Energy Audit Analysis of Residential Air- Conditioning Systems in Austin, Texas

Transcription

1 Energy Audit Analysis of Residential Air- Conditioning Systems in Austin, Texas Joshua Rhodes Brent Stephens Michael E. Webber, PhD Student Member ASHRAE Student Member ASHRAE ABSTRACT This work uses a unique database of nearly 5000 single-family home energy audits performed between 2009 and 2010 in Austin, TX to 1) characterize the homes and their air-conditioning systems, and 2) estimate the aggregated peak power savings attainable through the implementation of a series of conservation steps. The audit database is restricted to singlefamily detached units, which allows the analysis to be extrapolated to just under one-half of the Austin residential building stock. Characterization of the audit homes reveals that many homes still have low-hanging fruit efficiency improvements available. Theoretically enrolling all homes in the utility-sponsored efficiency program yields a potential reduction in the average air-conditioner size in the database from 3.1 tons (11 kw) to 2.2 tons (7.7 kw), which corresponds to a peak power demand reduction of 1.8 kw for the average individual system. The maximum utility-scale peak power reduction for the implementation of this program city-wide is estimated to be as high as 200 MW, which is almost 8% of Austin s peak electric demand in 2008 and enough to possibly offset new power plant acquisitions. Our calculations estimate that >97% of homes in the audit database would benefit from enrollment in a home efficiency program and that the cost of savings is significantly less than the cost of new generation facilities. Although this analysis is based on data from the City of Austin, understanding the methods described herein could allow electric utilities and homeowners in similar climates to make better-informed decisions when considering efficiency improvement programs. INTRODUCTION Several widespread factors have been shown to increase peak power demand (e.g., improper equipment sizing and low system efficiency) and total energy consumption (e.g., suboptimal refrigerant charge, excess duct leakage, and incorrect airflow rates) by air-conditioning systems in single-family residences (James et al., 1997; Proctor, 1997; Proctor, 1998; Neme et al., 1999; Downey and Proctor, 2002; Mowris et al., 2004; Pigg, 2008). However, there have been limited studies of the peak power consequences of residential air-conditioning systems that have sample sizes large and diverse enough to scale to the utility level. Thus, there is a knowledge gap that this paper is seeking to fill. This work uses a database of 4971 recently performed energy audits in Austin, Texas to characterize the prevalence of typical design and installation issues across the audit homes and predict the amount of peak power draw reductions that may be achieved in the average home. Additionally, the aggregated peak power savings potential to Austin Energy, the local electric utility, is estimated for widespread participation in a utility-scale efficiency program, and the costs of savings are compared to the costs of a new peaking generation plant. Understanding this dataset, especially the peak power consequences, can lead Austin Energy and similar utilities to make better-informed decisions when considering efficiency improvement programs. Joshua Rhodes and Brent Stephens are graduate students in the Department of Civil, Architectural and Environmental Engineering at The University of Texas, Austin, TX. Michael E. Webber is an assistant professor of Mechanical Engineering and associate director of the Center for International Energy and Environmental Policy at The University of Texas, Austin, TX.

