scholarly journals Layout optimization for offshore wind farms in India using the genetic algorithm technique

2020 ◽  
Vol 54 ◽  
pp. 79-87
Author(s):  
Narender Kangari Reddy ◽  
Somnath Baidya Roy
Keyword(s):  
Genetic Algorithm ◽  
Power Generation ◽  
Wind Farm ◽  
Selection Process ◽  
Wind Farms ◽  
Critical Issue ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Wake Model

Abstract. Wind Farm Layout Optimization Problem (WFLOP) is a critical issue when installing a large wind farm. Many studies have focused on the WFLOP but only for a limited number of turbines and idealized wind speed distributions. In this study, we apply the Genetic Algorithm (GA) to solve the WFLOP for large hypothetical offshore wind farms using real wind data. GA mimics the natural selection process observed in nature, which is the survival of the fittest. The study site is the Palk Strait, located between India and Sri Lanka. This site is a potential hotspot of offshore wind in India. A modified Jensen wake model is used to calculate the wake losses. GA is used to produce optimal layouts for four different wind farms at the specified site. We use two different optimization approaches: one where the number of turbines is kept the same as the thumb rule layout and another where the number of turbines is allowed to vary. The results show that layout optimization leads to large improvements in power generation (up to 28 %), efficiency (up to 34 %), and cost (up to 25 %) compared to the thumb rule due to the reduction in wake losses. Optimized layouts where both the number and locations of turbines are allowed to vary produce better results in terms of efficiency and cost but also leads to lower installed capacity and power generation. Wind energy is growing at an unprecedented rate in India. Easily accessible terrestrial wind resources are almost saturated, and offshore wind is the new frontier. This study can play an important role while taking the first steps towards the expansion of offshore wind in India.

Layout optimization for a large offshore wind farm using Genetic Algorithm

2020 ◽  
Author(s):  
K Narender Reddy ◽  
S Baidya Roy
Keyword(s):  
Genetic Algorithm ◽  
Wind Energy ◽  
Wind Farm ◽  
Selection Process ◽  
Wind Farms ◽  
Cost Effective ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Previous Generation ◽  
Large Wind

<p>Wind Farm Layout Optimization Problem (WFLOP) is an important issue to be addressed when installing a large wind farm. Many studies have focused on the WFLOP but only for a limited number of turbines (10 – 100 turbines) and idealized wind speed distributions. In this study, we apply the Genetic Algorithm (GA) to solve the WFLOP for large wind farms using real wind data.</p><p>The study site is the Palk Strait located between India and Sri Lanka. This site is considered to be one of the two potential hotspots of offshore wind in India. An interesting feature of the site is that the winds here are dominated by two major monsoons: southwesterly summer monsoon (June-September) and northeasterly winter monsoon (November to January). As a consequence, the wind directions do not drastically change, unlike other sites which can have winds distributed over 360<sup>o</sup>. This allowed us to design a wind farm with a 5D X 3D spacing, where 5D is in the dominant wind direction and 3D is in the transverse direction (D- rotor diameter of the turbine - 150 m in this study).</p><p>Jensen wake model is used to calculate the wake losses. The optimization of the layout using GA involves building a population of layouts at each generation. This population consists of, the best layouts of the previous generation, crossovers or offspring from the best layouts of the previous generation and few mutated layouts. The best layout at each generation is assessed using the fitness or objective functions that consist of annual power production by the layout, cost incurred by layout per unit power produced, and the efficiency of the layout. GA mimics the natural selection process observed in nature, which can be summarised as survival of the fittest. At each generation, the layouts performing the best would enter the next generation where a new population is created from the best performing layouts.</p><p>GA is used to produce 3 different optimal layouts as described below. Results show that:</p><p>A ~5GW layout – has 738 turbines, producing 2.37 GW of power at an efficiency of 0.79</p><p>Layout along the coast – has 1091 turbines, producing 3.665 GW of power at an efficiency of 0.82.</p><p>Layout for the total area – has 2612 turbines, producing 7.82 GW of power at an efficiency of 0.74.</p><p>Thus, placing the turbines along the coast is more efficient as it makes the maximum use of the available wind energy and it would be cost-effective as well by placing the turbines closer to the shores.</p><p>Wind energy is growing at an unprecedented rate in India. Easily accessible terrestrial resources are almost saturated and offshore is the new frontier. This study can play an important role in the offshore expansion of renewables in India.</p>

scholarly journals Theory and Verification of a new 3D RANS Wake Model

2020 ◽  
Author(s):  
Philip Bradstock ◽  
Wolfgang Schlez
Keyword(s):  
Wind Farm ◽  
Three Dimensional ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Ambient Flow ◽  
Turbulence Decay ◽  
Local Shear ◽  
Time Lagged ◽  
Wake Model

