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Smart Thermostats Trial John Ward & Stephen White ET/IR 970/R 12th June 2007 Prepared for Sustainability Victoria This project was supported by Sustainability Victoria’s CBEII program

Enquiries should be addressed to:

Dr Stephen D White CSIRO Energy Centre PO Box 330 Newcastle NSW 2300

Distribution list Sustainability Victoria – Nick Alsop 2

Investa Property Group – Naveen Radhappan 1

Investa Property Group – Stuart Wallace 1

Investa Property Group – David Raina 1

Investa Property Group – Craig Roussac 1

CSIRO Energy Technology – Stephen White 1

CSIRO Energy Technology – John Ward 1

CSIRO Energy Technology – Terry Jones 1

CSIRO Energy Technology - Records 1

This is copy number 7 of 10 Important Notice

© Copyright Commonwealth Scientific and Industrial Research Organisation (‘CSIRO’) Australia 2007 All rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO. The results and analyses contained in this Report are based on a number of technical, circumstantial or otherwise specified assumptions and parameters. The user must make its own assessment of the suitability for its use of the information or material contained in or generated from the Report. To the extent permitted by law, CSIRO excludes all liability to any party for expenses, losses, damages and costs arising directly or indirectly from using this Report.

Use of this Report The use of this Report is subject to the terms on which it was prepared by CSIRO. In particular, the Report may only be used for the following purposes. this Report may be copied for distribution within the Client’s organisation; the information in this Report may be used by the entity for which it was prepared (“the

Client”), or by the Client’s contractors and agents, for the Client’s internal business operations (but not licensing to third parties);

extracts of the Report distributed for these purposes must clearly note that the extract is part of a larger Report prepared by CSIRO for the Client.

The Report must not be used as a means of endorsement without the prior written consent of CSIRO. The name, trade mark or logo of CSIRO must not be used without the prior written consent of CSIRO.

Smart Thermostats Trial

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Contents Executive Summary................................................................................................................. 1 Acknowledgments.................................................................................................................... 1 1.0 Introduction ........................................................................................................................ 2 2.0 Electricity Industry Demand Response.............................................................................. 3

2.1 Wholesale Electricity ...................................................................................................... 3 2.2 Constrained Electricity Network ..................................................................................... 4

3.0 Trial Setup ......................................................................................................................... 6 3.1 Building Details .............................................................................................................. 6 3.2 Building Upgrade Works ................................................................................................ 6 3.3 Monitoring Equipment .................................................................................................... 7

4.0 Experimental Results and Performance Assessment........................................................ 8 4.1 Baseload Energy............................................................................................................ 8 4.2 Demand Response ...................................................................................................... 12

5.0 Discussion ....................................................................................................................... 17 5.1 Seasonal Thermostat Energy Reduction Summary ..................................................... 17 5.2 Electricity Industry Demand Response Summary........................................................ 17 5.3 Thermal Comfort of Building Occupants ...................................................................... 18

6.0 Recommendations........................................................................................................... 20 Appendix A – Trial Description .............................................................................................. 21 Appendix B – Building Selection............................................................................................ 22 Appendix C – Carrier quote for generic HVAC system upgrade............................................ 24 Appendix D – TAC quote for modifications to the BAS.......................................................... 26 Appendix E – Building Automation System Web Interface .................................................... 28 Appendix F – Experimental Schedule.................................................................................... 29


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Executive Summary Air conditioning is a major cause of peak electricity demand with building heating ventilation and air-conditioning (HVAC) accounting for around 60% of total energy consumption in commercial buildings. It also contributes significantly to peak electricity demand, and resulting electricity infrastructure investment. This report presents the results of “smart thermostat” control trials in a 10,555 m2 commercial building in Melbourne. The trials were conducted over the summer of 2006/2007 and focussed on both

• Seasonal temperature setpoint (thermostat) adjustment for reducing annual energy consumption and greenhouse gas emissions; and

• Temporary raising of temperature setpoint during times of summer peak electricity demand to reduce building peak demand for electricity industry benefit.

The smart thermostat technology was achieved through software changes to the Building Automation System (BAS) to (i) increase zone temperature setpoints uniformly across all zones, and (ii) provide a web interface for remote monitoring and changes to the setpoint. This relatively simple upgrade was achieved at a price of $4,680 excl GST. Increasing the temperature setpoint from 22.5°C to 23.5°C was found to reduce HVAC energy consumption by up to 15% on the days of the trial. This surprisingly large efficiency saving results from a combination of both reduced heat load on the building and improved chiller efficiency. A variety of steady state and transient data analysis techniques were used to confirm the validity of this result for this building. However, this level of savings may not be valid for all buildings and will be lower on a whole-of-year basis. Short duration demand response trials on the building showed that short term demand could be reduced by between 20% and 45%, corresponding to a demand response setup cost of $25 to $75/kW. The extent of demand response was highly dependent on ambient conditions, baseline assumptions and the available chiller capacity. Unfortunately, the measured control system response was not as clear as hoped for, in a number of key ways.

• A delay of around 30 minutes occurred between adjusting the thermostat setpoint and the change flowing through to a reduction in HVAC energy consumption.

• Large jumps in demand, resulting from compressor staging through the inbuilt chiller controls, prevented a uniform demand reduction.

• When chiller capacity was constrained, precooling was not effective and could potentially result in some zone temperatures rising.

Each of these issues could be addressed with more sophisticated controls, particularly controls that include forecasting and self learning capability. The trial was successful in demonstrating the benefits of utilising temperature setpoint as a means of reducing greenhouse gas emissions, or for providing a temporary reduction in peak demand for the electricity industry. However, additional buildings should be trialled to gather a greater sample set of successful implementations. Further research on building HVAC control strategies is also recommended to overcome the uncertainties experienced in the temporary demand reduction trials.

Acknowledgments CSIRO gratefully acknowledge Investa Property Group for providing the trial site and assistance in running these trials. This project was initiated by Sustainability Victoria and their assistance with project organisation was integral to the success of the trial. This project was supported by Sustainability Victoria’s CBEII program.


