NEGAWATT HOME AREA NETWORK DR ENABLED SMART APPLIANCES CONSULTING
SAN DIEGO GAS AND ELECTRIC COMPANY EMERGING TECHNOLOGIES PROGRAM ASSESSMENT REPORT PROJECT ID DR10SDGE0004
HOME AREA NETWORK DR ENABLED SMART APPLIANCES
FINAL REPORT
PREPARED FOR KATE ZENG, ERIC MARTINEZ SAN DIEGO GAS AND ELECTRIC COMPANY 8306 CENTURY PARK COURT SAN DIEGO, CA 92123
PREPARED BY M M VALMIKI, DOMINIC SHIOSAKI, MARC ESSER, AND TEAM NEGAWATT CONSULTING, INC. WWW.NEGAWATTCONSULT.COM
4/10/2013
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Acknowledgements
San Diego Gas and Electric Company and the authors of this report would like to acknowledge the assistance and cooperation of our host sites, SDG&E residential homes, and the residents who went out of their way to allow for our onsite testing and interviews. We would also like to thank the technology vendor that we evaluated, for their contributions above and beyond, and for their willingness to share more details about their products and services than the average customer would ever want to know.
Disclaimer
While SDG&E and the authors of this report did their best to come up with sensible results and recommendations, this report is provided as‐is. The models, figures, formulas, and recommendations may not be appropriate or accurate for some situations. It is the reader’s responsibility to verify this report and apply the findings appropriately when used in other settings or context. Readers are responsible for all decisions and actions taken based on this report and for all consequences, thereof.
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Executive Summary
This review investigates the demand response (DR) and control capability of a Home Area Network (HAN) technology with Smart Appliances. Secondly, it discusses energy savings potential, ease of installation, and user feedback on the technology. This review describes in detail the technology and its mass markey applicability in the context of the State of California.
The HAN with Smart Appliances technology under study claims to monitor and control home devices via a vendor‐supplied energy management system hub (EMS Hub), software interface, in‐home display, or mobile application. The HAN evaluation uses pricing signals in place of DR signals. It has direct support for Utility rate tiers with the devices having default responses and optional pricing event override. The suite of electric Smart Appliances available includes a refrigerator, microwave, range, electric water heater, dishwasher, clothes washer, and clothes dryer. The HAN can also integrate programmable communicating thermostats (PCT) for HVAC control and heavy duty load controllers for energy intensive devices such as pool pumps.
The system can monitor home energy consumption using a Whole Home Energy Sensor, plug‐load Energy Sensors, and appliance communication modules (ACM). These sensors and modules relay energy consumption to the Hub which commicates with user interfaces and the Smart Meter. The user interfaces can display the energy consumption costs of each device and whole home consumption using price signals from the Smart Meter or manually entered rates. The technology communication uses the ZigBee protocol.
For the field evaluation, we selected SDG&E territory residential customers who already use at least some of the types of electric appliances and have $100+ electricity bills. The customers were chosen for their potential of cost and energy savings and previous use of electric appliances which are applicable to DR events.
The smart appliance HAN was successful in providing a power drop during a simulated pricing event. The DR events were simulated by programming built‐in price tier schedules which caused power and consumption drops. The highest tier produces the same response as a DR signal. Thus, the highest tier price signal was sent to the HAN devices in order to simulate a Utility‐initiated DR event. Appliance‐ specific details are presented in the results section. DR event participation is automatic with appliance override capabilities presented to the user at each appliance.
Installation and commissioning is straight‐forward, but the appliances themselves are large and cumbersome and would typically be delivered and installed by professionals. Able‐bodied, “handy” customers could likely do most installations themselves. An exception is the pool pump shut‐off switch which needs to be installed by a licensed electrician to ensure code compliance. Also, older homes may not have compatible thermostat wiring. The software provides step‐by‐step instruction for the installation of appliance communication modules. The software itself is intuitive and extremely user‐
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friendly, utilizing customizable widgets and drag‐and‐drop configuration. It shows instantaneous and historical power consumption in an understandable format in units of kW, kWh and $.
The technology was not able to demonstrate successful communication with a Smart Meter due to Utility firewalls. This is important in California where Smart Meters are becoming ubiquitous. The Smart Meter supplies Utility rates to the HAN which can then be viewed on an IHD, personal computer, or mobile device.
Usability of the system was found to be good, in that devices respond to price signals quickly and mostly to specification. There is no way of programming time‐of‐use schedules independent of Utility rates as the appliances have pre‐programmed responses to pricing schedules. While this is good for some customers, some may also wish to have more control.
For a 10% SDG&E market penetration of customers with consumption greater than 900 kWh/month the demand drop and DR event energy savings were calculated. Results are presented both in the situation of guaranteed DR participation and a probabilistic power drop based on appliance usage patterns.
Power drops for guaranteed and probable participation across SDG&E territory for 10% market penetration.
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Energy savings for DR event participation for each appliance. Max Savings is the energy saved from switching from the highest consumption mode to the most effective DR function. Average Savings is the averaged savings across all tested DR
The customer economic benefit depends upon total energy consumption levels, appliance usage patterns, and appliances selected. Therefore, it behooves the customer to choose the rate program and appliance configuration wisely. Applicable (or nearly applicable) programs include the PTR, DR‐TOU, and CPP programs. Aside from the PTR, DR‐TOU, and CPP programs, the DR and TI programs are discussed.
Probable yearly customer rebates for 15 PTR days (1.25 $/kWh) in combination with the DR‐TOU and CPP schedules.
The payback times for the required communication module that needs to be attached to each appliance were calculated. The dishwasher and clothes washer do not pay back within their lifetime. The other appliances have ACM payback times ranging between 2.6 and 46.7 years, depending upon the program applied.
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Table of Contents
Acknowledgements ...... i Disclaimer...... i Executive Summary ...... ii List of Figures ...... vii List of Tables ...... viii Introduction ...... 1 Project Objectives ...... 3 Applicable codes and standards ...... 5 Market Overview ...... 6 Opportunity ...... 6 Products and Systems ...... 8 Measurement and Verification Overview ...... 9 Project Results ...... 10 Detailed Host System Description ...... 10 System deployment and operations‐related roles and responsibilities ...... 14 List of controlled points ...... 14 Sequence of operations ...... 14 System cost and cost‐influencing factors ...... 15 Preliminary Lab Test Results ...... 16 Evaluation of system operation and design in customer homes ...... 17 Customer feedback from in‐home testing ...... 18 Customer Details ...... 18 Customer Energy Habits ...... 20 Product Feedback ...... 21 Energy and Demand Savings ...... 22 Dishwasher ...... 23 Range ...... 25 Washing Machine ...... 27 Dryer ...... 29
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Refrigerator ...... 30 Water Heater ...... 32 PCT ...... 33 Whole Home Energy Sensor ...... 34 Response Time ...... 34 DR Response for SDG&E Territory ...... 35 Installation Experience ...... 37 Applicability of IOU programs and tariffs ...... 39 Technical Incentives ...... 39 Schedule PTR ...... 40 Schedule DR‐TOU ...... 41 Schedule CPP ...... 41 Other Residential Programs ...... 42 ACM Payback Times ...... 43 Conclusions ...... 44 Benefits of HAN with DR Enabled Smart Appliances ...... 44 System Improvement Opportunities ...... 46 Applicability of case study findings to other load types and sectors ...... 48 Considerations for large‐scale and persistent market implementation ...... 48 Impact of HAN devices on the SDG&E Roadmap 2011‐2020 ...... 48 Glossary and Acronyms ...... 51 References ...... 52 Appendix A: Measurement and Verification Plan ...... 55 Appendix B: Customer Survey Results ...... 66
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List of Figures
Figure 1: Electricity rate variance with time of use and California grid demand on a winter day...... 1 Figure 2 Utility and Customer advantages to energy efficiency (EE), demand response (DR), and peak‐ load shift (PLS). [2] ...... 2 Figure 3 – Vendor system architecture...... 10 Figure 4 ‐ Price tier settings ...... 11 Figure 5 ‐ Number of home owners that employ energy efficient technologies ...... 20 Figure 6 – Number of customers willing to buy HAN products for a specified price...... 21 Figure 7 – Customer product acceptance and use patterns...... 22 Figure 8 ‐ Appliance displaying "EP" during high price signals...... 23 Figure 9 ‐ Probability of dishwasher operation per hour [20] ...... 24 Figure 10 ‐ Range displaying "EP" during a high price tier ...... 25 Figure 11 ‐ Cooktop duty cycles are 75.8% and 76.8% at H (normal operation) and 8 (DR), respectively ‐ no reduction in kW or kWh...... 26 Figure 12 ‐ Washing maching displaying "EP" during high price tiers ...... 27 Figure 13 ‐ Normal operation and DR reduced duty cycle operation ...... 28 Figure 14 ‐ Probability of washing machine operation during a day [20] ...... 28 Figure 15 ‐ Dryer displaying "Delay EP" during high price tiers ...... 29 Figure 16 ‐ Refrigerator displaying current rate tier ...... 30 Figure 17 ‐ Water Heater display during normal and high price mode ...... 32 Figure 18 ‐ PCT setpoint adjustment to the four price tiers...... 33 Figure 19 ‐ Appliance response times ...... 34 Figure 20 ‐ Example of water heater response time going from DR mode to normal mode to DR mode . 34 Figure 21 ‐ Energy savings for DR event participation for each appliance. Max Savings is the energy saved from switching from the highest consumption mode to the most effective DR function. Average Savings is the averaged savings across all tested DR functions. Probable Savings and are similar but include the probability of operation during DR events...... 35 Figure 22 ‐ Guaranteed DR participation and probable power reduction for SDG&E market with Smart Appliances ...... 36 Figure 23. Heavy duty load controller installation: original wiring design (top), and actual, final installation ...... 38 Figure 24 ‐ Probable yearly customer rebate for 15 PTR days for both PTR schedules options...... 40 Figure 25 ‐ Probable yearly rebates for a DR‐TOU, PTR program ...... 41 Figure 26 ‐ Probable yearly rebates for a CPP, PTR program...... 42 Figure 27 ‐ ACM payback times for various appliances and program combinations ...... 43 Figure 28 – Widget user interface...... 45 Figure 29 ‐ Peak residential load across end‐uses [30] ...... 46 Figure 30 ‐ Mobile app requires WHEM to function ...... 47 Figure 31 ‐ SDG&E HAN timeline as of 11/2011 [29] ...... 49
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List of Tables
Table 1 Customer statistics evaluated against 1.24M installed Smart Meters in SDG&E territory [8] ...... 6 Table 2 Customer tiers/usage against large appliances [8] ...... 7 Table 3 ‐ System component descriptions ...... 12 Table 4 ‐ Smart appliance functionality responses. The highlighted cells were not tested in this study. Functions are cumulative with increasing price...... 13 Table 5 ‐ Complete system cost for selected appliances (MSRP) ...... 15 Table 6 ‐ Test site appliance and device matrix ...... 19 Table 7 ‐ Customer home characteristics ...... 19 Table 8 ‐ Smart appliance aspect importance as ranked by customers ...... 21 Table 9 ‐ Dishwasher Parameters ...... 24 Table 10 ‐ DR savings for dishwasher ...... 25 Table 11 ‐ Range Oven Parameters ...... 26 Table 12 ‐ Range DR power drops ...... 27 Table 13 – Washing Machine Parameters ...... 28 Table 14 ‐ DR savings for washing machine ...... 29 Table 15 – Dryer Parameters ...... 29 Table 16 ‐ DR savings for dryer ...... 30 Table 17 ‐ Refrigerator Parameters ...... 31 Table 18 ‐ DR savings for refrigerator ...... 32 Table 19 ‐ Water Heater Power and Energy Consumption ...... 33 Table 20 ‐ DR savings for water heater ...... 33 Table 21 ‐ SDG&E Market DR Power Reduction ...... 36 Table 22 Estimated power drop potential from HAN devices, per SDG&E ...... 37 Table 23 ‐ TI incentive for each appliance. Average implies the use of average power drop across all tested DR functions. Guaranteed means that the appliance will definitely operate during the DR event, probable considers the chances of operation during DR times...... 39 Table 24 ‐ DR‐TOU Savings ...... 41 Table 25 ‐ DR‐TOU Savings ...... 42
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Introduction
Demand is the momentary use of power from the grid and is of great impoortance to both the power generator (Utility) and the power consumer (Customer). Energy cost and demand can vary significantly with time of use as illustrated below.
Figure 1: Electricity rate variance with time of use and California grid demand on a winter day.
During peak hours and especially during critical peak pricing events, the ability for a utility to maintain electric reliability to all its customers is at risk and customers experience significant cost increases.
At first sight it would seem utilities stand to benefit from peak‐time, high‐rate sales of electricity. However, in today’s marketplace electricity supply is limited aand as prices increase, so does cost. Additionally, it becomes gradually more difficult for utilities to meet the market’s demand at all during peak times. This is further compounded by steady demand increases for the foreseeable future – according to the California Energy Demand 2012‐2020 Final Forecast, nonn‐coincidental peak demand in California is forecasted to increase at 1.3% per year from 2010 to 2018 [1].
