demonstrate an accurate, efﬁcient, and parallelizable optimization algorithm for the layout of hundreds, then 1000, turbines. It is modular and therefore allows different wake effect models to be incorporated. Its computational cost is a relation which depends upon how many candidate layouts it investigates and the complexity of its wake loss calculation. We demonstrate how well it maximizes energy capture and show how it allows one to examine how wake loss scales with energy capture and number of turbines. Keywords: Wind farm design, output maximization, wake consideration, Covariance Matrix Adaptation, Evolutionary strategy

(2011) Overall design optimization of wind farms, Renewable Energy 36(7), p. 1973-1982

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, pre- venting the use of classical analytical optimization techniques., url

(2011) Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms, Renewable Energy 36(11), p. 2838-2844, Elsevier Ltd, url

(2011) TOPFARM - NEXT GENERATION DESIGN TOOL FOR OPTIMISATION OF WIND FARM TOPOLOGY AND OPERATION(January 2011)

(2011) TopFarm: Multi-fidelity Optimization of Offshore Wind Farm, ISOPE International Society of Offshore and Polar Engineers, 2011.(2007), p. 516-524

A wind farm layout optimization framework based on a multi-fidelity model approach is applied to the offshore test case of Middelgrunden, Denmark. While aesthetic considerations have heavily influenced the famous curved design of Middelgrunden wind farm, this work focuses on testing a method that optimizes the profit of offshore wind farms based on a balance of the energy production, the electrical grid costs, the foundations cost, and the wake induced lifetime fatigue of different wind turbine components. The multi-fidelity concept uses cost function models of increasing complexity (and decreasing speed) to accelerate the convergence to an optimum solution. In the EU TopFarm project, three levels of complexity are considered. The first level uses a simple stationary wind farm wake model to estimate the Annual Energy Production AEP, a foundations cost function based on the water depth, and an electrical grid cost function. The second level calculates the AEP and adds a wake induced fatigue cost function based on the interpolation of a database of simulations done for various wind speed and wake setups with the aero-elastic code HAWC2 and the Dynamic Wake Meandering (DWM) model. The third level includes directly the HAWC2 and DWM models in the optimization loop in order to estimate both the fatigue costs and the AEP. The novelty of this work is the implementation of the multi-fidelity, the inclusion of the fatigue costs in the optimization framework, and its application on an existing offshore wind farm test case.

(2011) Wind Farm Layout Optimization (POSTER), B. Dilkina, L. A. Treinish (ed.)

We study the problem of choosing the optimal layout of individual turbines within the boundaries of a wind farm subject to proximity constraints and maximizing energy production. We present a mathematical model for wind turbine placement based on the wind distribution at the wind farm site. At present, this model considers the problem in two dimensions and can utilize estimates of the distribution of wind speed and direction at turbine hub height. Ideally, such distributions would be representative of the climatology of a potential wind farm site, if the required instrumentation is in operation. Alternatively, the data can be derived from simulation via the application of numerical weather prediction. In this case, we use the WRF-ARW community model configured to provide detailed wind estimates at an arbitrary target site. The layout model takes into consideration the hard constraints on turbine placement imposed by budget and turbine operational limitations. The objective is to maximize energy production subject to energy losses incurred due to wake effects between turbines. As opposed to existing approaches based on heuristic and global search methods, we describe an exact search method based on Branch and Bound that provides optimality guarantees. If given enough time, our solving method is guaranteed to find the optimal solution and if terminated early it provides a feasible solution together with an optimality gap. Existing methods find progressively better solutions but have no way of determining how far the best solution found so far is from the optimal and hence, provide solutions that may be arbitrarily bad., url

(2011) Wind Farm Layout Optimization with Representation of Landowner Remittances and Other Costs, 2012 Graduate Research Symposium, Department of Mechanical Engineering, Iowa State University March 29, 2012. 51(1), p. 2-3, Iowa: Department of Mechanical Engineering

Current wind farm layout optimization research focuses on advancing optimization methods. The research includes the assumption that a continuous piece of land is readily available. In reality, wind farm development projects rely on the permission of landowners for success. When a viable wind farm site location is identified, local residents are approached for permission to build turbines on their land, typically in exchange for monetary compensation. Landowners play a crucial role in the development of a wind farm, and some land parcels are more important to the success of the project than others. In order to advance the research on wind farm optimization, this research relaxes the assumption that a continuous piece of land is available, developing a novel approach that includes landowners’ decisions on whether or not to participate in the project. A Genetic Algorithm (GA) is adopted to solve the nonlinear constrained optimization problem, minimizing costs and maximizing power output of the wind farm. The optimization results of this new approach show that, for a specific wind farm layout case, we can identify the most crucial landowners prior to the negotiation process with landowners. Using this approach, a site developer can spend more resources on persuading these most-important landowners to take part in the project. This will ultimately increase the efficiency of wind farm projects, increasing energy output and saving time and money in the development stages., url, pdf