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1.0 Introduction HVAC energy consumption is responsible for approximately 60% of commercial building greenhouse gas emissions and around 12% of Australia’s total energy related greenhouse gas emissions. It is also responsible for a similar proportion of peak electricity demand in the national electricity market. Furthermore, in certain geographic locations, commercial building HVAC can account for up to 40% of peak electricity demand. With (i) $30billion of electricity infrastructure investment projected over the next 15 years to satisfy growing peak demand, and (ii) an environmental imperative to reduce greenhouse gas emissions, commercial building HVAC would appear to be an attractive target for new energy efficiency technologies. This report presents the results of a new energy management technology trial in a 10,555m2 commercial building in Melbourne. The new “smart thermostats” energy management technology can alter the temperature set point of the commercial building by either

• Seasonally raising the temperature setpoint during summer (cooling mode) and lowering the setpoint during winter (heating mode), to achieve a reduction in overall building energy usage. Energy savings are achieved because (i) the temperature difference between indoor and outdoor conditions is reduced resulting in reduced heat load on the building and (ii) the airconditioning system should operate more efficiently with a smaller differential between indoor and outdoor temperatures. The resulting energy savings will then lead to lower monthly energy usage bills and a reduction in greenhouse gas emissions.

• Temporarily raising the temperature setpoint during times of summer peak electricity demand to reduce the peak demand of a commercial building. This relies on the thermal inertia of the building to absorb heat for a period of time until the building has warmed up to its new setpoint temperature. In this way cooling and associated electricity demand is eliminated for a period of time after the setpoint is raised. This may be able to reduce maximum demand charges for a site or it could potentially attract an incentive payment from an electricity utility as a means of deferring electricity infrastructure investment as discussed in Section 2.

Similar approaches have been applied to HVAC systems in the past. For example ice storage has sometimes been used for shifting large quantities of cooling to periods where electricity can be supplied at low cost off-peak tariffs. The smart thermostats technology has the benefit of utilising existing building thermal mass and so does not require extra storage equipment and associated cost. Many building management systems have some level of inbuilt peak demand management functionality. Unfortunately, this functionality is rarely used or understood. The aim of this Smart Thermostats Trial is to provide a well documented demonstration of the technology functionality and to assess:

• the cost and benefits of smart thermostat technology; • the energy reductions possible from variations in temperature set-points • the suitability of smart thermostat technology for retail and network peak demand

reductions; and • acceptance of smart thermostat technology by building occupants.

This will help to enable Victorian commercial building owners to assess the suitability of the technology and deploy the technology where appropriate.


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2.0 Electricity Industry Demand Response Strong growth in electricity demand, since deregulation of the electricity industry, has resulted in a significant reduction in the reserve margin between peak demand and generation supply capacity. Growth has also resulted in a number of constrained network distribution areas, particularly in Western Sydney and South East Queensland. Air-conditioning is considered a major cause of this growth in demand. While the traditional solution has been to build additional generation, transmission and distribution infrastructure, there is an increasing interest in solving these problems from the demand side. There are two key scenarios where airconditioning demand management can feasibly make a significant contribution to operation of the electricity system:

1. During times of extreme price volatility on the wholesale electricity market. In this case reduced energy consumption saves the electricity retailer money when the wholesale spot price is higher than the agreed electricity tariff being paid by the end-user.

2. When parts of the electricity distribution network are constrained. In this case, the local network service provider saves money by deferring network infrastructure investment in the knowledge that demand can be reliably and sustainably reduced to a level where network limits are not exceeded.

These scenarios are discussed in more detail below.

2.1 Wholesale Electricity The Australian National Electricity Market (NEM) operates to balance electricity generation supply with demand. Generators are dispatched up to the required capacity to meet supply demand, with all generators receiving the price ($/MWh) of the highest cost generator that was dispatched over each interval. As an example of electricity price and demand characteristics, Figure 1 below shows these for the 22nd and 23rd January 2007 in the Victorian region of the NEM. Note that over the two days, demand follows a similar cycle, though at around 4pm on the 23rd the price spikes from a usual value of around $30-$40 per MWh up to almost $2000/MWh. Over these two days, this single price spike accounted for almost 30% of the traded value.

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Figure 1: Electricity demand and price showing a price spike, 22nd and 23rd January 2007

Note that the duration of the price spike shown above is only 30minutes. This is typical of most price spikes in the electricity market, as can be seen from Figure 2 which shows the average number of price spikes and their durations based on historic data from the Victorian NEM region over the years 2000 to 2006.


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Figure 2: Average price spike duration for different thresholds. Data is averaged over years 2000 to 2006.

The economic benefit of achieving a demand reduction at times of peak energy pricing can be further seen from Figure 3 which shows the impact of the highest cost energy on the total market value. This is based on data for Victoria over the 2006 calendar year. We note that in this case, the 20 hours of highest cost energy account for over 20% of the total market value for the year, leading to a massive economic benefit for a demand response program that is able to successfully targets these peak price periods.

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From the above characterisation, it is apparent that in order to respond to most spikes in the electricity market, a demand side response must:

• provide about 2 hours of load reduction; and • be able to reduce load for around 20 hours per year.

2.2 Constrained Electricity Network When the local capacity of the network is close to the limit of its ability to deliver peak electricity demand, the network operator will begin to plan how it will increase (augment) capacity to cope with future increasing demand. This can be achieved through a variety of network infrastructure measures, or by reducing demand so that the network can be left the way it is. In this way, the value of demand management is obtained from deferring capital expenditure on the network infrastructure measures.


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A typical summer demand profile from a predominantly commercial area is illustrated in Figure 4. This profile illustrates a relatively constant peak demand lasting as long 6 hours. If an individual demand management solution can only provide a temporary solution then numerous sources of demand management can be aggregated to satisfy the longer duration requirement of network demand management.

Figure 4: Example summer demand profile (DM Options Paper for Padstow Area, Energy Australia, 2004) While demand management in the wholesale energy market can be provided anywhere in the NEM Region, network demand management must be provided in the specific locality of the network constraint. We now describe the details of the trial.


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3.0 Trial Setup A reasonably typical Melbourne office block was selected to obtain quantitative data on the cost and performance of using smart thermostat controls to produce a demand response. Building selection and upgrade works, required to facilitate the remotely triggered demand response, are described below.