Utilities therefore benefit from managing peak demand in sevveral ways:
Increased grid stability Ability to service more customers with existing generation & distribution facilities Lower normalized operating cost, i.e. increased profittability
Additionally, utility customers reap cost savings from managiing peak demand. Home appliances, space heating, and space cooling consume a large portion of a residential customer’s energy bill. This bill can potentially be reduced if the devices within the home were to be more intelligently managed. Customers would be able to manage their own energy consumption better if empowered with information regarding their home areas and devices that have potential to save energy and cost.
The technology that is being reviewed herein is a compilation of ‘smart’ devices integrated into a Home Area Network (HAN) that allow a user to monitor and control his own energy consumption. The HAN facilitates both automatic and manual consumption cuts during times when energy rates are highest. Unique to the industry, this HAN includes a set of Smart Applliances. These are large household devices
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including a dishwasher, refrigerator, clothes washer, clothes dryer, water heater, and electric range. These devices are designed to be energy efficient and can altter their functionality in response to demand response (DR) events or pricing signals. Most HANs, including this one, do not alter the efficiency of an existing device but simply allow for controlling it more wisely. Appliances that are energy efficient and can change their functionality could pose great benefit to consumers and the Utility.
As Figure 2 illustrates, energy efficiency, load shifting, and demand response measures can help level the daily demand for energy. This makes the Utility more capable of supplying the required amount of electricity to its consumers at a minimal cost and reduces GHHG emissions.
Figure 2 Utility and Customer advantages to energy efficiency (EE), demand response (DR), and peak‐load shift (PLS). [2]
This study focuses on the appliance features that allow the usser to controol their energy consumption more effectively with regards to demand peaks and pricing changes.
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Project Objectives
The objective of this study is to evaluate the DR and utility rate response capability of the Smart Appliances HAN and to assess the overall acceptance and opinion from user surveys from a field evaluation. A collection of residential homes that passed predetermined criteria were selected. The energy and cost savings and demand‐response functions will be quantified. We will also review of the technology’s usability, ease of installation, and its qualitative DR functions. Please see Detailed Host System Description for a complete description of the HAN Smart Appliance system under study. We also go beyond one particular vendor, and assess benefits, validity and potential of the technology as a whole and briefly describe the marketplace as well as applicable codes and standards.
Our study has taken place in San Diego Gas & Electric territory. However, the results should be applicable throughout most of California due to consistent legislation and tariffs.
The particular HAN under study does not yet have support for direct DR signaling from the Utility. Rather, the Hub was designed to receive price signals from the Smart Meter which it then designates into one of four “tiers” called Low (Tier 1), Normal (2), High (3), and Critical (4). The designed interaction between the Smart Meter and the Hub is solely in terms of price signals. The best way in which Smart Meters will communicate DR events to residential HANs remains to be seen. For the sake of this report, it will be assumed henceforth that DR signals and the critical tier pricing signals are synonymous. Since the HAN system uses pricing tiers to change appliance function and consumption, the results and report will be described in that format. Even if ultimately the Hub differentiates between pricing and DR signals, the load shedding and avoidance transcends the distinction because the DR functional changes will be the same as critical pricing signal response. For this reason critical pricing (Tier 4) responses and power consumption drops should be considered representative of DR power drops.
In our project results section emphasis is placed on the following aspects:
Verification of system operation and design
Do the devices’ functionalities correctly respond to a simulated pricing event? Do the devices’ responses to a simulated pricing event result in an overall power drop? Do the devices correctly measure the energy consumption or status of the items that they are controlling or monitoring? Are the devices continuously networked and able to communicate effectively? Does the system effectively alert for pricing events and give users options to accept/decline?
Potential energy and demand savings
We observe energy and demand savings: by simulating a pricing event, a power drop is measured. Energy shown by the HAN technology display is cross‐referenced with a calibrated precision meter. We then perform savings calculations based on estimated power drop of appliances as per previous research reported averages and measured power consumption. Page 3 of 71
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Customer feedback
We developed a user survey to determine the demographics of the customers involved in device testing and their overall reactions to the technology. This was done to help understand the acceptance across different markets and barriers to market implementation. Some of the questions included:
Does the customer like the system? Does the customer like each individual product/device? How frequently does the user actually take advantage of the technology? Would the customer user this vendor’s product even without the mobile application feature? What changes would make the system more attractive?
Applicability of SDG&E incentive and rebate programs
We review various SDG&E programs with respect to this technology and provide recommendations for where program support may apply.
Concluding remarks and summary
Finally, we conclude our study with a discussion of
Benefits of Smart Appliance HAN technology Improvement opportunities for the tested product Applicability of this study to other load types, sectors, and sizes Considerations for large‐scale market implementation Potential future study HAN relevance with respect to the Smart Grid Roadmap
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Applicable codes and standards
Most of the standards and codes that relate to the home area network devices will pertain directly to the home appliance DR system that they are being used in conjunction with, i.e. electrical standards for home appliances such as UL listings or Energy Star ratings.
California Title 20 has a regulation requiring pool pumps that are replacements and 1 hp or larger, must be two‐speed or variable‐speed. The 240v load controller device including in the HAN system is only wired to work with two phase pumps; any variable speed pumps with the load controller may not be compatible with this regulation.
Currently there are proposals for future Title 24 amendments that may encompass these HAN devices. There is a specific proposal for Programmable Communicating Thermostats. The proposed measure title is Residential Demand Responsive Thermostatic Controls. The measure is a proposal for a Title 24 2013 update and it would require setback thermostats (as referenced in 2008 Title 26 Section 6 Section 112(c)) to be Upgradeable Setback Thermostats (USTs). This would give the specific thermostats the ability to add a communication module. [3,4]
There are also several well‐known alliances related to the Home Area Network market space. They defined their own standards and certifications for HAN products whereby they can place their logo on products that have met the criteria. These include HomePlug Powerline Alliance, Z‐Wave Alliance, and ZigBee Alliance.
HomePlug certifies products that use the IEEE 1901 standard which concerns devices that communicate over in‐home power lines. This type of certification also allows the consumers who buy HomePlug products to be confident that they will be compatible. [5]
Z‐Wave is a proprietary wireless communications protocol designed for home automation, specifically remotely controlled applications in residential and small commercial environments. The technology uses low‐power RF radio communication to avoid the interfering 2.4 GHz frequency that is common with many other (not HAN) products. The devices that follow this protocol are compatible with one another. [6]
The ZigBee standard is a wireless technology standard that communicates over the 2.4 GHz frequency. It is based off of the IEEE 802.15.4 standard that concerns low‐power short‐range wireless protocols. Multiple devices are able to communicate with each other on a ZigBee network. [7]
The technology reviewed uses ZigBee protocols to communicate with the Smart Meter and to each device. Since the Smart Meters in SDG&E territory have not yet been activated for most HAN systems, we were unable to verify the communication between the meter and Hub. Since both the HAN and Smart Meters use ZigBee protocols to communicate, it is anticipated that the meters should be able to send signals to the Hub.
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Market Overview
Opportunity SDG&E installed approximately 1.4 million Smart Meters from 2007‐2011 for residential and small business customers, with about 300,000 more to follow. This was the initial infrastructure needed for integration of HANs with the Utility. All of these customers are theoretically applicable to the HAN technology market. Any building with a Smart Meter and appliances is a potential target for appliance monitoring and control. However, we find that the technology appears the most useful to customers who consume comparatively more peak energy. This usually arises from sources such as pool pumps, air conditioning units, and energy intensive appliances; therefore, these appliances are also the primary focus in regulation with HAN technology. The continuing increase in all‐electric homes, which use electric water heaters, stoves, dryers, and other appliances, will help encourage the growth of this technology. Also, although this technology is applicable as a standalone system for monitoring and control, we determine that it will be more desirable to those wishing to participate in (and benefit from) DR events and variable pricing.
Table 1 and Table 2 data are taken from a paper by JBS Energy on behalf of the CPUC reviewing load research data and economic, demographic, and appliance saturation characteristics of CA utility residential customers taken from CA RASS data. The data displayed is specifically of SDG&E statistics [8].
Small Customers Large Customers Approximate kWh <425 425‐600 600‐900 900‐1500 >1500 % of customers 53.80% 19.50% 15.50% 8.90% 2.20% Aprox Smart Meter customers 666,582 241,605 192,045 110,271 27,258 1,239,000 % peaked 7.60% 7.00% 15.60% 15.30% 43.50% Potential optimal customers 50,660 16,912 29,959 16,871 11,857 126,260 Percent 10.19% Table 1 Customer statistics evaluated against 1.24M installed Smart Meters in SDG&E territory [8]
Table 1 shows SDG&E statistics for customers broken down by average usage tiers. The tables also show the percentage of those customers that have high peak consumption in summer months. This was calculated by comparing summer months against March and April. PG&E and JBS Energy attributed this peak directly to AC usage in summer months. Because the evaluated HAN technology focuses primarily on reducing energy during peak hours, we will deem these ‘peaked’ customers with Smart Meters as the primary market. This results in about 126,000 customers as the potential market in SDG&E territory [8]. Table 2 confirms that at least half of all customers with 600kWh or greater monthly energy use can indeed be expected to have air conditioners; pools pumps are prevalent for more than half of the customers with 900kWh per month. Therefore, our field evaluation, while not representative in a statistical sense, emphasized customers with monthly energy use of 900kWh or greater.
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Table 2 Customer tiers/usage against large appliances [8]
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Products and Systems
The following is a list of vendors in the Home Area Network sector. 1 Aclara [9] Calico Energy Systems [10] Control4 [11] Energate, Inc. [12] EnergyHub, Inc. [13] General Electric Company [14] Insteon [15] LG [34] Opower [16] Samsung [35] Silver Spring Networks [17] Space‐Time Insight [18] Universal Devices [19] Whirlpool[36]
1 The list is in alphabetical order, provided as is, not exhaustive, and the selection is arbitrary. The authors of this report do not endorse or guarantee, and disclaim any responsibility for: the content, products or services offered their performance or suitability, and any consequences or damages, incidental or otherwise, that may result from their consideration or use. Page 8 of 71
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Measurement and Verification Overview
The Smart Appliance HAN M&V gathered data on power demand, energy consumption, and connectivity with regards to appliance DR function. In order to gather controlled data, DR event were simulated for short periods of time for each individual appliance. The DR events were simulated by programming built‐in price tier schedules which caused power and consumption drops. The highest tier produces the same response as a DR signal. Thus, the highest tier price signal was sent to the HAN devices in order to simulate a Utility‐initiated DR event. Functional, power, and energy consumption drops were measured from reference states considered to be normal appliance operation.
In addition, the customers were given a short tutorial on the HAN software and given several months to form opinions on the technology. Following this, on‐line surveys collected customer responses to questions on appliance usage, DR and Smart Meter acceptance, HAN perception, and demographic makeup.
The test space consisted of many variables such as appliance settings, price tier progression, point in appliance cycle, customer use patterns, appliance state at price signal change. Due to the infeasibility of performing M&V on every variable combination, certain conditions were overlooked or not tested. This did not affect the study’s ability to predict Customer and Utility benefit.
For a detailed M&V plan, see Appendix A.
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Project Results
Detailed Host System Description
The Hub coordinates communication between in‐home appliances and devices, the Smart Meter, vendor servers, and energy management software as illustrated in Figure 3. The Hub relays Smart Meter price signals to the appliances which adjust their performance in order to reduce consumption during high‐cost times. The software allows the user to monitor the utility rates and energy consumption for all connected HAN devices within the home in order to make informed energy consumption decisions. It also allows for PCT setpoint control programming so that the energy and cost savings can be hands‐off and hassle‐free; the smart appliances are pre‐programmed for the same reason. The HAN can also be monitored with a computer or iPhone/iPad (only if certain components are present).
Figure 3 – Vendor system architecture.
It is yet to be determined exactly how a DR event will be signaled to customers. The technology under study can respond to Utility pricing schedules but does not have DR signal capabilities. If a Utility were to implement DR signaling in the form of price increases, then the Smart Appliances HAN could appropriately respond.
We were unable to connect the HAN to the Smart Meter due to Utility meter security measures, but the software allows for manual entry of variable rate schedules. In the manual variable rate settings, the
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user defines $/kWh rates for chosen timespans. These settings are repeated each day and can be set the same or different for weekend days. The prices are categorized into four “Tiers,” Tier 1 (Low), Tier 2 (Medium), Tier 3 (High), and Tier 4 (Critical) as seen in Figure 4. The price tier signal from the Hub will alert the Smart Appliance to function normally or to shed and delay loads. For this HAN, Tier 4 is synonymous with DR.
