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Parabolic-trough solar collector fields are large-scale systems, so the application of centralized optimizationbased control methods to these systems is often not suitable for real-time control. As such, this paper formulates a novel coalitional control approach as an appropriate alternative to the centralized scheme. The key idea is to split the overall solar collector field into smaller subsystems, each of them governed by a local controller. Then, controllers are clustered into coalitions to solve a local optimization-based problem related to the corresponding subset of subsystems, so that an approximate solution of the original centralized problem can be obtained in a decentralized fashion. However, the operational constraints of the solar collector field couple the optimization problems of the multiple coalitions, thus limiting the ability to solve them in a fully decentralized manner. To overcome this issue, a novel population-dynamics-assisted resource allocation strategy is proposed as a mechanism to decouple the local optimization problems of the multiple coalitions. The proposed coalitional methodology allows to solve the multiple local subproblems in parallel, hence reducing the overall computational burden, while guaranteeing the satisfaction of the operational constraints and without significantly compromising the overall performance. The effectiveness of proposed approach is shown through numerical simulations of a 10- and 100-loop version of the ACUREX solar collector field of Plataforma Solar de Almería, Spain., Peer Reviewed, Postprint (published version)

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., We consider a large population of decision makers that choose their evolutionary strategies based on simple pairwise imitation rules. We describe such a dynamic process by the replicator dynamics. Differently from the available literature, where the payoffs signals are assumed to be updated continuously, we consider a more realistic scenario where they are updated occasionally. Our main technical contribution is to devise two event-triggered communication schemes with asymptotic convergence guarantees to a Nash equilibrium. Finally, we show how our proposed approach is applicable as an efficient distributed demand response mechanism., This research was supported by the project PID2020-115905RB-C21 (L-BEST) funded by MCIN/ AEI /10.13039/501100011033, the ERC under research project COSMOS (802348), and the project BPIN 2021000100499 funded by CTeI - SGR and MinCiencias, Colombia., Peer Reviewed, Postprint (author's final draft)

En este artículo, consideramos el marco de dinámicas evolutivas y juegos poblacionales. Para una clase de dinámicas evolutivas y una familia de juegos poblacionales contractivos, desarrollamos un método novedoso para la búsqueda distribuida de equilibrios de Nash bajo información parcial respecto a las decisiones de los demás agentes. La principal contribución es que establecemos condiciones suficientes para garantizar la estabilidad asintótica del conjunto de equilibrios de Nash del juego poblacional subyacente. Además, ilustramos nuestros resultados teóricos en una aplicación numérica de un juego de congestión de gran escala., Juan Martinez-Piazuelo agradece a la Universitat Politécnica de Catalunya y al Banco Santander, España, por la financiación de su beca FPI-UPC. Adicionalmente, los autores agradecen a los proyectos PID2020-115905RB-C21 (L-BEST) y TED2021-129927B-I00 (MASHED), financiados por MCIN/ AEI/10.13039/501100011033 y por la European Union Next GenerationEU/PRTR, por financiar esta investigación. Finalmente, Nicanor Quijano agradece el proyecto BPIN2021000100499, de la asignación para CTeI-SGR y Minciencias., Peer Reviewed, Postprint (published version)

This paper proposes a coalitional model predictive control method for temperature regulation in parabolic-trough solar fields. The global optimization problem is divided into a set of local subproblems that will be solved in parallel by a set of coalitions. However, these local (smaller) problems remain coupled by a common global resource constraint. In this regard, we present a population-dynamics-assisted resource allocation approach to fully decouple the local optimization problems. By doing this, each coalition can address its corresponding optimization problem without relying on the solutions of the other coalitions. To illustrate the proposed methodology, we provide simulation results for a 100-loop parabolic-trough solar collector field., The authors gratefully acknowledge the financial support by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (OCONTSOLAR, grant agreement No 789051). In addition, the authors would like to thank the projects PID2020-119476RB-I00 (C3PO-R2D2) and TED2021-129927B-I00 (MASHED) funded by MCIN/ AEI /10.13039/501100011033 and by the “European Union NextGenerationEU/ PRTR” for supporting this research. Finally, Juan Martinez- Piazuelo gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the financial support of his predoctoral grant FPI-UPC., Peer Reviewed, Postprint (published version)

