Reducing Peak Demand with Energy Management Control Systemsinfohouse.p2ric.org/ref/40/39328.pdf ·...

R E P O R T

Reducing Peak Demand with Energy Management Control Systems

A field study

February 1998

Prepared by

BARAKAT & CHAMBERLIN, INC. 1800 Harrison Street, 18th Floor

Oakland, CA 94612 (510) 893-7800

In Association with

MICROGRID

Prepared for

595 Science Drive Madison, WI 53711-1060 Phone: (608) 238-4601

Fax: (608) 238-8733 Email: [email protected] Web: www.ecw.org

Copyright © 1998 Energy Center of Wisconsin All rights reserved

This report was prepared as an account of work sponsored by the Energy Center of Wisconsin (ECW). Neither ECW, participants in ECW, the organization(s) listed herein, nor any person on behalf of any of the organizations mentioned herein:

(a) makes any warranty, expressed or implied, with respect to the use of any information, apparatus, method, or process disclosed in this report or that such use may not infringe privately owned rights; or

(b) assumes any liability with respect to the use of, or damages resulting from the use of, any information, apparatus, method, or process disclosed in this report.

Project Manager Dave Shipley Energy Center of Wisconsin

Editor Eric Nelson Energy Center of Wisconsin

Contents Abstract .......................................................................................................................................................... i

Report Summary ...........................................................................................................................................iii

Introduction ................................................................................................................................................... 1 Market Assessment and Technical Potential ...................................................................................... 1

Method .......................................................................................................................................................... 3 Model Description ............................................................................................................................... 3 Estimating Load Reductions ............................................................................................................... 3 Input Data............................................................................................................................................ 5 Development of Control Strategies ..................................................................................................... 6 Testing Protocols ................................................................................................................................ 6

Store # 29 ....................................................................................................................................... 8 Office Headquarters ....................................................................................................................... 9

Results ........................................................................................................................................................ 11 Store #29........................................................................................................................................... 11 Office Headquarters .......................................................................................................................... 12

Discussion................................................................................................................................................... 17 Store #29........................................................................................................................................... 17 Office Headquarters .......................................................................................................................... 17 Changes in Test Protocols ................................................................................................................ 18 Validity of Results.............................................................................................................................. 19 Implementation Issues ...................................................................................................................... 19 Summary ........................................................................................................................................... 19 Conclusions....................................................................................................................................... 21 Recommendations ............................................................................................................................ 21

References.................................................................................................................................................. 23

Appendix A: Case Study Reports ..............................................................................................................A-1 Store #29..........................................................................................................................................A-1 Headquarters Office .........................................................................................................................A-4

Appendix B: Store #29 HVAC Schematic ..................................................................................................B-1

Appendix C: Glossary ............................................................................................................................... C-1

Tables and Figures Table 1 Nameplate lighting load shed from Office Headquarters ........................................................ 10 Table 2 Summary of demand savings at Office Headquarters ............................................................ 15 Table 3 Load control measures tested ................................................................................................. 20 Table 4 Percentage savings from tested load control measures ......................................................... 20 Table 5 Availability of tested load control measures in Phase I buildings............................................ 21 Table A1 Energy use analysis for Store #29 .........................................................................................A-3 Table A2 Energy use analysis for Office Headquarters.........................................................................A-8 Table A3 Estimated demand savings from potential control measures identified at Office HeadquartersA-8 Figure 1 Technical potential for demand reduction in Wisconsin............................................................ 2 Figure 2 Whole building demand at Store #29 ........................................................................................ 4 Figure 3 Whole building demand at Office Headquarters ....................................................................... 5 Figure 4 Outside temperature at Store #29 ............................................................................................. 7 Figure 5 Outside temperature at Office Headquarters ............................................................................ 7 Figure 6 Average percent of full capacity cooling at Store #29............................................................... 9 Figure 7 Demand on test day and peak day for Store #29.................................................................... 11 Figure 8 Average space temperature at Store # 29 on test day............................................................ 12 Figure 9 Demand at Office Headquarters on test day........................................................................... 13 Figure 10 Demand for Office Headquarters on peak day........................................................................ 14 Figure 11 Simulated demand savings due to temperature reset at Office Headquarters ....................... 14 Figure 12 Average indoor space temperature at Office Headquarters on test day................................. 15

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Abstract To study the potential for reducing peak demand with energy management control systems, we tested four load control measures at two sites using computer modeling and remote monitoring. We successfully tested three of these measures—shedding lighting, duty cycling of air conditioning, and shifting load onto a backup generator. The tested measures reduced peak demand by 14 percent, 15 percent and two percent, respectively. Because of changes in the test procedure and limited data, these results may not reflect the actual potential of these measures. We concluded that without incentives or strong pricing signals—such as premium rates during peak periods—it would be difficult to successfully market programs that used utility-controlled energy management control systems to reduce peak demand.

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Report Summary In this study we examined the potential for achieving demand reductions with Energy Management Control Systems (EMCS). We sought to identify potential demand reductions, comfort interactions, ways to mitigate negative effects, and ways the customer’s acceptance of control measures could be improved.

To achieve our objectives, we used computer modeling and remote monitoring to measure the demand reduction from four EMCS measures. The tests were conducted at two sites for one day. We examined the following measures:

• Duty cycling of air conditioning (retail store)

• Shedding lighting (office)

• Resetting the cooling temperature setpoint (office)

• Shifting demand onto the backup generator (office)

Results

One test—resetting the temperature setpoint—was not successful. The savings for the others measures are shown below, along with the statewide potential found in an earlier study.

Peak demand reduction due to four EMCS measures

The savings were greater than expected for shedding lights and duty cycling of air conditioning. The generator test yielded less savings than expected.

Several events affected our results. The building owner changed the lighting test to include most of the office lighting in order to get a better deal on an interruptible rate. The original test called for lighting to be shed only from perimeter offices, the basement, and the cafeteria. In addition, the building owner changed the generator test to include 60 kW less load than planned. Furthermore, because of objections to the test schedule, the testing period was scaled back from one week to one day.

Because of these changes, the results for the lighting load shed test and generator test may not be widely applicable, especially because the test was scheduled on a day when the company held a social event.

Measure Retail site Office site Potential in large buildings

Duty cycle AC 15% NA 3%

Shed lighting NA 14% 3%

Implement generator NA 2% 5%

Temperature reset NA Not implemented NA

D e m a n d S a v i n g s f r o m E M C S s

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We believe the test of duty cycling air conditioning is representative of what can be achieved, though we caution that this result is based on very limited data.