2 ENERGY AUDIT DATABASE Austin, Texas is unique in that it is one of the few cities in the U.S. that requires an energy audit to be performed on a home before it can be sold. This mandate is part of the Energy Conservation Audit and Disclosure (ECAD) ordinance (Austin Energy, 2011). Homes may be exempted from this ordinance under several conditions, including recent participation in utility-sponsored energy efficiency programs, classification as a condominium or manufactured home, or if the change of ownership occurs under a variety of extenuating legal conditions (e.g., foreclosure, exercise of eminent domain, or property settlements). The goal of the ECAD ordinance is to improve the energy efficiency of the entire Austin building stock, and it applies to both residential (single- and multi-family) and commercial buildings. Despite the wide applicability of the ECAD ordinance, we consider only single-family residences in this study. The ECAD program s intent is to 1) use market forces to increase the energy efficiency of on-the-market homes by providing prospective buyers with better information, and 2) address part of the Austin Climate Protection Plan, which includes reducing the City of Austin s peak power demand by 700 MW by There are over 200 companies in the greater Austin area that are permitted to conduct official ECAD audits. Each auditor receives training by Austin Energy and is given a detailed handbook explaining the steps necessary to conduct the official ECAD audit. Audits are all submitted on a uniform document to Austin Energy who then supplies the completed audit to prospective buyers. Auditor s results are internally checked against similar home audits to determine authenticity (Kisner, 2011). This work uses a database of almost 5000 energy audits that were performed on single-family detached homes between 2009 and 2010 as a basis for analysis. To the authors knowledge, no other analysis of this type has been conducted on a dataset this extensive. METHODS We first characterized common design and installation issues found in the homes in the audit database. Many of these parameters were assessed simply by visual inspection, and others were measured directly by the auditors. Subsequently, we estimated the amount of peak power draw reductions that might be achieved across the building stock in Austin for various penetration levels of participation in the most aggressive residential efficiency program in Austin Energy s Power Saver Program, Home Performance with ENERGY STAR. Home Performance with ENERGY STAR (HPwES) is a joint program between the US Department of Energy and the US Environmental Protection Agency. As stated on the program website, the program is a comprehensive, whole-house approach to improving energy efficiency and home comfort, while helping to protect the environment (Energy Star, 2011). Austin Energy, the municipally owned electric utility of Austin, TX, is the local sponsor, with over 7000 participants since Homeowners participate in the program by first contacting a participating home energy company to perform an energy audit on their home. The audit company then makes recommendations on improving the efficiency of the home in the following areas: duct sealing and repair, attic insulation, upgrading windows, caulking, weather stripping, radiant barriers, and correctly sizing the air-conditioner or heat pump to a unit with a SEER of 14 (12 EER, COP-3.5). The participant decides which improvements they would like to have performed, and Austin Energy reviews the proposal for work. Once approved, the homeowner has two options, 1) the homeowner may receive a rebate of up to 20% of the cost of repairs, up to $1575 or 2) the homeowner may be eligible for a low interest, unsecured loan from Austin Energy. To establish a best case scenario of improved homes, each home in the database was theoretically enrolled to receive the most commonly performed improvements in the HPwES program, and then each air-conditioning system was resized to meet its best case reduced cooling loads. The power draw savings of a resized air-conditioning unit, as compared to installed units at the time of the home audit, in each home were then summed and multiplied by a scaling factor to estimate the peak power consequences at the utility scale. For all homes in the database, attic insulation was brought to code (R-38 hr ft² F/Btu, RSI-6.7 m 2 K/W), caulking and weather-stripping was assumed to increase air tightness to 0.35 ACH (ASHRAE, 2009), leaky ducts were sealed, and windows were upgraded to a generic double-paned window (U-value of 0.56 Btu/hr ft² F, 0.97 W/m² K). Although nearly 5000 homes underwent energy audits as part of a sales transaction as required

