Offshore Wind Farm Construction: Easier, Safer and More Cost Effective

2011 ◽  
Author(s):  
Marc Eijssen
Keyword(s):  
Wind Energy ◽  
High Performance ◽  
Wind Farm ◽  
Steel Wire ◽  
Wind Farms ◽  
Cost Effective ◽  
Offshore Wind ◽  
Synthetic Fiber ◽  
Uhmwpe Fiber ◽  
Load Bearing

Converting wind energy into a useful form of energy is one of the fastest growing alternative sources of energy in the present and foreseeable future. At the end of 2009, worldwide nameplate capacity of wind-powered generators was 159 gigawatts (GW). Energy production at onshore, near shore and offshore locations was 340 TWh, which is about 2% of worldwide electricity usage, and has doubled in the past three years and will continue to grow substantially. This growth comprises not only more wind farms, but wind mills with more capacity as well [1–3]. To achieve this growth, (heavy) lift companies and offshore contractors meet various challenges. Since primary cost for producing wind energy is construction, with up to 10 lifts per wind mill, a very competitive environment has been created in which efficient logistic and lifting operations, flexibility, avoiding damage to loads and safety are paramount [4,5]. Appropriate lifting gear provides an essential tool to meet these challenges; especially at offshore locations. Currently, steel wire ropes and grommets are extensively being used as lifting gear during the construction of wind farms, with its low cost being the key driver. But its weight together with the risk for damage to loads and injuries to operators are serious concerns. Synthetic slings, especially those made from polyester fiber, have gained popularity as a way to overcome these concerns. But, such slings are very sensitive to damage, hence its value and risk-avoidance is more and more being questioned; especially considering currently applicable legislation such as the European Machinery Directive 2006/42/EC [6]. With the introduction of high performance fibers, such as Dyneema® (Ultra-High Molecular Polyethylene (UHMwPE) fiber), not “just another synthetic fiber” has been introduced. The use of this UHMwPE fiber in protective sleeves and load bearing constructions holds the characteristics to overcome the concerns of the traditional materials as it has been proven by well-respected lifting companies. Since durability of a synthetic sling is largely determined by the performance of the protective sleeves, this paper comprehensively presents the abrasion, cut and tear resistance improvement created by sleeves made with fibers such as Dyneema®. In combination with its functionality in load bearing constructions and the key elements of the European Machinery Directive 2006/42/EC, this paper proves that Ultralift® roundslings made with Dyneema® provide not only safer and easier but also more cost effective construction (logistics and lifting) operations. So, slings made with Dyneema® have been entrusted to meet the lifting challenges of today and tomorrow.

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>

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.

Offshore Wind Energy and the Mid-Atlantic Cold Pool: A Review of Potential Interactions

2021 ◽  
Vol 55 (4) ◽  
pp. 72-87
Author(s):  
Travis Miles ◽  
Sarah Murphy ◽  
Josh Kohut ◽  
Sarah Borsetti ◽  
Daphne Munroe
Keyword(s):  
Wind Energy ◽  
Wind Farm ◽  
Vertical Mixing ◽  
Wind Farms ◽  
The United States ◽  
Offshore Wind ◽  
Cold Pool ◽  
Ocean Bottom ◽  
Offshore Wind Farms ◽  
Seasonal Thermocline