At the retail site, we found that demand reductions due to locked-out cooling capacity equaled approximately three-quarters of the locked-out compressor load. Demand reductions due to shedding lighting at the office site were approximately 1.15 times the nameplate interior load shed. If confirmed by additional data, these results could be useful rules of thumb for estimating demand reduction.

Conclusions

• Observed savings suggest that most of the statewide potential for demand reduction using EMCSs is achievable.

An earlier study estimated a 20 percent potential reduction in demand using EMCSs. Based on a one-day test at two sites, we measured a 15 percent reduction. The sample size was small, however, and savings will vary.

• The savings were found to have relatively minor comfort impacts.

Shedding cooling loads resulted in space temperatures drifting upwards, but conditions remained within the comfort zone as defined by the American Society of Heating Refrigeration and Air-Conditioning Engineers.

Recommendations

• Technical assistance is needed for successful EMCS load reduction programs.

Customers were unaware of benefits, ease of implementation, and minimal impacts of control strategies.

• It is important to maintain contractual or financial leverage over a customer who agrees to be a host site for an experiment, to ensure that the experimental plan can be completed.

The customer’s primary motivation for participation was to obtain a new rate agreement with the utility. Once those negotiations were finalized and the equipment installed, the customer had no further interest in completing the experiment. This led to several changes in the test protocol that weakened the results of the study.

• Load shedding through EMCSs is apparently a “tough sell” and ways are needed to market the idea and motivate businesses to participate.

Few facility owners were willing to close a deal with the participating utility to participate in this project.

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Introduction Although direct load control has been a widespread practice in utility load management, it has mainly been used for residential end uses. Load management initiatives in the commercial, industrial, and agricultural sectors have been largely structured as interruptible load agreements between the utility and the customer. Under these arrangements, the utility offers the customer an incentive to initiate a certain control strategy according to a stipulated prenotification condition.

Some utilities are now beginning to consider the potential for direct load control in non-residential applications using energy management control systems—a computer system that makes control decisions. An EMCS can, for example, anticipate when temperature extremes are likely and respond before the building becomes uncomfortable. As an EMCS already controls operations within the building, it is relatively easy to program it for strategies that reduce peak electricity consumption. Load control can be achieved either through reprogramming EMCSs or through utility-generated signals transmitted to the facility.

In a two-part study we sought to examine the potential for such initiatives by Wisconsin utilities. Phase I focused on market data collection and analysis (Energy Center of Wisconsin, 1995), which is summarized below.

In this study we examined load control in two facilities—a large discount store and a large corporate office building. Our objective was to evaluate the impact of four control strategies identified in Phase I—duty cycling of air conditioning, shedding interior loads, shifting loads onto the emergency generator, and moving the cooling temperature setpoint. Our goal was to measure the demand savings and evaluate the comfort impact of each control strategy.

We chose a computer model as a convenient tool to evaluate the impact of load control. Using a model, we could estimate the building’s electrical demand to use as a base case. Then, by measuring the actual building load while demand control strategies were active, we could measure the impact of those measures.

Market Assessment and Technical Potential

Telephone surveys performed in Phase I indicated that over half of large buildings had EMCSs. The reported presence of an EMCS did not appear to be clearly related to any site characteristics, such as the facility's size and complexity. The presence of an EMCS did not appear to differ between customer segments.

On-site interviews indicated existing EMCSs were often old and were underutilized because customers were inhibited by their complexity. Customers were also cost-conscious and reluctant to consider new EMCS investments. Building owners were equally concerned that utility load control might reduce comfort conditions in their buildings, although operations managers recognized EMCSs can help maintain comfort.


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Relatively few customers were willing to accept a comfort tradeoff influenced by the incentive amount. Most customers either were already in favor of the measure, or were always opposed to the measure, regardless of its benefits. One measure showing a response to incentives was the willingness to reduce lighting during peak hours.

The technical potential assessment indicated little current use of EMCSs for peak reduction measures. Figure 1 shows the results from the on-site survey extrapolated for state-wide building stock.

Figure 1. Wisconsin’s technical potential for demand reduction in large buildings using EMCS measures

Not Cost-Effective10%

Hard EMCS 11%

ConservationPotential 6%

Easy EMCS 5%

Current EMCS4%Generation Potential

5%

Baseload 60%

About five percent of peak was already being reduced through EMCS measures in large buildings. An approximate five percent additional peak reduction was possible with on-site generators already installed. Another five percent was possible through “easy” EMCS measures that did not impact comfort conditions. About 11 percent was possible through “hard” EMCS measures that reduced comfort and were less acceptable to customers. About six percent savings were possible through cost-effective conservation measures, such as efficient lighting, window shade controls, and tuning, repair, and replacement of equipment. An additional 10 percent savings were technically possible, but these measures did not appear to be cost-effective. In all, we estimated that EMCSs could further reduce peak demand by about 20 percent.

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Method

Model Description

Both buildings were modeled using the computer analysis program Micro-AXCESS 10.2, a program developed by the Edison Electric Institute and supported by the Electric Power Research Institute. The program is designed to simulate energy consumption in buildings and their associated energy-using systems by utilizing an hourly weather simulation procedure. The program calculates energy consumption and demand for all major categories of building energy use, including direct consumption by end-uses. Utilizing input data provided by the user, Micro-AXCESS accounts for factors such as building construction materials, building location and orientation, weather, end-use loads, equipment operation schedules, occupancy, and HVAC equipment performance characteristics.

Using input design parameters, the program first calculates the base energy loads for each hour by multiplying the operating profile percentage by the peak usage for each load. Solar gain through fenestration and transmission gains and losses for each exposure are also calculated. As the final step in the zone-specific calculation, the program sums the component gains and losses previously calculated plus the heat gain due to internal loads, then calculates the resultant heating or cooling requirement for the zone.

Next, terminal system operation is simulated using specified control set points and design conditions. Based on input performance data and calculated loads, each primary system is simulated. Calculated energy usage is summed hourly and assigned to a specified meter.

The program uses standard engineering principles to simulate zone energy requirements. The calculation methodology is consistent with ASHRAE guidelines wherever applicable.

