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Executive Summary

As part of an effort to compile an update of the Minnesota Technical Reference Manual (TRM), EnerNex was tasked with assisting Franklin Energy in researching available end use electric load data, determining the most appropriate reference data for Minnesota, developing an end use load profile for each end use researched and identifying areas where the data could be refined for Minnesota with further studies or surveys. EnerNex reviewed multiple existing load profiles, including from such sources as the Electric Power Research Institute (EPRI), and Itron, and created approximate end use estimates based on national averages and Census region data. In order to refine this output, Minnesota specific end use surveys should be conducted. What follows is an overview of the process that was used to create the end use load profiles with a discussion of available data, and recommendations for refining the Minnesota load profiles in subsequent iterations.

The load profile estimations were developed by referencing source data that estimated the percentage of commercial and residential customers that utilize electricity for the end use specified and then finding estimated average annual energy consumption for those end uses. The tables below summarize these two variables for each residential and commercial end use. In some instances, the % of customers utilizing a specified end use can be equal to or greater than 100%. When the “% of customers utilizing” number exceeds 100%, it indicates that an average customer has more than one of the device (as is the case with residential refrigerators and televisions).

Residential End Use % of Customers Utilizing Estimated annual kWh Per Customer

Water Heater 36 4857

Space Heating 15 822

Air Conditioning 64 641

Indoor Lighting 100 1230

Exterior Lighting 100 645

Refrigerator 127 596

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Cooking Equipment 67 1185

Clothes Dryer 60 769

Clothes Washer 79 590

Computers 92 236

Televisions 286 166

Dishwasher 78 206

Pool/Spa 3 2300

Table 1 Major Residential End Uses and End Use Energy Estimates

Commercial End Use % of Total Sales Estimated annual kWh Per Customer

Interior Lighting 28.7 22,813

Exterior Lighting 5.84 4,637

Cooling 15 11,835

Space Heating 9 7,221

Motors 4.2 3,328

Cooking Equipment 4.2 3,321

Refrigeration 14 10,831

Ventilation 12 9,628

Air Compressors 0.96 760

Office Equipment 6.1 4,821

Water Heating 1.52 1,207

Total 101.5 80,405

Table 2 Major Commercial End Uses and End Use Energy Estimates

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1 Process and Sources for Load Profiles

The analysis is built with publically available U.S. Energy Information Administration (EIA)1 customer and usage data on each utility operating in Minnesota. The Energy Information Administration maintains electricity sales data by sector, state and utility.2 This utility data was used to create approximate end use load profiles for each utility for the identified end uses.

The general process was to estimate the annual energy (kWh) for each end use that each residential or commercial customer would be expected to use. Those annual estimates were then scaled according to each utility’s customer count to estimate end use energy consumption throughout the year. The annual consumption information was then converted into end use load profiles utilizing the publically available End Use Load Shapes data from Electric Power Research Institute (EPRI)3. EnerNex then created a dashboard front end that combines all end uses in a cumulative graph with each commercial or residential end use with selection criteria including by utility, month, and EPRI identified season-and-day-type.

EPRI load shapes were condensed into two season types and four day types each consisting of twelve (12) two-hour blocks of energy use. The EPRI load shape tool extrapolates annual energy usage into estimated consumption values for a twenty-four (24) hour period for each season and day type. The season and day type definitions are defined by EPRI as follows4:

• Peak season: Months of May through September. o Peak weekday in the peak season: Ten hottest weekdays that are not holidays

two in each of the months for the peak season namely May through September.

o Average weekday/weekend in the peak season: all other weekdays/weekends in May through September.

o • Off-peak season: Months of October through April.

1 http://www.eia.gov/electricity/ 2 http://www.eia.gov/electricity/data.cfm#sales Residential (table 6) and commercial utility (table 7) data from forms EIA-861- schedules 4A & 4D and EIA-861S. 3 Electric Power Research Institute (EPRI) End Use Load Shapes data - http://loadshape.epri.com/enduse 4 EPRI Load Shape Library 2.0 – Help: http://loadshape.epri.com/help

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o Peak weekday in the off-peak season: Ten coldest (or hottest weekdays depending on region) that are not holidays, for the months of the off-peak season namely October through April.

o Average weekday/weekend in the off-peak season: all other weekdays/weekends in October through April.”

When available, the EPRI identified region of Mid-Continent Area Power Pool (MAPP) shown in Figure 1 was utilized for load profiles as MAPP includes the state of Minnesota.

Figure 1 Geographic Depiction of Pre-2006 NERC Control Regions5

1.1 Residential

1.1.1 Water Heater The percentage of residential customers with electric water heaters, based on Energy Information Administration census sector breakdowns, was estimated to be 36%.6 Water Heaters annual electricity consumption was estimated using the national average, an average of

5 http://loadshape.epri.com/help 6 http://www.eia.gov/consumption/residential/data/2009/#water Table HC8.9

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4857 kWh annually.7 EPRI load shapes for the water heater end use were then scaled to this estimate. The region used for Minnesota was “MAPP” as that represented the most appropriate granularity of available data.

