Jorge Wilson González

Geomorphological stratification of the territory is rarely integrated explicitly into sizing models for hybrid solar–wind energy matrices, even though relief modulates both resources in opposite ways. This work proposes a four-stage model that combines geomorphological stratification by elevation ranges, resource-specific solar and wind characterization, a decision-tree surrogate model that reproduces the coupled physical simulation (PVWatts and PyWake) with high fidelity (R² > 0.99), and optimization through mixed-integer linear programming (MILP). MILP is subject to a hybridization constraint that prevents a single technology from occupying more than 80% of the installed area. The model was applied to a first location with four altitudinal strata and contrasted with a second one. For the first site, the wind resource was non-viable across all strata, and the model consistently selected exclusively solar configurations. At the second location, it selected a hybrid configuration with 73% wind and 27% solar energy, whose proportions are governed by the hybridization threshold. Uncertainty and sensitivity analyses showed that the technology selected is robust to the choice of representative point and to the out-of-sample behavior of the surrogate, whereas the wind non-viability of the first site depends on the reference height and shear assumptions of the reanalysis data.

The siting of utility-scale photovoltaic power plants requires the joint assessment of spatial feasibility, photovoltaic productivity, and electricity-demand conditions. This study proposes a two-stage GIS-based decision-support framework that separates the identification of a preferred photovoltaic site within each study area from the subsequent energy-demand assessment of the selected alternatives. In the first stage, spatial screening excludes protected areas, hydrographic restrictions, urbanized areas, incompatible land uses, and areas that do not satisfy the minimum contiguous land requirement. Proximity to suitable existing electrical infrastructure is then used as a strategic criterion to identify a preferred georeferenced photovoltaic location within each study area. In the second stage, the selected locations are evaluated according to location-specific photovoltaic power output, expected annual photovoltaic energy production, and a Demand Coverage Index (DCI). The methodology is applied to five study areas in Ecuador—Santa Elena, Catamayo, Manta, Quito, and Guayaquil—considering a standardized 100 MW photovoltaic plant. Expected annual photovoltaic energy production ranges from 125,210 to 159,560 MWh/year. Quito exhibits the highest annual photovoltaic energy production, whereas Catamayo achieves the highest DCI (0.4181), indicating that the estimated annual photovoltaic generation represents approximately 41.81% of the annual electricity demand associated with its distribution area. After normalization of photovoltaic energy production and demand coverage and the application of equal weights, Catamayo achieves the highest composite siting score (0.9847), followed by Santa Elena, Quito, Manta, and Guayaquil. The results demonstrate that maximizing photovoltaic energy production alone does not necessarily lead to the highest-ranked alternative when electricity-demand coverage is also considered. The proposed framework provides a transparent approach for integrating geospatial feasibility, photovoltaic productivity, and demand-oriented criteria in strategic utility-scale photovoltaic planning.

Geomorphological stratification of the territory is rarely integrated explicitly into sizing models for hybrid solar-wind energy matrices, even though relief modulates both resources in opposite ways. This work proposes a four-stage model that combines geomorphological stratification by elevation ranges, resource-specific solar and wind characterization, a decision-tree surrogate model that reproduces the coupled physical simulation (PVWatts and the Jensen wake model) with high fidelity (R² > 0.99), and optimization through mixed-integer linear programming (MILP). MILP is subject to a hybridization constraint that prevents a single technology from occupying more than 80% of the installed area. The model was applied to a first geographical location, including four altitudinal strata, and subsequently contrasted with a second one. For the first site, the wind resource was non-viable across all strata, and the model consistently selected exclusively solar configurations. At the second location, in contrast, it autonomously proposed the construction of a real hybrid matrix with a 73% wind and 27% solar contribution. The same procedure, without external reparameterization between territories, adapted the technological composition to the local resource of each territory, which constitutes the main evidence of the ability of the model to be generalized across territories with diverse resource conditions.

Traditionally, the dynamics of power systems have been governed by synchronous generators and their associated rotating masses. However, with the increasing penetration of renewable generation and power electronic interfaces, the inertia contributed by rotating machines has been gradually displaced. This makes it imperative to study alternative elements capable of mitigating the reduction in inertia in modern power systems. This article addresses the problem of optimal sizing and placement of Battery Energy Storage Systems to enhance frequency response in power grids through the application of optimization techniques such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Several inertia scenarios are analyzed, where the algorithms determine the optimal locations for Battery Energy Storage Systems units while minimizing the total installed Battery Energy Storage Systems capacity. As key contributions, this study models Battery Energy Storage Systems units, which emulate inertial responses based on the system’s Rate of Change of Frequency, and evaluates the impact of Battery Energy Storage Systems on frequency stability by analyzing parameters such as the frequency nadir, zenith, and steady-state frequency according to the installed Battery Energy Storage System’s size and location. A comparative analysis of the optimization scenarios shows that the Particle Swarm Optimization algorithm with 50% rotational inertia is the most efficient, requiring the lowest total installed power (277.11 MW). It is followed by the Particle Swarm Optimization algorithm with 100% rotational inertia (285.79 MW) and Genetic Algorithms with 50% rotational inertia (285.57 MW). In contrast, Genetic Algorithms with 25% rotational inertia demand the highest total installed Battery Energy Storage Systems power (307.44 MW), a result directly associated with a significant reduction in system inertia. Overall, an inverse relationship is observed between the available inertia level and the required Battery Energy Storage Systems capacity: the lower the inertia, the greater the power that the Battery Energy Storage Systems must supply to keep the system frequency within acceptable operational limits.

