Los nuevos desafíos técnicos asociados a variaciones drásticas en patrones típicos de despacho de centrales de generación y la distribución de flujos de potencia, debido a la interconexión de cargas dinámicas, interconexión con sistemas regionales y diversificación de las fuentes primarias de energía renovable han afectado a la estabilidad y confiabilidad de los sistemas eléctricos. Actualmente, los sistemas de almacenamiento de energía con baterías se constituyen como una de las soluciones más destacadas por su capacidad de contribuir a la estabilidad de frecuencia y voltaje en un sistema eléctrico. En este trabajo se modela y se analiza las bondades de la implementación de un sistema de almacenamiento para el control de Potencia – Frecuencia (P-f) en el Sistema Nacional Interconectado (S.N.I.). Para ello, se modela en DIgSILENT PowerFactory los componentes del sistema de almacenamiento en una base reducida del Sistema Nacional Interconectado S.N.I., la cual considera los reguladores de velocidad de las principales unidades de generación y mediante un equivalente dinámico se considera el aporte del sistema eléctrico colombiano. Se presenta el análisis del impacto de las baterías en diferentes escenarios teniendo en cuenta las incertidumbres en la programación de la generación.

A power system is never in a steady state due to continuous load variations, disturbances, maneuvers, and the operation of protection systems. A generation deficit causes a frequency drop in the system that must be controlled. If this frequency deviation is not properly managed, it can result in the loss of synchronism between generators and, eventually, lead to a partial or even total system collapse. This article presents a load shedding scheme applied to the IEEE 39-bus New England system. The scheme considers an N-1 contingency space to evaluate the dynamic frequency response, aiming to determine the appropriate settings for low-frequency relays activated by the rate of change of frequency (ROCOF).

Real-time voltage stability assessment, via conventional methods, is a difficult task due to the required large amount of information, high execution times and computational cost. Based on these limitations, this technical work proposes a method for the estimation of the voltage stability margin through the application of artificial intelligence algorithms. For this purpose, several operation scenarios are first generated via Monte Carlo simulations, considering the load variability and the n-1 security criterion. Afterwards, the voltage stability margin of PV curves is determined for each scenario to obtain a database. This information allows structuring a data matrix for training an artificial neural network and a support vector machine, in its regression version, to predict the voltage stability margin, capable of being used in real time. The performance of the prediction tools is evaluated through the mean square error and the coefficient of determination. The proposed methodology is applied to the IEEE 14 bus test system, showing so promising results.

An Electric Power System (EPS) is a dynamic system that, due to continuous variations in the load, the presence of disturbances, switching operations, and/or the operation of the protection system, is never in a steady state. A deficit in generation causes a drop in the system’s frequency that, if not controlled, could result in the loss of synchronism between generators or areas and, in the worst-case scenario, a total or partial system collapse. This article presents a methodology to identify a subset of representative events that generalizes the N-1 generation contingency space; this subset can later be applied in the development of Under-Frequency Load-Shedding (UFLS) schemes based on the Rate of Change of Frequency (ROCOF).

This article presents a unified framework for the early prediction of short-term voltage stability (STVS) using synchronized measurements of voltage, phase angle, and frequency from seven buses in a modified IEEE 14-bus system. A dataset comprising 5000 dynamic simulations was generated in DIgSILENT PowerFactory 2024 SP1 under probabilistic operating conditions and three-phase faults. Random Forest and Conv1D-LSTM models achieved F1 scores of 0.9782 and 0.9783, respectively, for the instability class. An observation window of 300 ms at 120 Hz yielded effective predictive performance while allowing sufficient time for supervisory assessment. Furthermore, an illustrative undervoltage load shedding scheme demonstrated that early warning information can support wide-area monitoring and subsequent control actions during severe contingency conditions.

The XXXII Electrical and Electronic Engineering Conference (XXXII JIEE-2024) is an annual event organized by the Faculty of Electrical and Electronic Engineering at the Escuela Politécnica Nacional, Quito, Ecuador