Figure 2 Average Residential Customer Water Heater Load Profile

1.1.2 Air Conditioning The estimation for residential central air conditioning load was a little more in depth than the estimates for other end uses. EIA estimates that 64% of residential customers in Minnesota use electric air conditioning.8 Because air conditioning is so dependent upon outside air temperature, EnerNex compiled hourly outdoor air temperature readings from January 1, 2011 through November 1, 2015 from the Minneapolis-St Paul International Airport through the

7https://www.energystar.gov/ia/partners/prod_development/new_specs/downloads/water_heaters/Water_Heater_Market_Profile_2010.pdf?30e7-9a7d 8 http://www.eia.gov/consumption/residential/data/2009/#ac Table HC7.9 / Minnesota Specific

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National Climatic Data Center, a part of the National Oceanic and Atmospheric Administration.9 These readings were then averaged for each hour of each month.

The temperature data was then correlated with a Lawrence Berkeley National Laboratory study10, which estimates the amount of air conditioning load available for demand response per customer based on temperature as shown in Figure 3.

Figure 3 Hourly Average Air Conditioner Load for Residential SmartAC Customers by Daily

Maximum Temperature10

These hourly Air Conditioning kW estimates were then used to create hourly load profiles for residential customers based on the expected range of temperature at a given time of day as shown in Figure 4.

9 Data request made through http://gis.ncdc.noaa.gov/map/viewer/#app=cdo&cfg=cdo&theme=hourly&layers=1&node=gi 10 Measuring Short-term Air Conditioner Demand Reductions for Operations and Settlement, 2012: http://emp.lbl.gov/sites/all/files/lbnl-5330e.pdf

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Figure 4 Average Per Customer A/C Load For Average Hourly Temperatures each Month

To estimate peak and average days, the sum of the hourly load profile within each month was then used as input for the EPRI end use tool. The result is a month by month breakdown of air conditioner load with estimates for peak weekday, average weekday, and average weekend as shown in Figure 5.

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Figure 5 Average Residential Air Conditioning Load Profile

The total air conditioning load for each utility is then calculated by estimating that 64% of their customers likely have air conditioning and scaling the estimated air conditioning load for a single customer.

1.1.3 Space Heating 15% of Minnesota residential customers utilize electricity as the primary fuel source for their space heating needs.11 Of those customers, space heating accounted for 55% of their annual electricity consumption.12 EnerNex then found the average kWh per customer for MN

11 http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=characteristics#sh Table HC6.9, using line 33 12 http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=consumption#end-use Table ce3.3, using line 19 for space heating/total energy

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commercial customer. EPRI MAPP load shapes for the Heating end use were then scaled to this estimate.

Figure 6 Average Residential Space Heating Load Profile

1.1.4 Indoor Lighting Annual indoor lighting energy use for a residential customer was estimated by the U.S Department of Energy’s Energy Office of Efficiency and Renewable Energy (EERE) to be 1230 kWh.13 This figure is calculated by multiplying the midpoint of the range provided by the Department of Energy for Minnesota times the percentage of lighting that is not exterior. Every residential customer was estimated to have indoor lighting. EPRI load shapes for the Lighting end use utilizes “All” regions because the more specific MAPP region data is not available.

13 Residential Lighting End-Use Consumption Study: Estimation Framework and Initial Estimates, 2012: http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/2012_residential-lighting-study.pdf

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Figure 7 Average Residential Indoor Lighting Load Profile

1.1.5 Exterior Lighting Annual exterior lighting energy use was estimated to be 645.2 kWh.14 This figure is calculated by multiplying the midpoint of the range provided by the Department of Energy for Minnesota times the percentage of lighting that is exterior. Every residential customer was estimated to have exterior lighting. EPRI load shapes for the Lighting end use were then scaled to this estimate. EPRI load shape selected was commercial exterior because EPRI does not have a load shape estimate for a residential external lighting profile. Additionally, for the exterior lighting end use utilizes “All” regions because the more specific MAPP region data is not available.

14Residential Lighting End-Use Consumption Study: Estimation Framework and Initial Estimates, 2012: http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/2012_residential-lighting-study.pdf

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Figure 8 Average Residential Exterior Lighting Load Profile

1.1.6 Refrigerator The number of refrigerators per residential customer was estimated to be 1.27. This comes from the national installed base statistics from the Department of Energy in 2008.15 Refrigerator annual electricity consumption was estimated using the national average of 596 kWh.16 EPRI load shapes for the Refrigerator end use were then scaled to this estimate. EPRI load shapes for the Refrigerator end use utilizes “All” regions because the more specific MAPP region data is not available.