Figure 4 ‐ Price tier settings
These manual settings were used to create schedules which allowed for monitoring and measurement of power demand changes during simulated DR events. In manual rate entry, the user cannot set the different tier limits so that the HAN can respond as desired giiven a certain price signal from the Smart Meter. Rather, the tiers are based on variability within the assigned pricees; e.g. the critical tier could start at 0.45 $/kWh or 1.50 $/kWh depending upon the range and numbeer of prices.
Since the Smart Meters are not configured for this particular HAN, we werre unable to confirm that the technology is able to receive price signals from the meter. Also, the software does not have support of DR signals or opt‐in/opt‐out choices. In other words, the HAN responds to signals automatically and without notice. However, the user can override most appliances locally (see Results section for details).
The following is a brief description of the devices that are inccluded by this vendor as part of the HAN (not all were included at every residence due to lack of appliances previously owned by home owner). This list includes the controlled points.
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Table 3 ‐ System component descriptions Device Purpose Data Energy Management Serves as the home base. Functionality: Confirm that energy System Hub (EMS Hub) Communicates with other consumption monitoring is shown and price devices on the network. signals are properly communicated. In‐Home Display (IHD) Shows real time energy Functionality: Confirm that the display consumption and Utility rate shows data for all HAN devices and alerts/pricing. Used for initial setup. Allows Functionality: During installation, confirm for user monitoring and PCT that the software adds all devices and Energy Management control. meter to HAN. Confirm monitoring ability and Monitoring of each device. Software Accuracy: Confirm the power measurements from each of the plugged appliances. Programmable Controls HVAC components Functionality: Confirm that during pricing Communicating based on programmed event, the thermostat responds correctly. Thermostat (PCT) temperature setpoints. Appliance Device that connects to each Functionality: Confirm that energy communication module appliance to make them consumption is relayed to Hub and during (ACM) DR/pricing enabled. DR/pricing event, the appliances respond correctly. Heavy Duty Load Controls the pool pump. Functionality: Confirm that during Controller (LCS) DR/pricing event, the pool pump responds correctly. Monitors energy consumption Functionality: Confirm that during Mobile App and cost and allows for PCT phone/tablet displays all devices and control. consumption.
Monitors and communicates Functionality: Confirm communication to Whole Home Energy total home energy Hub. Sensor consumption at main panel. Accuracy: Confirm total home energy consumption readings. Appliances that function Functionality: During a DR/pricing event, typically but also have control confirm the change in performance. DR Enabled Appliances strategy based on Hub signals and change their algorithm to Accuracy: Measure power to confirm power shed or shift loads to off peak. monitoring and to observe power drops.
Monitors energy consumption Functionality: Confirm that energy Energy Sensor of any device plugged into a consumption is relayed to Hub for 120V outlet. monitoring purposes.
Note that the energy sensor and whole home energy monitor do not allow for control or load shedding but rather provide only monitoring in order to inform energy consumption decision making.
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The smart appliances unique to this HAN are able to alter their normal functionality when the home energy manager receives price signals from the Utility. The Tier 4 rate causes the same HAN response that DR event signals would but without the acknowledgement for override or customer alert. These appliance responses are described in Table 4. All the tested Smart Appliances except for the water heater, dishwasher, and refrigerator can have their energy saving modes overridden manually during pricing events so homeowners can use their appliances normally at full power if needed. In general, the appliances shed or shift loads by reducing energy consuming features or delaying operation.
Table 4 ‐ Smart appliance functionality responses. The highlighted cells were not tested in this study. Functions are cumulative with increasing price. Device Tier 1 Tier 2 Tier 3 DR/Tier 4 (Low Cost) (Normal Cost) (High Cost) (Critical Cost) Dishwasher Normal Normal Delay start Turn heated dry Operation Operation off
Range Normal Normal Prevent starting larger Disable 3 burners Operation Operation lower oven, prevent self‐ clean, reduce burner by ~20% Front‐load Normal Normal Delay start, recommends Reduced duty Washer Operation Operation cold wash if overridden cycle wash and heater to 50% Front‐load Normal Normal Delay start, recommend Reduce heater Dryer Operation Operation Energy Saver mode if power to 0 for 20 overridden, cut to Energy minutes Saver mode if running Refrigerator Normal Normal Delay defrost, raise Disable electric Operation Operation freezer temp by 5 °F sweat heaters
Water Heater Normal Disable Lower setpoint to 110 °F Lower setpoint to Operation Resistance 100 °F Heaters Microwave Normal Cut 10% mag Cut lamp power by 50% Cut fan power by Operation power 75%
PCT 65/79 °F 66/80 °F 67/81 °F 68/82 °F (default settings heating/cooling)
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System deployment and operations‐related roles and responsibilities
Once the HAN system has been purchased, the end‐user is responsible for installation and configuration. The large appliances alone can be installed just as any other similar appliance would with the exception of networking; customers would opt to have appliances installed professionally should they be too heavy, cumbersome, or complex. It is likely that most customers, unless physically able and home maintenance savvy, would opt to have most of the devices and appliances professionally installed. The heavy duty load controller, whole home energy sensor, and PCT would likely require a contractor for electrical safety reasons.
The communication components, including PCT networking, in‐home display, EMS Hub, load controllers, whole home energy sensor, appliance communication modules (ACM), and energy sensors involve wireless setup and coordination. The software installation setup gives step‐by‐step instructions for establishing the lines of communication. Each communication component has MAC IDs and install codes that must be manually entered into the software during setup. LED lights on the EMS Hub, ACMs, and energy sensors indicate connectivity status. While the wireless setup is doable by a relatively technically savvy customer, some would likely choose to have a contractor install the communication devices and software.
After installation, the user can configure the PCT and software interface themselves. The software and settings are straight‐forward and intuitive with drag‐and‐drop widget programming and the vendor technical support is quite knowledgeable and readily available.
List of controlled points
The HAN is able to communicate with various devices as described in Tables 3 and 4. This evaluation included controllable smart appliances (refrigerator, range, washer, dryer, dishwasher, water heater), air conditioners (via PCT), and pool pumps (via HLC) in varying combinations. Note that not all sites had all appliances and controlled points due to the pre‐existing appliance and HVAC systems.
Settings are somewhat configurable through the energy management software. The user can define the utility rate scheduling in order to control when the smart appliances alter their functionality, use default settings, or allow the Smart Meter to define prices. The PCT can be set to three altered setpoint combinations for Tiers 2‐4, the smart appliances have preset modes for each tier, and the pool pump load controller operates in on‐off fashion during Tier 4 pricing events. All responses are automatic.
Sequence of operations
Once the HAN devices and appliances are installed and configured, they can be monitored and controlled via the energy management software or mobile application. The home computer software and in‐home display will show information regarding continuous energy use, pricing, and historical energy use. PCT pre‐programmed settings regarding pricing tiers can be configured so as to avoid
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missing energy savings opportunities. The smart appliances will automatically alter their functions when the price tier changes.
System cost and cost‐influencing factors
There are several factors that influence the initial purchase cost of this HAN technology. The greatest costs come from the smart appliance purchases. Depending on the number of smart appliances chosen, the system cost can vary widely. Table 5 shows the approximate cost for a complete smart appliance HAN system with 1 of each appliance used in this study, 2 Zigbee switches for energy intensive devices such as pool pumps and portable electrical heaters, and installation costs.
Table 5 ‐ Complete system cost for selected appliances (MSRP) Product Average Cost [$] Quantity Subtotal [$] Refrigerator 3099 1 3099 Range 2299 1 2299 Dishwasher 1599 1 1599 Washer 1499 1 1499 Dryer 1499 1 1499 Water Heater 1299 1 1299
Home Energy Monitor 161 1 161 Smart PCT 140 1 140 In‐Home Display 167 1 167 120v ZigBee Switch 161 1 161 240v ZigBee Switch 238 1 238 Communication Module 108 5 540 (per appliance)
Installation (per/hour) 90 3 270 Total 12971
There are different ways in which this system could be applied in customer homes. For new homes, purchasing a new suite of appliances is commonplace. If an existing home were to install a system that incorporated the energy monitors, switches, displays, and replaced appliances as is needed over time, the cost would be absorbed piece‐wise. If priority were given to the less expensive monitoring components only, more customer attention and care in making energy decisions would be required to reduce cost and peak demand.
There is no incremental cost once the system has been installed and commissioned except for maintenance costs typical to home appliances.
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Preliminary Lab Test Results
Preliminary installation and performance tests were performed in a laboratory environment prior to actual in‐home field evaluations. The lab test was performed at University of California – San Diego and was independent of the field evaluation. Below are the summarized results of the testing as delivered to Negawatt Consulting.
Ease of Installation Simple to install the Hub o Plug Hub into router via Ethernet cable o Plug Hub into wall outlet o Install software onto PC Step by step walk through to install devices and meters o Need devices MAC ID in order to set up equipment o Need account number, Hub MAC ID, and Hub Install Code in order to get a meter set up
Ease of Use Self‐explanatory user interface
Connectivity Type Connects via Zigbee o Connection is stable o Have not experience any difficulty connecting devices Has LEDs on the Hub to indicate connectivity
Accessibility (ex: home network, web server, etc) Accessible through web server o Can be accessed by double clicking on icon on desktop after installing software
Advantages (compared to other vendors) User friendly customer interface Easy to install equipment o Installation walk through is sufficient to get devices and meters connected to the network Programmable o Can program and control appliances
Disadvantages Need additional information to set equipment up o such as utility account number, Hub MAC ID, and Hub Install Code in order to get a meter set up Appliances to test were not available o Testing was limited to smart meter and a thermostat
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Evaluation of system operation and design in customer homes
Do the devices correctly respond to price tier changes/DR signals? Yes, all the appliances tested responded to the price tiers as described in the manufacturer’s specifications in Table 4 with one exception.
The water heater did have trouble shifting from Tier 2 to Tier 4 in a rapid timeframe. After switching from resistance heat to heat pump mode, a fan turns on in preparation for heat pumping. If ample time is given for the heat pump to turn on, then the design performs as specified, including switching from Tier 2 to Tier 4. However, if the price changed from Tier 2 to Tier 4 before the fan was finished and the heat pump came on, the water heater would go back to electrical resistance mode despite being in Tier 4. In all other instances the water heater performed as designed. While this is a minor problem, it indicates possible need for a comprehensive test plan of all appliance and HAN programming.
We did not test the microwave or the dishwasher’s Tier 4 ability to turn off heated dry.
All other appliance functions were observed without error.
Does the system effectively alert for DR/pricing events and give users options to accept/decline?
No, because the HAN is currently configured to only consider pricing schedules into decision making. Pricing events are not alerted to the customer but the information is readily available through the monitoring interfaces. Appliances automatically respond to pricing tiers and the user can override energy saving modes on all appliances except for the water heater, refrigerator, and dishwasher at the appliance menu. PCT settings can be overridden at the PCT, PC software, or on the mobile app.
If DR signal capabilities were enabled by the manufacturer, a signaling and opt‐in/opt‐out feature could potentially be added.
Do the devices’ responses to a simulated DR event result in an overall power drop? Depending on the operating status when the DR/pricing event initiates, some appliances will shed partial loads while others will delay starts so as to avoid adding loads. The refrigerator compressor and HVAC systems will not turn off or lower power demand, but they will reduce cycling in order to lower total energy consumption and reduce demand temporarily at the beginning of the event.
Do the devices correctly monitor the energy consumption or status of the items that they are connected to or are controlling? Yes, the in‐home display, mobile app, and home computer software accurately displays instantaneous and historical energy consumption and cost in five‐minute intervals with a delay of 10‐15 minutes. The user interfaces do not show current appliance status. However, the displays on the appliances
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themselves show “EP”, “eHeat,” “Energy Rate Critical”, or “Energy Saving” modes during altered function.
There was one instance of the appliance miscommunicating energy consumption to the network. See System Improvement Opportunities for details.
Are the devices continuously networked and able to communicate effectively? Several of the ACM’s ceased working properly and needed to be replaced or reset. Aside from this, there were no breaks in communication or significant impediments to connectivity. We were unable to confirm Smart Meter communication reliability.
Customer feedback from in‐home testing
After installation and the in‐home evaluation of the HAN devices, the users were asked to complete a demographical survey and provide responses to questions regarding their HAN. This allows insight into the potential market and how well‐suited this technology is for integration into homes. The reader should note that this survey was not made to supply representative data of a mass market implementation and is not normalized for this purpose. Thus, the survey results are informational only. Furthermore, the homes were not enabled with variable pricing schedules or Smart Meter connectivity so they were not able to utilize the energy saving and shifting features. However, if used strategically, the HAN was able to empower customers to make intelligent consumption decisions.
The individual survey questions and responses can be found in Appendix B.
Customer Details As detailed in the M&V Plan (Appendix A), selection criteria were established to choose home owners for the field evaluations. This included mostly home features that would be common for a user of this type of technology, such as higher energy bills and electric appliances.
Since each site had a unique original arrangement, the set of installed appliances and devices varied across sites. Table 6 shows the installed suite of devices for each site. The pool pump load controller was not tested due to non‐compliance to California electrical code as discussed in the Installation Experience section.
