Diseño y evaluación de técnicas de optimización multiobjetivo para sistemas de gestión de referencias en lazo de control

[EN] The term Real-Time Optimization (RTO) has spread rapidly in the industry, and provides a higher level control system. In this project the design and evaluation of a RTO for different types of control where the optimizer will calculate the best references from a multi-objective approach[ES] El término Optimización en Tiempo Real (RTO) se ha extendido rápidamente en la industria, y constituye un nivel superior del sistema de control. En el presente proyecto se realizará el diseño y evaluación de un RTO para diferentes tipos de control donde el optimizador calculará las referecias óptimas desde un enfoque multiobjetivoPajares Ferrando, A. (2014). Diseño y evaluación de técnicas de optimización multiobjetivo para sistemas de gestión de referencias en lazo de control. http://hdl.handle.net/10251/54190Archivo delegad

Desarrollo de una metodología de optimización multiobjetivo considerando soluciones casi-óptimas. Aplicación a problemas en ingeniería de control

[ES] En un problema de optimización multiobjetivo, habitualmente se busca caracterizar el conjunto de soluciones óptimas de Pareto, ignorando las soluciones casi-óptimas. Sin embargo, estas soluciones pueden proporcionar al diseñador una mayor diversidad de soluciones potencialmente útiles, lo que permite tomar una decisión final más informada. Pese a ello, obtener todas las soluciones casi-óptimas puede aumentar en exceso el número de ellas y, en consecuencia, ralentizar en exceso el proceso de optimización y complicar la etapa de decisión. Por ello, se propone obtener las soluciones casi-óptimas que mayor información relevante aporten al diseñador, descartando el resto de ellas. En este trabajo se asume que las soluciones más relevantes son, además de las óptimas (en el espacio de objetivos), las alternativas casi-óptimas significativamente diferentes (no vecinas en el espacio de parámetros) a las soluciones que le dominan, es decir, las soluciones casi-óptimas no dominadas en su vecindad. Este conjunto de soluciones proporciona alternativas diferentes sin aumentar en exceso el número de ellas. Para caracterizar este conjunto, en esta tesis, se presenta y valida un nuevo algoritmo (nevMOGA). Gracias a este algoritmo y la metodología descrita para su aplicación, el diseñador puede obtener estas soluciones con el objetivo de realizar un análisis más profundo, tomando la decisión final con mayor información. Además, en la tesis, se aplica esta nueva metodología en problemas de identificación de modelos y diseño de controladores multivariables. En ellos, se pone de manifiesto la utilidad de obtener las alternativas casi-óptimas no dominadas en su vecindad, proporcionando nueva información relevante para el diseñador. De hecho, en algunos de estos problemas, las alternativas casi-óptimas son preferidas en lugar de las óptimas.[CA] En un problema d'optimització multiobjectiu, habitualment se busca caracteritzar el conjunt de solucions òptimes de Pareto, ignorant les solucions quasi-òptimes. Aquestes solucions poden proporcionar al dissenyador una major diversitat de solucions potencialment útils, la qual cosa permet prendre una decisió final més informada. No obstant això, obtenir totes les solucions quasi-òptimes pot augmentar en excés el número d'elles, alentint en excés el procés d'optimització i complicant l'etapa de decisió. Per això, es proposa obtenir les solucions quasi-òptimes que major informació rellevant aporten al dissenyador, descartant la resta d'elles. En aquest treball s'assumix que les solucions més rellevants són, a més