Abstract:Purpose Concentrating Solar Power (CSP) plants based on parabolic troughs utilize auxiliary fuels (usually natural gas) to facilitate start-up operations, avoid freezing of HTF and increase power output. This practice has a significant effect on the environmental performance of the technology. The aim of this paper is to quantify the sustainability of CSP and to analyse how this is affected by hybridisation with different natural gas (NG) inputs.Methods A complete Life Cycle (LC) inventory was gathered for a c… Show more
“…Impact categories most salient in EU and World references replicate across the four LCA applications indicating the possibility of a systematic bias in the normalization approach. With the exception of Kasah (2014), this same pattern in ReCiPe externally normalized results was found in recent LCA applications such as concentrated solar power (Corona et al 2014), structural beams (Ibbotson and Kara 2013), industrial cleaning products (Kapur et al 2012), diapers (Mirabella et al 2013), laundry detergents (Prado-Lopez et al 2014), energy recovery from rice husks (Prasara-A and Grant 2011), and dishwashing soap (Van Hoof et al 2013).…”
Purpose Identification of environmentally preferable alternatives in a comparative life cycle assessment (LCA) can be challenging in the presence of multiple incommensurate indicators. To make the problem more manageable, some LCA practitioners apply external normalization to find those indicators that contribute the most to their respective environmental impact categories. However, in some cases, these results can be entirely driven by the normalization reference, rather than the comparative performance of the alternatives. This study evaluates the influence of normalization methods on interpretation of comparative LCA to facilitate the use of LCA in decision-driven applications and inform LCA practitioners of latent systematic biases. An alternative method based on significance of mutual differences is proposed instead. Methods This paper performs a systematic evaluation of external normalization and describes an alternative called the overlap area approach for the purpose of identifying relevant issues in a comparative LCA. The overlap area approach utilizes the probability distributions of characterized results to assess significant differences. This study evaluates the effects in three LCIA methods, through application of four comparative studies. For each application, we call attention to the category indicators highlighted by each interpretation approach. Results and discussion External normalization in the three LCIA methods suffers from a systematic bias that emphasizes the same impact categories regardless of the application. Consequently, comparative LCA studies that employ external normalization to guide a selection may result in recommendations dominated entirely by the normalization reference and insensitive to data uncertainty. Conversely, evaluation of mutual differences via the overlap area calls attention to the impact categories with the most significant differences between alternatives. The overlap area approach does not show a systematic bias across LCA applications because it does not depend on external references and it is sensitive to changes in uncertainty. Thus, decisions based on the overlap area approach will draw attention to tradeoffs between alternatives, highlight the role of stakeholder weights, and generate assessments that are responsive to uncertainty. Conclusions The solution to the issues of external normalization in comparative LCAs proposed in this study call for an entirely different algorithm capable of evaluating mutual differences and integrating uncertainty in the results.