3.1 Building Details In conjunction with Sustainability Victoria and Investa Property Group, a number of potential trial sites were identified and assessed for suitability for this trial. Details of the selection process are given in Appendix B. The selected site was ‘Site C’. The selected site is a reasonably standard office building comprising 11 floors and a total floor area of around 10555m2. The HVAC system comprises 2 chillers (a screw compressor and centripetal compressor with capacities 780 and 600 kW thermal respectively), 2 boilers (each around 700kW thermal) and 11 air handling units – one on each level. In addition to the main HVAC system, there is a supplementary unit that provides additional cooling on levels 6 & 9 where call centres operate. Prior to commencement of the trials, one of the HVAC chiller compressors broke down, reducing the peak cooling available from the chiller system. Consequently chiller capacity was more constrained than might be expected in a typical commercial building. This could be seen as an advantage, because building operation under constrained chiller capacity conditions is an important feature of hot day power demand profiles.

3.2 Building Upgrade Works To facilitate the trial, the Building Automation System (BAS) was upgraded to include:

• A global temperature setpoint control. The existing controls configuration allowed temperature setpoints for each zone within the building to be configured independently. The control feature allows these setpoints to be overridden to allow adjustment of all building temperature setpoints.

• A global temperature setpoint deadzone control was added to adjust the temperature difference between operation in heating mode and cooling modes. The air handling unit for each zone of the building has both a hot and cold water coil with proportional valves to control flow and consequently heating and cooling. The role of the temperature setpoint and deadzone in controlling these valves is illustrated in figure 5 (although the actual system has different gains). In the warmer seasons, increasing the deadzone reduces the amount of heating provided to the building. This is especially beneficial since any heat added to the building in the morning will ultimately result in additional cooling demand later in the day. Unfortunately, the operation of this feature was not able to be assessed because only cooling mode was used over the trial period.

Figure 5 – Typical heating & cooling characteristic illustrating the use of a dead-zone temperature band


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• A web interface was added so that a demand reduction can be triggered remotely, potentially in response to constraints on the electricity network. The web interface was used throughout the trial to monitor and control the experiments from the CSIRO Energy Centre in Newcastle. Screenshots of the interface are given in Appendix E

The building automation system upgrade works were carried out by TAC to CSIRO specifications at a cost of $4,680 excluding GST. The quote and further details of the upgrade works can be found in Appendix D. This price is based on upgrading an already reasonably advanced building automation system. A quote was also obtained to determine the likely cost of achieving similar functionality in a building without an existing BAS. While not directly comparable, the likely upgrade cost was around $8500 excluding GST. Details of this quote can be found in Appendix C. Additional costs are likely to be incurred in planning and implementing

3.3 Monitoring Equipment In order to allow a full analysis of the building response to the demand reduction trials, additional instrumentation and data logging facilities were added to the building. Using the BAS system, measurements were made of:

• all building zone temperatures, around six per level and 68 in total; • ambient temperature conditions; and • current consumption of each individual chiller

These were recorded every five minutes throughout the period of the trials. In addition, a power analyser was installed in the building switchroom to monitor:

• current, voltage and power of the total building HVAC system; and • current, voltage and power of the supplementary systems on level 6 & 9.

The power analyser was configured to also store these values each five minutes. By measuring the total power at the mechanical services board, we are able to assess the full impact of the demand reduction trial, including the effects on the chillers, air handling units, water circulation pumps, cooling towers and all other HVAC equipment. Data from the BAS was not always reliable. Analysis of the data showed that typically 5% of zone temperature sensors were providing false readings, reaching up to 10% at times. This data was excluded from the results whenever possible.


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4.0 Experimental Results and Performance Assessment There were two different control strategies under investigation in this trial:

1. improving baseline energy consumption, leading to annual energy savings; and 2. providing focused electricity demand reductions during specific times of high prices or

when the network is constrained. Either (or both) of these strategies can be implemented in commercial buildings. Experimental tests using each of these strategies are discussed below.

4.1 Baseload Energy The impact of thermostat setpoint on baseload energy consumption has been assessed in three ways. The first method compared the average power demand on three almost identical days with the thermostat set at 21.5°C, 22.5°C and 23.5°C. This method produces a single power demand data point for each temperature setpoint under comparable conditions. The second method compared the power demand at two set point temperatures (22.5°C and 23.5°C), with power measured during various steady state periods covering a wide variety of outdoor conditions. Regression of the much larger power demand data set enables steady state trends to be compared. The third method involves dynamic modelling of varying power demand over the full range of transient conditions. This model aims to fully characterise building thermal dynamics and its dependence on each variable. 4.1.1 Identical Ambient Conditions Data Points Experiments were conducted on three days to investigate the impact of thermostat setpoint on steady state energy consumption:

• 4th Dec – setpoint temperature at a nominal 22.5°C • 9th Jan – setpoint adjusted from 22.5°C to nominal 23.5°C between 10am to 4pm • 1st Feb – setpoint adjusted from 22.5°C to nominal 21.5°C between 10am to 4pm

These three days were chosen due to very similar ambient conditions, including maximum temperature, temperature profile, relative humidity, cloud cover and wind speed & direction – as recorded by the Commonwealth Bureau of Meteorology for Melbourne CBD. Results of these trials are shown in Figure 6. In the figure, TAverage is the average zone temperature within the building (based on 68 individual temperature measurement points), TSetpoint is the building setpoint temperature and ‘HVAC power’ is the total power consumption of the main HVAC system including chillers, controls, pumps and air handling units. It is apparent, from Figure 6, that the thermal conditions between 2pm and 3:30pm, on each day, are representative of steady state conditions. Data from these time periods is presented in Table 1. Cycling of power demand on the 1st of February results from compressor staging. The average power demand over the period is used for this data point. Table 1 shows that a decrease in setpoint temperature requires an additional 49kW/°C to maintain conditions, whilst an increase in setpoint temperature brings about an energy saving of around 33kW/°C. That is, there is around 15% change in total HVAC power per degree Celsius change in zone temperature.


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Figure 6: Experimental results from baseload energy consumption trials. Note that the cycling on 1st Feb is caused by a chiller compressor cycling on/off.

