This paper is a research article for finding the optimal control of smart power substations for improving the network parameters and reliability. The included papers are the most essential and main studies in the field, which propose a different approach to reach the best performance in electrical power systems. The parameters for improvement are the ability for tracking of the reference signal, stabilizing the system, reducing the error in steady state and controlling the behavior in transient state. The research focuses with the reaching a better transient stability considering voltage and frequency dynamic parameters. The optimal model for the control is focused on minimizing energy consumption but maintaining the controllable parameters, exploring some optimization techniques to find the optimal control, with of aim of minimizing the response time, the energy consumption, and maximizing the reliability by means of improving the controller to be more robust.

This paper addresses the challenges in forecasting electrical energy in the current era of renewable energy integration. It reviews advanced adaptive forecasting methodologies while also analyzing the evolution of research in this field through bibliometric analysis. The review highlights the key contributions and limitations of current models with an emphasis on the challenges of traditional methods. The analysis reveals that Long Short-Term Memory (LSTM) networks, optimization techniques, and deep learning have the potential to model the dynamic nature of energy consumption, but they also have higher computational demands and data requirements. This review aims to offer a balanced view of current advancements and challenges in forecasting methods, guiding researchers, policymakers, and industry experts. It advocates for collaborative innovation in adaptive methodologies to enhance forecasting accuracy and support the development of resilient, sustainable energy systems.

This article describes the development of an optimal and predictive energy management system (EMS) for a microgrid with a high photovoltaic (PV) power contribution. The EMS utilizes a predictive long-short-term memory (LSTM) neural network trained on real PV power and consumption data. Optimal EMS decisions focus on managing the state of charge (SoC) of the battery energy storage system (BESS) within defined limits and determining the optimal power contributions from the microgrid components. The simulation utilizes MATLAB R2023a to solve a mixed-integer optimization problem and HOMER Pro 3.14 to simulate the microgrid. The EMS solves this optimization problem for the current sampling time $(t)$ and the immediate sampling time $(t+1)$, which implies a prediction of one hour in advance. An upper-layer decision algorithm determines the operating state of the BESS, that is, to charge or discharge the batteries. An economic and technical impact analysis of our approach compared to two EMSs based on a pure economic optimization approach and a peak-shaving algorithm reveals superior BESS integration, achieving 59% in demand satisfaction without compromising the life of the equipment, avoiding inexpedient power delivery, and preventing significant increases in operating costs.

This paper proposes a three-layer model to find the optimal routing of an underground electrical distribution system, employing the PRIM algorithm as a graph search heuristic. In the algorithm, the first layer handles transformer allocation and medium voltage network routing, the second layer deploys the low voltage network routing and transformer sizing, while the third presents a method to allocate distributed energy resources in an electric distribution system. The proposed algorithm routes an electrical distribution network in a georeferenced area, taking into account the characteristics of the terrain, such as streets or intersections, and scenarios without squared streets. Moreover, the algorithm copes with scalability characteristics, allowing the addition of loads with time. The model analysis discovers that the algorithm reaches a node connectivity of 100%, satisfies the planned distance constraints, and accomplishes the optimal solution of underground routing in a distribution electrical network applied in a georeferenced area. Simulating the electrical distribution network tests that the voltage drop is less than 2% in the farthest node.

This paper attempts to elucidate the transformative integration of computational techniques within power systems, underscoring their critical role in enhancing system modeling, control, and the efficient integration of renewable energy. It breaks down the two-sided nature of technological progress, highlighting both gains in operational efficiency and new challenges such as real-time processing, data management, and cybersecurity. Through meticulous analysis of query-based research patterns and mathematical frameworks, this study delves into the balancing act between specificity and breadth in scholarly inquiries while evaluating the impact and evolution of research trends through citation analysis. The convergence of interests and transient research trends is evident, particularly in Artificial Intelligence and optimization. This comprehensive narrative anticipates a sophisticated trajectory for power systems, advocating for continuous innovation and strategic research to foster sustainable, resilient, and intelligent energy networks.

The paper presents the design and control strategy of an isolated DC microgrid, which is based on classical control techniques, predictive control and iterative algorithms. The design control parameters are maximum overshoot, settling time and voltage ripple. The strategy is designed to operate in two different modes, end-users minimum and maximum demand scenarios, and this is achieved through the incorporation of network dynamic loads. The control methodology developed allows to obtain a fast response of the design set points, and an efficient control for disturbance rejection. The simulation results obtained satisfy the proposed design guidelines by obtaining a maximum overshoot of 4.8%, settling time of 0.012 seconds and a voltage ripple of 0.1 percentage. The implemented system simulation was developed in Matlab-Simulink software.