Résumé
Model predictive control (MPC) has been a well-studied advanced supervisory control approach for optimizing the operations of building heating, ventilation and air-conditioning (HVAC) systems, with the objectives of reducing energy consumption and delivering better comfort. However, centralized MPC designs are often 1) not scalable to the increasing sizes of the building systems, 2) not adaptive to subsystem addition/attrition, i.e., ‘Plug-and-Play’ implementation. Agent-based approaches, such as distributed model predictive control (DMPC), are attractive alternatives.
In this paper, taking a multiple rooftop units (RTU) coordination problem as case study, we experimentally investigate the energy saving potential by implementing an agent-based DMPC strategy to coordinate the operations of multiple “virtual” RTUs with diverse unit efficiencies (COP) in an open space with multiple thermal zones. The operations of three RTUs are emulated by two groups of variable air volume (VAV) diffusers and a separate VAV box, that can be individually controlled to provide continuously changing sensible cooling rates into respective zones. Three laptop computers are dispatched into the three thermal zones as local agents. A server computer connected to both the Building Automation System (BAS) and the outside internet is responsible for predicting various exogenous inputs and exchanging information with the local agents. Experimental results show that the proposed agent-based DMPC design and implementation is able to achieve over 20% cost savings, in terms of electricity consumption charge with Time-of-Use pricing schedules, while at the same time maintaining local occupancy comfort.
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Détails
- Titre original : An agent-based control implementation for the optimal coordination of multiple rooftop units.
- Identifiant de la fiche : 30025062
- Langues : Anglais
- Source : 2018 Purdue Conferences. 5th International High Performance Buildings Conference at Purdue.
- Date d'édition : 09/07/2018
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