A grouped control strategy for the retrofit of post-war multi-unit residential building hydronic space heating systems

Energy & Buildings 208 (2020) 109604

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A grouped control strategy for the retrofit of post-war multi-unit residential building hydronic space heating systems Jamie P. Fine a,∗, Marianne F. Touchie a,b a b

Department of Mechanical and Industrial Engineering, University of Toronto, 27 King’s College Circle, Toronto, Canada Department of Civil and Mineral Engineering, University of Toronto, 35 St. George Street, Toronto, Canada

a r t i c l e

i n f o

Article history: Received 21 June 2019 Revised 4 October 2019 Accepted 10 November 2019 Available online 13 November 2019 Keywords: Energy efficiency Post-war buildings Multi-unit residential building Space heating Thermostatic radiator valve Energy model Payback period Thermal comfort Overheating Baseboard heating

a b s t r a c t This study proposes a novel HVAC retrofit for the space heating systems of post-war multi-unit residential buildings (MURBs). Typically, these buildings do not have in-suite temperature control and each suite must be retrofit with a thermostatic valve to provide this control to residents. However, given the potentially high installed cost of these valves, this retrofit strategy can be cost-prohibitive. To alleviate this issue, a retrofit strategy that uses temperature sensors in each suite with a control system that combines the signals from these sensors is proposed. This approach allows for suite control to be grouped, thereby reducing the equipment quantity, and cost of the retrofit. Field data from a case study building in Toronto, Canada, was used to develop and validate an EnergyPlus simulation tool. Results show that with a financially optimal grouping strategy, a 14% reduction in space heating energy consumption, a 68% reduction in overheating, and a payback period 10 years can be achieved. These results compare favourably to the typical strategy of installing one valve per suite, which resulted in a payback period of 15 years. These findings support that using this novel control strategy can be an effective way of economically improving post-war MURB performance.

1. Introduction 1.1. Research motivation Climate change is caused by greenhouse gas (GHG) emissions from human society and is currently viewed as one of humanity’s largest threats. In 2015, most nations around the world ratified the Paris agreement, which confirms their goals of reducing GHG emissions by 30% relative to 2005 emissions levels by 2030 [1]. In Canada, buildings account for 17% of total domestic GHG emissions [2], and almost 50% of GHG emissions from urban regions [3]. In southern Ontario, Canada, there are approximately 1200 post-war multi-unit residential buildings (MURBs) that were constructed between 1960 and 1970, and most do not have functional in-suite temperature control equipment. This lack of control, combined with potentially unbalanced hydronic systems that cause uneven heat distribution [4], typically results in the overheating of suites during the Fall, Winter and Spring months while the space heating system is operational. Current field studies show that this overheating leads to excessive window operation in at-

∗

Corresponding author. E-mail address: jamie.fi[email protected] (J.P. Fine).

https://doi.org/10.1016/j.enbuild.2019.109604 0378-7788/© 2019 Elsevier B.V. All rights reserved.

© 2019 Elsevier B.V. All rights reserved.

tempts to reduce indoor temperatures, even during cold weather periods [5]. This window operation results in unnecessary energy use, poor occupant comfort, and excessive GHG emissions. One existing strategy, which has been implemented to alleviate this overheating issue, involves retrofitting each suite with an individual thermostatic radiator valve (TRV), such as the Danfoss RA20 0 0 [6]. However, at quoted installed unit costs of $745 [private communication with contractor on August 17, 2019, non-unionized labour] to $1400 [private communication with building owner on September 19, 2018, unionized labour], this strategy would result in a total upfront cost of up to $250 M for southern Ontario alone and is cost-prohibitive for mass adoption. This study investigates the effectiveness of implementing a novel grouped building space heating system retrofit strategy that can reduce upfront costs, reduce space heating energy consumption, reduce overheating, and reduce building operating costs. This system is based on the grouping of suites with similar locations, orientation, and exterior exposure in a building, which can exhibit similar indoor temperatures. By grouping suites, a single flow control valve can then be installed to modulate the flow of space heating fluid being sent to each group, thereby reducing the total number of valves that are required within a building, and reducing the upfront cost of installation. With careful selection of a grouping strategy, this system also has the capability to improve building

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J.P. Fine and M.F. Touchie / Energy & Buildings 208 (2020) 109604

Nomenclature Acronyms MURB Multi-Unit Residential Building TRV Thermostatic Radiator Valve Variables Q Pxy Cxy nxy

air flow rate (m3 /s) pressure differential between spaces x and y (Pa) flow coefficient (m3 /s/Pan ) flow exponent (dimensionless)

