scholarly journals Wind Farm Layout Upgrade Optimization

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2465 ◽  
Author(s):  
Mamdouh Abdulrahman ◽  
David Wood
Keyword(s):  
Energy Production ◽  
Wind Farm ◽  
Lower Cost ◽  
Wind Farms ◽  
Offshore Wind ◽  
General Representation ◽  
Thrust Coefficient ◽  
Wind Farm Layout ◽  
Cost Of Energy ◽  
Wide Range

The problem of optimally increasing the size of existing wind farms has not been investigated in the literature. In this paper, a proposed wind farm layout upgrade by adding different (in type and/or hub height) commercial turbines to an existing farm is introduced and optimized. Three proposed upgraded layouts are considered: internal grid, external grid, and external unstructured. The manufacturer’s power curve and a general representation for thrust coefficient are used in power and wake calculations, respectively. A simple field-based model is implemented and both offshore and onshore conditions are considered. A genetic algorithm is used for the optimization. The trade-off range between energy production and cost of energy is investigated by considering three objective functions, individually: (1) annual energy production; (2) cost of added energy; and (3) cost of total energy. The proposed upgraded layouts are determined for the Horns Rev 1 offshore wind farm. The results showed a wide range of suitable upgrade scenarios depending on the upgraded layout and the optimization objective. The farm energy production is increased by 190–336% with a corresponding increase in the total cost by 147–720%. The external upgrade offers more energy production but with much more cost. The unstructured layouts showed clear superiority over the grid ones by providing much lower cost of energy.

Optimizing the Layout of Offshore Wind Energy Systems

2008 ◽  
Vol 42 (2) ◽  
pp. 19-27 ◽  
Author(s):  
Christopher N. Elkinton ◽  
James F. Manwell ◽  
Jon G. McGowan
Keyword(s):  
Wind Energy ◽  
Energy Production ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Energy ◽  
Offshore Wind Farm ◽  
Wind Farm Layout ◽  
Trade Offs ◽  
Cost Of Energy ◽  
The Cost

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.

Geometry-Based Models for Studying the Effects of Wind Farm Layout

2016 ◽  
Vol 33 (3) ◽  
pp. 481-501 ◽  
Author(s):  
Niranjan S. Ghaisas ◽  
Cristina L. Archer
Keyword(s):  
Wind Farm ◽  
Linear Models ◽  
Orientation Angle ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Farm Layout ◽  
Prevailing Wind Direction ◽  
Wake Effects ◽  
Well Correlation ◽  
Large Wind

AbstractLayout studies are critical in designing large wind farms, since wake effects can lead to significant reductions in power generation. Optimizing wind farm layout using computational fluid dynamics is practically unfeasible today because of their enormous computational requirements. Simple statistical models, based on geometric quantities associated with the wind farm layout, are therefore attractive because they are less demanding computationally. Results of large-eddy simulations of the Lillgrund (Sweden) offshore wind farm are used here to calibrate such geometry-based models. Several geometric quantities (e.g., blockage ratio, defined as the fraction of the swept area of a wind turbine that is blocked by upstream turbines) and their linear combinations are found to correlate very well (correlation coefficient of ~0.95) with the power generated by the turbines. Linear models based on these geometric quantities are accurate at predicting the farm-averaged power and are therefore used here to study layout effects in large wind farms. The layout parameters that are considered include angle between rows and columns, angle between incoming wind and columns (orientation), turbine spacings, and staggering of alternate rows. Each can impact wind power production positively or negatively, and their interplay is complex. The orientation angle is the most critical parameter influencing wake losses, as small changes in it can cause sharp variations in power. In general, for a prevailing wind direction, the orientation angle should be small (7.5°–20°) but not zero; staggering and spacing are beneficial; and nonorthogonal layouts may outperform orthogonal ones. This study demonstrates the utility of simple, inexpensive, and reasonably accurate geometry-based models to identify general principles governing optimal wind farm layout.

scholarly journals Wind farm layout optimization using pseudo-gradients

2021 ◽  
Vol 6 (3) ◽  
pp. 815-839
Author(s):  
Erik Quaeghebeur ◽  
René Bos ◽  
Michiel B. Zaaijer
Keyword(s):  
Large Scale ◽  
Wind Farm ◽  
Optimization Algorithms ◽  
Wind Farms ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Proof Of Concept ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Gradient Based

