# A Successful Case Study of Small Business Energy Efficiency and Demand Response with Communicating Thermostats 1

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5 Figure 1. Setpoints & weather- normalized compressor status, offices Compressor status, along the bottom of the figures, should be compared to the right axis labels, where 100% indicates that all of the compressors in the sample are running, and 0% indicates that none are running. Here, the effects of precooling on event days can barely be seen in the morning hours. In contrast, the event hours - indicated by the shading between 4 and 7 p.m. - show a marked decrease in compressor activity. Figure 2. Setpoints and weather-normalized compressor status, retail Logger data for restaurants paint a very different picture (Figure 3). Restaurants, like offices, attempt to precool on event days, lowering setpoints by as much as 6 before events. The new setpoints have no effect on the compressors, however, which run continuously under both scenarios. Following this precooling, loggers show an event offset of about 4 at 4 p.m., however, the compressors are unaffected again. This provides evidence that (1) the restaurants in our participant population have undersized AC units, and (2) buildings with undersized AC units are unlikely to provide load drop during demand response events.

6 Figure 3. Setpoints and weather-normalized compressor status, restaurants Load Impacts on Event Days The linear autoregressive model used to analyze the hourly load data estimates the hourly load (in kwh per hour) for an average customer. The model is fit using the maximum likelihood method with a lag of 1, and controls for several important factors: hour of the day, day of the week, month, cooling degree hours for the hour in question, and total cooling degree hours for the day (Equation 1). In Equation 1, Q i is the kwh/hr for hour i for an average customer, α is the intercept term, and the β s are the estimated parameters. Also: Q lag1 is the kwh/hr load for the previous hour, Hour is a set of 23 dummy variables for hour of the day for non-event days, EventHour is a set of 23 dummy variables for hour of the day for event days, Month is a set of three dummy variables for month, and Day is a set of four dummy variables for day of the week. The two other variables are calculated from weather data, where: CDH is the number of cooling degree hours (base 75) for hour i, and DayCDH is the total cooling degree hours for the day. The model was used to estimate average hourly event and non-event loads. Load impacts the difference between the event and non-event loads for a 100 reference day are presented in Table 5. These results show the greatest savings from offices and retail stores on both the CPP rate and the 4 ACC program. Overall, the model indicates an average load drop of 0.7 kw (14%) for a program with a similar distribution of participating business types and programs. Excluding Restaurants, who were unable to respond to events, load impacts averaged 20%. (1)

7 Business Type Office Restaurant Retail Table 5. Average load drop during event periods Program Average Hourly Usage (kwh/h) Impact Normal Event (kwh/h) (%) 4 ACC % CPP % 4 ACC* % CPP % 4 ACC % CPP % Total % Offices & Retail Only * Only one participant in the sample - 20% Bill Impacts for CPP Participants CPP bill impacts were calculated as the change in bill relative to the standard small commercial flat rate of \$0.113 per kwh. All but four of the CPP participants saved money on the experimental rate, with the greatest bill savings at \$178, and the greatest bill increase at \$20. On average, CPP participants saved about 5% on the CPP tariff, with restaurants having the highest savings in dollar terms (Table 6). Table 6. Mean summer bill savings by CPP participants Business Type Mean GSN Bill Mean CPP bill Mean CPP savings (\$) % Office \$557 \$529 \$28 5.0% Restaurant \$2,255 \$2,149 \$ % Retail \$654 \$623 \$32 4.9% All \$931 \$886 \$45 4.8% Figure 4 shows the change in participant bills compared to the change in their energy usage during the event period. The shaded bubbles indicate participants that had communicating thermostats. The size of the shaded bubbles indicates the number of events that each thermostat received and responded to the event notification. The white bubbles indicate participants without thermostats, and so size is of no consequence. On average, those with communicating thermostats dropped 0.58 kw during events and saved 0.8 kwh on event days, while those without PCTs dropped 0.16 kw during events and saved 0.4 kwh on event days.

8 Figure 4. Summer Bill Savings vs. load change for event period There appears to be a relatively good correlation between event response and bill savings, with all but nine of the CPP participants located in the third (bottom left) quadrant of the plot. A small group of participants saved money despite increasing their usage during event periods, while an even smaller group had higher bills despite dropping load. Load Shifting and Demand Response Behavior Figure 5 shows the percentage of participants that performed a variety of energy conservation and shifting measures every day before and during the pilot, plus the percentage of participants that performed the same actions only on event days. Figure 5. Load shifting and conservation actions before and during the pilot

10 air-conditioning units were undersized. In general, it is probably safe to say that buildings with undersized air-conditioning units are not good candidates for dropping air conditioning load on hot days. In conclusion, this study provides evidence that energy efficiency programs, dynamic rates and load control programs can be used concurrently and effectively in the small business sector. This study also provides further evidence that existing communicating thermostats can provide near immediate airconditioning demand response for load control, and enhance the ability of customers on CPP rates to respond to critical events. Acknowledgements The work described in this report was coordinated by the Demand Response Research Center (DRRC) and funded by the California Energy Commission s Public Interest Energy Research (PIER) Program under Work for Others Contract No and by the U.S. Department of Energy under Contract No. DE-AC02-05CH Thanks to everyone at the Demand Response Research Center, the Sacramento Municipal Utility District, Residential Control Systems, Inc. and eradio, Inc. that helped make this research project a success.

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