Intelligent control of domestic and commercial point of use water heating and cooling applications

Transcription

1 Intelligent control of domestic and commercial point of use water heating and cooling applications Michael Klopfer, Linyi Xia, Joy Pixley, Crystal Rapier, G.P. Li California Plug Load Research Center (CalPlug), University of California, Irvine (UC Irvine), Irvine, California, USA ABSTRACT Point-of-use water heating and cooling dispensing devices include coffee makers, bottled and bottle-less hot and cold water dispensers, and plug load counter top hot water hot pot dispensers. Many devices in this category use substantial energy maintaining water temperatures at a level that enables immediate. Additionally, many of these devices maintain the temperature at this level 24 hours a day, reducing the temperature during long unused periods would save considerable energy. In this study we evaluate the savings potential of a novel thermal control methodology, Active Thermal Observation and Management (ATOM), which uses a self-learning, probabilistic approach to observe occupancy and assess the probability of short-term occupancy. In this approach, the set-point holding temperature of the hot or cold water is actively adjusted to reduce thermal loss. With system knowledge of the required warm-up time to the user s set point, a balance is struck between user convenience and energy savings. Device-mounted sensors provide observational capability of the device to sense user interaction and user involvement. The correlation of events over time and patterns of device interaction are used to improve future predictive models for thermal set points. Thus, extended device usage leads to improved energy savings and improved convenience for the user. This algorithm can be applied to food-safe serving applications. The implications of this work are far reaching and can have substantial savings impact on many residential and commercial devices that use temperature-hold features. INTRODUCTION Recent improvements in lighting and indoor climate control efficiency have left plug loads as a growing challenge for improving total household and building energy efficiency. Countertop water heaters, water dispensers (supplying hot and cold or cold water alone), and commercial coffee brewers are major energy use plug load appliances.[1-3] For example, bottle type hot and cold water dispensers, specifically have a US EPA/DOE ENERGYSTAR standard (version 2.0) setting the maximum standby energy draw for this type at 0.87 kwh/day representing an annual energy usage of 318 kwh (see Figure 1).[4] In comparison, the energy use value for this single device is on-par with the total for an entertainment system area - the most concentrated plug load center in most homes.[5] For devices that are used intermittently, such as residential coffee makers, manufacturers have used simple inactivity or daily timers to turn off major appliance operations after an allotted time, a time of day, or a designated operation (such as brewing) is complete. Comparatively, for on-demand water dispensing devices, similar savings controls are not commonly used as these devices are expected to stay ready to dispense on demand at an arbitrary time (see Figure 1). The majority of energy use for these type of devices is used by heating and cooling (if equipped) functions rather than any additional electronics functions such as interfaces or controls. This brings thermodynamic factors as major efficiency limiters. With the exception to some operational dynamics of refrigeration systems (in devices that cool liquids), [6-10] minimal efficiency improvement other than insulation is possible for low cost resistive water heating systems.[11-13] This leaves operational control of these functions as a major direction to improve energy efficiency without impacting the lifestyle and habits of users. The California Plug Load Research Center (CalPlug) is currently investigating controls for this class or devices. CalPlug is an academic research center based at the University of California, Irvine which focuses on energy for commercial and household plug load devices. In the current reported work, we investigate plug load, counter-top, point of use water heaters (commonly referred to as electric hot pots ) and the impact on energy savings an intelligent control device could have with lessons to be drawn for additional similar devices.