(2011) Wind Park Layout Design Using Combinatorial Optimization, InTech

The energy sector has essential influence on climate change and atmospheric pollution. Wind energy, being a clean and renewable energy, can greatly contribute to decreasing of the air pollution negative impacts. Generally speaking, the production of renewable energy from wind can have a positive socioeconomic benefit – it not only help to reduce the climate changing but also support meeting of the long-term world economical goals. Taking that into account, many countries are encouraging the building of industrial wind parks. The building of a wind park is an expensive and complex task involving a wide range of engineering and scientific knowledge. The design of a wind park can have profound implications for its future profitability. Sustainable wind park development has to be done in an ecological way which means evaluating of the all possible positive and negative influences on the environment. The development of new wind energy projects requires also a significant consideration of land use issues. One of the most important factors in selecting a wind energy site is the availability of proper wind resources. The wind itself is a variable source of energy. The ability of a wind turbine to extract power from varying wind is a function of three main factors – the wind power availability, the power curve of the generator, and the ability of the turbine to respond to wind fluctuations (Üstüntaşa & Şahin, 2008). Wind turbines are available in various sizes and power output. They are designed to operate over a range of wind speeds (3-25 m/s) and can be erected singly by an individual property owner or grouped together to form a wind farm (wind park) connected to a public grid (Rodman & Meentemeyer, 2006). The common challenge for the wind park designer is to maximize the energy capture within the given restrictions (White et al., 1997; Kusiak & Song, 2010). As there is pressure to build more compact wind farms to optimize land utilization, an determination of array losses for very close turbine spacing is required (Smith et al., 2006). The investigations on wind energy using essentially cover four principal topics (Ettoumi et al., 2008). The first one deals with the sensors and instrumentation used for wind measurements. The second one examines the evaluation of wind energy potential for a given region using various statistical approaches. The third is focused on the design and characterization of wind energy turbines. The fourth is the design and development of wind parks. The results of investigations in those areas are used by wind park planners to develop cost-effective wind parks. The investigations discussed here concern the problems associated with the forth topic – design of the wind park layout, including choice of the turbines type, number and their placement in the wind park area. How to choose the number and the type of the turbines to install depends on a variety of factors – wind conditions, terrain, investments costs, power output, environmental influence, etc. More powerful turbine is usually preferred to the less powerful one since both the cost of a turbine and the energy it generates is usually proportional to its nominal power. The placement of wind turbines on the wind park site (i.e. wind park layout) is affected by several factors which have to be taken into account – the number of turbines, wind direction, wake interactions between wind turbines, land availability (area and shape), etc. For the goal a combinatorial design model for defining wind turbines type, number and placement is proposed. It is used for formulation of mixed- integer nonlinear discrete combinatorial optimization tasks satisfying different design requirements and restrictions. The tasks solutions results define different optimal wind park layout designs. The solutions results can be used also to evaluate the impact of alternative wind park layout schemes on the investment costs and wind park power output.

(2011) Wind Turbine Interference in a Wind Farm Layout Optimization Mixed Integer Linear Programming Model, Wind Engineering, Volume 35, Number 2 / April 2011 - Multi Science Publishing., p. 165-178

This work develops a wind intensity interference coefficient which captures the interference caused by an upwind turbine on a downwind turbine in the same wind flow. This coefficient includes the use of a Weibull distribution to handle variability in the wind velocity, and also accounts for the geometric relationship between the turbines and the boundaries of the wind sector. This interference coefficient then forms part of a mixed integer linear program (MILP) which is used to optimise the locations of wind turbines within a wind farm site. The MILP approach is an exact method that can guarantee that the turbine locations determined by the model are optimal with respect to an a priori chosen set of possible turbine locations. Tests on an example data set (based on a demonstration case in the WindFarmer software) show, for the particular wind resource used, that interference results in a loss of power of approximately 4.2% when compared to the power production which would be predicted without accounting for interference., url

(2011) Wind Turbine Placement in a Wind Farm Using a Viral Based Optimization, 41st International Conference on Computers & Industrial Engineering, p. 672-677

In the present research, the Wind Turbine placement problem for renewable energy utilization is solved using a viral systems optimization algorithm which finds the optimum configuration of wind turbines within a wind farm considering uniform wind-speed, and unidirectional wind. The problem is solved using a single type of turbine and constraining the available space to a flat scenario with a square shape with side of size equal to 50 rotor diameters corresponding to the utilized wind turbines. The impact of the wind wake is also considered in the performance of the wind turbines reducing the production of electricity when the wind passes through a wind turbine of the same wind farm. The objective of the viralbased algorithm is to find the best configuration of wind turbines which minimizes the cost of the electricity produced., pdf

