Abstract
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∘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.
Highlights
As the global focus to decrease Greenhouse Gas (GHG) emissions continues, the demand for cleaner electricity is increasing; this promising development puts a strain on electric grids
Business as usual (BAU) indoor temperature setpoint profile, with a nighttime setpoint of 18 ◦ C and a daytime setpoint of 22 ◦ C, was created as a benchmark, which is shown in the top graph of Figure 10 as a dashed black line
Typical known occupancy schedules for the warehouse (7 a.m.–6 p.m.) and weather data were used for this Model Predictive Control (MPC) study; an existing weather forecast tool such as CanMETEO [45] could be incorporated into eventual implementation within the building automation system in a real building
Summary
As the global focus to decrease Greenhouse Gas (GHG) emissions continues, the demand for cleaner electricity is increasing; this promising development puts a strain on electric grids. Utility grids have been incorporating clean renewable energy generating resources such as photovoltaics (PV) or wind turbines. This increase in intermittent production results in a supply side that has a variable output, creating new challenges for utilities and electricity customers. One major challenge linked to integrating distributed renewable energy sources into utility grids is that times of high consumption from buildings rarely correspond with the power generation from these intermittent renewable sources. Periods of peak consumption from buildings (morning and evening for residential and afternoon for commercial) do not match the time of greatest solar generation, which occurs midday, as depicted by the well-known illustration of the “duck curve” [1]. Similar supply–demand mismatch issues are observed elsewhere, for different reasons (for instance in heating dominated regions)
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