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Table 6 ‐ Test site appliance and device matrix Customer # 1 2 3 4 5 PCT 0 0 0 2 0 Washer/Dryer 1 1 1 1 1 Dishwasher 1 1 1 1 1 Range 1 1 1 0 1 Water Heater 1 0 1 0 0 Refrigerator 1 1 1 0 1 Whole Home 0 0 0 1 0 Energy Sensor In‐home 0 0 0 1 0 Display Plug Load 2 2 2 2 2 Energy Sensor
The customer survey gathered personal data that may be used in the design of a market study for determining the potential target population. The number of people living in the home ranged from 2‐4. It is important to note that in 3 of the 5 homes at least one person in the household was or used to be employed in a technical field.
Monthly energy bills were required to be over $100 for participation and the customers used an average of 943 kWh/month. Participant home sizes ranged from 1454 to 2600 ft2 and homeowner age ranged from 35 to 65 years.
Table 7 ‐ Customer home characteristics Customer CA Climate Occupancy Home Area (ft2) Average Monthly kWh Zone 1 7 4 1800 826 2 7 4 1800 1080 3 7 4 1454 1185 4 10 2 2600 552 (959 winter) 5 10 2 2200 1077
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Customer Energy Habits Overall, four of the five customers surveyed were energy conscious. Within the past two years, these four customers had purchased Energy Star appliances and installed CFL bulbs. Two customers used PV systems and one used solar hot water. Figure 5 shows energgy efficient measures that the home owners enacted in the preceding two years.
Figure 5 ‐ Number of home owners that employ energy efficient technologies
The home owners in the evaluation also had some knowledge of how the California electricity market and Utilities work. Four of the five homeowners at least “knew something about” TOU pricing and DR programs. All customers said they would participate in TOU ppricing and four would participate in DR programs. Four customers listed at least three appliance types when asked what devices would be appropriate for DR event inclusion. Also, four of the five home owners responded that someone in their household stays on top or new developments in energy efficiency, smart applications, home automation, and renewable energy.
In regards to smart appliances, home owners were asked to rank the importance of product aspects and their willingness to buy miscellaneous energy related devices. The results are shown in Table 8 and Figure 6.
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Table 8 ‐ Smart appliance aspect importance as ranked by customers Product aspect Rank Average Score (1 – most important, 6 – least) Total Cost of Appliance 1 1.5 Ability to Save Money on Energy 2 2 Costs Ability to Get 3 3 Maintenance/Repair Alerts Rebates/Cost Incentives 4 4 Brand 5 4.25 Ability to Montior/Control via 6 5 Internet or Smartphone
Figure 6 – Number of customers willing to buy HAN products for a specified price.
Product Feedback Concerning the technology tested here, home owners were asked a series of questions and asked to provide their level of agreement on a 5 point Likert scale. The customers were asked to respond in regard to the entire suite of smart appliances and HAN devices. Thus, the results may not be separable into monitoring and control devices and smart appliances. The results are shown below in Figure 7.
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Figure 7 – Customer product acceptance and use patterns.
In general, the customers did not use the smart features despite appreciating the simplicity and goals of the technology. They liked the layout and interaction with the energy monitoring software but never made attempts to use it regularly. This may be partially due to the customers not having information on their rate schedules or not having automatic responses since the Smart Meters were not connected. Most of the customers treated the installed HAN as nothing more than a typical suite of appliances.
Energy and Demand Savings
Overall, it is important to note that savings and payback times for this and similar projects will vary with the following:
Initial cost of system and installation Utility rates and incentives Control strategy Individual appliance energy consumption Frequency of Utility enacted DR/pricing Events Customer behavior regarding consumption and DR/TOU participation Time‐of‐use of appliances and correlation with start and end times of DR/pricing events
The vendor’s energy consumption monitoring software was validated with data from the power measurements during the simulated DR/pricing events. The HANs reported data was not used in calculations, however.
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The following sections provide the DR power drops for each appliance. Note that all power drops are from a reference normal operation as described for each appliance. Usage patterns are taken from literature to determine the probability of appliance operation during a seven hour DR event between 11:00 and 18:00 on any given day of the year. [20‐22, 24]
Dishwasher In general, the dishwasher load varies irregularly based upon load sensing, point in cycle, and selected options. Therefore, it is appropriate to consider the maximum power and the total kWh consumed for a typical cycle. The dishwasher is installed with a hot water supply rather than employing local, electrical water heating. The dishwasher was tested with on a normal cycle without heated dry as a reference state. The reference state required a power range of 210 to 470 Watts. A heated dry test was deemed infeasible since the dry occurs at the end of the long runtime. Rather, the appliance was tested for the first 10 minutes of its cycle.
Figure 8 ‐ Appliance displaying "EP" during high price signals.
The dishwasher will not turn off if started prior to receiving a Tier 4 price signal. Thus, if already on when the DR event occurs, no load will be shed other than the heated dry. In Tiers 3 and 4, the dishwasher delays starting until the price signal is reduced. Thus, during a DR event, the dishwasher shifts any possible loads to the completion of the event. Also, the dishwasher did not allow for any override; once in Tier 3 or 4, the dishwasher could not be started. This is likely a vendor error that will be fixed.
Although the heated dry was not measured, it can be assumed that the maximum electrical heating power will be equal the specified 875 Watt heaters. Choosing the heated dry option added up to 30 minutes of runtime to the cycle according to the display. Assuming an 80% heater duty cycle this would add .35 kWh to the cycle energy consumption at a power of .875 kW. This load would be shed during a DR event if the dishwasher was already running.
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A study in 2008 by the University of Bonn determined the probability of use of a dishwasher throughout the day [20]. The curve indicates a .37 probability of operation in the DR timeframe.
Figure 9 ‐ Probability of dishwasher operation per hour [20]
Table 9 lists the parameters used in the calculation of DR power drop potential.
Table 9 ‐ Dishwasher Parameters
Average T ‐ Runtime LD ‐ Loads/week PDR ‐ Operation DC ‐ Heater THD ‐ Heated Power [kW] [hr] [20‐22] Probability during Duty Cycle Dry Time [hr] DR event [20] .114 to .875 1.0 4.35 .37 .80 0.5
The energy consumption shifted with dishwasher participation in a DR event is
∗ (1)
The probable daily energy consumption during the DR period considering the probability of operation can be calculated by
∗ ∗ (2)
Where P is the power, LD is the average number of loads per week, PDR is the probability of operation during the DR timeframe, and T is the runtime in hours. EP allows for calculation of total energy saved from a number of DR events over a number of days. For instance, if there are 15 DR events in a year, the energy saved during those events can be estimated by 15*EP.
Table 10 lists the energy savings and power dropped during a DR event for the various dishwasher responses.
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Table 10 ‐ DR savings for dishwasher Function Average Power Energy Saved per DR Probable Energy Dropped [kW] participation [kWh] Savings [kWh/day] Delay of Normal Cycle .114 .114 .026 Disabling Heated Dry .875 .350 .080 Delaying Cycle with Added .178 to .875 .528 .121 Heat and Heated Dry
Range The range is a complicated appliance with many different electricity end‐use points including two ovens, four induction hotplates, and a warming plate. Additionally, each burner has multiple surface area options. Transient, steady state, and maximum power were measured for a single burner, both ovens, and the total range with no cooking loads as reference states.
Figure 10 ‐ Range displaying "EP" during a high price tier
In DR mode, three of the burners and the larger oven are disabled. Override consists of pressing the “start” or “on” button repeatedly after the appliance beeps to alert of peak pricing. “EP” is displayed to alert the user of a high energy price event. Also, burners are limited to a level of 8 on a scale of 10 and are reduced to 8 from higher points if already on. If burners are set to 8 or below, there is no change. Again, this can be overridden by pressing the temperature option button twice to increase back to 10. However, the reduction of burner level from 10 to 8 does not provide DR benefit (remember that no cooking load was added). Both settings use the same power of 1.96 kW at different duty cycle curves. However, both duty cycles are virtually the same with a different frequency. Since the override of the burners is so easy and if a customer is ready to cook, he will likely use as many burners as he needs, the energy savings calculations will not consider the burner disabling. Furthermore, there would only be benefit if the customer would otherwise use two or more burners simultaneously. Page 25 of 71
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Figure 11 ‐ Cooktop duty cycles are 75.8% and 76.8% at H (normal operation) and 8 (DR), respectively ‐ no reduction in kW or kWh.
It should also be noted that the burners do not cycle intelligently to minimize total power load. If multiple burners are on, they could potentially be cycled with phase shifts to lower average load. Instead of this, burners cycle only in relation to when they were turned on and reached their setpoint.
The lower, larger oven prevents start during DR mode. This may be overridden as described above. Lower oven transient heating incurs a load of 3.45 kW and requires .195 kWh per 100 °F change. The upper oven has a load of 2.85 kW and requires .070 kWh per 100 °F change. In other words, if the customer decides to not use the lower oven during a DR event, 3.45 kW are avoided. If the customer opts to use the upper oven instead, .61 kW and 64.1% of the total energy are saved. The transient operation was not observed, but it can reasonably be assumed that the heat loss rates from both ovens are equivalent due to design similarities. Therefore, the same energy savings are present during transient and steady‐state operation.
Table 11 lists the parameters measured or assumed for the calculation of DR power drops for the range ovens.
Table 11 ‐ Range Oven Parameters
Average T – LD ‐ PDR Tt ‐ Transient DC ‐ Steady Power Runtime Loads/week Heating Time State Duty [kW] [hr] [21] to 350 F [hr] Cycle [23] Larger Oven 3.46 1.07 3 7/16 .20 .25 Smaller Oven 2.85 1.07 3 7/16 .085 .25
The energy consumption during a DR event with oven participation is
∗ ∗ (3)
The average daily energy consumption during the DR period considering the probability of operation can be calculated by Equation (2). Page 26 of 71
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Table 12 summarizes the power drops and energy consumption savings during oven DR participation.
Table 12 ‐ Range DR power drops Functionality Average Power Drop [kW] Energy Savings per DR Probable Energy Savings participation [kWh] [kW] Delay of Using Large Oven 3.46 1.436 .269 Switching from Large to .61 .174 .033 Small Oven
Functionality Power Drop from Power Drop from H to 8 Energy Savings [kWh] Disabling a Burner [kW] [kW] “20% Reduction of 1.96 0 0 Burners”
Maximum Load Drop* 9.35 kW * Maximum load drop due to disabling of three burners and larger oven. Not a likely occurrence.
Washing Machine Similar to the dishwasher, the washer’s load will vary based upon load sensing, cycle options, and point in cycle. However, the washer’s load takes a periodic form that can be used to calculate energy savings during DR events. The washer was tested on a normal cycle with high‐temperature water for the first 15 minutes of the cycle as a reference state. The reference state has a periodic load operating between 103 and 8 Watts.
Figure 12 ‐ Washing maching displaying "EP" during high price tiers
The washer is designed to recommend cold wash during DR events but that was not observed. It should be noted that this would result in energy savings at the water heater, not at the dishwasher. The appliance does delay starting during a DR event. This delay is overridden by pressing the start button repeatedly.
The duty cycle in DR mode is reduced from 71.9% to 35.7% and adds roughly 38% of runtime (Figure 13). For a normal cycle this amounts to decrease of .023 kWh over the cycle and a small average power drop.
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Figure 13 ‐ Normal operation and DR reduced duty cycle operation
The University of Bonn determined that the total probability of use during the DR timeframe is about .35 [20].
Figure 14 ‐ Probability of washing machine operation during a day [20]
Table 13 lists the parameters and measurements used in calculating the DR potential of the washing machine.
Table 13 – Washing Machine Parameters
Pmax ‐ Peak Pmin – T – Tr – LD ‐ PDR DC ‐ DCr – Power Trough Runtime Reduced Loads/week [20] Normal Reduced [kW] Power [kW] [hr] Duty Cycle [20‐22] Duty Duty Time [hr] Cycle Cycle .103 .008 1.0 1.38 5.3 .35 .791 .357
The energy consumption of a single washing machine cycle is
∗ ∗ (4)
The average daily energy consumption during the DR period considering the probability of operation can be calculated by Equation (2). Table 14 lists the energy savings during DR events. Page 28 of 71
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Table 14 ‐ DR savings for washing machine Function Average Power Energy Saved per DR Probable Energy Drop [kW] participation [kWh] Savings [kWh/day] Delay of Normal Cycle .103 .082 .022 Reduced Duty Cycle 0 .020 .005
Dryer Similar to the washer and dishwasher, the dryer operation depends on load sensing, cycle selection, point in cycle, and other factors. The dryer was tested in normal mode with high temperature as a reference state. In the reference state the dryer requires about 5.15 kW. In a DR event the dryer switches to energy saver mode and cuts the heater power to 0 for 20 minutes if already running. If a customer tries to turn on the dryer during a DR event, the cycle is delayed unless overridden. If overridden, the design specifies that energy saver mode is suggested, although we did not always find that to be the case. The energy saver mode was not measured.
