# Presentation: 057623

## Electricity Use in California:  Past Trends and Present Usage Patterns 

**Rich Brown**
**http://enduse.lbl.gov/Projects/CAdata.html**
**May 2002**

**Notes:**

This talk focuses on the use of electricity in California, both by sector and end-use.  It was motivated by a series of questions related to the power crisis, but the talk does not focus on the crisis.  Other references are provided on our web site that go into more detail about the causes and events of the power crisis.

One of the main motivators for this study was the idea that examining how people use energy can yield important policy insights, yet much of the policy debate surrounding the power crisis was not informed by any such detailed analysis.  This study was intended to make these electricity use data available to a wider audience.

## Overview

- Background
- Was CA’s demand growth unprecedented?
- Was CA’s demand growth unforeseen?
- How many houses can 1 MW supply?
- Which end uses contribute most to peak?
- How do we know all this?
- Looking forward

**Notes:**
This talk is organized around several questions that motivated our study.
We also present some ideas for further research.

## Background

- Origins in EET demand-response white paper
- Mainly based on CEC and FERC data
- Electricity use values cited here exclude T&D losses, but include loads served by self-generation
- Further data in Brown & Koomey (2002), forthcoming in Energy Policy

**Notes:**
This study originally started with the EET demand-responsive buildings white paper led by Mary Ann Piette.
After looking at the building sector demand data, we had a lot of questions about how changes in demand had contributed to the power crisis.  We realized that no one else was making this type of information available to the general policy community in CA.
At the same time, Jerry Dion at DOE (BTS Analysis office) was getting more interested in peak demand issues and looking for ways to get his analysis efforts started.  Jerry funded this work as a logical first step in improving DOE’s peak demand analysis efforts.

The work presented here is a compilation of previous analyses and data, mainly published by CEC and FERC.  This study represents new work in that disparate data sets were combined and presented in a new way, and made available to the general energy analysis community.  

The consumption data presented here represent loads at the customer’s energy-using equipment.
Jon Koomey collaborated on this study.  Pre-print version is available on the web site listed on the title page.

## Was demand growth unprecedented or unforeseen?

**Notes:**
To answer this question, we first look at how much demand actually grew during the 80s and 90s

## CA Electricity Consumption	

**Notes:**
Both consumption and peak load grew about 50% in aggregate over 20 years.
Most growth was in the Buildings sector.
Compound Annual growth rates:
1980s:
Building sector: 3-4%
Overall: 3%
GSP: 5%, Population: 2%, service employment: 3%

1990s:  (1980s growth rate cut in half)
Building Sector: 1.5-2%
Overall: 1%
GSP: 3%, Population: 1%, service employment: 2%

95-2000:
Building Sector: 2-2.5%
Overall: 2%
GSP: 6%, Population: 1%, service employment: 3%

## CA Peak Load

**Notes:**
Compound Annual growth rates:
1980s:
Residential: 1%
Commercial: 5%
Overall: 3%
GSP: 5%, Population: 2%, service employment: 3%

1990s:  (1980s growth rate reversed)
Residential: 2%
Commercial: 1.6%
Overall: 1%
GSP: 3%, Population: 1%, service employment: 2%

95-2000:
Building Sector: 1-4%
Overall: 3%
GSP: 6%, Population: 1%, service employment: 3%

## Was Demand Growth Unprecedented?

- Growth slowed in the 90s
- Growth rates in 90s were lower than any time except Depression and early 70s
- Absolute growth in consumption in 80s was similar to 50s and 60s

**Notes:**
The consumption and load growth shown in the previous two charts was not unprecedented in California.
Growth in electricity use actually slowed during the early 1990s, with the late 1990s exhibiting growth rates similar to the 1980s.

## Was Demand Growth Unforeseen?

- As early as 1988, CEC overpredicted 2000 demand
- Early 90s recession led to lower growth
- Root cause of power crisis was little new generation added 1994-1997 in Western U.S.
- How accurate was the CEC forecast?

**Notes:**
Again, we conclude that the demand growth was expected.
Slower growth in the early 90s helped keep demand growth lower than expected.
After looking into this further, we conclude that the root cause of the power crisis was a supply/demand imbalance caused by little new generation between 1994 and 1997.  There are many reasons for little new generation, but the main one seems to be uncertainty about the restructured power market.

