• Energy Research

Coordinated operation of several industrial energy hubs (IEHs) to realize local energy management concepts at strategic points like industrial parks has attracted the attention of power grid operators worldwide. Deriving an operational model for integrating a large set of IEHs to trade energy in various markets is a fundamental challenge that has not yet been addressed. In this context, this paper presents an optimal market participation strategy for a virtual energy hub (VEH) consisting of multiple IEHs and industrial consumers. The proposed strategy seeks to answer two questions: (1) how can a VEH operator (VEHO) minimize its operation cost when participating in different energy markets (2) how can ancillary services affect the economic performance of VEH To address these questions, a two-stage robust-stochastic optimization model is proposed with the aim of minimizing the total operation cost of VEH and compensating the operational risks associated with the existing uncertainties considering the operational limits of the power grid. To this aim, the advanced ancillary services, i.e., market-based demand response programs and transactive energy management mechanism are used in line with the optimization problem. Furthermore, the role of the multi-supply facilities is included in the developed strategy to improve VEH flexibility.

This paper investigates how the implementation of Structural Health Monitoring Systems (SHMS) in the support structure (SS) of offshore wind turbines (OWT) affects capital expenditure (CAPEX) and operational expenditure (OPEX) of offshore wind farms (WF). In order to determine the added value of Structural Health Monitoring (SHM), the balance between the reduction in OPEX and the increase in CAPEX is evaluated. In this paper, guidelines for SHM implementation in offshore WF are developed and applied to a baseline scenario. The application of these guidelines consist of a review of present regulations in the United Kingdom and Germany, the development of SHM strategy, where the first stage of the Statistical Pattern Recognition (SPR) paradigm is explored, failure modes that can be monitored are identified, and SHM technologies and sensor distributions within the turbines are described for a baseline scenario. Furthermore, an inspection strategy where the different structural inspections to be carried out above and below water is also developed, together with an inspection plan for the lifetime of the structures, for the aforementioned baseline scenario. Once the guidelines have been followed and the SHM and inspection strategies developed, a cost-benefit analysis is performed on the baseline case (10% instrumented assets) and three other scenarios with 20%, 30% and 50% of instrumented assets. Finally, a sensitivity analysis is conducted to evaluate the effects of SHM hardware cost and the time spent in completing the inspections on OPEX and CAPEX of the WF. The results show that SHM hardware cost increases CAPEX significantly, however this increase is much lower than the reduction in OPEX caused by SHM. The results also show that an increase in the percentage of instrumented assets will reduce OPEX and this reduction is considerably higher than the cost of SHM implementation.

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.

AbstractFloating offshore wind energy is a new form of marine renewable energy which is attracting a great deal of attention worldwide. However, the concepts of floating offshore wind turbines (FOWTs) are still in early stages of development and their failure properties are not yet fully understood. Compared to bottom-fixed wind turbines, FOWTs are subject to more extreme environmental conditions and significant mechanical stresses which may cause a higher degradation rate and shorter mean-time-to-failure for components/structures. To fill the research gap, this paper aims to conduct qualitative and quantitative failure studies on an OC3 spar-type FOWT platform with 3 catenary mooring lines. The failure analyses are performed based on two well-established reliability engineering methodologies, namely, fault tree analysis (FTA) and failure mode and effects analysis (FMEA). The most critical FOWT components are prioritized according to their failure likelihood as well as the risk-priority-number. Our results show a good agreement between the two methods with regard to failure criticality rankings. However, some differences between the results are also observed that are attributed to the difference between FTA and FMEA methodologies as the former incorporates the causes of various failure modes into analysis, whereas the latter is mainly adopted for a single random failure analysis. The results obtained from the FMEA study for the FOWT system will also be compared with those reported for bottom-fixed offshore wind turbines and some interesting conclusions are derived.

A significant number of first-generation offshore wind turbines (OWTs) have either reached or are approaching the end of their operational lifespan and need to be upgraded or replaced with more modern units. In response to this concern, governments, regulatory bodies and industries have initiated the development of effective end-of-life (EOL) management strategies for offshore wind infrastructure. Lifetime extension is a relatively new concept that has recently gained significant attention within the offshore wind energy community. Extending the service lifetime of OWTs can yield many benefits, such as reduced capital cost, increased return on investment (ROI), improved overall energy output, and reduced toxic gas emissions. Nevertheless, it is important to identify and prepare for the challenges that may limit the full exploitation of the potential for OWT lifetime extension projects. The objective of this paper is to present a detailed PESTLE analysis to evaluate the various political, economic, sociological, technological, legal, and environmental challenges that must be overcome to successfully implement lifetime extension projects in the offshore wind energy sector. We propose a decision framework for extending the lifetime of OWTs, involving the degradation mechanisms and failure modes of components, remaining useful life estimation processes, safety and structural integrity assessments, economic and environmental evaluations, and the selection of lifetime extension technologies among remanufacturing, retrofitting, and reconditioning. Finally, we outline some of the opportunities that lifetime extension can offer for the wind energy industry to foster a more circular and sustainable economy in the future.

The environmental sustainability and business benefits of end-of-life products have led to worldwide growth in the remanufacturing market. However, customers are often sceptical about the quality and durability of remanufactured products. To ensure a risk-free customer experience, dealers carry out some upgrading actions on the most critical components and offer a reasonable warranty period on products at the time of resale. It is crucial for dealers to consider customers’ attitudes and preferences when deciding on an upgrading strategy, warranty coverage, and sales price for remanufactured products. This paper aims to establish an equilibrium between customers’ expected costs and dealers’ expected profit for remanufactured products sold with a two-dimensional warranty and post-warranty service. In our model, customers make their decisions based on cost–benefit balance, whereas dealers make decisions that maximise their profit margin. Owing to the nature of the conflict between the dealer and customers, a Stackelberg game model is developed to optimise the upgrade strategy, warranty policy, and pricing decisions for remanufactured products. The Karush–Kuhn–Tucker (KKT) optimality condition of the lower-level problem is used to solve the model. Finally, a numerical example is provided to illustrate its applicability.

AbstractThe structural integrity of offshore wind turbine (OWT) support structures is affected by one of the most severe damage mechanisms known as pitting corrosion‐fatigue. In this study, the structural reliability of such structures subjected to pitting corrosion‐fatigue is assessed using a damage tolerance modelling approach. A probabilistic model that ascertains the reliability of the structure is presented, incorporating the randomness in cyclic load and corrosive environment as well as uncertainties in shape factor, pit size and aspect ratio. A non‐intrusive formulation is proposed consisting of a sequence of steps. First, a stochastic parametric Finite Element Analysis (FEA) is performed using SMART© crack growth and Design Xplorer© facilities within the software package ANSYS. Secondly, the results obtained from the FEA are processed using an Artificial Neural Network (ANN) response surface modelling technique. Finally, the First Order Reliability Method (FORM) is used to calculate the reliability indices of components. The results reveal that for the inherent stochastic conditions, the structure becomes unsafe after the 18th year, before the attainment of the design life of 20 years. The FEA results are in very good agreement with results obtained from analysis steps outlined in design standard BS 7910 and other references designated as ‘theoretical analysis methods’ in this study. The results predict, for the case study, that the pit growth life is approximately 56% of the total pitting corrosion fatigue life. Sensitivity analysis results show that the aspect ratio of pits at critical size plays a significant role on the reliability of the structure.