This paper presents a literature review on optimal control techniques for energy management and control of microgrids. A classifi- cation of references linked with the design and development of optimal energy management systems (EMS) is made, considering mainly the objective functions to be solved and also the optimization techniques used for solving optimal control problems (OCPs) related with reliable operations of microgrids. A hierarchical control architecture for the development of EMS is the most com- monly found in literature, which implies the necessity of a telecommunications infrastructure to communicate a distributed control layer with an upper layer, where the optimization of the microgrid operation is done. Typically, this layer is developed at an entity called microgrid central controller (MGCC). A general architecture for optimal EMS is provided and analyzed in detail, as well as its future perspectives

This paper presents a methodology on the design of an optimal predictive control scheme applied to an islanded microgrid. The controller manages the batteries energy and performs a centralized load shedding strategy to balance the load and generation within the microgrid, and to keep the stability of the voltage magnitude. A nonlinear model predictive control (NMPC) algorithm is used for processing a data set composed of the batteries state of charge, the distributed energy resources (DERs) active power generation, and the forecasted load. The NMPC identifies upcoming active power unbalances and initiates automated load shedding over noncritical loads. The control strategy is tested in a medium voltage distribution system with DERs. This control strategy is assisted by a distribution monitoring system, which performs real-time monitoring of the active power generated by the DERs and the current load demand at each node of the microgrid. Significant performance improvement is achieved with the use of this control strategy over tested cases without its use. The balance between the power generated by the DERs and the load demand is maintained, while the voltage magnitude is kept within the maximum variation margin of ±5% recommended by the standard ANSI C84.1-1989.

This paper presents the methodology of design of a nonlinear robust controller for attitude regulation and its implementation in an experimental platform of an unmanned aerial vehicle (UAV) quadrotor. Details on the kinematic and dynamic modeling based on the Euler-Lagrange formalism are provided, as well as the particulars of the design of a nonlinear robust H-infinity PID controller to regulate the rotational moments. The performance and effectiveness of the proposed controller are tested in a simulation and an experimental platform. The performance of the proposed controller is compared with a conventional PID controller by using the integral square error (ISE) as performance parameter. Experimental results help to demonstrate the correct operation of the system for real-time applications in the presence of unmodeled dynamics and the uncertainties of the parameters.

This paper presents the design of a sliding mode control (SMC) for trajectory tracking of an unmanned aerial vehicle (UAV), quadrotor. A simplified model of the quadrotor is used for the controller design. The robustness of the controller is verified through simulations, and also through data analysis from the experiments in the 3DR Arducopter platform. The SMC algorithms are implemented in a microcontroller that communicates with a human machine interface (HMI), which monitors the behavior and stability of the state variables. The results show effectiveness of the control technique for maintaining stability in the quadrotor under different operating scenarios.

Integrating renewable energy sources into power systems is crucial for achieving global decarbonization goals, with wind energy experiencing the most growth due to technological advances and cost reductions. However, large-scale wind farm integration presents challenges in balancing power generation and demand, mainly due to wind variability and the reduced system inertia from conventional generators. This review offers a comprehensive analysis of the current literature on wind power forecasting and frequency control techniques to support grid-friendly wind energy integration. It covers strategies for enhancing wind power management, focusing on forecasting models, frequency control systems, and the role of energy storage systems (ESSs). Machine learning techniques are widely used for power forecasting, with supervised machine learning (SML) being the most effective for short-term predictions. Approximately 33% of studies on wind energy forecasting utilize SML. Hybrid frequency control methods, combining various strategies with or without ESS, have emerged as the most promising for power systems with high wind penetration. In wind energy conversion systems (WECSs), inertial control combined with primary frequency control is prevalent, leveraging the kinetic energy stored in wind turbines. The review highlights a trend toward combining fast frequency response and primary control, with a focus on forecasting methods for frequency regulation in WECS. These findings emphasize the ongoing need for advanced forecasting and control methods to ensure the stability and reliability of future power grids.