This paper addresses the problem of distributed Nash equilibrium (NE) seeking in strongly contractive aggregative multi-population games subject to partialdecision information. In particular, we consider the scenario where the so-called payoff providers of the multiple populations communicate through a possibly noncomplete network, and we formulate some consensus-like dynamics for the payoff providers to distributedly compute their payoff signals using local information only. Moreover, by exploiting the notions of d-passivity and d-antipassivity, we provide a unified analysis for several classes of evolutionary game dynamics. As the main contributions, we provide sufficient conditions to guarantee the d-antipassivity of a class of continuous-time dynamical systems, and we exploit such results to design distributed NE seeking dynamics for strongly contractive aggregative population games, as well as for a class of merely contractive aggregative population games. To the best of our knowledge, this is the first paper to consider the problem of distributed NE seeking for such classes of population games and from a unifying passivity-based perspective., This research has been supported by the projects PID2020-115905RB-C21 (LBEST) and TED2021-129927B-I00 (MASHED) funded by MCIN/AEI /10.13039/501100011033, and by the project BPIN2021000100499 funded by CTeI-SGR and MinCiencias,Colombia., Peer Reviewed, Postprint (published version)

This paper studies the joint production and maintenance scheduling in microalgae manufacturing systems comprised of multiple machines, which are subject to coupled production demand agreements and operational maintenance constraints. Namely, there are some microalgae production demands to be met over a given horizon, and the maintenance of each microalgae manufacturing unit must be done before a given deadline. Moreover, the number of units whose maintenance can be done simultaneously over the same day is limited, and the units that undergo maintenance cannot contribute to microalgae production during their maintenance day. To solve the considered problem, we design a mixed-integer nonlinear model predictive controller, which is implemented in two optimization stages. The former regards a mixed-integer model predictive control problem, while the latter considers a nonlinear model predictive control problem. The proposed approach allows us to decouple the mixed-integer and nonlinear parts of the whole problem, and thus provides more flexibility on the optimization solvers that can be employed. In addition, the first stage also evaluates the attainability of the demand agreements, and provides a mechanism to minimally adjust such constraints so that their satisfaction can be guaranteed at the second stage. The overall model predictive control approach is based on experimental data collected at VAXA Technologies Ltd., and the effectiveness of the proposed method is validated through numerical simulations including multiple manufacturing units and uncertainties., Juan Martinez-Piazuelo gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the financial support of his predoctoral grant FPI-UPC. In addition, the authors would like to thank VAXA Technologies Ltd. as well as the project PID2020-115905RB-C21 (L-BEST) funded by MCIN/ AEI /10.13039/501100011033 for supporting this research., Peer Reviewed, Postprint (published version)

This brief proposes a novel decision-making model for generalized Nash equilibrium seeking in the context of full-potential population games under capacity and migration constraints. The capacity constraints restrict the mass of players that are allowed to simultaneously play each strategy of the game, while the migration constraints introduce a networked interaction structure among the players and rule the strategic switches that players can make. In this brief, we consider both decoupled capacity constraints regarding individual strategies, as well as coupled capacity constraints regarding disjoint groups of strategies. As main technical contributions, we prove that the proposed decision-making protocol guarantees the forward time invariance of the feasible set, and we provide sufficient conditions on the connectivity level of the migration graph to guarantee the asymptotic stability of the set of generalized Nash equilibria of the underlying game when the game is a full-potential population game with concave potential function. Furthermore, we also provide an alternative discrete-time analysis of the proposed evolutionary game dynamics, which allows us to formulate a population-game-inspired distributed optimization algorithm that guarantees the hard satisfaction of the constraints over all iterations. Finally, the theoretical results are validated numerically on a constrained networked congestion game., Juan Martinez-Piazuelo gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the financial support of his predoctoral grant FPI-UPC. In addition, the authors would like to thank the project PID2020-115905RB-C21 (L-BEST) funded by MCIN/ AEI /10.13039/501100011033 for supporting this research., Peer Reviewed, Preprint