(2012) A GRASP-VNS algorithm for optimal wind-turbine placement in wind farms, Renewable Energy 48(null), p. 489-498

The wake effect is the key factor affecting the low efficiency of wind power production. It is very important to predict the relationship between the cost and the produced power for various wind-turbine placements under various wind speeds and directions. This paper proposes a GRASP-VNS algorithm for the optimal placement of wind turbines. Four different wind-farm conditions were considered: (a) uniform wind with single direction, (b) uniform wind with variable directions, (c) non-uniform wind with variable directions, and (d) non-uniform and variable-direction wind with land constraint. The proposed GRASP-VNS algorithm combines two well-known metaheuristics, GRASP and VNS, to create additional advantages in yielding the search trajectory. Intensive experiments assuming the four wind-farm conditions were performed. Statistical analyses show that the proposed GRASP-VNS algorithm significantly outperforms three existing GA-based methods., url

(2012) A mixed-discrete Particle Swarm Optimization algorithm with explicit diversity-preservation, Structural and Multidisciplinary Optimization 47(3), p. 367-388, url

(2012) Adequacy study of a wind farm considering terrain and wake effect, IET Generation, Transmission & Distribution 6(10), p. 1001

When wind passes over a hilly land, the complex terrain feature will observably change wind speed. Wind speed distribution on a hill is much different from that on a flat area. The wake produced by an upstream wind turbine generator (WTG) may affect power output of a downstream WTG. When a WTG fails, wind speed distribution over a wind farm also changes, which leads to different wind power output. This study proposes a time sequential Monte Carlo simulation technique to evaluate adequacy of a wind farm with considering the combined effect of terrain, wake and WTG reliability. A quadratic interpolation method is used to study the optimal locations of WTGs in order to maximise power output of a wind farm. A test wind farm and the IEEE reliability test system (RTS) are analysed to illustrate the models and proposed technique., url

(2012) Computing the Optimal Layout of a Wind Farm, NIK-2012 Conference, p. 93-104

In this paper, we develop computational procedures for optimizing the location of turbines in a wind farm. We consider two different scenarios: First, we assume that the number of turbines to be installed is given, and the goal is to find their locations such that the total power generation is maximized. Every point within a defined region is a potential turbine location. Second, we assume a finite set of possible locations, typically defined in terms of a grid, and the goal is to determine at what locations a turbine should be installed. The goal in this scenario is to maximize the net profit constituted by future sales income minus turbine installation and running costs. To reflect the interdependence between turbines, we need a model of the decline of the wind velocity behind a turbine. In the first part of the paper, we consequently develop a new wake model, which is shown to be an improvement of one of the most popular wake models in the literature. In the second part of the paper, we suggest heuristic methods for approaching the optimization problems, and evaluate them experimentally on a number of test cases. Based on results from the experiments, we conclude that our new wake model, combined with the method displaying the best performance in the experiments, is a useful tool in designing wind farms., pdf

(2012) Design of wind farm layout using ant colony algorithm, Renewable Energy 44, p. 53-62, url

(2012) Estimation of wind turbines optimal number and produced power in a wind farm using an artificial neural network model, Simulation Modelling Practice and Theory 21(1), p. 21-25

One of the most significant issues in the design of a new wind farm is the estimation of optimal number of wind turbines that has to be installed in it. The goal of every wind farm designer is the production of the maximum possible power, minimizing the installation cost. The cost can be significantly reduced using the minimum required number of wind turbines for specific power production, occupying at the same time the least possible acreage of land. In this work an artificial neural network (ANN) model is developed which has the ability to estimate the optimal number of wind turbines and the total produced power in a wind farm. The ANN model’s results are compared with those of earlier studies that have followed other approaches, proving that the ANN model is well working and has an acceptable accuracy. The proposed model can be useful in the studies of wind farm designers as a supportive tool for the estimation of the optimal number of wind turbines in a wind farm., url

(2012) Multi-objective Wind Farm Layout Optimization(August)

(2012) Offshore wind farm macro and micro siting protocol Application to Rhode Island, Proceeding of the 33rd Intl. Coastal Engineering. Conference, June 1-6 2012, Santander, Spain., p. 1-9