2 BACKGROUND CalPlug has previously investigated intelligent controls for set top box (STB) power management. The control system developed used sensors to determine user schedule and from this investigation we demonstrated 57.9% savings by enabling lower power operating modes when users were not present or not imminently ready to begin using the set top box under control.[14]. User detection from fused sensor feedback with time correlation permits self-learning to continually improve energy efficiency without impacting users. Point-of-use water dispensers provide the capability to scale energy usage by reducing the temperature set points to a lower energy settings when usage is not eminently expected. Knowledge of past schedules of device interaction permits the generation of a probability-based likelihood map of device interaction with respect to time. A period of time where hot water is unlikely dispensed (say 3 AM), and with no prior history of dispensing for a particular unit, the set points for hold temperature can be placed in a lower energy position. For periods of time where interaction is more likely (say 6 PM on a weekday) the set point can be altered to save energy, yet the required warmup period should be reduced to avoid user frustration, limiting energy savings potential during this period, but enabling the solution as viable. There are multiple human use factors that come into play regarding interaction triggers and warmup time tolerances to avoid frustration. These points are currently under investigation. This sensor mediates a dynamic balance between user-convenience against potential energy savings and is the key benefit to the Active Thermal Observation and Management (ATOM) system being investigated. Figure 1: Energy consumption for a hot and cold bottled water dispenser measured by CalPlug. Energy usage is constant to hold temperature set points at all times of the day and night even in periods where device usage is extremely unlikely. For a water heater, if the device is not used for a given period, the standby temperature can be lowered to reduce heat loss. Reducing liquid temperature reduces heat flow, yet increases the time period required to reheat the device when water is requested. A dynamic balance between energy savings and user satisfaction must be met. Thermal flow is approximated for an insulated water heater by Fourier s law of thermal conduction (shown in Equation 1(1)), effectively an insulated vessel assuming no mass loss, and no fluid convection, where heat loss is

3 only via conduction through the insulator. Correspondingly, the temperature of the liquid with respect to time is explicitly shown in Equation 1(4). Q = KA (T t d Liquid T Ambient ) (1) where Q = mc h (T Liquid T Ambient ) (2) mc h dt Liquid dt = KA d (T Liquid T Ambient ) (3) T Liquid (t) = T Ambient + (T T Ambient )e kat dmc h (4) Equation 1(1-4): Solution of Fourier s law of thermal conduction applied to an insulated vessel: An illustrative one dimensional solution is shown, where Q represents heat flow, t represents time, k is the conductivity of the insulator, d is thickness of the insulator, A is cross sectional area of the insulator, c h represents the liquid heat capacity, m is liquid mass. In (3) the final differential equation is solved for time in the form: U(t)=Be -t/τ +C. This relationship is effectively similar to the electrical analogy of a RC series circuit. The rapid addition of heat occurs at a timescale substantially greater than loss. For this reason, heating can be modeled by considering specific heat without considering heat loss. Accordingly, liquid temperature is linearly proportional to added heat (Q) if Q heating >>Q loss and constant, per Equation 1(2). The presented heat model Equation 1(4) provides a reasonable approximation for temperature change due to bulk heat loss. The effects of convection (in the liquid and vapor phase), and liquid loss due to evaporation do play a role in real-world systems yet are not taken into account in Equation 1. Consideration of these factors requires knowledge of system design for model acuity. Alternatively, knowledge of the governing first principles allows empirical fitting of experimentally collected data into simplified empirical models based on first principles. This semiempirical approach provides a balance between simplicity, flexibility, and first-principle model rigor. In this presentation, goodness-of-fit is shown using Pearson s R 2 value as a simple and conventional comparative metric. Unless otherwise noted, reduction of residuals as opposed to first principle fitting with constraints from known values was primarily used. This approach is used in this investigation to frame data analysis. MATERIALS AND METHODS A countertop water heater (hot pot) from a major manufacturer was filled with 1.0 L of water for all studies presented. All studies were performed at 20 C ambient temperature. A calibrated K-type thermocouple was used to measure liquid temperature and provide thermal control. The internal thermal controller on the countertop water heater device was bypassed for all reported studies. A PC based thermal controller constructed by the authors provided negative feedback direct control with 1.5 C of hysteresis to prevent rapid thermal cycling and match real-world control approaches for similar devices. For control of refrigeration systems, rapid cycling of compressors can cause mechanical failure due to successive starts with excessive refrigerant backpressure. Energy usage was measured and recorded using a calibrated Onset HOBO UX plug load logger. Passive Cooling Heat loss was assessed (via change in liquid temperature) by measuring a free decay in temperature from 98 C to 45 C. A temperature of 98 C was chosen as this was the highest temperature where phase change was not observed as free visible bubbles in the liquid. A fitting of Equation 1(4) was performed using a data analysis package, OriginLab Origin 9.0. This measurement was performed to assess both thermal loss and the temporal period a hold temperature varies for optimal ATOM controller thermal scheduling. Active Heating Heating rate was assessed (via change in liquid temperature) by measuring a change in temperature while applying continuous, maximum power (686 W) to the heating element from 28 C to 99 C. Temperature change was

