Articles
Publication Date: 2026
Energy Conversion and Management (0196-8904)348
This paper proposes a comprehensive framework for cyber-resilient optimal energy management system (EMS) in smart grids. A complete 24-hour closed-loop operational cycle is modeled and simulated. It begins with data measurement via a real-time updated nonlinear Digital Twin, followed by transmission through SCADA/RTUs, detection of cyberattacks, accurate reconstruction of corrupted data, and finally EMS. The outputs of EMS are then sent back to the Digital Twin, which is dynamically updated to reflect the actual network conditions and generate accurate synthetic measurements for the next hour. This entire process is embedded within a 24-hour rolling optimization scheme. The EMS includes a power flow model integrated with various distributed energy resources (DERs), such as renewables, diesel generator, battery, electric vehicle, and controllable loads. It also incorporates and ensures all technical, security, and dynamic constraints of the grid and DERs. Unlike previous studies that focus only on isolated aspects such as attack detection, data estimation, or day-ahead energy management, this work implements the entire process in a unified and dynamic framework. The model functions effectively in networks where PMUs are unavailable, as is the case in most real-world distribution grids, because it is designed solely based on RTU and SCADA data. Detection and reconstruction of coordinated attacks rely on physics-based recalculation methods, utilizing grid topology and data from neighboring buses to improve accuracy. The proposed model is validated on the IEEE 33-bus test system, successfully detecting various attack scenarios targeting different parameters, locations, and times. It reconstructs the correct values with high precision and optimizes the network operation accordingly. This 24-hour rolling simulation demonstrates the practicality and robustness of the approach in enabling secure, cost-effective, and resilient energy management in modern smart grids. © 2025 Elsevier Ltd.
Publication Date: 2026
Energy Conversion and Management: X (25901745)30
This paper proposes a real-time energy management optimization model for active distribution networks. In this model, the active distribution network connected to distributed energy resources exchanges data iteratively with a centralized energy management and control system at each time interval. Network-level parameters, including bus voltages and active and reactive power injections, are measured and sent to the central control system, where data are analyzed for variation, validation, noise detection, and cyberattack identification. Based on this analysis, the system performs rolling optimization for upcoming time-intervals and sends updated operational schedules back to the network, ensuring that generation units and controllable loads operate according to the newest optimal plan. As a result, the optimization of grid performance is carried out at every time interval, and the grid along with local generation–consumption resources are scheduled to operate according to the latest changes in grid parameters such as prices and power loads. Such adaptive scheduling guarantees both optimal and robust performance across all upcoming time periods. During data exchange, measurements may be corrupted by noise or falsified by stealthy false data injection (FDI) attacks with amplitudes close to measurement noise (low-magnitude FDI), making them difficult to detect. To address this challenge, several indices are proposed, including the Bus Current Imbalance Index (BCII), the Residual Current Magnitude Index (RCMI), and the Residual Current Angle Index (RCAI), which can effectively distinguish between noisy and falsified data while identifying the location, start time, and duration of cyberattacks. The results indicate that under varying input parameters such as electricity price, solar irradiance, and network load, the rolling optimization updates schedules and provides an optimal plan for upcoming hours. For example, at hour 6, the diesel generator schedule is adjusted for hours 6–24, and at hour 15, a new schedule is set for hours 15–24. Similarly, the battery plan is updated throughout the day; discharging initially scheduled at hours 17 and 19 is shifted to hours 18 and 19. These operational adjustments impacts operational cost. At hour 6 the total cost rises by 153.34%, whereas at hour 20 the total cost drops by 30.26%. The results also show that the model effectively detects small-magnitude FDI attacks under noise, with amplitudes equal to or 1–3 times the noise. Sensitivity analysis confirms that the proposed index consistently detects attacks under noise levels ranging from 1% to 5%. © 2026 The Author(s)