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1 Feb 2007 21.5°C 26.9°C 22.5°C 22.0°C 299 kW 49 kW/°C

Table 1: Results of Baseload Energy Reduction Experiments 4.1.2 Steady State Data Points at Varying Ambient Conditions Data collected throughout the trial period was examined and the power demand was recorded wherever periods of reasonably steady state conditions were identified. The resulting power demand data points are plotted as a function of external (ambient) temperature in Figure 7.


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Figure 7: scatter plot and fit of raw data showing HVAC power versus ambient temperature for different setpoints.

There is considerable spread in this data as there are many factors, other than setpoint and ambient temperature, which contribute to the measured power demand. For example:

• Performance of a HVAC system utilising cooling towers is much more dependant on ambient wetbulb temperature than the drybulb temperature measured;

• The heat load on the building changes throughout the day depending on usage patterns and solar radiation (including shadowing of the building at different times of the day).

• At low ambient temperatures the building operates in economy mode using outside air rather than building return air to condition the spaces. This mode of operation has different dynamics to those described in this report.

However, the general trends are certainly clear being • HVAC power demand increases with increasing external temperature; and • HVAC power demand increases with decreasing building temperature set-point.

Consistent with the identical conditions experiments in section 4.1.1, there is a 10% to 20% change in total HVAC power demand for a 1°C change in zone temperature. 4.1.3 Transient Modelling of All Data Most of the measured data was collected under conditions which could not be considered steady state. These transient data points have a wide range of influencing factors such as (i) building warm up/cool down in the morning (when the airconditioning system is turned on), (ii) rapidly changing outdoor conditions etc. While little can be interpreted from a single point in this non steady state data set, transient modelling of the full data set can be used to characterise the building and draw more sophisticated conclusions. A small signal model was developed for this purpose. The model utilises the HVAC power, ambient temperature and an identified thermal baseload profile for the building to estimate the average zone temperature for the building. This model is of the form:

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k1, k2, k3, k4, k5 adjustable parameters obtained by best fit to measured data

τ1, τ2 are the dominant thermal time constants for the building and HVAC system

s Is the complex Laplace variable

Initial Conditions accounts for uncertainty in the internal thermal states of the building fabric and HVAC system at the start of the measurement period. These initial conditions result in a transient that is a combination of the natural modes of the system and hence is of the form: . These modes are explicitly identified so as to not bias the system identification.

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Thermal baseload is an identified baseload profile that accounts for different thermal loads throughout the day. This will be dependant on factors such as solar gain and the activities of the building occupants. The thermal baseload is parameterised as a piecewise linear function. This baseload function is assumed to be the same for every day in the data set, so is found by determining the function that when subtracted from each day of the recoded building data yields the best fit to the measured data. The identified function is shown in Figure 9. Note that the temperature baseload is highest in the morning and decreases throughout the day. This is consistent with the solar gain for this building as there is significant eastern exposure, although the site is substantially shaded from the north and west.

5 minute interval data for 16 of the hottest days recorded was fitted to this model, using regression analysis, to determine the various parameters. The coefficient of determination of the fit over this data set is r2=0.956, suggesting that the model provides a good fit. Additional second order terms were evaluated (ie power squared) but they did not significantly alter the quality of the fit so are not included in the model. The fit is shown in figure 8 below.

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Of the model parameters k5 represents the dependence of power demand on indoor temperature. The best fit value of k5 is 12.1 kW/°C. Depending on the particular operating


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point, this corresponds to around 5-10% reduction in HVAC power for each degree the setpoint is raised. This is slightly lower than for the steady state data presented in sections 4.1.1 and 4.1.2. It is important to note that this method is based on all of the measured data for these days, so this figure is representative of the total (ie whole of season) savings available through thermostat setpoint changes – not just those achievable at the peak of hot days.

Figure 9: Identified thermal baseload – this accounts for factors such as building power usage patterns and thermal gain due to direct solar radiation.

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4.2 Demand Response In this section, we investigate how a change in the global temperature setpoint for the building is able to bring about a demand response. Triggered remotely, this functionality could be used by electricity retailers and distribution companies during times of high electricity prices or network constraints. As electricity retailers investigate new tariff structures (such as real-time-pricing (RTP), dynamic-peak-pricing and critical peak pricing (CPP)) building operators can utilise a demand response to minimise energy costs. A range of temperature setpoint control strategies were attempted (i) Simple temperature setpoint increase, (ii) Precooling followed by temperature setpoint increase and (iii) Precooling followed by ramped temperature setpoint increase. Results from each of these control strategies are discussed below 4.2.1 Simple Temperature Setpoint Increase Control Strategy The most straightforward method to implement a load reduction in an HVAC system, without completely shutting off the system, is to increase all temperature setpoints. This has two effects:

• There is a substantial initial load reduction as the cooling demand for all zones is reduced while indoor temperatures slowly rise to the new setpoint. The greater the building thermal mass, the longer this will take and consequently more energy can be saved at this time.

• Once the system has adjusted to the new setpoint, there will be lower power demand. This is described in Section 4.1. This lower energy consumption occurs because (1) the building internal temperature is closer to ambient conditions which reduces the heat load; and (2) the HVAC system may be more efficient when operating over a lower temperature differential.

Note that once the load reduction event is over, the building energy demand may increase significantly (rebound) as indoor zone temperatures are reduced back to original setpoints. Figure 10 shows an example of a direct load reduction experiment. In this case, the setpoint temperature was increased from 22.5°C to 24°C for 2.5 hours between 1:45pm and 4:15pm. We note that:


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• This was a very hot day, peaking at 37.8°C. With the HVAC compressors running at maximum (constant) power, average zone temperatures prior to the trial were around 23.75°C (1.25°C above setpoint) and still rising. It is evident that there was insufficient cooling capacity to maintain the normal 22.5°C setpoint conditions.

• During the load reduction period, the average zone temperature appears to be converging to a steady state value of 24.5°C (0.5°C above setpoint), with the HVAC system better able to meet the new setpoint. Although the setpoint changed by 1.5°C, the effect on the zone temperatures was only half this and consequently energy savings are not as great as might be imagined. The load reduction was only sustained for around 1 hr.

• In this trial around 65kWh (a 20% reduction) of electricity consumption was avoided over the period of 1 hour. This was achieved with a 0.75°C increase in average zone temperature

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Figure 10: Direct load reduction through increased global temperature setpoint.

