GHG emissions and energy performance of offshore wind power

Raadal, Hanne Lerche; Vold, Bjørn Ivar; Myhr, Anders; Nygaard, Tor Anders

doi:10.1016/j.renene.2013.11.075

Cited by 78 publications

(61 citation statements)

References 16 publications

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confidence: 99%

“…Several references in the literature have already dealt with the environmental impact assessment of offshore wind turbines (Kaldellis & Apostolou, 2017;Raadal, Vold, Myhr, & Nygaard, 2014;Tsai, Kelly, Simon, Chalat, & Keoleian, 2016;Weinzettel, Reenaas, Solli, & Hertwich, 2009). Despite the potential of LCA to evaluate a broad range of environmental impact categories, most of the LCA studies related to offshore wind turbines are restricted to only two impact indicators, namely climate change (analogous to greenhouse gas emissions) and energy demand (Arvesen & Hertwich, 2012;BRLi, 2015;Kaldellis & Apostolou, 2017;Raadal et al, 2014). Despite the potential of LCA to evaluate a broad range of environmental impact categories, most of the LCA studies related to offshore wind turbines are restricted to only two impact indicators, namely climate change (analogous to greenhouse gas emissions) and energy demand (Arvesen & Hertwich, 2012;BRLi, 2015;Kaldellis & Apostolou, 2017;Raadal et al, 2014).…”

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confidence: 99%

“…In addition to the techno-economic viability of floating wind turbines, the potential environmental benefits compared to other energy sources also need to be verified. Several references in the literature have already dealt with the environmental impact assessment of offshore wind turbines (Kaldellis & Apostolou, 2017;Raadal, Vold, Myhr, & Nygaard, 2014;Tsai, Kelly, Simon, Chalat, & Keoleian, 2016;Weinzettel, Reenaas, Solli, & Hertwich, 2009). Life cycle assessment (LCA) approach is the most common approach to quantify the impacts over the whole process life cycle, from the extraction of raw materials to the end-of-life phase (ISO 14040, 2006) and is well adapted to RES (Huang, Gan, & Chiueh, 2017;Pehnt, 2006).…”

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confidence: 99%

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Site‐specific life cycle assessment of a pilot floating offshore wind farm based on suppliers’ data and geo‐located wind data

Poujol¹,

Prieur‐Vernat

Dubranna

et al. 2020

J of Industrial Ecology

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Renewable energy systems are essential in coming years to ensure an efficient energy supply while maintaining environmental protection. Despite having low environmental impacts during operation, other phases of the life cycle need to be accounted for. This study presents a geo-located life cycle assessment of an emerging technology, namely, floating offshore wind farms. It is developed and applied to a pilot project in the Mediterranean Sea. The materials inventory is based on real data from suppliers and coupled to a parameterized model which exploits a geographic information system wind database to estimate electricity production. This multi-criteria assessment identified the extraction and transformation of materials as the main contributor to environmental impacts such as climate change (70% of the total 22.3 g CO 2 eq/kWh), water use (73% of 6.7 L/kWh), and air quality (76% of 25.2 mg PM2.5/kWh), mainly because of the floater's manufacture.The results corroborate the low environmental impact of this emerging technology compared to other energy sources. The electricity production estimates, based on geo-located wind data, were found to be a critical component of the model that affects environmental performance. Sensitivity analyses highlighted the importance of the project's lifetime, which was the main parameter responsible for variations in the analyzed categories. Background uncertainties should be analyzed but may be reduced by focusing data collection on significant contributors. Geo-located modeling proved to be an effective technique to account for geographical variability of renewable energy technologies and contribute to decision-making processes leading to their development. K E Y W O R D Sfloating offshore wind farm, geo-located mode, industrial ecology, life cycle assessment, renewable energy, wind energy

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Site‐specific life cycle assessment of a pilot floating offshore wind farm based on suppliers’ data and geo‐located wind data

