https://hdl.handle.net/20.500.12412/2540 2026-05-06T03:07:29Z 2026-05-06T03:07:29Z Sánchez de la Nieta, Agustín Gibescu, Madeleine https://hdl.handle.net/20.500.12412/7185 2026-04-17T21:00:21Z 2019-09-09T00:00:00Z

Day-ahead scheduling in a local electricity market Sánchez de la Nieta, Agustín; Gibescu, Madeleine Local electricity markets offer new trading opportunities for existing and emerging actors in the energy sector. In this study, a mathematical model is created for the hourly scheduling of a day-ahead local electricity market. The local electricity market has local producers and consumers and a connection with a retailer. The local resources considered are solar PV production, and an energy storage system. Therefore, the local electricity market has local resources, the inflexible and flexible residential loads, and the connection to the distribution network. This connection is used for buying energy from the wholesale market through a retailer. For evaluating the feasibility of the scheduling in a local energy market, an optimization model is proposed in this study to maximize the operational profits from the local electricity market, which is managed by an energy service company (ESCO). There are revenues from selling the energy to all residential loads, while the costs come from buying the electricity and managing all the local resources. A case study illustrates the energy profiles of all the resources managed, which have an impact on revenues and costs of the local market. From the case study, we draw some practical conclusions about the impact of local resources on the local electricity market.

2019-09-09T00:00:00Z Gantiva Osorio, Manuel Bejarano, Guillermo Millán Gata, Pablo https://hdl.handle.net/20.500.12412/7077 2026-02-04T22:00:31Z 2025-01-01T00:00:00Z

Propuesta de control coordinado para vehículos autónomos de superficie para la monitorización de recursos hídricos Gantiva Osorio, Manuel; Bejarano, Guillermo; Millán Gata, Pablo Este artículo resume los principales avances y trabajos en desarrollo de una tesis doctoral dedicada al diseño y evaluación de algoritmos de control distribuido y coordinado para vehículos autónomos de superficie. El contenido incluye una revisión sistemática del estado del arte, una descripción detallada de la plataforma experimental Yellowfish, utilizada para programar y validar los algoritmos, y la primera aproximación experimental, que valió una ley de guiado basada en Línea de Visión, combinada con un observador de estado no lineal y un controlador PID. Entre las mejoras propuestas se encuentra la implementación de un observador extendido con restricciones por conjuntos y una ley de control basada en backstepping, diseñada para incrementar la robustez del sistema frente a perturbaciones e incertidumbres. Asimismo, se desarrolla un controlador predictivo de alto nivel para coordinar múltiples vehículos en tareas colaborativas, como la navegación paralela. Este artículo subraya los avances alcanzados, identifica brechas en el estado actual del conocimiento y propone soluciones para mejorar el control y la eficiencia operativa de flotas autónomas.; This article summarizes the key advancements and ongoing work of a doctoral thesis focused on the design and evaluation ofdistributed and coordinated control algorithms for autonomous surface vehicles. The content includes a systematic review of thestate of the art, a detailed description of the experimental platform Yellowfish—used for programming and validating the algo-rithms—and the first experimental approach, which validated a line-of-sight-based guidance law, combined with a nonlinear stateobserver and a PID controller. Proposed improvements include the implementation of an extended observer with set-based cons-traints and a backstepping-based control law designed to enhance the system’s robustness against disturbances and uncertainties.Additionally, a high-level predictive controller is developed to coordinate multiple vehicles in collaborative tasks such as parallelnavigation. This article highlights the progress achieved, identifies gaps in the current state of knowledge, and proposes solutionsto improve the control and operational efficiency of autonomous fleets.