Este artículo se centra en resaltar la basta utilidad de la teoría de juegos, en particular aquella basada en la vertiente dinámica evolutiva, dentro del modelado y control de sistemas dinámicos complejos y de gran escala. Concretamente, y a través de ejemplos claros y de aplicación real, se motiva el uso de las dinámicas poblacionales para el modelado de sistemas ciberfísicos, así como los modelos dinámicos de pago para complementar un sistema de control en lazo cerrado con restricciones (físicas y operacionales), cuyos objetivos se alinean con la distribución dinámica de recursos y el alcance de equilibrios generalizados de Nash., This paper focuses on highlighting the vast usefulness of game theory, particularly that based on the evolutionary dynamic branch, within the modeling and control of complex and large-scale dynamic systems. Specifically, and through clear examples of real application, the use of population dynamics for modeling cyber-physical systems is motivated, as well as dynamic payment models to complement a closed-loop control system with restrictions (physical and operational), whose objectives are aligned with the dynamic resource allocation and the reachment of generalized Nash equilibria., Los autores agradecen al proyecto PID2020115905RB-C21 (L-BEST) financiado por MCIN/ AEI /10.13039/501100011033., Peer Reviewed, Postprint (author's final draft)

Els problemes de control automàtic poden ser extremadament difícils de resoldre per a qualsevol algoritme no adaptatiu. El control automàtic implica l'avaluació en temps real de les respostes d'un sistema que es retroalimenta sota condicions incertes. Aquestes circumstàncies d'incertesa són freqüents en diferents àrees d'enginyeria, que van des de simples controls de vàlvules fins a la conducció autònoma d'última generació. Tot i que s'ha demostrat que els algoritmes de control clàssics funcionen, actualment la tendència de crear sistemes altament complexos i més grans fa que els mètodes clàssics anteriors no sempre es puguin aplicar amb efectivitat. Això sol ser degut per la falta de dades per dissenyar el model matemàtic, o per tenir múltiples elements controlables dins del sistema. Veient com altres branques, com la Teoria de Jocs, modelen problemes amb aquestes característiques, proposem enfocaments procedents de l'Aprenentatge Per Reforç per resoldre aquest tipus de situacions en una metodologia basada en dades i sense models matemàtics. Concretament, presentem un estudi sobre diversos algoritmes d'aprenentatge de reforç multiagent i els apliquem als sistemes de drenatge urbà per provar-ne l'eficàcia. Els sistemes de drenatge urbà (UDS) són xarxes d'interconnexió de canonades i instal·lacions d'emmagatzematge que transporten les aigües residuals a les estacions de tractament d'aigües residuals, abans d'alliberar l'aigua a les masses d'aigua properes, eliminant-les de productes químics perillosos., Automatic Control problems can be extremely challenging for any non-adapting algorithm to solve. Control involves the real-time evaluation of system responses under uncertain circumstances, and can be encountered in different areas of engineering, ranging from simple valve controls to State-of-the-Art autonomous driving. While classical control algorithms have been proven to work, in a trend of creating more complex and larger systems these previous methods cannot be always applied. This is usually due to incomplete data to design the mathematical model, or having multiple controllable elements in a system. Seeing how these types of problem can be modeled with Game Theory, we propose approaches coming from Reinforcement Learning to solve these sorts of situations in a model-free data-driven methodology. Concretely, we present a study on various Multi-Agent Reinforcement Learning algorithms and apply them on Urban Drainage Systems to test their efficacy. Urban Drainage Systems (UDS) are networks of interconnecting pipes and storage facilities that transport wastewater to Waste Water Treatment Plants, before releasing the water into nearby water bodies, removing them of dangerous chemicals.