Since 2008, the Rhode Island (RI) Coastal Resources Management Council has been leading the development of an Ocean Special Area Management Plan (Ocean SAMP), in partnership with the University of Rhode Island, resulting in an extensive multidisciplinary analysis of the Rhode Island offshore environment and its suitability to site offshore wind farms. As part of SAMP, a comprehensive macro-siting optimization tool: the Wind Farm Siting Index (WIFSI), integrating technical, societal, and ecological constraints, was developed within the conceptual framework of ecosystem services. WIFSI uses multivariate statistical analyses (principal component and k-means cluster analyses) to define homogeneous regions, which integrate and balance ecological and societal constraints as part of a Cost/Benefit tool. More recently, a Wind Farm micro-Siting Optimization Tool was developed (WIFSO), which uses a genetic algorithm to derive the optimal layout of a wind farm sited within one of the macro-siting selected regions. In this work, we present an overview of the current state of development of the integrated macro- and micro- siting tools., pdf

(2012) Offshore wind farm siting using a genetic algorithm, 2012 International Conference on Green Technologies (ICGT), p. 208-214, IEEE

This study uses a genetic algorithm to optimize a wind farm layout considering the engineering challenges and the ecosystem services as constraints to the turbine siting. Included is an analysis of how wake effects influence the power produced using a simple wake model called the WAsP model. The current study considers the location of the proposed Deep Water Wind Inc. Offshore Wind Farm Project, southeast of Block Island, Rhode Island. The proposed project consists of six, 6MW Siemens wind turbines located within the Rhode Island State waters that extend roughly 4.8 km off of the Block Island coast. The optimum solution produces turbine locations best conforming to areas of low technical, ecological, and social costs, while simultaneously distributing the turbines to minimize turbine wake interaction. Future model improvements will consist of more accurately describing wind conditions within the wind farm and incorporating turbine cable interconnection installation costs., url

(2012) Optimal turbine spacing in fully developed wind farm boundary layers, Wind Energy 15(2), p. 305-317, url

(2012) Optimising land use for wind farms, Energy for Sustainable Development 16(4), p. 471-475

The optimal scales and densities for wind turbine arrays are examined from the perspective of maximising power density, defined as power per unit area of land occupied. This is different from the usual aim of minimising the cost of electricity production but could become increasingly important if available sites for wind farms become a limiting factor in their construction. A simple model is used to calculate the theoretical maximum power densities available from various configurations of wind turbine array, taking into account the wake effect. The effects of array size, turbine separation and perimeter set-back are investigated. It is observed that a wind farm designed to maximise power production per unit area of land could be very different from one designed to maximise economic gain., url

(2012) Optimization Locations of Wind Turbines with the Particle Swarm Optimization, Springer Berlin Heidelberg 7331, Ying Tan, Yuhui Shi, Zhen Ji (ed.), p. 133-141, Berlin, Heidelberg: Springer Berlin Heidelberg

In this paper, a new algorithm is presented for the locations of wind turbine in the distribution systems. Technical constraints such as feeder capacity limits, bus voltage, and load balance are considered. The Particle Swarm Optimization(PSO) is applied to solve this problem. To enhance the performance of the new algorithm, a load flow program with Equivalent Current Injection (ECI) is used to analyze the load flow of distribution systems. Based on ECI load flow model, a constant Jacobian matrix is determined to improve the existing power-based model by using the Norton Equivalent Theorem. Example of IEEE 69-bus system is adopted to illustrate the efficiency and feasible of the proposed algorithm. Test results show that with proper site selections of wind turbines can be used to reduce system losses and maintain the voltage profile., url

(2012) Optimization Models for Turbine Location in Wind Farms

(2012) Optimization of the wind turbine layout and transmission system planning for a large-scale offshore wind farm by AI technology, 2012 IEEE Industry Applications Society Annual Meeting, p. 1-9, IEEE

Interest in using offshore wind power is rapidly increasing worldwide. A typical offshore wind farm may have hundreds of generators installed within an area ranging from several to tens of square kilometers. Therefore, many feasible schemes have been developed for optimizing wind turbine locations and internal line connections in a wind farm. The planner must search for the optimal scheme in terms of wind power output and installation and operation costs. This study proposes a novel procedure for optimizing wind turbine location and connection line topology by using artificial intelligent techniques: the Genetic Algorithm (GA) is used to optimize the layouts for the offshore wind farm, and Ant Colony System (ACS) algorithm is used to optimize line connection topology. The wake effect, real cable parameters and wind speed series are also considered. The proposed concepts and methods can improve the economy and efficiency of offshore wind farms worldwide., url

(2012) Optimization of Wind Farm Turbine Layout Including Decision Making Under Risk, IEEE Systems Journal 6(1), p. 94-102