15 Refrigerator Market Profile, 2009: http://apps1.eere.energy.gov/states/pdfs/ref_market_profile.pdf 16http://www.energystar.gov/sites/default/files/assets/images/ES_Appliance_Energy_Use_pie_graph_8%2022%2014-large.jpg

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Figure 9 Average Residential Refrigerator Load Profile

1.1.7 Residential Cooking Equipment It was estimated that 67% of residential cooking equipment in Minnesota is powered by electricity. 17 This was estimated utilizing the EIA Table HC3.9 Appliances in Homes in Midwest Region, Divisions, and States, 200918. The average annual electricity consumption of cooking equipment was found by adding the oven, stovetop and microwave oven annual averages from across the US.19 Next, the Itron eShape 8760 data for Minnesota were scaled to the annual averages compiled by EIA.20 eShapes were scaled because they are from 2001. Next, the

17 http://www.eia.gov/consumption/residential/data/2009/#fueluses Table HC1.9 18 Table HC3.9 Appliances in Homes in Midwest Region, Divisions, and States, 2009: http://www.eia.gov/consumption/residential/data/2009/#appliances 19 http://www.eia.gov/consumption/residential/data/2001/index.cfm?view=consumption Table 2 20 How do we cite eShapes?

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eShapes were averaged across all residential types and Minnesota regions to create a single chart containing the averages of every hour from all 12 months.

Figure 10 Average Residential Cooking Load Profile

1.1.8 Clothes Dryer The percentage of residential customers with clothes dryers was estimated to be 80%.21 Of the customers with clothes dryers, 75% were estimated to be electric.22 Clothes dryer annual electricity consumption was estimated using the national average, an average of 769 kWh

21 http://www.energystar.gov/products/appliances/clothes_dryers 22 http://www.nrdc.org/energy/efficient-clothes-dryers.asp

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annually.23 EPRI MAPP load shapes for the clothes dryer end use were then scaled to this estimate.

Figure 11 Average Residential Clothes Dryer Load Profile

1.1.9 Clothes Washer The number of electric clothes washers per residential customer was estimated to be .79. This comes from the national average reported by the Environmental Protection Agency (EPA).24 Clothes washer annual electricity consumption was estimated using the national average, an

23http://www.energystar.gov/sites/default/files/assets/images/ES_Appliance_Energy_Use_pie_graph_8%2022%2014-large.jpg 24 https://www.energystar.gov/ia/partners/reps/pt_reps_res_retail/files/CW_ProductSnapshot_May08.pdf

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average of 590 kWh annually.25 EPRI MAPP load shapes for the clothes washer end use were then scaled to this estimate.

Figure 12 Average Residential Clothes Washer Load Profile

1.1.10 Computers EnerNex excluded smartphones and tablets in defining “computers.” The percentage of residential customers with personal computers, based on 2013 national Census data, was estimated to be 92%.26 In the high turnover world of high tech, it is difficult to determine a precise annual electricity consumption. Computers draw different load based on hours used, and

25http://www.energystar.gov/sites/default/files/assets/images/ES_Appliance_Energy_Use_pie_graph_8%2022%2014-large.jpg 26 https://www.census.gov/history/pdf/acs-internet2013.pdf

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computing power, with the best estimate for annual electricity use at 236 kWh.27 EPRI load shapes for the PC and Computer end use were then scaled to this estimate. EPRI load shapes for the computer end use utilizes “All” regions because the more specific MAPP region data is not available.

Figure 13 Average Residential Computers Load Profile

1.1.11 Televisions The number of televisions per residential customer was estimated to be 2.86. This comes from the national average reported by Nielson.28 Television annual electricity consumption was estimated using the national average, an average of 166kWh annually. 29 EPRI load shapes for

27 http://www.savewithsrp.com/advice/ask/computers.aspx 28 http://www.bloomberg.com/bw/articles/2012-03-22/will-more-britons-buy-dishwashers 29 http://www.ce.org/CorporateSite/media/environment/Energy-Consumption-of-Consumer-Electronics.pdf

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the TVandPC end use were then scaled to this estimate. EPRI load shapes for the television end use utilizes “All” regions because the more specific MAPP region data is not available.

Figure 14 Average Residential TV Load Profile

1.1.12 Dishwasher The number of dishwashers per residential customer was estimated to be .78. This comes from the national average reported by Bloomberg Businessweek.30 Dishwasher annual electricity consumption was estimated using the national average, an average of 206kWh annually.31 EPRI MAPP load shapes for the Dishwasher end use were then scaled to this estimate.

