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This paper presents a full-year hourly district emobility model and its integration into a Positive Energy District simulation and assessment model including building operation, use and embodied energy and emissions. The aim of this work is to model the operation and energy flexibility potential of an EV fleet in a district through mono- and bi-directional charging and enable its assessment in terms of self-utilization of local and volatile regional RES surpluses. Results of example residential, office, school and supermarket use cases show an increase in self-utilization of local PV of up to 30% due to EV inclusion, even if PV installation size exceeds legal building code requirements by a factor of two to four. Bi-Directional charging can cut annual grid electricity by up to 30% but require an increase in battery full equivalent cycles of 20%. © 2025 Elsevier B.V., All rights reserved.

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The global transition to a low-carbon energy system requires innovative solutions that integrate renewable energy production with storage and utilization technologies. The growth in energy demand, combined with the intermittency of these sources, highlights the need for advanced management models capable of ensuring system stability and efficiency. This paper presents the development of an optimized energy management system integrating renewable sources, with a focus on green hydrogen production via electrolysis, storage, and use through a fuel cell. The system aims to promote energy autonomy and support the transition to a low-carbon economy by reducing dependence on the conventional electricity grid. The proposed model enables flexible hourly energy flow optimization, considering solar availability, local consumption, hydrogen storage capacity, and grid interactions. Formulated as a Mixed-Integer Linear Programming (MILP) model, it supports strategic decision-making regarding hydrogen production, storage, and utilization, as well as energy trading with the grid. Simulations using production and consumption profiles assessed the effects of hydrogen storage capacity and electricity price variations. Results confirm the effectiveness of the model in optimizing system performance under different operational scenarios.

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The Eighth International Workshop on Narrative Extraction from Texts (Text2Story'25) was held on April 10 th , 2025, in conjunction with the 47 th European Conference on Information Retrieval (ECIR 2025) in Lucca, Italy. During this half-day event, more than 30 attendees engaged in discussions and presentations focused on recent advancements in narrative representation, extraction, and generation. The workshop featured a keynote address and a mix of oral presentations and poster sessions covering nineteen papers. The workshop proceedings are available online 1 . Date: 10 April 2025. Website: https://text2story25.inesctec.pt/.

Abstract
The rapid proliferation of uncontrolled resources poses significant voltage regulation challenges in low-voltage (LV) distribution grids. In this condition, conventional charging strategies, often based on fixed or static schedules, may lead to adverse voltage deviations under unpredictable load conditions and variable renewable generation. To address these challenges, this paper studies a hybrid deep reinforcement learning (DRL) framework based on a Proximal Policy Optimization (PPO) policy network enriched by a Graph Convolution Variation (GCV) feature extractor to improve voltage regulation issues in LV grids. In addition to ensuring that electric vehicles (EVs) achieve their required state-of-charge (SoC), the framework dynamically adjusts charging rates in real time to maintain LV-grid voltage within acceptable limits. Extensive simulation results, including detailed analysis and comparisons with the static charging method, demonstrate significant improvements in voltage regulation, and enhanced overall grid performance. The obtained results demonstrate the effectiveness of controlling EVs' charging controls in an intelligent manner to address the voltage regulation issue in low-voltage grids. © 2025 Elsevier B.V., All rights reserved.