Abstract
—The main objective of this work is to summarize the most commonly used dielectric fluids in the power distribution transformers, as well as to discuss what are the latest and the rationale behind those trends. The favorable and unfavorable reasons for any choice behind each of those dielectric fluids will be discussed. Additionally, this work also advances the power distribution transformers health index most commonly used to assess the condition of the transformer.

Abstract
Ester-based dielectric fluids have now been on the market for several decades, providing fire-safe and environmentally friendly alternatives to mineral oils, which have traditionally been used in transformers and other electrical equipment. This opens the door to innovation in power transformers. However, the use of esters-based dielectrics in power transformers is still very limited, especially for the higher voltage levels. The usage of these esters-based dielectrics in higher voltage power transformers is not yet consensual. this work present results with the use of natural esters in power distribution transformers. Tests carried out on mineral oil and natural ester oil found that the ester-based dielectric can withstand higher voltage thresholds for AC and Impulses tests, mainly within the specs of destructive tests, e.g., the natural ester was able to withstand a 185kV impulse without registering dielectric rupture while the natural oil registered a dielectric rupture with a 160kV impulse. Heating and mechanical tests demonstrated that ester-based dielectric oils for power transformers lead to a flow reduction between 16,8% and 18,2% in the cooling system that was design for mineral oils but they achieve a higher heat transfer coefficient, between 0,5% to 5% depending on the location of measurement. © Published under licence by IOP Publishing Ltd.

Abstract
Renewable energy communities (REC) are bound to play a crucial role in the energy transition, as their role, activities, and legal forms become clearer, and their dissemination becomes larger. Even though their mass grid integration, is regarded with high expectations, their diffusion, however, has not been an easy task. Its legal form and success, entail responsibilities, prospects, trust, and synergies to be explored between its members, whose collective dynamics should aim for optimal operation. In this regard, the pairing methodology of potential participants ahead of asset dimensioning seems to have been overlooked. This article presents a methodology for pairing consumers, based on their georeferenced load consumptions. A case study in an area of Porto (Asprela) was used to test the methodology. QGIS is used as a geo-representation tool and its PlanHeat plugin for district characterization support. A supervised statistical learning approach is used to identify the feature importance of an overall district energy consumption profile. With the main variables identified, the methodology applies standard K-means and Dynamic Time Warping clustering, from which, users from different clusters should be paired to explore PV as the main generation asset. To validate the assumption that this complementarity of load diagrams could decrease the total surplus of a typical PV generation, 18 pairings were tested. Results show that, even though it is not true that all pairings from different clusters lead to lower surplus, on average, this seems to be the trend. From the sample analyzed a maximum of 36% and an average of 12% less PV surplus generation is observed.

Abstract
Battery degradation remains a major challenge in electric vehicle (EV) adoption, directly affecting long-term performance, cost, and user satisfaction. This paper proposes a data-driven charging strategy that reduces battery wear while meeting the user's daily range needs. By integrating manufacturer guidelines, battery aging models, and thermal dynamics, the proposed optimization algorithm dynamically adjusts the charging current and timing to minimize stressors, such as high temperatures and prolonged high state of charge (SoC). The methodology is responsive to user inputs such as departure time and required driving range, enabling personalized charging behavior. Simulation results show that this approach can reduce battery degradation by up to 2.7% over a 30-day period compared to conventional charging habits, without compromising usability. The framework is designed for integration into Battery Management Systems (BMS), with applications for both private EV users and fleet operators. We address EV battery aging driven by high core temperature and prolonged high state of charge (SoC) during overnight/home charging. Given a user-specified departure time and required driving range, we schedule charging power over time to minimize predicted degradation exposure while still meeting the range requirement. The scheduler optimizes charging timing/current under SoC dynamics, thermal constraints, and charger/ BMS limits.

Abstract
As electric vehicle (EV) adoption accelerates, residential buildings-particularly multi-dwelling structures-face increasing challenges to electrical infrastructure, notably due to conservative sizing practices of electrical feeders based on maximum simultaneous demand. Current sizing methods assume all EVs charge simultaneously at maximum capacity, resulting in unnecessarily oversized and costly electrical installations. This study proposes an optimized methodology to estimate accurate coincidence factors, leveraging simulations of EV user charging behaviors in multi-dwelling residential environments. Charging scenarios considering different fleet sizes (1 to 70 EVs) were simulated under two distinct premises of charging: minimization of current allocation to achieve the desired battery state-of-charge and maximization of instantaneous power delivery. Results demonstrate significant deviations from conventional assumptions, with estimated coincidence factors decreasing non-linearly as fleet size increases. Specifically, applying the derived coincidence factors can reduce feeder section requirements by up to 86%, substantially lowering material costs. A fuzzy logic inference model is further developed to refine these estimates based on fleet characteristics and optimization preferences, providing a practical tool for infrastructure planners. The results were compared against other studies and real-life data. Finally, the proposed methodology thus contributes to more efficient, cost-effective design strategies for EV charging infrastructures in residential buildings.

Abstract
This research presents an anomaly detection algorithm for a Vanadium Redox Flow Battery (VRFB) using battery dataset as an example. The algorithm determines the anomaly detection threshold by fitting a Gaussian mixed model (GMM) to an anomaly-free dataset and testing it against a dataset containing only anomalies. By forcing the test dataset to classify all observations as anomalies, the threshold can be found. Applying again the model to the training dataset, classifies 11% of normal observations as failures, indicating that, not all observations were captured by the GMM, resulting in false positives. A percentage based on the likelihood values is suggested for replication to other systems, and a ratio of anomaly detection over time is proposed for preventive maintenance alerts.