This document describes the load composition methodology for estimating the load on a feeder. This methodology is important to identifying the response of the feeder to voltage changes.

The methodology is implemented Load composition.xls spreadsheet locating in the Load Composition download. If you want to load different TMY data files, you will also need to extract the TMY files from the TMY folders using the Load TMY Data button on the Conditions worksheet.

# Aggregation method

Load composition is the term used to describe the breakdown of end-use load according to the nature of the load. The current load composition breakdown is as follows.

1. Electronic ${\displaystyle P_{E}}$ loads are those that use power electronics. These loads typically have constant power requirements, but often have adverse harmonic characteristics that make them appear to have poor power factors.
2. Motor A ${\displaystyle P_{A}}$ are three-phase induction motors that drive constant torque loads, such as industrial and commercial compressors and refrigerators.
3. Motor B ${\displaystyle P_{B}}$ are three-phase induction motors that drive speed-squared loads with high inertia, such as fans.
4. Motor C ${\displaystyle P_{C}}$ are three-phase induction motors that drive speed-squared loads with low inertia, such as pumps.
5. Motor D ${\displaystyle P_{D}}$ are single-phase induction motors that drive constant torque load such as residential A/C compressors, refrigerators and heat-pumps.
6. ZIP ${\displaystyle I_{p}}$ is the real part of constant current loads.
7. ZIP ${\displaystyle I_{q}}$ is the reactive part of the constant current loads.
8. ZIP ${\displaystyle P_{p}}$ is the real part of constant power loads (often denoted as P).
9. ZIP ${\displaystyle P_{q}}$ is the reactive part of constant power loads (often denoted as Q).
10. ZIP ${\displaystyle Z_{p}}$ is the resistance part of constant impedance loads (often denoted as G)
11. ZIP ${\displaystyle Z_{q}}$ is the reactance part of constant impedance loads (often denoted as B)

Each load type has a contribution from residential, commercial, industrial and agricultural components. The residential includes single-family homes and multi-family buildings. Commercial loads include small and large offices, small and large retail, hotels, and motels. Industrial and agricultural loads are idiosyncratic and are not modeled in detail.

Taken together the combined power factor of the constant loads is

${\displaystyle PF={\begin{cases}P_{q}+I_{q}+Z_{q}=0&1\\P_{q}+I_{q}+Z_{q}>0&{\frac {P_{p}+I_{p}+Z_{p}}{\sqrt {(P_{p}+I_{p}+Z_{p})^{2}+(P_{q}+I_{q}+Z_{q})^{2}}}}\\P_{q}+I_{q}+Z_{q}<0&-{\frac {P_{p}+I_{p}+Z_{p}}{\sqrt {(P_{p}+I_{p}+Z_{p})^{2}+(P_{q}+I_{q}+Z_{q})^{2}}}}\end{cases}}}$

The total power (magnitude) ${\displaystyle P_{total}}$ of the composite load is

${\displaystyle P_{total}\approx |P_{E}|+|P_{A}|+|P_{B}|+|P_{C}|+|P_{D}|+{\sqrt {(P_{p}+I_{p}+Z_{p})^{2}+(P_{q}+I_{q}+Z_{q})^{2}}}}$

# Climate

TMY2 climate data [1] is used to determine the weather conditions for any particular month, day of week, and hour of day. In the TMY2 file, the following data is used

1. Dry Bulb Temperature [1/10 °C], which is converted to °F;
2. Wind speed [tenths m/s], which is converted to miles/hour;
3. Relative humidity [%];
4. Diffuse Horizontal Radiation [Wh/m^2], which is converted to Btu/sf.h; and
5. Direct normal Radiation [Wh/m^2], which is converted to Btu/sf.h.

In addition, the following data is either extracted from the TMY2 data or looked up elsewhere

1. Latitude, which is obtained using the city;
2. Heating design temperature [F], which is the minimum TMY2 observation;
3. Cooling design temperature [F], which is the maximum TMY2 observation; and
4. Peak solar radiation [Btu/sf.h], which is the maximum direct normal observation.

Single and multi family dwellings are represented by typical loads, which are used to characterize a population of homes on a feeder. If multiple characteristics are needed, multiple models must be used and the results summed before computing the composite load. The load contribution to the feeder is always multiplied by the number of dwellings having those characteristics.

## Single-family dwellings

The basic characteristics of single-family residences are shown in Table 1.