Estimating Load Reductions

For the demand shed test we prepared building models based on site audit data. The building demand, outside temperature, and other key parameters were trended for a period beginning the day before the test day until two days after the test day. Then the model was fine-tuned to the observed building demand on the test day. The remainder of the trended data served as a check on the similarity of the test day to other occupied days. The fine-tuned model provided the reference from which the load shed was calculated. This was compared to the observed demand, as shown in Figure 2 and 3.

For both buildings, the models were used to estimate demand on the maximum demand day. This maximum demand day designated the peak reference day used in evaluating the potential control strategies. For each building, the reference peak hourly demand was the hourly demand output from the model for the maximum demand day. This was compared to the experimental peak hourly demand developed from the test results to estimate peak savings. The potential control strategies for each building were modeled for hourly demands during the maximum demand day, and were compared to the


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reference peak hourly demand. The differences in demand—candidate strategy versus reference peak—were attributed to the candidate demand control strategy.

Figure 2. Whole building demand at Store #29

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Figure 3. Whole building demand at Office Headquarters

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Input Data

The models were developed using information collected through site visits and interviews with the building staff. See Appendix A.

In order to perform a building energy analysis, Micro-AXCESS requires a specially formatted binary weather file for the location of the building being studied. This file contains dry bulb, wet bulb, wind speed, cloud cover, and cloud type data for 8760 hours per year. Raw actual weather data for the period 10/92 through 9/93 for both sites were obtained from National Oceanic and Atmospheric Administration (NOAA). They were then converted to Micro-AXCESS format for the simulations completed here.

We calibrated our model against actual weather coming from NOAA. Average weather would not have been useful for this purpose. Once the model was calibrated, we picked a typical “extreme” day from the Typical Meteorological Year (TMY) weather files. Then we modeled the building behavior for this “average” extreme day. We followed this process because the weather during the monitored period was not sufficiently extreme to provide a representative peak day.


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Development of Control Strategies

Demand control strategies were devised to conform to the customer's demand control goals and requirements. These goals and requirements were:

• Work area or sales area comfort and usability were not to be compromised. Where lighting levels were lowered, it would be done by shedding overlit situations. Where space temperatures were changed, they would be maintained as close to existing deadbands as possible.

• Control strategies would remain sufficiently reliable to avoid compromising comfort, the system’s primary purpose.

• Control strategies that could be replicated in most stores would be given primary consideration as they could create the most significant utility cost savings.

The building hourly demand model was used to evaluate all potential control strategies because it allowed HVAC control strategies to be reasonably evaluated and could reasonably simulate the interaction of multiple control strategies (for example, HVAC and reduced lighting). Generally, the model for each building was used in an iterative manner to identify the largest demand reductions that had the least impact on comfort. This was done as follows:

• All demand savings associated with lighting changes and use of standby generators were identified and building descriptions were modified to reflect these changes.

• Candidate HVAC control strategies were developed, starting with a building model reflecting operations with reduced internal gains.

• The HVAC control strategy was devised to maximize specific opportunities afforded by the building's zoning and orientation.

Testing Protocols

For both buildings, load shedding test procedures were similar. Both sites had mechanical service contracts with the same firm and used one manufacturer's automation system. This simplified the procedure for standardizing the sites and obtaining trend log printouts and other data from the EMCSs.

The tests were conducted on August 14, 1996. The date was chosen to exercise the load shed strategy under maximum summer conditions to capture the full effect of interactive effects that would accompany a summer load shed. On the TMY tape for this location, the hottest day of the year was August 13. In fact, the test day turned out to be warm, but did not achieve the summer maximum. Fortunately, the actual test day was proceeded by a warm night, characteristic of hottest summer conditions. Figure 4 and 5 compare test day temperatures to estimates of temperatures on the peak summer day.

M e t h o d

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Figure 4. Outside temperature at Store #29

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Figure 5. Outside temperature at Office Headquarters

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To minimize the effect on the customer, the test was a single-day load shed test instead of the originally planned one-week flip-flop test, which had two load shed days. The methodology used to define, detect,


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and quantify the load shed was essentially as documented, except that the test consisted of a one-day exercise instead of a one-week exercise.

Store # 29 In discussions with the customer, only one control measure was identified as feasible—duty cycling of the air conditioners during peak periods.

Duty cycling of air conditioning was tested using a simplified procedure. The load shed consisted of disconnecting nine rooftop units from 3 p.m. to 6 p.m. (the fans on these load shed units remained running). Load shed units were units 1-9 as shown in the schematic included in Appendix B. These units were the largest of the rooftop units and served the 45,000 square foot core of the store's display area.

The compressor load for each of these units was 17.8 kW per nameplate full-load amps (assuming the power factor =.8 and the usage factor = .85), giving an aggregate switched-off load of 160.4 kW. In practice, however, the units would be cycling on and off several times an hour in the course of maintaining the temperature set point. The load to be shed would not be the whole switched load but a smaller load proportional to the fraction of the total load actually on at any one time. For purposes of establishing this fraction, the OFF/ON status of each of the nine shed units was trended during the test. The mean ON status of all switched units times the switched load was taken as a measure of the load which was actually switched off.

As shown in Figure 6, the mean OFF/ON status showed some variability, but for these purposes the mean ON status for the 3-hour period prior to load shed was used as the measure of the compressor load on the test day. The mean ON status for the 3-hour period was .42. This mean ON status lead to the estimate of the effective compressor load of 67 kW (.42*160.4) at the point of load shed.

M e t h o d

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Figure 6. Average percent of full capacity cooling at Store #29 on test day

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Office Headquarters Discussions with the customer generated the following list of feasible control measures:

• Shedding lighting

• Using the emergency generator to peak shave dedicated load

• Resetting cooling setpoints upwards

The load shed test at office headquarters consisted of disconnecting 137 kW of general office lighting located on two office floors and the cafeteria and of using the emergency generator to meet 30 kW of equipment load. A third component consisted of raising the cooling set point by two degrees F. The test was conducted between 3 p.m. and 6 p.m.

The “nameplate” lighting load shed was 137 kW, as shown in Table 1.