performance by reducing overheating and the corresponding space heating loads. To achieve this goal, the proposed system leverages wireless in-suite temperature sensors, installed in each suite of the building, to measure indoor temperatures. These temperature signals can then be combined in various ways and adjusted according to the suite and riser layouts in a particular building to determine the current predominant thermal status of a group. Using these data, the flow control valves for each group can then modulate the flow rate of space heating fluid being sent to each group, achieving grouped space heating system control. This retrofit approach is novel as other in-suite radiator control retrofit strategies have only focused on the installation of a control device in each suite of a building, or hydraulic balancing of unbalanced hydronic systems, and not on the grouping of suites to reduce equipment quantity requirements. This paper contributes to the discourse on this topic by proposing this alternative retrofit approach, detailing the techniques needed to analyze performance and identify effective grouping strategies, and determining the efficacy of the proposed retrofit in the context of field measurements from a case study building in Toronto, Canada. To begin, a discussion of existing studies on MURB TRV retrofits will follow. 1.2. Review of existing post-war MURB retrofits The retrofit of MURBs that utilize hydronic baseboard space heating systems, which tend to be from the post-war era, is a topic of increasing interest in many parts of the world. Currently, buildings that do not include in-suite temperature control equipment often rely on outdoor air dry bulb temperatures as an input to a control system that adjusts the water supply temperature to insuite radiators [7]. In these systems, as the outdoor air temperature decreases, the supply water temperature to the radiators increases, and vice-versa. This control method, known as the outdoor air reset method, is commonly used in post-war MURBs in southern Ontario. As discussed in Section 1.1, the lack of in-suite system control in these buildings leads to overheating in many suites since the space heating demands throughout the building are not always uniform, and this overheating motivates residents to open their windows to regulate indoor temperatures [8]. However, only one retrofit study has been carried out in this jurisdiction where four buildings were outfit with smart thermostats and TRVs, and reported a space heating energy savings of 22% - 27% [9]. Unfortunately, little additional detail about this study is currently available, so the remainder of this review includes projects from other locales. In China, many buildings are operated using district heating, where a fluid loop connects multiple buildings to a common heat source [10]. Owners of these buildings are now beginning to investigate the installation of TRVs in apartments since overheating is a common issue leading to 15% to 30% excess space heating energy consumption [11]. A study by Xu et al. investigated the effect of TRVs on overheating and found that the installation of these valves can have an 80% effectiveness in reducing overheating [12]. A study

by Zhang et al. investigated the implementation of TRVs in a building in Harbin, China, using both field studies and computer simulation tools [13]. In their study, a subset of suites within a high-rise apartment building was outfit with TRVs so that the performance of the system could be monitored. This monitoring campaign was used to calibrate a simulation tool, which was then used to predict the impact of a building-wide installation of TRVs. The result of their study showed that a reduction of up to 17% of space heating energy consumption could be achieved by installing TRVs, along with a reduction of over 42% of pumping power for the system. Another study, carried out by Cholwea et al., investigated the use of TRVs in nine MURBs in Lubin, Poland [14]. Each building had an individual heating plant located in the basement of the building. Each apartment in each of these buildings were outfit with TRVs, and depending on the building, hydraulic balancing was also carried out. Multi-season monitoring was completed such that the effectiveness of the TRV and hydraulic balancing retrofits could be compared to pre-retrofit data. The results of the study showed that a space heating energy savings of almost 21% could be achieved if both the TRV and hydraulic balancing retrofits were carried out. Alternatively, if only TRVs were added without hydraulic balancing, a savings of approximately 10% was realized, and if hydraulic balancing was carried out on already installed TRVs, an additional savings of 8.8% was realized. This study clearly illustrates that system balancing and the use of TRVs are both important to achieving optimal energy savings. Monetti et al. investigated the implementation of TRVs to improve the energy performance of a historic building with district heating in Turin, Italy [15]. Changes to the building façade were restricted due to the historical nature of the building, meaning that envelope retrofits were prohibited, thus necessitating a mechanical system retrofit to improve building performance. The historic building consisted of twelve apartments on four floors, and a TRV was added to the hydronic baseboard in each room of each apartment. Suite overheating was not a major concern prior to the retrofit, but room-based temperature control was thought to potentially reduce energy consumption. Building primary space heating loop water flow rate, space heating rate, and temperature data were used to develop and calibrate a base-case energy model, which was then modified to estimate the impacts of the added TRVs. The results of this study showed that energy savings between 2% and 10% could be achieved by installing TRVs, depending on the temperature setpoint control scenario. As illustrated by these studies, TRVs have the potential to reduce building energy consumption as well as improve occupant comfort, especially when combined with hydronic balancing. However, studies in the literature have only investigated the installation of these control devices in each suite of a MURB, which can be financially prohibitive based upon the previously mentioned costs. The study presented here builds on this existing literature by investigating the effectiveness of grouping multiple suites within a building into a single control group, thereby reducing the number of required TRVs. 2. Methodology The purpose of this study is to determine if implementing a grouped building space heating system retrofit can reduce upfront costs, reduce space heating energy consumption, reduce overheating, and reduce building operating costs. To realize this purpose, a variety of grouping strategies were tested using a calibrated case study building energy model, such that the performance and costeffectiveness of multiple group configurations could be compared. The development of the grouping strategies was based on grouping suites with similar space heating loads, such as those suites on adjacent floors or with the same orientation [5], which will

J.P. Fine and M.F. Touchie / Energy & Buildings 208 (2020) 109604

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Table 1 Summary of different grouping strategies. Strategy name

Strategy description

Valves required

Base Case Operation

No control groups utilized, and boiler outlet temperature is only a function of the outdoor air temperature The whole building is treated as one control group All north-facing suites are one control group and all south facing suites are one control group All suites on floors 1–5 are one control group, all suites on floors 6–10 are one control group, etc. The same grouping is used as the “five floors per group” strategy, but each group is also split by north and south exposure The suits from all floors that are vertically aligned are in one group All suites on floor 1 are one control group, all suites on floor 2 are one control group, etc. The same grouping is used as the “one floor per group” strategy, but each group is also split by north and south exposure Each suite in the building has a unique control group

0

One Group for Building North-South Split Five Floors per Group Five Floors per Group with North-South Split One Group per Vertical Suite Stack One Floor per Group One Floor per Group with North-South Split One Group per Suite