Abstract. This paper presents a heuristic building block for wind farm layout optimization algorithms. For each pair of wake-interacting turbines, a vector is defined. Its magnitude is proportional to the wind speed deficit of the waked turbine due to the waking turbine. Its direction is chosen from the inter-turbine, downwind, or crosswind directions. These vectors can be combined for all waking or waked turbines and averaged over the wind resource to obtain a vector, a “pseudo-gradient”, that can take the role of gradient in classical gradient-following optimization algorithms. A proof-of-concept optimization algorithm demonstrates how such vectors can be used for computationally efficient wind farm layout optimization. Results for various sites, both idealized and realistic, illustrate the types of layout generated by the proof-of-concept algorithm. These results provide a basis for a discussion of the heuristic's strong points – speed, competitive reduction in wake losses, and flexibility – and weak points – partial blindness to the objective and dependence on the starting layout. The computational speed of pseudo-gradient-based optimization is an enabler for analyses that would otherwise be computationally impractical. Pseudo-gradient-based optimization has already been used by industry in the design of large-scale (offshore) wind farms.

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 Optimal Micro-Siting of Weathervaning Floating Wind Turbines

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 886
Author(s):  
Javier Serrano González ◽  
Manuel Burgos Payán ◽  
Jesús Manuel Riquelme Santos ◽  
Ángel Gaspar González Rodríguez
Keyword(s):  
Wind Direction ◽  
Wind Farm ◽  
Single Point ◽  
Wind Farms ◽  
Offshore Wind ◽  
Classical Solutions ◽  
Feasible Region ◽  
Offshore Wind Farms ◽  
Cost Of Energy ◽  
Wind Farm Design

This paper presents a novel tool for optimizing floating offshore wind farms based on weathervaning turbines. This solution is grounded on the ability of the assembly (wind turbine plus floater) to self-orientate into the wind direction, as this concept is allowed to freely pivot on a single point. This is a passive yaw potential solution for floating wind farms currently in the demonstration phase. A genetic algorithm is proposed for optimizing the levelised cost of energy by determining the geographical coordinates of the pivot points (i.e., the position over which the assembly can rotate to self-orient to the incoming wind direction). A tailored evaluation module is proposed to take into account the weathervaning motion around the pivot point depending on the incoming wind direction. The results obtained show the suitability of the proposed method to solve the addressed problem under realistic conditions. Additionally, the influence of the feasible region defined by the plot and the maximum area occupied on floating offshore wind farm design are also analysed in the proposed test cases. These deployable area constraints are of great importance for the viability of this technology, as it requires more space than classical solutions anchored to a fixed point.

Levelized Cost of Energy Assessment for Offshore Wind Farms—An Examination of Different Methodologies, Input Variables, and Uncertainty

2021 ◽  
Vol 7 (4) ◽  
Author(s):  
Fiona Devoy McAuliffe ◽  
Miriam Noonan ◽  
Jimmy Murphy
Keyword(s):  
Discount Rate ◽  
Energy Production ◽  
Capital Expenditure ◽  
Wind Farms ◽  
Business Case ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Tax Rate ◽  
Levelized Cost ◽  
Cost Of Energy

Abstract Levelized cost of energy (LCoE) is the most common metric used in renewable energy assessments. However, this can be a very complex calculation with numerous methodologies depending on the perspective taken. Inputs including costs, energy production are generally forecasts and predictions based on publicly available information; therefore, they are key areas of uncertainty. Elements of the calculation are site or region specific such as the tax rate or inclusion of grid connection costs. The business case and financial assumptions applied will be very project specific, e.g., the discount rate applied. These numerous variables and uncertainties must be fully understood in order to effectively apply the metric or review and compare LCoEs. Therefore, this paper provides a comprehensive set of LCoE methodologies that provide a reference basis for researchers. A case study demonstrates the application of these methods and the variation in results illustrates the importance of correctly selecting the discount rate and cash flow based on the perspective and motivation of the user. Sensitivity studies further investigates the potential impact of key variables and areas of uncertainty on results. Analysis indicates that the energy production and discount rate applied will have the most significant impact on LCoE, followed by Capital Expenditure (CAPEX) costs. While the key areas of uncertainties cannot necessarily be solved, this paper promotes consistency in the application and understanding of the metric, which can help overcome its limitations.