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Table 1. Nameplate lighting load shed from Office Headquarters

Description Number of fixtures Connected load (kW)

2 tube F96 T12 74 10.36

2-175 MH 11 3.58

MR 16 16 0.8

2 tube F32 6 0.33

1 tube F32 43 1.2

2 tube F32 1386 76.2

3 tube F32 274 22.7

4 tube F32 56 6.1

2 bulb PL-13 68 1.8

Cafeteria -- 11.1

Basement hall -- 3

Total lighting shed -- 137.17

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Results

Store #29

The demand observed during the air conditioning load shed is shown in Figure 7, along with the model’s baseline demand for the test day and peak day. The load shed resulted in a reduction in demand of 50 kW, averaged for the 3-hour test period. The load reduction on the peak day would have been 54 kW.

Figure 7. Demand on test day and peak day for Store #29

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The mean space temperature on the test day is shown in Figure 8. This figure shows the mean space temperature drifting upwards from 74 to 77 degrees during the load shed test.


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Figure 8. Average space temperature at Store # 29 on test day

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Office Headquarters

In Figure 9, the observed demand on the test day is compared to the modeled demand for the day. The baseline demand was estimated by running the model with actual test day weather but without the load shed.

This shows the results of shedding lighting and shifting demand onto the backup generator. Due to a programming failure, the temperature reset test was not performed. The mean difference between the modeled baseline (no load shed) and the observed demand was 176 kW.

R e s u l t s

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Figure 9. Demand at Office Headquarters on test day

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The observed demand on test day was atypical (there was a company social event that day). To account for this in estimating demand reduction on the peak day, we approximated what the demand would have been by running the building model with the peak day weather with and without the load shed. Peak demand reduction was then estimated by subtracting the modeled actual demand from the modeled baseline. Figure 10 indicates the mean demand reduction expected on the peak day would be 188 kW.


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Figure 10. Demand for Office Headquarters on peak day

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Figure 11. Simulated demand savings due to temperature reset at Office Headquarters

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Demand savings from the four measures are summarized in Table 2.

R e s u l t s

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Table 2. Summary of demand savings at Office Headquarters

The mean space temperature on the test day is shown in Figure 12. This figure shows the mean space temperature remained constant at approximately 72 degrees during the load shed test.

Figure 12. Average indoor space temperature at Office Headquarters on test day 6566676869707172737475

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Control measure Observed demand savings (kW)

Demand savings on peak day (kW)

Lighting curtailment 137 137

Additional cooling reduction from reduced internal gain

9 21

Emergency generator used to peak shave dedicated load

30 30

Commanded reset of cooling setpoints

None (estimated) None (estimated)

Total demand savings 176 188

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Discussion

Store #29

At Store #29, the EMCS shut off nine air conditioning units to simulate duty cycling of A/C. This reduced building demand by 50 kW for the 3-hour load shed interval. The observed load shed was less than the load switched off. Clearly, the un-shed HVAC units picked up part of the load. In this case, 74 percent of the reduced cooling load actually resulted in an observable reduction in demand.

If this load shed had occurred on the hottest summer day, the realized load shed would have been 54 kW. This was because the modeled compressor load increased to 94 kW on the peak demand day, an increase of seven percent over the test day compressor demand. The demand on the maximum demand day was not much greater than on the test day, as would be expected for a building dominated by internal gains.

During the load shed interval, the interior space temperature rose by three degrees to 77° F. According to ASHRAE’s comfort criteria (ASHRAE, 1997), 77°F is considered a comfortable indoor temperature in the summer, provided the relative humidity is not above approximately 60 percent in the space. In Store #29, the temperature climbed approximately 1°F per hour of shed under the test conditions. A longer load shed period might not be achievable. For example, if humidity in the space was 50 percent and the temperature continued to climb at the same rate for two more hours to 79°F, the space would no longer be considered thermally comfortable by ASHRAE’s definition.

Other buildings will also change temperature at different rates, depending on overall shape, thermal mass, insulation, window area, internal loads, and other factors. The feasibility of cooling load sheds needs to be evaluated case by case, considering minimum acceptable comfort requirements, building response characteristics, length of the expected utility demand control periods, and potential savings of demand and cost.

Office Headquarters

At the Office headquarters, the EMCS shed lighting loads and shifted load onto the emergency generator. This reduced demand from the modeled baseline by 176 kW on the test day. However, the actual demand before the load shed was about 100 kW larger than that modeled. Demand then decreased by 269 kW in the first hour after load shed. Review of demand for the prior year during a hot spell did not show instances where demand reached 1200 kW. Therefore, the extra 100 kW was assumed to be due to a company social event, which ended at approximately 4 p.m. This caused higher than normal loads for most of the day, followed by a sudden drop in demand at 4 p.m., when people left early.

The demand change expected on the peak demand day was 188 kW, including 30 kW of demand reduction due to shifting the fire and safety loads to the emergency generator. Most of the load connected to the generator was emergency lighting, which consisted of scattered ceiling fixtures on a separate


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circuit. When the load shed took place, most other ceiling lights were switched off, but the emergency fixtures stayed on, draining power from the generator as it came on line.

The company social event did affect our ability to measure the impact of any task lighting employees might have switched on when the ceiling lights went off. During the customer’s earlier manual test, however, the affected employees, most of whom work on computer screens, found the daylighting from the windows adequate. But on other visits utility personnel observed that most employees had task lights on even when the ceiling lights were on. The situation is probably a mixture: some employees, perhaps those nearest the windows, do not use the task lights and would not turn them on when the ceiling light are controlled; others use the task lights all the time.

In any case, we are confident that only a modest number of task lights are switched on when the ceiling lights go off, because the utility has found that the demand savings on real control days has very closely matched what’s predicted. On July 2, 1997, for instance, demand reduction was 210 kW—only four percent lower than the predicted value of 218 kW. We conclude, therefore, that task lighting has only very small effect on the load shed for this building. However, we recognize that task lighting would have a much larger impact in a building with different lighting usage patterns or less daylighting.

The remaining 158 kW of demand reduction was greater than the 137 kW of lighting load shed. The increased demand reduction of 15 percent was due to cooling load reductions, which accompanied the lighting load shed. The demand on the maximum demand day was not much greater than on the test day, as would be expected for a building dominated by internal gains.

The cooling setpoint reset was not engaged as planned. The mean space temperature remained constant at approximately 72 degrees during the load shed test. Had this set point reset been engaged, the temperature would have drifted upward during the test, and the temperature reset would have produced a larger initial demand reduction. Accordingly, the impact of the temperature reset strategy could not be verified experimentally.