1 2 4 8 16 20 40 320

Table 2 Summary of building characteristics. Parameter

Description

Gross Floor Area Walls Glazing Roof

28,730 m2 (24,840 m2 above grade and 3890 m2 below grade unconditioned garage) Brick facade with concrete block backup wall (no insulation), gypsum board interior (U = 3.086 W/m2 K, ρ = 1460 kg/m3 ) 27% of wall area, double-glazed, low-emissivity with thermally-broken aluminium frames (U = 2.687 W/m2 K, g-value = 0.41) Concrete slab with 50 mm polyurethane foam board, built-up roof membrane topped with ballast (U = 0.403 W/m2 K, ρ = 1600 kg/m3 ) 3 × 860 kW natural gas boilers, name plate efficiency of 84%, installed in 2006 Two to three SlantFin 90-A Baseboard in series [16] Makeup air unit on roof. Two central exhaust fans (one for kitchen fans and one for bathroom fans)

Centralized Space Heating System In-suite Radiators Ventilation System

be described in Section 2.1. The particular case study building, described in Section 2.2, was selected because it is representative of the post-war MURB archetype, as mentioned in Section 1.2, and is the target for this type of retrofit. Development of the calibrated energy model for the case study building is described in Section 2.3, which begins with a general overview of the model in Section 2.3.1. In order to simulate occupants’ use of operable windows to reduce overheating, a novel window modelling technique, described in Section 2.3.2, was also developed and can be applied beyond this study. Finally, to assess the efficacy of various grouping strategies, the calibrated building energy model was used in conjunction with a post-processing tool that was developed to select the required baseboard heater mass flow rate. This post-processing tool is described in Section 2.3.4.

Fig. 1. Typical Floorplan of case study building (Note: BR = Bedroom).

2.3. Modelling approach 2.1. Description of grouping strategies Nine different grouping strategies were tested, which resulted in different quantities of control valves being required. These grouping strategies were selected based on similar suite orientation to maximize similarity for solar and wind exposure, and height in the building to maximize similarity for stack effect. A description of each of these grouping strategies for a 20-storey building is presented in Table 1. 2.2. Description of case study building The case study building is a student family residence located in Toronto, Canada, and was built in 1968. The Canadian Weather Year for Energy Calculation (CWEC) Toronto data file was used for the simulation. The building includes an exposed floor slab edge, a pressurized corridor ventilation system, hydronic baseboard heating, and no central air conditioning. In the model, the total height of the building is 20-stories with 320 suites, housing approximately 700 people. A simplified typical floorplan is presented in Fig. 1, an image of the 3D EnergyPlus model is presented in Fig. 2, and additional building characteristics are presented in Table 2.

An energy model of the case study building was developed in EnergyPlus version 8.9 [17] such that hourly building thermal loads and temperature profiles could be generated. 2.3.1. General energy model overview Each individual suite was represented by an individual thermal zone in the building energy model. This model allowed for the temperature and thermal loads of each suite to be analyzed independently, which was necessary since individual suite data is needed to determine the effectiveness of the proposed system. Internal gains: Internal gains were modelled in EnergyPlus using the People, Lights, and ElectricEquipment objects with magnitudes and schedules based on field measurements and a previously calibrated eQuest model, with maximum values of 28 m2 /person, 50 W/m2 , and 50 W/m2 , respectively [18]. These loads accounted for the heat gains from occupants, interior task and general lighting, cooking equipment, and miscellaneous electrical equipment. Plots of the profiles for these loads are shown in Fig. 3. It is important to note that due to constraints with the operable window model (described in Section 2.3.2), the window airflow was still adjusted during scheduled low-occupancy times.

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J.P. Fine and M.F. Touchie / Energy & Buildings 208 (2020) 109604 Table 3 Flow parameters used to determine economizer flow rates.

Fig. 2. Image of EnergyPlus model.

However, since unoccupied hours typically occurred during daytime hours, which exhibited most of the overheating, the assumption that the windows were used to reduce this overheating produces a conservative estimate for the building energy consumption. Similarly, since the main purpose of this model is to compare different grouping cases, and this operational assumption was consistent between the different cases, the comparative results will not be affected. Envelope losses: The envelope characteristics presented in Table 2 and architectural plans were used to model the building envelope. Additional field data from a similar building was used to characterize the air leakage of the exterior envelope and the corridor door of a test suite, which was then used to determine the economizer flow rates as described in Section 2.3.2. These data were used to determine the flow parameters of the exterior envelope and showed that leakage through the exterior envelope was dominated by the air leakage through the windows [19]. Characterization of the corridor door pressure boundary was also carried out using blower door tests in accordance with ASTM-E779 to determine the flow parameters for this pressure boundary [20]. A summary of the flow parameters for the combined door-window air

Window position

Flow coefficient (m3 /s/Pan )

Flow exponent

Closed (infiltration) Open (infiltration) Closed (exfiltration) Open (exfiltration)