With the advancement of technology, most physical systems have become cyber-physical due to their level of connectivity; therefore, are not only the effects produced by non-malicious faults being analyzed but also the faults caused by malicious attacks. This paper presents a dynamic analysis of the impact of voltage sensor faults on the two-level Hierarchical Control designed for managing the one-phase Distributed Energy Resources (DER) based on Grid-Forming Control (GFC). The hierarchical control strategy implemented and further analyzed under the influence of sensor faults states the capability of regulating the voltage in the isolated Microgrid (MG). In addition, the proposed control system can compensate for the voltage unbalance produced at the point of common coupling or critical bus below a reference quality value of 2%. The hierarchical control design seeks simplicity, low processing effort, and fast response. Finally, a dynamic analysis is carried out about the controller's capability to maintain the operating point in the event of different voltage sensor faults on the secondary control and the effect it imposes on the power quality generated in the MG. The Matlab/Simulink simulation platform has been used to test the performance of the proposed control strategy.

The data presented in this paper are related to the paper entitled “Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection”, available in the Energies journal. Here, data are included to show the results of an Under-Frequency Load Shedding (UFLS) scheme that considers the injection of virtual inertia by a VSC-HVDC link. The data obtained in six cases which were considered and analyzed are shown. In this paper, each case represents a different frequency response configuration in the event of generation loss, taking into account the presence or absence of a VSC-HVDC link, traditional and optimized UFLS schemes, as well as the injection of virtual inertia by the VSC-HVDC link. Data for each example contain the state of the relay, threshold, position in every delay, load shed, and relay configuration parameters. Data were obtained through Digsilent Power Factory and Python simulations. The purpose of this dataset is so that other researchers can reproduce the results reported in our paper.

This paper addresses a proposal for the optimization and automation of the methodology of selectivity analysis in protection relays by fault sweeps, to improve selectivity and evaluate the operation times and coordination margins in a complex network composed by a large amount of overcurrent units. Through the operating times of the protection devices, it can be quickly identified the lack of coordination that may occur in the relays of the network for several topological conditions. The coordination times are obtained by a set of injected failures in the assets delimited by the scope of the selectivity study, and through an automatic systemic analysis it is sought to generate a diagnosis of the most critical points in which there is no correct selectivity within protection functions. The automation strategy of this work is designed and evaluated in DIgSILENT PowerFactory and Python computational tools. Through the scripting and automation module, a Python script is executed in the PowerFactory environment, and in this way the protection relays and the measurement transformers are modeled by assigning their respective settings and directionality. Finally, an analysis is carried out with criteria of the good setting of protection devices created by experts in the field, and a calculation file is generated with the optimized operating times of the protection devices and an analysis of selectivity between the overcurrent functions of the system.

Under-frequency load shedding (UFLS) schemes are the latest safety measures applied for safeguarding the integrity of the grid against abrupt frequency imbalances. The overall inertia of electrical power systems is expected to decrease with an increased penetration of renewable energy as well as elements connected through power electronic interfaces. However, voltage source converter-based high voltage direct current (VSC-HVDC) links can provide virtual inertia through a control loop that allows for a reaction to occur at certain frequency fluctuations. This paper evaluates a UFLS scheme that considers the injection of virtual inertia through a VSC-HVDC link. A genetic algorithm (GA) is used to determine the location of the UFLS relays, the activation threshold of each stage, the delay time and the percentage of load shedding at each stage. It was found that the virtual inertia causes the nadir to delay and sometimes reach a greater depth. Furthermore, the implemented GA approximates the frequency response to the limits set with the constraints, reducing the load shedding but achieving a steeper nadir and a lower steady-state frequency level than traditional UFLS. The simulations were performed using the IEEE 39-bus test system.

The frequency of an electrical power system can be interpreted as an indicator of the equilibrium between generation and demand. Under fault conditions where this balance is disrupted, the Under-Frequency Load Shedding scheme is typically the ultimate resource employed to prevent the frequency from dropping to undesirable thresholds that could lead to a blackout. Smart loads have the capability to dynamically adjust their consumption in response to frequency variations, which has the potential to enhance frequency regulation and mitigate the need for abrupt load disconnection through the Under-Frequency Load Shedding scheme. The objective of this paper is to present a literature review of Under-Frequency Load Shedding schemes, an innovative approach to their classifications, and the introduction of a new category within these schemes that incorporates smart loads. Additionally, an overview of smart loads is included to be potentially employed in the design of new schemes and contribute to a shift in the operation philosophy, considering that the primary mission of electric power systems is to provide energy to loads rather than disconnecting them.