By taking into account the social behaviors of renewable distributed generations (RDGs) and electric vehicles (EVs), the multi-operator AC/DC hybrid distribution system (HDS) has evolved into a cyber-physical-social system (CPSS), necessitating an appropriate architecture. In this paper, a distributed CPSS architecture is proposed for the multi-operator AC/DC HDS using a cloud-edge-device cooperation. The architecture incorporates a model that distinguishes different types of EVs, enabling the demonstration of the interaction among the cyber, physical, and social systems. To address issues of transmission delay and noisy, a Non-ideal Communication Alternating Direction Method of Multipliers is employed. The proposed framework consists of three stages: day-ahead economic dispatch, intra-day security dispatch, and real-time adjustment. This enables efficient dispatching of EVs based on economic and node voltage considerations while optimizing the performance of renewable distributed generators for each operator. Case studies are conducted to demonstrate the effectiveness of the CPSS structure in achieving distributed dispatching the HDS.

The output capacity of Hybrid Renewable Energy Systems (HRES) is crucial for dispatching plans and spinning reserve capacity, but it's constrained by renewable energy generator output and transmission tie-line capacity. Considering both dynamic line rating for tie-lines and the entire HRES is beneficial due to their susceptibility to micro weather conditions. Unfortunately, considering them collectively for capacity forecasting involves long-term regular fluctuations and short-term uncertainty changes in weather factors, which reduces prediction accuracy. Thus, a novel data-driven model is introduced in this paper to address the aforementioned issue. Initially, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed to segment the capacity sequence into high-, medium-, and low-frequency components. Subsequently, the high-frequency component, characterized by wind-induced randomness, is predicted using Newton-Raphson-Based Optimizer (NRBO) - Bidirectional Gated Recurrent Unit (BiGRU); the medium-frequency component, reflecting seasonal regularities, is forecasted using Seasonal Autoregressive Integrated Moving Average (SAIMA); and the smooth and periodic low-frequency component is anticipated using Multivariable Linear Regression (MLR). Finally, the predictions from these models are combined to derive the ultimate predictive value. Case studies demonstrate that comprehensive consideration of transmission tie-lines equipped with DLR, as well as HRES, can enhance the external output capability of HRES, especially during periods of abundant wind resources. The proposed data-driven model can capture high-frequency fluctuations, medium-frequency periodicity, and low-frequency trends in capacity to enhance prediction accuracy.

All-electric ships (AESs) with efficient direct current (DC) shipboard microgrids become a significant means to reduce carbon emissions. Large-scale energy storage systems (ESSs) can provide sufficient operating flexibility to mitigate power fluctuations for the shipboard microgrid system. However, the power supply capability of the shipboard battery can significantly vary during actual operation, potentially causing voltage instability in the shipboard microgrid system. This article proposes a large-signal stability analysis method that considers battery dynamics for the shipboard microgrid system. Firstly, a novel full-order shipboard microgrid system model is proposed to comprehensively capture system electrodynamics. Then, conservative stability conditions with battery peak current constraints are derived. Based on these conditions, the influences of battery dynamics on the large-signal stability of the system are revealed for the first time. The results show that the domain of attraction (DOA) of the system varies significantly with different battery states. To verify these influences in the application, a general model predictive control (MPC) method that considers battery dynamics is adopted. The simulation results demonstrate that the direct current (DC) voltage sags can be reduced by 11.27%, and the battery can always operate within a healthy power range.