scholarly journals Simultaneous Optimisation of Cable Connection Schemes and Capacity for Offshore Wind Farms via a Modified Bat Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 265 ◽  
Author(s):  
Yuanhang Qi ◽  
Peng Hou ◽  
Liang Yang ◽  
Guangya Yang
Keyword(s):  
Capital Investment ◽  
Wind Farm ◽  
Mathematical Problem ◽  
Wind Farms ◽  
Bat Algorithm ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Cost Of Energy ◽  
Modified Bat Algorithm ◽  
Cable Connection

Offshore wind energy has attracted worldwide attention and investments in the last decade due to the stability and abundance of wind resources. As one of the main components of this, internal array cables have a great impact on the levelised cost of energy of offshore wind farms, and thus their connection layout is a matter of concern. In this paper, a classical mathematical problem—the traveling salesman problem, which belongs to the field of graph theory—is applied to solve the offshore wind farm cable connection layout optimization problem. Both the capital investment on cables, cable laying, and the cost of power losses associated with array cables are considered in the proposed model. A modified bat algorithm is presented to resolve the problem. Furthermore, a cable crossing detection method is also adopted to avoid obtaining crossed cable connection layouts. The effectiveness was verified through a case study.

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 Economic Feasibility of Floating Offshore Wind Farms in the North of Spain

2020 ◽  
Vol 8 (1) ◽  
pp. 58 ◽  
Author(s):  
Laura Castro-Santos ◽  
A. Rute Bento ◽  
Dina Silva ◽  
Nadia Salvação ◽  
C. Guedes Soares
Keyword(s):  
Wind Farm ◽  
Net Present Value ◽  
Wind Farms ◽  
Economic Feasibility ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
The North ◽  
Wind Structure ◽  
Cost Of Energy ◽  
North Of Spain

This paper assesses the economic feasibility of offshore wind farms installed in deep waters considering their internal rate of return (IRR), net present value (NPV), and levelized cost of energy (LCOE). The method proposed has three phases: geographic phase, economic phase, and restrictions phase. The purpose of the geographic step is to obtain the input values, which will be used in the economic phase. Then, the economic parameters are calculated considering the inputs provided previously. Finally, the bathymetric restriction is added to the economic maps. The case study focused on the Cantabric and North-Atlantic coasts of Spain, areas that have not been studied previously in economic terms regarding floating offshore wind technology. Moreover, several alternatives have been considered, taking into account the type of floating offshore wind structure and the electric tariff. Results indicate which is the best floating offshore wind structure with respect to LCOE, IRR, and NPV, and where is the best location for the connection of a floating offshore wind farm in the region selected.

Numerical Optimization and Noise Analysis of High-Tip-Speed Wind Turbine

2017 ◽  
Vol 9 (6) ◽  
pp. 1461-1484 ◽  
Author(s):  
Long Wang ◽  
Guoping Chen ◽  
Tongguang Wang ◽  
Jiufa Cao
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Noise Analysis ◽  
Wind Farms ◽  
Offshore Wind ◽  
Class Iii ◽  
Offshore Wind Farms ◽  
Multi Objective ◽  
Cost Of Energy

AbstractWith lower turbulence and less rigorous restrictions on noise levels, offshore wind farms provide favourable conditions for the development of high-tip-speed wind turbines. In this study, the multi-objective optimization is presented for a 5MW wind turbine design and the effects of high tip speed on power output, cost and noise are analysed. In order to improve the convergence and efficiency of optimization, a novel type of gradient-based multi-objective evolutionary algorithm is proposed based on uniform decomposition and differential evolution. Optimization examples of the wind turbines indicate that the new algorithm can obtain uniformly distributed optimal solutions and this algorithm outperforms the conventional evolutionary algorithms in convergence and optimization efficiency. For the 5MW wind turbines designed, increasing the tip speed can greatly reduce the cost of energy (COE). When the tip speed increases from 80m/s to 100m/s, under the same annual energy production, the COE decreases by 3.2% in a class I wind farm and by 5.1% in a class III one, respectively, while the sound pressure level increases by a maximum of 4.4dB with the class III wind farm case.

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