de les òptimes (en l'espai d'objectius), les alternatives quasi-òptimes significativament diferents (no veïnes en l'espai de paràmetres) a les solucions que li dominen, és a dir, les solucions quasi-òptimes no dominades en el seu veïnatge. Aquest conjunt de solucions proporciona alternatives diferents sense augmentar en excés el número d'elles. Per a caracteritzar aquest conjunt, en aquesta tesi, es presenta i valida un nou algorisme (nevMOGA). Gràcies a aquest algorisme i la metodologia descrita per a la seua aplicació, el dissenyador pot obtenir aquestes solucions amb l'objectiu de realitzar una anàlisi més profunda, prenent la decisió final amb major informació. A més, en la tesi, s'aplica aquesta nova metodologia en problemes d'identificació de models i disseny de controladors multivariables. En ells, es posa de manifest la utilitat d'obtenir les alternatives quasi-òptimes no dominades en el seu veïnatge, proporcionant nova informació rellevant per al dissenyador. De fet, en diversos casos, les alternatives quasi-òptimes són preferides en lloc de les òptimes.[EN] In a multiobjective optimization problem, the aim is usually to characterize the set of optimal solutions (Pareto set) and the nearly optimal solutions are ignored. Proceeding in this way has a drawback, namely, some of these nearly optimal solutions are potentially useful for the designer and their consideration can lead him or her to make a better informed decision. However, finding all the nearly optimal solutions would excessively slow down the optimization process and would complicate the decision stage unnecessarily. In order to overcome this problem, we propose a new methodology to obtain only the nearly optimal solutions that really provide relevant information to the designer, discarding the rest of them. In this work, it is assumed that the most relevant solutions are, apart from the optimal ones, the nearly optimal solutions which are significantly different (not neighbors in the parameter space) from the solutions that dominate them, that is to say, the nearly optimal solutions non dominated in their neighborhood. In this way, a set of potentially useful alternatives is provided, without increasing their number unnecessarily. In order to characterize this new set of solutions, a novel algorithm (nevMOGA) is presented and validated. Thanks to this algorithm and to the methodology described for its application, a designer will be able to obtain these new solutions and, therefore, this will enable him or her to perform a deeper analysis of the problem, which eventually will result in a more knowledgeable decision. In addition, this new methodology is applied to several engineering problems in the areas of model tuning and multivariable control design. Through these application examples, the usefulness of obtaining and taking into account the nearly optimal solutions non dominated in their neighborhood is demonstrated. In effect, in some of these cases, a nearly optimal solution is preferred to any of the optimal ones.Este trabajo ha sido parcialmente subvencionado por el Ministerio de Economía y Competitividad a través de la beca FPU15/01652, y los proyectos DPI2015- 71443-R y RTI2018-096904-B-I00, por la administración local Generalitat Valenciana a través de la beca ACIF/2015/079 y los proyectos GV/2017/029 y AICO/2019/055, y por la Universitat Politècnica de València a través de la beca FPI-2014/2429.Pajares Ferrando, A. (2019). Desarrollo de una metodología de optimización multiobjetivo considerando soluciones casi-óptimas. Aplicación a problemas en ingeniería de control [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/133974TESI

