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Browsing ENERGIA by Sustainable Development Goals (SDG) "11:Cidades e Comunidades Sustentáveis"
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- Analysis of Techno-Economic and Social Impacts of Electric Vehicle Charging Ecosystem in the Distribution Network Integrated with Solar DG and DSTATCOMPublication . Bonela, Ramesh; Ghatak, Sriparna Roy; Swain, Sarat Chandra; Lopes, Fernando; Nandi, Sharmistha; Sannigrahi, Surajit; Acharjee, ParimalABSTRACT: In this work, a comprehensive planning framework for an electric vehicle charging ecosystem (EVCE) is developed, incorporating solar distributed generation (DG) and a distribution static compensator (DSTATCOM), to assess their long-term techno-economic and environmental impacts. The optimal locations and capacities of the EVCE, solar DG, and DSTATCOM are determined using an improved particle swarm optimization algorithm based on the success rate technique. The study aims to maximize the technical, financial, and social benefits while ensuring that all security constraints are met. To assess the financial viability of the proposed model over a 10-year horizon, a detailed economic analysis comprising installation cost, operation, and maintenance cost is conducted. To make the model more realistic, various practical parameters, such as the inflation rate and interest rate, are incorporated during the financial analysis. Additionally, to highlight the societal benefits of the approach, the study quantifies the long-term carbon emissions and the corresponding cost of emissions. The proposed framework is tested on both a 33-bus distribution network and a 108-bus Indian distribution network. Various planning scenarios are explored, with different configurations of the EVCE, solar-based DG, and DSTATCOM, to assist power system planners in selecting the most suitable strategy.
- Bridging Regional Divides in Decarbonization: Firm Strategies, Policy Tensions, and Structural Trade-offs in Portugal [Resumo]Publication . Vale, Mário; Alves, Tiago; Fontes, Margarida; Mamede, Ricardo; Bento, NunoABSTRACT: The transition to a low-carbon economy is shaped by structural tensions and trade-offs that impact firms, regions, and policymakers. A central challenge is balancing regional equity, industrial specialization, and technological innovation in decarbonization policies (Markard & Rosenbloom, 2022). This study critically examines these tensions by analysing firm-level decarbonization strategies within the Portugal 2020 (PT2020) program, revealing how economic structures shape sustainability transitions and the effectiveness of policy interventions.
- Critical transitions: Unpacking decarbonization strategies in Portuguese industry and regional disparitiesPublication . Vale, Mário; Alves, Tiago; Duarte de Castro Fontes, Maria Margarida; Mamede, Ricardo; Bento, NunoABSTRACT: In the wake of the Paris Agreement, the urgency for decarbonization has intensified globally, prompting varied responses from different regions and sectors. This study critically examines the uneven decarbonization trajectories of Portuguese firms within the framework of the Portugal 2020 (PT2020) program, informed by transition theory and regional innovation systems. Employing a multi-method approach that combines natural language processing and a systematic literature review, we identify and categorize the decarbonization strategies of 278 out of 2,793 firms funded by PT2020 between 2020 and 2023. Our findings reveal a modest (less than 10 % of all projects) but pivotal engagement in decarbonization, predominantly focused on the Porto metropolitan area and adjacent regions, indicating a pattern of uneven geographical transitions. Larger, established firms predominantly undertake these initiatives, reflecting a skew in policy effectiveness towards more stable entities. The most common pathways—demand and co-benefits (49 %) and decarbonization of electricity (34 %)—suggest a preference for immediately actionable strategies (electrification of uses and technological breakthroughs). This study underscores the disparity in decarbonization efforts across firms, but also regions, correlating higher industrial productivity and urbanization with increased activity. Such trends reveal the influence of existing economic structures and regional capacities on the adoption of green technologies, which exacerbate regional inequalities in the face of global decarbonization mandates. This study improves the understanding on the potential of decarbonization to increase or decrease inequalities among companies and regions. It provides crucial lessons for policies aiming to accelerate decarbonization to achieve the 2030 goals. Further research is required to explore the impact of regional specialization on decarbonization strategies and to develop more inclusive and equitable policies.