4 measured against time. Assuming a high rate of energy addition versus energy loss, a linear relationship was regressed. Holding Temperature Energy Consumption The energy required to hold a set point temperature within the set 1.5 C hysteresis limits was assessed by a 30 minute power consumption test that was started once the initial set point was met. This was repeated for all evaluated set points. During the heating phase, a constant maximum power (686 W) to the heating element was applied. A period of 30 minutes was chosen to balance sufficient cycles while minimizing evaporation to improve extrapolation. Set points between 40 C to 95 C were chosen. Measured energy values from the 30 minute periods values were extrapolated to kwh/day. Energy usage was plotted against set point temperature. Assuming the required energy to maintain temperature is equaled to loss, the fitting of Equation 1(4) was performed similar to the passive cooling measurement evaluation. RESULTS Passive Cooling As shown in Figure 2, a clear exponential cooling relationship is observed between temperature and time elapsed. A strong fit is observed regressing Equation 1(4) with no physical fitting constraints. If the first principle form of x Equation 1(4) is used, the following regression is found: y = 20( C) + 79( C) exp ( ) with R2 =0.996 using T ambient and initial liquid temperatures. The differentiation of this expression combined with Equation 1(2) results in the heat loss (Q) in watts represented as: Q(t in min) = 2500 e loss in the measured range above 50 C is 23W t kg 4186(J/kgC). The average rate of heat Figure 2: Passive cooling of 1.0L of water in the closed countertop water heater from 98 C to 45 C.

5 As shown in Figure 3, an approximately linear relationship is observed between temperature and time elapsed. A linear regression provided: y = x with R 2 = It is worthwhile to note a strong fit is observed when regressing the same data with an exponential polynomial function: y = exp ( x x ) with R 2 = The R 2 value from this fit is deceptively high as substantial residual error is balanced between positive and negative residual differences an all too common source of issue when using R 2 as a goodness-of-fit metric. The breakdown of a strongly linear relationship between heat added and temperature shown in Figure 3, is likely due to both liquid and vapor phase convection and evaporative cooling effects. During heating, at around 75 C the appearance of small surface-attached bubbles and the increasing water vapor pressure (trending towards atmospheric at boiling point) lend credence to this speculation. A secondary effect of thermal mass of the vessel creating a temporal lag in heating likely explains the delay to heating after the element is activated. Both of these factors, in addition to conductive cooling add additional and potentially non-negligible error to the linear heating model. Figure 3: Active heating of 1.0L of water in the closed countertop water heater from 28 C to 99 C. As shown in Figure 4, a non-linear relationship is observed between energy consumed at multiple holding temperatures. This is expected as Q in = Q out when the temperature is constant, hence a relationship similar to that expressed in Equation 1(4) is not surprising. The following relationship was found from an unconstrained regression: x y = exp ( ) with R2 =

6 Figure 4: Energy required to maintain (hold) a set temperature within 1.5 C hysteresis bands of a given multiple set temperature values. DISCUSSION Dynamic temperature control can save substantial energy. In Figure 1, we present the required energy for a hot and cold bottled water dispenser in a commercial building. From the period of 6PM until 8AM, there is no usage of this device this is 13 hours of a 24 hour day. A CalPlug study showed that 0.18 kwh of energy was required to bring this water cooler from ambient temperature (75 C) to the hot and cold set points. If typical daily usage of this device requires kwh, a revised operation schedule with 7 hours requires 0.34 kwh. If the overhead (pick up load) for reaching the set-point is added, this new daily energy requirement is 0.53 kwh. This results in a 29% decrease in energy usage. For the counter top water heater, heat loss ranges between 25W and 8.5W between 98 C to 45 C, respectively. This heat loss rate is substantially less than the heater supplying 686W of thermal energy when heating. The ideal heating relationship expanded from Equation 1(4) is y(t) = x + 28 for a starting temperature of 28 C degrees (Figure 3). Heat balance based on addition and loss can be calculated at any arbitrary temperature and time from a given staring point based on the presented information. A single heating event requires kwh to return the water to 95 C from an ambient equilibrium temperature of 20 C. As the behavioral control factors of the ATOM system are still in experimental determination, only the savings potential is calculated assuming the operation of the ATOM system can reach these performance goals. We provided multiple simplified scenarios illustrating potential application:

