The proliferation of community energy storage systems (CESSs) necessitates effective energy management to address financial concerns. This paper presents an efficient energy management scheme for heterogeneous power consumers by analyzing various cost factors relevant to the power system. We propose an authority transaction model based on a multi-leader multi-follower Stackelberg game, demonstrating the existence of a unique Stackelberg equilibrium to determine optimal bidding prices and allocate authority transactions. Our model shows that implementing a CESS can reduce total electricity costs by 16% compared to the conventional case that does not account for authority transactions among CESS users, highlighting its effectiveness in practical power systems.

The integration of Internet of Things (IoT) technologies into modern vehicles has facilitated the emergence of the Internet of Vehicles (IoV), revolutionizing the automotive industry by enabling advanced connectivity and data-driven functionalities. Among the many applications made possible by these advancements, accurate driver identification has become essential for enhancing vehicle security, personalizing user experiences, and supporting usage-based services. However, existing driver identification methods often struggle to maintain accuracy across various road environments, as driving behavior varies with road characteristics. This paper introduces a novel driver identification framework that dynamically adapts to varying road geometries by integrating road curvature analysis to improve both accuracy and robustness across diverse road environments. Using global positioning system (GPS) sensor data, road curvature is estimated using the Menger curvature method, and road curvature segments are classified into distinct types through k-means clustering with dynamic time warping. Separate driver identification models are then developed for each road type using machine learning algorithms, including Random Forest, XGBoost, and LightGBM, to capture the subtle differences in driving behavior with varying road types. Extensive experiments using real-world driving data demonstrate that the proposed method achieves up to 86.02% accuracy on unseen road environments and outperforms existing methods by up to 18.64%. These experimental results highlight the improved generalization capability and comprehensive validation of the proposed model, emphasizing its effectiveness for robust driver identification in realistic scenarios.

Container relocations are a major source of inefficiency in storage yard operations because they increase yard crane movements and delay subsequent handling tasks. In multi-block container yards, the total number of relocations depends on the interaction between block assignment and container rearrangement planning. Block assignment determines the container sequence processed by each block, while rearrangement planning determines how that sequence is handled within the block. Most existing studies have addressed these decisions separately, limiting their ability to capture the relocation effects arising from this interaction. This paper proposes a relocation-aware planning framework that coordinates block assignment and container rearrangement planning. The framework employs a queue-guided rearrangement policy and a cost-balanced assignment rule. The rearrangement policy is trained using reinforcement learning to generate container movement plans based on the current block state and the scheduled container sequence assigned to the block. The trained policy is then used to estimate the relocation consequences of candidate block assignments. Based on these estimates, the assignment rule evaluates candidate blocks by considering both the relocation cost of the current assignment and a lookahead-based estimate of its effect on subsequent rearrangement operations. Computational experiments under different block layouts, occupancy levels, and queue lengths show that the proposed framework reduces total relocations compared with baseline approaches. The results further indicate that coordinating assignment and rearrangement planning provides greater benefits in larger and more congested yard configurations, where assignment decisions have stronger downstream effects on rearrangement workload.