However, our building model predicted that savings from the temperature reset would have disappeared. Savings from this measure would be much less significant in the office building than in the store, because the characteristics of the office building caused its temperature to drift upwards more rapidly. The cooling system in the office would therefore have to come back on before the end of the demand control period. These results were considered evidence that the temperature reset would not be an effective demand control measure and suggested the building's cooling load was almost entirely due to internal gains, influenced very little by outdoor climate conditions.

Changes in Test Protocols

The testing approach was implemented in consultation with the customer. Originally, the plan was to collect monitoring data over two seasons, the first a control period and the second implementing the improved control measures. However, due to construction delays at the customer’s facility, an alternative test procedure was proposed. The shorter duration test utilized a “flip-flop” design in which the demand control sequence would be operated or not operated on successive days. However, the customer

D i s c u s s i o n

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objected to repeated changes of the control strategy. As a result, a simplified test was developed where the control sequence was exercised for a period of several hours on one day.

For the lighting shed test at Office Headquarters, the actual demand control measures implemented by the customer turned out to be quite different from those initially proposed. The customer determined it was in its interest to maximize the amount of interruptible power in exchange for an incentive rate from the utility. Accordingly, the customer agreed to implement demand controls on all lighting, instead of just the perimeter office, basement display, basement hallways, and cafeteria. The customer expected curtailment would occur rarely, and that task lights would be adequate if that did happen. The customer also modified the equipment connected to the emergency generator to include only 30 kW of load.

Validity of Results

Although we had only one day of experimental observations, we are confident that we were able to establish the behavior of the buildings based on our model because we had two years of monitoring data. However, the estimates of demand reduction were based on the observations of only one day at two sites. One should not assume that these findings will necessarily be valid at other sites.

Implementation Issues

“Bounceback,” or an increase in consumption at the end of the control period, appears to be a manageable issue. This is because the peak times are typically in the afternoon. By the time the control period is over, the Office Headquarters should be shutting down for the end of the day. In the case of Store #29, however, opening hours typically extend beyond the utility’s peak demand period. The EMCS would need to be carefully programmed to bring the air conditioning back on gradually to avoid a spike. If typical demand control periods for the site were expected to start in the morning and last throughout the day, air conditioning demand shedding may not be feasible. This needs to be evaluated case by case.

Specifications for EMCSs were not critical to this project. Essentially, any EMCS on the market is capable of implementing the simple load control strategies demonstrated during this project. A more sophisticated approach would have involved a demand-limiting algorithm that cycled the air conditioning to minimize hot spots in the building. In that case, demand savings would be similar, but the impact on human comfort might be even less than that indicated during the test.

Summary

Phase I identified potential control savings from the following measures:

• Demand shifting—preheat domestic water

• Demand shifting—sequential morning start-up of chillers

• Fan slowdown


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• Upgrade controls

• Window film

• Energy-efficient air-conditioning equipment

• Convert 100 percent outdoor air units to recirculation

• Energy-efficient lighting

• Demand shifting

• Load shedding—duty cycling

• Turn off electric reheats used for temperature control

In Phase II, we identified and tested opportunities at two specific locations. Of the eleven measures identified in Phase I, we developed strategies for four, and tested three:

Table 3. Load control measures tested

Facility type Measure

Retail Duty cycling of air conditioning

Office Shed interior loads (lights)

Office Implement back-up generator

Office Temperature reset (not implemented)

In the chart below, the savings obtainable from the measures in the two test sites are compared to those predicted for the same measures in the mix of buildings examined in Phase I.

Table 4. Percentage savings from tested load control measures

Measure Retail site Office site Phase I buildings

Duty cycle AC 15% NA 3%

Shed lighting NA 14% 3%

Implement generator

NA 2% 5%

Temperature reset NA Not implemented NA

D i s c u s s i o n

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These results suggest that demand reduction from these measures will vary considerably from site to site. The savings from duty cycling air conditioning and shedding lights were higher at these two sites than Phase I predicted. No potential for other measures identified in Phase I was found at all.

The three tested measures have wide applicability to other locations, as shown by the following table, which shows how typical they are of control options available in offices and retail buildings.

Table 5. Availability of tested load control measures in Phase I buildings

Measure Offices Retail buildings

Duty cycle AC 2 of 10 1 of 5

Implement generator 4 of 10 1 of 5

Shed lighting All All

Conclusions • Savings observed (about 15 percent for two cases) suggest that most of the statewide potential

identified in Phase I (about 20 percent for a mix of facilities) is possible. The sample size was small, however, and savings will vary.

• “Hard” EMCS savings, such as shedding cooling loads, had relatively minor comfort impacts, as defined by ASHRAE.

• The influence of incentive rates on customer choice was clear. This suggests the influence of market-based rates that relay the appropriate price signal for capacity.

• Technical assistance was needed. Customers were not aware of control strategies' benefits, ease of implementation, and minimal impacts.

Recommendations

Technical assistance was necessary to implement EMCS measures. The customer was not well acquainted with the reality of load control strategies. The customer demonstrated a reluctance to consider anything that might curtail operations. Yet, as the project was actually implemented, the customer deployed load controls greater and deeper than those originally proposed.

What changed the customer’s opinion? It may have emerged from the process of working out the engineering details of load control strategies. This project allowed the customer (and the customer's engineer) to become more familiar with control mechanisms and to realize the impacts would be minor. But in large part, it was the incentive of interruptible rates.


22

The customer was willing to implement load controls to receive the benefits of an interruptible rate schedule. During site recruitment we found that customers were reluctant to permit direct control by the utility, but would consider doing it with a sufficient incentive. This suggests a market-based approach, such as real-time pricing, might be perceived as a “fair” way to influence customers to adopt load control strategies.

Implementation of this project revealed a serious program design problem. Since we did not provide the incentive, we had little leverage to ensure the participant’s cooperation. The customer saw only his responsibility to the utility and not to a third party. If such a program is conducted in the future, it would be better to assure that incentive funds pass through the experimental program so that the customer understands his obligation to cooperate with the experiment.

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References ASHRAE. 1997. ASHRAE Handbook, Fundamentals, I-P Edition (p. 8.12). American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. Atlanta, GA:ASHRAE.

Energy Center of Wisconsin. 1995. Potential for Reducing Peak Demand with Energy Management Control Systems (WCDSR-137-1). Madison, WI: ECW.