30 http://www.bloomberg.com/bw/articles/2012-03-22/will-more-britons-buy-dishwashers 31http://www.energystar.gov/sites/default/files/assets/images/ES_Appliance_Energy_Use_pie_graph_8%2022%2014-large.jpg

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Figure 15 Average Residential Dishwasher Load Profile

1.1.13 Pool Spa It was estimated that 3% of residential customers in Minnesota own or operate a pool, spa or hot tub. 32 This was found by dividing the number of homes with Pool/Hot Tub/Spa Heater by the total number of homes. Next, the average annual electricity consumption of the Pool/Hot Tub/Spa Heater was estimated to be 2300 kWh from the same table. 33 Next, the Itron eShape 8760 data for Minnesota were scaled to the annual averages compiled by EIA.34 eShapes were scaled because they are from 2001. Next, the eShapes were averaged across all residential types

32 http://www.eia.gov/consumption/residential/data/2001/index.cfm?view=consumption Table 2 33 http://www.eia.gov/consumption/residential/data/2001/index.cfm?view=consumption Table 2 34 How do we cite eShapes?

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and Minnesota regions to create a single chart containing the averages of every hour from all 12 months.

Figure 16 Average Residential Pool and Spa Load Profile

1.2 Commercial and Industrial

1.2.1 Interior Lighting Interior Lighting for the commercial sector was estimated to be 28.72% of total commercial electricity sales. This was found utilizing the California Commercial End-Use Survey.35 EnerNex

35 California Commercial End-Use Survey, April 17, 2007: http://capabilities.itron.com/CeusWeb/ChartsSF/Default2.aspx

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then found the average kWh per customer for MN commercial customer, assuming that every commercial customer had interior lighting. EPRI MAPP load shapes for the Lighting Internal end use were then scaled to this estimate.

Figure 17 Average Commercial Interior Lighting Load Profile

1.2.2 Cooling (A/C) The estimation for commercial air conditioning load was performed similarly to the residential air conditioning estimate. EnerNex estimated that Cooling (A/C) represented about 15% of commercial end use electricity consumption based on the California Commercial End Use Survey.36 Because air conditioning is so dependent upon outside air temperature, EnerNex compiled hourly outdoor air temperature readings from January 1, 2011 through November 1,

36 California Commercial End-Use Survey, April 17, 2007: http://capabilities.itron.com/CeusWeb/ChartsSF/Default2.aspx

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2015. Outdoor air temperature readings over a the nearly four year period were gathered from the Minneapolis-St Paul International Airport through the National Climatic Data Center, a part of the National Oceanic and Atmospheric Administration.37 These readings were then averaged for each hour of each month.

The hourly air conditioning kW estimates were then used to create hourly load profiles for commercial customers. Then, to estimate peak and average days, the sum of the hourly load profile within each month was used as input for the EPRI end use tool. The result is a month by month breakdown of air conditioner load with estimates for peak weekday, average weekday, and average weekend. The front end simply multiplies the number of utility the kW load profile estimate.

Figure 18 Average Commercial AC Load Profile

37 http://gis.ncdc.noaa.gov/map/viewer/#app=cdo&cfg=cdo&theme=hourly&layers=1&node=gi

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1.2.3 Space Heating Commercial space heating was estimated to be 9% of total commercial electricity sales.38 EnerNex then found the average kWh per customer for MN commercial customer. EPRI MAPP load shapes for the Heating end use were then scaled to this estimate.

Figure 19 Average Commercial Space Heating Load Profile

1.2.4 Motors Motors and motor drives were estimated to be 4.2% of total commercial electricity sales based on the Itron California End Use Survey.39 The 8760 data was then scaled to the average

38http://www.eia.gov/consumption/commercial/data/archive/cbecs/cbecs2003/detailed_tables_2003/detailed_tables_2003.html#enduse03 Table E5A - Utilizing Row 55 for Appropriate Census Region 39California Commercial End-Use Survey, Limited Statewide-All Commercial Workbook, April 17, 2007: http://capabilities.itron.com/CeusWeb/ChartsSF/Default2.aspx

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Minnesota customer. A table was then developed containing the Minnesota averages of every hour from all 12 months.

Figure 20 Average Commercial Motors Load Profile

1.2.5 Cooking Equipment Commercial cooking equipment was estimated to be 4.2% of total commercial electricity sales based on the Itron California End Use Survey.40 The 8760 data was then scaled to the average Minnesota customer. A table was then developed containing the Minnesota averages of every hour from all 12 months.

40California Commercial End-Use Survey, Limited Statewide-All Commercial Workbook, April 17, 2007: http://capabilities.itron.com/CeusWeb/ChartsSF/Default2.aspx

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Figure 21 Average Cooking Equipment Load Profile

1.2.6 Refrigeration Refrigeration for the commercial sector was estimated to be 14% of total commercial electricity sales.41 EnerNex then found the average kWh per customer for MN commercial customer. EPRI MAPP load shapes for the Refrigeration end use were then scaled to this estimate.