Figure 15 ‐ Dryer displaying "Delay EP" during high price tiers
Table 15 lists the parameters and measurements used in calculating the DR potential of the washing machine. The probability of occurring in the DR timeframe is the same as for the dryer.
Table 15 – Dryer Parameters
P ‐ Normal Pmin – Power w/o T – Runtime Th – Heater Cut‐ LD ‐ Loads/week PDR Power [kW] heater [kW] [hr] out Time [hr] [20, 21] [20] 5.15 .300 .75 .33 2.75 .35
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The energy consumption of a single washing machine cycle with and without the heater is
∗ (5a)
∗ (5b)
The average daily energy consumption during the DR period considering the probability of operation can be calculated by Equation (2). Table 16 lists the energy savings during DR events.
Table 16 ‐ DR savings for dryer Function Average Power Drop Energy Saved per DR Probable Energy [kW] participation [kWh] Savings [kWh/day] Delay of Normal Cycle 5.15 3.863 .531 Cutting Heater for 20 min 4.85 (for 20 minutes) 1.617 .222
Refrigerator The refrigerator energy consumption is primarily due to a compressor and electric sweat heaters. The compressor cycles at a rate proportional to the ambient temperature, heat generation within the refrigerated space, and setpoints. In order to reduce energy consumption during DR events and peak price periods, the freezer temperature setpoint is raised 5 degrees and the sweat heaters are disabled in DR mode. Sweat heaters manifest as large spikes in power which is used to control condensation. Following the sweat heater operation, the compressor has to work for an extended period of time to re‐ establish the setpoint.
Figure 16 ‐ Refrigerator displaying current rate tier
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When the freezer setpoint is raised, the compressor will turn off for an extended period of time until the internal temperature reaches the upper deadband limit. Then the compressor will run at a reduced duty cycle until the DR event is complete due to the reduced ΔT between the refrigerated space and ambient.
The refrigerator was tested at default settings in a well‐controlled environment with no significant variation in ambient temperature as a reference state. At this reference state, the refrigerator operates at a duty cycle of 55.7% with power ranging from .060 to .215 kW. The power also spikes to about .680 kW during .35% of time for the sweat heaters to operate. Since the sweat heaters operate so infrequently, they are not included in the DR savings analysis.
Table 17 lists the parameters and measurements used in calculating the DR potential of refrigerator and some of validated functions.
Table 17 ‐ Refrigerator Parameters
Operational Normal P – Avg DC ‐ DC Sweat Freezer Fridge Td ‐ Cmpsr State Power Power Duty above Heater Avg Avg Delay Time Range [kW] Cycle .11 kW Power Temp Temp [hr] [kW] [%] [kW] [°F] [°F] Reference .005 to .099 .557 .108 .672 ‐0.20 42.37 N/A State .215 Freezer Temp .005 to .089 .462 .017 .645 6.69 42.77 1.33 Increase (5 .215 °F)
The freezer temperature rises by 7 degrees as designed (slightly higher than the design point). From this, the average power drop is only about 10 W but it should be noted that the duty cycle decreases by about 10% and power draw above .11 kW is virtually eliminated.
The energy consumption of the refrigerator in normal operation and with an increase in freezer setpoint during a seven hour DR event is
7 ∗ (6a)
∗ 7 (6b)
Since the refrigerator is always on, the probability of it being able to participate in a DR event is 1. Thus, the probably energy used is the same as the energy used on a normal day.
(7)
Table 18 lists the energy savings during DR events.
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Table 18 ‐ DR savings for refrigerator Function Temporary Power Drop Energy Savings during DR (~80 min) [kW] event [kWh] Increase Freezer Temp .210 .153
It should be noted that the refrigerator analysis assumes a constant demand pattern each day when in fact the demand will vary slightly based upon freezer door opening and closing. Also, the compressor remained off for 80 minutes following the freezer setpoint increase. This was without any food in the freezer. With food, the time could be substantially higher.
Water Heater The 50 gallon water heater is a hybrid design that can heat in a number of modes using either electrical resistance heaters or an air‐supply heat pump. Since water heating accounts for a significant portion of home energy use, all‐electric homes could benefit from a hybrid water heater. The water heater can operate in several modes, energy saving mode, hybrid mode, high demand mode, and standard mode.
Energy saving mode uses only the heat pump. Hybrid mode (the default state) gives priority to the heat pump but will activate electrical resistance heating after a large draw High demand mode is similar to hybrid mode but will activate resistance heat sooner. Standard mode only runs the electrical resistance heaters.
Figure 17 ‐ Water Heater display during normal and high price mode
The heat pump delivers about 1.3 kW of heat while using about .422 kW of electrical power while the resistance heating uses about 4.5 kW. In normal operation, the water heater will operate in hybrid mode at the setpoint defined by the customer. The reference state used here is the default of 120 °F in hybrid mode. In Tier 2 the water heater switches to energy saving mode, in Tier 3 the setpoint is lowered to 110 °F, and in Tier 4 the setpoint is lowered to 100 °F. The responses are cumulative (as with all the appliances) and the altered setpoints are fixed without regard to what the original customer setting was. The DR mode cannot be overridden.
The total energy consumption for heating a large draw of 30 gallons from standard ambient temperature (77 °F) to the setpoints using the heat pump and resistance heaters is shown in Table 19.
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Table 19 ‐ Water Heater Power and Energy Consumption Power [kW] Energy to 120 Energy to 110 Energy to 100 [kWh] [kWh] [kWh] Heat Pump .422 1.04 .80 .56 Resistance Heaters 4.51 3.15 NA NA
Assuming the probability of one large draw per DR event (~30 gallons) to be .32 [24], the power demand and energy consumption drops from the reference state are as shown in Table 20.
Table 20 ‐ DR savings for water heater Function Power Probable Power Energy Savings during Probable Energy Drop [kW] Drop [kW] DR event [kWh] Savings [kWh] Switch to energy 4.09 1.31 2.11 .68 saving mode Lower setpoint to 110 4.09 1.31 2.36 .75 Lower setpoint to 100 4.09 1.31 2.60 .83
PCT The PCT works as intended and responds to the EMS signals within one second. With each increase in price tier the setpoint for heating and cooling adjusts to the programmed values. This is similar to PCTs reviewed elsewhere and will not be considered in the savings and DR power drop calculations. However, it is useful to note that the user interface is very intuitive and the setpoints can be controlled on the PC software, smart devices, and at the PCT itself. The PCT has programmable settings for both normal, daily schedules and for Tier 2, 3, and 4 pricing. During Tier 1 the PCT will act according to the daily programmed schedule. If the normal, daily schedule setpoints require less on‐time then the higher tier settings, the setpoints will default to the lower energy consuming option.
Figure 18 ‐ PCT setpoint adjustment to the four price tiers.
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Whole Home Energy Sensor The whole home energy sensor is device that provides power transmission data from the main home panel to the HAN monitors. This provides information on the total home consumption. The WHES was only tested on one PV system. The power measurement was validated with readings of 3.4 kW with an error of about 2%.
Response Time The response time for the appliances was measured to ensure that the DR functions would occur quickly. All the appliances reacted in less than one minute with only the washer and refrigerator responding slower than 8 seconds.
Appliance Response Time [s] WH 4 Dryer 8 Washer 23.5 Range 4 PCT 1 DW NA Fridge < 1 compressor cycle
Figure 19 ‐ Appliance response times
Figure 20 shows an example power curve of an appliance moving from DR mode to normal mode back to DR mode with a response time of 4 seconds.
Figure 20 ‐ Example of water heater response time going from DR mode to normal mode to DR mode
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DR Response for SDG&E Territory
It is difficult to produce a realistic estimate of potential energy savings or reduction of power because the power drops will be largely dependent on customer behavior. If a customer determines they need appliances more or less during certain days or times, it will influence their choice to opt in or out of a DR or pricing event. Also all customers will vary in their potential power drop capabilities as well. Despite this, the probable energy savings and power drops were used to calculate the estimated effect on the SDG&E territory.
As shown in this field evaluation, power drops for appliances varied from .05 to 5 kW. Probable energy consumption during a DR event varied from .01 to .831 kWh while the energy savings from assured participation range from .05 to 3.9 kWh.
Figure 21 ‐ Energy savings for DR event participation for each appliance. Max Savings is the energy saved from switching from the highest consumption mode to the most effective DR function. Average Savings is the averaged savings across all tested DR functions. Probable Savings and are similar but include the probability of operation during DR events.
Table 21 lists the total probable DR power reduction of the SDG&E territory for a 10% market penetration of the 126,260 customers described in the Market Opportunity section. The calculation also takes into account the electric appliance market penetration [25].
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Table 21 ‐ SDG&E Market DR Power Reduction Appliance Avg Power Probable 10% of Electric Total Probable Drop with DR Power Drop in Market Appliance Market Total Market Participation DR Timeframe Size Penetration Avg Power Power Drop [kW] [kWh] [25] Drop [MW] [MW] Dishwasher 0.14 0.03 12626 .702 1.20 .28 Range 2.04 0.38 12626 .391 10.05 1.88 Washer 0.05 0.01 12626 .71 .46 .12 Dryer 5.00 0.69 12626 .253 19.97 2.20 Refrigerator 0.15 0.15 12626 .987 1.89 1.89 Water 4.09 1.31 12626 .192 9.90 3.17 Heater Total 39.48 9.54
The water heater, dryer, refrigerator, and range have the most potential for impact. However, the range DR functions are easily overridden in such a way that they may not be effective. In addition, customers may tend to use the smaller oven naturally. While this is good from an energy effectiveness perspective, it may preclude the DR benefit of disabling the larger oven.
Figure 22 ‐ Guaranteed DR participation and probable power reduction for SDG&E market with Smart Appliances
It should be noted that as the market for electric appliances grows, so will the DR potential of this type of HAN. These calculations are for current populations, not projections.
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In addition to these savings, the HAN also includes a PCT for smart HVAC control and a pool pump switch compatibility (not CA compliant). Although these were not part of the study, they should be included in the HAN description since they will add to the energy and power savings. A 2011 third party load impact study provided by SDG&E estimates the power drop potential from each device during a DR event as shown in Table 22.
Table 22 Estimated power drop potential from HAN devices, per SDG&E
Installation Experience
Issues during installation
Mobile app is unavailable without the whole home energy sensorr. Heavy duty load controller wiring instructions were unclear (2‐phase vs. 1‐phase, as well as where to place it in line, see figure and details below). Pool pump load controller could not be installed due to non‐compliance with California electrical code. The controller cuts only one leg of the two phase legs which is against California electrical code and could possibly cause pump failure. However, a brief test did show that the controller communicated with the EMS Hub. Furthermore, HLC is not compatible with new 3‐ phase variable power pumps. Occasionally, the appliance communication modules would not receive power or communicate to EMS Hub. Sometimes replacing one module with another or re‐installation would solve the problem. Vendor should buffer quality assurance to avoid customer returns and device malfunction. The first whole home energy sensor did not register with the EMS and needed replacing. PCT requires three‐wire electrical connection which is not always present.
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Figure 23. Heavy duty load controller installation: original wiring design (top), and actual, final installation
Figure 23 shows diagrams of the original and modified wiring designs for the heavy duty load controller. Originally, the 240V power for the pool controller and pump was the output of the 240V heavy duty load controller. This would, however, have caused not only the pool pump but also the home owner’s pool controller to lose power when the HAN signaled to cut power to the pool pump. This in turn would in most cases result in the pool controller to lose settings, time, and date. Our electrician modified the original design as detailed in the installation instructions to independently power the heavy duty controller and the pool controller. This allowed for the HAN’s load controller to override the home owner’s pool controller during a DR event without losing information.
Another issue concerned the wiring methods within the heavy duty controller. The installation instructions detailed how to wire a 240V line through a relay so that cutting power only broke one of the two legs of the 240V line. This is against CA electrical code so the electrician correctly wired the system to cut both legs of the power. The actual components supplied allowed for the correct installation but the instructions detailed the process incorrectly. Due to incorrect wiring being potentially very hazardous, heavy duty load controllers should always be installed by licensed electricians and technology vendors should provide state‐by‐state (and correct) instructions.
Overall Programming
There is little programming necessary for the Smart Appliance HAN installation. Initial settings for the PCT and Utility rate schedules are needed, but this is straightforward and intuitive. The provided software guides the user through the process in steps and the configuration is self‐explanatory. Smart appliances have pre‐set price and DR event function adjustments and the PCT has and easy TOU and setpoint interface.
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Applicability of IOU programs and tariffs
San Diego Gas & Electric provides various customer rates that fit a wide variety of customer groups. There are several programs, current and forthcoming, that provide benefits to residential customers that are willing to alter energy usage according to peak times or demand events [26‐28]. In addition to the customer rebates and cost savings, money will also be saved due to the empowerment of conscious energy decisions.
It should be noted that it is likely that range savings are over‐estimated and refrigerator savings are underestimated.
