Abstract
The wine production, included in the primary sector is a great cultural and economic deal, both nationally and internationally matters. However, it is highly dependent on natural resources, and traditionally involves high energy and water consumption. Given the global climate change scenario and the need for efficient resource management, it is necessary to implement a sustainable plan for the wine sector to realize sustainable practices. Data from the International Organization of Vine and Wine (OIV), states that global wine production exceeded 260 million hectoliters, in 2022. These has resulted in significant water and energy consumption, with around 500-1200 m(3) of water used per hectare for irrigation and 1.2 gigajoules per hectoliter of wine produced, concluding that more than 80% of total water consumption is associated with irrigation, while more than 90% of energy consumption, is associated with winery processes. In this context, the scarcity of water or the need to achieve carbon neutrality by 2050 makes it essential to adopt energy and water efficiency measures that allow for the sustainable management of resources without endangering the sector's viability. With this in mind, a case study applied to a Portuguese wine industry is presented, including data analysis from water and energy consumption. Also, efficiency metrics will be analyzed, proposing management and decision-support tools based on monitoring and sensor-based techniques. In fact, one example of these efficiency measures deals with the adoption of systems that provide real-time data on consumption patterns and resource availability in order to improve sustainability of the global process production.

Abstract
This study provides a comprehensive review of how game theory can be applied to model and optimize dynamic coalitions in contemporary energy markets. With the increasing decentralization of energy systems driven by technologies such as solar photovoltaics, home energy storage, and electric vehicles, consumers have begun to play a more active and influential role in the market. In this new context, where cooperative and collective decision-making is gaining importance, game theory emerges as a valuable tool for analyzing and structuring these interactions. The primary objective of this work is to systematically review existing models, assess their methodological strengths and limitations, and identify open research gaps that hinder their applicability to real-world settings. By synthesizing the current state-of-the-art, this study aims to highlight pathways toward the development of more realistic and effective models that capture the dynamic and interdependent behaviors of energy consumers and the coalitions they form. Ultimately, this review seeks to provide an updated overview of this growing field, serving both as a basis for future research and as a foundation for the design of solutions that promote fairer, more efficient, and more participatory energy markets, especially for small-scale consumers, who now have greater voice and power of choice. © 2026 IEEE.

Abstract
The growing adoption of electric vehicles (EVs) requires strategic planning of charging infrastructures to ensure greater efficiency and accessibility. In this context, forecasting EV trips becomes essential to identify travel patterns, anticipate demand for charging in different locations and strategically optimize the distribution of charging stations. This study proposes the use of Artificial Intelligence (AI) techniques to analyze mobility patterns and predict demand for charging in different locations. Three AI techniques will be explored: Fuzzy Logic, to deal with uncertainties associated with driver behavior; Supervised Machine Learning, encompassing Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Linear Regression, to model and predict travel patterns; and Reinforcement Learning (RL), applied to the dynamic optimization of charging station distribution. The combination of these techniques aims to provide an intelligent and adaptive system for managing charging stations, contributing to sustainable mobility and the energy efficiency of the network.

Abstract
[No abstract available]

Abstract
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.