41http://www.eia.gov/consumption/commercial/data/archive/cbecs/cbecs2003/detailed_tables_2003/detailed_tables_2003.html#enduse03 Table E5A - Utilizing Row 55 for Appropriate Census Region

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Figure 22 Average Commercial Refrigeration Load Profile

1.2.7 Ventilation Ventilation for the commercial sector was estimated to be 12% of total commercial electricity sales.42 EnerNex then found the average kWh per customer for MN commercial customer. EPRI MAPP load shapes for the Ventilation end use were then scaled to this estimate.

42http://www.eia.gov/consumption/commercial/data/archive/cbecs/cbecs2003/detailed_tables_2003/detailed_tables_2003.html#enduse03 Table E5A - Utilizing Row 55 for Appropriate Census Region

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Figure 23 Average Commercial Ventilation Load Profile

1.2.8 Air Compressors Air Compressors were estimated to be 0.96% of total commercial electricity sales based on the Itron California End Use Survey.43 The 8760 data was then scaled to the average Minnesota customer. A table was then developed containing the Minnesota averages of every hour from all 12 months.

43California Commercial End-Use Survey, Limited Statewide-All Commercial Workbook, April 17, 2007:http://capabilities.itron.com/CeusWeb/ChartsSF/Default2.aspx

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Figure 24 Average Commercial Air Compressors Load Profile

1.2.9 Office Equipment Office Equipment for the commercial sector was estimated to be 6.1% of total commercial electricity sales.44 EnerNex then found the average kWh per customer for MN commercial customer. EPRI MAPP load shapes for the Office Equipment end use were then scaled to this estimate.

44http://www.eia.gov/consumption/commercial/data/archive/cbecs/cbecs2003/detailed_tables_2003/detailed_tables_2003.html#enduse03 Table E5A - Utilizing Row 55 for Appropriate Census Region

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Figure 25 Average Commercial Office Equipment Load Profile

1.2.10 Exterior Lighting Exterior Lighting for the commercial sector was estimated to be 5.84% of total commercial electricity sales. This was found utilizing the California Commercial End-Use Survey.45 EnerNex then found the average kWh per customer for MN commercial customer. EPRI load shapes for the Lighting External end use were then scaled to this estimate. EPRI load shapes for the exterior lighting end use utilizes “All” regions because the more specific MAPP region data is not available.

45 California Commercial End-Use Survey, April 17, 2007: http://capabilities.itron.com/CeusWeb/ChartsSF/Default2.aspx

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Figure 26 Average Commercial Exterior Lighting Load Profile

1.2.11 Water Heating Water heating for the commercial sector was estimated to be 1.52% of total commercial electricity sales.46 EnerNex then found the average kWh per customer for MN commercial customer. EPRI MAPP load shapes for the Water Heater end use were then scaled to this estimate.

46http://www.eia.gov/consumption/commercial/data/archive/cbecs/cbecs2003/detailed_tables_2003/detailed_tables_2003.html#enduse03 Table E5A - Utilizing Row 55 for Appropriate Census Region

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Figure 27 Average Commercial Water Heater Load Profile

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2 Identifying Gaps

In the absence of Minnesota specific data, regional and national averages were substituted. In the cases of Commercial air compressors, commercial cooking equipment, and commercial motors, the model was based on California data as that was the only data available at the granularity necessary to provide specific end use estimates. Minnesota is likely to have regional variation when compared to both EIA national averages, the California Commercial End-Use Survey and the EPRI load profiles for all regions as well as the MAPP region. A Minnesota specific end use survey would refine the estimates with more regionally specific data at the foundation.

Because of Minnesota’s cold climate, the estimates for both air conditioning and heating are primary candidates for Minnesota-specific refinement. While an effort was made to refine the air conditioning load estimate based on average temperatures for each hour of each month, the base data was developed for California demand response potential, not load profile estimates.

Water heating is another significant end use that would benefit from a Minnesota specific survey. The load profile for commercial water heating uses Minnesota’s census region estimates, but the residential assumes a water heating national average.

Because of Minnesota’s northern latitude, lighting loads may be shifted due to different daylight hours. Lighting estimates were difficult to classify, and estimates for commercial lighting varied greatly. The 2003 Commercial Buildings Energy Consumption Survey (CBECS) seemed to indicate that 38% of commercial building load was due to lighting.47 Meanwhile, the EIA states that about 19% of commercial sector electricity consumption in 2014 was due to lighting. 48 Other sources separately identified outdoor lighting and compared survey results.49 Minnesota specific lighting load profiles and estimates for commercial, residential, and public street/highway lighting would provide needed refinement to the lighting profiles.