For reference purposes, SDG&E labeled the following amount of days ‘DR days’ in the associated years: 2 in 2008, 9 in 2009, 14 in 2010, and 6 in 2011.
Technical Incentives The TI program offers an incentive to large commercial and industrial customers for automated DR mechanisms. The program is paid out as a one‐time incentive of 300 $/kW automatic, dispatchable power drop. Dispatchable implies that the power is shed upon Utility signal with little chance of it not occurring.
It is conceivable that a similar program could be offered to residential customers. It would be necessary to establish a specific incentive for each type of appliance measure since having a Utility technician perform a load shed test at each homesite would not be done as with the current TI program. Therefore, a specific incentive for each type of appliance would be based upon the maximum appliance power drop, average appliance power drop, or probabilistic power drops.
Table 23 ‐ TI incentive for each appliance. Average implies the use of average power drop across all tested DR functions. Guaranteed means that the appliance will definitely operate during the DR event, probable considers the chances of operation during DR times.
Avg Guaranteed Avg Probable Participation Participation Incentive[$] Incentive [$] DW 14.17 3.26 Range 34.49 6.47 Washer 2.19 0.58 Dryer 117.41 16.14 Fridge 6.56 6.56 WH 111.33 35.63
The more likely incentive program would consider the probability of a power drop, so the second column of incentives is more appropriate. This is necessary because the power drop is not
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“dispatchable” as the traditional TI program stipulates. From this, the dryer and water heater are most beneficial in terms of a TI incentive.
Schedule PTR The Schedule PTR program (Peak Time Rebate) is applicable to customers receiving electric bundled residential service through a rate schedule that requires separate metering. CARE customers and Net Energy Metering customers are also eligible. The program provides bill credits based on kWh reductions (from a baseline) during peak time hours, known as PTR events. Customers that participate in this solely by reducing their energy consumption during PTR events will receive a 0.75 $/kWh bill credit for their efforts. However if customers have enabling technology, such as approved HAN devices, they will receive a higher rate of 1.25 $/kWh [26].
On this schedule, the Utility will notify customers of PTR events by mass media, e‐mail, and notifications on the Utility’s website. ‘Enabling’ technology is defined as technology that satisfies three conditions:
can be initiated via a Utility signal to reduce end‐use electric energy for specific electric equipment or appliances, is included in a designated Utility DR program, and has been registered with the Utility by the customer.
The yearly rebates for each appliance participating in 15 PTR days/year are shown in Figure 24. The water heater and dryer have the largest rebate per year.
Figure 24 ‐ Probable yearly customer rebate for 15 PTR days for both PTR schedules options.
Using the calculated rebates, the payback time in years was calculated for an ACM attached to each appliance. It is assumed that the added cost from purchasing a smart appliance is contained in the necessary networking component. In other words, the smart appliance itself would cost as much as a typical appliance.
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Schedule DR‐TOU
A new residential schedule for 2013 called DR‐TOU (Time‐of‐use) allows customers to save money by shifting loads to off‐peak times. Rates differentiate between winter and summer and on‐peak (1100‐ 1800 weekdays) and off‐peak (all other hours) [27].
Using average summer rates of .301 $/kWh on‐peak and .187 $/kWh off‐peak and winter rates of .163 $/kWh and .159 $/kWh off‐peak, yearly savings for shifting appliance loads to off‐peak are listed in Table 24.
Table 24 ‐ DR‐TOU Savings Appliance Yearly Savings [$] Dishwasher 1.17 Range 2.31 Washer 0.21 Dryer 5.78 Refrigerator 2.35 Water Heater 12.75
The Smart Appliances HAN is particularly well‐suited to the DR‐TOU schedule because the rate changes throughout the day will allow for automatic participation. When used in combination with the PTR and TI programs, the rebates are amplified as seen at the end of this section.
Figure 25 ‐ Probable yearly rebates for a DR‐TOU, PTR program Schedule CPP Schedule CPP (Critical Peak Pricing) is a pricing schedule with varying rates across the day (Off‐peak, semi‐peak, and on‐peak) with a Utility option to initiate up to 18 CPP events each year. During the CPP events, rates are up to 18.7 times the lowest off‐peak rate. This variable schedule allows customers to
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manage their energy consumption in order to reduce cost and gives the Utility freedom to manage high demand days.
Using averaged rates similar to the DR‐TOU treatment above, yearly savings for shifting appliance loads from peak, semi‐peak, and CPP events to off‐peak times are listed in Table 25. CPP rates are collapsed into average summer rates of .0887 $/kWh peak and .057 $/kWh off‐peak, winter rates of .091 $/kWh peak and .063 $/kWh off‐peak, and CPP event rate of 1.058 $/kWh.
Table 25 ‐ DR‐TOU Savings Appliance Yearly Savings [$] Dishwasher 1.58 Range 3.14 Washer 0.28 Dryer 7.84 Refrigerator 3.18 Water Heater 17.30
The CPP schedule is not yet developed for residential customers, so commercial rates were used. Therefore, the savings may not necessarily represent what a residential customer would realize. However, the CPP program is well‐suited to the Smart Appliances HAN because it incorporates multiple price tiers rather than just on‐peak, off‐peak.
Figure 26 ‐ Probable yearly rebates for a CPP, PTR program Other Residential Programs Other residential schedules include Domestic Residential (DR), Domestic with Solar Energy Systems (DR‐ SES), Alternative Rate Programs (E‐CARE), and other programs of various specific purposes such as multifamily or Electric Vehicle Services. None of these programs benefit via incentive or rebate for reducing load specifically during a demand‐response event. However, shedding loads at a needed time
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is beneficial to both the Utility and the customer. Customers benefit by reducing their personal consumption and cost.
California utilities must eventually transition to real‐time & time‐of‐day pricing schedules. As will be discussed, SDG&E plans to test more pilot schedules and projects in the next few years to introduce enabling technology and demand reduction to the public [29].
ACM Payback Times The time needed to recoup the extra investment in the appliance communication module (the remainder of added HAN and smart appliance costs were not considered) varies across appliance and programs enrolled. The following figure shows the payback in years for the $108 module for four of the appliances and various program combinations. Payback times are reasonable for the dryer and water heater with DR‐TOU/PTR, DR‐TOU/PTR/TI, and CPP‐TI programs.
Figure 27 ‐ ACM payback times for various appliances and program combinations
Again, it is likely that the range savings are quite generous and the refrigerator savings are quite conservative. This would suggest that the smart appliances worth the extra investment would be the dryer, water heater, and refrigerator.
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Conclusions
Benefits of HAN with DR Enabled Smart Appliances
Energy Efficient Smart Appliances with Intelligent Load Shedding
The Smart Appliances HAN is unique in its approach to DR and smart pricing schedules in that it does not respond to Utility signals by simply shutting down devices that may be needed by the customer. All other HANs control electrical grid demand by turning off or cycling loads through plug load controllers. Cycling loads may be useful for air conditioners, heat pumps, and pool pumps (which this HAN includes), but it is not well‐suited to ranges, washers, and other appliances that must remain on when the customer needs them. A range cannot be cycled on and off during a DR event if the customer is expecting to cook dinner. The Smart Appliances HAN addresses this conflict by offering unique, state‐of‐ the‐art Energy Star rated appliances which have an intelligent reaction to DR or pricing signals.
During a DR or pricing event, the clothes washer, dryer, and dishwasher all delay adding loads to the household demand until the event has completed, unless overridden. The range reduces maximum hotplate temperatures and number of available ovens. The refrigerator avoids demand spikes due to electric sweat heaters. The water heater turns off electrical heating capabilities and operates as an air‐ source heat pump. The PCT, water heater, range, and refrigerator adjust their setpoints in order to reduce the cycling durations and frequencies. In general, all the appliances reduce or avoid adding electrical loads that are used for heating in various processes.
In this manner, the Smart Appliances HAN is able to offer a host of household appliances that can be used as needed by the customer while still participating in DR events. Additionally, the HAN products offered are varied and modular such that a customer can design his own system as required or desired. An on‐off HAN control is more restrictive and may be less attractive to large appliance customers.
Customer Use of New Technology
Customers responded favorably to the HAN and all elected to keep the system installed after the evaluation. The customers liked the ability to monitor their consumption and the simplicity of the monitoring and control software. The appliances themselves saved money on monthly bills through efficiency savings and customers, in general, liked the appliance functions.
Appliance Energy Consumption Control & Monitoring
The user interface is intuitive and easily customizable. The PC software allows for multiple widget arrangements that display real‐time usage, historical data on differing timescales, tips, energy equivalents, and utility rates. Data can be displayed in units of $, $/hr, kW, and kWh. Appliance consumption data is stored on vendor servers for up to 3 years and can be accessed on a number of devices. The smart phone app can be used to access PCT control and HAN monitoring remotely.
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The DR and price function changes occur automatically so that customers can participate in events without active management. Most of the appliance changes are easily overridden if necessary. In this manner, the customer is empowered without inconvenience. Furthermore, the appliance functions are all pre‐programmed which frees the user from complicated installation.
Figure 28 – Widget user interface.
Utility Communication & Benefits
The Utility will benefit from the automatic DR and price event participation and can be assured of a power drop at the initialization of an event. Loads and energy consumption are reduced in varying degree across the appliances. The appliance response time to signals is on the order of seconds. The load and energy reductions have been detailed. Especially effective are the measures enacted in the dryer, water heater, and refrigerator. As seen in Figure 29, the peak load incurred by the dryer, water heater, refrigerator, and range roughly equal the peak load due to air conditioning, the focus of many DR efforts to date.
As an added bonus, the server database could allow for future analysis of individual appliance and device consumption patterns for targeting of future energy reduction efforts.
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Figure 29 ‐ Peak residential load across end‐uses [30]
Reduce Energy Bills
The smart features of this HAN can allow for customers to lower their monthly electricity bills by avoiding consumption during periods of expensive rates. The customer can input a rate schedule as defined by the Utility which will cause appliances to automatically avoid or shed expensive loads. This is a financial benefit to the customer that will be realized each month. It also allows the customer a hassle‐free DR participation with easy override on most appliances. In this way, the customer does not have to worry about adding too many active energy consumption decisions to their daily to‐do lists.
System Improvement Opportunities
Many other HANs include plug load contollers in order to monitor and control miscellaneous home devices such as televisions, lighting, fans, and portable space heaters. Since the Smart Appliance HAN already includes plug load monitors, it would be natural to include plug load controllers, as well.
The smart app and in‐home display require the installation of the whole home energy sensor (Figure 30). This is an unreasonable requirement that restricts usability unnecessarily. In one of the test homes, the Page 46 of 71
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WHES was attached to the PV circuit in order to monitor PV output as opposed to home consumption. After the installation, the mobile app and IHD worked even though the WHES was monitoring an unrelated system. This exemplifies the unnecessary requirement for the smart app and IHD.
Figure 30 ‐ Mobile app requires WHEM to function
The smart appliance functional changes are not programmable with exception of the PCT. Some technically savvy customers would prefer the ability to choose their own energy saving measures from a list of possible choices. The vendor might consider implementing an “advanced” mode that allows for detailed schedules for each device, in the manner of Energy‐Star approved programmable thermostats.
The user override is extremely easy and does not cause the user to think much before doing so. It would be wise to include a more apparent signaling of a DR and pricing event to the user so that overriding would be a more conscious decision.
In some instances, the appliances cannot be overridden or do not alter function to design specifications. Also, the ACMs had reliability issues with regard to connectivity both before and after installation. The vendor should enact a comprehensive test plan to catch imperfections before further market adoption.
Finally, the price tier settings are not customizable. The tiers are automatically set based on the variability and range of rates entered. It is necessary to allow customers to set tier designations on their own, if they so choose.
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Applicability of case study findings to other load types and sectors
The technology included in the field evaluation is intended for residential applications. Commercial and industrial applications require more complex methods for monitoring and controlling energy.
This technology may also be applicable to small work groups or small businesses in small office buildings where building technology resembles residential systems. However, small commercial energy and DR tariffs and incentives are different from their residential counterparts. It was not in the scope of this study to determine applicability and financial benefits under these circumstances. There is not an obvious reason why the technology should not be beneficial in small commercial environments; we therefore recommend further research in this area.
Considerations for large‐scale and persistent market implementation
This technology is ready for large‐scale implementation, but only some of the appliances display reasonable payback in energy cost savings. Unfortunately, the utility infrastructure may not be as ready: as of the writing of this report, residential Smart Meters have not yet been enabled for in‐home communication with this type of HAN so the technology could not operate at its full potential. Furthermore, the HAN operates via price signal schedules. Individual utilities may or may not use currently use such a structure in their TOU and DR signaling. Eventually, they must transition to real‐ time pricing schedules. For this reason, a further study of energy and cost savings based on price tiers rather than DR programs should be considered.
The installation and setup process of the appliances and networking was straight forward and can be handled by an average “handy” homeowner. The applications support is knowledgeable and readily available.
