A Novel Formulation of Economic Model Predictive Control for Periodic Operations

This paper proposes a novel formulation of economic model predictive control (MPC) for linear systems with periodic operations. In this economic MPC design, the optimal periodic trajectory from an economic point of view is unknown, hence it is not possible to follow a standard control strategy in which the MPC uses this trajectory to define a terminal constraint to guarantee closed-loop convergence. The economic cost function is optimized with a periodicity constraint at each time step considering all periodic trajectories in a period including the current state. The recursive feasibility and closed-loop convergence to the optimal periodic trajectory are analyzed using the Karush-Kuhn-Tucker conditions. Finally, two simulations are provided to demonstrate the main results.Agencia Estatal de Investigación DPI2013-48243-C2Agencia Estatal de Investigación DPI2016-76493- C3Ministerio de Ciencia, Innovación y Universidades MDM-2016-065

Resilient Distributed Energy Management for Systems of Interconnected Microgrids

In this paper, distributed energy management of interconnected microgrids, which is stated as a dynamic economic dispatch problem, is studied. Since the distributed approach requires cooperation of all local controllers, when some of them do not comply with the distributed algorithm that is applied to the system, the performance of the system might be compromised. Specifically, it is considered that adversarial agents (microgrids with their controllers) might implement control inputs that are different than the ones obtained from the distributed algorithm. By performing such behavior, these agents might have better performance at the expense of deteriorating the performance of the regular agents. This paper proposes a methodology to deal with this type of adversarial agents such that we can still guarantee that the regular agents can still obtain feasible, though suboptimal, control inputs in the presence of adversarial behaviors. The methodology consists of two steps: (i) the robustification of the underlying optimization problem and (ii) the identification of adversarial agents, which uses hypothesis testing with Bayesian inference and requires to solve a local mixed-integer optimization problem. Furthermore, the proposed methodology also prevents the regular agents to be affected by the adversaries once the adversarial agents are identified. In addition, we also provide a sub-optimality certificate of the proposed methodology.Comment: 8 pages, Conference on Decision and Control (CDC) 201

Distributed Augmented Lagrangian Method for Link-Based Resource Sharing Problems of Multi-Agent Systems

A multi-agent optimization problem motivated by the management of energy systems is discussed. The associated cost function is separable and convex although not necessarily strongly convex and there exist edge-based coupling equality constraints. In this regard, we propose a distributed algorithm based on solving the dual of the augmented problem. Furthermore, we consider that the communication network might be time-varying and the algorithm might be carried out asynchronously. The time-varying nature and the asynchronicity are modeled as random processes. Then, we show the convergence and the convergence rate of the proposed algorithm under the aforementioned conditions.Comment: 9 page

Accelerated Multi-Agent Optimization Method over Stochastic Networks

We propose a distributed method to solve a multi-agent optimization problem with strongly convex cost function and equality coupling constraints. The method is based on Nesterov's accelerated gradient approach and works over stochastically time-varying communication networks. We consider the standard assumptions of Nesterov's method and show that the sequence of the expected dual values converge toward the optimal value with the rate of $\mathcal{O}(1/k^2)$. Furthermore, we provide a simulation study of solving an optimal power flow problem with a well-known benchmark case.Comment: to appear at the 59th Conference on Decision and Contro

Generalized Nash equilibrium seeking in population games under the Brown-von Neumann-Nash dynamics

© 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 worksThis paper investigates the problem of generalized Nash equilibrium (GNE) seeking in population games under the Brown-von Neumann-Nash dynamics and subject to general affine equality constraints. In particular, we consider that the payoffs perceived by the decision-making agents are provided by a so-called payoff dynamics model (PDM), and we show that an appropriate PDM effectively steers the agents to a GNE. More formally, using Lyapunov stability theory, we provide sufficient conditions to guarantee the asymptotic stability of the set of generalized Nash equilibria of the game, for the case when the game is a so-called stable game (also known as contractive game). Furthermore, we illustrate the application of the considered framework to an energy market game considering coupled equality constraints over the players decisions.Peer ReviewedPostprint (author's final draft

Trabajo de investigación previo a la obtención del Título de Psicólogo General de la Universidad Tecnológica Indoamérica. Modalidad Proyecto de Investigación.

La Carga Mental, en la actualidad, es una realidad presente en las organizaciones de numerosos países, incluido el Ecuador. La Carga Mental guarda relación con las características de la labor, pero también con la valoración que la propia persona realiza de su actividad y con otros factores entre los que se incluye la Resiliencia. La relación entre Carga Mental y Resiliencia fue evaluada en los trabajadores de la Cooperativa Andalucía, con una metodología cuantitativa, correlacional y transversal. La población quedó conformada por 200 trabajadores y se utilizó el cuestionario NASA TLX para evaluar la Carga Mental y el cuestionario Escala de Resiliencia (ER) para evaluar Resiliencia. Se obtuvo como resultado que la mayoría de los trabajadores estudiados perciben una alta Carga Mental y la presencia de una alta Resiliencia en la población estudiada. Se pudo comprobar que existe correlación entre la Carga Mental y la Resiliencia, por lo que se elaboró un programa de intervención para modificar los resultados obtenidos