Abstract. This paper details the background to the WakeBlaster model: a purpose built, parabolic three-dimensional RANS solver, developed by ProPlanEn. WakeBlaster is a field model, rather than a single turbine model; it therefore eliminates the need for an empirical wake superposition model. It belongs to a class of very fast (a few core seconds, per flow case) mid-fidelity models, which are designed for industrial application in wind farm design, operation and control. The domain is a three-dimensional structured grid, with approximately 80 nodes covering the rotor disk, by default. WakeBlaster uses eddy viscosity turbulence closure, which is parameterized by the local shear, time-lagged turbulence development, and stability corrections for ambient shear and turbulence decay. The model prescribes a profile at the end of the near-wake, and the spatial variation of ambient flow, by using output from an external flow model. The WakeBlaster model is verified, calibrated and validated using a large volume of data from multiple onshore and offshore wind farms. This paper presents example simulations for one offshore wind farm.

Wind Flow Conditions in Offshore Wind Farms: Validation and Application of a CFD Wake Model

Green ◽  
2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Annette Westerhellweg ◽  
Beatriz Cañadillas ◽  
Friederike Kinder ◽  
Thomas Neumann
Keyword(s):  
Wind Farm ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Flow ◽  
Flow Conditions ◽  
Offshore Wind Farms ◽  
Speed Reduction ◽  
Wake Model ◽  
The Impact

AbstractSince August 2009, the first German offshore wind farm ‘alpha ventus’ is operating close to the wind measurement platform FINO1. Within the research project RAVE-OWEA the wind flow conditions in ‘alpha ventus’ were assessed in detail, simulated with a CFD wake model and compared with the measurements. Wind data measured at FINO1 have been evaluated for wind speed reduction and turbulence increase in the wake. Additionally operational data were evaluated for the farm efficiency. The atmospheric stability has been evaluated by temperature measurements of air and water and the impact of atmospheric stability on the wind conditions in the wake has been assessed. As an application of CFD models the generation of power matrices is introduced. Power matrices can be used for the continual monitoring of the single wind turbines in the wind farm. A power matrix based on CFD simulations has been created for ‘alpha ventus’ and tested against the measured data.

scholarly journals Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 146
Author(s):  
Joongjin Shin ◽  
Seokheum Baek ◽  
Youngwoo Rhee
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Optimization Procedure ◽  
Pso Algorithm ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Metropolitan City ◽  
Wind Potential

This paper examines the solution to the problem of turbine arrangement in offshore wind farms. The two main objectives of offshore wind farm planning are to minimize wake loss and maximize annual energy production (AEP). There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. South Korea’s offshore wind farms, which are deep in water and cannot be installed far off the coast, are affected by land complex terrain. Thus, domestic offshore wind farms should consider the separation distance from the coastline as a major variable depending on the topography and marine environmental characteristics. As a case study, a 60 MW offshore wind farm was optimized for the coast of the Busan Metropolitan City. For the analysis of wind conditions in the candidate site, wind conditions data from the meteorological tower and Ganjeolgot AWS at Gori offshore were used from 2001 to 2018. The optimization procedure is performed by evolutionary algorithm (EA) and particle swarm optimization (PSO) algorithm with the purpose of maximizing the AEP while minimizing the total wake loss. The optimization procedure can be applied to the optimized placement of WTs within a wind farm and can be extended for a variety of wind conditions and wind farm capacity. The results of the optimization were predicted to be 172,437 MWh/year under the Gori offshore wind potential, turbine layout optimization, and an annual utilization rate of 26.5%. This could convert 4.6% of electricity consumption in the Busan Metropolitan City region in 2019 in offshore wind farms.

scholarly journals Prediction of Power Generation by Offshore Wind Farms Using Multiple Data Sources

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 700 ◽  
Author(s):  
Cheng-Dar Yue ◽  
Che-Chih Liu ◽  
Chien-Cheng Tu ◽  
Ta-Hui Lin
Keyword(s):  
Power Generation ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Offshore Area ◽  
Multiple Data ◽  
Central Weather Bureau ◽  
Wind Resources

In this study we evaluated the wind resources of wind farms in the Changhua offshore area of Taiwan. The offshore wind farm in Zone of Potential (ZoP) 26 was optimized through an economic evaluation. The annual energy production (AEP) of the offshore wind farm in ZoP 26 was predicted for 10 and 25 years with probabilities of 50%, 75%, and 90% by using measured mast data, measure-correlate-predict (MCP) data derived from Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Central Weather Bureau (CWB) data. When the distance between the turbines in a wind farm was decreased from 12D to 6D, the turbine number increased from 53 to 132, while the capacity factor decreased slightly from 48.6% to 47.6%. MCP data derived from the inland CWB station with similar levels of wind resources can be used to accurately predict the power generation of the target offshore wind farm. The use of MCP with mast data as target data, together with CWB and MERRA data as reference data, proved to be a feasible method for predicting offshore wind power generation in places where a mast is available in a neighboring area.

