An Extended Pattern Search Method for Offshore Floating Wind Layout and Turbine Geometry Optimization

2016 ◽  
Author(s):  
Caitlin Forinash ◽  
Bryony DuPont
Keyword(s):  
Wind Farm ◽  
Cost Model ◽  
Geometry Optimization ◽  
Interaction Model ◽  
Solution Space ◽  
Wind Farms ◽  
Power Production ◽  
Pattern Search ◽  
Offshore Wind ◽  
Extended Pattern Search

An Extended Pattern Search (EPS) method is developed to optimize the layout and turbine geometry for offshore floating wind power systems. The EPS combines a deterministic pattern search with stochastic extensions. Three advanced models are incorporated: (1) a cost model considering investment and lifetime costs of a floating offshore wind farm comprised of WindFloat platforms; (2) a wake propagation and interaction model able to determine the reduced wind speeds downstream of rotating blades; and (3) a power model to determine power produced at each rotor, and includes a semi-continuous, discrete turbine geometry selection to optimize the rotor radius and hub height of individual turbines. The objective function maximizes profit by minimizing cost, minimizing wake interactions, and maximizing power production. A multidirectional, multiple wind speed case is modeled which is representative of real wind site conditions. Layouts are optimized within a square solution space for optimal positioning and turbine geometry for farms containing a varying number of turbines. Resulting layouts are presented; optimized layouts are biased towards dominant wind directions. Preliminary results will inform developers of best practices to include in the design and installation of offshore floating wind farms, and of the resulting cost and power production of wind farms that are computationally optimized for realistic wind conditions.

Optimization of Floating Offshore Wind Energy Systems Using an Extended Pattern Search Method

2016 ◽  
Author(s):  
Caitlin Forinash ◽  
Bryony DuPont
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Power Production ◽  
Pattern Search ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Power Development ◽  
Offshore Wind Farms ◽  
Onshore Wind ◽  
Extended Pattern Search

An Extended Pattern Search (EPS) approach is developed for offshore floating wind farm layout optimization while considering challenges such as high cost and harsh ocean environments. This multi-level optimization method minimizes the costs of installation and operations and maintenance, and maximizes power development in a unidirectional wind case by selecting the size and position of turbines. The EPS combines a deterministic pattern search algorithm with three stochastic extensions to avoid local optima. The EPS has been successfully applied to onshore wind farm optimization and enables the inclusion of advanced modeling as new technologies for floating offshore wind farms emerge. Three advanced models are incorporated into this work: (1) a cost model developed specifically for this work, (2) a power development model that selects hub height and rotor radius to optimize power production, and (3) a wake propagation and interaction model that determines aerodynamic effects. Preliminary results indicate the differences between proposed optimal offshore wind farm layouts and those developed by similar methods for onshore wind farms. The objective of this work is to maximize profit; given similar parameters, offshore wind farms are suggested to have approximately 24% more turbines than onshore farms of the same area. EPS layouts are also compared to those of an Adapted GA; 100% efficiency is found for layouts containing twice as many turbines as the layout presented by the Adapted GA. Best practices are derived that can be employed by offshore wind farm developers to improve the layout of platforms, and may contribute to reducing barriers to implementation, enabling developers and policy makers to have a clearer understanding of the resulting cost and power production of computationally optimized farms; however, the unidirectional wind case used in this work limits the representation of optimized layouts at real wind sites. Since there are currently no multi-turbine floating offshore wind farm projects operational in the United States, it is anticipated that this work will be used by developers when planning array layouts for future offshore floating wind farms.

Design Analysis of Critical Concepts Influence Wind Farm Production and Efficiency

2018 ◽  
Vol 40 ◽  
pp. 136-150
Author(s):  
Naima Charhouni ◽  
Mohammed Sallaou ◽  
Khalifa Mansouri
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Production Efficiency ◽  
Wind Farms ◽  
Power Production ◽  
Separation Distance ◽  
Design Analysis ◽  
Offshore Wind ◽  
Farm Production ◽  
Wind Farm Design

Wind farm deficiency caused by wake turbine interactions has received an important attention by scientific researchers in recent years. However the quality of power production is strongly depends on wind turbines location from others. In this regard, this paper proposes a comprehensive design analysis of crucial concepts that aid to plan for an efficient wind farm design. Indeed, the wake modeling problem is addressed in this analysis by comparing three models with available measured data gotten from literature. A configuration of wind turbines placement within the offshore wind farm as a function of separation distance is investigated in this study considering four wind farms layout. In addition to these elements, four rotor diameters size are evaluated as critical concept for wind turbine selection and production .The results obtained demonstrate that it is complicated to make a balance between three conflicted objectives related to the power production, efficiency and surface land area required for wind farm as a function of these crucial concepts.

