Browsing by Author "Adedipe, Tosin"

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    A Bayesian network model for the probabilistic safety assessment of offshore wind decommissioning
    (SAGE, 2022-09-02) Shafiee, Mahmood; Adedipe, Tosin
    With increasing the number of wind turbines approaching the end of their service life, it has become crucial for businesses to understand and assess safety and security issues related to the decommissioning phase of wind farm asset lifecycle. This paper aims to develop, for the first time, a Bayesian Network (BN) model for the safety assessment of offshore wind farm decommissioning operations. The most critical safety incidents are identified and their corresponding risk-influencing factors (RIF) are determined. The impacts of human errors as well as procedural and mechanical/electrical failures on the safety and efficiency of decommissioning operations are thoroughly analysed. The findings of the study revealed that the most critical RIFs during offshore wind decommissioning operations include: visibility, crew fatigue, number of personnel per operation, proper safety procedures, crane integrity, number of lifts available in the wind farm, inspection frequency, as well as equipment design.
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    Bayesian network modelling for the wind energy industry: an overview
    (Elsevier, 2020-05-29) Adedipe, Tosin; Shafiee, Mahmood; Zio, Enrico
    Wind energy farms are moving into deeper and more remote waters to benefit from availability of more space for the installation of wind turbines as well as higher wind speed for the production of electricity. Wind farm asset managers must ensure availability of adequate power supply as well as reliability of wind turbines throughout their lifetime. The environmental conditions in deep waters often change very rapidly, and therefore the performance metrics used in different life cycle phases of a wind energy project will need to be updated on a frequent basis so as to ensure that the wind energy systems operate at the highest reliability. For this reason, there is a crucial need for the wind energy industry to adopt advanced computational tools/techniques that are capable of modelling the risk scenarios in near real-time as well as providing a prompt response to any emergency situation. Bayesian network (BN) is a popular probabilistic method that can be used for system reliability modelling and decision-making under uncertainty. This paper provides a systematic review and evaluation of existing research on the use of BN models in the wind energy sector. To conduct this literature review, all relevant databases from inception to date were searched, and a total of 70 sources (including journal publications, conference proceedings, PhD dissertations, industry reports, best practice documents and software user guides) which met the inclusion criteria were identified. Our review findings reveal that the applications of BNs in the wind energy industry are quite diverse, ranging from wind power and weather forecasting to risk management, fault diagnosis and prognosis, structural analysis, reliability assessment, and maintenance planning and updating. Furthermore, a number of case studies are presented to illustrate the applicability of BNs in practice. Although the paper details information applicable to the wind energy industry, the knowledge gained can be transferred to many other sectors.
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    Decommissioning strategy to reduce the cost and risk-driving factors in the offshore wind industry.
    (Cranfield University, 2021-05) Adedipe, Tosin; Hart, Phil; Shafiee, Mahmood
    With the increasing number of wind turbines approaching their end of life, there has to be a decommissioning strategy in place as the removal of these assets is not as direct as reverse installation. Offshore asset decommissioning involves technical, financial, operational, safety, policy, and environmental considerations on handling offshore marine assets at their end-of-life, with phases from the planning to site clean-up and monitoring. Offshore decommissioning activities cost significantly more than onshore; thus, adequate financial and safety provisions are essential, and more research required in this area. Decommissioning projects have hitherto been performed on a small scale, but with large-scale aging structures, they must be optimised for lowered costs and risks. In terms of planning, execution and costs, there have been significant cost overruns on decommissioning projects, which are not profit-generating projects. These forecasted large-scale decommissioning activities also have associated risks. Although risk management is a well-researched area, there is limited literature on offshore wind decommissioning risk management. This research thus, applies risk management methods and strategies to develop a robust decommissioning risk framework. In addition, to improve decommissioning processes and technologies, there is a need to develop new protocols for decommissioning. This research identifies potentials for computational simulations and automations that need to be developed to identify and manage the highest cost and risk-drivers. This study seeks to close the research gap in understanding how to decrease decommissioning costs and risks. This research addresses potential opportunities in cost and risk estimation research, impact analysis and reduction frameworks that can be adapted to decommissioning activities specific to the offshore wind industry.
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    An economic assessment framework for decommissioning of offshore wind farms using a cost breakdown structure
    (Springer, 2021-01-04) Adedipe, Tosin; Shafiee, Mahmood
    Purpose As wind power generation increases globally, there will be a substantial number of wind turbines that need to be decommissioned in the coming years. It is crucial for wind farm developers to design safe and cost-effective decommissioning plans and procedures for assets before they reach the end of their useful life. Adequate financial provisions for decommissioning operations are essential, not only for wind farm owners but also for national governments. Economic analysis approaches and cost estimation models therefore need to be accurate and computationally efficient. Thus, this paper aims to develop an economic assessment framework for decommissioning of offshore wind farms using a cost breakdown structure (CBS) approach. Methods In the development of the models, all the cost elements and their key influencing factors are identified from literature and expert interviews. Similar activities within the decommissioning process are aggregated to form four cost groups including: planning and regulatory approval, execution, logistics and waste management, and post-decommissioning. Some mathematical models are proposed to estimate the costs associated with decommissioning activities as well as to identify the most critical cost drivers in each activity group. The proposed models incorporate all cost parameters involved in each decommissioning phase for more robust cost assessment. Results and discussion A case study of a 500 MW baseline offshore wind farm is proposed to illustrate the models’ applicability. The results show that the removal of wind turbines and foundation structures is the most costly and lengthy stage of the decommissioning process due to many requirements involved in carrying out the operations. Although inherent uncertainties are taken into account, cost estimates can be easily updated when new information becomes available. Additionally, further decommissioning cost elements can be captured allowing for sensitivity analysis to be easily performed. Conclusions Using the CBS approach, cost drivers can be clearly identified, revealing critical areas that require attention for each unique offshore wind decommissioning project. The CBS approach promotes adequate management and optimisation of identified key cost drivers, which will enable all stakeholders involved in offshore wind farm decommissioning projects to achieve cost reduction and optimal schedule, especially for safety-critical tasks.