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4.2.2 Precooling Prior to Setpoint Increase Control Strategy In the event that there is advanced warning that a load reduction is going to be required, a greater energy reduction can be achieved by first pre-cooling the building to build up a “thermal buffer” that can help extend the peak demand period with minimal loss of conditions. Figure 11 shows the results of one such trial.

Figure 11: Load reduction with an initial pre-cool prior to increasing global temperature setpoint

Salient features of this trial include:

• The test was performed on a somewhat cooler day (peaking at 32.8°C) than the previously test described in Section 4.2.1. However with the HVAC compressors


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running at full power, the average zone temperature prior to the trial was around 23.75°C (1.25°C above setpoint). It appears that cooling capacity was still constrained.

• During the load reduction, the average zone temperature increased to around 24.5°C and approximately 128kWh of energy consumption was avoided. In the first hour of the load reduction, around 77kWh was avoided (a 23% reduction). Once steady state conditions are established (with the compressor cycling on/off to meet demand) there is an average load reduction of around 43kW – this equates to a reduction in energy consumption of 12.7% achieved from a 0.75°C increase in average zone temperature.

• There was a delay of around 30mins from initiation of the load reduction to the peak energy reduction occurring. This may limit the immediacy of this method for providing a real-time response to sudden price spikes in the electricity market.

• Operation of Individual compressors in the HVAC chiller package is staged. During the load reduction, one of the compressors cycles on/off as the load reduction is insufficient to completely remove the requirement for it to run. Compressor cycling would need to be overridden to achieve a “firm” and continuous reduction in power demand.

• With no additional HVAC capacity available for the pre-cool, any additional cooling supplied to some zones must be achieved through reduced cooling to other zones. That is, the pre-cool was only able to cause a redistribution of cooling between zones. Figure 12 shows this effect. When the building temperature setpoint was reduced, the temperature in zone22 reduced as desired but the temperature in zone 42 increased.

4 6 8

27

10 12 14 16 18 20 2220

21

22

23

24

25

3rd Jan - Ambient max 32.8C

26TSetpoint

TZone22

TZone42

Time (hours)

Tem

pera

ture

(C)

Figure 12: Two of the individual zone temperatures during the load reduction with pre-cool trial.

4.2.3 Precooling Prior to Ramped Setpoint Increase Control Strategy One limitation of the load reduction strategy discussed in the previous section is that compressor cycling creates a lumpy power reduction profile and prevents a “firm”, continuous load shed. In this section, we investigate using a pre-cool followed by a gradually increasing temperature setpoint to facilitate a more even load reduction. The gradual increase in setpoint may also make the load reduction less perceptible to building occupants. The plots in Figure 13 show the results of this trial. PHVAC, TSepoint and TAverage show the measured HVAC power, building thermostat setpoint and average zone temperature respectively.


14

4 6 8 10 12 14 16 18 20 2220

21

22

23

24

25

26

27

28

29

Time (hours)

Tem

pera

ture

(C)

02-Feb-2007

TAverage

TSetpoint

HVAC Power

4 6 8 10 12 14 16 18 20 220

50

100

150

200

250

300

350

400

450

HV

AC

Pow

er (k

W)

Figure 13: Pre-cool followed by increasing setpoint to achieve a sustained load reduction.

From this trial we note:

• The maximum temperature on the day of this trial was 34.2°C which is between that of the previous two cases. Capacity was not fully constrained because, following maintenance, additional chiller capacity had become available compared with that available in the previous scenarios.

• With this additional capacity available, pre-cooling was effective in reducing the building temperature prior to the load shed event. However, a longer pre-cool would have been necessary for the building to reach steady state.

• The apparent energy saved during the load reduction was 475kWh at an average power demand reduction of 190kW (ie 46%) and temperature rise of 2.5°C. However this is not directly comparable with previous results since:

o The chillers were operating at higher power before and after the load reduction in order to provide the pre-cool and then to recover from the load shed;

o Previous trials did not have an effective pre-cool; and o The zone temperatures were allowed to deviate further than in previous trials.

• This setpoint temperature profile was partially effective at creating the desired more uniform load reduction.

The model described in Section 4.1.3 was used to further explore this scenario. In particular, the model was used to determine the indoor temperature profile that would result from a completely uniform load reduction equal to the measured average load reduction. Such a uniform load reduction would be most desirable for electricity industry demand response.

Previous research has considered open-loop temperature setpoint shaping to achieve uniform load reductions. We believe a closed loop response based on intelligent zone controllers will provide better load shaping and greater firmness of response.

Figure 14 shows the results of these temperature profile simulations for three cases. (i) Temperature profile using the experimental temperature setpoint steps (ii) Temperature profile (Sim1) to achieve uniform load reduction when the chiller is

operated in the same way as the experiments, up until the end of the initial precool period.

(iii) Temperature profile (Sim2) to achieve uniform load reduction, when a demand limit of 330kW is placed on the chiller throughout the simulation time period.


15

8 9 10 11 12 13 14 15 16 17 1820

21

22

23

24

25

26

Time (hours)

Tem

pera

ture

(C)

TSetpoint TAverage TModelled TSim1 TSim2

8 9 10 11 12 13 14 15 16 17 180

100

200

300

400

500

Pow

er (k

W)

02-Feb-2007 - Ambient max 34.2C

PHVAC PSim1 PSim2

Figure 14: Simulation of various control scenarios involving pre-cool followed by increasing setpoint to achieve a

sustained load reduction The simulated average zone temperatures, in scenario one, show a good fit with the actual measured values. This gives confidence in the predictions for the uniform load reduction scenario simulations.

The uniform load reduction simulations show that the desired uniform load reductions should be achievable without the indoor temperature exceeding that experienced in the experimental trials. Encouragingly, placing a demand limit on the chiller of 330kW (approx 80% of full capacity), does not significantly alter the maximum temperature experienced inside the building. However, indoor temperatures remain above 24°C for longer due to the less effective precool and a slower return to normal after the demand reduction event stops.

For the two uniform load simulations, the building power demand during the load reduction event was the same (ie independent of the chiller capacity constraint either side of the event). Hence they would appear to be equally useful for electricity demand management purposes. However, the load reduction appears much stronger when the capacity is not limited. This highlights the importance and difficulty of determining baseline demand.