Table 1 - Single-family residential model
Parameter Unit Default Description
Floor area sf 2200 The total conditioned floor area of the dwelling
Building height ft 10 The exterior wall height
Wall area sf ${\displaystyle 3{\sqrt {2Floorarea}}Buildingheight=}$ 1990 Exterior wall area
Wall R-value °F.h/Btu.sf 19 From local building code
Roof R-value °F.h/Btu.sf 60 From local building code
Window R-value °F.h/Btu.sf 3.5 From local building code
Window-wall ratio % 15% From local building code
Ventilation rate puV/h ${\displaystyle {\dot {V}}_{thermal}+{\dot {V}}_{wind}}$ (see Notes 1 & 2) Typically between 0.5 and 5 air-changes per hour
Balance temperature °F ${\displaystyle T_{setpoint}-{\frac {Heatgains}{UA}}}$ Temperature at which neither heating nor cooling is required
Heating design temperature °F ${\displaystyle T_{min}\,\!}$ Usually set to the lowest dry-bulb temperature in the TMY data, but can be adjusted to oversize equipment or account for TMY's lack of extremes
Cooling design temperature °F ${\displaystyle T_{max}\,\!}$ Usually set to the highest dry-bulb temperature in the TMY data, but can be adjusted to oversize equipment or account for TMY's lack of extremes
Heating capacity Btu/h ${\displaystyle UA\left(T_{setpoint}-T_{heatingdesign}\right)}$ Oversizing of heating can also be done here
Cooling capacity Btu/h ${\displaystyle UA\left(T_{setpoint}-T_{coolingdesign}\right)+Q_{peaksolar}}$ (see Note 3) Oversizing of cooling can also be done here
Thermostat setpoint °F 72 Typically between 68°F and 72°F in the winter and between 72°F and 78°F in summer
Building UA Btu/°F.h ${\displaystyle H_{vent}+UA_{wall}+UA_{window}+UA_{roof}\,\!}$ (see Notes 4,5,6,7) Typically around 500 Btu/°F.h
Internal heat gains Btu/h ${\displaystyle \sum _{x=enduse}{r_{x}Q_{x}}\,\!}$ ${\displaystyle r_{x}\,\!}$ is the fraction of heat from all fuel sources that goes to indoor air
External shading % 20% Fraction of solar radiation that is blocked by external shading (tree, overhangs, etc.)
Solar gains Btu/h ${\displaystyle Directnormal\times Solarexposure}$ (see Note 8) Directnormal include atmospheric losses (clouds, haze, etc.)
Latent load Btu/h ${\displaystyle 0.3{\frac {Internalheatgains+Solargains}{1+e^{4-10RH}}}}$ Latent gains account for humidity effect on cooling coils
Heating duty cycle % ${\displaystyle \left[0,{\frac {T_{balance}-T_{out}}{T_{balance}-T_{heat}}},1\right]}$ This is the diversified heating duty cycle
Cooling duty cycle % ${\displaystyle \left[0,{\frac {T_{out}-T_{balance}}{T_{cool}-T_{balance}}},1\right]}$ This is the diversified cooling duty cycle
Notes
1. ${\displaystyle {\dot {V}}_{thermal}\approx 1.877Floorarea{\sqrt {Buildingeight|T_{in}-T_{out}|/T_{in}}}/Airvolume}$ in puV/h
2. ${\displaystyle {\dot {V}}_{wind}\approx Floorarea\times Windspeed\times 10^{-5}}$ in puV/h
3. ${\displaystyle Q_{peaksolar}=Peaksolar\times Windowarea\times Shading\times Exposurefraction/8}$ in Btu/h with ${\displaystyle Exposurefraction={\begin{cases}Solarelevation>0&:{\sqrt {2}}\cos(Solarelevation)\sin(Solarelevation)\\Solarelevation\leq 0&:0\end{cases}}}$
4. ${\displaystyle H_{vent}=0.182Ventilationrate\times Airvolume}$
5. ${\displaystyle UA_{wall}=Wallarea(1-Windowwallratio)/Wallrvalue\,\!}$
6. ${\displaystyle UA_{roof}=Flooarea/Roofrvalue\,\!}$
7. ${\displaystyle UA_{window}=Wallarea\times Windowallratio\times Windowrvalue}$
8. ${\displaystyle Solarexposure=Windowarea(1-Externalshading)Exposurefraction/8\,\!}$

The end-use electricity is used to determine what fraction of the end-use load ends up as electric load, as shown in Table 2.

Table 2 - Single-family residential end-use electrification
End-use Default
Resistive heating 20%
Heat-pump 40%
Hotwater 50%
Cooking 50%
Clothesdrying 50%

The system efficiency is used to determine that operating load of the end-uses, as shown in Table 3.

Table 3 - Single-family residential end-use efficiency
End-use Default
Heating efficiency 4.6 (COP)
Cooling efficiency 10.0 (SEER)

The installed capacity is used to determine the total end-use load capacity per unit floor area, as shown in Table 4.

Table 4 - Single-family residential end-use installed capacity (W/sf)
End-use Default
Cooking 3.00
Hotwater 2.50
Lighting 1.00
Plugs 1.50
Washing 2.50
Heating ${\displaystyle {\frac {Heatingcapacity}{3.412Floorarea\times Heatingefficiency}}}$
Cooling ${\displaystyle {\frac {Coolingcapacity}{Floorarea\times Coolingefficiency}}}$
Refrigeration 0.20
Figure 1 - ELCAP winter/weekday end-use load shapes
Figure 2 - ELCAP winter/weekend end-use load shapes
Figure 3 - ELCAP summer/weekday end-use load shapes
Figure 4 - ELCAP summer/weekend end-use load shapes

Before computing the diversified load, the end-use load shapes for the 5 demand-based end-uses (cooking, hotwater, lighting, plugs and washing) are used to determine the fraction of the load operating at a given time. For end-use load shapes are used for winter/summer and weekend/weekday conditions, as shown in Figures 1-4.

