Abstract
Renewable Energy Communities (RECs) need performance-based methods to share locally generated energy to prevent free-riding, incentivize consumer behavior, and improve overall social well-being through sector interaction. We tackle the challenge of ranking REC members for local energy allocation factor purposes, based on multidimensional household waste sorting performance, where efficiency changes over time and trade-offs exist among waste streams. We created a ranking system that balances stability (for fairness) with responsiveness (to reward improvement), compensating the REC manager promoter (municipality). The method combines historical frontier analysis with Mahalanobis distance, following: (1) DEA-derived weights to combine inputs, (2) temporal frontiers for each waste stream, (3) projects current performance onto past benchmarks with a customized rolling window, (4) calculates multivariate z-scores through Mahalanobis distance, and (5) ranks members by their statistical distance from historical norms. The proposed methodology enhancement is verified with synthetic data from 30 households over 14 months, with 8 evaluation periods. It shows 71.4% rank category stability compared to 49.0% for monthly DEA, a 22.4 percentage point increase, while still detecting performance changes. The system accounts for output correlations, with mostly positive links between waste streams ((Formula presented) glass-packages, (Formula presented) glass-organic). Mahalanobis distance fairly rewards balanced performance across related dimensions. Sensitivity tests indicate that the approach is robust to variations in parameter choices. The framework provides a straightforward computational method (<1 s per evaluation) that yields rankings with statistical significance for consumer communication. It is the first framework designed specifically for temporal performance ranking in incentive allocation. © 2026 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC license. http://creativecommons.org/licenses/by-nc/4.0/

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.

Abstract
The integration of electric vehicles (EVs) into the power grid poses significant challenges and opportunities for energy management systems. This is especially concerning for parking lots or private building condominiums in which refurbishing is not possible or is costly. This paper presents a real-time monitoring approach to EV charging dynamics with battery storage support over a 24 h period. By simulating EV demand, state of charge (SOC), and charging and discharging events, we provide insights into the operational strategies for energy storage systems to ensure maximum charging simultaneity factor through internal power enhancement. The study uses a time-series analysis of EV demand, contrasting it with the battery's SOC, to dynamically adjust charging and discharging actions within the constraints of the upstream infrastructure capacity. The model incorporates parameters such as maximum power capacity, energy storage capacity, and charging efficiencies, to reflect realistic conditions. Results indicate that real-time SOC monitoring, coupled with adaptive charging strategies, can mitigate peak demands and enhance the system's responsiveness to fluctuating loads. This paper emphasizes the critical role of real-time data analysis in the effective management of energy resources in existing parking lots and lays the groundwork for developing intelligent grid-supportive frameworks in the context of growing EV adoption.