Abstract
Portugal's electricity generation relies heavily on renewable sources, which accounted for over half of the country's production in recent years. The Portuguese government has set ambitious renewable energy targets for 2030. The R3EA project (https://r3ea.web.ua.pt/pt/projeto) evaluates the impact of new investments in solar and wind energy capacity in the Centro Region of Portugal, focusing on the costs and benefits of externalities. This study examines Portugal's electricity market outcomes in terms of prices, generation mix, and emissions for different wind and solar capacities, using the National Energy and Climate Plans (NECP) of Portugal and Spain as the reference scenario. The electricity markets of both countries are modelled together, reflecting the integrated Iberian market with significant interconnections. The NECP scenario results in lower market prices and emissions, but less significantly than scenarios with lower demand and higher renewable energy share. In all scenarios, increasing renewable energy sources drives market prices down from over 200/MWh in 2022 to under 100/MWh during peak hours in 2030. Demand is the main driver of emissions, as higher demand leads to more reliance on fossil fuel plants. Lower demand scenarios in 2030 show 20 % fewer CO2 emissions per TWh than higher demand ones.

Abstract
Purpose of Review: This paper reviews the flexibility-centric value chain (FCVC) and analyses how coordinating digital platforms along the FCVC is essential for enabling FCVC activities and supporting key actors. Based on the FCVC, the digital infrastructure needed to support flexibility provision in power systems is reviewed, with special focus on the role of energy communities (ECs) as emerging relevant actors and potential aggregators of its members. Recent Findings: We review the Grid Data and Business Network (GDBN), a platform developed by the authors to support the FCVC, with special focus on those stages of the FCVC not properly supported by existing solutions. It also analyses platforms used in local flexibility markets (LFMs), and it presents the RECreation digital platform designed to manage ECs to support the participation in flexibility markets. Summary: Digital platforms are necessary for scaling flexibility services. The GDBN offers a comprehensive approach by enabling the FCVC and facilitating interoperability with existing platforms dedicated to specific segments, such as ECs and LFMs. By addressing current limitations in platform integration, this paper contributes to a clearer understanding of how digital tools can enable an efficient flexibility ecosystem. © The Author(s) 2025.

Abstract
Distributed energy resources (DERs) offer untapped potential to meet the flexibility needs of power systems with a high share of non-dispatchable renewable generation, and local flexibility markets (LFMs) can be effective mechanisms for procuring it. In LFMs, energy communities (ECs) can aggregate and offer flexibility from their members' DERs to other parties. However, since flexibility prices are only known after markets clear, flexibility bidding curves can be used to deal with this price uncertainty. Building on previous work by the authors, this paper employs a two-stage methodology to calculate flexibility bids for an EC participating in an LFM, including not only batteries and photovoltaic panels, but also cross-sector (CS) flexible assets like thermal loads and electric vehicles (EVs) to assess their impact. In Stage 1, the EC manager minimizes the energy bill without flexibility to define its baseline. In Stage 2, it computes the optimal flexibility to be offered for each flexibility price to build the flexibility bidding curve. Case examples allow to assess the impact of CS flexible assets on the final flexibility offered. © 2025 IEEE.

Abstract
Planning Energy communities (ECs) requires engaging members, designing business models and governance rules, and sizing distributed energy resources (DERs) for a costeffective investment. Meanwhile, the growing share of nondispatchable renewable generation demands more flexible energy systems. Local flexibility markets (LFMs) are emerging as effective mechanisms to procure this flexibility, granting ECs a new revenue stream. Since sizing with flexibility becomes a highly complex problem, we propose a 2 -stage methodology for estimating DERs size in an EC with collective self-consumption, flexibility provision and cross-sector (CS) assets such as thermal loads and electric vehicles (EVs). The first stage computes the optimal DER capacities to be installed for each member without flexibility provision. The second stage departs from the first stage capacities to assess how to modify the initial capacities to profit from providing flexibility. The impact of data clustering and flexibility provision are assessed through a case study. © 2025 IEEE.

Abstract
Recently, there has been growing attention on the decarbonisation of maritime transport, particularly regarding the landside operations at ports. This has spurred the development and implementation of strategies and policies aimed at enhancing the environmental performance of port activities. Among these strategies, the electrification of port infrastructure is emerging as a potential industry standard for the future. However, there remains a significant gap in understanding the patterns of electricity consumption in ports and how to forecast them accurately. To address this gap, this paper provides a review of the current literature on electricity demand in ports, examining practical applications, methodologies employed, and their key limitations. The findings indicate that, despite its importance in supporting the electrification process, electricity demand forecasting in ports has not received substantial attention in either industry or academic research, and there are no clearly established policies to support port authorities in obtaining the necessary data. Finally, the paper outlines potential directions for future research and how port authorities or local government agencies can contribute to these efforts.

Abstract
Data-based approach for diagnosis of thyroid disorders is still at its early stage. Most of the research outcomes deal with binary classification of the disorders, i.e. presence or not of some pathology (cancer, hyperthyroidism, hypothyroidism, etc.). In this paper we explore deep learning (DL) models to improve the multi-class diagnosis of thyroid disorders, namely hypothyroid, hyperthyroid and no pathology thyroid. The proposed DL models, including DNN, CNN, LSTM, and a hybrid CNN-LSTM architecture, are inspired by state-of-the-art work and demonstrate superior performance, largely due to careful feature selection and the application of SMOTE for class balancing prior to model training. Our experiments show that the CNN-LSTM model achieved the highest overall accuracy of 99%, with precision, recall, and F1-scores all exceeding 92% across the three classes. The use of SMOTE for class balancing improved most of the model’s performance. These results indicate that the proposed DL models not only effectively distinguish between different thyroid conditions but also hold promise for practical implementation in clinical settings, potentially supporting healthcare professionals in more accurate and efficient diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.