The ELCAP end-use load shape are rescaled according to the daily energy use estimates, as shown in Table 5. The estimates used a approximately 50% of the original daily ELCAP consumption.

Table 5 - Single-family residential daily end-use energy demand (kWh/day)
End-use Winter weekday Winter weekend Summer weekday Summer weekend
Cooking 0.67 0.82 0.56 0.59
Hotwater 7.08 7.40 5.64 5.52
Lighting 7.15 7.45 5.52 5.47
Plugs 14.56 15.81 15.56 7.54
Washing 1.66 2.02 1.66 1.99

The non-demand end-uses (heating, cooling and refrigeration) are computed directly from the power density and the end-use duty-cycle (if any)

${\displaystyle Q_{x}=DC_{x}\times PD_{x}\times Floorarea/1000}$

The final diversified end-use load is computed by looking up the rescaled ELCAP demand for the season (winter/summer) and day type (weekday/weekend), multiplying by the power density and the floor area. The final diversified load is weighted between the winter and summer values based on the day of year.

${\displaystyle Q_{winter}=Q_{ELCAP_{winter}}{\frac {E_{daily_{winter}}}{E_{ELCAP_{winter}}}}\times PD_{x}\times Floorarea/1000}$

${\displaystyle Q_{summer}=Q_{ELCAP_{summer}}{\frac {E_{daily_{summer}}}{E_{ELCAP_{summer}}}}\times PD_{x}\times Floorarea/1000}$

{\displaystyle {\begin{aligned}Q_{diversified}&=Q_{winter}(1-|\sin({\frac {3.14}{12}}(month-1.5))|)\\&+Q_{summer}|\sin({\frac {3.14}{12}}(month-1.5))|\end{aligned}}}

Finally, the end-use load composition is determined by multiplying by the end-use composition matrix for single-family residential dwellings:

Table 5 - Single-family residential load composition matrix
End-use Electronic Motor-D Ip (Iq) Pp (Pq) Zp (Zq)
Cooking 0.25 0.25
Hotwater 0.50
Lighting 0.50 0.50
Plugs 0.75 0.25 (0.10)
Washing 0.35 0.15
Heating 0.40 0.20
Cooling 1.00
Refrigeration 0.80 0.20

## Multi-family dwellings

Multi-family buildings are very similar to single family dwellings, except that some of the default parameters are different or calculated differently. Specifically

Floor area
The floor area per dwelling unit is used as the basic parameter. When combined with the floors per building' and units per floor this give a rough approximation of the total building floor area (excluding conditioned circulation space).
Floor to floor height
The floor height is used to compute the total building height and air volume.

The load composition matrix for multi-family buildings is shown in Table 6.

Table 6 - Multi-family residential load composition matrix
End-use Electronic Motor-D Ip (Iq) Pp (Pq) Zp (Zq)
Cooking 0.40 0.40
Hotwater 0.80
Lighting 0.50 0.50
Plugs 0.75 0.25 (0.10)
Washing 0.35 0.15
Heating 0.20 0.80
Cooling 1.00
Refrigeration 0.80 0.20

All commercial load composition models are developed using the California End-Use Survey (CEUS) results. These results are largely valid for the WECC, but care should be take to account to differences in construction types and building codes in regions not covered by the survey.

Note
The CEUS data used is for the whole state of California. There is CEUS data available for specific utilities, but that was not deemed helpful for load compositions that would apply WECC-wide.

The CEUS data from the the Itron CEUS results website was used to obtain the commercial load tables. The exp16day data set is used because it is more compact than the 8760 data set, but provides end-use load shapes for each season, day type, and hour.

The load densities for the following end-uses are estimated for each building type.

• Heating
• Cooling
• Ventilation
• Water heating
• Cooking
• Refrigeration
• Exterior lighting
• Interior lighting
• Office equipment
• Miscellaneous
• Process equipment
• Motors
• Air compression

The basic method for determining commercial load composition is to estimate the hourly load density (W/sf) using the hourly CEUS energy data. The load densities are then multiplied by the average building floor area to yield the load (MW).

The heating and cooling loads are interpolated based on the building's balance temperature. The heating load is multiplied by the heating duty cycle

${\displaystyle \rho _{heating}={\begin{cases}T_{out}

and similarly the cooling load is multiplied by the cooling duty cycle

${\displaystyle \rho _{cooling}={\begin{cases}T_{out}>T_{balance}&:{\frac {T_{out}-T_{balance}}{T_{design_{cooling}}-T_{balance}}}\\T_{out}\leq T_{balance}&:0\end{cases}}}$

## Small office

Each small office load vector is multiplied by the small office end-use composition map to determine the end-use load composition for small offices, as shown in Table 7.

