Heat pumps in Ontario

Table 1 contains a list of the cities examined and a measure of the hours below 18.3 \(^\circ\)C converted to days. These heating degree days (HDD) are as few as 3444 in Windsor, and as many as 6017 in Timmins. Also listed are the normal daily minimum, average, and maximum temperatures in the month of January. January is typically the coldest month of the year. Warmest temperatures usually occur during the midafternoon, and coldest temperatures usually occur in the very early mornings prior to sunrise. Wind can have a more significant effect on heating needs in cities like Kingston than in Ottawa or Timmins, but all of these particulars are captured in the TMY created for the city by Environment Canada.

It is assumed that the climate data used will result in average heating needs. However, it should be noted that, in Ontario, from 1990 to 2015 inclusive, the HDD index has been 8% lower on average with a 95% confidence interval of 2.7% [46]. Warmer weather usually results in better heat pump performance, resulting in a greater proportion of heating supplied by heat pump, but may or may not result in greater economic benefit as there may be less opportunity to provide heat overall.

Heating demand is profoundly affected by building construction, and we therefore lay out in detail the average prototypical SDD. Homes in Ontario are typically constructed using wood frames with exterior cladding and gypsum wall board on the interior wall. The cavities left between wood studs in the frame are hopefully filled with insulation. However, the average level of insulation taken from Swan and Ugursal et al. [62] results in a partially filled wall cavity if fibreglass batt insulation is used (see Table 4). Most homes have basements [62] with less wall insulation than upper levels, and an asphalt shingle covered peaked roof. The greatest level of insulation found in the home is usually at the juncture between the attic and upper living area. Table 2 enumerates the basic properties of the prototypical SDDs used in this study.

Dwellings can be SDDs, semi-detached duplexes, townhouses or row-houses, low-rise apartments, or even high-rise apartments and condominiums. Only SDDs are considered in this work. This narrow scope reduces the number of results to present while still showing the benefit of heat pump use for the majority (54.3%, see Table 3) of dwellings in Ontario [42, 59]. SDDs are often built by similar methods, the details of which are discussed in the following sections.

Two SDDs were used in the weather simulations—a one-storey and a two-storey building. These both had square footprints and a living area of 173 m\(^2\) (see Table 3) was maintained for both. As a result, the footprint of the two-storey building was reduced. The four walls (Walls) of the structures are oriented to face the cardinal directions: north, south, east, and west. All features like windows and doors (see “Fenestration”) are spread evenly across all the walls. The roof is a hip roof enclosing an attic space. Both are described in “Attic and roof”. The effects of all weather conditions, including solar insolation, are calculated by Energyplus™ for each city using the appropriate TMY weather file (see “Estimation of hourly heating needs, Air infiltration, Heat loss estimation in EnergyPlus”). Both prototypical homes have a basement extending 1.5 m below grade that is not considered part of the conditioned living area.

Walls are a wood-frame wall common to North American home construction [1, 3, 36]. Wood studs are 39 mm wide by 90 mm deep and spaced on 400 mm centres (2 \(\times\) 4 on 16 inch centres). The wall interior has a gypsum wall board 12.7 mm (0.5 in) thick and the exterior consists of a 25.4 mm (1 in) wood board sheathing, a 12.7 mm (0.5 in) felt air gap and finally, a 12.7 mm (0.5 in) hardboard wood siding.

A roof and attic was added to more accurately model common residential building designs. A 6/12 roof pitch was used. This denotes a 6 unit rise per 12 units of length, or a rise of 26.6\(^\circ\). The roof is covered with asphalt shingles on top of 19 mm wood sheathing. These are supported by 39 \(\times\) 140 mm rafters. Details of the thermal modelling are shown in Table 4. Not shown in the table is attic ventilation. This was modelled as a leakage area of 5000 cm² for the single-storey detached home as per the work of Kneifel and Hendron et al. [22, 36] where 1 unit of ventilation is added for every 300 units of attic floor area. A two-storey home requires half of this leakage area because it has half the attic area due to the fact that the interior living area remains constant and is spread across two levels.