We have no information on whether this particular vendors’ manufacturing process, supply chain, support workflow and back‐end infrastructure can scale to a significantly larger customer volume at this time. The vendor does have significant resources and already operates within large‐scale markets.
Impact of HAN devices on the SDG&E Roadmap 2011‐2020
SDG&E Advice Letter 2307‐E [29] discusses implementing a Smart Grid plan with the goals
1. To deliver customer benefits and value leveraging the Smart Meter infrastructure, 2. To give customers choice, control, and convenience regarding their energy usage; 3. To provide rates that empower customers with choice;
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4. To protect the security and privacy of customers’ data; 5. To target and coordinate technology efforts related to the HAN platform enabling the expansion of the market for customer products and services; and 6. To enable customers to actively participate in demand response programs and rates while maintaining or improving their overall satisfaction levels.
The Smart Appliances HAN certainly addresses each of these.
Home Area Network devices are currently in the field test stage within the CA Utilities. These devices represent forward progress of the Utilities to establish a smart grid and educate its consumers on energy consciousness.
Figure 31 ‐ SDG&E HAN timeline as oof 11/2011 [29] Page 49 of 71
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Figure 31 shows the general timeline that SDG&E has established for Home Area Network development within its territory. The highlighted region represents ongoing projects within the HAN outline that may pertain to the technology reviewed in this evaluation. As stated earlier, the only current residential schedule rates that take advantage of HAN technology are the PTR and DR‐TOU schedules.
The second highlighted row within the timeline shows the development process of ZigBee HAN standards. ZigBee is a standard communications protocol used by Smart Meters to communicate with in‐home devices. This type of communication makes automated demand‐response possible. For the simulated DR event a signal was artificially implemented by setting the Utility rate to the “critical tier”. Critical tier rates cause the same functional response as a Smart Meter DR signal would. It is important to note that currently the Utility specifically states in the PTR rate schedule that they DO NOT guarantee consistent communication to customers of DR events via mass media, internet site, or email. Because of this caveat, it is highly desirable for a HAN to be able to receive DR and price signals from the Smart Meter.
The third highlighted row in Figure 31 shows various pilot programs for HAN devices. These include low‐ income In‐home display (LI‐IHD), low‐income programmable controlling thermostats (LI‐PCT), Solar Energy Systems (SES), Residential Automated Controls Technology (RACT), and Price‐Driven Load Management (PDLM). All of these pilot programs are meant to educate Utility customers first hand in the future of energy management. Users with more opportunities to gather information on their energy consumption (IHD and PCT) will be more conscious of their daily needs and behavior.
The last highlighted row shows future and pending HAN offerings and programs in continuation of HAN technology adoption. In D.11‐07‐056 Ordering Paragraph 11 the CPUC ordered PG&E, SCE, and SDG&E to develop a HAN implementation plan that includes a roll‐out strategy with a timetable for making HAN functionality and benefits similarly accessible to customers across all three companies. This order also required HAN planning to include key issues such as costs, expanded data access and granularity, current and evolving national standards, and security risk mitigation [31]. This field evaluation covers many of these issues that follow the roadmap timeline. Also, the pending Small Customer Technology Deployment (SCTD) will similarly increase the understanding of implementation issues by distributing more HAN technologies to customers for more in depth evaluations [32].
The overall impact of HAN in the SDG&E roadmap is “customer empowerment”. SDG&E defines this term for educating customers on new energy saving technology and the future of the Utility. Currently SDG&E is planning to begin roll out of dynamic rate plans for residential customers in 2013. By using HAN technology empowered customers will be able to minimize their energy consumption and avoid peak usage.
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Glossary and Acronyms
AC Air Conditioning App Application, commonly for mobile phones CFL Compact Fluorescent CPUC California Public Utilities Commission DR Demand Response and also common residential tariff EE Energy Efficiency EMS Energy Management System HAN Home Area Network IEEE Institute of Electrical and Electronics Engineers LCS Hardwired Control System IHD In‐Home Display PCT Programmable Communicating Thermostat PDLM Price Driven Load Management PG&E Pacific Gas & Electric PLC Plug Load Controller PLS Peak Load Shift PSH Peak Shift at Home PTR Peak Time Rebate RACT Residential Automated Controls Technology RASS Residential Appliance Saturation Survey SCE SoCal Edison SCTD Small Customer Technology Deployment SDG&E San Diego Gas & Electric SES Solar Energy System TOD Time of Day TOU Time‐of‐Use UCSD University of California, San Diego DR‐TOU Domestic Residential ‐ Time of Use DR‐PTR Domestic Residential ‐ Peak Time Rebate CPP Domestic Residential – Critical Peak Pricing HVAC Heating, Ventilation, and Air Conditioning ZigBee Low‐power high‐frequency wireless protocol Z‐wave Low‐power RF protocol WiFi Wireless local area network RF Radio Frequency LAN Local Area Network
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References
[1] Kavalec, Chris, Nicholas Fugate, Tom Gorin, Bryan Alcorn, Mark Ciminelli, Asish Gautam, Glen Sharp, and Kate Sullivan. 2012. California Energy Demand Forecast 2012‐2022 Volume 1: Statewide Electricity Demand and Methods, End‐User Natural Gas Demand, and Energy Efficiency. California Energy Commission, Electricity Supply Analysis Division. Publication Number: CEC‐200‐2012‐001‐CMF‐VI.
[2] DemandResponse2012‐2014‐Projects_Test_SDGE_20110301Atch01_207224.ppt (please contact the authors for access to this document)
[3] California Utilities Statewide Codes and Standards Team. 2011. Working Draft Measure Information Template – Upgradeable Setback Thermostats. Codes and Standards Enhancement Initiative. http://www.energy.ca.gov/title24/2013standards/prerulemaking/documents/2011‐ 06‐09_workshop/review/2013_CASE_Upgradeable_Setback_Thermostats_2011_06_08.pdf
[4] California Energy Commission. 2008. 2008 Building Energy Efficiency Standards for Residential and Nonresidential Buildings. California Energy Commission. Publication Number: CED‐400‐ 2008‐001‐CMF. http://www.energy.ca.gov/2008publications/CEC‐400‐2008‐001/CEC‐400‐2008‐ 001‐CMF.PDF
[5] Home Plug Powerline Alliance. https://www.homeplug.org/home/
[6] Z‐Wave Alliance. http://www.z‐wavealliance.org/
[7] ZigBee Alliance. http://www.zigbee.org/
[8] Marcus, William, Greg Ruszovan. 2007. “Know Your Customers”: A Review of Load Research Data and Economic, Demographic, and Appliance Saturation Characteristics of California Utility Residential Customers. JBS Energy, Inc. on behalf of The Utility Reform Network.
[9] Aclara. http://www.aclaratech.com/Pages/default.aspx
[10] Calico Energy Systems. http://calicoenergy.com/page.php?page_id=5
[11] Control4. http://www.control4.com/
[12] Energate, Inc. http://www.energateinc.com/
[13] EnergyHub, Inc. http://www.energyhub.com/
[14] General Electric Company http://www.geappliances.com/home‐energy‐manager/
[15] Insteon. http://www.insteonsmartgrid.com/about‐us.html
[16] Opower. http://opower.com/
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[17] Silver Spring Networks. http://www.silverspringnet.com/
[18] Space‐Time Insight. http://www.spacetimeinsight.com/
[19] Universal Devices. http://www.universal‐devices.com/
[20] Stamminger, Rainer, et. al. 2008. Synergy Potential of Smart Appliances. University of Bonn.
[21] Mansouri, Iman, Marcus Newborough, Douglas Probert. 1996. “Energy Consumption in UK Households: Impact of Domestic Electrical Appliances.” Applied Energy, Vol. 54.
[22] Roberts, Peter. 2012. Yarra Valley Future Water – 2011 Appliance Stock and Usage Patterns Survey. Yarra Valley Water.
[23] Fisher, Don, et. al. 2002. Commericial Cooking Appliance Technology Assessment. “Chapter 7: Ovens.” Fisher‐Nickel, Inc. Publication Number: 5011.02.26
[24] Hendron, R., J. Burch. 2007. “Development of Standardized Domestic Hot Water Event Schedules for Residential Buildings.” Energy Sustainability 2007 Conference. National Renewable Energy Laboratory.
[25] American Housing Survey. 2011. US Census.
[26] PTR Electric Schedule. http://regarchive.sdge.com/tm2/pdf/ELEC_ELEC‐SCHEDS_PTR.pdf
[27] DR‐TOU Electric Schedule. http://regarchive.sdge.com/tm2/pdf/ELEC_ELEC‐SCHEDS_DR‐ TOU.pdf
[28] Personal correspondence with Eric Martinez of SDG&E, March 2013.
[29] Advice letter 2307‐E. http://regarchive.sdge.com/tm2/pdf/2307‐E.pdf
[30] Brown, Richard E., Jonathan G. Koomey. 2002. Electricity Use in California: Past Trends and Present Usage Patterns. Ernst Orlando Lawrence Berkeley Laboratory, Energy Analysis Department. Publication Number: LBL‐47992
[31] http://www.part68.org/documents/meetingrec/11‐Meetings/ACTA‐11‐021_ELEC_3956‐ E%20.pdf
[32] http://www.energy.ca.gov/electricity_analysis/notices/2011‐04‐ 26_drmec_workshop/presentations/SDGE_DR_Enrollment_2010.pdf
[33] US Department of Energy. 2012. Buildings Energy Data Book. 2012. http://buildingsdatabook.eren.doe.gov/
[34] LG. http://www.lg.com/global/products/home‐network/
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[35] Samsung. http://www.samsung.com/us/
[36] Whirlpool. http://www.whirlpool.com/
[37] Technology Incentives Program. http://www.sdge.com/technical‐assistance‐technology‐ incentives
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Appendix A: Measurement and Verification Plan
Introduction
This measurement plan is an integral part of the project described in “REMA Phase 2 Evaluation Statement of Work and Estimate” [1] and “REMA Phase II Project Plan” [3].
It follows the guidelines established in [2].
It has been designed to accurately assess both the baseline performance of the incumbent technology (or standard practice in the absence of an incumbent) and the performance of the technology under study.
It has been designed in compliance with one of the evaluation methods identified in the International Performance Measurement and Verification Protocol (IPVMP) except where site‐ or technology‐specific circumstances dictated a deviation from one of these protocols. The Measurement Plan identifies selected IPMVP method to be used or the justification for any deviations from IPMVP.
All instrumentation under the control of evaluation staff shall be calibrated in accordance with guidelines established in the IPMVP as described in [2].
For field evaluations, all reasonable efforts shall be made to calibrate or replace any customer‐owned instrumentation or where this is not possible, to document the calibration status of such instrumentation.
Measurement uncertainty for each monitoring device will be documented. An error analysis evaluating the uncertainty associated with energy and demand savings estimates will be required for the Final Report.
All instrumentation will be commissioned prior to initiating data collection to ensure that measurement and logging systems are functioning properly, to minimize risk of unusable data sets.
Any anomalous data will be investigated and explained. Following investigation, careful consideration will be given to whether such data should be incorporated in the analysis or replaced by additional data collection.
Any events that occur at customer premises during the data collection period that are likely to compromise the validity of the assessment project and that are beyond the control of evaluation staff will be communicated to program management without delay.
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Test site description
The test sites for this project are San Diego based existing residential homes, which are the market of the technology. This project is a functional test to gather initial feedback and data for a future project with a representative list of test sites. The selection process of the residential homes in the project was based on the following desired criteria:
Preferably mostly electric single family home with electric resistance water heater (as opposed to heat pump or propane) and electric stove. Preferably Active use of an air conditioning system Broadband internet (must have) Used 700 kWh of electricity or higher per month (must have) Preferably a home with a swimming pool.
From these criteria, 5 homes were chosen to participate in the project. The table below provides a detailed description of the 5 residential sites as well as which technology will be installed and tested in that home.
Project Customer # Climate AC Pool Spa Elec Elec Solar Smart Zone Dryer Water Phone [7] Heater Smart Appliances 12 7 Yes No No Yes Yes No Yes Smart Appliances 13 10 No No No Yes Yes Yes Yes Smart Appliances 14 7 No No Yes Yes No Yes (HW) Yes Smart Appliances 15 10 Yes Yes Yes Yes Yes Yes (PV) No Smart Appliances 16 10 Yes Yes Yes Yes No No No
For the different sites, the site‐specific factors that could obfuscate the impact of the functionality of the technology under study as well as energy and demand savings are:
The technical savviness of the customer Climate zone and age of the home, as they will affect AC usage The customer utilizing a Smart Phone application The amount of time the customer is at home and uses energy Any fluctuation of normal energy using patterns Behavioral patterns and habits of the customer
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Data collection procedures
The main objective of this project, as also specified in [3], is to assess whether the technologies perform as designed and to obtain homeowner feedback in order to determine the readiness for a large‐scale, persistent implementation. The data to be monitored in this project will be measurements from sensors as well as personal feedback of those involved.