Table 7 - Small office end-use composition map
Electronic Motor-A Motor-B Motor-C Motor-D ZIP Ip ZIP Iq ZIP (P) ZIP (Q) ZIP (G) ZIP (B)
Heating 0.40 0.50 0.10 0.02
Cooling 0.75 0.25
Ventilation 0.30 0.70
Water heating 1.00 0.15
Cooking 0.20 0.20 0.60
Refrigeration 0.20 0.80
Exterior lighting 1.00 -0.35
Interior lighting 1.00 -0.35
Office equipment 1.00
Miscellaneous 1.00
Process equipment 0.50 0.50
Motors 0.50 0.50
Air compression 1.00

## Large office

Each large office load vector is multiplied by the large office end-use composition map to determine the end-use load composition for large offices, as shown in Table 8.

Table 8 - Large office end-use composition map
Electronic Motor-A Motor-B Motor-C Motor-D ZIP Ip ZIP Iq ZIP (P) ZIP (Q) ZIP (G) ZIP (B)
Heating 0.50
Cooling 0.25 0.75
Ventilation 0.70
Water heating 0.50
Cooking 0.50 0.50
Refrigeration 0.20 0.80
Exterior lighting 1.00 -0.35
Interior lighting 1.00 -0.35
Office equipment 1.00
Miscellaneous 1.00
Process equipment 0.50 0.50
Motors 0.50 0.50
Air compression 1.00

## Retail

Each retail load vector is multiplied by the retail end-use composition map to determine the end-use load composition for retail buildings, as shown in Table 9.

Table 9 - Retail end-use composition map
Electronic Motor-A Motor-B Motor-C Motor-D ZIP Ip ZIP Iq ZIP (P) ZIP (Q) ZIP (G) ZIP (B)
Heating 0.40 0.50 0.10 0.02
Cooling 0.50 0.50
Ventilation 0.30 0.70
Water heating 1.00 0.15
Cooking 0.20 0.20 0.60
Refrigeration 0.20 0.80
Exterior lighting 1.00 -0.35
Interior lighting 1.00 -0.35
Office equipment 1.00
Miscellaneous 1.00
Process equipment 0.50 0.50
Motors 0.50 0.50
Air compression 1.00

## Lodging

Each lodging load vector is multiplied by the lodging end-use composition map to determine the end-use load composition for lodging buildings, as shown in Table 10.

Table 10 - Lodging end-use composition map
Electronic Motor-A Motor-B Motor-C Motor-D ZIP Ip ZIP Iq ZIP (P) ZIP (Q) ZIP (G) ZIP (B)
Heating 0.40 0.50 0.10 0.02
Cooling 0.25 0.75
Ventilation 0.30 0.70
Water heating 1.00 0.15
Cooking 0.20 0.20 0.60
Refrigeration 0.20 0.80
Exterior lighting 1.00 -0.35
Interior lighting 1.00 -0.35
Office equipment 1.00
Miscellaneous 1.00
Process equipment 0.50 0.50
Motors 0.50 0.50
Air compression 1.00

## Grocery

Each grocery load vector is multiplied by the grocery end-use composition map to determine the end-use load composition for grocery stores, as shown in Table 11.

Table 11 - Grocery store end-use composition map
Electronic Motor-A Motor-B Motor-C Motor-D ZIP Ip ZIP Iq ZIP (P) ZIP (Q) ZIP (G) ZIP (B)
Heating 0.40 0.50 0.10 0.02
Cooling 0.25 0.75
Ventilation 0.30 0.70
Water heating 1.00 0.15
Cooking 0.20 0.20 0.60
Refrigeration 0.20 0.80
Exterior lighting 1.00 -0.35
Interior lighting 1.00 -0.35
Office equipment 1.00
Miscellaneous 1.00
Process equipment 0.50 0.50
Motors 0.50 0.50
Air compression 1.00

## Restaurant

Each restaurant load vector is multiplied by the restaurant end-use composition map to determine the end-use load composition for restaurants, as shown in Table 12.

Table 12 - Restaurant end-use composition map
Electronic Motor-A Motor-B Motor-C Motor-D ZIP Ip ZIP Iq ZIP (P) ZIP (Q) ZIP (G) ZIP (B)
Heating 0.40 0.50 0.10 0.02
Cooling 0.50 0.50
Ventilation 0.30 0.70
Water heating 1.00 0.15
Cooking 0.20 0.20 0.60
Refrigeration 0.20 0.80
Exterior lighting 1.00 -0.35
Interior lighting 1.00 -0.35
Office equipment 1.00
Miscellaneous 1.00
Process equipment 0.50 0.50
Motors 0.50 0.50
Air compression 1.00

## School

Each school load vector is multiplied by the school end-use composition map to determine the end-use load composition for schools, as shown in Table 13.

Table 13 - School end-use composition map
Electronic Motor-A Motor-B Motor-C Motor-D ZIP Ip ZIP Iq ZIP (P) ZIP (Q) ZIP (G) ZIP (B)
Heating 0.40 0.50 0.10 0.02
Cooling 0.75 0.25
Ventilation 0.30 0.70
Water heating 1.00 0.15
Cooking 0.20 0.20 0.60
Refrigeration 0.20 0.80
Exterior lighting 1.00 -0.35
Interior lighting 1.00 -0.35
Office equipment 1.00
Miscellaneous 1.00
Process equipment 0.50 0.50
Motors 0.50 0.50
Air compression 1.00

## Health

Each health load vector is multiplied by the health end-use composition map to determine the end-use load composition for health care facilities, as shown in Table 14.