0.0036 0.0053 0.0038 0.0064

0.572 0.535 0.616 0.583

flow path for when the window is opened and when the window is closed, for both the infiltration and exfiltration flow directions, is presented in Table 3. These parameters were used with differential pressure measurements from the case study building to determine the pressure differentials throughout the building, which were −50 Pa to +26 Pa at the bottom and top of the building, respectively, in January [5]. These pressure differentials were assumed to vary linearly through the height of the building and were modified as a function of temperature differential between the indoor and outdoor such that the effects of stack could be modified for each month of the simulation period [17]. Air infiltration due to wind was modelled using the ZoneVentilation:WindandStackOpenArea object with a constant opening area to account for wind-induced leakage through the inoperable wall components. However, the stack effect component in this object was set to zero because stack effect was modelled via the operable window model in Section 2.3.2. Space heating system: The primary space heating system was modelled as a closed-loop hydronic system as shown by the process flow schematic of the system in Fig. 4. The boiler was modelled using a Boiler:HotWater object, which allowed for return water from the in-suite radiators to be heated to a controllable temperature setpoint. This setpoint was controlled using the SetpointManager:OutdoorAirReset object, which allowed the temperature at the boiler outlet to be a function of the outdoor air dry bulb temperature. As the outdoor air temperature decreased, the hot water temperature increased, and vice-versa for increasing outdoor air temperatures. Each hydronic baseboard was modelled using the ZoneHVAC:Baseboard:Convective:Water object, along with manufacturer-specified mass flow rates and “U-Factor Times Area” values. This baseboard object was used for simplicity because the quantification of the separated impacts of both radiative and convective heat transfer components on the space was not required, since detailed thermal comfort calculations were not part of the analysis. Furthermore, these two EnergyPlus objects result in the same total zone energy transfer [17, 21]. The pump was modelled using a Pump:ConstantSpeed object, which had a total flow rate equal to the combined flow rate of all in-suite radiators.

Fig. 3. Plot of occupancy, light, and equipment load profiles for a suite.

J.P. Fine and M.F. Touchie / Energy & Buildings 208 (2020) 109604

Fig. 4. Process flow schematic of the building space heating system.

This pump was always set to operate at 100% of the rated flow rate, which is consistent with the operating scheme in the many of the post-war buildings being targeting for this retrofit. Only the outlet temperature from the boiler was changed to control the heat supply rate to thermal zones. It is important to again acknowledge that this operating scheme is both energy inefficient and offers poor indoor comfort given that there is no manual or automated in-suite space heating system control. Therefore, given the ongoing use of this technique in buildings of the post-war vintage, the need for a cost-effective retrofit solution is increasingly significant. To approximate how the boiler outlet temperature varied with outdoor air temperature, measurements of the radiator surface temperatures within the building were used. From a review of these data, the boiler outlet temperatures set in this model were 80°C at −10°C, and 35°C at 15°C [5]. The overall space heating system thermal efficiency was assumed to be 80%, which was based on the boiler name-plate efficiency of 84%, and an assumed 4% additional system heat loss. A simulation time step of 20 min was used, which was selected based upon a convergence study that

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showed a change of less than 1% in temperature and energy consumption profiles with further time step reduction. Base case simulation: Using these building characteristics, a target indoor temperature of 22°C was used for all suites for cooling via operable windows, and the space heating system water flow rate was set to 100% of the design capacity to represent base case operation. A simulation of the building was carried out for the heating season of October 1st to May 15th. The resultant hourly total building space heating load and a sample temperature plots from a north-facing suite on the 13th floor are presented in Fig. 5. The monthly natural gas consumption was compared to measured utility data and a plot of this data is shown in the supporting information of this article. The resulting coefficient of variation of the root mean square error was found to be 7.0%, and the normalized mean bias error was found to be 1.8%. These two parameters fall within the ASHRAE-14 model calibration guideline requirements of 15% and 5% [22], verifying that the baseline model can accurately estimate the monthly energy consumption of the building. Based upon this model, the resulting space heating energy use intensity for the building was found to be 185 kWh/m2 of heated floor area. The temperature data from the model was also compared to measured temperature data from the building. From the model, the average heating season temperature in a 13th-floor north-facing suite was 23.5°C, with a range of 13.3°C to 29.7°C. From the measured data, which was taken with a measurement interval of 4 h and occasional data drops of up to two weeks, the average heating season temperature in the same suite was 23.4°C with a range of 15.2°C to 25.3°C. Therefore, given these temperature results, combined with the acceptable energy prediction, this model was deemed acceptable for use as a tool to determine the effectiveness of installing TRVs, and compare the impacts of different grouping strategies. 2.3.2. Modelling of operable windows to achieve cooling As mentioned in Section 1.2, year-round window operation in this building type is common as a means for residents to regulate their suite temperature in lieu of in-suite control of the hydronic baseboards. Given that a reduction in building energy consumption of over 20% has been found by implementing temperature control in buildings that previously relied on window operation to reduce heating-season overheating [9], heat loss through window operation is expected to account for a significant portion of this associated energy loss, and an analysis of this relative loss was carried out as part of this study. In EnergyPlus, there are two typical methods used to model natural ventilation with operable windows.

Fig. 5. Heating season total building space heating load (left) and sample temperature profile from a 13th-floor north facing suite (right).

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J.P. Fine and M.F. Touchie / Energy & Buildings 208 (2020) 109604

Fig. 7. Different pressure zones for window flow rate determination.

Fig. 6. Schematic of the theoretical suite ventilation system.