As an envisioned technology for future smart city networks, this paper studies the real-time decentralized charging coordination of a fleet of plug-in electric vehicles (PEVs) under feeder capacity constraints. In particular, inspired by some ideas in the field of population games and payoff dynamics models, we propose a novel form of continuous-time primal-dual gradient dynamics and develop a real-time control method for the charging coordination of PEVs in smart city networks. The proposed method is able to coordinate the charging profiles of multiple PEVs in a decentralized fashion under a general convex optimization objective, and guarantees the satisfaction of the operational constraints of the PEVs and the feeder lines of the distribution network for all times. The optimality and asymptotic stability of the proposed dynamics are formally proven, and the advantages of the proposed method are illustrated through numerical simulations considering a fleet with several PEVs., The work of Juan Martinez-Piazuelo was supported in part by the Universitat Politècnica de Catalunya and in part by Banco Santander under Grant FPI-UPC. The work of Nicanor Quijano was supported by the U.K.-PACT Project (2019–2021), systemic perspectives on low-carbon cities in Colombia—An integrated urban modeling approach for policy and regulatory analysis. This work was supported by Project PID2020-115905RB-C21 (L-BEST) funded by MCIN/ AEI /10.13039/501100011033., Peer Reviewed, Postprint (author's final draft)

Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energètica

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., In this paper, we consider the framework of population games and evolutionary dynamics. Based on such a framework, we formulate a novel approach for distributed Nash equilibrium seeking under partial-decision information for a class of evolutionary dynamics and a family of contractive population games. As the main contribution, we provide sufficient conditions to guarantee the asymptotic stability of the set of Nash equilibria of the underlying game. To the best of our knowledge, this is the first paper to address the problem of distributed Nash equilibrium seeking under partial-decision information in the aforementioned context of population games and evolutionary dynamics., The authors would like to thank the project PID2020-115905RB-C21 (L-BEST) funded by MCIN/AEI /10.13039/501100011033 for supporting this research., Peer Reviewed, Postprint (author's final draft)

This letter proposes a novel form of continuous-time evolutionary game dynamics for generalized Nash equilibrium seeking in equality-constrained population games. Using Lyapunov stability theory and duality theory, we provide sufficient conditions to guarantee the asymptotic stability, non-emptiness, compactness, and optimality of the equilibria set of the proposed dynamics for certain population games. Moreover, we illustrate our theoretical developments through a numerical simulation of an equality-constrained congestion game., Peer Reviewed, Postprint (author's final draft)

This paper studies the problem of generalized Nash equilibrium seeking in population games under general affine equality and convex inequality constraints. In particular, we design a novel payoff dynamics model to steer the decision-making agents to a generalized Nash equilibrium of the underlying game, i.e., to a self-enforceable state where the constraints are satisfied and no agent has incentives to unilaterally deviate from her selected strategy. Moreover, using Lyapunov stability theory, we provide sufficient conditions to guarantee the asymptotic stability of the corresponding equilibria set in stable population games. Auxiliary results characterizing the properties of the equilibria set are also provided for general continuous population games. Furthermore, our theoretical developments are numerically validated on a Cournot game considering various market-related and production-related constraints., Juan Martinez-Piazuelo gratefully acknowledges the Universitat Politècnica de Catalunya, Spain and Banco Santander, Spain for the financial support of his predoctoral grant FPI-UPC, Spain . In addition, the authors would like to thank the project PID2020-115905RB-C21 (L-BEST) funded by MCIN/ AEI /10.13039/501100011033 for supporting this research. Finally, Nicanor Quijano would like to thank the UK-PACT Project (2019-2021), Systemic perspectives on low-carbon cities in Colombia – An integrated urban modelling approach for policy and regulatory analysis., Peer Reviewed, Postprint (published version)