Energy efficient planning and scheduling of HVAC services in smart buildings

• Main Author: Radhakrishnan, Nikitha
• Other Authors: Su Rong
• Format: Theses and Dissertations
• Language: English
• Published: 2017
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
• Online Access: http://hdl.handle.net/10356/69896
• Institution: Nanyang Technological University

The building sector represents at least 40% of the worldwide primary energy consumption and in tropical Singapore, electricity comprises the single largest building operating expense with up to 60% of the energy going into air-conditioning. Much of this energy consumption is wasted. Environmental Protection Agency (EPA) studies suggest that 30% energy savings can be realized through improvements to facilities and facility management. This work proposes a novel distributed architecture for controlling Heating, Ventilation, and Air conditioning (HVAC) systems in commercial buildings. We regard heating/cooling as a service. The provider of the service is the HVAC system and customers are the thermal zones. Zone Modules in each thermal zone use local models and measurements to compute requests for HVAC service over various future time windows. These requests are expressed in terms of the heating/cooling service required, which we can conceptually regard as tokens. A Central Scheduler balances token requests and allocates tokens to each zone for the next time slot. This allocation attempts to minimize total energy consumption while respecting operational constraints. Zone modules update their local models based on the measured thermal responses resulted from allocated tokens and re-compute forward token requests. This strategy is implemented in a Model Predictive Control framework. The proposed token based architecture is inspired by medium access control protocols in communication networks and is called Token Based Scheduling Strategy. It offers several advantages in the context of HVAC systems. The architecture is scalable to large buildings with 200-500 thermal zones, it is robust relative to non-stationary environmental conditions and unanticipated changes in user needs, and it is modular enabling low-cost deployment without requiring expensive custom thermal modeling of buildings. The proposed architecture can readily accommodate a wide variety of operational factors like chiller efficiency through Coefficient of Performance (COP) specifications, as well as constraints on cooling air mass flow rates, fan capacities, duct pressure distribution, and damper opening constraints. Simulation studies reveal that the proposed approach suffers modest performance loss as compared with centralized non-linear scheduling strategies. These centralized strategies, however, are not scalable to buildings with 200+ zones and suffer prohibitive deployment costs. The token based scheduling strategy is further extended towards minimizing energy costs by incorporating the Time-of-Use electricity pricing strategy. This helps in shifting electricity usage to off-peak periods and reducing energy demand peaks. Further, by arbitraging among consumer comfort margins, buildings can change their energy consumption patterns to provide flexibility to the grid. A new framework for contracting flexibility in buildings that includes temporal constraints and a decentralized approach for computing the online flexibility in buildings is also proposed. While this strategy has applications to HVAC systems in general, the focus of this thesis will be air-conditioning systems in tropical climates