Fenestration consists often of only one or two windows per room. For this reason, windows are assumed to be operable. In the event that some windows are in reality inoperable this assumption results in a slightly conservative (higher) estimate of heating needs (inoperable windows have a slightly lower heat loss—U = 2.24 W/m²K). Table 5 details the properties used to model fenestration. These properties are sourced from ASHRAE Fundamentals 2013, Chapter 17 [3].

Infiltration of air into the home’s heated volume is an important factor in the overall heat load calculation. From Swan et al., the average number of air changes per hour at a 50 Pa (ACH₅₀) wind-induced pressure differential is 6.5 ACH₅₀ [63]. From the ACH₅₀ value, we can calculate an equivalent leakage area (ELA)—the sum total area of all the openings in the building envelope that would produce an equivalent ACH at that pressure (50 Pa)—using Eq. 2:\(Q_\mathrm{r}\) is the volume of exchanged air per second for the heated volume (V) in question, and it is calculated using Eq. 1 [3]:ELA is calculated using a discharge coefficient, \(C_\mathrm{D}\), which can be approximately 0.611 for a sharp-edged orifice or 1.0 as used by Sherman and Grimsrud [57]. We use \(C_\mathrm{D}=1.0\) for both Eqs. 2 and 3. The density of air in Eq. 2 is represented by \(\rho\), and \(\varDelta P_r\) is the pressure differential of either 50 Pa or 4 Pa:The lower pressure of 4 Pa is used in Energyplus™ to model normal wind loading conditions, and the \(\mathrm{ELA}_{4Pa}\) is determined using Eq. 3 also from ASHRAE Fundamentals [3]. As previously mentioned, \(C_{D1}=C_{D2}=1.0\) and n is a pressure exponent found empirically to be 0.65 [3].

From this starting point, the \(ELA_{4Pa}\) is used to calculate infiltration-induced heat loss according to weather conditions–wind speed \(v_\mathrm{wind}\) and the difference between indoor (\(T_\mathrm{in}\)) and outdoor (\(T_\mathrm{out}\)) temperatures (see Eq. 4). The coefficients \(C_\mathrm{s}\) and \(C_\mathrm{w}\) modify the stack effect and wind effects, respectively. The infiltration model described is based on the work of Sherman and Grimsrud [57] elaborated within chapter 16 of the ASHRAE handbook of fundamentals [3]. \(C_\mathrm{s}\) varies depending on the height of the building. Single-storey buildings have a \(C_\mathrm{s}\) of 0.000145, and two-storey buildings 0.000290. The wind speed coefficient \(C_\mathrm{w}\) is based on the level of sheltering to be expected and again the height of the building. Coefficients corresponding to an urban setting, “where obstacles are more than one building height away” 0.000104 and 0.000137 are used for buildings of one- and two-storey heights, respectively [3]:

As previously mentioned, EnergyPlus™ modelling software is used to estimate the hourly heating needs of the average SDD in each of the seven cities analyzed. The particulars of construction of the home as described in the preceding sections are inputs to the EnergyPlus™ IDF files used to define the dwelling in each city. Weather is also specified in the IDF, using a TMY file for each location. Outputs from the model include heat losses and outdoor temperatures. With the knowledge of these two pieces of information and the interior set point, the heating system response is calculated from manufacturer-provided performance data elaborated in the following sections.

Table 6 shows the resultant yearly heating needs in kWh for the average SDD in each city, alongside the average residential heating needs gleaned from Statistics Canada natural gas consumption data [60] for 2014 (latest year with HDD index of 1.00—heating needs similar to the expected average), and also from Natural Resources Canada’s Comprehensive Energy Use Database for SDDs in Ontario in 2014 [39, 44, 45, 47]. The Statistics Canada average applies to all residential natural gas customers. These data are a proxy for estimating heating needs. It is assumed that natural gas consumption in July (672 kWh per household) is not for home heating, but instead represents a base energy need for cooking and hot water. We have therefore subtracted this value from all months of the year to arrive at the average in Table 6. 31,420 kWh/year is the estimate without subtracting July consumption. No heating system efficiencies were applied to the Statistics Canada data set, as there are no such data available. In the case of the NRCan estimate, efficiencies for heating system types are applied because NRCan provides heating system efficiencies that can be weighted by secondary energy consumption [44]. The weighted efficiency is multiplied by the total energy used for heating to arrive at the approximately 25 MWh/year in Table 6. While these estimates are not not used for calibration of the simulations, the resultant heating needs are reasonably close to the province-wide averages in cities like Kingston, Ottawa, and Sudbury. In much colder, but less populous cities like Thunder Bay and Timmins the deviation is between 17 and 38%. More concerning is the fact that heating needs in Toronto, the most populous city in Ontario, are estimated to be 15–20% lower than the average, although there are relatively fewer SDDs and more apartments in Toronto than in other cities [59].