Recent computer, office equipment and television annual kWh estimates were difficult to find and continue to change as devices get more energy efficient but also diversify and proliferate with smart phones, tablets and other plug load “smart” devices. One industry estimate states that the computer and data center market represents 2% of the electricity consumption in the

47http://www.eia.gov/consumption/commercial/data/archive/cbecs/cbecs2003/detailed_tables_2003/detailed_tables_2003.html#enduse03 48 http://www.eia.gov/tools/faqs/faq.cfm?id=99&t=3 49 http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/2010-lmc-final-jan-2012.pdf Table 6.1

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US, with a compound annual growth rate between 6-8%.50 Given the growth in that industry, finding accurate electricity consumption estimates is difficult.

2.1 Focusing on Customer Demand A recent national trend has been for the power (kW) and delivery/capacity (distribution) component of a customer’s electricity bill continues to be the cost driver in raising electricity costs while the energy (kWh) is relatively stable. This is due to the cost of upgrading an aging infrastructure as well as the distribution and generation capacity needed to meet resource adequacy requirements for peak demand.

According to the U.S. Energy Information Administration’s 2015 Annual Energy Report51

“In the AEO2015 Reference case, increasing costs of electric power generation and transmission and distribution, coupled with relatively slow growth of electricity sales (averaging 0.7%/year), result in an 18% increase in the average retail price of electricity (in real 2013 dollars) over the projection period.”

In part, this conclusion reflects the success of Energy Efficiency efforts which have been supported with government programs such as the Environmental Protection Agency (EPA) Energy Star® program as well as utility programs to promote consumer purchase of energy efficient devices. However, the EE programs have not addressed the peak demand issue related to coincident load. Because the distribution and capacity (kW) component (rather than the energy (kWh) component) is becoming the cost driver for utility electricity procurement, cost reduction efforts should expand from not only focusing on Energy Efficiency goals for kWh, but also demand savings goals for kW. Expanding focus to include the peak kW demand will help to: 1) ensure that the capacity of current delivery infrastructure is sufficient to meet demand while utility infrastructure upgrade efforts continue; and 2) avoid construction of “peaker” generation stations and firing up older generation units to meet peak demand approximately 12 days per year (less than 3% power factor utilization).

2.1.1 Managing Demand Some categories of commercial and industrial electricity customers have a demand component to their bill. However, many of these customers do not understand how to monitor or manage

50 Jonathan Koomey. 2011. Growth in data center electricity use 2005 to 2010. Oakland, CA: Analytics Press. July. 51 U.S. Energy Information Administration’s 2015 Annual Energy Report http://www.eia.gov/forecasts/aeo/

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their demand to reduce their coincident load and reduce demand charges. Therefore, the first step is to work with Minnesota utilities to help customers better understand any applicable demand charges and communicate demand reduction strategies for managing and reducing peak demand usage.

2.1.2 Demand Response Many areas of the country are now utilizing Demand Response (DR) programs to interact with customers to request demand reduction during specific days that are anticipated to be peak days of the year or to reduce demand when generation supply costs are expensive or there are infrastructure or capacity issues in the distribution system that could affect delivery reliability:

• Reliability DR Programs are designed to request customer load reduction in response to system conditions in order to minimize the potential for broader consumer impacts. These programs are dispatched when distribution capacity or generation supply is constrained. Reliability DR Programs require communication to participating custimers in order to trigger load reductions either automatically by interfacing with customer systems or through manual interaction by the customer.

• Dynamic Pricing based DR programs attempt to identify the peak times of the day and incentivize customers to curtail usage during those periods through economic signals. For example, Time-of-Use (TOU) rates take into account that electricity supply is more expensive during times of peak demand and are structured to generally align the retail price with the average cost of procurement for those periods as illustrated in Figure 22.

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Figure 28 Time of Use Pricing to align procurement cost and retail price

• Dynamic Pricing can also attempt to identify the peak days of the year and incentivize customers to further curtail usage during those days. Critical Peak Pricing (CPP) or Peak Time Rebate (PTR) rates can build upon the TOU concept with either additional cost for energy usage on the peak days of the year (CPP) or providing rebates for curtailing usage during the peak days of the year.

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Figure 29 Critical Peak Pricing differential with TOU for peak days of the year

• As an alternative to rate based CPP or PTR structures, optional DR programs can offer customers incentives for demand reductions on peak days.

