Search

The scale of offshore wind energy has been rapidly growing worldwide in recent years. Accurate assessment of the offshore wind energy potential and emission reduction benefits is essential to realize the ambitious targets for future installations. In this study, the Shandong Sea of China was selected as a case study, and the long-term offshore wind energy potential was assessed using ERA5 reanalysis data for the last 30 years, including the spatio-temporal variation and the technical potential of wind resources. An innovative approach to evaluate the technical potential of installed offshore wind farms based on deep learning, satellite images and ERA5 reanalysis data was proposed and applied to the Shandong Sea. The results show that (1) the highest offshore wind energy potential is observed in the northeast sea of Weihai; (2) the inter-annual variation of wind resources is relatively modest, with a pronounced monthly variability. The richest wind resources are found in the spring; (3) the electricity generation of wind resources is 12.66–30.53 GWh/year, equivalent to a reduction of 9.35–22.55 Kt of CO 2 emissions; (4) as of 2023, there were 12 OWFs including 633 offshore wind turbines in Shandong Sea, generating 14,218 GWh/year of clean electricity and reducing 10,505 kt/year of CO 2 emissions. The method proposed in this study is applicable to both small- and large-scale areas and allows for an accurate assessment of the electricity generation from installed offshore wind farms. [Display omitted] • Long-term offshore wind energy potential in Shandong Sea is assessed using ERA5 data. • Long-term changes and technical potential of offshore wind resources are revealed. • A method to assess technical potential of installed offshore wind farms is proposed. • Electricity production and CO 2 abatement of offshore wind farms were quantified. [ABSTRACT FROM AUTHOR]

Due to the complementarity of renewable energy sources, there has been a focus on technology hybridization in recent years. In the area of hybrid offshore power plants, the current research projects mostly focus on the combinational implementation of wind, solar, and wave energy technologies. Accordingly, considering the already existing offshore wind farms, there is the potential for the implementation of hybrid power plants by adding wave energy converters and floating photovoltaics. In this work, a stochastic sizing model is developed for the hybridization of existing offshore wind farms using wave energy converters and floating photovoltaics considering the export cable capacity limitation. The problem is modeled from an investor perspective to maximize the economic profits of the hybridization, while the costs and revenues regarding the existing units and the export cable are excluded. Furthermore, to tackle the uncertainties of renewable energy generation, as well as the energy price, a scenario generation method based on copula theory is proposed to consider the dependency structure between the different random variables. Altogether, the hybridization study is modeled in a mixed integer linear programming optimization framework considering the net present value of the project as the objective function. The results showed that hybrid-sources-based energy generation provided the highest economic profit in the studied cases in the different geographical locations. Furthermore, the technical specifications of the farms have also been considerably improved providing more stable energy generation, guaranteeing a minimum level of power in a high share of the time, and with a better utilization of the capacity of the cable while the curtailment of energy is maintained within the acceptable range. [Display omitted] • A mathematical model is developed for the hybridization of existing offshore farms. • Effects of existing turbines and cable capacity are considered in the model. • Wind, wave, and solar energy are the sources considered for expansion of the farm. • Uncertainty of renewable energy generation and electrical market price is modeled. • Copula theory is applied to model the dependency of random variables. [ABSTRACT FROM AUTHOR]