Electricity is subject to time-of-use (TOU) pricing. This means that during winter months the price is lowest during the evening hours of 7 pm–7 am. It is moderately priced between the hours of 11 am and 5 pm, and most costly from 7 to 11 am and 5 to 7 pm. Prices are 6.5, 9.5, and 13.2 cents per kWh. Saturdays, Sundays, and holidays are billed at the lowest rate for all hours of the day.

All electrical consumption measured at the meter is multiplied by a total loss factor (TLF) intended to account for losses in transmission and some of the costs of maintaining the grid system. The TLF is different for each utility and regulated by the OEB. Costs tend to be higher for medium- and low-density populations, and lowest for those living in higher density urban environments. All cities analyzed are urban in terms of population density and are therefore treated as high density for calculating the TLF.

Beyond the TLF, charges are applied per kWh consumed to compensate utilities for delivery and regulatory costs. These are shown in Table 8 as a Delivery and Regulatory Adder (DRA). Again, the OEB approves applications made by the utilities to adjust these prices and also applies reductions or increases where it is demonstrated that the true costs were different than expected. The total cost of electricity per kWh consumed at offpeak, midpeak and peak times is shown in Table 9.

Charges applied monthly at fixed rates are not included in the calculations because the homeowner would have to cease all use of electricity to avoid them. This is rarely feasible and would render the discussion of economic feasibility here within moot. However, for reference a fixed delivery charge of approximately $21 per 30 days of service is applied alongside a $0.25 regulatory administration fee and a $0.79 smart metering charge [17, 20, 23, 24, 35, 65, 67].

Fossil fuel prices are often subject to similar fixed monthly costs. Natural gas in particular tends to have an identical $21 per month delivery charge. Energy consumption costs are generally calculated per cubic metre of gas volume, but for this study all pricing is shown per kWh of energy consumed at the meter. The conversion factor used is 10.361 kWh/m³ [48]. Delivery, transportation, storage and other consumption-related fees are shown in Table 9 to allow direct comparison of costs with other energy sources.

Furnace oil prices are collected per jurisdiction; an average of the monthly prices over the year preceding July 2017 are used in this study. These prices are also shown in Table 9 [38].

As shown in Table 10, GHG emissions due to the use of electricity vary hourly. This variability occurs because of the number and type of generators supplying electricity changes throughout the day. Data obtained from the Independent Electricity System Operator (IESO) provides historical generator output by type and hour [26]. An average day in January is shown in Fig. 14.

Emissions from all electricity generators in Ontario are available from Environment and Climate Change Canada [14]. GHG emissions reported are exclusively for natural gas (and a few oil) fired generators. Taking the reported emissions for these generators and dividing by the electricity output of these same natural gas generators for the year, an average GHG emission intensity per kWh generated is calculated (560gCO₂e/kWh_e). The hourly generating mix for each month is averaged over the whole month to produce an average day.

Electric conventional furnaces or baseboard heaters are the only conventional heating systems capable of achieving 100% efficiency. In this study we attribute this high efficiency to all electric heating systems. The electrical energy consumptions shown in the Fig. 17 are therefore equivalent to the heating needs of our prototypical average home in each city (shown in the left).

We see, in Fig. 17, that Timmins has the greatest heating requirements and Windsor has the least heating required. It is apparent that all heat pumps, whether controlled with a balance temperature point or the advanced regime, use less energy over the year than conventional electric heat. Energy reductions are substantial, ranging between 21% in Timmins using the single-stage heat pump to nearly 80% in Windsor with the variable ductless heat pump.

