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Abstract Accurately forecasting electric vehicle (EV) charging demand is critical for managing peak loads and ensuring grid stability in regions with increasing EV adoption. Residential household peak energy usage and EV charging patterns vary significantly across areas, influenced by geographic accessibility, sociodemographic factors, charging preferences, and EV attributes. Averaging data across regions can overlook these differences, leading to an underestimation of charging demand disparities and risking grid overload during peak periods. This study introduces a spatiotemporal trip chain-based EV charging schedule simulation method to address these challenges. The methodology integrates sociodemographic and geographic data with the large language model to generate trip chains, which serve as the basis for simulating EV charging schedules and aggregating regional energy loads to forecast peak demand. A case study of Pescadero, CA employs synthetic profiles, derived from Census statistics, to model local households as EV owners and validate the practical applicability of this approach. The results emphasize the representativeness of the trip chain generation model and the effectiveness of the EV charging schedule simulation model in accurately forecasting energy consumption patterns and assessing peak load impacts. By combining sociodemographic and geographic insights, this study provides a robust tool for evaluating the peak load impacts of EV charging. 

Identifying IoT devices is crucial for network monitoring, security enforcement, and inventory tracking. However, most existing identification methods rely on deep packet inspection, which raises privacy concerns and adds computational complexity. Moreover, existing works overlook the impact of wireless channel dynamics on the accuracy of layer-2 features, thereby limiting their effectiveness in real-world scenarios. In this work, we define and use the latency of specific probe-response packet exchanges, referred to as "device latency," as the main feature for device identification. Additionally, we reveal the critical impact of wireless channel dynamics on the accuracy of device identification based on device latency features. Specifically, this work introduces "accumulation score" as a novel approach to capturing fine-grained channel dynamics and their impact on device latency when training machine learning models. We implement the proposed methods and measure the accuracy and overhead of device identification in real-world scenarios. The results confirm that by incorporating the accumulation score for balanced data collection and training machine learning algorithms, we achieve an F1 score of over 97% for device identification, even amidst wireless channel dynamics, a significant improvement over the 75% F1 score achieved by disregarding the impact of channel dynamics on data collection and device latency. 

In smart grids, two-way communication between end-users and the grid allows frequent data exchange, which on one hand enhances users' experience, while on the other hand increase security and privacy risks. In this paper, we propose an efficient system to address security and privacy problems, in contrast to the data aggregation schemes with high cryptographic overheads. In the proposed system, users are grouped into local communities and trust-based blockchains are formed in each community to manage smart grid transactions, such as reporting aggregated meter reading, in a light-weight fashion. We show that the proposed system can meet the key security objectives with a detailed analysis. Also, experiments demonstrated that the proposed system is efficient and can provide satisfactory user experience, and the trust value design can easily distinguish benign users and bad actors.