Operational strategies for offshore wind turbines to mitigate failure rate uncertainty on operational costs and revenue

Several operational strategies for offshore wind farms have been established and explored in order to improve understanding of operational costs with a focus on heavy lift vessel strategies. Additionally, an investigation into the uncertainty surrounding failure behaviour has been performed identifying the robustness of different strategies. Four operational strategies were considered: fix on fail, batch repair, annual charter and purchase. A range of failure rates have been explored identifying the key cost drivers and under which circumstances an operator would choose to adopt them. When failures are low, the fix on fail and batch strategies perform best and allow flexibility of operating strategy. When failures are high, purchase becomes optimal and is least sensitive to increasing failure rate. Late life failure distributions based on mechanical and electrical components behaviour have been explored. Increased operating costs because of wear-out failures have been quantified. An increase in minor failures principally increase lost revenue costs and can be mitigated by deploying increased maintenance resources. An increase in larger failures primarily increases vessel and repair costs. Adopting a purchase strategy can negate the vessel cost increase; however, significant cost increases are still observed. Maintenance actions requiring the use of heavy lift vessels, currently drive train components and blades are identified as critical for proactive maintenance to minimise overall maintenance costs

Analysis of offshore wind turbine operation & maintenance using a novel time domain meteo-ocean modeling approach

This paper presents a novel approach to repair modeling using a time domain Auto-Regressive model to represent meteo-ocean site conditions. The short term hourly correlations, medium term access windows of periods up to days and the annual distibution of site data are captured. In addition, seasonality is included. Correlation observed between wind and wave site can be incorporated if simultaneous data exists. Using this approach a time series for both significant wave height and mean wind speed is described. This allows MTTR to be implemented within the reliability simulation as a variable process, dependent on significant wave height. This approach automatically captures site characteristics including seasonality and allows for complex analysis using time dependent constaints such as working patterns to be implemented. A simple cost model for lost revenues determined by the concurrent simulated wind speed is also presented. A preliminary investigation of the influence of component reliability and access thresholds at various existing sites on availability is presented demonstrating the abiltiy of the modeling approach to offer new insights into offshore wind turbine operation and maintenance

Forecasting long term jack up vessel demand for offshore wind

Offshore wind deployment has greatly increased in the last decade. The number and size of installations have historically determined the requirement for jack up vessels, as installation was the only driver for heavy lift vessel requirement. The next phase of offshore wind development will change this situation, as jack up requirement for operations and maintenance will certainly increase as assets age, and in parallel larger sites con-tinue to be installed and commissioned. The approach taken in this paper is to develop a long-term vessel demand model. This is achieved by a two stage process: firstly an installation jack up demand model is developed using UK offshore wind data for projects installed in the period 2003-2016. This model is utilized in tandem with an operations and maintenance jack up requirement model which is based on published reliability figures and failure duration data. The combined model captures the requirement for jack up vessels in the period 2012-2030. The paper concludes that demand for jack up vessels in UK waters will ramp up significantly in this decade, with an initial peak in 2014. A secondary peak around 2028 is highly dependent on assumptions regarding the trajectory of the turbine failure rate over time

Statistical forecasting for offshore wind helicopter operations

The influence of the wind and wave climate on offshore wind operations and maintenance is well known. These environmental factors dictate to a large extent whether turbine crew transfer (carried out by small vessels) or major lifting actions (carried out by large vessels) can be executed at sea. However the role of helicopter operations has received much less attention. In this paper the authors explore the helicopter access problem via statistical forecasting and implement a model innovation, by including cloud base as a key access metric. By understanding the practical limits of helicopter operation, offshore wind access calculations will be much improved and reflect more closely the reality of operations at sea

Offshore wind turbine operation and maintenance analysis using a time domain meteo-ocean modelling approach

Paper describes offshore wind turbine operation and maintenance analysis using a time domain meteo-ocean modelling approach

