scholarly journals New Tendencies in Wind Energy Operation and Maintenance

2021 ◽  
Vol 11 (4) ◽  
pp. 1386
Author(s):  
Ángel M. Costa ◽  
José A. Orosa ◽  
Diego Vergara ◽  
Pablo Fernández-Arias
Keyword(s):  
Power Systems ◽  
Wind Energy ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Levelized Cost Of Electricity ◽  
Onshore Wind ◽  
Maintenance Strategies ◽  
Operation And Maintenance ◽  
Wind Power Systems

Both the reduction in operating and maintenance (O&M) costs and improved reliability have become top priorities in wind turbine maintenance strategies. O&M costs typically account for 20% to 25% of the total levelized cost of electricity (LCOE) of current wind power systems. This paper provides a general review of the state of the art of research conducted on wind farm maintenance in recent years. It shows the new methods and techniques, focusing on trends and future challenges. In addition to this, this work includes a review of the following items: (i) operation and maintenance, (ii) failure rate, (iii) reliability, (iv) condition monitoring, (v) maintenance strategies, (vi) maintenance and life cycle and (vii) maintenance optimization As for offshore wind turbines, it is crucial to limit the maximum faults, since the maintenance of these wind farms is more complex both technically and logistically. Research into wind farm maintenance increased by 87% between 2007 and 2019, with more than 38,000 papers (Scopus) including “wind energy” as the main topic and some keywords related to O&M costs. The LCOE in onshore wind projects has decreased by 45%, while in offshore projects it has decreased by 28%. The O&M costs of onshore wind projects fell 52%, while in the case of offshore projects, they have declined 45%. Thus, the results obtained in this paper suggest that there is a change in research on wind farm operation and maintenance, as in recent years, scientific interest in failure has been increasing, while interest in the various techniques of wind farm maintenance and operation has been decreasing.

Study on Offshore Wind Farm Maintenance Strategies Optimization

2011 ◽  
Vol 347-353 ◽  
pp. 795-799 ◽  
Author(s):  
Xiao Yan Bian ◽  
Guang Yue Li ◽  
Yang Fu
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Planning Method ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Onshore Wind ◽  
Maintenance Strategies ◽  
Total Cost ◽  
The Cost ◽  
Maritime Environment

With the harsh maritime environment, the accessibility of the site leads to the high cost directly. The cost on Operation & Maintenance for offshore wind farm is about twice of equivalent onshore wind farms. Consequently the study on the maintenance strategies is necessary. The objective of this work is to optimize the offshore wind farm maintenance strategies by network planning method, to minimize the total cost, by considing the effect of several factors, such as weather, boat availability, the location of the wind farm and ect.

Community Benefits and UK Offshore Wind Farms: Evolving Convergence in a Divergent Practice

2021 ◽  
pp. 2150001
Author(s):  
John Glasson
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Host Community ◽  
Offshore Wind ◽  
Energy Industry ◽  
Offshore Wind Farms ◽  
Onshore Wind ◽  
Community Benefits ◽  
The Uk ◽  
Renewable Energy Industry

The Offshore Wind sector is a major, dynamic, and rapidly evolving renewable energy industry. This is particularly so in Europe, and especially in the UK. Associated with the growth of the industry has been a growth of interest in community benefits as voluntary measures provided by a developer to the host community. However, in many cases, and for some of the large North Sea distant offshore wind farms, the benefits packages have been disparate and pro rata much smaller than for the well-established onshore wind farm industry. However, there are signs of change. This paper explores the issues of community benefits for the UK offshore sector and evolving practice, as reflected in a macro study of the adoption of community benefits approaches across the industry. This is followed by a more in-depth micro- approach, which explores approaches that have been adopted in three case studies of recent OWF projects — Aberdeen, Beatrice and the Hornsea Array. Whilst there is still much divergence in practice, there are also examples of some convergence, and the development of a more replicable practice. Particularly notable is the adoption of annual community benefits funds, as the key element of community benefits schemes/agreements between developers, local authorities and local communities.

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 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 Managing Wind Power Generation via Indexed Semi-Markov Model and Copula

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4246 ◽  
Author(s):  
Guglielmo D’Amico ◽  
Giovanni Masala ◽  
Filippo Petroni ◽  
Robert Adam Sobolewski
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Energy ◽  
Output Power ◽  
Wind Turbines ◽  
Wind Farm ◽  
Energy Systems ◽  
Wind Farms ◽  
Energy System ◽  
Spatial Dependencies

Because of the stochastic nature of wind turbines, the output power management of wind power generation (WPG) is a fundamental challenge for the integration of wind energy systems into either power systems or microgrids (i.e., isolated systems consisting of local wind energy systems only) in operation and planning studies. In general, a wind energy system can refer to both one wind farm consisting of a number of wind turbines and a given number of wind farms sited at the area in question. In power systems (microgrid) planning, a WPG should be quantified for the determination of the expected power flows and the analysis of the adequacy of power generation. Concerning this operation, the WPG should be incorporated into an optimal operation decision process, as well as unit commitment and economic dispatch studies. In both cases, the probabilistic investigation of WPG leads to a multivariate uncertainty analysis problem involving correlated random variables (the output power of either wind turbines that constitute wind farm or wind farms sited at the area in question) that follow different distributions. This paper advances a multivariate model of WPG for a wind farm that relies on indexed semi-Markov chains (ISMC) to represent the output power of each wind energy system in question and a copula function to reproduce the spatial dependencies of the energy systems’ output power. The ISMC model can reproduce long-term memory effects in the temporal dependence of turbine power and thus understand, as distinct cases, the plethora of Markovian models. Using copula theory, we incorporate non-linear spatial dependencies into the model that go beyond linear correlations. Some copula functions that are frequently used in applications are taken into consideration in the paper; i.e., Gumbel copula, Gaussian copula, and the t-Student copula with different degrees of freedom. As a case study, we analyze a real dataset of the output powers of six wind turbines that constitute a wind farm situated in Poland. This dataset is compared with the synthetic data generated by the model thorough the calculation of three adequacy indices commonly used at the first hierarchical level of power system reliability studies; i.e., loss of load probability (LOLP), loss of load hours (LOLH) and loss of load expectation (LOLE). The results will be compared with those obtained using other models that are well known in the econometric field; i.e., vector autoregressive models (VAR).

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.

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.

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.

Scheduling and Routing Optimization of Maintenance Fleet for Offshore Wind Farms Using Duo-ACO

2014 ◽  
Vol 1039 ◽  
pp. 294-301 ◽  
Author(s):  
Zhen You Zhang
Keyword(s):  
Wind Farm ◽  
Optimal Solution ◽  
Wind Farms ◽  
Daily Basis ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Routing Optimization ◽  
Renewable Power ◽  
Operation And Maintenance ◽  
The Cost

Wind energy is one of the fast growing sources of renewable power production currently and there is a great demand to reduce the cost of operation and maintenance to achieve competitive energy price in the market especially for offshore wind farms. An offshore wind farm usually comprises a large number of turbines and thus needs a number of service vessels for maintenance. It is already a complicated task to plan the schedule and route for each of the vessels on a daily basis, dealing with several constraints, such as weather window and maintenance demand, at the same time. Even more challenging is to find an optimal solution. This paper propose a method, i.e. Duo Ant Colony Optimization (Duo-ACO), to improve the utilization of the maintenance resources, specifically the efficient scheduling and routing of the maintenance fleet and thus reduce the operation and maintenance (O&M) cost. The proposed metaheuristic method can help operator to avoid a time-consuming process of manually planning the scheduling and routing.

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