2025-01-01T00:00:00Z Millán, M. Ceballos González, Manuel Orihuela Espina, Diego Luis https://hdl.handle.net/20.500.12412/6961 2025-12-18T22:00:30Z 2024-01-01T00:00:00Z

Graph theory applications: addressing battery challenges in sensor networks Millán, M.; Ceballos González, Manuel; Orihuela Espina, Diego Luis This paper deals with the link among graph theory and sensor networks. Graph theory offers a robust platform for representing and studying intricate networks, whereas sensor networks consist of interconnected devices that gather and share data in a distributed manner. Leveraging a well-established theoretical framework for this purpose has resulted in notable progress in comprehending the dynamics, enhancement, and administration of sensor networks. The main goal of our paper is to take advantage of some graph-based tools to ease the battery level management in sensor networks and to solve some fault situations. Thus, we aim to develop an algorithmic method that allows the identification of those sensors with low battery level and, later, proceed with changes in the topology that avoid possible failures in the communication. In order to do so, we explore several graph procedures such as minimum spanning trees and shortest path algorithms. We have also used the edge contraction operation, graph coloring techniques and some computational geometry tools such as Voronoi diagrams and Delaunay triangulation. Our algorithm initiates by assessing the coordinates and battery statuses of each sensor. Subsequently, we generate a weighted matrix based on various criteria such as Euclidean distance or communication expenses between sensor pairs. Following this, we construct the Voronoi diagram to delineate the influence region of each sensor. Then, we use the Delaunay graph corresponding to the Voronoi diagram, where edge weights are determined by the previously derived weighted matrix. Upon completing these preliminary steps, we implement several procedures. The first procedure aims to identify sensors with depleted battery levels. The second procedure determines the shortest path between such low-battery vertices and other vertices with sufficient power to prevent data loss. The third procedure involves the edge contraction operation, eliminating vertices associated with sensors having low battery levels. The fourth one computes the minimum spanning tree to efficiently retrieve data from all sensors within the network. Finally, the last procedure assumes that there have been a failure in several sensors throughout a period of time and it generates the different Delaunay graphs associated to the network during that period. We believe that the tools and algorithms considered in this paper may be useful and helpful for understanding the link between graph theory and sensor networks. In addition, this link may provide new methods to deal with various challenges in network optimization, routing, clustering, localization and coverage. We would also like to point out the importance of further research in this field to unlock the full potential of graph-based approaches in sensor networks.

2024-01-01T00:00:00Z Ceballos González, Manuel Orihuela Espina, Diego Luis https://hdl.handle.net/20.500.12412/6960 2025-12-18T22:00:29Z 2023-01-01T00:00:00Z

Graph theory tools for battery level management in sensor networks Ceballos González, Manuel; Orihuela Espina, Diego Luis In this paper, we deal with the link between graph theory and sensor networks. Graph theory provides a powerful framework for modeling and analyzing complex networks, while sensor networks offer a distributed system of interconnected devices that collect and transmit data. The use of a well-established theoretical framework to this application has led to sig\-ni\-fi\-cant advancements in understanding the behavior, optimization and management of sensor networks. The main goal of our paper is to take advantage of some graph-based tools to ease the battery level management in sensor networks. Thus, we aim to develop an algorithmic method that allows the identification of those sensors with low battery and, later, proceed with changes in the topology that avoid possible failures in the communication. In order to do so, we explore several graph procedures such as minimum spanning trees and shortest path algorithms. We have also used the edge contraction operation, graph coloring techniques and some computational geometry tools such as Voronoi diagrams and Delaunay triangulation. Our algorithmic method starts considering the coordinates of every sensor and its battery level. After that, a weighted matrix is constructed based on come criteria such as the euclidean distance or the communication cost between each pair of sensors. Next, we have constructed the Voronoi diagram in order to analyze the influence area of every sensor. Then, we have considered the Delaunay graph associated to the previous diagram where the weight of the edges is given by the weighted matrix obtained before. After the previous steps, we have implemented several routines. The first one is devoted to locating the sensors with low battery level. The second one obtains the shortest path from one of those vertices to another vertex with a non-low battery level in order to avoid losing data. The third one applies the edge contraction operation deleting the vertices of sensors with low battery level. The last one computes the minimum spanning tree in order to collect the data from all the sensors of the network. We believe that the tools and algorithms considered in this paper may be useful and helpful for understanding the link between graph theory and sensor networks. In addition, this link may provide new methods to deal with various challenges in network optimization, routing, clustering, localization and coverage. We would also like to point out the importance of further research in this field to unlock the full potential of graph-based approaches in sensor networks.

2023-01-01T00:00:00Z