Multivariable controller design for the cooling system of a PEM fuel cell by considering nearly optimal solutions in a multi-objective optimization approach

[EN] This paper presents a design for the multivariable control of a cooling system in a PEM (proton exchange membrane) fuel cell stack. This system is complex and challenging enough: interactions between variables, highly nonlinear dynamic behavior, etc. This design is carried out using a multiobjective optimization methodology. There are few previous works that address this problem using multiobjective techniques. Also, this work has, as a novelty, the consideration of, in addition to the optimal controllers, the nearly optimal controllers nondominated in their neighborhood (potentially useful alternatives). In the multiobjective optimization problem approach, the designer must make decisions that include design objectives; parameters of the controllers to be estimated; and the conditions and characteristics of the simulation of the system. However, to simplify the optimization and decision stages, the designer does not include all the desired scenarios in the multiobjective problem definition. Nevertheless, these aspects can be analyzed in the decision stage only for the controllers obtained with a much less computational cost. At this stage, the potentially useful alternatives can play an important role. These controllers have significantly different parameters and therefore allow the designer to make a final decision with additional valuable information. Nearly optimal controllers can obtain an improvement in some aspects not included in the multiobjective optimization problem. For example, in this paper, various aspects are analyzed regarding potentially useful solutions, such as (1) the influence of certain parameters of the simulator; (2) the sample time of the controller; (3) the effect of stack degradation; and (4) the robustness. Therefore, this paper highlights the relevance of this in-depth analysis using the methodology proposed in the design of the multivariable control of the cooling system of a PEM fuel cell. This analysis can modify the final choice of the designer.This study was supported in part by the Ministerio de Ciencia, Innovacion y Universidades (Spain) (grant no. RTI2018-096904-B-I00) and by the Generalitat Valenciana regional government through project AICO/2019/055.Pajares-Ferrando, A.; Blasco, X.; Herrero Durá, JM.; Simarro Fernández, R. (2020). Multivariable controller design for the cooling system of a PEM fuel cell by considering nearly optimal solutions in a multi-objective optimization approach. Complexity. 2020:1-17. https://doi.org/10.1155/2020/8649428S1172020Gunantara, N. (2018). A review of multi-objective optimization: Methods and its applications. Cogent Engineering, 5(1), 1502242. doi:10.1080/23311916.2018.1502242Engau, A., & Wiecek, M. M. (2007). Generating ε-efficient solutions in multiobjective programming. European Journal of Operational Research, 177(3), 1566-1579. doi:10.1016/j.ejor.2005.10.023Loridan, P. (1984). ?-solutions in vector minimization problems. Journal of Optimization Theory and Applications, 43(2), 265-276. doi:10.1007/bf00936165White, D. J. (1986). Epsilon efficiency. Journal of Optimization Theory and Applications, 49(2), 319-337. doi:10.1007/bf00940762Pajares, A., Blasco, X., Herrero, J. M., & Reynoso-Meza, G. (2018). A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA. Complexity, 2018, 1-22. doi:10.1155/2018/1792420Schutze, O., Vasile, M., & Coello, C. A. C. (2011). Computing the Set of Epsilon-Efficient Solutions in Multiobjective Space Mission Design. Journal of Aerospace Computing, Information, and Communication, 8(3), 53-70. doi:10.2514/1.46478Pajares, A., Blasco, X., Herrero, J. M., & Reynoso-Meza, G. (2019). A New Point of View in Multivariable Controller Tuning Under Multiobjective Optimization by Considering Nearly Optimal Solutions. IEEE Access, 7, 66435-66452. doi:10.1109/access.2019.2915556Fredriksson, A., Forsgren, A., & Hårdemark, B. (2011). Minimax optimization for handling range and setup uncertainties in proton