Table 14 - Health end-use composition map
Electronic Motor-A Motor-B Motor-C Motor-D ZIP Ip ZIP Iq ZIP (P) ZIP (Q) ZIP (G) ZIP (B)
Heating 0.40 0.50 0.10 0.02
Cooling 0.50 0.50
Ventilation 0.30 0.70
Water heating 1.00 0.15
Cooking 0.20 0.20 0.60
Refrigeration 0.20 0.80
Exterior lighting 1.00 -0.35
Interior lighting 1.00 -0.35
Office equipment 1.00
Miscellaneous 1.00
Process equipment 0.50 0.50
Motors 0.50 0.50
Air compression 1.00

Industrial and agricultural loads are considered idiosynchratic and must be entered directly as an end-use load composition on their respective worksheets.

# Analysis results

A number of analysis results are provided with the worksheets. The Feeders analysis enumerates the load component compositions for residential, commercial, and mixed (50/50) feeders. The Loadshapes worksheets provides daily load component shapes for Portland OR. The sensitivity analysis provides the sensitivities of component loads to temperature.

## Feeder compositions

The feeder component compositons were computed for winter peak, typical shoulder, and summer peak conditions at 6:00, 9:00, 15:00, and 18:00 hours for a 100% single-family residential feeder, 50% single-family residential/small-office commercial mixed feeder, and a 100% small-office commercial feeder in each of the cities for which climate data was available. The tables were generated using Version 1.6.3.

### Winter peak (6:00) component compositions

Table 1 - Winter peak 6:00 residential feeder load composition
City ST Electronic Motor-A Motor-B Motor-C Motor-D ZIP PF
Albuquerque NM 6.4% 0.0% 0.0% 0.0% 38.7% 54.9% 1.000
Bakersfield CA 11.5% 0.0% 0.0% 0.0% 32.8% 55.8% 1.000
Boise ID 7.5% 0.0% 0.0% 0.0% 37.0% 55.5% 1.000
Cheyenne WY 6.3% 0.0% 0.0% 0.0% 38.0% 55.7% 1.000
Denver CO 6.4% 0.0% 0.0% 0.0% 37.9% 55.7% 1.000
Eugene OR 9.6% 0.0% 0.0% 0.0% 35.3% 55.1% 1.000
Fresno CA 12.0% 0.0% 0.0% 0.0% 32.4% 55.6% 1.000
Helena MT 4.3% 0.0% 0.0% 0.0% 40.3% 55.5% 1.000
Las Vegas NV 11.1% 0.0% 0.0% 0.0% 33.2% 55.7% 1.000
Long Beach CA 15.5% 0.0% 0.0% 0.0% 29.2% 55.3% 1.000
Los Angeles CA 17.5% 0.0% 0.0% 0.0% 27.8% 54.7% 1.000
Phoenix AZ 12.3% 0.0% 0.0% 0.0% 32.0% 55.7% 1.000
Portland OR 9.6% 0.0% 0.0% 0.0% 35.3% 55.1% 1.000
Redmond OR 4.4% 0.0% 0.0% 0.0% 40.2% 55.4% 1.000
Reno NV 6.6% 0.0% 0.0% 0.0% 37.8% 55.5% 1.000
Sacramento CA 13.4% 0.0% 0.0% 0.0% 30.8% 55.8% 1.000
San Diego CA 16.7% 0.0% 0.0% 0.0% 28.3% 55.0% 1.000
San Francisco CA 14.2% 0.0% 0.0% 0.0% 30.1% 55.8% 1.000
Santa Maria CA 11.6% 0.0% 0.0% 0.0% 32.9% 55.6% 1.000
Seattle WA 10.9% 0.0% 0.0% 0.0% 33.8% 55.3% 1.000
Spokane WA 6.2% 0.0% 0.0% 0.0% 38.1% 55.7% 1.000
Yakima WA 7.3% 0.0% 0.0% 0.0% 37.1% 55.5% 1.000