Monitoring fisheries resources at offshore wind farms: BACI vs. BAG designs

2020 ◽  
Vol 77 (3) ◽  
pp. 890-900
Author(s):  
Elizabeth T Methratta
Keyword(s):  
Statistical Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Cultural Value ◽  
Analytical Models ◽  
Offshore Wind Farms ◽  
Fisheries Resources ◽  
Unaffected Control ◽  
Before And After

Abstract Offshore wind farms often co-occur with biodiverse marine ecosystems with high ecological, economic, and cultural value. Yet there are many uncertainties about how wind farms affect marine organisms and their environment. The before–after–control–impact (BACI) design, an approach that compares an impact location with an unaffected control both before and after the intervention, is the most common method used to study how offshore wind farms affect finfish. Unfortunately, this design has several methodological limitations that undermine its ability to detect effects in these studies. An alternative approach, the before–after-gradient (BAG) design, would sample along a gradient with increasing distance from the turbines both before and after the intervention, and could overcome many of the limitations of BACI. The BAG design would eliminate the difficult task of finding a suitable control, allow for the assessment of the spatial scale and extent of wind farm effects, and improve statistical power by incorporating distance as an independent variable in analytical models rather than relegating it to the error term. This article explores the strengths and weaknesses of the BACI and BAG designs in the context of offshore wind development and suggests an approach to incorporating the BAG design into existing fisheries surveys and a regional monitoring framework.

scholarly journals Analysis of offshore wind farm located on Baltic Sea

2019 ◽  
Vol 137 ◽  
pp. 01049
Author(s):  
Anna Sobotka ◽  
Kajetan Chmielewski ◽  
Marcin Rowicki ◽  
Justyna Dudzińska ◽  
Przemysław Janiak ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
The European Union ◽  
European Union Policy ◽  
Offshore Wind Farms ◽  
Wind Speeds ◽  
Wind Measurements ◽  
The Baltic

Poland is currently at the beginning of the energy transformation. Nowadays, most of the electricity generated in Poland comes from coal combustion. However, in accordance to the European Union policy of reducing the emission of carbon dioxide to the atmosphere, there are already plans to switch to low-emission energy sources in Poland, one of which are offshore wind farms. The article presents the current regulatory environment of the offshore wind energy in Poland, along with a reference to Polish and European decarbonisation plans. In the further part of the article, the methods of determining the kinetic energy of wind and the power curve of a wind turbine are discussed. Then, on the basis of historical data of wind speeds collected in the area of the Baltic Sea, calculations are carried out leading to obtain statistical distributions of power that could be generated by an exemplary wind farm with a power capacity of 400 MW, located at the place of wind measurements. On their basis, statistical differences in the wind power generation between years, months of the year and hours of the day are analysed.

A Study on the New Grid Integration Solutions of Offshore Wind Farms

2011 ◽  
Vol 383-390 ◽  
pp. 3610-3616 ◽  
Author(s):  
Xin Yin Zhang ◽  
Zai Jun Wu ◽  
Si Peng Hao ◽  
Ke Xu
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Grid Integration ◽  
Variable Speed ◽  
Reliable Operation ◽  
Offshore Wind Farms ◽  
Electrical Interconnection ◽  
Investment Cost ◽  
And Performance

Offshore wind farm is developed in the ascendant currently. The reliable operation, power loss, investment cost and performance of wind farms were effect by the integration solutions of electrical interconnection system directly. Several new integration configurations based on VSC-HVDC were comparative analyzed. For the new HVDC topology applied the wind farm internal DC bus, the Variable Speed DC (VSDC) system that is suitable for those topologies was proposed. The structure of VSDC was discussed and maximum wind power tracking was simulated on the minimal system. It is clear that new integration configurations based on VSC-HVDC has good prospects.

Optimization of O&M for Offshore Wind Farms Modelling for WMTC Conferences

2015 ◽  
Author(s):  
Thomas Nivet ◽  
Ema Muk-Pavic
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Cost Effective ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Offshore Wind Farms ◽  
Climate Conditions ◽  
Operation Analysis ◽  
Cost Effective Approach ◽  
Failure Evaluation

Offshore wind energy is one of the most upcoming sources of energy, and it is already partially replacing the fossil fuelled power production. However, offshore wind turbine technology is also associated with harsher weather environment. Indeed, it experiences more challenging wind and wave conditions, which in turn limits the vessels capabilities to access the wind farms. Additionally, with the constant rise of power utilization, improvements in the Operation Maintenance (O&M) planning are crucial for the development of large isolated offshore wind farms. Improvements in the planning of the O&M for offshore wind farms could lead to considerable reduction in costs. For this reason, the interest of this research paper is the investigation of the most cost effective approach to offshore turbine maintenance strategies. This objective is achieved by implementing a simulation approach that includes a climate conditions analysis, an operation analysis, a failure evaluation and a simulation of the repairs. This paper points out how different O&M strategies can influence the sustainability of a wind farm.

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