This paper presents a new contribution to optimal wind farm design, including the main risk management aspects. The objective of the algorithm is to optimize the expected profits of the wind farm by taking into account that the wind data used to design the wind farm involves some degree of uncertainty that affects the final return of the project. Net present value (NPV) will be used as a figure of the profitability in the proposed method. The maximization of the NPV means the maximization of the cumulative net cash flows (by maximizing the generation of net energy) and minimization of the investment. Both terms mainly depend on the number and type of wind turbines, tower height, and geographical position, among other factors. Therefore, the tool developed in this paper is intended to determine the wind farm configuration most suitable in the presence of risk due to uncertainty in the wind data., url

(2012) Optimization of wind turbine micrositing: A comparative study, Renewable and Sustainable Energy Reviews 16(8), p. 5485-5492

The need for energy is an attention required issue for the developing countries. Developing countries are in the grip of the deficit of fossils or hydrocarbons sources of energy. Many countries are looking for the optimal solutions of energy production which are more reliable, pollution free and presume less cost. Pakistan is also in the list of those countries who want to get rid of expensive and polluted means of power production. Power production to fulfill the demand of the country is the biggest challenge for Pakistan. Therefore, many sites are under consideration for greener solutions of the problem. The proposed study is undertaken for the under consideration site, Gharo, Sindh, Pakistan. The present research is undertaken to find out the optimal solution for the wind turbine micrositings. A comparison of present study with different past studies (using different optimization techniques, i.e., genetic algorithm, Monte Carlo simulation method etc.) have been undertaken to prove the results of the present study as better results. The basic objective of the study is to find out the most optimal solution for cost per unit power; therefore, the number of wind turbines is not an issue in the undertaken study however, cost is the function of number of wind turbines and to optimize the solution, MS-Excel is used first to prove that power is a function of Wind speed. Second, genetic algorithm is also used for minimal value of fitness function., url

(2012) Optimizing energy output and layout costs for large wind farms using particle swarm optimization, 2012 IEEE Congress on Evolutionary Computation, p. 1-7, Ieee, url

(2012) State of the Art of Wind Farm Optimization, Proceedings of EWEA 2012 - European Wind Energy Conference & Exhibition, p. 1-11

In recent years the trend has been to collect wind generators into larger and larger wind farms. As the investments are substantial, the optimization of the wind farm layout plays a major role today. The scope of the present work is to define the state of the art in wind farm optimization. To do so the literature of the last two decades has been analyzed, and the structure of the problem has been defined. The most effective techniques and models used in the past are described. The common pitfalls are listed as well, with the aim to create a blueprint for future development of wind farm optimization tools/softwares. The main findings concern the high dependency of the resulting layout on the objective function chosen, which objective should be as detailed as possible; the energy yield alone has been proven not to be the best function for practical purposes. The need for all-encompassing functions requires the costs to be computed besides the production yield. New strategies have been developed to handle comprehensive objective functions and to reduce long computational times, namely the “two-steps” optimization, which consist of a combination of two algorithms, usually a meta-heuristic and a local search approach. The last point touched by this work highlights the areas where a better understanding is needed and more research should be addressed, like the models for degradation and the solving algorithms used., url

(2012) State of the Art of Wind Farm Optimization

The present work attempts to outline the state of the art in the field of wind farm layout optimization. To do so the literature of the last two decades has been analyzed and the common structure of the problem has been defined. The most effective techniques and models are described. The usual pitfalls are as well listed, whose aim is the creation of a blueprint for future development of wind farm optimization tools/softwares. The last point touched by this work highlights the areas where a better understanding is needed and more research should be addressed to determine realistic layouts.

(2012) TOPFARM : Multi-fidelity Optimization of Wind Farms, Wind Energy, p. 1-36

A wind farm layout optimization framework based on a multi-fidelity optimization approach is applied to the offshore test case of Middelgrunden, Denmark as well as to the onshore test case of Stag Holt - Coldham wind farm, UK. While aesthetic considerations have heavily influenced the famous curved design of the Middelgrunden wind farm, this work focuses on demonstrating a method that optimizes the profit of wind farms over their lifetime based on a balance of the energy production income, the electrical grid costs, the foundations cost, the cost of wake turbulence induced fatigue degradation of different wind turbine components. A multi-fidelity concept is adapted which uses cost function models of increasing complexity (and decreasing speed) to accelerate the convergence to an optimum solution. In the EU-FP6 TOPFARM project, three levels of complexity are considered. The first level uses a simple stationary wind farm wake model to estimate the Annual Energy Production (AEP), a foundations cost model depending on the water depth, and an electrical grid cost function dictated by cable length. The second level calculates the AEP and adds a wake induced fatigue degradation cost function based on the interpolation in a database of simulations performed for various wind speeds and wake setups with the aero-elastic code HAWC2 and the Dynamic Wake Meandering (DWM) model. The novelty of this work is the implementation of the multi-fidelity approach in the context of wind farm optimization, the inclusion of the fatigue degradation costs in the optimization framework, and its application on the optimal performance as seen through an economical perspective.

