Development and Experimental Validation of a TRNSYS Dynamic Tool for Design and Energy Optimization of Ground Source Heat Pump Systems

[EN] Ground source heat pump (GSHP) systems stand for an efficient technology for renewable heating and cooling in buildings. To optimize not only the design but also the operation of the system, a complete dynamic model becomes a highly useful tool, since it allows testing any design modifications...

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Detalles Bibliográficos
Autores: Ruiz-Calvo, F., Corberán, José M., Montagud- Montalvá, Carla|||0000-0002-7118-6119, Cazorla-Marín, Antonio|||0000-0003-3314-0395
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/104884
Acceso en línea:https://riunet.upv.es/handle/10251/104884
Access Level:acceso abierto
Palabra clave:Ground source heat pump
Geothermal energy
Heating and cooling
TRNSYS modeling
Experimental validation
TERMODINAMICA APLICADA (UPV)
MAQUINAS Y MOTORES TERMICOS
Descripción
Sumario:[EN] Ground source heat pump (GSHP) systems stand for an efficient technology for renewable heating and cooling in buildings. To optimize not only the design but also the operation of the system, a complete dynamic model becomes a highly useful tool, since it allows testing any design modifications and different optimization strategies without actually implementing them at the experimental facility. Usually, this type of systems presents strong dynamic operating conditions. Therefore, the model should be able to predict not only the steady-state behavior of the system but also the short-term response. This paper presents a complete GSHP system model based on an experimental facility, located at Universitat Politècnica de València. The installation was constructed in the framework of a European collaborative project with title GeoCool. The model, developed in TRNSYS, has been validated against experimental data, and it accurately predicts both the short- and long-term behavior of the system.