Abstract The U.S. East Coast has 1.7 million acres of federal bottom under lease for the development of wind energy installations, with plans for more than 1,500 foundations to be placed. The scale of these wind farms has the potential to alter the unique and delicate oceanographic conditions along the expansive Atlantic continental shelf, a region characterized by a strong seasonal thermocline that overlies cold bottom water, known as the “Cold Pool.” Strong seasonal stratification traps cold (typically less than 10°C) water above the ocean bottom sustaining a boreal fauna that represents vast fisheries, including the most lucrative shellfish fisheries in the United States. This paper reviews the existing literature and research pertaining to the ways in which offshore wind farms may alter processes that establish, maintain, and degrade stratification associated with the Cold Pool through vertical mixing in this seasonally dynamic system. Changes in stratification could have important consequences in Cold Pool setup and degradation, processes fundamental to high fishery productivity of the region. The potential for these multiple wind energy arrays to alter oceanographic processes and the biological systems that rely on them is possible; however, a great deal of uncertainty remains about the nature and scale of these interactions. Research should be prioritized that identifies stratification thresholds of influence, below which turbines and wind farm arrays may alter oceanographic processes. These should be examined within context of spatial and seasonal dynamics of the Cold Pool and offshore wind lease areas to identify potential areas of further study.

scholarly journals A Framework for the Selection of Optimum Offshore Wind Farm Locations for Deployment

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1855 ◽  
Author(s):  
Varvara Mytilinou ◽  
Estivaliz Lozano-Minguez ◽  
Athanasios Kolios
Keyword(s):  
Decision Making ◽  
Wind Energy ◽  
Wind Farm ◽  
Cost Effective ◽  
Optimization Techniques ◽  
Offshore Wind ◽  
Moray Firth ◽  
The Uk ◽  
The Given ◽  
Nondominated Sorting

This research develops a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 available zones in the UK. The framework includes optimization techniques, decision-making methods and experts’ input in order to support investment decisions. Further, techno-economic evaluation, life cycle costing (LCC) and physical aspects for each location are considered along with experts’ opinions to provide deeper insight into the decision-making process. A process on the criteria selection is also presented and seven conflicting criteria are being considered for implementation in the technique for the order of preference by similarity to the ideal solution (TOPSIS) method in order to suggest the optimum location that was produced by the nondominated sorting genetic algorithm (NSGAII). For the given inputs, Seagreen Alpha, near the Isle of May, was found to be the most probable solution, followed by Moray Firth Eastern Development Area 1, near Wick, which demonstrates by example the effectiveness of the newly introduced framework that is also transferable and generic. The outcomes are expected to help stakeholders and decision makers to make better informed and cost-effective decisions under uncertainty when investing in offshore wind energy in the UK.

scholarly journals Optimization of Wind Turbine Interconnections in an Offshore Wind Farm Using Metaheuristic Algorithms

2020 ◽  
Vol 12 (14) ◽  
pp. 5761 ◽  
Author(s):  
Chakib El Mokhi ◽  
Adnane Addaim
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Farm ◽  
Renewable Energy Sources ◽  
Wind Farms ◽  
Optimization Methods ◽  
Network Routing ◽  
Metaheuristic Algorithms ◽  
Offshore Wind ◽  
Offshore Wind Farms

Wind energy is currently one of the fastest-growing renewable energy sources in the world. For this reason, research on methods to render wind farms more energy efficient is reasonable. The optimization of wind turbine positions within wind farms makes the exploitation of wind energy more efficient and the wind farms more competitive with other energy resources. The investment costs alone for substation and electrical infrastructure for offshore wind farms run around 15–30% of the total investment costs of the project, which are considered high. Optimizing the substation location can reduce these costs, which also minimizes the overall cable length within the wind farm. In parallel, optimizing the cable routing can provide an additional benefit by finding the optimal grid network routing. In this article, the authors show the procedure on how to create an optimized wind farm already in the design phase using metaheuristic algorithms. Besides the optimization of wind turbine positions for more energy efficiency, the optimization methods of the substation location and the cable routing for the collector system to avoid cable losses are also presented.

scholarly journals Offshore wind energy potential for Bahrain via multi-criteria evaluation

2020 ◽  
pp. 0309524X2092539
Author(s):  
Mohamed Elgabiri ◽  
Diane Palmer ◽  
Hanan Al Buflasa ◽  
Murray Thomson
Keyword(s):  
Wind Energy ◽  
Geographical Information Systems ◽  
Wind Farm ◽  
Electricity Consumption ◽  
Pairwise Comparison ◽  
Wind Farms ◽  
Offshore Wind ◽  
Geographical Information ◽  
Energy Yield ◽  
Energy Potential