Poujol¹,

Prieur‐Vernat

Dubranna

et al. 2020

J of Industrial Ecology

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“…In practice, common features of the two types of installation mean that there is likely to be a positive correlation, which would reduce the variance of the difference. It should also be noted that the LCA used in this estimate considered a single type of HAWT, whereas an LCA of five types of HAWT found a range of 18-31 g CO 2 e/kWh generated (Raadal et al, 2014), which is an additional source of uncertainty. (Direct comparison of the two LCAs is difficult due to differing assumptions and choice of functional unit, but Raadal et al appear to estimate much higher total emissions.)…”

Section: Novel Offshore Vertical Axis Wind Turbinesmentioning

confidence: 99%

Carbon brainprint – An estimate of the intellectual contribution of research institutions to reducing greenhouse gas emissions

Chatterton

Parsons

Nicholls

et al. 2015

Process Safety and Environmental Protection

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ResearchHigher education Greenhouse gas a b s t r a c t Research and innovation have considerable, currently unquantified potential to reduce greenhouse gas emissions by, for example, increasing energy efficiency. Furthermore, the process of knowledge transfer in itself can have a significant impact on reducing emissions, by promoting awareness and behavioural change. The concept of the 'carbon brainprint' was proposed to convey the intellectual contribution of higher education institutions to the reduction of greenhouse gas emissions by other parties through research and teaching/training activities. This paper describes an investigation of the feasibility of quantifying the carbon brainprint, through six case studies. The potential brainprint of higher education institutes is shown to be significant: up to 500 kt CO 2 e/year for one project. The most difficult aspect is attributing the brainprint among multiple participants in joint projects.

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“…Then, Data acquisition is required to quantify the input and output flows associated with the power generation technologies listed in Table 1. Inventory data for the new technologies, i.e., those potentially participating in the electricity production mix according to the optimisation of each scenario, are based on specific literature: coal thermal power plants with/without capture [37][38][39], NGCC with/without capture [40,41], nuclear fission (III and IV generation) [42,43], nuclear fusion [44], cogeneration [45], hydropower dam and run-of-river plants [46], wind farms [47,48], tidal power [49], wave power [50], photovoltaics (open-ground and rooftop systems) [51], concentrated solar power plant with/without storage [52,53], geothermal (binary cycle) power plants [54], bioresource technology (biomass integrated gasification combined cycle, biogas and waste-to-energy plants) [55][56][57], PEMFC [58], and SOFC [59]. Finally, data for both existing technologies and background processes are retrieved from the ecoinvent database [60].…”

Section: Electricity Productionmentioning

confidence: 99%

Prospective Analysis of Life-Cycle Indicators through Endogenous Integration into a National Power Generation Model

et al. 2016

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Abstract:Given the increasing importance of sustainability aspects in national energy plans, this article deals with the prospective analysis of life-cycle indicators of the power generation sector through the case study of Spain. A technology-rich, optimisation-based model for power generation in Spain is developed and provided with endogenous life-cycle indicators (climate change, resources, and human health) to assess their evolution to 2050. Prospective performance indicators are analysed under two energy scenarios: a business-as-usual one, and an alternative scenario favouring the role of carbon dioxide capture in the electricity production mix by 2050. Life-cycle impacts are found to decrease substantially when existing fossil technologies disappear in the mix (especially coal thermal power plants). In the long term, the relatively high presence of natural gas arises as the main source of impact. When the installation of new fossil options without CO 2 capture is forbidden by 2030, both renewable technologies and-to a lesser extent-fossil technologies with CO 2 capture are found to increase their contribution to electricity production. The endogenous integration of life-cycle indicators into energy models proves to boost the usefulness of both life cycle assessment and energy systems modelling in order to support decision-and policy-making.

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GHG emissions and energy performance of offshore wind power

Cited by 78 publications

References 16 publications

Site‐specific life cycle assessment of a pilot floating offshore wind farm based on suppliers’ data and geo‐located wind data

Site‐specific life cycle assessment of a pilot floating offshore wind farm based on suppliers’ data and geo‐located wind data

Carbon brainprint – An estimate of the intellectual contribution of research institutions to reducing greenhouse gas emissions

Prospective Analysis of Life-Cycle Indicators through Endogenous Integration into a National Power Generation Model

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