therapy. Medical Physics, 38(3), 1672-1684. doi:10.1118/1.3556559Lee, J., & Johnson, G. E. (1993). Optimal tolerance allotment using a genetic algorithm and truncated Monte Carlo simulation. Computer-Aided Design, 25(9), 601-611. doi:10.1016/0010-4485(93)90075-yAndújar, J. M., & Segura, F. (2009). Fuel cells: History and updating. A walk along two centuries. Renewable and Sustainable Energy Reviews, 13(9), 2309-2322. doi:10.1016/j.rser.2009.03.015Mehta, V., & Cooper, J. S. (2003). Review and analysis of PEM fuel cell design and manufacturing. Journal of Power Sources, 114(1), 32-53. doi:10.1016/s0378-7753(02)00542-6De las Heras, A., Vivas, F. J., Segura, F., Redondo, M. J., & Andújar, J. M. (2018). Air-cooled fuel cells: Keys to design and build the oxidant/cooling system. Renewable Energy, 125, 1-20. doi:10.1016/j.renene.2018.02.077Kandlikar, S. G., & Lu, Z. (2009). Thermal management issues in a PEMFC stack – A brief review of current status. Applied Thermal Engineering, 29(7), 1276-1280. doi:10.1016/j.applthermaleng.2008.05.009Yan, Q., Toghiani, H., & Causey, H. (2006). Steady state and dynamic performance of proton exchange membrane fuel cells (PEMFCs) under various operating conditions and load changes. Journal of Power Sources, 161(1), 492-502. doi:10.1016/j.jpowsour.2006.03.077Maghanki, M. M., Ghobadian, B., Najafi, G., & Galogah, R. J. (2013). Micro combined heat and power (MCHP) technologies and applications. Renewable and Sustainable Energy Reviews, 28, 510-524. doi:10.1016/j.rser.2013.07.053Notter, D. A., Kouravelou, K., Karachalios, T., Daletou, M. K., & Haberland, N. T. (2015). Life cycle assessment of PEM FC applications: electric mobility and μ-CHP. Energy & Environmental Science, 8(7), 1969-1985. doi:10.1039/c5ee01082aMartinez, S., Michaux, G., Salagnac, P., & Bouvier, J.-L. (2017). Micro-combined heat and power systems (micro-CHP) based on renewable energy sources. Energy Conversion and Management, 154, 262-285. doi:10.1016/j.enconman.2017.10.035Elmer, T., Worall, M., Wu, S., & Riffat, S. B. (2015). Fuel cell technology for domestic built environment applications: State of-the-art review. Renewable and Sustainable Energy Reviews, 42, 913-931. doi:10.1016/j.rser.2014.10.080Hawkes, A., Staffell, I., Brett, D., & Brandon, N. (2009). Fuel cells for micro-combined heat and power generation. Energy & Environmental Science, 2(7), 729. doi:10.1039/b902222hEllamla, H. R., Staffell, I., Bujlo, P., Pollet, B. G., & Pasupathi, S. (2015). Current status of fuel cell based combined heat and power systems for residential sector. Journal of Power Sources, 293, 312-328. doi:10.1016/j.jpowsour.2015.05.050Strahl, S., & Costa-Castelló, R. (2017). Temperature control of open-cathode PEM fuel cells. IFAC-PapersOnLine, 50(1), 11088-11093. doi:10.1016/j.ifacol.2017.08.2492Zhang, G., & Kandlikar, S. G. (2012). A critical review of cooling techniques in proton exchange membrane fuel cell stacks. International Journal of Hydrogen Energy, 37(3), 2412-2429. doi:10.1016/j.ijhydene.2011.11.010Navarro Gimenez, S., Herrero Dura, J. M., Blasco Ferragud, F. X., & Simarro Fernandez, R. (2019). Control-Oriented Modeling of the Cooling Process of a PEMFC-Based $\mu$ -CHP System. IEEE Access, 7, 95620-95642. doi:10.1109/access.2019.2928632Herrero, J. M., García-Nieto, S., Blasco, X., Romero-García, V., Sánchez-Pérez, J. V., & Garcia-Raffi, L. M. (2008). Optimization of sonic crystal attenuation properties by ev-MOGA multiobjective evolutionary algorithm. Structural and Multidisciplinary Optimization, 39(2), 203-215. doi:10.1007/s00158-008-0323-7Bristol, E. (1966). On a new measure of interaction for multivariable process control. IEEE Transactions on Automatic Control, 11(1), 133-134. doi:10.1109/tac.1966.1098266Blasco, X., Herrero, J. M., Sanchis, J., & Martínez, M. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20), 3908-3924. doi:10.1016/j.ins.2008.06.010Schmittinger, W., & Vahidi, A. (2008). A review of the main parameters influencing long-term performance and durability of PEM fuel cells. Journal of Power Sources, 180(1), 1-14. doi:10.1016/j.jpowsour.2008.01.07