3 by the ECAD ordinance, it is assumed that no home energy improvements were previously implemented in these homes. Thus, it is assumed that the home characteristics as listed in the ECAD database are an accurate reflection of the homes today, as well as the rest of the single-family homes in Austin. To assess peak power improvements, the optimal air-conditioner capacity for each theoretically improved home in the audit database was estimated by performing a Manual J calculation (Rutkowski, 2004). This analysis was accomplished with a custom spreadsheet program following the Manual J (ACCA) reference (James et al., 1997). Assuming the homes went through the home energy upgrades, the values mentioned above were used in place of each home s existing characteristics. The calculated unit size (EER 12, COP-3.5) was then compared to the installed unit size (installed EER) and the difference in peak power draw was calculated. Peak power draw was estimated in both cases by dividing equipment nominal capacity by rated EER and adjusting for likely operating conditions, as shown in using Equation 1:!"# !"!! =!!" (1) where P is the peak power draw (kw), CAP is the nominal capacity of the unit (tons), 12 is the unit conversion factor ([BtukW]/[hr-tons-W]), EER is the rated efficiency of the unit (BTU/hr-W), and CF is the consumption factor used to account for increased power draw at outdoor conditions during the peak hour in Austin that are likely higher than rated conditions. The average power draw for homes in the database was calculated and extrapolated to 156,000 single-family detached homes with an assumed average 70% run time for each unit during peak summer demand (Pigg, 2008; Stephens et al., 2011). There are approximately 332,000 residential buildings in Austin Energy s service area, 41.7% (156,000) of which are single-family detached units (US Census, 2009). Thus, this database of 4971 energy audits represents over 3% of all singlefamily homes in Austin, making it a statistically relevant representation of the building stock. Because the audits in the ECAD database in our possession were single-family detached homes, we extrapolated the results of our analysis of the ECAD data to all single-family units in the City of Austin. Percentage)of)Homes)(%)) 25" 20" 15" 10" 5" 0" Audit"Database" Austin"Census" Year)Built) Figure 1 ECAD audit database homes compared to projected 2009 Austin census data (single-family detached homes only). Based on Figure 1, the ECAD data are similar to the demographic data for Austin as of Census data shows a higher percentage of recently built homes (these homes are not subject to the ECAD ordinance because of their age). The owners of these newer homes may have chosen to obtain an ECAD audit as an incentive to give to potential buyers. Furthermore, the audit data includes a higher percentage of older homes.

4 RESULTS Findings from the Audit Database This section first summarizes building and system characteristics of homes in the audit database. Figure 2 shows the distribution of the year that each home in the database was built. The majority (~99%) of homes in the audit database were built prior to 2000, with a mean and median year built of 1972 and 1976, respectively. Number of Homes Figure 2 Distribution of year built of the homes in the ECAD audit database (N = 4893). The mean floor area of the homes in the audit database was approximately 1798 ft 2 (167 m 2 ) (std. dev. 764 ft 2, 71 m 2 ), with a median value of 1615 ft 2 (150 m 2 ). Figure 3 shows the measured attic insulation levels for ECAD homes. Number of Homes Mean: R-21 Median: R-21 Std. Dev.: R-9.5 Number: 4555 < Attic Insulation R-value (hr ft² F/Btu) Figure 3 Distribution of Attic insulation levels of homes in the ECAD audit database. Austin Energy recommends an attic R-value of 38 hr ft² F/Btu (RSI-6.7 m 2 K/W) for homes in the Austin area, although the majority of homes in the audit database (92%) fall below this recommendation. Homes in the audit database had an average attic insulation R-value of 21.5 hr ft² F/Btu (std. dev. 9.5) (RSI-3.8 m 2 K/W; std. dev. 1.7). High thermal conductivity between conditioned spaces and attic spaces increases cooling demand in the summer, as the temperature difference between the attic and conditioned space can be greater than 54 F (30 C) (Parker et al., 2002). Although there were 4971 homes in the audit database, only a negligible fraction (< 0.2%) did not have central air-conditioning.