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Appendix A: Case Study Reports

Store #29

Store #29 was constructed in 1979 and remodeled in 1993. The building's long dimension runs along a north/south axis, and its narrow dimension runs along an east/west axis. There is one floor and a mezzanine; the main floor is occupied by retail space, stockrooms, shipping, and receiving, while the mezzanine is occupied by offices and stockrooms. Packaged heating and cooling units are mounted on the roof. Total gross square footage is about 120,000 square feet.

Preliminary Load Analysis Store #29 purchases electricity from Wisconsin Power and Light. There is one main metered service for 480V/3-phase distribution. Gas is purchased from the Madison Gas & Electric and is used to fire rooftop gas furnaces and direct-fired domestic hot water heaters.

In 1993 and 1994, Store #29 consumed over 1.5 million kWh of electricity and 19,000 therms of natural gas. Based on a gross area of 120,000 square feet, its energy use index was roughly 59,500 Btu per gross square foot per year.

Peak electrical demands registered for this same period varied from 250 kW to 415 kW, with pronounced summertime peak demand and energy use. These peak demand profiles were manifestations of greater HVAC plant energy use during summer months. As expected, natural gas usage peaked in winter. Base gas usage averaged 250 therms during summer months.

Space Conditioning Most of the facility's space heating and cooling is provided by 21 packaged rooftop AC units equipped with mixed air dampers, air-cooled DX cooling, and integral gas furnaces. The units range from 5 through 15 tons of cooling and about 220,000 Btu/hour of heating. All units are air-cooled. In the energy analysis, they were modeled to use approximately 1.4 kW/ton at full load conditions.

Supply fans distribute a constant volume of conditioned air and ventilation using large diffusers located throughout the store. All systems are equipped with a mixed air plenums and control dampers that allow outside air economizing.

Lighting Existing retail and office lighting is provided by standard 2x4 drops in 4-tube fluorescent fixtures using energy-efficient ballasts and T-8 tubes and averaging about 1.6 watts/sq.ft.

The store's retail area is outlined with a continuous strip of 1x4 energy-efficient fluorescent lighting. Incandescent and high-intensity discharge (HID) lighting units are used sparingly for highlighting effects. In areas where extra lighting is required, lighting levels are increased by 2x2 three “U” tube fluorescent fixtures or a slightly higher fixture density.


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Warehouse area lighting is provided with 8-foot, single-tube fluorescent strip fixtures using standard coil and core ballasts and T-12 tubes, averaging about 0.95 watts/sq.ft. Light switching is controlled via a direct digital control (DDC) system.

Envelope The building envelope is constructed with six-inch hollow core building blocks on the inside and six-inch solid building blocks on the outside, equating to a “R” value of about 3.5.

The eastern exposure has one loading dock door (R=2.5), and the two front entrances utilize vestibules for customer entrances and exits. Vestibules are constructed of double pane glass (R=1.5) supported by a metal framework. Each entrance has glass panels attached to the exterior wall above.

The western exposure contains three main loading dock bay doors (R=2.5) and seven standard foam core steel doors (R=2.5). The northern side has one loading door and three standard foam core steel doors. The southern side of the building is shared with the mall; heat transfer does not occur.

Roof construction is a standard 4-inch, built-up type of approximately 100,000 sq.ft. with a “R” value of 25.

Environmental Controls HVAC controls are primarily DDC in nature. Currently, a Control Systems International (CSI) direct digital control energy management system (DDC EMS) is installed. This is used for monitoring and automation of rooftop HVAC units and lighting schedules. The DDC EMS monitors space, outside, mixed, and supply air temperatures, enables start/stop of rooftop units based on input schedules and optimum start/stop criteria, and schedules major lighting circuits. Zone temperatures are sensed by the DDC system, and cooling and heating stages are sequenced to maintain temperature setpoints.

Since the summer of 1993, the store has participated in a utility-sponsored electric demand control program. In return for a reduced electric tariff, the store allows the utility to shed a portion of the rooftop HVAC unit load on peak summer days via utility dispatched direct load control relays. Fifteen of the 21 rooftops are now part of this utility dispatched load, accounting for about 130 kW of demand.

Miscellaneous Miscellaneous electrical loads include display and checkout equipment plugged into power outlets, miscellaneous convenience outlets, ten electric duct heaters in the front of the building for zonal temperature control, and miscellaneous exhaust fans. Domestic hot water is provided by gas-fired domestic water heaters.

Operation and Scheduling

Occupancy • Monday-Saturday: 8:00 A.M.-12:00 P.M. (occupancy varying from 10%–95%).

• Sunday-Holidays: 10:00 A.M.-6:00 P.M. (occupancy varying from 20%–100%).

A p p e n d i x A : C a s e S t u d y R e p o r t s

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Lighting • Monday-Saturday: 8:00 A.M.-12:00 P.M. (with a 5% base and varying from 5%–100%).

• Sunday-Holidays: 7:00 A.M.-6:00 P.M. (5% base load).

HVAC System • Heating: 70°F occupied–60°F unoccupied.

• Cooling: 74°F occupied–85°F unoccupied.

• Monday-Saturday: 7:00 A.M.-9:00 P.M. (fans and other related equipment).

• Sunday: 7:00 A.M.-6:00 P.M. (fans and other related equipment).

Baseline End-Use Consumption Total energy use breaks out as follows.

Table A1. Energy use analysis for Store #29

Category Annual energy use (therms)

Percentage

Lighting 21,315 30.7%

Heating 19,346 28.0%

Cooling 4297 6.2%

Auxiliary 13,837 20.0%

Other 10,444 15.1%

Potential Strategies Identified In discussions with the customer, only one control measure was identified as feasible—duty cycling of the air conditioners during peak periods.

The customer already was on an interruptible rate that allowed the utility to directly control the rooftop units (RTU). The customer expected occasional curtailment of air conditioning, and did not feel the slight temperature increase was a serious burden. One of the experiment's purposes was to verify the amount of temperature increase that occurred during control periods, then determine if it affected the customer.

The control mechanism was rather crude. The utility sent a radio signal to each controlled RTU, and required a portion of these to cease operation for short periods during peak conditions. By “rolling” the shut-down signal, the curtailments were staggered between units. For example, by staggering shut-off periods of 20 minutes in an hour, the overall effect was that one-third of the units were off during the peak hour. The actual amount of peak reduction depended on whether the RTUs had been on without the radio signal. Assuming the peak day represented the extreme design conditions, and that units were


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appropriately sized, they would otherwise have been on. Hence, this was a reasonable assumption. However, these conditions were unlikely in the real world. Part of the purpose of the experiment was to determine a “rule of thumb” for estimating more accurate demand savings, taking into account the probability that RTUs would otherwise be on.