A Comparison of Archiving Strategies for Characterization of Nearly Optimal Solutions under Multi-Objective Optimization

[EN] In a multi-objective optimization problem, in addition to optimal solutions, multimodal and/or nearly optimal alternatives can also provide additional useful information for the decision maker. However, obtaining all nearly optimal solutions entails an excessive number of alternatives. Therefore, to consider the nearly optimal solutions, it is convenient to obtain a reduced set, putting the focus on the potentially useful alternatives. These solutions are the alternatives that are close to the optimal solutions in objective space, but which differ significantly in the decision space. To characterize this set, it is essential to simultaneously analyze the decision and objective spaces. One of the crucial points in an evolutionary multi-objective optimization algorithm is the archiving strategy. This is in charge of keeping the solution set, called the archive, updated during the optimization process. The motivation of this work is to analyze the three existing archiving strategies proposed in the literature (ArchiveUpdateP(Q,epsilon)D(xy), Archive_nevMOGA, and targetSelect) that aim to characterize the potentially useful solutions. The archivers are evaluated on two benchmarks and in a real engineering example. The contribution clearly shows the main differences between the three archivers. This analysis is useful for the design of evolutionary algorithms that consider nearly optimal solutions.This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades (Spain) (grant number RTI2018-096904-B-I00), by the Generalitat Valenciana regional government through project AICO/2019/055 and by the Universitat Politecnica de Valencia (grant number SP20200109).Pajares-Ferrando, A.; Blasco, X.; Herrero Durá, JM.; Martínez Iranzo, MA. (2021). A Comparison of Archiving Strategies for Characterization of Nearly Optimal Solutions under Multi-Objective Optimization. Mathematics. 9(9):1-28. https://doi.org/10.3390/math9090999S1289

Analyzing the Nearly Optimal Solutions in a Multi-Objective Optimization Approach for the Multivariable Nonlinear Identification of a PEM Fuel Cell Cooling System

[EN] In this work, the parametric identification of a cooling system in a PEM (proton exchange membrane) fuel cell is carried out. This system is multivariable and nonlinear. In this type of system there are different objectives and the unmodeled dynamics cause conflicting objectives (prediction errors in each output). For this reason, resolution is proposed using a multi-objective optimization approach. Nearly optimal alternatives can exist in any optimization problem. Among them, the nearly optimal solutions that are significantly different (that we call nearly optimal solutions nondominated in their neighborhood) are potentially useful solutions. In identification problems, two situations arise for consideration: 1) aggregation in the design objectives (when considering the prediction error throughout the identification test). When an aggregation occurs in the design objectives, interesting non-neighboring (significantly different) multimodal and nearly optimal alternatives appear. These alternatives have different trade-offs in the aggregated objectives; 2) new objectives in decision making appear. Some models can, with similar performance in the design objectives, obtain a significant improvement in new objectives not included in the optimization phase. A typical case of additional objectives are the validation objectives. In these situations, nearly optimal solutions nondominated in their neighborhood play a key role. These alternatives allow the designer to make the final decision with more valuable information. Therefore, this work highlights, as a novelty, the relevance of considering nearly optimal models nondominated in their neighborhood in problems of parametric identification of multivariable nonlinear systems and shows an application in a complex problem.This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades, Spain, under Grant RTI2018-096904-B-I00, and in part by the Generalitat Valenciana Regional Government under Project AICO/2019/055.Pajares-Ferrando, A.; Blasco, X.; Herrero Durá, JM.; Salcedo-Romero-De-Ávila, J. (2020). Analyzing the Nearly Optimal Solutions in a Multi-Objective Optimization Approach for the Multivariable Nonlinear Identification of a PEM Fuel Cell Cooling System. IEEE Access. 8:114361-114377. https://doi.org/10.1109/ACCESS.2020.3003741S114361114377