Current global commitments to reduce the emissions of greenhouse gases encourage national targets for renewable generation. Due to its small land mass, offshore wind could help Bahrain to fulfil its obligations. However, no scoping study has been carried out yet. The methodology presented here addresses this research need. It employs analytical hierarchy process and pairwise comparison methods in a geographical information systems environment. Publicly available land use, infrastructure and transport data are used to exclude areas unsuitable for development due to physical and safety constraints. Meteorological and oceanic opportunities are ranked and then competing uses are analyzed to deliver optimal sites for wind farms. The potential annual wind energy yield is calculated by dividing the sum of optimal areas by a suitable turbine footprint to deliver maximum turbine number. In total, 10 favourable wind farm areas were identified in Bahrain’s territorial waters, representing about 4% of the total maritime area, and capable of supplying 2.68 TWh/year of wind energy or almost 10% of the Kingdom’s annual electricity consumption. Detailed maps of potential sites for offshore wind construction are provided in the article, giving an initial plan for installation in these locations.

scholarly journals Cost Benefit of Implementing Advanced Monitoring and Predictive Maintenance Strategies for Offshore Wind Farms

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4922
Author(s):  
Alan Turnbull ◽  
James Carroll
Keyword(s):  
Wind Farm ◽  
Cost Benefit ◽  
Wind Farms ◽  
Cost Effective ◽  
Predictive Maintenance ◽  
Offshore Wind ◽  
Component Failure ◽  
Maintenance Strategies ◽  
Early Results ◽  
The Impact

Advancements in wind turbine condition monitoring systems over the last decade have made it possible to optimise operational performance and reduce costs associated with component failure and other unplanned maintenance activities. While much research focuses on providing more automated and accurate fault diagnostics and prognostics in relation to predictive maintenance, efforts to quantify the impact of such strategies have to date been comparatively limited. Through time-based simulation of wind farm operation, this paper quantifies the cost benefits associated with predictive and condition-based maintenance strategies, taking into consideration both direct O&M costs and lost production. Predictive and condition-based strategies have been modelled by adjusting known component failure and repair rates associated with a more reactive approach to maintenance. Results indicate that up to 8% of direct O&M costs can be saved through early intervention along with up to 11% reduction in lost production, assuming 25% of major failures of the generator and gearbox can be diagnosed through advanced monitoring and repaired before major replacement is required. Condition-based approaches can offer further savings compared to predictive strategies by utilising more component life before replacement. However, if weighing up the risk between component failure and replacing a component too early, results suggest that it is more cost effective to intervene earlier if heavy lift vessels can be avoided, even if that means additional major repairs are required over the lifetime of the site.

scholarly journals Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models

2021 ◽  
Author(s):  
Jana Fischereit ◽  
Kurt Schaldemose Hansen ◽  
Xiaoli Guo Larsén ◽  
Maarten Paul van der Laan ◽  
Pierre-Elouan Réthoré ◽  
...  
Keyword(s):  
High Resolution ◽  
Wind Energy ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wake Effect ◽  
Disk Model ◽  
Computational Costs ◽  
Rans Simulations ◽  
Wind Resource