The decarbonisation of industry, heating and transportation is a major challenge for many countries' energy transition. Hydrogen is a direct low-carbon fuel alternative to natural gas offering a higher flexibility in the range of possible applications, yet currently most hydrogen is produced using carbon-intensive steam methane reforming due to cost considerations. Therefore, this study explores the economics of a prominent low-carbon method of hydrogen production, comparing the cost of hydrogen generation from offshore wind farms with and without grid electricity imports to conventional hydrogen production methods. A novel techno-economic model for offshore electrolysis production costs is presented, which makes hydrogen production fully dispatchable, leveraging geological salt-cavern storage. This model determines the lifetime costs aportioned across the system components, as well as the Levelised Cost of Hydrogen (LCOH). Using the United Kingdom as a case study, LCOH from offshore wind power is calculated to be €8.68/kg H2 using alkaline electrolysis (AEL), €10.49/kg H2 using proton exchange membrane electrolysis (PEMEL), and €10.88/kg H2 with grid electricity to backup the offshore wind power. A stochastic Monte-Carlo model is used to asses the uncertainty on costs and identify the cost of capital, electrolyser and wind farm capital costs, and cost of electricity as the most important drivers of LCOH across the different scenarios. Reducing the capital cost to comparative levels observed on today's wind farms alone, could see AEL LCOH fall to €5.32/kg H2 , near competitive with conventional generation methods. • Development of a techno-economic model for offshore electrolysis production cost. • Lifetime hydrogen production and Levelized cost of gas (LCOH) are determined. • LCOH is calculated using deterministic and stochastic Monte-Carlo models. • Alkaline electrolysis with offshore wind power cost is the UK's lowest cost option. [ABSTRACT FROM AUTHOR]

Maximising the energy potential of offshore wind farms requires an in-depth assessment of technological, economic, sociopolitical, and environmental aspects. Given the large economic impact of large-scale projects, a robust site selection procedure is critical for limiting financial risks while supporting informed investments. This research uncovers a novel and multidisciplinary approach for boosting the efficacy of Norwegian and global offshore wind farm siting investments. The proposed method uses a two-stage fuzzy mathematical model that considers technical, economic, logistical, and environmental factors. It combines the Ordinal Priority Approach (F-OPA) and Trigonometric Weighted Assessment (TRWA) technique by using an in-depth techno-economic assessment. An alternative reactive power compensation model, power loss calculations, and associated techno-economic analysis were performed for the investigated offshore wind farm locations. Furthermore, the energy economic calculations are carried out to provide support for the proposed decision-making framework. The proposed methodology was tested through a case study, focusing on ranking Norwegian offshore wind farm sites selected from potential locations announced by The Norwegian Water Resources and Energy Directorate (NVE). Within the Norwegian offshore wind farm sites, the approach demonstrated a versatile and efficient decision-making process at both individual and collective levels, identifying the Sandskallen-Sørøya Nord project as a pivotal investment priority and providing valuable managerial insights to enhance Norway's offshore wind initiatives. The model's stability was affirmed through a sensitivity analysis, underscoring its potential to enhance renewable energy policy and decision-making globally. [Display omitted] • A thorough power system analysis via detailed power loss calculations. • Tailored reactive power compensation model and a techno-economic assessment. • Decision-making algorithm ranks Norwegian wind farms by interdisciplinary criteria. • A fuzzy OPA-TRWA multicriteria framework handles uncertain data and reduces bias. • Our approach aids Norwegian wind sector, assists decisions, boosts sustainability. [ABSTRACT FROM AUTHOR]

A large-scale offshore wind power integration has a reverse peak regulation effect on the electric load and requires newly built transmission corridors. This work adopts a wind-thermal (WT) bundled system for offshore wind power integration and transmission line capacity factor improvement. The Qingzhou I-VII offshore wind farms (5000 MW) and the Yangxi coal-fired power plant units #5, #6 (2 × 1240 MW) are taken as a case study. The offshore wind farms (OWF) act as the must-take generator to increase the renewable fraction while the thermal power plants (TPP) provide peak regulation service to trace the changing net load. The OWF and TPP share the transmission line and point of interconnection. The OWF integration level is determined by the 8760-h production simulation and the wind-thermal (W-T) capacity ratio is 1.4516:1 (3600 MW OWF) in the studied site. The curtailment rate, electricity shortage rate, transmission line capacity factor (CF), load following precision, renewable fraction, CO 2 emission of WT-W3600 condition are 9.02%, 5.41%, 61.36%, 91.43%, 50.46%, 6.97 million ton/year. There is a trade-off relationship between renewable fraction and curtailment rate in the WT system which restricts higher OWF integration. The demand-side response technologies such as power-to-X and energy storage are required to break the trade-off relation and reduce the electricity shortage rate. The WT system is economically viable and the net present value (NPV), levelized cost of electricity (LCOE), internal rate of return (IRR) of WT-W3600 are 3.55 billion¥, 0.4859¥/kWh, 4.82%. Co-locating the OWF and the floating solar photovoltaic (PV) is the future direction that benefits the intensive exploitation of sea area and offshore renewable energy development. Fig. Schematic of offshore wind-thermal (WT) power system at the same point of interconnection (for illustrative purpose to show the connection). [Display omitted] • WT system combines offshore wind power and thermal power plants. • The recommended wind-thermal (W-T) capacity ratio in Yangxi is 1.4516:1. • WT system improves transmission line capacity factor to 61.36%. • WT system improves renewable fraction and reduces CO 2 emission. • The NPV, LCOE, IRR of WT-W3600 are 3.55 billion¥, 0.4859¥/kWh, 4.82%. [ABSTRACT FROM AUTHOR]