A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA

[EN] Traditionally, in a multiobjective optimization problem, the aim is to find the set of optimal solutions, the Pareto front, which provides the decision-maker with a better understanding of the problem. This results in a more knowledgeable decision. However, multimodal solutions and nearly optimal solutions are ignored, although their consideration may be useful for the decision-maker. In particular, there are some of these solutions which we consider specially interesting, namely, the ones that have distinct characteristics from those which dominate them (i.e., the solutions that are not dominated in their neighborhood). We call these solutions potentially useful solutions. In this work, a new genetic algorithm called nevMOGA is presented, which provides not only the optimal solutions but also the multimodal and nearly optimal solutions nondominated in their neighborhood. This means that nevMOGA is able to supply additional and potentially useful solutions for the decision-making stage. This is its main advantage. In order to assess its performance, nevMOGA is tested on two benchmarks and compared with two other optimization algorithms (random and exhaustive searches). Finally, as an example of application, nevMOGA is used in an engineering problem to optimally adjust the parameters of two PI controllers that operate a plant.This work was partially supported by the Ministerio de Economia y Competitividad (Spain) Grant numbers DPI2015-71443-R and FPU15/01652, by the local administration Generalitat Valenciana through the project GV/2017/029, and by the National Council of Scientific and Technological Development of Brazil (CNPq) through the grant PQ-2/304066/2016-8.Pajares-Ferrando, A.; Blasco, X.; Herrero Durá, JM.; Reynoso-Meza, G. (2018). A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA. Complexity. 2018:1-22. https://doi.org/10.1155/2018/1792420S1222018Reynoso-Meza, G., Sanchis, J., Blasco, X., & Martínez, M. (2013). Algoritmos Evolutivos y su empleo en el ajuste de controladores del tipo PID: Estado Actual y Perspectivas. Revista Iberoamericana de Automática e Informática Industrial RIAI, 10(3), 251-268. doi:10.1016/j.riai.2013.04.001Reynoso-Meza, G., Sanchis, J., Blasco, X., & García-Nieto, S. (2014). Physical programming for preference driven evolutionary multi-objective optimization. Applied Soft Computing, 24, 341-362. doi:10.1016/j.asoc.2014.07.009SANCHIS, J., MARTINEZ, M., & BLASCO, X. (2008). Integrated multiobjective optimization and a priori preferences using genetic algorithms. Information Sciences, 178(4), 931-951. doi:10.1016/j.ins.2007.09.018Loridan, P. (1984). ?-solutions in vector minimization problems. Journal of Optimization Theory and Applications, 43(2), 265-276. doi:10.1007/bf00936165White, D. J. (1986). Epsilon efficiency. Journal of Optimization Theory and Applications, 49(2), 319-337. doi:10.1007/bf00940762Vasile, M., & Locatelli, M. (2008). A hybrid multiagent approach for global trajectory optimization. Journal of Global Optimization, 44(4), 461-479. doi:10.1007/s10898-008-9329-3Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257-271. doi:10.1109/4235.797969Herrero, J. M., García-Nieto, S., Blasco, X., Romero-García, V., Sánchez-Pérez, J. V., & Garcia-Raffi, L. M. (2008). Optimization of sonic crystal attenuation properties by ev-MOGA multiobjective evolutionary algorithm. Structural and Multidisciplinary Optimization, 39(2), 203-215. doi:10.1007/s00158-008-0323-7Schütze, O., Coello Coello, C. A., & Talbi, E.-G. (2007). Approximating the ε-Efficient Set of an MOP with Stochastic Search Algorithms. Lecture Notes in Computer Science, 128-138. doi:10.1007/978-3-540-76631-5_13Schutze, O., Vasile, M., & Coello, C. A. C. (2011). Computing the Set of Epsilon-Efficient Solutions in Multiobjective Space Mission Design. Journal of Aerospace Computing, Information, and Communication, 8(3), 53-70. doi:10.2514/1.46478Sareni, B., & Krahenbuhl, L. (1998). Fitness sharing and niching methods revisited. IEEE Transactions on Evolutionary Computation, 2(3), 97-106. doi:10.1109/4235.735432Schutze, O., Esquivel, X., Lara, A., & Coello, C. A. C. (2012). Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 16(4), 504-522. doi:10.1109/tevc.2011.2161872Blasco, X., Herrero, J. M., Sanchis, J., & Martínez, M. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20), 3908-3924. doi:10.1016/j.ins.2008.06.01

Diseño multiobjetivo del sistema de gestión de las energías en vehículos híbridos