Performance Analysis of Offshore Mono-Pile Wind Turbine Co-Located With Floating Photovoltaic Systems in Shark Bay, Australia

2021 ◽  
Author(s):  
Morteza Bahadori ◽  
Hassan Ghassemi
Keyword(s):  
Wind Turbine ◽  
Hybrid System ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Production ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Shark Bay

Abstract In recent years, as more offshore wind farms have been constructed, the possibility of integrating various offshore renewable technologies is increased. Using offshore wind and solar power resources as a hybrid system provides several advantages including optimized marine space utilization, reduced maintenance and operation costs, and relieving wind variability on output power. In this research, both offshore wind and solar resources are analyzed based on accurate data through a case study in Shark Bay (Australia), where bathymetric information confirms using offshore bottom-fixed wind turbine regarding the depth of water. Also, the power production of the hybrid system of co-located bottom-fixed wind turbine and floating photovoltaic are investigated with the technical characteristics of commercial mono-pile wind turbine and photovoltaic panels. Despite the offshore wind, the solar energy output has negligible variation across the case study area, therefore using the solar platform in deep water is not an efficient option. It is demonstrated that the floating solar has a power production rate nearly six times more than a typical offshore wind farm with the same occupied area. Also, output energy and surface power density of the hybrid offshore windsolar system are improved significantly compared to a standalone offshore wind farm. The benefits of offshore wind and solar synergies augment the efficiency of current offshore wind farms throughout the world.

Analysis of Wind Turbine Capacity Factor Improvement by Correcting Yaw Error Using LIDAR

2017 ◽  
Author(s):  
Roozbeh Bakhshi ◽  
Peter Sandborn
Keyword(s):  
Energy Production ◽  
Wind Farm ◽  
Production Capacity ◽  
Wind Farms ◽  
Power Production ◽  
Capacity Factor ◽  
Offshore Wind ◽  
The Common ◽  
Turbine Capacity ◽  
The Cost

Yaw error is the angle between a turbine’s rotor central axis and the wind flow. The presence of yaw error results in lower power production from turbines. Yaw error also puts extra loads on turbine components, which in turn, lowers their reliability. In this study we develop a stochastic model to calculate the average capacity factor of a 50 turbine offshore wind farm and investigate the effects of minimizing the yaw error on the capacity factor. In this paper, we define the capacity factor in terms of energy production, which is consistent with the common practice of wind farms (rather than the power production capacity factor definition that is used in textbooks and research articles). The benefit of using the energy production is that it incorporates both the power production improvements and downtime decreases. For minimizing the yaw error, a nacelle mounted LIDAR is used. While the LIDAR is on a turbine, it collects wind speed and direction data for a period of time, which is used to calculate a correction bias for the yaw controller of the turbine, then it will be moved to another turbine in the farm to perform the same task. The results of our investigation shows that although the improvements of the capacity factor are less than the theoretical values, the extra income from the efficiency improvements is larger than the cost of the LIDAR.

scholarly journals Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

2018 ◽  
Author(s):  
Thomas Duc ◽  
Olivier Coupiac ◽  
Nicolas Girard ◽  
Gregor Giebel ◽  
Tuhfe Göçmen
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Decay Constant ◽  
Wind Farms ◽  
Power Production ◽  
Original Model ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Onshore Wind ◽  
Wake Model