The first method uses the ZoneVentilation:WindandStackOpenArea object, which models an opening with a pre-defined area that is opened/closed at a pre-defined temperature. This technique can take stack and wind effects into account, but does not allow for individual windows to be partially opened. This lack of partial opening functionality resulted in convergence issues for the model, even with simulation time steps of one minute, and necessitated an investigation into an alternative technique. One typical alternative technique, the AirFlowNetwork, can be used to calculate air movement between different thermal zones in the model, natural ventilation, wind-driven infiltration, and stack effect. However, using this method requires highly detailed information about the air flow pathways within a building, and since these detailed data were not available, an alternative method was developed as part of this study. The method that was developed as part of this study implemented the equivalent of a theoretical economizer system in each suite, using the AirLoopHVAC:OutdoorAirSystem object. In this study, the goal of this window model is to simulate the use of windows to maintain a constant indoor setpoint temperature, similar to the WindandStackOpenArea object. This model does not account for the detailed occupant behavioural aspects that lead to their opening and closing of windows, or occupancy patterns, but the integration of this functionality is a goal for investigation in future model development. This technique allowed for outdoor air to be used to cool an interior space, as shown by the schematic diagram in Fig. 6. This approach prevented convergence issues since the outdoor air flow rate could be more precisely controlled. This approach also required limited building information compared to the AirFlowNetwork, which made it easier to apply given the data available from the case study building. The main components required to execute this approach are a Fan:ConstantVolume, an OutdoorAir:Mixer, and a Controller:OutdoorAir. The fan is required to facilitate the theoretical air transfer between the mixing box and the thermal zone, but has all heat gain and energy consumption parameters set to zero such that the fan has no impact on the thermal profile of the zone. The flow rate of the fan is set to a constant value, and this value must be tuned for each size suite such that simulation convergence can be achieved, while also ensuring that this value does not constrain the cooling capacity of the system. This cooling capacity is a function of the outdoor air flow rate and temperature, and minimum and maximum limits for this flow rate must be set within the outdoor air controller. Since this system is meant to simulate an

operable window, these minimum and maximum limits correspond to the ventilation air flow rate into the test suite when the window is closed and open, respectively. By using this theoretical system, the simulation program can adjust the air flow through the simulated window between these minimum and maximum limits to achieve the desired reduction in overheating. To ensure that this simulation setup can accurately represent an actual operable window, air leakage testing must be carried out to determine pressure-flow correlations for the pressure boundaries within a suite of interest. These pressure-flow correlations must then be combined with measured pressure data for a building to generate the minimum and maximum flow limits used in the controller, such that they correspond to real-world conditions. The pressure boundaries that must be considered are the boundary between the suite and the exterior (boundary 1–2), and the boundary between the corridor and the suite (boundary 2–3). A schematic showing the different pressure zones and boundaries is shown in Fig. 7. After determining the pressure-flow characteristics of these boundaries through field testing or other methods, the air flow relationship shown in Eq. (1) can used to determine the air flow rate through the suite, which is based upon the power law equation. n23 n12 Q = C12 P12 = C23 P23 = C13 (P12 + P23 )n13

(1)

where Q is the air flow rate, Pxy is the pressure difference between space x and y, Cxy is the flow coefficient for the boundary between space x and y, and nxy is the flow exponent for the boundary between space x and y. To determine the minimum flow rate limit, it can be assumed that the windows in a suite are closed and there is a pressure drop across both pressure boundaries. Alternatively, when the window is open, it can be assumed that the total pressure drop that normally would act across both boundaries only acts across the boundary between the suite and the corridor. These minimum and maximum flow limits can also be adjusted as a function of time in the simulation to capture the effect of changing building differential pressures due to stack effect. 2.3.3. System schematic and operating simulation technique A simplified schematic of the proposed retrofit to the space heating system with a total of four suites and two groups is presented in Fig. 8. It is important to note that any hardware needed for flow bypass or to prevent backflow are not included in this schematic. As shown in Fig. 8, Suites 1 and 2 form Group 1, and Suites 3 and 4 form Group 2. In a real system, there can be more than two groups, and there can also be more than two suites in each group. The flow rate to each group is then controlled by a flow control valve, where there is one valve for each group, depicted as valves

J.P. Fine and M.F. Touchie / Energy & Buildings 208 (2020) 109604

Fig. 8. Schematic of the grouped space heating system.

V1 and V2 for Group 1 and Group 2, respectively. It is important to note that these control valves are upstream of any passive flow splitting that takes place within each group. Then, a central boiler, which supplies heat to the building hydronic loop, is connected to the radiator in each suite of the building. A centralized pump is used to pressurize the system, and it is salient to note that flow bypass or a global pressure regulator may be needed to maintain overall system pressures if a constant speed pump is used. The control valves receive a control signal from a main system supervisory controller, which provides them with the inputs needed to control the flow rate for each group. The supervisory controller receives wireless signals from indoor air temperature sensors in each suite (located based on typical thermostat placement), which are then processed to determine the control signals that should be sent to the flow control valves. 2.3.4. Post-Processing algorithm for suite grouping and system control The energy model is used to determine the hourly indoor temperature profiles and baseboard heating loads for each suite in the building. This information is then combined with a post-processing script, developed in MATLAB [23] for this study, to test different suite grouping strategies and implement the crowd-sourced control strategy. Before the post-processing is carried out, the energy model must be used to determine building performance for a range of baseboard fluid mass flow rates. In each simulation,