16

5.0 Discussion

5.1 Seasonal Thermostat Energy Reduction Summary The seasonal thermostat adjustment strategy showed that an energy saving of up to 15% could be achieved with a 1°C increase in setpoint temperature in summer. These savings would result from both reduced thermal heat load on the building and increased chiller efficiency.

Despite these dual benefits, the magnitude of the saving is very surprising. From thermodynamics, one would expect a saving of around 5% rather than the measured 15% saving. However, a number of analysis strategies have been used to confirm the higher savings result. It should be noted that other buildings may not respond as well as this.

5.2 Electricity Industry Demand Response Summary The smart thermostats system is designed to provide a demand response that can benefit the electricity industry. Features/limitations of the system that have been identified include:

• Demand response potential - the magnitude (and achieved duration) of the load reduction is heavily dependant on ambient conditions. A load reduction of around 20% for a 0.75°C rise in zone temperatures over a time period of around one hour was obtained during these tests. This is equivalent to a reduction of 25% over a period of 1 hour for a 1°C rise in zone temperatures. On cooler days, where existing capacity is better able to maintain set point temperature, a larger temperature rise could be possible along with increased demand reduction. Conversely, extreme temperature days (>40°C) may not be able to achieve this magnitude of load reduction.

• Timeliness – there is typically around 30 minutes delay between the start of a load shed and a substantial reduction in HVAC power due to the inherent lag in the building control response.

• Firmness –.the response of the package chiller controls led to some cycling of the compressors. This resulted in substantial variation in the shape of the response during the load shed rather than a uniform demand reduction. In aggregation with other buildings this may not be a problem for a satisfactory electricity demand response. However, a uniform demand reduction would be preferred and should be achievable with more sophisticated set point ramping algorithms as discussed in Section 4.2.3.

• Cost – Load reductions between 66kW (20% of 330kW) and 190 kW (46% of 410kW) were measured during different load shed events. The magnitude of savings was particularly dependent on the existence of a chiller capacity constraint and the resulting effectiveness of building precooling. With BAS modifications costing $4680, this equates to a setup cost of $25-$75/kW.

• Electrical issues – the load reduction causes changes in the electrical characteristic of the HVAC system which may affect the electrical network.

o Figure 15 shows the power factor of the HVAC system during the load shed on 2nd Feb 2007. In addition to the reduction in real system power, the power factor of the HVAC system has also reduced during the load shed. We believe that this is due to equipment operating at part load during the load reduction.

o During the load shed on 2nd Feb, the voltage at the connection point between the building and the electricity distribution network increased by around 2% during the load reduction.


17

8 9 10 11 12 13 14 15 16

500

17 180

100

200

300

2nd Feb

400

Pow

er (k

W)

Time (hours)

PHVAC

P69

PF

1

8 9 10 11 12 13 14 15 16 17 180.5

0.6

0.7

0.8

0.9

Figure 15 – HVAC power, Level 6 & 9 supplementary system power and HVAC power factor.

Pow

er fa

ctor

Figure 15 also shows the power consumption of the supplementary HVAC system for levels 6 & 9 of the test site. Note that these units were unaffected by the setpoint changes during the trial and hence have been excluded from the analysis.

5.3 Thermal Comfort of Building Occupants Occupancy satisfaction with conditions during the trial was not actively assessed. Building occupants were not informed of trial dates or specifications, and any dissatisfaction reported to the site building manager was by usual site arrangements. There were no significant reports of dissatisfaction with thermal comfort, which is reasonable as the setpoints utilised in this trial were well within the ASHRAE recommended comfort limits. One significant aspect of thermal comfort observed during these trials was the impact of a building pre-cool on individual zone temperatures. This was briefly discussed earlier with reference to Figure 12. In this case, the pre-cool resulted in temperatures in some zones actually rising as they were starved of cool by other zones. Results are further explained with reference to figure 16 below for 2nd Feb. This figure shows the setpoint temperature (TSetpoint), measured mean zone temperature (TMean) and the bounds (T50%) which show the region in which 50% of the samples above and below the mean lie.

8 9 10 11 12 13 14 15 16 17

26

1820

21

22

23

24

2nd Feb

TSetpoint

TMean25

T50%

Time (hours)

Tem

pera

ture

(C)

Figure 16: Zone temperature characteristics during a load shed event.

For most of the day, half of all zones are within 0.5°C of the mean zone temperature. During the pre-cool however, the spread of zone temperatures increases markedly to the point at 12:30pm where the 50% region is ±1°C. The reason for this is that when the setpoint is reduced at the start of the pre-cool, all zones are initially well above setpoint so all chilled water valves open to 100%, and cooling is reasonably evenly spread across all the zones.


18

Consequently, the cooler zones (with less load) initially cool fastest and hotter zones can actually increase in temperature. It is not until the cooler zones have further cooled that there is sufficient capacity to start seeing temperature reductions in the hotter zones. The situation where the hotter zones increase in temperature during a pre-cool could be particularly problematic since occupants of such zones may begin to experience a loss of thermal comfort even before the actual load reduction begins. Comfort considerations are clearly an important consideration for this technology which has not been fully assessed in this study. A large body of literature is available on thermal comfort showing factors that can ameliorate perceived comfort issues. For example the “Cool Biz” program in Japan has used setpoint temperatures as high as 28°C with a promotional campaign encouraging workers to come to work dressed with appropriate (lighter) clothing.


19

6.0 Recommendations Experimental testing on a 10,555m2 commercial building showed that raising the thermostat temperature by 1°C during summer could achieve an energy saving of up to 15%. More buildings should be tested to determine the extent to which this result can be applied generically to other buildings. Short duration demand response trials on the building showed that short term demand could be reduced by between 20% and 45%. The extent of demand response was highly dependent on ambient conditions, baseline assumptions and the available chiller capacity. More demand response events and a variety of buildings should be surveyed to improve the precision and generality of these findings. The measured control system response to the demand response request was not as clear as hoped in a number of key ways.

• A delay of around 30 minutes occurred between adjusting the thermostat setpoint and the change flowing through to a reduction in HVAC energy consumption.

• Switching compressors on and off (staging of capacity) is the primary method of chiller control. Unfortunately, this resulted in large jumps in demand, preventing a uniform demand reduction.