Abstract. In this paper, a new calculation procedure to improve the accuracy of the Jensen wake model for operating wind farms is proposed. In this procedure the wake decay constant is updated locally at each wind turbine based on the turbulence intensity measurement provided by the nacelle anemometer. This procedure was tested against experimental data at onshore wind farm La Sole du Moulin Vieux (SMV) in France and the offshore wind farm Horns Rev-I in Denmark. Results indicate that the wake deficit at each wind turbine is described more accurately than when using the original model, reducing the error from 15–20 % to approximately 5 %. Furthermore, this new model properly calibrated for the SMV wind farm is then used for coordinated control purposes. Assuming an axial induction control strategy, and following a model predictive approach, new power settings leading to an increased overall power production of the farm are derived. Power gains found are in the order of 2.5 % for a two wind turbine case with close spacing and 1 to 1.5 % for a row of five wind turbines with a larger spacing. Finally, the uncertainty of the updated Jensen model is quantified considering the model inputs. When checked against the predicted power gain, the uncertainty of the model estimations is seen to be excessive, reaching approximately 4 %, which indicates the difficulty of field observations for such a gain. Nevertheless, the optimized settings are to be implemented during a field test campaign at SMV wind farm in scope of the national project SMARTEOLE.

scholarly journals Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 680 ◽  
Author(s):  
Zhenzhou Shao ◽  
Ying Wu ◽  
Li Li ◽  
Shuang Han ◽  
Yongqian Liu
Keyword(s):  
Wind Farm ◽  
Model Simulation ◽  
Interaction Model ◽  
Wind Farms ◽  
Offshore Wind ◽  
Analytical Models ◽  
Percentage Error ◽  
Stress Profile ◽  
Offshore Wind Farms ◽  
Classical Energy

In a wind farm some wind turbines may be affected by multiple upwind wakes. The commonly used approach in engineering to simulate the interaction effect of different wakes is to combine the single analytical wake model and the interaction model. The higher turbulence level and shear stress profile generated by upwind turbines in the superposed area leads to faster wake recovery. The existing interaction models are all analytical models based on some simple assumptions of superposition, which cannot characterize this phenomenon. Therefore, in this study, a mixing coefficient is introduced into the classical energy balance interaction model with the aim of reflecting the effect of turbulence intensity on velocity recovery in multiple wakes. An empirical expression is also given to calculate this parameter. The performance of the new model is evaluated using data from the Lillgrund and the Horns Rev I offshore wind farms, and the simulations agree reasonably with the observations. The comparison of different interaction model simulation results with measured data show that the calculation accuracy of this new interaction model is high, and the mean absolute percentage error of wind farm efficiency is reduced by 5.3% and 1.58%, respectively, compared to the most commonly used sum of squares interaction model.

scholarly journals A Minimalistic Prediction Model to Determine Energy Production and Costs of Offshore Wind Farms

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 448
Author(s):  
Jens Nørkær Sørensen ◽  
Gunner Christian Larsen
Keyword(s):  
Prediction Model ◽  
Wind Turbines ◽  
Water Depth ◽  
Energy Production ◽  
Wind Farm ◽  
Cost Model ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
The Cost

A numerical framework for determining the available wind power and associated costs related to the development of large-scale offshore wind farms is presented. The idea is to develop a fast and robust minimal prediction model, which with a limited number of easy accessible input variables can determine the annual energy output and associated costs for a specified offshore wind farm. The utilized approach combines an energy production model for offshore-located wind farms with an associated cost model that only demands global input parameters, such as wind turbine rotor diameter, nameplate capacity, area of the wind farm, number of turbines, water depth, and mean wind speed Weibull parameters for the site. The cost model includes expressions for the most essential wind farm cost elements—such as costs of wind turbines, support structures, cables and electrical substations, as well as costs of operation and maintenance—as function of rotor size, interspatial distance between the wind turbines, and water depth. The numbers used in the cost model are based on previous but updatable experiences from offshore wind farms, and are therefore, in general, moderately conservative. The model is validated against data from existing wind farms, and shows generally a very good agreement with actual performance and cost results for a series of well-documented wind farms.

scholarly journals On the Robustness of Active Wake Control to Wind Turbine Downtime

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3152
Author(s):  
Stoyan Kanev
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Production ◽  
Offshore Wind ◽  
Negative Effects ◽  
Collaborative Control ◽  
Potential Benefits ◽  
Production Increase ◽  
The Impact

Active wake control (AWC) is an operational strategy for wind farms that aims at reducing the negative effects of wakes behind wind turbines on the power production and mechanical loads at the wind turbines’ downstream. For a given wind direction, the strategy relies on collaborative control of the machines within each row of turbines that affect each other through their wakes. The vast amount of research performed during the last decade indicates that the potential upside of this technology on the annual energy production of a wind farm can be as high as a few percentage points. Although these predictions on the potential benefits are quite significant, they typically assume full availability of all turbines within a row operating under AWC. However, even though the availability of offshore wind turbines is nowadays quite high (as high as 95%, or even higher), the availability of a whole row of turbines is shown to be much lower (lower than 60% for a row of ten turbines). This paper studies the impact of turbine downtime on the power production increase from AWC, and concludes that the AWC is robust enough to be kept operational in the presence of turbines standing still.