There were advantages to implementing the control strategy through the EMCSs. The utility only had to provide one radio receiver to the EMCS rather than one to each bank of RTUs. In this case, the EMCS took over and transmitted control signals to the RTUs after receiving the radio signal. A second and more important advantage was that the EMCS could achieve more precise temperature controls within the building. After receiving the radio signal, the EMCS could implement a demand-limiting algorithm where the appropriate RTUs were cycled, depending on where the heat build-ups occurred inside the store. This permitted better temperature control for the customer, even though the same demand savings were being realized.

Customer Acceptance and Perceptions Based on conversations with the client, it became readily apparent that any strategy delivering an acceptable payback would be considered for actual testing, assuming employee or customer comfort were not compromised beyond a certain level. The customer contact indicated a two-and-a-half year simple payback threshold for any energy or demand saving measure. Additionally, the client had to be certain load shedding measures and timing were such that new peak electric demand levels were not set directly following the test, essentially negating any cost savings achieved through the load shed. A resumption of normal mode temperature setpoints without a sufficient time delay could very well result in an electric demand excursion as fans and compressors suddenly loaded up.

Headquarters Office

The Office Headquarters building was constructed in 1987. In 1990, it was remodeled to include an optical department in the basement. The building's long dimension runs along an east/west axis, and its narrow dimension runs along a north/south axis. Overall, there are three floors (basement through second), two mechanical penthouses above the second floor, and a cafeteria on the ground floor. The basement is occupied by the display, photo, and optical departments, shipping/receiving, and equipment mechanical rooms. The upper two floors are used as office space. The mechanical penthouses above the west and east wings of the second floor house air handlers, chillers, boilers, and humidification equipment used for conditioning the upper two floors. Total gross square footage is about 220,000 square feet.

Preliminary Load Analysis Office Headquarters purchases electricity from Wisconsin Public Service. There is one main metered service for 480V/3-phase distribution. Gas is purchased from the same utility, and is used to fire space heating hot water boilers, humidification steam boilers, and direct-fired domestic hot water heaters.


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For 1993 and 1994, the Office Headquarters consumed over 5.5 million kWh of electricity and 94,000 therms of natural gas. Based on a gross area of 220,000 square feet (excluding the mechanical penthouses), the energy use index was roughly 128,350 Btu per gross square foot per year.

Peak electrical demands registered for this same period varied from 835 kW to 1,160 kW, with a pronounced summertime peak demand and energy use. These peak demand profiles were a manifestation of greater HVAC fan and chiller plant energy use during summer months.

As expected, natural gas usage exhibited prominent peak usage during winter months. Base gas usage was about 2,400 therms during the summer months.

Space Conditioning Most of the building's space cooling is provided by four multistage, air-cooled, reciprocating chillers that serve two independent cooling circuits. The largest units are rated at 125 tons; one unit is about 40 tons. The energy analyses modeled the units to use approximately 1.2 kW/ton at full load conditions. Chillers, circulation pumps, and air coolers were not operating during the site visit, but appeared to be in good condition. The cooling plant provides chilled water through two separate circuits to nine major air handlers that supply conditioned air to the building.

Additional mechanical cooling is provided by dedicated package terminal units that handle the load imposed by the building's mainframe computing system.

Space heating is provided by a modular bank of six gas-fired hot water boilers in parallel for each mechanical penthouse. Each cast iron boiler has a capacity of approximately 10 boiler-HP, and is equipped with off/low/high fire controls. Hot water is piped to heating coils in each of the air handlers, the perimeter baseboard convectors in the first and second floor offices, and to the unit heaters and reheat coils in basement variable air volume (VAV) terminal units.

There are nine main air handling systems that supply conditioned air and ventilation to offices and the basement. The west penthouse houses supply air/return air (SA/RA) systems 1 & 2 that distribute conditioned air and ventilation to the building's west side; the east penthouse houses SA/RA systems 3 & 4 that distribute conditioned air and ventilation to the building's east side. The remainder of the air handlers (except for SA-9) are located in the basement and serve areas of the basement. The SA-9 unit serves the second floor board and executive room.

These systems supply a variable volume of air to cooling-only VAV boxes on the first and second floors, and to cooling-only VAV and VAV reheat boxes in the basement. The building houses approximately 220 VAV boxes.

All systems are equipped with mixed-air plenums and control dampers that allow outside air economizing. All are equipped with return fans, except for systems 8 and 9. All fans are equipped with variable inlet vanes to allow throttling of supply and return air quantities.


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Lighting Existing first and second floor general open work area lighting is provided by standard 2x4 drops in 2-tube fluorescent fixtures using energy-efficient ballasts and T-8 tubes and averaging about 1.16 watts/sq.ft. First and second floor offices along the south side have a higher power density of about 1.65 watts/sq.ft.

By design, the cafeteria has a high amount of daylighting. This is supplemented by fluorescent lighting and large amounts of incandescent lighting. The open area around the main stairwell and main entrance also has some daylighting, supplemented by HID lamps and compact fluorescent down lights. Stairwell and restroom lighting fixtures are fluorescent.

The basement offices along the western wing's southern exposure have a power density of about 1.13 watts/sq.ft. The optical area lighting is provided with standard 2x4 drops in 2-tube fluorescent fixtures using energy-efficient ballasts and T-8 tubes averaging about 1.16 watts/sq.ft. Lighting for the photo studio, setup area, and storage is provided by 8-foot, single-tube fluorescent strip fixtures using standard coil and core ballasts and T-12 tubes. Lighting is controlled through a combination of low voltage and line voltage switches.

Envelope The building envelope is of standard wall construction with a brick facing and an estimated “R” value of 11. All windows are tinted, double-pane glass with aluminum framing (R=1.54). Areas above the main entrance are detailed with a standing seam copper fascia. This standing seem copper fascia also covers the outward facing exterior stairwell walls and spandrels between the first and second floor windows. The basement is either below grade or bermed.

The southern exposure contains the main entrance and provides a walkway canopy for sheltered access. The eastern and western exposures are utilized for employee access. The northern exposure's wall area is similar to the southern exposure's except for the addition of a central area. This central area includes a receiving bay door and a cafeteria with a solarium. The roof is a conventional built-up type with an “R” value of 25 and is about 75,000 sq.ft.