- Decarbonization Transition Pathways and Regional Trends: Insights from One Million StudiesPublication . Bento, Nuno; Alves, Tiago; Ribeiro, Ricardo; Fontes, MargaridaABSTRACT: As global temperatures near critical thresholds and emissions continue to rise, the urgency for strategic, accelerated decarbonization grows. Despite a vast climate mitigation literature, a systematic understanding of actionable pathways remains limited. Here, we apply artificial intelligence to analyze over one million scientific papers (2011–2021), generating a data-driven typology of six archetypal decarbonization pathways: Technology Breakthrough, Electrification of Uses, Integrated Policy, Decarbonization of Electricity, Demand Reduction & Co-benefits, and Land Use & Circularity. Regional patterns show Electrification of Uses prevailing in Europe (EU27), while Technology Breakthrough dominates in China, the US, and Japan. Increasing political and societal resistance to mitigation makes the strategic selection and combination of pathways even more critical. Our analysis highlights key synergies between pathways, the scientific competencies required to support them, and persistent gaps—particularly in Land Use and Circularity. We also compare current climate policy directions with the typology, revealing alignment gaps that may weaken policy effectiveness. This framework enables policymakers to better match strategies with regional capacities and research strengths, offering a more coherent approach to decarbonization. Strengthening the integration of science, technology, and policy is essential to overcome fragmentation and deliver the emissions reductions needed to meet the net-zero climate targets.
- Design optimisation of five pilot-scale two-stage vertical flow-constructed wetlands for piggery wastewater treatmentPublication . Karan, N.; Gogoi, Jayanta; Ganguly, Anasuya; Brito, António; Marques dos Santos, C.; de Oliveira Corrêa, Diego; Gouveia, Luisa; Mutnuri, SrikanthABSTRACT: With growing pig farming, sustainable piggery wastewater treatment methods are essential for environmental protection. This study evaluated five pilot-scale two-stage vertical flow-constructed wetlands (VFCWs) with varying configurations of aeration, plantation, and saturation zones. Three VFCW configurations (1VFCW, 2VFCW, and 3VFCW) were unsaturated, while 4VFCW and 5VFCW were saturated in the second stage (up to 60 and 90 cm, respectively). The 5VFCW featured a stacked configuration with no space between its two stages. Passive aeration was selectively applied in 2VFCW, 3VFCW, 4VFCW, and 5VFCW, while plants were present in most configurations except the control. Saturated 4VFCW achieved the highest removal efficiency for TN (77.03 ± 16.24%) and NO3− (46.06 ± 45.96%), while the stacked 5VFCW showed the highest removal for chemical oxygen demand (COD) (94.17 ± 4.85%) and Total ammoniacal nitrogen (TOC) (86.35 ± 6.78%). Unsaturated 1VFCW excelled in TAN removal (98.89 ± 0.33%), and the control system (C) showed the highest removal efficiency for PO43− (90.38 ± 6.52%) and TOC (87.52 ± 9.83%). Overall, 4VFCW emerged as the most balanced and effective system, supported by an optimal combination of aerobic and anaerobic conditions that facilitated sequential nitrification and denitrification, along with an extended hydraulic retention time due to saturation.
- Driving Transformative Change: Assessing the Direction and Design of Decarbonization Policies in the EU, US, China, and JapanPublication . Fontes, Margarida; Sousa, Cristina; Bento, NunoABSTRACT: The transition to low‐carbon economies demands policies that drive both decarbonization and deep socioeconomic transformation. This paper assesses the “transformative potential” of 3,400 decarbonization policies from Europe, the US, China, and Japan. We define transformative potential considering both policy direction – alignment with sustainability goals, and policy design ‐ presence of mechanisms that can induce transformation, such as experimentation, actor diversity, multiscale coordination, and reflexivity. The research shows that when we consider a broad universe of decarbonization policies, transformative potential is still limited: only 20% of policies align with at least one transition pathway and include at least one transformative mechanism; and just 2% include three or more mechanisms. By identifying distinguishing features of these higher transformative potential policies, the paper contributes to understanding how technological, sectoral and contextual factors shape the capacity of policies to enable transformative change.