5 Table 1 describes selected characteristics for the primary air-conditioning systems in the homes in the audit database, and the subsequent sections describe selected measured (or estimated) air-conditioning system parameters. Although there were 4971 homes in the audit database, only a negligible fraction (< 0.2%) did not have central air-conditioning. Table 1. Air-Conditioning System Characteristics Parameter Mean Median Standard Number of Deviation Units Nominal Capacity in tons [kw] 3.1 [11] 3.0 [10.6] 0.8 [2.8] 4763 Airflow Rate in CFM [m 3 /s] 1296 [0.61] 1200 [0.56] 651 [0.31] 4714 Rated Efficiency in Btu/hr/W [COP] 10.0 [2.9] 10.0 [2.9] 1.7 [0.5] 3818 Floor Area/Capacity in ft 2 /ton [m 2 /kw] [13.3] 502 [13.2] 91.4 [2.4] 4693 Unit Age (years) Figure 4 shows the distribution of total duct leakage across the audit homes, measured as the leakage airflow rate at -0.1 IWC (-25 Pa), normalized to the estimated system airflow rates. Duct leakage fractions include the combined supply and return leakage to both interior and exterior spaces. Figure 4 Distribution of duct leakage as a fraction of total duct flow for homes in the database. The majority of homes (approximately 77%) had duct leakage that would typically require sealing (greater than 10%), although there is considerable uncertainty associated with both the leakage measurements and the estimated system airflow rates. The mean duct leakage fraction was 19% (std. dev. 13%), with a median of 16% and an interquartile range of 10-24%. The mean duct leakage airflow rate, measured at -0.1 IWC (-25 Pa), was approximately 234 cfm (0.108 m 3 /s), which was at the low end of Neme et al. (1999), who summarized 19 duct studies that yielded a range of cfm ( m 3 /s) at -0.1 IWC (-25 Pa). The temperature difference across the cooling coil is another parameter measured in the audit homes and is an important indicator of how well the system is functioning. Figure 5 shows the distribution of temperature differences measured across the audit homes. The mean temperature difference was 17.1 F (9.5 C) (std. dev. 5.9 F (3.3 C), N = 3687). Austin Energy recommends that the temperature difference across the cooling coil be in the range of F ( C). Approximately 47% of the systems were operating outside of the recommended range, split approximately equal between too high (24%) and too low (23%). Temperatures that are below this range may be indicators of low airflow rates, fouled coils, or improper refrigerant charge, all of which can reduce the cooling capacity of the unit (Proctor, 1997). Excessive temperature differences may be indicators of improper sizing or overcharging, but can be an indication of increased sensible capacity. Additionally, the distribution of rated energy efficiency ratios (EER) of the installed units is presented in Figure 6, in units of cooling output (BTU/hr) per electrical power input (W).

6 Number of Homes Mean:"17.1" F"" Median:"17" F"" Std."Dev.:"5.9" F"" Number:"3687" Temperature Diference Across the Coil ( F) Figure 5 Distribution of temperature Difference across the evaporator coil for homes in the ECAD audit database. Number of Homes Up To 6 Mean:"EERC9.9" Median:"EERC10" Std."Dev.:"EERC1.7" Number:"3839" Installed EER (BTU/hr per W) Figure 6 Distribution of the installed efficiency of air-conditioning units in the ECAD audit database. The installed unit efficiency has a direct impact on the energy consumption and power draw of the air-conditioning unit. Approximately 86% of homes had systems with a rated EER less than Austin Energy s recommendation of EER 12 (COP- 3.5), and less than 2% had an EER of 14 (COP-4.1) or more. Using Equation 1 with CF = 0, we estimate that the average rated power draw across all individual units is approximately 3.9 kw (std. dev. 1.2 kw). This estimate is conservative for summer peak, as the outdoor temperature during the summer peak in Austin typically exceeds 95 F (35 C). Using an increase in outdoor unit power draw of 1.8% per C rise in outdoor temperatures (Stephens et al., 2011) and an outdoor temperature of 105 F (41 C) during the peak hour, the average peak power draw across all individual units is likely 4.2 kw (std. dev. 1.4 kw). Scaling to the approximately 156,000 single-family units in the City of Austin, and considering this dataset to be representative of all single-family detached homes in Austin, this analysis leads to a collective total possible rated power draw of approximately 660 MW for air-conditioning (or approximately 460 MW if we assume that 70% of air-conditioners are operating during the peak hour). These values represent approximately 25% and 18% of Austin s peak power demand in 2008, respectively (Duncan, 2009). Increasing the Efficiency of the Homes with the Home Performance with Energy Star Program Using the previously described sizing procedure and assigning oversized units with new correctly sized units (in commercially-available increments of 0.5 tons (1.8 kw)), the mean unit size in the database would decrease from 3.1 tons (~11 kw) (std. dev. 0.8 tons, 2.8 kw) to 2.2 tons (7.7 kw) (std. dev. 0.5 tons, ~1.8 kw) using HPwES retrofits, which would

