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 frameworks 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 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.