7 1. A household uses the countertop water heater daily with no usage from 9AM to 5PM, occasional usage during the period of 6 PM to 8 PM, and rare usage from 9 PM to 12 AM, with no normal usage from 12AM to 6 AM. In normal use with a set point of 95 C, daily energy usage is 1.1 kwh. If in the new schedule, 95 C is maintained between 6 PM and 8 PM, 50 C is maintained between 9 PM and 12 AM, and ambient is maintained during the balance of the time, the new daily energy usage is 0.53 kwh after the energy for the heating events are added. This results in a comparative energy savings of 51.8%. 2. An office kitchen uses the countertop water heater from 8 AM until 5 PM for tea, followed by no usage over the evening period. As before, the daily, baseline usage is 1.1 kwh to maintain a 95 C holding temperature. No intermediate holding temperature is assumed in this case. The new daily energy usage after intervention is kwh. This results in a comparative energy savings of 79%. 3. A residential user with a high tolerance for waiting for water to reheat to the set point uses the countertop water heater intermittently from 7 AM to 8 AM for preparing instant coffee and makes tea in the evening intermittently from 6 PM until 12 PM. During the remaining period the device is unused. As before, the daily, baseline usage is 1.1 kwh to maintain a 95 C holding temperature. As this user is more willing to tolerate longer warmup savings, deeper energy savings approaches can be applied. The ATOM system can search for periods of time where usage is possible but unlikely and progressively lower or raise the temperature to automatically match usage based on past models of usage. In simplified form, for 6 hours at night, there is no usage and the set point is reduced to ambient. In the morning, the set-point is raised to 95 C. In the evening, deep energy savings are applied based on the artificial intelligence algorithm of the ATOM system. Effectively 2 hours are maintained at 50 C, 2 hours at 70 C, and 2 hours at 95 C. a summarized simplification of the usage schedule. The resulting daily energy use from this schedule would be kwh considering intermediate warmup power requirements. This results in a comparative energy savings of 40.5%. Beyond what has been presented, other considerations must be taken into account. For energy calculations, the authors use energy required to maintain the temperature at a holding point as a major calculation figure. If water is dispensed, depending on the type of dispenser, the energy used is higher. This case equally affects both the baseline and intervention scenarios, so it was not included in calculations. Every user has a different threshold to waiting for reheating from a reduced set-point. This programing aspect is key to making the solution practically successful. Long wait times for reheating will cause user frustration which could result in discontinuation of this solution entirely. A healthy balance between energy usage and user satisfaction is critical for successful application. The overhead energy usage of the implementation of the ATOM system is expected to be under 1.0 watt (0.024 kwh daily), yet this must be factored in for calculation of true energy savings for a complete system. CONCLUSION The work presented describes the foundation for a new type of dynamic energy management system for plug load device control. Water heating and cooling applications have largely been overlooked within the broad scope of plug loads. However, they do have an impact on the rising plug load burdens for residences and commercial buildings. The ATOM system s occupancy and interaction based control system extends upon CalPlug s previous work in power management of STBs. Continued work on the behavioral and sensing aspects of the learning control system is required to achieve the savings potentials presented in this report. Adaptive, behavioral based control provides the next major horizon in plug load energy savings to help users save energy without adversely affecting their lifestyle. ACKNOWLEDGEMENTS This research was sponsored by support from the California Energy Commission, Southern California Edison, and the University of California, Irvine. The authors would like to thank Jason Luo, Omair Farooqui, Sid Kasat, Binh Nguyen, Duy Nguyen, Hugh Dang and Phat Quach for their experimental assistance to this study. REFERENCES 1. Analysis and Representation of Miscellaneous Electric Loads in NEMS. 2013, US Energy Information Administration. 2. Kwatra, S., J.T. Amann, and H.M. Sachs. Miscellaneous energy loads in buildings

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