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all radiators in the building receive the same fraction of the design mass flow rate of space heating fluid, which assumes that hydraulic balancing is carried out during the retrofit process and using the installed control valves. A range of mass flow rate fractions from 0%, which represents the space heating system being off, to 100%, which represents the system operating at the designed capacity, should be used. In this study, flow rate fraction increments that corresponded to space heating rate capacity changes of approximately 5% were used. It is important to note that cooling via window operation using the process described in Section 2.3.2 was still utilized in conjunction with all grouping strategies when overheating took place as one of the goals of the modified control strategy is to reduce the amount of window operation. The next step involves combining the suites in the building into control groups. Each suite in a control group is supplied the same radiator mass flow rate fraction at a given time step, but different control groups in the building can have different mass flow rate fractions. Once the suites that belong to each control group are selected, post-processing of simulation data can begin. This post-processing analysis uses a time stepping approach with the previously generated simulation data. Minimum and maximum temperature thresholds must be set for the building, which represent when the installed suite temperature sensor will signal that the suite is too warm or too cold. In this study, the “too cold” threshold is 21°C and the “too warm” threshold is 24°C. At the first time step, the system is run at the 100% mass flow fraction and the temperature of each suite in a control group is checked to determine if it is above the maximum temperature threshold, or below the minimum temperature threshold. If a suite temperature is above the maximum threshold, a signal is generated for the suite to indicate that there is a need to decrease the temperature because the suite is too warm. Alternatively, if a suite temperature is below the minimum temperature threshold, that suite would generate a signal to increase the temperature because the suite is too cold. However, if a suite temperature is in-between these two temperature thresholds, no signal is produced. All of the signals for a control group are then assessed based upon a pre-defined control method to determine if the mass flow rate fraction for the radiators in that control group should be changed using the group flow control valve. If a change is needed, a step up or down to the next mass flow rate is made for the control group, resulting in a change of approximately 5% in space heating system capacity. In this study, the pre-defined control method that was implemented was based upon a minimum allowable suite temperature. In this method, if any suite in a control group signalled to increase the temperature, the flow rate fraction for that control group would increase, even if other suites signalled to decrease the temperature. At time steps when no suites signalled to increase the temperature, or decrease the temperature, the mass flow rate fraction was decreased compared to the previous time step to allow the lowest interior temperature to be maintained at the lower temperature threshold. Alternatively, if any suite signalled to decrease the temperature, and no suites signalled to increase the temperature, then the mass flow fraction was also decreased. The indoor temperature and space heating load from the corresponding mass flow fraction energy model simulation results were then selected to represent the suite temperatures and space heating rates for each control group. This process was implemented at each time step in the simulation and allowed for building performance to be determined for each hour in the simulation period, and for a variety of grouping strategies. 2.3.5. Summary of modelling approach To summarize and illustrate the solution process, a flow chart that represents the overall modelling methodology is presented in Fig. 9.

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J.P. Fine and M.F. Touchie / Energy & Buildings 208 (2020) 109604 Table 4 Summary of grouping strategy simulation results (Natural gas carbon intensity = 1.879 kg/m3 [24]). Grouping strategy

Space heating energy use intensity (kWh/m2 )

Space heating energy savings

“Too Warm” Signal Reduction

CO2 Emission reduction (tonne)

Base Case Operation One Group for Building North-South Split Five Floors per Group Five Floors per Group with North-South Split One Group per Vertical Suite Stack One Floor per Group One Floor per Group with North-South Split One Group per Suite

185 174 172 160 154 168 154 147 137

0% 6% 7% 14% 17% 9% 17% 21% 26%

0% 24% 31% 68% 82% 42% 80% 92% 93%

0 44 52 100 124 68 124 152 192

Alternatively, with the maximum retrofit expenditure of one group per suite, as implemented in the buildings discussed in the literature review, the energy consumption of the building can be reduced by 26% and the number of too warm signals can be decreased by 93%. This energy reduction result compares well with the reported energy savings of overheated buildings of approximately 15% to 30% in the literature [9,11,13,14], and further supports the effectiveness of this tool for determining the potential savings of different grouping strategies. When using this grouping strategy, the simulation results also showed that 70% of the energy consumption reduction is attributed to reduced window operation. These reductions are achieved as the heat output to each suite is controlled on a suite-by-suite basis, and overheating is then only a result of factors that are independent of the space heating system. Therefore, there can remain some too warm signals. 3.2. Analysis of grouping strategy and system performance trends

Fig. 9. Modelling methodology flow chart.

3. Results and discussion This section first presents the results specific to the case study building energy model, followed by a discussion of how these results can be applied in other buildings. Finally, this section concludes with a discussion of the financial implications of the proposed retrofit strategy.

3.1. Case study building results A summary of the results from testing the different grouping strategies is presented in Table 4. For reference, the base case operating scenario resulted in a total of approximately 921,0 0 0 registered “too warm” signals over the simulation period, which corresponds an average of 49% of the building being above the upper temperature threshold over the heating season. With the minimum retrofit expenditure, by adding only one control valve to the building, a reduction in annual space heating energy consumption of 6% can be realized, and the number of “too warm” signals can be reduced by 24%. These reductions are achieved since the existing outdoor air reset strategy does not allow for reductions of space heating system output during periods with high solar or internal gains, which are accounted for when using indoor temperature sensors as a control input.

To better understand the broader trends that are exhibited when comparing the grouping strategies, such that this approach can be applied to other buildings, comparisons between intermediate options were also carried out. When comparing the “Five Floors per Group” and “One Floor per Group” strategies, there is both a reduction in energy consumption and too warm votes when using the “One Floor per Group” strategy. This trend is exhibited because, as more suites are combined on a wide range of floors, such as in the “Five Floors per Group” strategy, there is decreasing similarity between each of the suites. To quantify the similarity between suite temperatures, the variance of suite temperature at each timestep within each group was determined and averaged over the simulation period. It was found that the “Five Floors per Group” control strategy had an average intragroup temperature variance of 1.6°C, while the “One Floor per Group” strategy had an average intragroup temperature variance of 1.1°C. This decrease in temperature similarity is due to changes in stack effect as a function of suite height in the building, which results in changing infiltration rates on each floor of the building. Similarly, a comparison was carried out between the “One Floor per Group” and the “One Floor per Group with North-South Split” strategies, and it was found that the average intragroup temperature variances were 1.1°C and 0.6 °C, respectively. In this comparison, the splitting of suites on the south exposure, which receive more solar heating than those with northern exposure, allowed for increased similarity between suites in each group. This increased similarity then results in more accurate space heating system control, producing a reduction in energy use and too warm votes. Plots of the energy consumption reduction and too warm vote reduction as a function of intragroup temperature variances are shown in Fig. 10 for each grouping strategy. As shown in Fig. 10, there is a relationship between the intragroup temperature variance parameter and both space heating energy reduction and overheating reduction. In all of the tested

J.P. Fine and M.F. Touchie / Energy & Buildings 208 (2020) 109604

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Fig. 10. Plots of relationship between intragroup temperature variance effects on system performance.