“…Impact categories most salient in EU and World references replicate across the four LCA applications indicating the possibility of a systematic bias in the normalization approach. With the exception of Kasah (2014), this same pattern in ReCiPe externally normalized results was found in recent LCA applications such as concentrated solar power (Corona et al 2014), structural beams (Ibbotson and Kara 2013), industrial cleaning products (Kapur et al 2012), diapers (Mirabella et al 2013), laundry detergents (Prado-Lopez et al 2014), energy recovery from rice husks (Prasara-A and Grant 2011), and dishwashing soap (Van Hoof et al 2013).…”
Purpose Identification of environmentally preferable alternatives in a comparative life cycle assessment (LCA) can be challenging in the presence of multiple incommensurate indicators. To make the problem more manageable, some LCA practitioners apply external normalization to find those indicators that contribute the most to their respective environmental impact categories. However, in some cases, these results can be entirely driven by the normalization reference, rather than the comparative performance of the alternatives. This study evaluates the influence of normalization methods on interpretation of comparative LCA to facilitate the use of LCA in decision-driven applications and inform LCA practitioners of latent systematic biases. An alternative method based on significance of mutual differences is proposed instead. Methods This paper performs a systematic evaluation of external normalization and describes an alternative called the overlap area approach for the purpose of identifying relevant issues in a comparative LCA. The overlap area approach utilizes the probability distributions of characterized results to assess significant differences. This study evaluates the effects in three LCIA methods, through application of four comparative studies. For each application, we call attention to the category indicators highlighted by each interpretation approach. Results and discussion External normalization in the three LCIA methods suffers from a systematic bias that emphasizes the same impact categories regardless of the application. Consequently, comparative LCA studies that employ external normalization to guide a selection may result in recommendations dominated entirely by the normalization reference and insensitive to data uncertainty. Conversely, evaluation of mutual differences via the overlap area calls attention to the impact categories with the most significant differences between alternatives. The overlap area approach does not show a systematic bias across LCA applications because it does not depend on external references and it is sensitive to changes in uncertainty. Thus, decisions based on the overlap area approach will draw attention to tradeoffs between alternatives, highlight the role of stakeholder weights, and generate assessments that are responsive to uncertainty. Conclusions The solution to the issues of external normalization in comparative LCAs proposed in this study call for an entirely different algorithm capable of evaluating mutual differences and integrating uncertainty in the results.
“…On the contrary, the environmental impact of the HYSOL plant increases significantly when it is operated with natural gas as the auxiliary fuel. Still, the carbon footprint of the HYSOL configuration operating with 55% of natural gas hybridization resulted to be 294 kg CO 2 eq/MWh, which is lower than the 311 kg CO 2 eq/MWh obtained for a conventional hybrid parabolic trough CSP plant hybridized with only 35% of natural gas and also lower than conventional power plants based on the combustion of natural gas using combined cycle technology (365-425 kg CO 2 eq/MWh) [18,28,29].…”
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confidence: 72%
“…Life Cycle Assessment (LCA) is an appropriate methodology to evaluate the environmental performance of renewable technologies, as has been proven in scientific literature [7][8][9]. The environmental impacts of conventional CSP plants have been previously evaluated by the scientific community [10][11][12][13][14][15][16][17][18][19][20]. These analyses are all based on LCA methodology, and evaluate the environmental performance of CSP plants of varying capacity, operating with different technologies (parabolic trough/Fresnel reflectors, central tower, Stirling dish), including specific component characteristics (air/wet cooling, thermal storage technology) and hybridization with different fuels.…”
Concentrating Solar Power (CSP) technology is developing in order to achieve higher energy efficiency, reduced economic costs, and improved firmness and dispatchability in the generation of power on demand. To this purpose, a research project titled HYSOL has developed a new power plant, consisting of a combined cycle configuration with a 100 MWe steam turbine and an 80 MWe gas-fed turbine with biomethane. Technological developments must be supported by the identification, quantification, and evaluation of the environmental impacts produced. The aim of this paper is to evaluate the environmental performance of a CSP plant based on HYSOL technology using a Life Cycle Assessment (LCA) methodology while considering different locations. The scenarios investigated include different geographic locations (Spain, Chile, Kingdom of Saudi Arabia, Mexico, and South Africa), an alternative modelling procedure for biomethane, and the use of natural gas as an alternative fuel. Results indicate that the geographic location has a significant influence on the environmental profile of the HYSOL CSP plant. The results obtained for the HYSOL configuration located in different countries presented significant differences (between 35% and 43%, depending on the category), especially in climate change and water stress categories. The differences are mainly attributable to the local availability of solar and water resources and composition of the national electricity mix. In addition, HYSOL technology performs significantly better when hybridizing with biomethane instead of natural gas. This evidence is particularly relevant in the climate change category, where biomethane hybridization emits 27.9-45.9 kg CO 2 eq per MWh (depending on the biomethane modelling scenario) and natural gas scenario emits 264 kg CO 2 eq/MWh.
“…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].…”
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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