AN EVOLUTIONARY ALGORITHM-BASED MODEL PREDICTIVE CONTROL FOR COMBINED ELECTRICAL AND THERMAL ENERGY SYSTEMS

Sarah Noye; Asier Urueña Andreu; Ruben Mulero Martinez; Ricardo Alonso Segade; Angel Carrera Martin

doi:10.35490/EC3.2024.241

AN EVOLUTIONARY ALGORITHM-BASED MODEL PREDICTIVE CONTROL FOR COMBINED ELECTRICAL AND THERMAL ENERGY SYSTEMS

Sarah Noye
, Asier Urueña Andreu
, Ruben Mulero Martinez
, Ricardo Alonso Segade
, Angel Carrera Martin

Basque Research and Technology Alliance (BRTA)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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Resumen

Energy storage is crucial to increase renewable energy adoption in construction. By optimizing their control strategies, operational costs decrease and return on investment improves. Model Predictive Controls (MPC) have been used to optimize the use of energy storage but are costly to implement. This paper presents an MPC with a generalized mathematical model for electrical and thermal storage. A methodology is introduced to account for physical restrictions. Three evolutionary algorithms were compared for the optimization and a Genetic Algorithm was found to best reduce the energy bill with average daily savings of 38.7 %.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2024 European Conference on Computing in Construction
Editores	Marijana Srećković, Mohamad Kassem, Ranjith Soman, Athanasios Chassiakos
Editorial	European Council on Computing in Construction (EC3)
Páginas	703-710
Número de páginas	8
ISBN (versión impresa)	9789083451305
DOI	https://doi.org/10.35490/EC3.2024.241
Estado	Publicada - 2024
Publicado de forma externa	Sí
Evento	European Conference on Computing in Construction, EC3 2024 - Chania, Grecia Duración: 14 jul 2024 → 17 jul 2024

Serie de la publicación

Nombre	Proceedings of the European Conference on Computing in Construction
Volumen	2024
ISSN (versión digital)	2684-1150

Conferencia

Conferencia	European Conference on Computing in Construction, EC3 2024
País/Territorio	Grecia
Ciudad	Chania
Período	14/07/24 → 17/07/24

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 7: Energía asequible y no contaminante

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10.35490/EC3.2024.241

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Noye, S., Urueña Andreu, A., Mulero Martinez, R., Alonso Segade, R., & Carrera Martin, A. (2024). AN EVOLUTIONARY ALGORITHM-BASED MODEL PREDICTIVE CONTROL FOR COMBINED ELECTRICAL AND THERMAL ENERGY SYSTEMS. En M. Srećković, M. Kassem, R. Soman, & A. Chassiakos (Eds.), Proceedings of the 2024 European Conference on Computing in Construction (pp. 703-710). (Proceedings of the European Conference on Computing in Construction; Vol. 2024). European Council on Computing in Construction (EC3). https://doi.org/10.35490/EC3.2024.241

Noye, Sarah ; Urueña Andreu, Asier ; Mulero Martinez, Ruben et al. / AN EVOLUTIONARY ALGORITHM-BASED MODEL PREDICTIVE CONTROL FOR COMBINED ELECTRICAL AND THERMAL ENERGY SYSTEMS. Proceedings of the 2024 European Conference on Computing in Construction. editor / Marijana Srećković ; Mohamad Kassem ; Ranjith Soman ; Athanasios Chassiakos. European Council on Computing in Construction (EC3), 2024. pp. 703-710 (Proceedings of the European Conference on Computing in Construction).