Fig. 11. Space heating energy reduction as a function of temperature.

configurations, as the variance metric reduces, which indicates increasing suite temperature similarity, system performance improves. These general trends related to improved suite similarity can be used to guide the design of grouping strategies for other buildings, which can then be tested in energy simulation software to quantify the energy savings and overheating reductions. Since climate change has been shown to cause increasing outdoor temperatures, an investigation into the relationship between the outdoor air temperature and the heat loss related to window operation was also completed. This investigation was conducted by comparing the “Base Case Operation” strategy with the “One Group per Suite” strategy and determining the changes in space heating energy consumption and overheating as a function of outdoor air temperature. The results of this comparison are shown in Fig. 11. As shown in Fig. 11, from a space heating energy perspective, as the outdoor air temperature increases, the effectiveness of implementing in-suite temperature control also increases since space heating energy is reduced as a function of outdoor temperature. Alternatively, from an overheating perspective, there is a decreasing trend in the effectiveness of in-suite control. However, this apparent decrease is only because there are very few overheating occurrences when the outdoor temperature is less than 10°C when in-suite temperature control is most effective, but as the outdoor temperature increase there are still hours with overheating caused by factors external to the building space heating system, which could only be compensated for with additional cooling measures. Therefore, based on the energy consumption results, it is expected that as outdoor temperatures increase due to climate change there

will be an increasing importance of implementing improved space heating system control. 3.3. Financial analysis Given that a major motivation of using this retrofit strategy is to reduce the capital cost of the retrofit by reducing the number of valves being installed, a financial analysis that determines the economic practicality of these strategies was undertaken. For this analysis, a valve unit cost based on the required flow rate for each valve in each grouping strategy was used, and a summary of these costs is presented in Table 5. Please note that the Danfoss RA20 0 0 cost used in this financial analysis, which is the conventional TRV, corresponds to the non-unionized price mentioned in Section 1.1. All costs presented in Table 5 were based off of quotes from industry in Toronto, Canada, in August 2019. Similarly, based on additional consultation with industry, a fixed cost of $33,500 was also added to all strategies [private communication with equipment supplier on March 12, 2019, non-unionized labour], except for one group per suite, to account for the purchase and installation of wireless temperature sensors ($42/suite) and a supervisory controller ($20,0 0 0), which will facilitate controlling the valves with based on the control signal from suite temperature sensors. This cost was not added to the one group per suite since it is assumed that individual in-suite thermostats could be used in that scenario, which would not require a supervisory controller. The cost savings associated with each of the different strategies is related to the reduction in natural gas usage, which is related to

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J.P. Fine and M.F. Touchie / Energy & Buildings 208 (2020) 109604 Table 5 Summary and breakdown of installed valve costs (excluding local tax) [costs based upon private communication with Toronto-area contractor on August 16, 2019). Model

Applicable grouping strategy

Material cost

Labour cost

Danfoss RA2000 Belimo G250-N Belimo G680C Belimo G6100C Belimo G6125C Belimo F6150HD Belimo F6200L

One Group per Suite One Floor per Group & One Floor per Group with NS Split Vertical Stack Five Floors per Group with NS Split Five Floors per Group NS Split One Group for Building

$390 $1420 $2895 $3660 $4860 $4710 $6015

$265 $970 $1970 $2490 $3310 $3206 $4090

Table 6 Payback period results.

Grouping strategy One Group for Building North-South Split Five Floors per Group Five Floors per Group with North-South Split One Group per Vertical Suite Stack One Floor per Group One Floor per Group with North-South Split One Group per Suite

Annual space heating energy savings

Initial Cost ($) (including 13% local tax)

Estimated annual space heating energy cost savings

Estimated simple payback period (years)

6% 7% 14% 17%

$ $ $ $

$3100 $3600 $7000 $8600

14.6 14.2 10.1 10.3

9% 17% 21%

$ 121,500 $ 87,500 $141,500

$4700 $8600 $10,600

25.6 10.1 13.3

26%

$ 238,300

$13,400

17.8

44,900 51,400 70,400 89,100

Table 7 Results of overheating reduction economic analysis.

Grouping strategy

Initial cost ($)

One Group for Building North-South Split Five Floors per Group Five Floors per Group with North-South Split One Group per Vertical Suite Stack One Floor per Group One Floor per Group with North-South Split One Group per Suite

$ $ $ $

44,900 51,400 70,400 89,100

24% 31% 68% 82%

5 6 9 8

$ 121,500

42%

3

$ 87,500 $141,500

80% 92%

8 6

$ 238,300

93%

4

the space heating energy reduction. For this case study, the cost of natural gas was taken as 0.131 $/m3 [25] and a natural gas energy density of 37.3 MJ/m3 [26] was used. Using these values and the simple payback formula shown in Eq. (2), an economic analysis of each grouping strategy was carried out. The payback period analysis does not take into account energy price forecasting, and is therefore likely more conservative than a amortization analysis since energy prices have historically increased at a greater rate than inflation in Toronto [27]. The results of these financial calculation are presented in Table 6.