• Increasingly, DR programs are striving to align wholesale electricity costs with DR resource dispatches. Aggregated or large DR resources can now be bid into Independent System Operator (ISO) markets52. DR resources can bid into the market as an alternative to generation and DR resource dispatch is triggered by market prices & conditions. However, there is some uncertainty related to the market rules for DR as detailed in FERC orders 71953 (Wholesale Competition in Regions with Organized Electric Markets, 2008) and 74554 (Demand Response Compensation in Organized Wholesale Energy Markets, 2011). The uncertainty is due to the legal situation regarding the FERC Order 745 when

52 Midwest Independent System Operator (MISO) Demand Response Strategic Initiative: https://www.misoenergy.org/WHATWEDO/STRATEGICINITIATIVES/Pages/DemandResponse.aspx 53 FERC, Order 719, October 2008; http://www.ferc.gov/whats-new/comm-meet/2008/101608/E-1.pdf 54 FERC, Order 745, March 2011; http://www.ferc.gov/EventCalendar/Files/20110315105757-RM10-17-000.pdf

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the DC Circuit Court of Appeals vacating the rule55 on May 23, 2014. FERC appealed to the Supreme Court and Oral Arguments56 occurred on October 14, 2015.

DR programs have been implemented in a variety of ways. Price response programs utilizing rates and tariffs are obviously coordinated by electric utilities. However, implementing customer strategies to respond to price response, economic DR or reliability DR programs can also be implemented either by the utility or by third party suppliers known as DR providers (aka Curtailment Service Providers (CSP) or DR Aggregators).

Minnesota has several sources of DR potential. For example, technologies such as Grid Interactive Hot Water Heaters or interfacing with customers’ Building Energy Management Systems can facilitate customer participation in optional DR programs or assist customers that are on dynamic rate structures.

2.1.2.1 Residential Hot Water

Other regions of the country have had significant success with utilizing electric hot water heaters for DR programs. An example is rural electric cooperatives’ adoption of grid interactive water heaters.57 More than 250 electric cooperatives in 34 states have a significant amount of customers participating in DR programs (often combined with permanent load shifting) that center on cycling of large-capacity electric water heating equipment. Such programs have allowed some electric coops to levelize their load and avoid significant changes in peak and off-peak usage.

However, electric water heaters also highlight the tradeoffs between EE and DR. Therefore, these programs were threatened by efficiency standards adopted in 201058 that limited the capacity of electric water heaters to less than 55 gallons. This prompted utilities with the larger-

55 United States Court of Appeals For The District Of Columbia Circuit No. 11-1486, May 23, 2014 Electric Power Supply Association, Petitioner vs. Federal Energy Regulatory Commission, Respondent and Madison Gas And Electric Company, Et Al., Intervenors (Consolidated with 11-1489, 12-1088, 12-1091, 12-1093) http://www.cadc.uscourts.gov/internet/opinions.nsf/DE531DBFA7DE1ABE85257CE1004F4C53/$file/11-1486-1494281.pdf 56 http://www.supremecourt.gov/oral_arguments/argument_transcripts/14-840_p86a.pdf 57 Delaware County Electric Cooperative DR Capability and Predictability, November, 2013: http://www.nreca.coop/wp-content/uploads/2014/01/NRECA_DOE_DCEC_Demand-Response_a.pdf 58 DOE Appliance and Equipment Standard Program, http://www1.eere.energy.gov/buildings/appliance_standards/residential/pdfs/htgproducts_finalrule_notice.pdf

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capacity water heater DR programs to engage with the Department of Energy (DOE)59 to discuss alternatives discussions that resulted in a proposed rule60 that provides waivers to large-capacity water heaters equipped with grid-interactive capabilities.

With an estimated 1% of commercial electricity consumption and 36% of residential electricity consumption attributed to water heating, Minnesota has the potential to implement demand side management or demand response utilizing DR programs grid interactive water heaters.

2.1.2.2 Commercial Building Automation Systems

Another potential source of both demand management and DR is the Building {Energy} Management Systems (BMS or {BEMS}) for commercial buildings. These systems are capable of controlling building loads such as HVAC, lighting and more. The systems are also capable of managing building load to remain beneath a threshold demand as defined by the building occupant or customer. Recent industry studies highlight the adoption of BMS systems:

All told, the U.S. building automation systems market will reach a projected $1.65 billion by year-end, up 5 percent from $1.57 billion in 2012. Solid growth ranging from 7 to 9 percent will follow in the next four years, with industry revenue forecast to hit $2.24 billion by 2017, equivalent to a 43 percent increase from 2012...61

Given the increasing importance of energy savings in the small and medium commercial building (SMCB) market, BEMSs are poised to be a tool that enables cost and carbon savings in small and medium buildings. These systems have traditionally focused on two key functions – energy visualization and energy analytics. Today, the market is expanding into adjacent functions, such as demand response (DR) and property management, broadening the capabilities of next-generation BEMSs and engaging a broader set of

59 DOE Rulemaking for Residential Water Heater Standards Waiver Process, http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/63 60 2013-02-26 Energy Conservation Program for Consumer Products: Energy Conservation Standards for Residential Water Heaters; Notice of proposed rulemaking, http://www.regulations.gov/#!documentDetail;D=EERE-2012-BT-STD-0022-0158 61 Sensors Online - US Building Automation Market Primed for Growth, November 12, 2013: http://www.sensorsmag.com/specialty-markets/building-management/news/us-building-automation-market-primed-growth-12071

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players in the global buildings market. Navigant Research forecasts that the global SMCB BEMS market will grow from $231.3 million in 2013 to $1.3 billion in 2022.62

To utilize the install base of PMS that already exist as well as the market growth and proliferation of these systems, customer collaboration and education can assist commercial customers utilize their BMS to optimize their energy utilization and building management to align with their applicable rate structures, to reduce their overall electricity bills and/or attempt to reduce consumption during peak demand to help lower utility procurement costs.