Heavy lift vessel strategy analysis for offshore wind

To minimise the future cost of energy for future large, remote offshore wind farms, new maintenance methodologies may be required. This paper presents a modelling framework to test these maintenance methodologies in order to determine where different operational choices represent the cost optimal solution. The sensitivity of operational strategies to wind farm size as well as failure rates of major components has been examined in order to demonstrate the capability of the modelling approach as well as identify the strengths and weaknesses of the strategies. Fix on fail methodology has been identified as cost effective only for small wind farms where failure rates are low. Vessel purchase becomes cost effective when the wind farm is sufficiently large or failure rate is high. A batch repair approach is shown to provide comparable costs to purchasing a vessel without the large capital costs but reduces the overall power produced. Several additional areas for future model development and research have been identified

Optimum CTV fleet selection for offshore wind farm O&M activities

Operation and Maintenance (O&M) contributes a significant share of the expenses during the lifetime of offshore wind farms. When compared to onshore wind, O&M costs are increased, due to the use of specialised vessels, shorter weather windows and challenging environmental conditions. Furthermore, increased frequency of failures, longer downtime and limited accessibility create uncertainties in the planning stage of the O&M tasks. In order to decrease the cost of power generation and increase the competitiveness of offshore wind industry against other alternative energy sectors, it is essential to keep the costs of the vessel fleet used for O&M tasks at minimum level while providing sufficient support to sustain power generation. In order to address these issues, the focus of this paper is to provide decision support for the selection of a Crew Transfer Vessel (CTV) fleet for the offshore wind farm maintenance operations. This is achieved through analyses of environmental conditions, investigation of failures, and assessment of vessel operations. The developed methodology and analysis enable operators to decide the specification of CTVs which will bring the optimum financial benefit, considering both the enhancement of the offshore wind farm power generation as well as the minimisation of the total O&M cost

Sensitivity of offshore wind turbine operation & maintenance costs to key operational parameters

Due to the lack of operator knowledge and the deployment of new technology in future large offshore wind farms, significant uncertainty exists in the field of offshore wind farm operation and maintenance (O&M). In order to investigate this uncertainty as well as explore the feasibility of novel O&M strategies, simulation is required. This paper outlines a new modelling approach which allows identification of the key costs and operational parameters of O&M in order to quantify the sensitivity of overall O&M costs to variations of these parameters. In addition, several key areas requiring greater understanding are identified and two case studies demonstrating how the developed model can provide new insights into these areas are presented. This paper applies an auto-regressive (AR) climate modelling approach to concurrently simulate representative wind and wave time series coupled with Markov Chain Monte Carlo (MCMC) based failure simulation. Both these simulation approaches are well established in the field of reliability modelling however, this work represents the first time that both wind and wave climate simulations have been coupled and applied to offshore wind O&M. The AR climate modelling approach allows a synthetic time series to be rapidly produced based on site data. The hourly short term correlations as well as medium term access windows of up to several days required for maintenance operations are captured while preserving the overall observed distribution. The seasonality observed in wind speed and wave heights is also incorporated. Failures are simulated based on failure rates available in the public domain and associated time to repair is based on the simulated weather climate. This time series based approach allows constraints on access vehicle capabilities, type and availability to be applied and the influence on wind farm availability and O&M costs examined. Lost earnings associated with downtime are also captured using the simulated wind speed time series. An initial study, examining the influence of reliability and time to repair of key components on overall availability and costs is presented for different sized turbines demonstrating how the benefits of reduced maintenance action is affected by turbine size. A further study, exploring the degree to which overall O&M costs are influenced by variation in vessel hire costs is also shown, demonstrating the capability of the modelling approach. Finally, various potential novel areas for investigation are identified for future work highlighting the benefit of the modelling approach

Understanding the new financial risks in the operational life of offshore wind projects

This speech discussed the new financial risks in the operational life of offshore wind projects