(2012) Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation, Renewable Energy 38(1), p. 16-30

A new methodology, the Unrestricted Wind Farm Layout Optimization (UWFLO), that addresses critical aspects of optimal wind farm planning is presented in this paper. This methodology simultaneously determines the optimum farm layout and the appropriate selection of turbines (in terms of their rotor diameters) that maximizes the net power generation. The farm layout model obviates traditional restrictions imposed on the location of turbines. A standard analytical wake model has been used to account for the velocity deficits in the wakes created by individual turbines. The wind farm power generation model is validated against data from a wind tunnel experiment on a scaled down wind farm. Reasonable agreement between the model and experimental results is obtained. The complex nonlinear optimization problem presented by the wind farm model is effectively solved using constrained Particle Swarm Optimization (PSO). It is found that an optimal combination of wind turbines with differing rotor diameters can appreciably improve the farm efficiency. A preliminary wind farm cost analysis is per- formed to express the cost in terms of the turbine rotor diameters and the number of turbines in the farm. Subsequent exploration of the influences of (i) the number of turbines, and (ii) the farm land size, on the cost per Kilowatt of power produced, yields important observations., url

(2012) WIND FARM LAYOUT OPTIMIZATION CONSIDERING ENERGY GENERATION AND NOISE PROPAGATION, Proceedings of the ASME 2012 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2012 August 12-15, 2012, Chicago, IL, USA, p. 1-10

(2012) Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy, Renewable Energy 48(null), p. 276-286

The micro-siting of wind farms has recently attracted much attention due to the booming development of wind energy. The paper aims to maximize the electrical power extracted from a wind farm while satisfying the required distance between turbines for operation safety. The micro-siting problem is by nature a constrained optimization problem, in which the coupling of wake effects is strong and the number of position constraints between turbines is large. An improved Gaussian particle swarm optimization algorithm is proposed to optimize the positions of turbines in the continuous space. To prevent the premature of the algorithm, a local search strategy based on differential evolution is incorporated to search around the promising region achieved by the particle swarm optimization. A simple feasibility-based method is employed to compare the performance of different schemes. Comprehensive simulation results demonstrate that the micro-siting schemes obtained by the proposed algorithm increase the power generation of the wind farm. Moreover, the execution time of the algorithm is significantly reduced, which is important especially for large-scale wind farms., url

(2012) Wind Farm Optimization Based on CFD Model of Single Wind Turbine Wake, EWEA 2012

The present work is focused on the problem of wind farm design optimization. An optimal wind farm design guarantees high power output and low operational costs. The best positioning of the wind turbines is found by considering that in general the wind turbines operate in the wake of the others and therefore the wake interaction plays a crucial role in the energy production. The easiest approach makes use of analytical models for both the single wakes and the wake interactions. The analytical approach ensures a fast resolution and is therefore suitable to be used within an optimization framework. In the present work, the analytical model used for the single wake is replaced by a model directly derived from a Computational Fluid Dynamics CFD simulation of the wind turbine. The results are then compared with a fully analytical approach., pdf

(2012) Wind Turbine Layout & Performance Optimization A manufacturer’s perspective, p. 11, pdf

(2013) A fast and effective local search algorithm for optimizing the placement of wind turbines, Renewable Energy 51, p. 64-70, Elsevier Ltd, url

(2013) A Research on Wind Farm Micro-sitting Optimization in Complex Terrain, Proceedings of the 2013 International Conference on aerodynamics of Offshore Wind Energy Systems and wakes (ICOWES2013), p. 669-679

Wind farm layout optimization in complex terrain is a pretty difficult issue for onshore wind farm. In this article, a novel optimization method is proposed to optimize the layout for wind farms in complex terrain. This method utilized Lissaman and Jensen wake models for taking the terrain height and the wake loss from the upstream turbines into the wind turbine power output calculation. Wind direction is divided into sixteen sections, and the wind speed is processed using the Weibull distribution. The objective is to maximize the total wind farm power output and the free design variables are the wind turbines’ park coordinates which subject to the boundary and minimum distance conditions between two wind turbines. A Cross Particle Swarm Optimization (CPSO) method is developed and applied to optimize the layout for a certain wind farm case. Compared with the uniform and experience method, results show that the CPSO method has a higher optimal value, and could be used to optimize the actual wind farm micro-sitting engineering projects., url

(2013) Bionic optimization for micro-siting of wind farm on complex terrain, Renewable Energy 50, p. 551-557, url