• When chiller capacity was constrained, precooling was not effective and could potentially result in some zone temperatures rising.

• Since chiller capacity was constrained, demand ‘rebound’ following a demand response event was not able to be assessed.

Each of these issues could be addressed with more sophisticated controls, particularly controls that include forecasting and self learning capability. Further research and demonstration of these control options is recommended.


20

Appendix A – Trial Description Smart Thermostats Trial

Introduction Air-conditioning is a major cause of peak demand. Residential air conditioner switching (where the consumers air conditioner is remotely switched off for ‘x’ minutes in the hour) has wide acceptance in the USA as a method for reducing this peak demand on critical days. Typical costs of using this technology is around US$30/kVAyr. The more recent trend is toward controlling thermostat temperature rather than switching off the air conditioner. This reduces the air-conditioning demand while still maintaining some level of control over space conditions. In this way it is more likely to be acceptable to consumers and it eliminates potential difficulties associated with switching new inverter air-conditioning technology. There is very little experience of controlling air-conditioners (either method) in Australia. This project aims to demonstrate the viability of this technology for demand management in Australia. Initial investigations suggest that the technology which is used for residential applications in the USA, uses radio switching frequencies that are not available in Australia. Consequently, the proposed trial is for commercial application and will focus on the thermostat control approach rather than the air conditioner curtailment approach.

Benefits to SEAV The demonstration will provide information on thermostat control technology which will enable SEAV to

• assess the cost and benefits of smart thermostat technology • assess the suitability of smart thermostat technology for retail and network peak

demand reductions • assess consumer acceptance of smart thermostat technology

SEAV will then be able to promote the technology where appropriate, including submissions to policy and regulatory bodies investigating the potential benefits of demand management.

Trial details The trial would be performed on a commercial building of about 5000m2

floor area, which would be enlisted by SEAV. A web interface/control module would provide high level load shedding instructions to the package air-conditioning units. This would enable remote communication and control of the building air-conditioning system. Exact hardware requirements would depend to some extent on the existing brand and type of package unit and its associated control system. The trial hardware would be installed and commissioned by a leading air-conditioning company such as Carrier (see attached quote). Monitoring and reporting of trial results would be performed by CSIRO with performance interpreted by assessing the deviation from measured baseline power consumption data. Expected peak demand reduction for the 5000m2

floor area building would be around 30 kW. If rolled out at a cost of $10,000 for 30kW, this gives a capital cost of about $330/kVA of demand reduction. This is competitive with other supply side options. At the conclusion of the trial, the host commercial building will have the equipment necessary to enable shifting of electrical loads. This may enable the building owner to negotiate an improved tariff from retailers by offering an improved load profile.


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Appendix B – Building Selection The Smart Thermostats Trial has been established to assess:

• the cost and benefits of smart thermostat technology; • the energy reductions possible using seasonal variations in temperature set-points • the suitability of smart thermostat technology for retail and network peak demand

reductions; and • consumer acceptance of smart thermostat technology.

This will provide a quantitative measure to allow assessment of the suitability of this technology for Victorian commercial buildings.

Building Selection Ideally, this trial will assess the performance of a smart thermostat strategy on a standard commercial office building. In collaboration with Sustainability Victoria, Investa Property Group have identified four possible sites for this deployment. Of these, the preferred option is ‘Site C’.

Assessment Criteria In reaching this position, each of the four buildings were inspected and evaluated against the following criteria:

• HVAC setup – whether the building has a “typical” HVAC system, and ease of working with it.

• Standard test environment – is the building usage normal for a commercial office environment?

• Ease of metering – can the existing metering provide us with a suitable history of the building energy consumption to serve as a baseline for the trial? How difficult it is to connect up a power analyser to the mechanical plant (chillers, air handling units, etc) to observe energy reductions during transients in thermostat set-point.

• BAS interface – Difficulty in making the required changes to the building automation system to allow building thermostat to be controlled remotely.

• Other factors – factors such as occupancy rates, recent changes to configurations, energy saving practices, tenant concerns which may impact on the success of the trail.

Evaluation of each building against these criteria is given in the following table. For each aspect, scores were given between 1 (bad) and 5 (good), and these were summed to give a final score out of 25. The Preferred site with the highest ranking is Site C. Site A Site B Site C Site D

HVAC setup Two towers. Multiple

plant rooms and air

handling units.

3 Single plant room co-

located with four air

handling units.

5 Single plant room.

One AHU per floor.

Levels 6 & 9 (call

centres) have

supplementary

cooling provided by

an additional system.

4 Distributed. Many small

systems, installed at

different times.

Conditioning is

provided substantially

for the production

equipment rather than

comfort of personnel.

Also has humidity

control.

1


22

Standard test environment

Typical multi-storey

commercial offices. 5 Typical multi-storey

commercial offices.

Only around 70%

occupancy rate.

4 Typical multi-storey

commercial offices.

Level 6 & 9 (call

centres) have more

staff than would be

typical.

4 Most of the floor space

is dedicated to an

electronics

manufacturing facility.

Many production

machines In operation.

1

Ease of Metering Multiple circuits feeding

the AHU and plant

rooms.

Multiple interval meters

for the site.

3 Single mechanical

services board

supplying all AHU

and plant.

Multiple interval

meters for the site.

3 Single circuit for AHU

and plant rooms.

Single interval meter

for whole of site.

Exception to above is

extra meter &

supplementary

HVAC equipment for

the call centres.

4 Distributed setup, but

was stated that the

mechanical services

were fed from single

point (not verified)

Single interval meter for

whole site.

3

BAS Interface Johnson Controls.

Impression is that these

will be hard to adapt for

the trial.

Need to add remote

access.

2 Automated Logic

System should be

fairly straightforward

to upgrade.

Need to add remote

access.

3 BAS already has

remote access.

BAS should be fairly

easy to upgrade.

5 No BAS (which is ok)

Multiple HVAC systems

are distributed

throughout the facility.

This will make it

extremely hard to

adjust the site setpoint.

2

Other Factors No real additional

issues. 5 On a previous

summer there were

some failures of

HVAC equipment

and tenants may be

unwilling to be part of

the trial.

Building already has

substantial dead-

zone between

heating and cooling

modes.

