In recent years, microgrid (MG) deployment has significantly increased, utilizing various technologies. MGs are essential for integrating distributed generation into electric power systems. These systems’ economic dispatch (ED) aims to minimize generation costs within a specific time interval while meeting power generation constraints. By employing ED in electric MGs, the utilization of distributed energy resources becomes more flexible, enhancing energy system efficiency. Additionally, it enables the anticipation and proper utilization of operational limitations and encourages the active involvement of prosumers in the electricity market. However, implementing controllers and algorithms for optimizing ED requires the independent handling of constraints. Numerous algorithms and solutions have been proposed for the ED of MGs. These contributions suggest utilizing techniques such as particle swarm optimization (PSO), mixed-integer linear programming (MILP), CPLEX, and MATLAB. This paper presents an investigation of the use of model predictive control (MPC) as an optimal management tool for MGs. MPC has proven effective in ED by allowing the prediction of environmental or dynamic models within the system. This study aims to review MGs’ management strategies, specifically focusing on MPC techniques. It analyzes how MPC has been applied to optimize ED while considering MGs’ unique characteristics and requirements. This review aims to enhance the understanding of MPC’s role in efficient MG management, guiding future research and applications in this field.

Currently, the global use of electric vehicles is still low; however, a significant increase is expected in the coming years. Determining the optimal location of charging stations in distribution systems can influence the increased adoption of this technology in transportation, as it contributes to the proper functioning of distribution networks. There are several optimization methods, which can be classified into exact, heuristic, and metaheuristic methods, each with different characteristics and applications. This article presents a literature review of the main optimization methods currently used to determine the location of charging stations in distribution systems. It concludes that metaheuristic optimization methods are the most widely used. In addition, the review identifies current research gaps, particularly the limited use of real EV demand data and the lack of stochastic approaches to represent demand variability. The main contribution of this work lies in emphasizing the importance of incorporating stochastic methods to adequately address the uncertainty of EV demand in distribution networks.

The transition from centralized to decentralized power generation presents a significant opportunity to enhance grid resilience and energy independence, particularly in regions with unstable grids or vulnerability to natural disasters. Distributed Generation (DG), particularly solar photovoltaics, has become a key component of modern power systems, especially in distribution networks. However, its increasing penetration at both utility and distributed levels poses challenges related to voltage, frequency, and angular stability, requiring advanced monitoring and control strategies. In this context, energy storage systems (ESS) emerge as a crucial player, playing a significant role in mitigating these challenges. ESS stabilizes power supply fluctuations and enhances grid reliability, providing a safety net for the intermittent nature of renewable energy sources. This study examines the impact of DG on the stability and operation of the National Interconnected Electric System (SENI) of the Dominican Republic, with a focus on integrating ESS at both utility and distribution levels. Using DIgSILENT, future scenarios (2027) with high renewable penetration were simulated. The methodology included data collection, modeling of actual automatic load shedding (EDAC) events from 2024, and the simulation of three scenarios: without storage, with utility-scale storage, and with both utility-scale and distributed storage. The results indicate that energy storage systems (ESS) significantly enhance system stability, reducing both the total disconnected load and the number of EDAC steps. In the absence of storage, 640 MW were disconnected; with utility-scale storage, this was reduced to 540 MW, and the addition of distributed storage further decreased the disconnected load to 440 MW while also delaying EDAC activation times. Findings highlight the critical need for ESS investments and grid flexibility mechanisms to accommodate increasing renewable penetration, particularly in Small Island Developing States (SIDS). Future research should optimize ESS deployment and conduct detailed economic analyses to support the development of sustainable power systems.

The rapid dynamic responses of predictive control algorithms are widely acknowledged. However, achieving accurate steady-state reference tracking hinges not just on a precise mathematical model of the system but also on its parameters. This document presents a predictive control scheme augmented with integral state feedback tailored to a photovoltaic (PV) application. In scenarios with uncertain system parameters, steady-state errors can particularly impact reactive power regulation, where the absence of an integral term in the loop exacerbates this issue. The robustness and sensitivity of both predictive control and the proposed enhanced predictive controller are thoroughly examined. Simulation and experimental results are included to validate the effectiveness of this approach.

Power systems are complex and often subject to faults, requiring accurate mathematical models for a thorough analysis. Traditional time-domain models are employed to evaluate the dynamic response of power system elements during transmission system faults. However, only the positive sequence components are considered for unbalanced faults, so the small-signal stability analysis is no longer accurate when assuming balanced conditions for asymmetrical faults. The dynamic phasor approach extends traditional models by representing synchronous machines with harmonic sequence components, making it suitable for an unbalanced condition analysis and revealing dynamic couplings not evident in conventional methods. By modeling electrical and mechanical equations with harmonic sequence components, the study implements an eigenvalue sensitivity analysis and participation factor analysis to identify the variable with significant participation in the critical modes and consequently in the dynamic response of synchronous machines during asymmetric faults, thereby control strategies can be proposed to improve system stability. The article validates the dynamic phasor model through simulations of a single-phase short circuit, demonstrating its accuracy and effectiveness in representing the transient and dynamic behavior of synchronous machines, and correctly identifies the harmonic sequence component with significant participation in the critical modes identified by the eigenvalue sensitivity to the rotor angular velocity and rotor angle.