62 Navigant Research, Energy Management for Small and Medium Buildings - Building Energy Management System Software, Services, and Hardware Global Market Analysis and Forecasts, Q2 2014: http://www.navigantresearch.com/research/energy-management-for-small-and-medium-buildings

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3 Source Data

Residential

• Residential figures: Data from forms EIA-861- schedules 4A & 4D and EIA-861S. o http://www.eia.gov/electricity/data.cfm#sales

• Percent of res customers with Central Air Conditioning in IA, MN, ND, and SD from Table HC7.9

o http://www.eia.gov/consumption/residential/data/2009/ • Number of TVs per household in US

o http://www.nielsen.com/us/en/insights/news/2009/more-than-half-the-homes-in-us-have-three-or-more-tvs.html

• Annual electricity use per TV (Figure ES-3) o http://www.ce.org/CorporateSite/media/environment/Energy-Consumption-of-

Consumer-Electronics.pdf • Percent of US households with dishwasher

o http://www.bloomberg.com/bw/articles/2012-03-22/will-more-britons-buy-dishwashers

• Annual energy use of dishwasher, clothes washer, clothes dryer, refrigerator in US o http://www.energystar.gov/sites/default/files/assets/images/ES_Appliance_Ener

gy_Use_pie_graph_8%2022%2014-large.jpg • Number of refrigerators per household, US

o http://apps1.eere.energy.gov/states/pdfs/ref_market_profile.pdf • Percent of US households with clothes washer

o https://www.energystar.gov/ia/partners/reps/pt_reps_res_retail/files/CW_ProductSnapshot_May08.pdf

• Percent of US households with clothes dryer o http://www.energystar.gov/products/appliances/clothes_dryers

• Percent of US electric residential clothes dryers in the US o http://www.nrdc.org/energy/efficient-clothes-dryers.asp

• Average annual electricity use of residential lighting in Minnesota, indoor and Exterior (Utilizes Figure ES.4. and Table 4.4)

o http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/2012_residential-lighting-study.pdf

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• Percent of res customers with electric water heaters in IA, MN, ND, and SD from Table HC8.9

o http://www.eia.gov/consumption/residential/data/2009/#water • Federal Standard for Annual kWh consumption by electric water heaters

o https://www.energystar.gov/ia/partners/prod_development/new_specs/downloads/water_heaters/Water_Heater_Market_Profile_2010.pdf?30e7-9a7d

• Number of households with a personal computer (From 2013; Does not include smartphones)

o https://www.census.gov/history/pdf/acs-internet2013.pdf • Average annual computer electricity usage

o http://www.savewithsrp.com/advice/ask/computers.aspx • Average annual pool/spa/heater electricity usage, Percent of households with pool/hot

tub, Annual cooking equipment kWh averages o http://www.eia.gov/consumption/residential/data/2001/index.cfm?view=consu

mption • Percent of MN Households that use electric cooking equipment

o http://www.eia.gov/consumption/residential/data/2009/#fueluses Table HC1.9

Commercial

• Commercial figures: Data from forms EIA-861- schedules 4A & 4D and EIA-861S. o http://www.eia.gov/electricity/data.cfm#sales Table 7

• Lighting electricity use in commercial sector across the US, 2014 o http://www.eia.gov/tools/faqs/faq.cfm?id=99&t=3

• Annual Lighting electricity use per commercial building (Table 5.1) across US o http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/2010-lmc-final-jan-

2012.pdf • Annual water heating, office equipment, refrigeration electricity estimates, by Midwest

West Census division o http://www.eia.gov/consumption/commercial/data/archive/cbecs/cbecs2003/de

tailed_tables_2003/detailed_tables_2003.html#enduse03 Table E5A • Percent of Annual Commercial Electricity consumption by air compressors

o California Commercial End-Use Survey, April 17, 2007: http://capabilities.itron.com/CeusWeb/ChartsSF/Default2.aspx (Download "Limited Statewide - All Commercial" Workbook)

• Load shape curve of air compressors

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o California Commercial End-Use Survey, April 17, 2007: http://capabilities.itron.com/CeusWeb/ChartsSF/Default2.aspx (Download "Limited Statewide - All Commercial" Workbook)