Browsing by Issue Date, starting with "2024-04-03"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
- Identification, readiness and potencial of technology domains for decarbonisation and role of the eletric power systemPublication . Duarte, António Francisco Costa; Costa, Paulo Moisés Almeida da; Tomé, Paulo Rogério PerfeitoThe intensive use of fossil fuels in recent years has led to a significant increase in carbon dioxide (CO2) levels in the atmosphere, contributing to global warming and its harmful effects on the climate. Global warming has become one of humanity’s most urgent challenges. As a result, there is increasing pressure to reduce carbon emissions and take measures to limit the extent of climate change and its severe consequences. This dissertation, developed under the research project “Sustainable Transformative Transitions - reconcile the acceleration of low carbon transitions with system transformations” – (PTDC/GES-AMB/0934/2020), funded by the Foundation for Science and Technology, focuses on the decarbonisation theme. Its content can be subdivided into two main parts. In the first part, a methodology based on two text-mining tools was developed and applied to identify the domains of innovative decarbonisation technologies and instruments that have received significant attention from the scientific community in recent years. To achieve this, documents from databases of scientific publications (Scopus and Web of Science), research projects (Cordis), and patents (Patstat) were examined. Computational software was developed and used to process the outputs of the text-mining software and obtain a relevant set of terms related to decarbonisation technologies/instruments. Those terms were then consolidated based on their characteristics to establish a set of 41 domains of technology/instruments that emerged as the most relevant to support the decarbonisation process. Analyses were carried out to assess the significance of these domains in scientific production by examining their relevance and occurrence, both in absolute and relative terms. The readiness of the domains was assessed by calculating the average technology readiness level (TRL) for a specific set of innovative technologies associated with each domain. This process was supported by information from a database provided by the International Energy Agency, which contained data on 368 distinct technology designs and components related to achieving net-zero emissions. The technologies of that database were categorised into specific domains based on their unique characteristics. The domains were then intersected with six literature-derived decarbonisation pathways (defined elsewhere). This analysis revealed that twelve domains emerged as the more crucial in supporting these pathways. In addition, the risk levels for each pathway were determined based on the average Technology Readiness Level (TRL) and relevance-occurrence rank ratios. Furthermore, the potential for decarbonisation of each pathway was assessed using information from the Intergovernmental Panel on Climate Change (IPCC) on the potential for reducing net carbon dioxide emissions (CO2) by 2030. The “Electrification of uses” pathway has emerged as the most promising for decarbonisation, demonstrating the highest readiness and average potential for CO2 emissions reduction and a low-risk value. Furthermore, the “Decarbonisation of electricity” pathway ranks as the fourth most promising option. Consequently, the electrical sector plays a crucial role in achieving decarbonisation goals. Based on these findings, the second part of this work concentrates on developing and applying a model to forecast avoided CO2 emissions for the Portuguese power system until 2050. The model relies on predictions from reliable documents regarding electricity consumption and renewable electricity generation. Hence, four main scenarios were formulated to incorporate electricity consumption and production forecasts. Furthermore, these four scenarios were further categorised to include sub-scenarios related to different charging options for electric vehicles. It is essential to mention that these charging alternatives can significantly impact consumption patterns. The study also examined the effects on emissions due to incorporating storage systems into the electrical system. Additionally, it investigated how the distribution of renewable electricity production and storage systems across different networks within the electrical system influences emissions. The results indicate that, in the base case study, significant values of avoided emissions can be expected from 2023 onwards. Depending on the scenario, the estimated avoided emissions for the 2023-2050 period ranges from 214.2 MtonCO2 (low increase in electricity consumption and conservative growth in non-conventional generation scenario) to 266.5 MtonCO2 (high increase in electricity consumption and ambitious increase in non-conventional generation scenario). The change in the consumption pattern due to the concentration of electric vehicle charging in specific periods (peak or night empty hours) revealed a reduction in the value of avoided emissions, which, in the most unfavourable scenario (represented by a high increase in electricity consumption and ambitious increase in non-conventional generation scenario), reaches 90.4% of the value obtained for the base scenario. Integrating storage systems in the electric networks can potentially enhance the value of avoided emissions. The magnitude of this potential depends on various factors, including the specific scenario considered (in terms of consumption and generation), the indirect emissions associated with storage system manufacturing, and the changes in the load profile due to EV charging. Moreover, the proper planning for storage capacity installation over the years and its location assumes a significant relevance on the value of avoided emissions, namely when considering the indirect emissions from manufacturing the storage systems. The results highlight that storage systems can increase the expected avoided emissions by up to 32.4%, depending on the scenario. Furthermore, the results show that the distribution of the non-conventional generation across various networks directly impacts the expected avoided emissions.