This work is aimed at optimizing the wind turbine rotor speed setpoint algorithm. Several intelligent adjustment strategies have been investigated in order to improve a reward function that takes into account the power captured from the wind and the turbine speed error. After different approaches including Reinforcement Learning, the best results were obtained using a Particle Swarm Optimization (PSO)-based wind turbine speed setpoint algorithm. A reward improvement of up to 10.67% has been achieved using PSO compared to a constant approach and 0.48% compared to a conventional approach. We conclude that the pitch angle is the most adequate input variable for the turbine speed setpoint algorithm compared to others such as rotor speed, or rotor angular acceleration.

The introduction of energy efficiency as a new goal into already complex production plans is a difficult challenge. Decision support systems can help with this problem but these systems are often resisted by end users who ultimately bear the responsibility for production outputs. This paper describes the design of a decision support tool that aims to increase the interpretability of decision support outputs. The concept of ‘grey box’ optimisation is introduced, where aspects of the optimisation engine are communicated to, and configurable by, the end user. A multi-objective optimisation algorithm is combined with an interactive visualisation to improve system observability and increase trust.

ResumenEn el presente trabajo se expone una metodología para modelar mediante un Sistema Multi-Agente (SMA) sistemas biológicos y fisiológicos dinámicos con variables cuantificadas discretas, como el crecimiento y decrecimiento de poblaciones o el modelado epidemiológico de enfermedades. Se muestra un procedimiento para transformar un sistema de Ecuaciones Diferenciales Ordinarias (EDO) (que modela un entorno de forma correcta) en un SMA equivalente mediante un esquema basado en el método de Monte Carlo. Se utiliza un caso práctico fundamentado en un modelo matemático de Leucemia Mieloide Crónica (LMC) para comparar la metodología basada en agentes con el modelado tradicional basado en un sistema de EDO. Se realiza una simulación con cada modelo (SMA y EDO) y se compara los resultados obtenidos con ambas metodologías

Hydrogen-powered heavy-duty vehicles will transform the logistics landscape, but their extensive adoption presents substantial challenges. Matching hydrogen demand with supply, scaling up infrastructure, controlling carbon emissions targets, and integrating with renewable energy sources are significant obstacles to overcome. This paper addresses these challenges by modeling a hydrogen station for heavy-duty vehicle fleets using Matlab-Simulink software. The hydrogen station components proposed are individually modeled: (1) the electrolyzer model generates hydrogen and oxygen by electrolysis consuming water and electricity; (2) the hydrogen reformer model generates hydrogen and carbon dioxide through steam methane reforming or ethanol reforming; (3) the hydrogen storage tank; and (4) carbon capture and storage. These models were compiled into functional mock-up units (FMU) to facilitate further exploration. This paper incorporates metaheuristic optimization techniques to address the design complexities and enhance the performance of hydrogen stations under various operating conditions. Multiple optimization objectives have been considered, including reducing carbon emissions and reducing the total monetary cost. Furthermore, several critical constraints are integrated to ensure realistic scenarios. These constraints include the accumulated hydrogen production that meets daily demand and the limitations in resource consumption. Finally, the combination of the FMU approach with metaheuristics techniques demonstrates the potential for the optimal hydrogen infrastructure design.