The advanced control system seems to provide greater reductions in energy consumption than the usual balance point control. This is because both the heat pumps and the electric backup heating systems are powered with electricity and any time that a heat pump can be operated it will cost less than electric heat. Balance point control switches over to the conventional furnace when the heat pump can no longer provide the full heating. This switch occurs once the outdoor temperature is low enough that the home’s heat loss exceeds the heat pump’s capacity. While this is a simple control strategy to implement, it prevents energy from being saved due to the fact that the heat pump could still provide for part of the heating demand, while the electric furnace can supply the remainder. Simultaneous operation is possible if the heat pump indoor heat exchanger is installed before the electric furnace coils.

Natural gas prices are far lower per unit of energy than electricity. This has no effect on energy consumption for the balance point controlled heat pump results, but has a significant effect on those using the advanced controls. It is simply not economical to use a heat pump as often when natural gas backup heat is available. Because of this trade-off between cost and energy savings, we see that, in Fig. 18, the energy consumption is nearly always greater under advanced control.

Despite the negative effects of energy prices, we see that energy consumption can still be reduced by using heat pumps instead of conventional natural gas heating. Depending on the city and control strategy chosen, energy consumption can be reduced by approximately 20–80%. These significant energy consumption reductions will necessarily lead to fewer GHG emissions in a jurisdiction like Ontario, where electricity is generated with few fossil fuel inputs.

Oil furnaces tend to have the lowest energy efficiency of any type of heating system. We used 80% efficiency for oil furnaces in our model (2% higher than NRCan) [44]. This lack of efficiency results in the greatest energy needed to replenish the heat lost during the year of any of the heating systems analyzed. Figure 19 shows the high furnace oil energy needs alongside the much lower energy requirements of the six heat pump simulations. Because oil heating is often more expensive than using a heat pump, energy abatement remains economical. That is, energy consumption is always lower when heat pumps are modelled with advanced control, partly because heat pumps can operate at lower temperatures than with a simple economic balance point, but also because the control system can avoid times when heat pump performance is low and electricity prices are high. We can see an overall reduction of energy consumption of between 36% and 84% throughout the seven cities and six scenarios in Fig. 19.

The cost of heating with electricity is one of the most expensive options available (Fig. 23). Conventional furnaces or baseboard heaters show costs ranging from approximately $2000 per year in Windsor, where both the weather is mild and the cost of electricity is low, to more than $3600 per year in Timmins where electricity is more costly and the climate is much colder.

For homes using electric heat, even the single-stage heat pump offers a considerable annual savings of approximately $600 to almost $1000, using a balance point of − 4 \(^\circ\)C (Fig. 24). With advanced controls, we can see that maximal savings occur in Kingston. The advanced control system provides a significant increase in economic performance for both the single-stage (SS) and the variable speed centrally ducted (VC) heat pumps.

Variable speed heat pumps are even less expensive to operate. The variable speed ductless (VD) heat pump system has a clear advantage regardless of the control system used. Savings often exceed $2000 per year for the ductless system.

Natural gas heat is widely regarded as the least costly, but Fig. 25 and 26 show that heat pumps can provide lower cost heating in all climates. Unfortunately, it appears that only the variable ductless system can produce any significant savings. In fact, the variable centrally ducted heat pump tends to cost more to operate than conventional heating because it possesses the capacity for heating but not at high enough COPs to overcome the economic disadvantage imposed by very low natural gas prices.

Advanced control is able to ensure some savings, however marginal, in every jurisdiction with every heat pump, but as seen before this means that the heat pumps are often turned off in favour of conventional natural gas heat. Though cost-efficient, natural gas furnaces are not as energy efficient as heat pumps, and energy consumption results in “Natural gas backup heat” and Fig. 18 confirm this.

The best of today’s technology—the variable speed ductless heat pump—is capable of providing heat for all or most of the heating season throughout Ontario, and it can also overcome the enormous price advantage of natural gas heat (see Fig. 26). However, it is unlikely that these yearly savings are sufficient to pay back the initial capital investment quickly enough for most homeowners.

Oil heating is similar to electric heat in that it is costly. Oil furnaces are also less efficient than either natural gas or electric heat, and this further adds to their operating costs. There is therefore much opportunity to save on annual heating costs by adding a heat pump to an oil home heating system. Figures 27,  28 show both the high cost of oil heating and the significant savings to be had when using a heat pump.