This project will be focused on one vendor for a Home Area Network (HAN) with DR enabled devices. This will include various household devices that are able to respond to demand, pricing, and user‐ defined events. Also included in this review are various other products by the same vendor that are compatible to this HAN. The following table describes the devices that will be included in this study.
Smart appliances and HAN devices
Device Purpose Data Energy Management Serves as the home base. Functionality: Confirm that during DR System (EMS) Communicates with event, the alerts are properly other devices on the communicated. network. Programmable Controls the internal Functionality: Confirm that during DR Thermostat (PCT) temperature event, the thermostat responds correctly. Communication module Device that connects to Functionality: Confirm that during DR each appliance to make event, the appliances respond them DR enable correctly. Load Control Switch (LCS) Controls the pool pump Functionality: Confirm that during DR event, the pool pump responds correctly. Energy Sensor Monitors energy Functionality: Confirm that energy consumption of any consumption is relayed to EMS for device plugged into a monitoring purposes. 120V outlet Mobile App Monitors and manages Accuracy: Confirm the power energy consumption and measurements from the home cost consumption. DR Smart Appliances: Appliances that function Functionality: During a DR event, ‐ Refrigerator as typical, but also confirm the change in performance. ‐ Range (stove/oven) respond to controls from ‐ Dishwasher the EMS and change their Accuracy: Measure power to confirm ‐ Dryer algorithm to shift loads power drop during DR event ‐ Washer for off peak or reduce ‐ Heat pump water heater consumption.
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Data points
The data that is necessary in order to accomplish the specified objectives are:
1) The products’ technical specifications versus its performance on a lab work bench. Students at the University of California, San Diego will provide lab testing for each technology and explain the product and its operation. 2) The power drop of the entire home during a demand/response event. For homes with DR enabled appliances or other plug loads (see section Instrumentation) this will also show the power drop for individual appliances. 3) The general behavior of the technology during the demand/response simulation. This will be based off a survey evaluation completed during the simulation. This will include observing the devices, energy management system, web portal, and Smart Phone alerts during the simulation (for those technologies that apply). 4) Customer satisfaction and implementation which will be evaluated through a survey that each customer will complete at the completion of the project. This will give an overall view of the customer’s perception of the technology. 5) Utility bills of each residential home. We will study the possible impact on the customer’s energy consumption and cost with the new technologies. The results will be used to determine the population necessary for a future detailed analysis, which in turn would allow for a statewide prediction of saving potential.
Data sampling, recording, and collection intervals
Each residential customer home involved in this project will be tested and evaluated. The controls for the tests in this project are the results of the homes when the technologies are not in operation.
A main portion of this project is the demand/response simulation at the residential homes. This simulation will occur after the technologies are installed in each of the homes. Each home will have at least one simulated and evaluated DR event with NegaWatt Consulting’s presence. During the simulation the technology will be alerted of a DR signal from the ‘utility’ via an online web portal or energy manager. For some of the technologies, this alert may be sent to their mobile device and/or computer. Once the DR event begins, the technologies will be observed to gauge whether they react properly. The power drop (kW) of the home will be directly measured for immediate impact. The power drop will depend on the response from the technology and whether the controlled appliances were operating before the DR event. At the conclusion of the DR event, the technology will be observed to determine if the home returns back to its original state and all appliances are properly working.
The customers will receive one online survey by NegaWatt Consulting only at the completion of the demand/response simulation. They will complete and submit the survey on the spot. This evaluation
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will reflect the feedback the customers had of the technologies. The table below is a detailed description of how to conduct DR Simulation during the visit:
Smart Appliaces DR event simulation
Technology DR Simulation Gamma Option 1: Manually set price tier time ranges within energy management system. Utility prices have four tiers: Low, Normal, High, and Critical. 1) In energy management system in home PC, set utility rates to create a “critical” tier event during desired time frame. 2) Price tier preceding and following critical tier should be normal rate. Note: In a price tier event, there are no HAN‐device status‐responses as with a real DRLC, such as acknowledgment of a DRLC command and completion, indication of user override, etc.
Option 2: 1) Receive the customer ID and serial numbers during installation 2) Contact the Field Applications Engineer for each home via email. Vendor will create the DR event for the homes. Details to provide vendor are: ‐ Day and time of DR simulation ‐ Price tier ‐ Price rate
Below is a checklist that describes the task for NegaWatt Consulting to take during a DR simulation visit:
DR simulation visit checklist
1 Look at utility and home online portal ‐ figure out the username and password a. Observe what is on/off, DR enabled, thermostat settings, etc. 2 Customer to fill out survey 3 Observe the PLCs and confirm they are all operational 4 Check power usage at the home and compare to the online portal 5 Run DR simulation: a. Check alerts: email, phone, online portal, in‐home display b. DR simulation begins at the indicated time c. Power drop d. DR simulation ends at the indicated time e. Do appliances turn on/off as they should f. Thermostat 6 Run DR again and opt out to check functionality 7 Check random plug load appliances with Kill‐o‐wattz 8 Check online usage rating price v. SDG&E's tiered charge rates
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The collection interval for the energy and cost analysis will be up to two billing periods, one of the months prior to the installation and another after installation. The utility bills under observation should be as similar as possible in terms of outside factors such as weather which will affect HVAC use and the amount of time the customer is home; normalization may be performed as needed if outside factors vary too much during the observation period. Instrumentation
Tools and instruments that will be used in the project are:
Outside weather temperature for the utility bill comparison. Weather Underground and Weather Bug (http://www.wunderground.com/ and weatherbug.com) are an online source that provides several local climate measurements including weather. There are several stations of weather measurements that are located relatively close to the homes in order to have accurate comparisons. Since the utility comparison will be based from monthly bills, the weather measurements will be downloaded as a daily average as a comma delimited text file, then stored and charted with Microsoft Excel 2007. A Fluke 1735 Three Phase Power Logger device for whole house power consumption. The 1735 conducts energy consumption testing by logging most electrical power parameters and captures voltage events. Calibration of the Fluke 1735 was done on 3/30/2011. The values that will be measured are energy and power. Measuring range and accuracy for the main variables of the power logger are: o Voltage (V‐RMS Wye measurement) Range (V‐RMS Wye): 57 / 66 / 110 / 120 / 127 / 220 / 230/ 240 / 260 / 277 / 347 / 380 / 400 / 417 / 480 V AC Range (V‐RMS Delta): 100 / 115 / 190 /208 / 220 / 380 / 400 / 415 / 450 / 480 / 600 / 660 / 690 / 720 / 830 V AC Resolution: 0.1 V Intrinsic error: ± (0.2% of measured value + 5 digits) Operating error: ± (0.5% of measured value + 10 digits) o Current (A‐RMS) Range: 15 A / 150 A / 3000 A RMS (non‐distorted sine wave) Resolution: 0.01 A For ranges 150 A/3000 A Intrinsic error: ± (0.5 % of m. v. + 10 digit) Operating error: ± (1 % of m. v. + 10 digit) For range 15 A Intrinsic error: ± (0.5 % of m. v. + 20 digit) Operating error: ± (1 % of m. v. + 20 digit)
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Note: Power measurement errors from the Fluke 1735 are calculated by adding the errors of voltage and current and including error of power factor (specified error x [1‐ power factor error]). Kill‐a‐watt EZ will be used to check if the devices on the plug load/power strips are operational during a DR simulation by measuring the energy (kWh). This will be connected to all the devices in each of the residential homes that are controlled, which will ultimately depend on the technology. Factory accuracy for this device is a 0.2% A survey for the DR simulation and another for customer feedback. Questions in the NegaWatt Consulting DR survey will be completed by NegaWatt staff; the customer survey will be filled out by the customers in a presence of NegaWatt staff to assist in case of questions.
The NegaWatt DR survey is shown below:
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Questions in the online customer feedback survey include (extract):
To view the full survey, see Appendix B.
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Data analysis procedures
As stated in the Introduction, all data will be reviewed before analysis and any anomaly will be investigated and explained.
Data manipulation (aggregation, statistical analysis, etc)
The demand response simulation survey from NegaWatt Consulting will evaluate wether the technologies actually perform the way they are designed and configured. From the survey, an overall determination of the product will be based off all the houses with the same installations, as some failures may depend on different factors. If any of the overall questions indicate a failed test, then it will give insight into public use of the product. As indicated in the evaluation, a root cause analysis will be conducted for the source of these failures such as the technology itself orr the installation process. The responses to this evaluation will further the lab observations that the UCSD students provide.
The online survey will export the results into Excel once all the surveys are completed. The results of the customer feedback survey will be converted to a mathematical format. For the survey questions with a 4‐point Likert scale (strongly agree to strongly disagree), the responses will be presented as a percentage of 0‐100%. The positive feedback will be associated with a higher percent and vice versa. The response answers and the overall score will be illustrated in histogram curves. The rest of the questions will be in different mathematical format based on the question. These will then also be charted, as shown in the example figure below. With this format the overall ‘score’ of the technology will be calculated. The scores will then be compared to the other technologies to determine which technology had the best feedback. The curves will determine the personal satisfaction and functionality of the products based on the customer.
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Calculation of energy, demand and cost savings
During the DR event, the Fluke 1735 Power Logger will collect the power measurements for each home. The Fluke device has two components: one that measures voltage and one that measures current. Tools to measure these values will be installed on the residential home’s circuit breaker to measure the entire home consumption. The Fluke 1735 will calculate and log the power from these factors while including the effects of errors and the power factor. The power will be logged at 1 minute intervals for the full DR simulation period. The measurements will be downloaded as a tab‐separated text file per residential home. The data will then be transferred to a spreadsheet where calculations will be performed and charts created.
With the power measurements, the DR power drop and/or energy reductions for each home will be calculated and then normalized into a percent of power drop or energy reduction as compared to the original. Further analysis of this value will calculate an average of these factors and standard deviation for each technology. Error analysis of these calculations will be based off the inaccuracies of the power logger as provided in the Instrumentation section.
The monthly utility bills for the homes will be used to conduct a simple calculation of energy consumed with the technologies installed. First, the weather data and general information from each customer will determine which month prior to installation will be used. The mean temperature of each month will be calculated and compared. Of the months that are within a ±5⁰ margin, temperature differences will be calculated. The daily measurements will be collected for a year prior to the installation month in order to find a month similar in climate as the tested month. The temperature discrepancy from the two months will be part of the error analysis. In addition, the customer will confirm that their schedule during the month observed and a potential month prior will be similar, including vacations and energy habits at home.
The energy consumption will then be compared to the customer feedback survey. The energy savings will show if those who used the technologies to the most extent actually saved significantly more than those who did not. This will give an overall picture of the functionality of the technologies.
The cost calculations will use the same utility bills as the energy savings. The cost savings will take into account the amount of energy used in both months and calculate a value. Due to the various rates during the year, this value will be calculated at a rate that will normalize the cost. Similar to the energy savings calculations, the average and standard deviation of the cost savings will also be determined.
A market analysis will be conducted focusing on smart electronic appliances. This will begin with using the survey to calculate a percentage of customers with the electric appliances in order to observe the market at hand for each appliance. A major factor will include the responses from the customer regarding their attitude towards smart electric appliances. This will include examining the different factors that are significant to the consumer when buying a new appliance. In particular, there will be a Page 64 of 71
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discussion of what entices a consumer to choose a smart appliance over the regular appliance. Barriers to the smart appliance market will also be discussed based off the responses to the survey. The table below is an example of the type of analysis that will be conducted for each of the smart appliances. All numerical figures are for sake of example only and may turn out different in reality.
Appliance Electric Dryer Percentage of residents with this Electric Appliance 50% Average EOL (end of life) for the Electric Appliance [*] 8 years Average Cost of Smart Appliance ‐ present day $1100 Average Cost of Regular Electric Appliance [5] $ 800 Average Load Shift with the Smart Appliance 500 W Total hours during day Smart Appliance Used 5 hours/ day
* Researched source for EOL
The findings of our data will be extrapolated to the potential for the entire California market. Research will be conducted to determine the penetration of each electric appliance in the future and the market potential. From these results and research, an incentive rebate amount will be recommended in order to improve the market adoption curve (see figure below) and avoid the gaps that may occur during the “Early Adopters” and “Early Majority” phases.
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References
[1] HAN Eval SOW and Estimate_v1.1.docx
[2] Draft ETP assessment protocol 061610.docx
[3] REMA Phase II Project Plan. E. Martinez, Emerging Technologies, SDG&E.
[4] The California Energy Commission. http://www.energy.ca.gov/maps/renewable/Climate_Zones_Zipcode.pdf
[5] Samsung 7.4 cu. Ft. 11‐ Cycle Steam Electric Dryer http://www.bestbuy.com/site/Samsung+‐+7.4+Cu.+Ft.+11‐Cycle+Steam+Electric+Dryer+‐ +White/2363664.p?id=1218323020217&skuId=2363664&cmp=RMX&ref=06&loc=01&ci_src=14110944 &ci_sku=2363664
Appendix B: Customer Survey Results
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*Question misunderstood. Result disregarded.
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(end of document)
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