While the potential of model-based control is recognized, the development of reasonably accurate models for control applications remains a challenging, cumbersome and time-consuming task. This paper proposes a systematic and generalizable approach – based on low-order control-oriented thermal network (RC) archetypes – for the development, testing and implementation of readily scalable control solutions for buildings. These archetypes, focusing specifically on control applications, can significantly facilitate assessing the effect of control strategies on energy efficiency and load management. Furthermore, this approach can also be used for characterization, design and testing of simple retrofit strategies. The utilization of RC-based archetypes for common types of zones (such as those heated/cooled with forced-air or radiant systems) is proposed. These simple models (often 1st to 4th order models suffice), can also be used for the control of residential buildings. For larger buildings, zonal models can be combined to form whole building models.

This paper presents an experimental and theoretical study of the dynamic response of residential buildings with different levels of thermal mass and their respective space heating peak demands for different room temperature set point profiles. Experiments were conducted at two identical and highly instrumented houses. One of them is modified with different floor coverings, while the other one is kept unchanged and used for reference. Their dynamic response to a night time setback set point profile with step changes is monitored and analyzed. Equivalent RC network thermal models are developed for a north zone of the houses. These models are then used to study the impact of set point ramping lengths and “near-optimal” transition curves between two temperatures on the peak demand reductions, while maintaining thermal comfort. It was found that, while taking into consideration the thermal response of the building due to the level of mass, an appropriate yet simple set point strategy to reduce peak electricity demand can be established. For a room with wood flooring, by replacing the conventional night time setback temperature profile with a one hour or two hour ramp, peak demand reductions of up to 10% and 25% can be achieved, respectively

Optimal management of thermal energy storage in a building is essential to provide predictable energy flexibility to a smart grid. Active technologies such as Electric Thermal Storage (ETS) can assist in building heating load management and can complement the building’s passive thermal storage capacity. The presented paper outlines a methodology that utilizes the concept of Building Energy Flexibility Index (BEFI) and shows that implementing Model Predictive Control (MPC) with dedicated thermal storage can provide predictable energy flexibility to the grid during critical times. When the utility notifies the customer 12 h before a Demand Response (DR) event, a BEFI up to 65 kW (100% reduction) can be achieved. A dynamic rate structure as the objective function is shown to be successful in reducing the peak demand, while a greater reduction in energy consumption in a 24-hour period is seen with a rate structure with a demand charge. Contingency reserve participation was also studied and strategies included reducing the zone temperature setpoint by 2${}^{\circ }$C for 3 h or using the stored thermal energy by discharging the device for 3 h. Favourable results were found for both options, where a BEFI of up to 47 kW (96%) is achieved. The proposed methodology for modeling and evaluation of control strategies is suitable for other similar convectively conditioned buildings equipped with active and passive storage.

A control-oriented model of an Electrically heated Thermal Energy storage device (ETS) is presented. The ETS consists of bricks heated up to 871 °C with electric coils; heat is discharged with an airflow passing through channels in the bricks. The device stores energy during off-peak periods to meet future on-peak building heating loads. While its rationale is understood, improvement regarding operation strategies is needed. The charging/discharging could be adjusted according to predicted heating loads to reduce bills, and enhance building energy flexibility in its interaction with the grid. The model, to facilitate implementation of model predictive control (MPC), is based on the following features: (a) calibration of grey-box resistance-capacitance models with a heat exchanger model; (b) periodic state updates from sensors; (c) “effective” brick conductivity. A 1-capacitance model with state updates and “effective” brick conductivity was comparable to a detailed 140-capacitance model. Therefore, a simple model with modifications gives adequate predictions and thus can aid with MPC. Finally, strategies are evaluated to study energy flexibility potential. Using the ETS during the morning critical demand hours and anticipating the rise in setpoint temperature from night-time setback, peak reduction 11% is obtained. A reduction of 73% is found when HVAC heating coil is limited.

A total of 2, 302 gravity stations established in the Sudbury area (N.T. S. map sheet 41 I) in the period 1945-1969, was used to compile the Bouguer gravity anomaly map (scale 1:250, 000) with 2 mgal contour interval. Most of the area is characterized by a relatively positive regional gravity anomaly which in its western part includes the Sudbury structure; it may reflect the distribution of gabbroanorthositic rocks within the crust. Regional gravity lows appear to be related to large bodies of granite, such as the Cartier granite and the Killarney granite , or to granitegneisses of the Grenville Province. Many local gravity anomalies correlate well with the geology but the Sudbury structure itself cannot be related to a simple gravity anomaly.