- Dynamic Line Rating Models and Their Potential for a Cost-Effective Transition to Carbon-Neutral Power SystemsPublication . Estanqueiro, Ana; Algarvio, Hugo; Couto, António; Michiorri, Andrea; Salas, Sergio; Pudjianto, Danny; Hagglund, Per; Dobschinski, Jan; Bolgaryn, Roman; Kanefendt, Thomas; Gentle, Jake; Alam, S. M. Shafiul; Priest, Zachary M.; Abboud, Alexander W.ABSTRACT: Most transmission system operators (TSOs) currently use seasonally steady-state models considering limiting weather conditions that serve as reference to compute the transmission capacity of overhead power lines. The use of dynamic line rating (DLR) models can avoid the construction of new lines, market splitting, false congestions, and the degradation of lines in a cost-effective way. DLR can also be used in the long run in grid extension and new power capacity planning. In the short run, it should be used to help operate power systems with congested lines. The operation of the power systems is planned to have the market trading into account; thus, it computes transactions hours ahead of real-time operation, using power flow forecasts affected by large errors. In the near future, within a "smart grid" environment, in real-time operation conditions, TSOs should be able to rapidly compute the capacity rating of overhead lines using DLR models and the most reliable weather information, forecasts, and line measurements, avoiding the current steady-state approach that, in many circumstances, assumes ampacities above the thermal limits of the lines. This work presents a review of the line rating methodologies in several European countries and the United States. Furthermore, it presents the results of pilot projects and studies considering the application of DLR in overhead power lines, obtaining significant reductions in the congestion of internal networks and cross-border transmission lines.
- Influence of Inhibitors Generated in Lignocellulosic Hydrolysates from Group of Acids on the Growth of Strains TG1 and Tuner of Escherichia coliPublication . Gaspar, Suelen S.; Alves Ferreira Caturra, Júnia Aparecida; Moniz, Patricia; Silva-Fernandes, Talita; Silvestre, Adriana I. R; Torrado, Ivone; Pesce, Gaetano R.; Carvalheiro, Florbela; Duarte, Luís; Fernandes, Maria da ConceiçãoABSTRACT: Concerns over fossil fuels are of increasing interest in biorefineries that utilize lignocellulosic residues. Besides sugars, inhibitors are formed during biomass pretreatment, including acetic acid (AI) and formic acid (FI), which can hinder microbial fermentation. The TG1 and Tuner strains of Escherichia coli were subjected to various acid concentrations. Samples were taken during fermentation to monitor growth, sugar consumption, biomass yield, and product yield. With increasing AI, the TG1 strain maintained stable growth (0.102 1/h), while xylose consumption decreased, and product formation improved, making it better suited for high-acetic-acid industrial applications. In contrast, the Tuner strain performed better under low-inhibitor conditions but suffered metabolic inhibition at high AI levels, compensating by increasing lactic acid production-an adaptation absent in TG1. However, Tuner showed greater resistance to formic acid stress, sustaining higher growth and ethanol production, whereas TG1 experienced a greater metabolic decline but maintained stable acetic acid output. Both strains experienced inhibition in formic acid metabolism, but TG1 had a higher yield despite its lower overall robustness in formic acid conditions. The use of TG1 for value-added compounds such as ethanol or formic acid may help to avoid the use of chemicals that eliminate acetic acid. Tuner could be used for lactic acid production, especially in hydrolysates with under moderate concentration.
- A Machine Learning Model for Procurement of Secondary Reserve Capacity in Power Systems with Significant vRES PenetrationsPublication . dos Santos, Joao; Algarvio, HugoABSTRACT: The growing investment in variable renewable energy sources is changing how electricity markets operate. In Europe, players rely on forecasts to participate in day-ahead markets closing between 12 and 37 h ahead of real-time operation. Usually, transmission system operators use a symmetrical procurement of up and down secondary power reserves based on the expected demand. This work uses machine learning techniques that dynamically compute it using the day-ahead programmed and expected dispatches of variable renewable energy sources, demand, and other technologies. Specifically, the methodology incorporates neural networks, such as Long Short-Term Memory (LSTM) or Convolutional neural network (CNN) models, to improve forecasting accuracy by capturing temporal dependencies and nonlinear patterns in the data. This study uses operational open data from the Spanish operator from 2014 to 2023 for training. Benchmark and test data are from the year 2024. Different machine learning architectures have been tested, but a Fully Connected Neural Network (FCNN) has the best results. The proposed methodology improves the usage of the up and down secondary reserved power by almost 22% and 11%, respectively.
- Mapping less sensitive areas with a view to the potential installation of solar and wind electricity generation units [Comunicação oral]Publication . Simoes, Sofia; Barbosa, Juliana Pacheco; Oliveira, Paula; Simões, Teresa; Quental, Lídia; Costa, Paula; Picado, Ana; Catarino, Justina; Patinha, Pedro