7 allow the average home to realize a peak power demand reduction of approximately 1.8 kw (std. dev. 1.2 kw) if replaced by a correctly sized EER 12 (COP-3.5) unit. Results indicate that more than 97% of the homes in the audit database would benefit from this program. Aggregated across the entire 156,000 single-family detached homes in Austin, TX, we estimate that application of HPwES retrofits to all applicable single-family homes in Austin could reduce Austin Energy s peak power demand by as much as 200 MW, or almost 8% of peak demand in Our estimate of the potential peak power savings of HPwES retrofits assumes that the fraction of systems operating during the peak hour (assumed 70%) does not change after correctly sized units are installed, although this fraction could potentially increase as smaller systems will generally operate for longer periods of time to meet the same cooling load. Oversized systems often cycle on and off frequently, which can help reduce the aggregate instantaneous demand on a utility. If the fraction of air-conditioning systems operating during the peak hour increased from 70% to 100%, as a result of the home improvements, the peak savings would be reduced to 86 MW. However, it is not expected that the fraction would increase to that level because other HPwES retrofits would also decrease the cooling load during the peak hour. The aggregation of air-conditioning units drawing less power because of their decreased size, increased efficiency (EER 12, COP- 3.5), and reduced cooling loads from improvements to the efficiency of the home, should still enable substantial reductions in peak power demand (Neme et al., 1999). Comparison of the Costs of Efficiency Upgrades to the Costs of Peaking Power Plant Acquisitions The above analysis suggests that up to ~200 MW of peak demand can potentially be shaved with a very aggressive level of home energy efficiency improvements. Reducing this peak demand in Austin might offset the need for Austin Energy to obtain additional peak generation units, which are used to meet peak demand and usually only operate a small number of hours during the hottest parts of the summer when the air-conditioning load is high. A generic 160 MW conventional natural gas combustion turbine is estimated to cost $685 per kw (overnight costs), or approximately $110 million, with $2 million in fixed O&M cost per year (EIA, 2010). Assuming that fuel costs are $75.60 per MWh, depending on the generating unit and prevailing market prices for natural gas (NRRI, 2007), 200 hours per year of operation, and a modest 5% yearly rise in fuel prices, the total fuel cost for 20 years would be about $80 million. Thus, considering a 20-year life span, the total cost associated with a 160 MW natural gas peaking unit could be approximately $230 million (or $1438 per kw of generation). The maximum rebate from Austin Energy to homeowners enrolled in HPwES is currently $1575. Considering an average peak power reduction of 1.8 kw per home, the cost per kw (savings) to Austin Energy is approximately $865 per kw, or only 60% of the cost of a new peaking generation plant. Austin Energy could even increase the maximum rebate to approximately $2600 per home and the cost of savings would still be at parity with the cost per kw of generation. This increased maximum rebate is over half the average cost (approximately $5000) to homeowners for enrolling in this program (Belzer et al., 2007). Although there are other more cost-effective methods to reduce peak demand on electric utilities, such as direct load control and critical peak pricing (Newsham and Bowker, 2010), this analysis is limited to building retrofits using HPwES, which can still be more cost-effective than new plant acquisitions while simultaneously benefiting homeowners by reducing their overall energy consumption. CONCLUSION Given this unique dataset of energy audit information for almost 5000 single-family homes, we were able to characterize a significant portion of the Austin residential building stock. We