Providing the energy demands of various sectors through the utilization of renewable energy sources offers a more sustainable and long-term approach compared to fossil fuel consumption. In this study, the proposition is made to address Turkey's rail industry energy demand through Offshore Wind Farms (OWF). Accordingly, the spatial analysis for determining the most suitable OWF location is explored. The research methodology is conducted based on a Geographic Information System-Multi-Criteria Decision Making (GIS-MCDM) hybrid approach. Alternative OWF locations are identified using GIS. Location selection criteria are established based on literature review, and a fuzzy-based decision matrix is constructed, incorporating expert opinions. To leverage two distinct fuzzy number sets, Intuitionistic Fuzzy Weighted Averaging (IFWA) and Spherical Fuzzy Stepwise Weight Assessment Ratio Analysis (SF-SWARA) methods are employed for criteria weighting, followed by the aggregation of criterion weights using the Einstein Operations t-norm technique. To ensure comparable OWF alternative rankings, five different alternative ranking methods are employed to obtain alternative rankings. Ultimately, the most suitable OWF location for meeting the energy demand of the rail industry is determined to be the Izmir region. The research findings contribute to managerial perspectives by proposing recommendations concerning strategic location selection, optimized resource allocation, risk mitigation and policy alignment and sustainable long-term planning. • Turkey's sustainable rail transportation energy demand problem is addressed. • OWF is proposed to meet the rail transportation energy demand. • A framework for OWF location determination is presented for policy makers. • Criteria for OWF location selection problem are determined. • Hybrid GIS-MCDM model is innovatively used in OWF location selection. [ABSTRACT FROM AUTHOR]

Disposing of end-of-life wind turbine blades has become a pressing environmental concern due to the increasing repowering of wind turbines, resulting in significant waste. This study explores the potential of reusing shredded composite (SC) derived from recycling these blades for new building components. The SC underwent a thorough characterisation, including evaluating essential performance requirements as a building insulation material, such as thermal conductivity, acoustic absorption, and flammability classification, following established procedures and standard specifications to ensure reliable and standardised results. The SC outperforms traditional materials regarding sound absorption properties, with peak Noise Reduction Coefficient (NRC) values reaching 0.65. Additionally, it demonstrates effective sound transmission loss, with a peak value of 35.7 dB at 1000 Hz. However, the SC has limitations regarding flammability classification due to the presence of resin. The study examines the thermal conductivity of SC in maximum fibre length (MFL) of 2 mm and 10 mm, with the latter showing lower conductivity. These findings highlight the value of reusing and repurposing materials for creating sustainable building components and practices, supporting circular economy principles. They also underscore the importance of addressing the large-scale disposal of waste materials through their effective reuse in the construction industry. • Recycled wind turbine blades for sustainable building components. • Shredded composite surpasses typical materials in sound absorption & transmission loss. • Shredded composite thermal conductivity varies with 10 mm MFL. • Study emphasises circular economy in material reuse for construction. [ABSTRACT FROM AUTHOR]