Consulta en la Biblioteca ETSI Industriales (Riunet)Pajares Ferrando, A. (2013). Diseño multiobjetivo del sistema de gestión de las energías en vehículos híbridos. http://hdl.handle.net/10251/35176.Archivo delegad

Parameter uncertainty modeling for multiobjective robust control design. Application to a temperature control system in a proton exchange membrane fuel cell

[EN] Advanced control systems are tuned using dynamic models and optimization techniques. This approach frequently involves satisfying multiple conflicting objectives. Tuning robust controllers requires considering a framework that represents the system uncertainties, and its definition is not a trivial task. When dealing with a nonlinear model with many parameters, a high-quality representation requires a massive sampling of variations. In many cases, this represents an inaccessible computational cost for the optimization process. This work presents a new methodology for parameter uncertainty modeling that is oriented to tuning robust controllers based on multiobjective optimization techniques. The uncertainty modeling formulated represents a feasible computational cost and leads to robust solutions without attributing excessive conservatism. The novelty of this process consists in using the multiobjective space to define a set of scenarios with highly representative properties of the global uncertainty framework that formulate the control problem under a predefined minimization strategy. To demonstrate the effectiveness of this methodology, we present a temperature control design in a micro-CHP system under worst-case minimization. Based on the results, particular interest is given to verifying the appropriate formulation of the uncertainty modeling, which represents a 92.8% reduction of the computational cost involved in solving the robust optimization problem under a global uncertainty framework.This work was supported in part by grant PID2021-124908NB-I00 founded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe"; by grant SP20200109 (PAID-10-20) funded by Universitat Politecnica de Valencia; and by grant PRE2019-087579 funded by MCIN/AEI/10.13039/501100011033 and by "ESF Investing in your future"; and by the Generalitat Valenciana regional government through project CIAICO/2021/064. Funding for open access charge: CRUE-Universitat Politecnica de Valencia.Veyna-Robles, U.; Blasco, X.; Herrero Durá, JM.; Pajares-Ferrando, A. (2023). Parameter uncertainty modeling for multiobjective robust control design. Application to a temperature control system in a proton exchange membrane fuel cell. Engineering Applications of Artificial Intelligence. 119:1-18. https://doi.org/10.1016/j.engappai.2022.10575811811

Fundamentals for the design of energy management strategies for smart grids based on predictive control techniques. Methodology and case studies (EMS validation test)

This document compiles all the simulations performed to validate the EMS developed in the paper "Fundamentals for the design of energy management strategies for smart grids based on predictive control techniques. Methodology and case studies"This work was supported in part by grant PID2020-116616RB-C31 and grant PID2021-124908NB-I00 founded by MCIN/AEI/10.13039/501100011033 and by ‘‘ERDF A way of making Europe’’; by the Generalitat Valenciana regional government through project CIAICO/2021/064, by Andalusian Regional Program of R+D+i (P20- 00730), and by the project “The green hydrogen vector. Residential and mobility application”, approved in the call for research projects of the Cepsa Foundation Chair of the University of Huelva. Funding for open access charge: CRUE-Universitat Politècnica de València.Pajares Ferrando, A.; Vivas Fernandez, FJ.; Blasco Ferragud, FX.; Herrero Durá, JM.; Segura Manzano, F.; Andújar Márquez, JM. (2023). Fundamentals for the design of energy management strategies for smart grids based on predictive control techniques. Methodology and case studies (EMS validation test). http://hdl.handle.net/10251/19329

Proyectos Zero: activar la comunidad de prácticas para recibir a los nuevos alumnos de Arquitectura activando vocaciones y ampliando y diversificando la profesión de Arquitecto

Investigación sobre metodologías docentes que permitan redirigir vocaciones dispares en los estudiantes de primer año hacia la Arquitectura gracias a la diversificación del perfil profesional del Arquitecto con ejemplos de jóvenes profesionales egresados de la Universidad de Alicante y la puesta en cuestión de forma lúdica del paradigma de la disciplina del Proyecto Arquitectónico heredado de la Modernidad y también de algunas de las alternativas contemporáneas