Payback Period =

Number of “Too Warm” signals reduced per dollar spent

“Too Warm” Signal reduction

Initial Cost Annual Savings

(2)

As shown in Table 6, the payback periods range from 10 to 26 years, which can be compared to a typically acceptable payback period of approximately 10 years [28]. Given this typically acceptable payback period, the “Five Floors per Group”, “Five Floors per Group with North-south Split”, and “One Group per Floor” strategies offer economically practical options for this building, and are all more economical than the conventional retrofit strategy (i.e. the one group per suite retrofit). One additional investigation of interest is the relationship between the capital cost of the system and the reduction of overheat-

ing in the building. Table 7 shows the relationship between capital cost and the reduction of “too warm” signals per dollar spent. As shown in Table 7, the most economically effective way to reduce the amount of overheating in the building is to implement the “Five Floors per Group” strategy. This strategy offers the highest reduction in “too warm” signals per dollar spent, and when coupled with the payback period analysis also offer acceptable periods. The effectiveness of this strategy is due to the similarity of suite temperatures within the group, allowing for the suite with the lowest temperature within each group to be representative of the overall group, resulting in both reduced overheating and a low quantity of valves being required. It is important to note that depending on the riser layout of the building, not all tested grouping strategies may be possible. For example, although the detailed riser layout for the case study building is not available, a review of other post-war MURBs in Toronto, Canada, shows that risers that run vertically to each suite stack are often used. Therefore, it may be necessary to install additional bypass valves to allow for grouping of suites along the height of the building, as needed for the two optimal grouping strategies in this case study. This requirement for additional valves may increase the cost of some grouping strategies, which would need to be considered on a case-by-case basis for each building retrofit.

J.P. Fine and M.F. Touchie / Energy & Buildings 208 (2020) 109604

Fig. 12. Plot of retrofit payback period as a function of capital cost fraction.

Finally, the cost of each valve will have an impact on the payback period of the system and may negate the benefits of reducing the number of valves being installed. For example, based upon the two quotations for conventional TRVs in Toronto that were acquired as part of this study, the installed unit cost showed considerable range from $745 to $1400. However, an installed cost closer to $50 per valve was mentioned by both Cholewa [14] and Monetti [15], which is much lower than the quotations received in Toronto. This discrepancy is likely due to different local material and labours costs as the values reported in these studies were from within the last 5 years. Therefore, although the quotations that were acquired as part of this study cannot be adjusted to the other referenced costs, a sensitivity study that investigates payback period sensitivity as a function of capital cost was carried out for the Five Floors per Group, One Group per Vertical Stack, and One Group Per Suite strategies. These strategies were selected since they represent the optimal strategy, a riser layout constrained strategy, and the base case retrofit. This sensitivity study was carried out by multiplying the initial capital cost of each respective strategy by a “capital cost fraction” of 0.05 to 1.25, and using the originally calculated annual cost savings for each respective strategy. The results of this study are presented in Fig. 12. As shown in Fig. 12, depending on the capital cost fraction that is utilized, the practicality of each strategy will change based upon a payback period limit of approximately 10 years. However, it is important to note that the relative payback periods of each strategy remain constant if the same capital cost fraction is applied. Even at lower capital cost fractions, the Five Floor strategy remains more economical than the One Group per Suite retrofit. Similarly, the Vertical Stack retrofit consistently remains the least economical option. Therefore, depending on the riser layout, specific building thermal profile, energy cost, and system capital cost, the most effective retrofit strategy may vary, and detailed analysis of each building and economic situation should be carried out to determine the strategy that should be implemented. 4. Conclusion

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tical stack strategy, were more cost effective than a conventional TRV installed in each suite. The effectiveness of each strategy was also found to be a function of the intragroup in-suite temperature variance, with lower variances resulting in higher retrofit effectiveness, and this trend is expected to be applicable to other buildings of this construction type, HVAC design and control, and vintage. It was also found that if different pricing is used to estimate the system capital cost, and if the same relative pricing of each strategy is used, the relative cost-effectiveness of each strategy will remain constant. These findings demonstrate that using this novel control and retrofit method can be an effective way of improving the performance of post-war MURBS. Future work will include refining the signal generation algorithm, replacing temperature sensors in each suite with occupant-driven data collection, development of the software needed to implement this control strategy, and a pilot project in Ontario, Canada, to further investigate the effectiveness of this strategy. Funding Funding for this work was provided through the Dean’s Spark Professorship (held by Dr. Touchie), which supported by the Dean of the Faculty of Applied Science and Engineering at the University of Toronto. Funding for this work was also provided through a CMHC-NSERC Postdoctoral Fellowship (held by Dr. Fine). Author declaration We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from: jamie.fi[email protected] Declaration of Competing Interest None. Acknowledgements

The results of this study show that the proposed grouped retrofit strategy is an effective way to reduce energy consumption and overheating, while still being cost-effective. For the case study building, it was found that creating groups with five floors per group, which required eight control valves, was the most effective strategy from an economical perspective. The strategy resulted in a 14% reduction in annual building space heating energy consumption, an 68% reduction in overheating, and a payback period 10 years. Other strategies were tested, and it was found that all implementations of the grouped strategy, except for the ver-

The authors would like to thank Xinxiu Tian for the air tightness testing used in the model, and the building owners for supplying the utility data and floor plans used for the model construction and validation. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.enbuild.2019.109604.

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