Abstract
Renewable Energy Communities (RECs) are emerging as key enablers of decentralized, sustainable, and consumer-driven energy systems. Beyond environmental benefits, RECs possess significant potential to enhance resilience against extreme weather, price volatility, and infrastructure fragility. This article integrates resilience and relia bility constraints directly into the planning and operation of RECs, assessing their impact on system cost, sizing, and dispatch. Two optimization models are developed: a design model that sizes community assets (PV and BESS) using varying resilience indicators, and an operational model that minimizes costs while monitoring reliability. The analysis introduces two resilience metrics, deterministic hourly autonomy and average autonomy, and eval uates them using real-world data from the Caxias Living Lab. Results demonstrate that average resilience can be increased with minimal cost impacts due to non-linear trade-offs, whereas strict hourly resilience requires signifi cant storage investment. Furthermore, a Value of Lost Load (VoLL) reliability indicator is shown to cost-effectively trigger maintenance events. This framework offers actionable guidance for designing sustainable, adaptive, and economically viable energy communities.

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
The increasing deployment of individual and collective self-consumption systems is reshaping Energy Management Systems (EMSs) under evolving regulatory frameworks. This paper presents a techno-economic comparison between a centralized EMS and a decentralized EMS for flexible resources dispatching and sharing under collective self-consumption schemes. The centralized EMS is formulated as a Mixed-Integer Non-Linear Programming (MINLP) optimization problem, whereas the decentralized EMS employs a rule-based algorithm that requires no information exchange among members. Both strategies have been evaluated under the Spanish regulatory framework, a) using fixed allocation coefficients and b) introducing improvements borrowed from the Portuguese regulation, selected as a benchmark due to its advanced regulatory maturity. For the case of ex-ante allocation coefficients computation, an optimization-based methodology is proposed combining Mixed-Integer Linear Programming (MILP) with data clustering techniques. Results indicate that both EMS architectures achieve comparable energetic performance. The centralized EMS achieves the highest levels of self-consumption, self-sufficiency and energy sharing, particularly when proportional allocation coefficients are used, while the decentralized EMS performs closely. From an economic perspective, the centralized EMS provides the highest cost reductions, while the decentralized EMS yields lower economic savings but with significantly less computational effort, with runtimes up to eighteen times shorter. These findings highlight a clear trade-off between economic optimality and computational efficiency, positioning decentralized EMS solutions as a scalable and privacy-preserving alternative for individual self-consumers transitioning to collective self-consumption schemes in evolving regulatory frameworks.

Abstract
The need to improve the flexibility and dynamism of the Electric Power Systems (EPS) has driven the development and integration of new devices. Among these, FACTS controllers are particularly notable for their ability to regulate multiple system variables. Incorporating FACTS into the grid requires integrating their electrical models into the analysis and operational tools of the EPS, ensuring precise monitoring and effective system control. This article presents novel steady-state models for FACTS controllers, specifically designed for decoupled state estimation methods. The framework updates the algorithm decoupled and model decoupled state estimators and introduces the modified algorithm decoupled estimator, which offers enhanced robustness and convergence. These improvements are validated through theoretical analysis and simulations. The methodology introduces new state variables and decoupled nonlinear functions to represent FACTS controllers, enabling seamless integration into decoupled estimation frameworks. The study assesses the effectiveness of bad data processing using the Largest Normalized Residual Test (LNR-Test), ensuring robustness under decoupled FACTS modeling. Simulations on the IEEE 30-bus and IEEE 118-bus test systems include shunt, series, series-shunt, and multiple FACTS controllers, as well as single and multiple bad data. Results demonstrate the accuracy and effectiveness of the decoupled estimators with FACTS controllers and confirm the practical applicability of LNR-Test within the proposed approaches.

Abstract
This work examines the impact of time-slice resolution on renewable energy integration outcomes in long-term energy planning using OSeMOSYS. The analysis focuses on the Portuguese power system over the period 2024-2050, analysed under three scenarios: one coarse (six time slices) and two finer (twelve and twenty-four time slices), all evaluated under strict cost optimisation. Key outputs include system costs, technology deployment, dispatch behaviour, and emissions trajectories. Results indicate that temporal structure directly shapes long-term planning outcomes. The coarse scenario smooths short-term variability and promotes investment in technologies, particularly solar photovoltaic and wind, while reducing the share of natural gas combined cycle (NGCC), presenting an optimistic decarbonisation pathway. Finer resolutions capture intra-day and seasonal fluctuations, revealing operational constraints, increasing NGCC capacity (1.3 to 2 GW), and moderating Solar PV and wind output. Overall, the findings demonstrate that temporal resolution is not a secondary modelling choice but a critical determinant of the credibility of long-term energy planning. Appropriate temporal segmentation is therefore essential for robust evaluation of policy options, system flexibility requirements, and sustainable energy transition strategies © 2026 IEEE.

Abstract
The increasing reliance on Networked Microgrids (NMGs) for decentralized energy management introduces unprecedented cybersecurity risks, particularly in the context of False Data Injection Attacks (FDIA). While traditional FDIA studies have primarily focused on network-based intrusions, this work explores a novel cyber-physical attack vector leveraging Uncrewed Aerial Vehicles (UAVs) to execute sophisticated cyberattacks on microgrid operations. UAVs, equipped with communication jamming and data spoofing capabilities, can dynamically infiltrate microgrid communication networks, manipulate sensor data, and compromise power system stability. This paper presents a multi-objective optimization framework for UAV-assisted FDIA, incorporating Non-dominated Sorting Genetic Algorithm III (NSGA-III) to maximize attack duration, disruption impact, stealth, and energy efficiency. A comprehensive mathematical model is formulated to capture the intricate interplay between UAV operational constraints, cyberattack execution, and microgrid vulnerabilities. The model integrates flight path optimization, energy consumption constraints, signal interference effects, and adaptive attack strategies, ensuring that UAVs can sustain long-duration cyberattacks while minimizing detection risk. Results indicate that UAV-assisted cyberattacks can induce power imbalances of up to 15%, increase operational costs by 30%, and cause voltage deviations exceeding 0.10 p.u.. Furthermore, analysis of attack success rates vs. detection mechanisms highlights the limitations of conventional rule-based anomaly detection, reinforcing the need for adaptive AI-driven cybersecurity defenses. The findings underscore the urgent necessity for advanced intrusion detection systems, UAV tracking technologies, and resilient microgrid architectures to mitigate the risks posed by airborne cyber threats.