Table 2 - Winter peak 6:00 residential feeder load composition
City ST Electronic Motor-A Motor-B Motor-C Motor-D ZIP PF
Albuquerque NM 8.2% 1.5% 7.2% 4.9% 29.1% 49.1% -0.999
Bakersfield CA 11.7% 2.2% 8.6% 5.9% 23.1% 48.5% -0.998
Boise ID 8.9% 1.7% 6.5% 4.5% 28.4% 50.1% -0.999
Cheyenne WY 7.8% 1.5% 5.7% 3.9% 30.3% 50.9% -0.999
Denver CO 7.9% 1.5% 5.8% 4.0% 30.0% 50.8% -0.999
Eugene OR 10.5% 1.9% 7.7% 5.2% 25.8% 48.9% -0.998
Fresno CA 12.3% 1.5% 10.0% 6.9% 20.9% 48.5% -0.997
Helena MT 6.2% 0.7% 5.5% 3.9% 32.6% 51.2% -0.999
Las Vegas NV 11.5% 2.1% 8.5% 5.8% 23.5% 48.6% -0.998
Long Beach CA 14.2% 1.7% 11.5% 8.0% 17.5% 47.2% -0.996
Los Angeles CA 15.1% 1.8% 12.2% 8.5% 16.0% 46.4% -0.996
Phoenix AZ 12.5% 1.5% 10.1% 7.0% 20.5% 48.4% -0.997
Portland OR 10.5% 1.9% 7.7% 5.2% 25.8% 48.9% -0.998
Redmond OR 6.2% 1.1% 5.4% 3.8% 32.6% 50.9% -0.999
Reno NV 8.3% 1.0% 6.8% 4.7% 28.6% 50.7% -0.999
Sacramento CA 12.9% 2.4% 9.5% 6.5% 21.0% 47.9% -0.998
San Diego CA 14.8% 1.8% 12.0% 8.3% 16.6% 46.7% -0.996
San Francisco CA 13.3% 2.5% 9.8% 6.7% 20.3% 47.6% -0.997
Santa Maria CA 12.1% 1.4% 9.8% 6.8% 21.5% 48.6% -0.997
Seattle WA 11.6% 1.4% 9.4% 6.5% 22.5% 48.7% -0.998
Spokane WA 7.7% 1.4% 5.7% 3.9% 30.4% 50.9% -0.999
Yakima WA 8.7% 1.6% 6.4% 4.4% 28.7% 50.2% -0.999

Table 3 - Winter peak 6:00 commercial feeder load composition
City ST Electronic Motor-A Motor-B Motor-C Motor-D ZIP PF
Albuquerque NM 12.3% 4.8% 23.3% 16.1% 7.3% 36.8% -0.971
Bakersfield CA 12.1% 5.6% 22.3% 15.2% 7.7% 37.8% -0.971
Boise ID 12.1% 5.6% 22.3% 15.2% 7.7% 37.8% -0.971
Cheyenne WY 12.1% 5.6% 22.3% 15.2% 7.7% 37.8% -0.971
Denver CO 12.1% 5.6% 22.3% 15.2% 7.7% 37.8% -0.971
Eugene OR 12.1% 5.6% 22.3% 15.2% 7.7% 37.8% -0.971
Fresno CA 12.8% 3.6% 24.2% 16.8% 4.5% 39.0% -0.968
Helena MT 12.9% 3.4% 24.9% 17.6% 5.3% 36.7% -0.969
Las Vegas NV 12.1% 5.6% 22.3% 15.2% 7.7% 37.8% -0.971
Long Beach CA 12.8% 3.6% 24.2% 16.8% 4.5% 39.0% -0.968
Los Angeles CA 12.8% 3.6% 24.2% 16.8% 4.5% 39.0% -0.968
Phoenix AZ 12.8% 3.6% 24.2% 16.8% 4.5% 39.0% -0.968
Portland OR 12.1% 5.6% 22.3% 15.2% 7.7% 37.8% -0.971
Redmond OR 12.3% 4.8% 23.3% 16.1% 7.3% 36.8% -0.971
Reno NV 12.8% 3.6% 24.2% 16.8% 4.5% 39.0% -0.968
Sacramento CA 12.1% 5.6% 22.3% 15.2% 7.7% 37.8% -0.971
San Diego CA 12.8% 3.6% 24.2% 16.8% 4.5% 39.0% -0.968
San Francisco CA 12.1% 5.6% 22.3% 15.2% 7.7% 37.8% -0.971
Santa Maria CA 12.8% 3.6% 24.2% 16.8% 4.5% 39.0% -0.968
Seattle WA 12.8% 3.6% 24.2% 16.8% 4.5% 39.0% -0.968
Spokane WA 12.1% 5.6% 22.3% 15.2% 7.7% 37.8% -0.971
Yakima WA 12.1% 5.6% 22.3% 15.2% 7.7% 37.8% -0.971

