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Publicações

Publicações por Fernando Fontes

2019

Guaranteed Constraint Satisfaction in Continuous-Time Control Problems

Autores
Fontes, FACC; Paiva, LT;

Publicação
IEEE Control Systems Letters

Abstract

2019

Optimal power consumption for demand response of thermostatically controlled loads

Autores
Halder, A; Geng, XB; Fontes, FACC; Kumar, PR; Xie, L;

Publicação
OPTIMAL CONTROL APPLICATIONS & METHODS

Abstract
We consider the problem of determining the optimal aggregate power consumption of a population of thermostatically controlled loads such as air conditioners. This is motivated by the need to synthesize the demand response for a load serving entity (LSE) catering a population of such customers. We show how the LSE can opportunistically design the aggregate reference consumption to minimize its energy procurement cost, given day-ahead price, load forecast, and ambient temperature forecast, while respecting each individual load's comfort range constraints. The resulting synthesis problem is intractable when posed as a direct optimization problem after Euler discretization of the dynamics, since it results in a mixed-integer linear programming problem with number of variables typically of the order of millions. In contrast, in this paper, we show that the problem is amenable to continuous-time optimal control techniques. Numerical simulations elucidate how the LSE can use the optimal aggregate power consumption trajectory thus computed, for the purpose of demand response.

2019

Optimal Control of Thermostatic Loads for Planning Aggregate Consumption: Characterization of Solution and Explicit Strategies

Autores
Fontes, FACC; Halder, A; Becerril, J; Kumar, PR;

Publicação
IEEE Control Systems Letters

Abstract

2019

SAMPLED-DATA MODEL PREDICTIVE CONTROL: ADAPTIVE TIME-MESH REFINEMENT ALGORITHMS AND GUARANTEES OF STABILITY

Autores
Paiva, LT; Fontes, FACC;

Publicação
DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B

Abstract
This article addresses the problem of controlling a constrained, continuous-time, nonlinear system through Model Predictive Control (MPC). In particular, we focus on methods to efficiently and accurately solve the underlying optimal control problem (OCP). In the numerical solution of a nonlinear OCP, some form of discretization must be used at some stage. There are, however, benefits in postponing the discretization process and maintain a continuous-time model until a later stage. This is because that way we can exploit additional freedom to select the number and the location of the discretization node points. We propose an adaptive time-mesh refinement (AMR) algorithm that iteratively finds an adequate time-mesh satisfying a pre-defined bound on the local error estimate of the obtained trajectories. The algorithm provides a time-dependent stopping criterion, enabling us to impose higher accuracy in the initial parts of the receding horizon, which are more relevant to MPC. Additionally, we analyze the conditions to guarantee closed-loop stability of the MPC framework using the AMR algorithm. The numerical results show that the proposed AMR strategy can obtain solutions as fast as methods using a coarse equidistant-spaced mesh and, on the other hand, as accurate as methods using a fine equidistant-spaced mesh. Therefore, the OCP can be solved, and the MPC law obtained, faster and/or more accurately than with discrete-time MPC schemes using equidistant-spaced meshes.

2019

A BRKGA for the integrated scheduling problem in FMSs

Autores
Mahdi Homayouni, S; Fontes, DBMM; Fontes, FACC;

Publicação
Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '19

Abstract

2019

A decision support system for TV self-promotion Scheduling

Autores
Fontes, DB; LIAAD-INESC L.A., Faculdade de Economia, Universidade do Porto, 4200-464 Porto, Portugal,; Pereira, PA; Fontes, FA; Universidade do Minho 4800-058 Guimarães, Portugal,; Universidade do Porto, 4200-465 Porto, Portugal,;

Publicação
International Journal of Advanced Trends in Computer Science and Engineering

Abstract
This paper describes a Decision Support System (DSS) that aims to plan and maintain the weekly self-promotion space for an over the air TV station. The self-promotion plan requires the assignment of several self-promotion advertisements to a given set of available time slots over a pre-specified planning period. The DSS consists of a data base, a statistic module, an optimization module, and a user interface. The input data is provided by the TV station and by an external audiometry company, which collects daily audience information. The statistical module provides estimates based on the data received from the audiometry company. The optimization module uses a genetic algorithm that can find good solutions quickly. The interface reports the solution and corresponding metrics and can also be used by the decision makers to manually change solutions and input data. Here, we report mainly on the optimization module, which uses a genetic algorithm (GA) to obtain solutions of good quality for realistic sized problem instances in a reasonable amount of time. The GA solution quality is assessed using the optimal solutions obtained by using a branch-and-bound based algorithm to solve instances of small size, for which optimality gaps below 1% are obtained.

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