| dc.description.abstract | Heating and cooling in residential buildings, provided by Heating, Ventilation, and Air-Conditioning (HVAC) systems, represent a crucial load for electric utilities. Fluctuations of heating and cooling loads in residential buildings have a significant impact on a utility’s load profile. Electricity suppliers have introduced time-of-day (TOD) or time-of-use (TOU) electricity pricing, making peak electricity very expensive to consumers, as a means of managing load demand when the grid is near capacity. The impact on the utility’s load profile can be mitigated by grid-interactive efficient HVAC operations that reduce the peak load demand. Pre-cooling is a strategy that reduces the load during on-peak hours by shifting cooling operation from on-peak hours to off-peak hours. Accordingly, many manufacturers have built in rule-based pre-cooling operation strategies into their smart thermostats by setting the space temperature a few degrees lower for a period preceding the start of on-peak hours. However, common rule-based pre-cooling operation strategies might not be an optimal solution for a specific home with specific thermal properties and HVAC system cooling capacity under a given utility rate structure and varying weather conditions in terms of cost savings. Moreover, even though the smart thermostat and utility industries have increasingly collected abundant operational data, there is still a lack of a systematic framework that can utilize such data to generate actionable information for advanced home HVAC system diagnosis and control, and for realizing home energy cost savings and grid-interactive efficient operations. Therefore, the primary research question to address in this study is — What is the fundamental system science underlying the design of such a framework using the data collected from smart devices for the intelligent dynamic management of cooling energy use in a home?
Recognizing that a home thermal model, which is capable of connecting the data such as weather with HVAC operations, is at the heart of this framework, this study first aims to develop such model that is built upon the standard RC (Resistance–Capacitance) approach for one lumped virtual envelope to describe the thermal dynamics of a home. A parameter estimation scheme is also developed that enables automatic, sequential, and optimal estimation of the model parameters, i.e., the thermal properties, of a home, using the data collected through smart thermostats and internet connections. The technical approach includes the development and validation of the home thermal model and its parameter estimation scheme using data collected from a test home. Moreover, with reasonable simplifications to the home thermal model, a model-based envelope performance evaluation method is also proposed to assess the thermal performance of a home envelope in this study. The simplicity of the method allows the parameter to be automatically estimated using a short period of indoor and outdoor air temperature data through data screening without the need for a home’s physical information.
Then, an optimal pre-cooling strategy is developed based on an optimization algorithm that is constructed utilizing the automatically identified home thermal model, which is unique for each home, to search optimal HVAC operations for minimizing energy cost with a given TOU utility rate structure, HVAC system capacity, and weather condition. The algorithm determines the HVAC on/off control signal that minimizes the 24-h energy cost while maintaining thermal comfort and calculates the corresponding optimal indoor air temperature. Through simulations, the results demonstrate that the optimal pre-cooling strategy is indeed significantly more effective than the common rule-based pre-cooling strategies.
Since the optimal pre-cooling is heavily dependent on a specific set of conditions, such as specific thermal properties, HVAC system capacity, utility rate structure, and weather condition, the impact of different sets of conditions on the optimal pre-cooling is investigated by the operation and energy performance analysis on the thermal dynamics, total energy consumption, and energy cost and is also compared with a rule-based pre-cooling through simulations. It is found that the optimal pre-cooling is adaptive based on changing conditions and its performance is significantly dependent on weather conditions and home thermal properties, while its performance may vary for different cooling capacities and utility rate structures. The better the home thermal condition is, the less energy cost the operation requires. In terms of weather condition, it has the dominant impact on the performance of the optimal pre-cooling operation. The hotter the weather is in summer, the more cost savings a good thermal condition home can achieve. Moreover, less energy cost can be achieved for a HVAC system with a higher cooling capacity only when a home has a better thermal condition, and also tends to be achieved for a utility rate structure with a much higher on-peak electricity price than the price during off-peak or/and mid-peak hours. For a home with a poor thermal condition, however, it is found that the optimal pre-cooling strategy may need more energy consumption, while the least energy consumption can always be achieved without sacrificing thermal comfort for a home with a good or better thermal condition, compared with rule-based operation pre-cooling strategies. The superb energy performance of the optimal strategy is attributed to a longer runtime of the HVAC system in cool outdoor air conditions and to the elimination of deadband in the HVAC operation, which is required by the rule-based operation strategies, to allow the indoor air temperature to stay near the thermal comfort upper bound as much as possible. These observations are in line with the analysis and expectations and experience.
Additionally, this study conducts several experiments through a real test home, including the investigations of the impact of internal heat gains on the home thermal model and cooling load calculations using the mode-based method and the HVAC efficiency. This study also investigates the implementation of the optimal pre-cooling strategy and meanwhile demonstrates the effectiveness of the optimal pre-cooling strategy in terms of the operation and energy performance analysis through experiments.
Overall, this study has helped to answer important questions about effective decision making for the operation of HVAC systems, with tremendous potential for minimizing home energy cost. This study is a fundamental research that has culminated in understanding of thermal interactions and investigation of methodologies for achieving grid-interactive efficient operation of HVAC system. This study also contributes to knowledge through the development of step-by-step approach that may be followed to achieve optimal operation of HVAC systems, based on consideration of thermal properties, weather condition, HVAC cooling capacity, and utility rate structure in a smart grid environment. Therefore, the developed framework in this study is useful for advanced home HVAC system diagnosis and control, and for realizing home energy savings and grid-interactive efficient operations. | en_US |