### Summer peak (15:00) component compositions

Table 4 - Summer peak 15:00 residential feeder load composition
City ST Electronic Motor-A Motor-B Motor-C Motor-D ZIP PF
Albuquerque NM 17.5% 0.0% 0.0% 0.0% 60.7% 21.9% 0.999
Bakersfield CA 15.1% 0.0% 0.0% 0.0% 65.8% 19.1% 0.999
Boise ID 15.7% 0.0% 0.0% 0.0% 64.3% 20.0% 0.999
Cheyenne WY 17.1% 0.0% 0.0% 0.0% 61.2% 21.7% 0.999
Denver CO 16.3% 0.0% 0.0% 0.0% 63.1% 20.7% 0.999
Eugene OR 16.9% 0.0% 0.0% 0.0% 61.8% 21.4% 0.999
Fresno CA 15.1% 0.0% 0.0% 0.0% 65.8% 19.1% 0.999
Helena MT 17.9% 0.0% 0.0% 0.0% 59.8% 22.4% 0.999
Las Vegas NV 14.4% 0.0% 0.0% 0.0% 67.4% 18.2% 0.999
Long Beach CA 16.4% 0.0% 0.0% 0.0% 62.8% 20.8% 0.999
Los Angeles CA 16.9% 0.0% 0.0% 0.0% 61.8% 21.3% 0.999
Phoenix AZ 14.0% 0.0% 0.0% 0.0% 68.2% 17.8% 0.999
Portland OR 17.4% 0.0% 0.0% 0.0% 60.7% 22.0% 0.999
Redmond OR 18.2% 0.0% 0.0% 0.0% 59.2% 22.7% 0.999
Reno NV 16.2% 0.0% 0.0% 0.0% 63.3% 20.5% 0.999
Sacramento CA 15.5% 0.0% 0.0% 0.0% 64.8% 19.7% 0.999
San Diego CA 17.8% 0.0% 0.0% 0.0% 59.7% 22.5% 0.999
San Francisco CA 16.9% 0.0% 0.0% 0.0% 61.8% 21.4% 0.999
Santa Maria CA 16.6% 0.0% 0.0% 0.0% 62.6% 20.9% 0.999
Seattle WA 16.6% 0.0% 0.0% 0.0% 62.5% 21.0% 0.999
Spokane WA 16.1% 0.0% 0.0% 0.0% 63.6% 20.4% 0.999
Yakima WA 16.1% 0.0% 0.0% 0.0% 63.6% 20.4% 0.999
Table 5 - Summer peak 15:00 mixed residential/commercial feeder load composition
City ST Electronic Motor-A Motor-B Motor-C Motor-D ZIP PF
Albuquerque NM 13.1% 8.1% 15.1% 8.9% 33.4% 21.4% -0.995
Bakersfield CA 12.9% 5.0% 16.8% 10.6% 30.8% 24.2% -0.987
Boise ID 12.5% 7.6% 17.1% 9.8% 29.9% 23.3% -0.988
Cheyenne WY 12.9% 7.8% 17.7% 10.2% 27.4% 24.1% -0.988
Denver CO 12.7% 7.7% 17.4% 10.0% 28.9% 23.6% -0.988
Eugene OR 12.9% 7.8% 17.6% 10.1% 27.9% 23.9% -0.988
Fresno CA 12.9% 5.0% 16.8% 10.6% 30.8% 24.2% -0.987
Helena MT 14.0% 4.8% 14.7% 9.7% 33.8% 23.2% -0.995
Las Vegas NV 12.6% 4.9% 16.5% 10.4% 32.2% 23.7% -0.987
Long Beach CA 12.7% 7.7% 17.4% 10.0% 28.7% 23.7% -0.988
Los Angeles CA 12.9% 7.8% 17.6% 10.1% 27.9% 23.9% -0.988
Phoenix AZ 12.5% 4.8% 16.3% 10.3% 33.0% 23.4% -0.987
Portland OR 13.7% 5.3% 17.8% 11.2% 26.6% 25.6% -0.987
Redmond OR 13.3% 8.3% 15.4% 9.1% 32.2% 21.8% -0.995
Reno NV 12.7% 7.7% 17.3% 10.0% 29.1% 23.5% -0.988
Sacramento CA 12.4% 7.5% 17.0% 9.8% 30.3% 23.1% -0.988
San Diego CA 13.8% 5.3% 18.0% 11.3% 25.9% 25.9% -0.987
San Francisco CA 12.9% 7.8% 17.6% 10.1% 27.8% 23.9% -0.988
Santa Maria CA 13.2% 6.0% 17.6% 10.9% 27.9% 24.6% -0.987
Seattle WA 12.8% 7.7% 17.5% 10.1% 28.4% 23.8% -0.988
Spokane WA 12.6% 7.6% 17.3% 9.9% 29.3% 23.5% -0.988
Yakima WA 12.6% 7.6% 17.3% 9.9% 29.3% 23.5% -0.988

Table 6 - Summer peak 15:00 commercial feeder load composition
City ST Electronic Motor-A Motor-B Motor-C Motor-D ZIP PF
Albuquerque NM 8.8% 16.1% 29.9% 17.6% 6.7% 21.5% -0.967
Bakersfield CA 11.1% 8.8% 29.8% 18.8% 3.6% 28.5% -0.965
Boise ID 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964
Cheyenne WY 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964
Denver CO 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964
Eugene OR 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964
Fresno CA 11.1% 8.8% 29.8% 18.8% 3.6% 28.5% -0.965
Helena MT 9.7% 10.0% 30.9% 20.3% 5.1% 24.6% -0.967
Las Vegas NV 11.1% 8.8% 29.8% 18.8% 3.6% 28.5% -0.965
Long Beach CA 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964
Los Angeles CA 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964
Phoenix AZ 11.1% 8.8% 29.8% 18.8% 3.6% 28.5% -0.965
Portland OR 11.1% 8.8% 29.8% 18.8% 3.6% 28.5% -0.965
Redmond OR 8.8% 16.1% 29.9% 17.6% 6.7% 21.5% -0.967
Reno NV 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964
Sacramento CA 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964
San Diego CA 11.1% 8.8% 29.8% 18.8% 3.6% 28.5% -0.965
San Francisco CA 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964
Santa Maria CA 10.9% 10.2% 29.7% 18.3% 3.9% 27.6% -0.964
Seattle WA 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964
Spokane WA 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964
Yakima WA 10.2% 13.0% 29.5% 17.0% 4.9% 26.1% -0.964