assessed the contribution of single-family residential air-conditioning units to Austin s peak power demand, and estimated the potential peak power savings of widespread participation in a residential efficiency program. Our analysis leads to similar conclusions of previous studies: residential homes and air-conditioning systems are often inefficient. However, because of the scale and diversity of the dataset, broader conclusions can be drawn for the city of Austin s residential building stock. We estimate that residential airconditioning systems in single-family homes likely account for ~18% of Austin s peak power demand in the summer, and that repairing system inefficiencies and poor building performance characteristics could reduce peak demand by almost 200

8 MW, or almost 8%. We estimate that more than 97% of the homes in the audit database would benefit from home efficiency upgrades, and that the cost of savings to the utility is currently only ~60% of the cost of an additional peaking generation unit. Additionally, full participation in an efficiency program could help achieve up to 28% of Austin s peak power demand reduction goal of 700 MW by Although our analysis is limited to the City of Austin, implementation of a program similar to Austin s ECAD in any other city would allow for similar analysis of the existing building stock and associated peak power savings. ACKNOWLEDGMENTS Joshua Rhodes portion of this work was funded by The Pecan Street Project, Inc. (a 501(c)3 non-profit public-private partnership in Austin, Texas) and the University of Texas at Austin. Brent Stephens portion of this work was funded by the National Science Foundation (IGERT Award DGE # ). The authors would also like to thank Christopher Frye and Timothy Kisner (both Austin Energy) for their intellectual contributions and their help with understanding the audit database. REFERENCES ASHRAE, ASHRAE Handbook of Fundamentals. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. Austin Energy, About the Energy Conservation Audit and Disclosure (ECAD) Ordinance. Web. 21 Apr < Belzer, D., G. Mosey, P. Plympton, and L. Dagher, Home Performance with ENERGY STAR: Utility Bill Analysis on Homes Participating in Austin Energy s Program. Report No. NREL/TP Golden, CO: NREL. Downey, T. and J. Proctor, What can 13,000 air-conditioners tell us?, In the Proceedings of ACEEE Summer Study on Energy Efficiency in Buildings. Duncan, R., Resource & Climate Protection Plan to Aug Web. 16 May < EIA, Electricity Market Module. Report No. DOE/EIA-0554(2010). Washington, DC: DOE/EIA. Energy Star, Home Performance with ENERGY STAR. USDOE & USEPA. Web. 19 May < James, P., J.E. Cummings, J. Sonne, R. Vieira, and J. Klongerbo, The Effect of Residential Equipment Capacity on Energy Use, Demand, and Run-Time. ASHRAE Transactions 103(2): Kisner, T., Personal communication with Tim Kisner, Austin Energy. April 19, Mowris, R.J., A. Blankenship, E. Jones, Field Measurements of Air Conditioners with and without TXVs. In the Proceedings of the ACEEE Summer Study on Energy Efficiency in Buildings. Newsham, G. R. and B. G. Bowker, The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review. Energy Policy 38: Neme, C., J. Proctor, and S. Nadel, Energy Savings Potential From Addressing Residential Air Conditioner and Heat Pump Installation Problems. Report No. A992. Washington, DC: American Council for an Energy Efficient Economy. NRRI, McGarvey, J., K. Costello, R.S. Potter, M. Murphy, and P. Laurent. What Generation Mix Suits Your State? Tools for Comparing Fourteen Technologies across Nine Criteria. Report No Columbus, Ohio: NRRI. Parker, D.S., J.K. Sonne, and J.R. Sherwin, Comparative Evaluation of the Impact of Roofing Systems on Residential Cooling Energy Demand in Florida. Report No. FSEC-CR Florida Solar Energy Center. Pigg, S, Central Air Conditioning in Wisconsin. Report No Madison: Energy Center of Wisconsin. Proctor, J., Field Measurements of new residential air-conditioners in Phoenix, Arizona. ASHRAE Transactions 103(1): Proctor, J., Monitored In-Situ Performance of residential Air-Conditioning Systems. ASHRAE Transactions 104(1B): Rutkowski, H., Residential Load Calculation: Manual J. Arlington, VA: Air Conditioning Contractors of America. Stephens, B., Siegel, J.A., and Novoselac, A., Operational Characteristics Of Residential And Light-Commercial Air- Conditioning Systems In A Hot And Humid Climate Zone. Building and Environment 46(10): U.S. Census Bureau, American Community Survey, 2009 Summary Tables; generated by Joshua Rhodes; using American FactFinder; < (15 May 2011).