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    Hydrogen production with integrated CO2 capture via sorbent enhanced reforming
    (SSRN, 2022-11-10) Lesemann, Markus; Mays, Jeff; Clough, Peter T.; Oakey, John; Adedipe, Tosin; Duncan, Angus
    GTI Energy has been developing a novel process technology for hydrogen production from natural gas with inherent carbon capture. The GTI process, based on sorbent enhanced reforming (SER), has the advantage that it captures CO2 inherently in the process via a pre-combustion technique instead of secondary capture from a flue gas stream. This approach is fundamentally different from conventional technologies such as steam methane reforming (SMR) and autothermal reforming (ATR) which require additional process steps to avoid CO2 emissions. The inherent carbon capture capability results in step-out economics of the GTI process. The GTI process is based on GTI’s Hydrogen Generator (CHG) technology which converts natural gas and steam into H2 and CO2 in separate streams. The inherent carbon capture in the GTI process leads to its higher carbon capture potential, its substantially lower capital cost (by 40-60%) and substantially smaller footprint compared to the conventional approaches, resulting in overall lower levelized cost of hydrogen (by 10-30%). In its optimized configuration, carbon capture rates over 97.5%, with 10% lower levelized cost of hydrogen (LCOH) and ~50% reduction in CAPEX compared to conventional SMR with CO2 capture are achievable. By relaxing the carbon capture rate to 96%, a LCOH ~20% lower than the SMR case can be achieved. LCOH and CAPEX advantages of the process compared to Autothermal Reforming (ATR) are even more pronounced. Development of the process technology is currently supported by the U.S. Department of Energy (DOE) and by the U.K. Department for Business, Energy, and Industrial Strategy (BEIS). DOE has been supporting the development and operation of a 0.071 MWth pilot plant at GTI’s main test facility near Chicago, USA, to demonstrate the process chemistry and fluidized bed operation. Under BEIS funding, a team comprised of Cranfield University, GTI Energy, and Doosan Babcock has been developing a 1 MWth pilot plant at a dedicated new test site at Cranfield University in the UK (“HyPER Project”).
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    Offshore wind decommissioning: an assessment of the risk of operations
    (Taylor & Francis, 2022-01-12) Shafiee, Mahmood; Adedipe, Tosin
    Despite the need to ensure that operations at the end of the infrastructure life cycle are carried out in a safe and efficient manner, there is no systematic risk analysis study tailored specifically for renewable energy decommissioning. This paper aims to propose qualitative and quantitative approaches for identifying and prioritising different hazards associated with decommissioning of offshore wind farms. The potential hazards are identified through well-established techniques such as hazard identification (HAZID), fault tree analysis (FTA), event tree analysis (ETA) and risk matrix. Four levels of consequence are considered in the risk analysis process. The results reveal that the lifting and loading are the most safety-critical operations during the decommissioning; hence, they will require specific attention for safety management improvement.
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    A stochastic petri net model for O&M planning of floating offshore wind turbines
    (MDPI, 2021-02-20) Elusakin, Tobi; Shafiee, Mahmood; Adedipe, Tosin; Dinmohammadi, Fateme
    Abstract With increasing deployment of offshore wind farms further from shore and in deeper waters, the efficient and effective planning of operation and maintenance (O&M) activities has received considerable attention from wind energy developers and operators in recent years. The O&M planning of offshore wind farms is a complicated task, as it depends on many factors such as asset degradation rates, availability of resources required to perform maintenance tasks (e.g., transport vessels, service crew, spare parts, and special tools) as well as the uncertainties associated with weather and climate variability. A brief review of the literature shows that a lot of research has been conducted on optimizing the O&M schedules for fixed-bottom offshore wind turbines; however, the literature for O&M planning of floating wind farms is too limited. This paper presents a stochastic Petri network (SPN) model for O&M planning of floating offshore wind turbines (FOWTs) and their support structure components, including floating platform, moorings and anchoring system. The proposed model incorporates all interrelationships between different factors influencing O&M planning of FOWTs, including deterioration and renewal process of components within the system. Relevant data such as failure rate, mean-time-to-failure (MTTF), degradation rate, etc. are collected from the literature as well as wind energy industry databases, and then the model is tested on an NREL 5 MW reference wind turbine system mounted on an OC3-Hywind spar buoy floating platform. The results indicate that our proposed model can significantly contribute to the reduction of O&M costs in the floating offshore wind sector.