Figure 1 - Daily clustomer type shape (Portland OR, weekday, summer peak)

Figure 2 - Daily customer type composition (Portland OR, weekday, summer peak)

Figure 3 - Daily load component shape (Portland OR, weekday, summer peak)

Figure 4 - Daily Customer component composition (Portland OR, weekday, summer peak)

## Sensitivities analysis

The load composition sensitivity analysis computes the changes in important output values with respect to changes in certain input values. The output values considered load (MW) and composition (%) for each of the building types. The input values considered are the temperature (F) and the number of buildings.

In the spreadsheet, the sensitivity analysis is performed only when the Update sensitivities button on the Composition worksheet is pressed.

The following load sensitivities were calculated with Version 1.6.2 using a –1 °F perturbation on peak cooling conditions.

Notation
The value 0 indicates that no change was detected. The value 0.000 indicates the change was less than 0.0005. The value - indicates that the difference is between two very small or zero values.
Table 1 - Single-family residential temperature sensitivities
Output Summer peak
Phoenix AZ San Francisco CA Portland OR
Electronic 0 -0.15% 0 -0.19% 0 -0.20%
Motor-A - - - - - -
Motor-B - - - - - -
Motor-C - - - - - -
Motor-D +0.0526 +0.32% +0.0507 0.43% +0.0486 0.44%
ZIP (Ip) - - - - - -
ZIP (Iq) - - - - - -
ZIP (P) 0 -0.15% 0 -0.21% 0 -0.21%
ZIP (Q) - - - - - -
ZIP (G) 0 -0.02% 0 -0.03% 0 -0.03%
ZIP (B) 0 -0.01% 0 -0.01% 0 -0.01%
ZIP PF 0 (na) 0 (na) 0 (na)
Total +0.0526 0% +0.0507 0% +0.0486 0%

Table 2 - Multi-family residential temperature sensitivities
Output Summer peak
Phoenix AZ San Francisco CA Portland OR
Electronic 0 -0.15% 0 -0.20% 0 -0.23%
Motor-A - - - - - -
Motor-B - - - - - -
Motor-C - - - - - -
Motor-D +0.9444 +0.33% +0.9949 0.49% +0.9685 0.51%
ZIP (Ip) - - - - - -
ZIP (Iq) - - - - - -
ZIP (P) 0 -0.15% 0 -0.26% 0 -0.26%
ZIP (Q) - - - - - -
ZIP (G) 0 -0.02% 0 -0.03% 0 -0.03%
ZIP (B) 0 -0.01% 0 -0.01% 0 -0.01%
ZIP PF 0 (na) 0 (na) 0 (na)
Total +0.9444 0% +0.9948 0% +0.9685 0%

Table 3 - Small-office commercial temperature sensitivities
Output Summer peak
Phoenix AZ San Francisco CA Portland OR
Electronic 0 -0.06% 0 -0.08% 0 -0.06%
Motor-A +0.0223 +0.10% +0.1107 +0.06% +0.0383 +0.17%
Motor-B 0 -0.02% 0 -0.06% 0 -0.04%
Motor-C 0 -0.01% 0 -0.03% 0 -0.02%
Motor-D +0.0074 +0.03% +0.0369 +0.08% +0.0128 +0.05%
ZIP (Ip) 0 -0.05% 0 -0.12% 0 -0.08%
ZIP (Iq) - - - - - -
ZIP (P) - - - - - -
ZIP (Q) - - - - - -
ZIP (G) 0 -0.02% 0 -0.04% 0 -0.03%
ZIP (B) 0 -0.01% 0 -0.04% 0 -0.03%
ZIP PF 0 (na) 0 (na) 0 (na)
Total +0.0295 0% +0.1469 0% +0.0508 0%

Table 4 - Large-office commercial temperature sensitivities
Output Summer peak
Phoenix AZ San Francisco CA Portland OR
Electronic 0 -0.03% 0 -0.05% 0 -0.05%
Motor-A +0.1097 +0.03% +0.3423 +0.06% +0.1782 +0.05%
Motor-B +0.3292 +0.06% +1.0268 +0.12% +0.5345 +0.10%
Motor-C 0 -0.01% 0 -0.06% 0 -0.04%
Motor-D 0 +0.00% 0 +0.00% +0.0128 +0.00%
ZIP (Ip) 0 -0.03% 0 -0.06% 0 -0.04%
ZIP (Iq) - - - - - -
ZIP (P) 0 +0.00% 0 +0.00% 0 +0.00%
ZIP (Q) - - - - - -
ZIP (G) 0 -0.02% 0 -0.01% 0 -0.01%
ZIP (B) 0 +0.01% 0 +0.02% 0 -0.02%
ZIP PF 0 (na) 0 (na) 0 (na)
Total +0.4380 0% +1.3663 0% +0.7111 0%