Energies

This paper proposes and validates a method for assessing the resilience of cyber–physical microgrids integrating Photovoltaic (PV) generation and Battery Energy Storage Systems (BESS). The approach combines two operational performance indicators—Voltage Deviation Index (VDI) and Energy Not Supplied (ENS)—with a composite resilience index that captures recovery dynamics following physical and cyber disturbances. The method is implemented in MATLAB Simulink R2022b on the IEEE 33-bus feeder, with PV at bus 6 and a BESS at bus 18. Two stress scenarios are analyzed: (i) loss of the main supply at bus 2 and (ii) a cyber-induced communication failure that triggers local (fallback) operation. Compared with the base case, the proposed strategy reduces VDI by approximately 27% and ENS by 12%, demonstrating significantly improved resilience without noticeable performance penalties.

In gas turbine fire-resistant oil systems, valve actuations induce transient pressure fluctuations and the water hammer effect, causing pressure oscillations and structural vibrations. This study uses a coupled CFD and transient structural simulation to analyze the effects of different valve strategies on pressure wave propagation and structural response. Results show that a higher valve opening rate leads to a more significant water hammer effect, increasing structural deformation and stress. The maximum equivalent stress was verified at 201.9 MPa, maintaining a 30% safety margin and meeting American Society of Mechanical Engineers (ASME) B31.3 requirements. Finally, a “slow-fast-slow” (S-shaped) valve strategy is proposed to significantly improve the system’s pressure response characteristics, providing theoretical and engineering guidance for safe operation.

To address the issue of inaccurate load forecasting affecting the effectiveness of minimum demand scheduling in railway traction stations, this study introduces a multi-objective grey wolf optimizer (MOGWO) to jointly optimize the parameters of variational mode decomposition (VMD) and long short-term memory network (LSTM) within the forecasting framework. The proposed MOGWO-VMD-LSTM model enhances the data decomposition capability of VMD and improves LSTM training, prediction accuracy, and inverse normalization reconstruction. Using a 10-day load dataset from a traction station, the model’s performance is compared against LSTM and VMD-LSTM baselines. Simulation results demonstrate superior performance in terms of mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) metrics. Application of the forecasting results to traction station scheduling reduces the single-peak power purchase from 22.279 MW to 20.052 MW, achieving a 9.995% reduction, indicating strong practical potential.

Frequent extreme-weather events pose severe challenges to the secure and economical operation of power systems with high renewable energy penetration. To strengthen grid resilience against such low-probability, high-impact events while maintaining good performance under normal conditions, this paper proposes an optimal energy storage allocation method for power systems with high-wind-power penetration. We first identify two representative extreme wind power events and develop a risk assessment model that jointly quantifies load-shedding volume and transmission-line security margins. On this basis, a multi-scenario joint siting-and-sizing optimization model is formulated over typical-day and extreme-day scenarios to minimize total system cost, including annualized investment cost, operating cost, and risk cost. To solve the model efficiently, a two-stage hierarchical solution strategy is designed: the first stage determines an investment upper bound from typical-day scenarios, and the second stage optimizes storage allocation under superimposed extreme-day scenarios within this bound, thereby balancing operating economy and extreme-weather resilience. Simulation results show that the proposed method reduces loss-of-load under extreme-weather scenarios by 32.46% while increasing storage investment cost by only 0.18%, significantly enhancing system resilience and transmission-line security margins at a moderate additional cost.