Asset modelling challenges in the wind energy sector

The research area of asset management and cost reduction for wind energy is increasing in importance as over 100,000 turbine assets are currently installed worldwide. Such large numbers of turbine assets, with a growing offshore component, drive demand for research into improved decision support for wind farm planners and operators - as the operational risk for the assets is removed from OEMs (while under warranty) and is transferred to the power utility owners (post-warranty). The current and future demand for research in this field has thus been driven by the needs of owner/operators and central government policy formultation. The huge potential energy available from offshore wind in particular, is well known. The most crucial aspect of future deployment levels is the economic recovery of this potential energy. The long-term viability of the offshore wind power as a large scale energy solution hinges on reduction of the cost of energy, as a purely cost-based energy market would currently produce a gas- and coal- based system. The areas of research identified by the authors to aid this transition can be summarised into 3 key themes: 1. Asset and meteo-ocean modelling to support offshore operations and maintenance The first modeling innovation is in the advanced planning of logistics for offshore operations and maintenance. Research produced by Ernst and Young in 2009 has shown that far from reducing O&M costs through learning processes, the real and expected O&M spend for offshore wind has increased year on year: this is backed up by more recent anecdotal evidence. Wind farm owners - often utilities with no experience of offshore operations – whish to improve their knowledge of how to plan for the big risks such as vessel chartering for maintenance, spares provision and location/type of O&M service base. The unique blend of modeling methodologies utilized by the authors build on existing research on Markov chain reliability models applied to wind power assets, and with an increased focus on accurate statistical characterization of weather conditions (particularly working constraints such as wave heights and wind speeds). This advanced approach enables key questions regarding site accessibility in winter, jack up vessel strategy, and life cycle cost issues to be addressed in a way more comprehensive and innovative than any other approach in the literature. This work has far-reaching implications for utilities and offshore wind farm owners, representing a step change in modeling accuracy compared to existing industry standard tools. The future for this work stream will involve this work converging with research activity on condition based maintenance and decision support. 2. Analysis of wind farm maintenance data for asset life cycle optimization The second area of innovative research is analysis of wind turbine SAP and other work order and maintenance data to investigate possible links between failure events and weather conditions. Such analysis will also aid wind asset engineers in their anticipation of failure modes, traceability of faults via improved failure reporting, and calculation of risk exposure to specific failure mechanisms. Current analysis methods are primitive and ripe for improvement by porting data analysis methods from other sectors. Current research by the authors focuses on developing algorithms to mine these rich data sets and establish new understanding of failure patterns with respect to environmental conditions. Industrial interest in this area of work is growing as more assets exit 3-5 year warranty guarantees and the risk associated with serial defect, wear issues, and end of life ramping of failure rates becomes the responsibility of the wind farm owner/operator. Increased knowledge of asset deterioration and failure behaviour can also drive feedback to manufacturers, as well as providing a bargaining chip in 3rd party O&M contract negotiations. Academic novelty is also apparent as most research to date has focused on analysis of turbine SCADA/ condition monitoring data streams: the potential of SAP-type systems to provide a rich source of data to calibrate failure models and understand the impact of environmental conditions on reliability has not been adequately explored. Although at a less advanced stage of maturity compared with the offshore logistics work, this work stream again has the potential to be fused with parallel work on condition monitoring and asset management. 3. Role of condition monitoring in decreasing OPEX As wind turbines increase in size and move offshore, operations and maintenance procedures need to be optimised to increase reliability, safety and maximise cost effectiveness. The true economic value of a condition based maintenance strategy for offshore wind farms has yet to be calculated. Many existing studies have focused on either the functionality of the turbine, or on the structural integrity, but not both. This theme involves closing the research gap which exists between reliability modelling of wind turbine functionality and modelling of structural integrity. The aim is to produce a comprehensive approach which will facilitate to quantify the overall economic benefits of condition monitoring. With a case study, the industrial application of the approach will be shown and the benefits will be demonstrated. This modelling allows operators to examine a condition based maintenance approach that theoretically allows reduced costs over both preventive and corrective maintenance strategies. There have been several studies into the possible benefits and cost advantages of using a condition based maintenance strategy. However, few have examined the implications of false alarms. Investigating false alarms or ignoring false positives in a remote offshore environment will incur costs that may alter the cost benefit of condition monitoring systems. Probabilistic models are used in the paper to determine the possible benefits of using condition monitoring systems and the detect that false positives and negatives have on the reliability of the system. The methods used include Markov chains, Monte-Carlo simulations and time-series modelling