Multi-Stage Optimization of Wind Farms With Limiting Factors

2013 ◽  
Author(s):  
Bryony L. DuPont ◽  
Jonathan Cagan
Keyword(s):  
Wind Farm ◽  
Solution Space ◽  
Wind Farms ◽  
Objective Evaluation ◽  
Pattern Search ◽  
Limiting Factors ◽  
Limiting Factor ◽  
Test Case ◽  
Wind Speeds ◽  
Multi Stage

Larger onshore wind farms are often installed in phases, with discrete smaller sub-farms being installed and becoming operational in succession until the farm as a whole is completed. An extended pattern search (EPS) algorithm that selects both local turbine position and geometry is presented that enables the installation of a complete farm in discrete stages, exploring optimality of both incremental sub-farm solutions and the completed project as a whole. The objective evaluation is the maximization of profit over the life of the farm, and the EPS uses modeling of cost based on an extensive cost analysis by the National Renewable Energy Laboratory (NREL). The EPS uses established wake modeling to calculate the power development of the farm, and allows for the consideration of multiple or overlapping wakes. A limiting factor is used to determine the size of wind farm stages: optimization stages based on the number of turbines currently available for development (representative of limitations in initial capital, which is commonly encountered in wind farm stage development). Two wind test cases are considered: a unidirectional test case with constant wind speed and a single wind direction, and a multidirectional test case, with three wind speeds and a defined probability of occurrence for each. The test case shown in the current work is employed on a 4000 km by 4000 km solution space. In addition, two different methods are performed: the first uses the optimal layout of a complete farm and then systematically “removes” turbines to create smaller sub-farms; the second uses a weighted multi-objective optimization over sequential, adjacent land that concurrently optimizes each sub-farm and the complete farm. The exploration of these resulting layouts indicates the value of full-farm optimization (in addition to optimization of the individual stages) and gives insight into how to approach optimality in sub-farm stages. The behavior exhibited in these tests cases suggests a heuristic that can be employed by wind farm developers to ensure that multi-stage wind farms perform at their peak throughout their completion.

Micrositing Optimization of the Block Island Wind Farm, RI, USA

2013 ◽  
Author(s):  
Christopher M. O’Reilly ◽  
Annette R. Grilli ◽  
Gopu R. Potty
Keyword(s):  
Renewable Energy ◽  
Objective Function ◽  
Rhode Island ◽  
Wind Farm ◽  
Clustering Algorithm ◽  
Cost Model ◽  
Wind Farms ◽  
Offshore Wind ◽  
Mitigating Factors ◽  
Initial Objective

The Rhode Island Ocean Special Area Management Plan (RIOSAMP) has been implemented in Rhode Island since 2008 to provide guidance to local regulators in the zoning of renewable energy, with a focus on the siting of offshore wind farms. The project culminated in the siting of the first North American offshore wind project, optimized using a spatial planning approach combining exclusionary and mitigating factors. The optimization of mitigating factors is based on a standard cost model approach and extended to include ecological and societal factors. This macro-siting optimization phase provided the framework to define a Renewable Energy Zone (REZ) for wind farm development and the present study seeks the siting optimization of the wind farm layout within this zone. The optimization considers the loss in power resulting from turbine wake interaction, a cable cost clustering algorithm, and the spatial variation of both foundation cost and the available wind resource within the REZ through a micrositing objective function. This initial objective function is extended to include ecological and social costs. The layout optimization is based on a Genetic Algorithm (GA) optimization scheme. The method is applied to the REZ area, demonstrating that a gain of approximately $10 million over 20 years could be obtained if an “optimal layout” would be selected over the initial layout chosen by the developers.

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