(2013) Cloud Scale Distributed Evolutionary Strategies for High Dimensional Problems, Springer Berlin Heidelberg 7835, Anna I. Esparcia-Alcázar (ed.), p. 519-528, Berlin, Heidelberg: Springer Berlin Heidelberg

We develop and evaluate a cloud scale distributed covariance matrix adaptation based evolutionary strategy for problems with dimensions as high as 400. We adopt an island based distribution model and rely on a peer-to-peer communication protocol. We identify a variety of parameters in a distributed island model that could be randomized leading to a new dynamic migration protocol that can prove advantageous when computing on the cloud. Our approach enables efficient and high quality distributed sampling while mitigating the latencies and failure risks associated with running on a cloud. We evaluate performance on a real world problem from the domain of wind energy: wind farm turbine layout optimization., url

(2013) Effects of Uncertain Land Availability , Wind Shear, and Cost on Wind Farm Layout, Proceedings of the ASME 2013 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2013 August 4-7, 2013, Portland, Oregon, USA, p. 1-13

The robust optimization presented in this paper is formulated to assist in early-stage wind farm development. It can help wind farm developers predict project viability and can help landowners predict where turbines will be placed on their land. A wind farm layout is optimized under multiple sources of uncertainty. Landowner participation is represented with a novel uncertain model of willingness-to-accept monetary compensation. An uncertain wind shear parameter and economies-of-scale cost reduction parameter are also included. Probability Theory, Latin Hypercube Sampling, and Compromise Programming are used to form the robust design problem and minimize the two objectives: the normalized mean and standard deviation of Cost-of-Energy. The results suggest that some landowners that will only accept high levels of compensation are worth pursuing, while others are not., pdf

(2013) Evolutionary computation approaches for real offshore wind farm layout: A case study in northern Europe, Expert Systems with Applications 40(16), p. 6292-6297, url

(2013) Fast and effective multi-objective optimisation of wind turbine placement, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference - GECCO '13, p. 1381, New York, New York, USA: ACM Press, url

(2013) Implementing Particle Swarm Optimization in Wind Farm to Place Wind Turbines, Australian Journal of Basic and Applied Sciences 7(7), p. 77-84

In this paper, particle swarm optimization is utilized to optimize the placement of wind turbines in a wind farm. Since wind is one of the sustainable energy sources, it is important to build the wind parks as efficient as possible to extract the largest possible amount of electrical power. Here, the location of each wind turbine is adjusted within a predefined square field considered as a wind farm. The output power of the farm and the total cost of construction are the parameters considered to create an objective function. The simulated results are shown in graphs and tables. Also comparisons are carried out with previous presented optimizations. The results prove that in present study, the efficiency and electrical power extraction of the wind farm are increased regarding the total cost of construction., pdf

(2013) Improved Formulation for the Optimization of Wind Turbine Placement in a Wind Farm, Mathematical Problems in Engineering 2013(1), p. 1-5, url

(2013) Irregular-shape wind farm micro-siting optimization, Energy 57, p. 535-544, Elsevier Ltd, url

(2013) Iterative non-deterministic algorithms in on-shore wind farm design: A brief survey, Renewable and Sustainable Energy Reviews 19, p. 370-384, Elsevier, url

(2013) Modular approach for the optimal wind turbine micro siting problem through CMA-ES algorithm, Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion - GECCO '13 Companion, p. 1561, New York, New York, USA: ACM Press, url

(2013) Multi-Objective Wind Farm Design: Exploring the Trade-Off Between Capacity Factor and Land Use, 10th World Congress on Structural and Multidisciplinary Optimization, p. 1-8

The performance of a wind farm is aﬀected by several key factors that can be classiﬁed into two categories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is aﬀected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary performance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning eﬀective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-oﬀ is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-oﬀs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-oﬀ is parameterized in the upper level. In this work, the CF - LAMI trade-oﬀ is successfully quantiﬁed by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important insights into the trade-oﬀs between the two performance objectives, which can signiﬁcantly streamline the decision-making process in wind farm development.

(2013) Offshore wind farm layout optimization using mathematical programming techniques, Renewable Energy 53, p. 389-399, Elsevier Ltd, url

(2013) On learning to generate wind farm layouts, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference - GECCO '13, p. 767, New York, New York, USA: ACM Press, url

(2013) Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients, Renewable Energy 55, p. 266-276, Elsevier Ltd, url

(2013) Optimal wind turbines placement within a distribution market environment, Applied Soft Computing 13(10), p. 4038-4046, url

(2013) Optimization of wind farm design taking into account uncertainty in input parameters, EWEA 2013

