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 Unmanned 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.

Abstract
The effective management of shared resources within energy communities poses a significant challenge, particularly when balancing renewable energy generation and fluctuating demand. This paper introduces a novel optimization framework that integrates people flow data, modeled using the Social Force Model (SFM), with energy management strategies to enhance the efficiency and sustainability of energy communities. By combining SFM with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the framework addresses multi-objective optimization problems, including minimizing energy costs, reducing user waiting times, and maximizing renewable energy utilization. The study employs synthesized data to simulate an energy community with shared facilities such as electric vehicle (EV) charging stations, communal kitchens, and laundry rooms. Results demonstrate the framework's ability to align energy generation with resource demand, reducing peak loads and improving user satisfaction. The optimization model effectively incorporates real-time behavioral dynamics, showcasing significant improvements in renewable energy utilization-reaching up to 88% for EV charging stations-and cost reductions across various scenarios. This research pioneers the integration of people flow modeling into energy optimization, providing a robust tool for managing the complexities of energy communities.

Abstract
The inclusion and arrangement of protection devices within the LV distribution network often neglected. By exemption of protection devices during network modelling, may result in overestimation of reliability performances. Detail network representation of UK LV residential model is used to assess network reliability performance. The analytical and improved Monte-Carlo Simulation (MCS) approaches are used to estimate system-related reliability indices.

Abstract
This paper presents a reliability-based approach for the design and deployment of an energy management system (EMS) by using 'smart' applications, such as energy storage (ES), to control battery power output in residential dwellings, and thus improve distribution-network reliability performance. The state of charge (SOC) of the battery system is designed based on time-varying electricity tariff, load demand and solar photovoltaic (PV) generation data to investigate a realistic test-case scenario. Additionally, a typical MV/LV urban distribution system is fully modelled and scripted to investigate the potential benefits that 'smart' interventions can offer to customers' quality of power supply. In this research, Monte-Carlo simulation method is further developed to include the time-variation of electricity demand profiles and failure rates of network components. Accordingly, the reliability-based effects from SOC variation in batteries are compared with an uncontrolled microgeneration (MG) scenario, by using different PV penetration levels to justify the value of control. The benefits are assessed through standard reliability indices measuring frequency and duration of power interruptions and most importantly, the energy not supplied to customers during sustained interruptions.

Abstract
This paper, which is part one of a two-part series, presents a general methodology for reducing system complexity by calculating the electrical and reliability equivalent models of low and medium voltage distribution networks. These equivalent models help to reduce calculation times while preserving the accuracy assessment of power system reliability performance. The analysis is applied to typical UK distribution systems, which supply four generic load sectors with different networks and demand compositions (residential, commercial and industrial). This approach allows for a direct correlation between reliability performance and network characteristics, while assessing the most representative aggregate values of failure rates and repair times of power components at each load sector. These are used in the Part 2 paper for assessing the potential benefits of energy storage and demand-side resources on the reliability performance of different generic distribution networks. © 2013 IEEE.

Abstract
This paper, which is the second part of a two-part series, considers the influence of distributed energy resource functionalities on reliability performance of active networks. The reliability and network equivalent models defined in the Part 1 paper are used to assess the potential improvements that different demand-side management and energy storage schemes will have on the frequency and duration of customer interruptions. Particular attention is given to energy-related reliability indices which measure the energy and power not supplied to residential and commercial customers. A new theoretical interruption model is also introduced for a more accurate correlation between the different low-voltage and medium-voltage demand profiles and the time when both long and short interruptions are more likely to occur. © 2013 IEEE.