3 No real additional

issues.

Only drawbacks with

this site are the call

centres. It may be

possible to exclude

these 2 levels from

the trial.

5 HVAC is primarily for

the manufacturing

process not personnel.

As such, there was

reluctance to modifying

set-points.

Building manager is not

stationed on site.

2

Overall Score ( /25) 18 18 22 9


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Appendix C – Carrier quote for generic HVAC system upgrade Carrier Air Conditioning Pty Ltd ABN 81 000 024 742 Unit 2 / 89-91 Tennant Street Fyshwick ACT 2609 AUSTRALIA Telephone: 61-2-6280 6066 Fax: 61-2-6280 4015 Attention: Stephen White January 6, 2005 Re: Energy Curtailment Trial Introduction: Carrier Air Conditioning Pty Ltd, a subsidiary of United Technologies Corporation, is the world’s largest manufacturer of air conditioning, heating and refrigeration equipment for commercial, residential and transport applications. With revenues of over $US 9 billion annually Carrier manufactures sells and services 50 brands in residential, commercial, refrigeration and transportation markets. The Carrier Comfort Network is a proprietary protocol linking all Carrier manufactured controls products. As discussed, for a residential trial our contact for the Comfort Choice program in the U.S. is Pete Pierret. Our recommendation for a commercial building would be a site of approx 5,000 sqM with DX or chilled water air conditioning. We could then install hardware to control the equipment and invoke strategies to either demand limit or load shed as required by the Energy Company. Communication to the system can be via dial up using a web browser or via a LAN in the building. Comfort Trail System Overview: CCNWeb is a device designed to provide Internet/Intranet Connectivity to a Carrier Comfort Network (CCN). CCNweb can be accessed using a web browser over an Ethernet-based Local Area Network or point to point over an analogue telephone line. CCNweb is cost effective and does not require an onsite computer. CCNweb provides a link to all available CCN controllers. Using a web browser and the controller links, a user can access status display, occupancy, and set point tables. In addition CCNweb allows overriding (forcing) and autoing (returning points to a controller’s automatic control) of point values. It allows the user to change parameters in the tables for the purpose of Energy Management over the internet/intranet via an http:// web address. This initial trial allows for all networked information to be displayed on web pages in tabular format. Standard graphics are offered. Premierlink Rooftop Controller is an intelligent control that continuously monitors and regulates a package unit operation with reliability and precision that maximizes downtime


24

and ensures maximum occupant comfort for Package Units up to 90KW and can control two stages of cooling and up to three stages of heat. Building Type: Trail Building to Consist of Package Units up to 90kw with up to two stages of cooling and three stages of heating, relatively straight forward cabling accessibility and distances, Ethernet enabled accessible to CSIRO over the intranet/internet. Client to provide the following:

1. Installation of Ethernet cabling to your Network via RJ45 connector to our BMS Panel. 2. A Fixed IP Address with a subnet mask and a default gateway. 3. OR a fixed/dedicated analogue telephone line with a phone port connector to our

CCNWeb (modem) for dial up connection. Our Budget Price for the above trail is $8,500 excluding GST. (Includes labour, materials, sundries, installation, engineering, commissioning, and twelve months defects liability of newly installed equipment) Scalability of the CCNweb System is possible with the addition of extra CCN or in some cases third party DDC hardware as required to control additional air conditioning in buildings once connected to the CCN System. Power Meters can be connected to the CCN Controllers via pulse I/O or direct to the CCN System via LON or Modbus compatible integrators. Our trail does not allow for this but can be easily achieved. Other Building Components such as lighting, other Air Conditioning Plant and Equipment, may be connected to the CCN System via I/O or third party integrators dependant on compatibility to the CCN System. Should you require a presentation or demonstration on what Carrier can offer your organisation in regards to the Energy Curtailment Program please do not hesitate to contact me direct. Kind regards Alan Watts National Manager Building Automation Systems


25

Appendix D – TAC quote for modifications to the BAS.


26


27

Appendix E – Building Automation System Web Interface

Figure 17 – Web based interface for temperature setpoint and deadzone controls

Figure 18 – Web based interface for monitoring the building automation system


28

Appendix F – Experimental Schedule Experiments were run over the period from November 2006 to February 2007 according to the schedule below. The specific dates chosen were based upon Commonwealth Bureau of Meteorology forecasts with an emphasis on days with ambient temperatures over 30°C, which is characteristic of the conditions when a load shed event is likely to be required. The normal building setpoint is 22.5°C with a 0.5°C deadzone. This was maintained whenever a trial was not being conducted.

Experimental Schedule

Trial Date Max Temp Setpoint Details

4th Jan 35.1°C Setpoint raised to 25°C from 1:45pm to approx 2:25pm. (Cut short due to reported user discomfort before the trial was started) Direct load shed

10th Jan 37.6°C Setpoint raised to 24°C from 1:45pm to 4:15pm.

29th Nov 18.7°C

30th Nov 31.1°C Load shed with pre-cool

3rd Jan 32.8°C

Setpoint reduced to 21°C from 11:45 to 1:45 to pre-cool the building. Setpoint raised to 24°C from 1:45pm to 4:15pm for load reduction.

9th Jan 26.1°C Deadzone changed to 2°C from 10:00am to 4:00pm. This makes the proportional cooling band active above 23.5°C.

Constant setpoint / deadzone

1st Feb 26.9°C

Setpoint changed to 21°C with 1°C deadzone from 10:00am to 4:00pm. This makes the proportional cooling band active above 21.5°C.

17th Jan

32.2°C

Sustained load reduction

2nd Feb 34.2°C

Setpoint adjusted to 21°C from 10:45am to1:45pm to pre-cool the building From 1:45pm, setpoint is raised by 1°C each half hour

• 22°C from 1:45pm to 2:15pm, • 23°C from 2:15pm to 2:45pm, • 24°C from 2:45pm to 3:15pm, • 25°C from 3:15pm to 3:45pm.

Note: • On 17th Jan, the 25°C trial was not

intended to run, but the BMS would not respond to the configuration change. The 25°C setpoint may have terminated earlier than the above specification.

• On 2nd Feb, the pre-cool failed to start, so was initiated at 11am instead.

Table 2: Smart thermostats trial experimental schedule


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