Acciona (Spain)

The aim of this contribution is to define active customer relationships within the renewable-based energy communities in the EU, and, namely, in Catalonia [...]

Endophytic fungi were isolated from ginger (Zingiber officinale) tubers and identified through molecular characterization of ITS and 28S rRNA regions. Nine species were obtained, belonging to the genera Aspergillus, Penicillium, Plectosphaerella, and Pseudogymnoascus. Several isolates, particularly Penicillium melinii, Aspergillus ustus, and Plectosphaerella cucumerina, exhibited strong antagonistic activity against Botrytis cinerea (up to 98.6% growth inhibition), while moderate effects were observed against Colletotrichum acutatum, Staphylococcus aureus and Klebsiella pneumoniae. All isolates produced at least one extracellular enzyme, with lipolytic and cellulolytic enzymes being the most frequently observed, and showed measurable antioxidant activity (EC50 values ranging from 21.7 to 673.6 µg/mL). P. melinii and P. cucumerina demonstrated the highest radical scavenging capacities. These findings reveal the multifunctional potential of ginger-associated endophytic fungi as sustainable sources of bioactive compounds, with promising applications in biocontrol, food preservation, and industrial biotechnology.

Dissolved oxygen (DO) concentration in nutrient solution is critical for maximizing yield and optimizing quality traits of lettuce plants grown in floating systems. This study evaluated the effects of two aeration systems—a Venturi system (V) and a Venturi system combined with a nanobubble generator using electromagnetic waves (VN)—compared with a non-aerated control (C), on quali-quantitative traits of lettuce plants grown in a floating system over two consecutive harvests. Both aeration treatments significantly increased DO levels in the nutrient solution compared to C, with the VN treatment maintaining the highest value throughout the crop cycle. Although no significant differences in lettuce yield were observed, both aeration treatments enhanced the leaf concentration of P, Mn, Zn, and Cu in the second harvest, and Mg in both harvests. Moreover, the VN treatment lowered leaf nitrate concentration in both harvests compared to the other treatments. The increase in DO in the nutrient solution delayed leaf senescence, as evidenced by higher chlorophyll index and lower anthocyanin levels in the lettuce leaves harvested at the end of the trial for both aeration systems. These results suggest that aeration, particularly with nanobubbles, can be an effective and sustainable strategy to enhance the quality traits of lettuce grown in a floating system.

Distribution systems have traditionally received the least attention in the bulk picture of power systems; they are yet the most "critical component" as they directly impact customers’ perception and utilities’ reputation. While transmission systems have seen abundant literature, innovations, and applications of power electronic-based devices to optimize their performance, distribution systems still require more attention toward a flexible and dynamic scheme to cope with volatile energy consumption and production. As a replacement or enhancement of the traditional ones, Solid-State Transformers can play a central role in the energy management of distribution systems towards this goal, in addition to the mere voltage conversion. This manuscript, developed within the IEEE Task Force on Solid-State Transformer integration in distribution grids, offers a critical review of the Solid-State Transformer potential, services, and technical challenges for its integration in distribution systems. Particular attention is given to scientific trends and open points in the current research that must be addressed before utilities extensively integrate the Solid-State Transformer in distribution systems.

The Synchronous Reference Frame Phase-Locked Loop is a central component of grid-following Voltage Source Converters. Previous research has shown that the differential equation introduced by this loop is a swing equation, similar to that of the synchronous machine. This paper analyzes the largesignal stability of a single converter connected to an infinite-bus system. The outcome is a boundary in the plane of the parameters separating a region of insufficient damping and a region of sufficient damping. Moreover, the boundaries of stability from the small-signal analysis are also included for completeness. The findings are validated via time-domain simulation in EMT models.

This work presents the development of an adaptive thermal protection system for synchronous machines (SMs), taking into consideration the final cooling temperature and the operation point of the machine. This system aims to improve current thermal protections, which consist of a fixed alarm and trip thresholds regardless of the generator’s operating point or ambient temperature. A recurrent neural network (RNN)-based approach has been employed to predict SM temperatures during operation. Multiple tests have been conducted on a specially designed test bench. Inside the windings and iron core of the 5.5 kVA generator, multiple Pt100 sensors have been installed to train the neural network with real temperature values, enabling accurate predictions. The selected RNN model is Long Short-Term Memory (LSTM). Its inputs include electrical variables and the inlet and outlet air temperatures of the SM’s cooling system. The results show that the model accurately defines warning and trip thresholds, significantly improving thermal protection, as these thresholds are no longer fixed values. Additionally, the study suggests validating the model under cooling system failures and exploring its application in water-cooled systems. This research is supported by a patent on real-time thermal diagnostics for synchronous machines, highlighting its potential contribution to predictive maintenance and the monitoring of power generation systems.

A microgrid (MG) is part of a distribution system that comprises loads and distributed energy resources, capable of operating either connected to or islanded from the primary grid. Having an appropriate design, MG controllers improve energy efficiency, playing a vital role in the modern distribution system. Thus, MG management and control has become a broad area of research due to its complex operation. Reinforcement learning (RL) offers adaptive solutions for handling MG complex dynamics and nonlinearity. It is an alternative to traditional algorithms and control methods in tasks, such as load frequency control, resource allocation, and energy management. Due to the relevance of the topic, this survey examined the role of RL in MG control and management, offering a comprehensive update on previous reviews, categorising articles by RL type, control objectives, and MG operational modes. Additionally, hardware implementations and performance assessments across RL-based solutions were evaluated. The present survey identified key research trends and gaps, contributing to understanding the role of RL in MG management and control and guiding future solutions in the field.