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title = "AN EVOLUTIONARY ALGORITHM-BASED MODEL PREDICTIVE CONTROL FOR COMBINED ELECTRICAL AND THERMAL ENERGY SYSTEMS",

abstract = "Energy storage is crucial to increase renewable energy adoption in construction. By optimizing their control strategies, operational costs decrease and return on investment improves. Model Predictive Controls (MPC) have been used to optimize the use of energy storage but are costly to implement. This paper presents an MPC with a generalized mathematical model for electrical and thermal storage. A methodology is introduced to account for physical restrictions. Three evolutionary algorithms were compared for the optimization and a Genetic Algorithm was found to best reduce the energy bill with average daily savings of 38.7 \%.",

author = "Sarah Noye and \{Urue{\~n}a Andreu\}, Asier and \{Mulero Martinez\}, Ruben and \{Alonso Segade\}, Ricardo and \{Carrera Martin\}, Angel",

note = "Publisher Copyright: {\textcopyright} 2024 European Council on Computing in Construction.; European Conference on Computing in Construction, EC3 2024 ; Conference date: 14-07-2024 Through 17-07-2024",

year = "2024",

doi = "10.35490/EC3.2024.241",

language = "English",

isbn = "9789083451305",

series = "Proceedings of the European Conference on Computing in Construction",

publisher = "European Council on Computing in Construction (EC3)",

pages = "703--710",

editor = "Marijana Sre{\'c}kovi{\'c} and Mohamad Kassem and Ranjith Soman and Athanasios Chassiakos",

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Noye, S , Urueña Andreu, A, Mulero Martinez, R, Alonso Segade, R & Carrera Martin, A 2024, AN EVOLUTIONARY ALGORITHM-BASED MODEL PREDICTIVE CONTROL FOR COMBINED ELECTRICAL AND THERMAL ENERGY SYSTEMS. En M Srećković, M Kassem, R Soman & A Chassiakos (eds.), Proceedings of the 2024 European Conference on Computing in Construction. Proceedings of the European Conference on Computing in Construction, vol. 2024, European Council on Computing in Construction (EC3), pp. 703-710, European Conference on Computing in Construction, EC3 2024, Chania, Grecia, 14/07/24. https://doi.org/10.35490/EC3.2024.241

AN EVOLUTIONARY ALGORITHM-BASED MODEL PREDICTIVE CONTROL FOR COMBINED ELECTRICAL AND THERMAL ENERGY SYSTEMS. / Noye, Sarah ; Urueña Andreu, Asier; Mulero Martinez, Ruben et al.
Proceedings of the 2024 European Conference on Computing in Construction. ed. / Marijana Srećković; Mohamad Kassem; Ranjith Soman; Athanasios Chassiakos. European Council on Computing in Construction (EC3), 2024. p. 703-710 (Proceedings of the European Conference on Computing in Construction; Vol. 2024).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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AU - Alonso Segade, Ricardo

AU - Carrera Martin, Angel

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Y1 - 2024

N2 - Energy storage is crucial to increase renewable energy adoption in construction. By optimizing their control strategies, operational costs decrease and return on investment improves. Model Predictive Controls (MPC) have been used to optimize the use of energy storage but are costly to implement. This paper presents an MPC with a generalized mathematical model for electrical and thermal storage. A methodology is introduced to account for physical restrictions. Three evolutionary algorithms were compared for the optimization and a Genetic Algorithm was found to best reduce the energy bill with average daily savings of 38.7 %.

AB - Energy storage is crucial to increase renewable energy adoption in construction. By optimizing their control strategies, operational costs decrease and return on investment improves. Model Predictive Controls (MPC) have been used to optimize the use of energy storage but are costly to implement. This paper presents an MPC with a generalized mathematical model for electrical and thermal storage. A methodology is introduced to account for physical restrictions. Three evolutionary algorithms were compared for the optimization and a Genetic Algorithm was found to best reduce the energy bill with average daily savings of 38.7 %.

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Noye S , Urueña Andreu A, Mulero Martinez R, Alonso Segade R, Carrera Martin A. AN EVOLUTIONARY ALGORITHM-BASED MODEL PREDICTIVE CONTROL FOR COMBINED ELECTRICAL AND THERMAL ENERGY SYSTEMS. En Srećković M, Kassem M, Soman R, Chassiakos A, editores, Proceedings of the 2024 European Conference on Computing in Construction. European Council on Computing in Construction (EC3). 2024. p. 703-710. (Proceedings of the European Conference on Computing in Construction). doi: 10.35490/EC3.2024.241

AN EVOLUTIONARY ALGORITHM-BASED MODEL PREDICTIVE CONTROL FOR COMBINED ELECTRICAL AND THERMAL ENERGY SYSTEMS

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