Optimization of wind farm design with risk assessment is presented in this paper. The net present value (NPV) is used to evaluate the yield of the laid-down capital of the wind farm. Monte Carlo simulation method is applied to obtain probability distribution of the objective function. The uncertainties of the wind speed and direction and power curve of the wind turbine are studied by incorporating them in the annual energy production (AEP) uncertainty, which can be directly translated into NPV uncertainty. Differential Evolution (DE) is used as the optimization algorithm., url

(2013) Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions, Renewable Energy 52(null), p. 273-282

The development of large scale wind farms that can compete with conventional energy resources presents significant challenges to today's wind energy industry. A powerful solution to these daunting challenges can be offered by a synergistic consideration of the key design elements (turbine selection and placement) and the variations in the natural resource. This paper significantly advances the Unrestricted Wind Farm Layout Optimization (UWFLO) method, enabling it to simultaneously optimize the placement and the selection of turbines for commercial-scale wind farms that are subject to varying wind conditions. The advanced UWFLO method avoids the following limiting traditional assumptions: (i) array/grid-wise layout pattern, (ii) fixed wind condition, or unimodal and univariate distribution of wind conditions, and (iii) the specification of a fixed and uniform type of turbine to be installed in the farm. Novel modifications are made to the formulation of the inter-turbine wake interactions, which allow turbines with differing features and power characteristics to be considered in the UWFLO method. The annual energy production is estimated using the joint distribution of wind speed and direction. A recently developed Kernel Density Estimation-based model that can adequately represent multimodal wind data is employed to characterize the wind distribution. A response surface-based wind farm cost model is also developed and implemented to evaluate and favorably constrain the Cost of Energy of the designed farm. The selection of commercially available turbines introduces discrete variables into the optimization problem; this challenging problem is solved using an advanced mixed-discrete Particle Swarm Optimization algorithm. The effectiveness of this wind farm optimization methodology is illustrated by applying it to design a 25-turbine wind farm in N. Dakota. A remarkable improvement of 6.4% in the farm capacity factor is accomplished when the farm layout and the turbine selection are simultaneously optimized., url

(2013) Site specific optimization of wind turbines energy cost: Iterative approach, Energy Conversion and Management 73, p. 167-175, Elsevier Ltd, url

(2013) Solving Wind Farm Layout Optimization with Mixed Integer Programming and Constraint Programming, 10th International Conference, CPAIOR 2013, Yorktown Heights, NY, USA, May 18-22, 2013. Proceedings(7874), p. 284-299

The wind farm layout optimization problem is concerned with the optimal location of turbines within a fixed geographical area to maximize energy capture under stochastic wind conditions. Previously it has been modelled as a maximum diversity (or p-dispersion-sum) prob- lem, but such a formulation cannot capture the nonlinearity of aerody- namic interactions among multiple wind turbines. We present the first constraint programming (CP) and mixed integer linear programming (MIP) models that incorporate such nonlinearity. Our empirical results indicate that the relative performance between these two models reverses when the wind scenario changes from a simple to a more complex one. We also propose an improvement to the previous maximum diversity model and demonstrate that the improved model solves more problem instances., url

(2013) The investigation of tower height matching optimization for wind turbine positioning in the wind farm, Journal of Wind Engineering and Industrial Aerodynamics 114, p. 83-95, Elsevier, url

(2013) TOPFARM – A TOOL FOR WIND FARM OPTIMIZATION, p. 32

(2013) Topics in Wind Farm Layout Optimization: Analytical Wake Models, Noise Propagation, and Energy Production, pdf

(2013) Wind Farm Layout Optimization A Refinement Method by Random Search

(2013) Wind farm layout optimization using genetic algorithm with different hub height wind turbines, Energy Conversion and Management 70, p. 56-65, Elsevier Ltd, url

(2013) Wind farm layout optimization using particle filtering approach, Renewable Energy 58, p. 95-107, url

(2013) Wind Farms Design and Optimization

(2013) Wind turbine positioning optimization of wind farm using greedy algorithm, Journal of Renewable and Sustainable Energy 5(2), p. 023128, American Institute of Physics

In this paper, the greedy algorithm is used to solve the wind turbine positioning optimization problem. Various models are employed to describe the problem, including the linear wake model, the power-law power curve model with power control mechanisms, Weibull distribution, and the profit function. The incremental calculation method is developed to consider the influence of the adding turbine on other turbines in the wind farm and accelerate the wind power assessment process. The repeated adjustment strategy is used to improve the optimized result. Three cases with simple models and a case with realistic models are used to test the present method. The results show that the greedy algorithm with repeated adjustment can obtain a better result than bionic algorithm and genetic algorithm in less computational time. The proposed greedy algorithm is an effective solution strategy for wind turbine positioning optimization. © 2013 American Institute of Physics, url

[Could not find the bibliography file(s)]