Environmental Controls HVAC controls are currently a combination of local pneumatic and DDC. Currently, an American Auto Matrix (AAM) DDC EMS is used for monitoring and control of central mechanical plant equipment and main lighting circuit scheduling.

The DDC EMS monitors key space and supply water temperatures, enables start/stop of plant and air handler systems based on input schedules and optimum start/stop criteria, and supervises control of air handling unit heating and cooling coils, relief fans, humidifiers, and return fan inlet vanes.

Zone temperatures are controlled by local pneumatic thermostats for VAV boxes and unit heaters and by the DDC system for perimeter baseboards. The supply fan's inlet vanes are controlled through a local pneumatic loop, as are air handler mixed air dampers.


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Since the fall of 1994, the building has undergone a retrofit of its DDC EMS system. The existing system will eventually be removed and replaced by a Control Systems International (CSI) automation platform. This new platform will take over control of all equipment currently under control of the AAM system, and will include extensions such as chiller/boiler start/stop and lead/lag, VAV box, supply/return fan speed, and mixed air damper control.

Miscellaneous Miscellaneous electrical loads include equipment plugged into convenience outlets by tenants, the headquarters mainframe computer, photo department lighting and equipment, duplicating equipment, kitchen equipment, optical department machinery, a passenger elevator and a freight elevator, miscellaneous exhaust fans, optical and control air compressors, and city water booster pumps. Domestic hot water is provided by electric domestic water heaters located in the mechanical rooms. There is also a 54 kW electric water heater used for photo processing.

As part of the premonitoring efforts, MicroGrid attached electrical current data loggers on the electrical feeder supplying the west penthouse mechanical equipment and read the meters on the uninterruptible power supply supplying the main computer system. This was done to help determine the magnitude and profiles of these loads. This information helped better determine how electrical energy is used in the building.

Operation and Scheduling

Office Occupancy • Monday-Friday: 7:00 A.M.-12:00 P.M. (occupancy varying from 5%–95%).

• Saturday: 7:00 A.M.-7:00 P.M. (occupancy varying from 5%–30%).

• Sunday-Holidays: 7:00 A.M.-6:00 P.M. (occupancy at 5%).

Office Lighting • Monday-Friday: 6:00 A.M.-12:00 P.M. (with a 5% base and varying from 10%–90%).

• Saturday: 7:00 A.M.-7:00 P.M. (with a 5% base and varying from 10%–30%).

• Sunday-Holidays: 7:00 A.M.-6:00 P.M. (5% base load).

HVAC System • Heating: 75°F occupied—65°F unoccupied.

• Cooling: 75°F occupied—85°F unoccupied.

• Monday-Friday: 6:00 A.M.-8:00 P.M. (fans and other related equipment).

• Saturday: 7:00 A.M.-1:00 P.M. (fans and other related equipment).


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Optical and Duplicating • Monday-Friday: 8:00 A.M.-5:00 P.M. (equipment operation).

• Saturday: Off.

• Sunday-Holidays: Off.

Cafeteria Equipment • Monday-Friday: 6:00 A.M.-1:00 P.M. (equipment operation).

• Saturday: Off.

• Sunday-Holidays: Off.

Main Frame Computer and Dedicated Cooling • Sunday-Saturday: On.

• Holidays: On.

Baseline End-Use Consumption Total energy use breaks out as follows:

Table A2. Energy use analysis for Office Headquarters

Category Annual energy use (therms)

Percentage

Lighting 28,342 10.2%

Heating 93,905 33.8%

Cooling 11,570 4.2%

Auxiliary HVAC 17,460 6.3%

Mainframe Computer & Cooling 101,976 36.7%

Other 24,915 8.8%

Potential Strategies Identified Discussions with the customer generated the following list of potential control measures:

Table A3. Estimated demand savings from potential control measures identified at Office Headquarters

Control Measure Estimated demand savings (kW)


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Perimeter office, basement display, basement hallways, and cafeteria light shedding

38

Emergency generator used to peak shave dedicated load

90

Photo department processing hot water load shed 6–12

Commanded reset of cooling setpoints upwards by 3° F 22

Total demand savings estimate 160

As none of the potential conservation measures had simple paybacks meeting the customer's threshold, the above load shedding strategies did not include effects of these proposed conservation efforts. This building already included energy- efficient lighting.

Customer Acceptance and Perceptions Based on conversations with the client, any strategy delivering an acceptable payback would be considered for actual testing, assuming employee or customer comfort were not compromised beyond a certain level. The customer contact indicated a two-and-a-half year simple payback threshold for any energy or demand saving measure. Additionally, the client had to be certain load shedding measures and timing were such that new peak electric demand levels were not set directly following the test, essentially negating any cost savings achieved through the load shed. Potentially, setting a peak demand following a test would be especially possible where space or chilled water temperature setpoints were reset upwards as a strategy. A resumption of normal mode temperature setpoints without a sufficient time delay could very well result in an electric demand excursion as fans and chiller compressors suddenly loaded up.

The customer had some concerns regarding load shedding of the hot water heater in the photo lab. Staff were reluctant to accept curtailments in photo processing operations. Therefore, the participant considered this measure to be unacceptable.

Appendix B: Store #29 HVAC Schematic

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Appendix C: Glossary Hard EMCS Demand reduction measures implemented through the building’s energy

management control system that potentially have some negative impact on customer comfort

Easy EMCS Demand reduction measures implemented through the building’s energy management control system that have no perceived impact on customer comfort

Critical areas Areas of the building that are occupied by staff members working or customers shopping—as opposed to peripheral areas such as hallways

Hunting The transient response of a control system (for example, a temperature controller) as it oscillates about the new setpoint until settling into a new steady-state condition. Unstable systems may oscillate or hunt indefinitely

TMY Typical Meteorological Year. A “constructed” weather year based on at least ten years of hour-by-hour data at the location, intended to represent an average year while at the same time maintaining the kinds of patterns and “runs” of weather that exist in reality

ASHRAE American Society of Heating Refrigeration and Air-Conditioning Engineers

Direct Load Control Shedding of load in a facility through a direct signal sent by an outside organization, usually the electric utility

VAV Variable air volume. A heating, ventilating, and air-conditioning system in which the air supplied to the space can be varied according to requirements

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