Abstract. Numerical wind resource modelling across scales from mesoscale to turbine scale is of increasing interest due to the expansion of offshore wind energy. Offshore, wind farm wakes can last several tens kilometres downstream and thus affect the wind resources of a large area. So far, scale-specific models have been developed and it remains unclear, how well the different model types can represent intra-farm wakes, farm-to-farm wakes as well as the wake recovery behind a farm. Thus, in the present analysis the simulation of a set of wind farm models of different complexity, fidelity, scale and computational costs are compared among each other and with SCADA data. In particular, two mesoscale wind farm parameterizations implemented in the mesoscale Weather Research and Forecasting model (WRF), the Explicit Wake Parameterization (EWP) and the Wind Farm Parameterization (FIT), two different high-resolution RANS simulations using PyWakeEllipSys equipped with an actuator disk model, and three rapid engineering wake models from the PyWake suite are selected. The models are applied to the Nysted and Rødsand II wind farms, which are located in the Fehmarn Belt in the Baltic Sea. Based on the performed simulations, we can conclude that average intra-farm variability can be captured reasonable well with WRF+FIT using a resolution of 2 km, a typical resolution of mesoscale models for wind energy applications, while WRF+EWP underestimates wind speed deficits. However, both parameterizations can be used to estimate median wind resource reduction caused by an upstream farm. All considered engineering wake models from the PyWake suite simulate intra-farm wakes comparable to the high fidelity RANS simulations. However, they considerably underestimate the farm wake effect of an upstream farm although with different magnitudes. Overall, the higher computational costs of PyWakeEllipSys and WRF compared to PyWake pay off in terms of accuracy for situations when farm-to-farm wakes are important.

scholarly journals Offshore Wind Energy Potential for Bahrain via Multi-criteria Evaluation

2020 ◽  
Author(s):  
Diane Palmer ◽  
Mohamed Elgabiri ◽  
Hanan Al Buflasa ◽  
Murray Thomson
Keyword(s):  
Wind Energy ◽  
Geographical Information Systems ◽  
Wind Farm ◽  
Electricity Consumption ◽  
Pairwise Comparison ◽  
Wind Farms ◽  
Offshore Wind ◽  
Geographical Information ◽  
Energy Yield ◽  
Energy Potential

Current global commitments to reduce emissions of greenhouse gases encourage national targets for renewable generation. Due to its small land mass, offshore wind could help Bahrain to fulfill its obligations. However, no scoping study has yet been carried out. The methodology presented here addresses this research need. It employs Analytical Hierarchy Process and pairwise comparison methods in a Geographical Information Systems environment. Publicly available land use, infrastructure and transport data are used to exclude areas unsuitable for development due to physical and safety constraints. Meteorological and oceanic opportunities are ranked, then competing uses are analyzed to deliver optimal sites for wind farms. The potential annual wind energy yield is calculated by dividing the sum of optimal areas by a suitable turbine footprint, to deliver maximum turbine number. Ten favourable wind farm areas were identified in Bahrain’s territorial waters, representing about 4% of the total maritime area, and capable of supplying 2.68 TWh/yr of wind energy or almost 10% of the Kingdom’s annual electricity consumption. Detailed maps of potential sites for offshore wind construction are provided in the paper, giving an initial plan for installation in these locations.

scholarly journals A Framework to Select Optimum Offshore Wind Farm Locations for Deployment

2018 ◽  
Author(s):  
Varvara Mytilinou ◽  
Athanasios Kolios
Keyword(s):  
Decision Making ◽  
Wind Energy ◽  
Wind Farm ◽  
Cost Effective ◽  
Economic Life ◽  
Decision Makers ◽  
Offshore Wind ◽  
Moray Firth ◽  
The Uk ◽  
Economic Life Cycle

This research proposes a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 zones in the UK. The framework includes optimisation techniques, decision-making methods and experts’ input in order to help stakeholders with investment decisions. Techno-economic, Life Cycle Costs (LCC) and physical aspects for each location are considered along with experts’ opinions to provide deeper insight into the decision making process. A process on the criteria selections is also presented and seven conflicting criteria are being considered in TOPSIS methods in order to suggest the optimum location that was produced by the NSGAII algorithm. Seagreen Alpha was the most probable solution, followed by Moray Firth Eastern Development Area 1, which demonstrates by example the effectiveness of the newly introduced framework that is also transferable and generic. The outcomes are expected to help stakeholders and decision makers to make more informed and cost-effective decisions under uncertainty when investing in offshore wind energy in the UK.

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