Este estudio presenta la implementación de una arquitectura de control y comunicación para un tanque de características no lineales, utilizando una red redundante basada en el protocolo MQTT y tecnologías open source. Se muestra el modelamiento del tanque, la técnica de control y la topología de comunicación utilizada, junto a conceptos de control Hot StandB y HSBY. La arquitectura se implementa con un lazo de control principal que funciona en la nube y un lazo secundario de control local redundante. El objetivo en el sistema remoto es mantener la variable de operación en el valor de referencia definida previamente, sintetizando un controlador digital tipo PID, estudiando las incertidumbres asociadas al enlace de comunicación entre el controlador digital y la planta. Los resultados consideran la reducción súbita de ancho de banda en el enlace de comunicaciones y presencia de ruido blanco en la medición. Se concluye que esta arquitectura de control pueda funcionar en una zona remota, en presencia de incertidumbres.

This paper presents a control scheme applied to superconducting magnetic energy storage systems (SMES). The SMES is integrated to the grid using a pulse-width-modulated current source converter (PWM-CSC) which is an straightforward alternative to conventional topologies due to its intrinsic current characteristic. A novel representation of the system is obtained using feedback linearization. An average representation of the converter is considered with the modulation indices as control inputs. The dynamical system is transformed form no-autonomous into an autonomous system by application the power invariant Park's transformation from the abc reference frame into a dq reference frame. Results demonstrate the efficiency of the proposed control to modify the total energy stored in the superconducting coil by modification of its dc current guaranteeing stability in closed-loop.

This work proposes a technique for placing electric vehicle charging stations using a bootstrapping-based probabilistic power flow. The methodology employs maximum likelihood estimation to model uncertainties in EV charging demand and establish robust confidence intervals for key system metrics. This approach was implemented in the Matpower simulation software within the IEEE-14 bus system, modeling the probabilistic load profile of 4500 EVs while considering 4000 realizations to obtain a wide spectrum of operation scenarios. The main results identified bus 9 as the optimal location for EV charging infrastructure, obtaining minimal active power losses (26.5 +/- 0.5 MW) and a maximum efficiency of 92.87 +/- 0.08 %. The strategic placement of charging stations is closely linked to the lowest active power losses, offering optimal efficiency. However, beyond an optimal placement, this paper aims to increase the robustness of modern grids, overcoming drawbacks related to the integration of electromobility infrastructure. The selection of the most representative features, combined with uncertainty analysis, contributes to an improved decision-making, emphasizing the need for supporting sustainable mobility.