Microgrids are gaining considerable attention as a promising solution for integrating distributed energy resources and enhancing grid resilience. Model predictive control (MPC) has emerged as a powerful control strategy for microgrids due to its ability to handle complex dynamics and optimization problems. This study aims to conduct a comprehensive assessment of MPC applications and evaluate their overall effectiveness across various microgrid functionalities. Previous studies have not collectively examined MPC and have not explored its advantages and disadvantages in the microgrid. This study systematically categorizes and addresses this gap in the existing literature. An extensive list of suitable research papers was compiled from the Web of Science and analyzed, considering the method of the studies, main focus and objectives, publication year, and findings. Moreover, this research incorporates co-occurrence keyword analysis, covering MPC applications, systematic reviews, microgrids, and review articles. The visualization and analysis of the data obtained from the Web of Science database were conducted using VOS viewer. This discussion includes approaches that help electrical engineers evaluate the benefits and disadvantages of MPC within the microgrid setup. This knowledge enables electrical practitioners to select the appropriate methods for providing a resilient and reliable ecosystem.

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system’s security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators.

In the past, providing an online and real-time response to cyber–physical attacks in large-scale power microgrids was considered a fundamental challenge by operators and managers of power distribution networks. To address this issue, an innovative framework is proposed in this paper, enabling real-time responsiveness to cyberattacks while focusing on the techno-economic energy management of large-scale power microgrids. This framework leverages the large change sensitivity (LCS) method to receive immediate updates to the system’s optimal state under disturbances, eliminating the need for the full recalculation of power flow equations. This significantly reduces computational complexity and enhances real-time adaptability compared to traditional approaches. Additionally, this framework optimizes operational points, including resource generation and network reconfiguration, by simultaneously considering technical, economic, and reliability parameters—a comprehensive integration often overlooked in recent studies. Performance evaluation on large-scale systems, such as IEEE 33-bus, 69-bus, and 118-bus networks, demonstrates that the proposed method achieves optimization in less than 2 s, ensuring superior computational efficiency, scalability, and resilience. The results highlight significant improvements over state-of-the-art methods, establishing the proposed framework as a robust solution for real-time, cost-effective, and resilient energy management in large-scale power microgrids under cyber–physical disturbances.

As renewable energy sources become more widespread and energy consumption continues to grow, there is an urgent requirement for smarter, more flexible control methods to manage microgrids (MGs) effectively. This study proposes a data-driven nonlinear model predictive control (NLMPC) framework for optimized MG operation, emphasizing energy storage system (ESS) integration. Effective MG management is crucial given increasing renewable penetration and energy demands. This framework coordinates distributed generation (DG) units, including rotating and non-rotating resources, with a battery ESS in a dynamic MG environment. Leveraging Gaussian Process Regression (GPR), the framework accurately models the complex dynamics of both DG units and the ESS. Unlike traditional model-based approaches, GPR learns system behavior from operational data, enabling precise performance prediction under varying conditions. This accuracy is crucial for optimized resource dispatch and efficient MG operation. GPR models capture ESS charging/discharging characteristics, efficiency, and state-of-charge (SOC) dynamics for informed ESS utilization. To address renewable energy uncertainties, Monte Carlo simulations are incorporated. This allows robust evaluation of the control strategy under various scenarios, ensuring MG stability and reliability despite fluctuating renewable generation. By considering these uncertainties, the NLMPC controller proactively manages DG and ESS dispatch, mitigating forecast errors and maximizing renewable energy use. The framework aims to achieve optimal power flow, balancing supply and demand while respecting operational constraints. This includes constraints on DG units, the ESS (SOC limits, charge/discharge rates), and overall MG operation (voltage and frequency stability). The NLMPC controller dynamically adjusts DG and ESS setpoints to minimize costs, maximize renewable energy use, and ensure MG stability and reliability. Simulation results demonstrate the framework’s effectiveness. Significant cost savings (approximately 39.2% compared to Conventional MPC and 41.5% compared to Adaptive MPC) and voltage stability improvements (28.57% and 52.38% respectively) are achieved. These improvements stem from accurate system dynamics modeling, robust uncertainty handling, and coordinated DG and ESS control.

This paper proposes an innovative transient stability index (TSI) designed to enhance the real-time assessment of power system stability. The TSI integrates a corrected kinetic energy approach with a modified equal area criterion, offering a novel methodology for evaluating transient stability margins in power systems. Unlike traditional methods, the proposed TSI operates without relying on post-fault data, making it particularly suitable for online applications. A structure-preserving model is utilized to represent the power network, accounting for key factors such as controller behavior during transient events. Additionally, a new statistical classification method is introduced to efficiently determine the individual contribution of generators to the overall system stability. The effectiveness of the proposed approach is validated through comprehensive case studies on IEEE 9-bus and IEEE 39-bus systems. The simulation results confirm that the proposed method provides accurate, real-time insights into the transient stability margins of power systems, demonstrating its practical advantages in both analysis and operation.