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.

Theory of cutset area is defined in the context of network behavior, as much in steady state as in dynamic operation. With reference to cutset angle theory, a proposed methodology for identifying stress on the network is stated for cutset angle evolution. Potential applications for Phase Measurement Units, PMU, are validated on a Colombian network.

This paper presents a novel methodology for the calculation of angular indexes of an electrical system from stationary analysis, using load flow and nose curves (P–V) in each of the buses of the system to perform control actions and preserve or improve voltage stability. The control actions are proposed considering a novel method based on the concepts of the cutset angle (CA) and center of angle (COA). The target is a fast estimation of voltage-stability margins through an appropriate angular characterization of the whole system and for each load bus with a complete network and N-1 contingency criteria. The most significant enhancement is that the angular characterization is based on the COA, which is related to the angular dynamics of the system, and indirectly reflects the inertia and the respective angles of the generator rotor, as well as the impact on the angular equivalent-system model. Simulations showed that the COA is an important index to determine the location of occurrence of the events. The COA can also help aim where control actions, like the amount of load shedding, should be carried out to remedy the voltage problems. The proposed method is assessed and tested in the benchmark IEEE 39-bus system.

In the present paper is shown a methodology for characterization of the electric load carried out using as baseline the information obtained from the measurement of the electrical parameters at the medium voltage level by means of the phasorial measurement units (PMUs) and low voltage level by means of the smart meters (SM) in a smart campus. A network topology is also developed for the connection of both PMUs and SMs to the control center using the Ethernet network that is available in the place, the control center is the site where all the optimization algorithms are merged for the energy management system (EMS) on smart campus. Load characterization is done by means of the ZIP model, in which the impedance, current, active and reactive power constants are calibrated. Then, simply with the voltage information obtained from the measurements, the load characterization curve over time is shown. For the location of the PMUs, the clustering technique of k-medoids is used, where the number of clusters represents the number of PMUs in the SEP which is calculated by a MILP optimization considering the 100% observability of the SEP, in terms of the location of SM is performed in each of the substations of the SEP on the low voltage side. Finally, a comparison of load characterization at the medium and low voltage level is performed, obtaining very similar values.

En este artículo se proponen indicadores normalizados, o en por unidad, para análisis de estabilidad de tensión en un sistema eléctrico de potencia. Los indicadores son simples y se conciben en función de la potencia reactiva de reserva y del margen de cargabilidad del sistema. La metodología presentada con los indicadores propuestos hace posible conjugar la evolución del margen de cargabilidad y las reservas de potencia reactiva. Esto es particularmente útil o beneficioso para analistas y operadores al permitir que se interprete de manera adecuada si la presencia de potencia reactiva de reserva es una garantía de la estabilidad de tensión o no en determinado punto del sistema de potencia. Los indicadores se aplicaron con éxito, exponiendo tendencias similares para una red IEEE pequeña ampliamente conocida y para una red de gran cantidad de barras y generadores. Por otro lado, a través de una arquitectura básica propuesta para la integración de tecnologías de información, se indicó la conveniencia del uso de indicadores para aplicaciones modernas de sincrofasores y conciencia situacional, en la que los operadores y analistas podrán contar con información precisa de lo que ocurre en el sistema y concebir probables soluciones con éxito.

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.