Looking in more detail, how accurate was the CEC forecast?

## 1987-2000 Electricity Consumption Growth

**Notes:**
The forecast data shown here are from the CEC’s 1990 Electricity Report (ER90).  Their forecast of building sector electricity consumption was very accurate (within 1%).  There’s an element of luck to this, but it also represents our better understanding of energy use in buildings.
The forecast overestimated use in the industrial and agricultural sectors, probably because the recession was not factored into the forecast.

## 1987-2000 Electricity Peak Load Growth

- Peak load in Ag sector declined by 300 MW.

**Notes:**
The CEC forecasted about 30 new central station power plants would be needed, while load actually grew by about 1/3 less.

## How Many Houses Can 1 MW Supply?

- Conventional wisdom says 1000 houses
- On peak: 600 houses
- On average: 1,200 houses
- Rule of thumb can be misleading
- CA ISO now uses 750 houses
- Only relevant for dispatchable resources

## CA Households per MW of Capacity

- Source:  CEC; 1999 data

**Notes:**
The range of demand is 900-1400 houses/MW on average
300-800 houses/MW on peak.
SMUD has lower values because the climate is hot and has more SF houses.

## Which End Uses Contribute Most to Peak?

**Notes:**
To answer this question, first focus on buildings sector, then look at load shapes of all sectors

## 1999 CA Building Sector Consumption and Peak Demand - Top 10 Highest-Peak End Uses

**Notes:**
The building-sector peak load is dominated by cooling and lighting.
Residential miscellaneous includes lighting.
Miscellaneous and Other are large parts of the peak, which suggests further work is warranted to understand their components.
Refrigeration, ventilation, and cooking round out the top 10 end-uses.
One surprising end-use is residential clothes dryers.  There is about 800MW used to dry clothes at a time when the statewide average temperature is over 80 degrees and the RH is around 30%.
The next slide looks at these top 10 end-uses over the course of the peak day, in the context of system electrical load.

## California 1999 Summer Peak-day Load 

**Notes:**
The peak load data on the previous slide was measured at the peak hour.
This slide adds in the rest of the buildings sector, industrial, and ag sectors, and shows the load shape over the whole day.
The top of the curve is the statewide load according to FERC data.
The top slice is a calibration that adjusts the CEC forecast data to the FERC system data.  Note the real variation around 8pm not captured in the forecast model.
Next we look at all the end uses in each sector.

## 1999 Residential Summer Peak-day Load

**Notes:**
This chart shows the end use load shapes in the residential sector.
The sector peaks around 6pm, after the system peak.
Note the dominance of cooling and miscellaneous (lighting and fans).
Cooking also shows a large peak.

## 1999 Summer Peak-day Commercial Building Load

**Notes:**
This chart shows the end use load shapes in the commercial sector.
The sector peaks around 2pm, before the system peak.
Note the dominance of cooling and lighting
The Other end-use also shows more load variation that one would expect, probably due to building occupancy schedules.

## 1999 Summer Peak-day Industry & Other Load

**Notes:**
This chart shows the end use load shapes in the non-building sectors.
Note the relatively flat load shape, which may reflect load curtailment programs to some extent.

## Slide 19

**Notes:**
Winter peak is much lower than summer
~ 30GW vs. ~50GW

Peak occurs later in the day, around 8pm

Dominated by lighting end-use

Winter has been analyzed much less in CA than summer

CEC doesn’t track lighting separately from miscellaneous

## How have end use shares changed over time?

**Notes:**
One of the goals of this study was to determine how end-use shares have changed over time.  We were able to find only one historical study that had comprehensive data only on the residential sector.

## CA Residential Electricity Consumption (and Annual Growth Rate)

**Notes:**
Went back into archives and found 1976 LBNL report.
Blumstein says first end-use energy analysis conducted anywhere.
Compared 1975 to 1999 end-use consumption.
Growth was concentrated in a few end-uses.
Growth in Miscellaneous is expected.
1975 Refrigerator consumption probably too low due to exponential retirement model.
1975 TV consumption looks somewhat too high.
Space conditioning basically flat due to Title 24 and equipment standards.

## A Note About the Data

- Empirical data sources
- Monthly consumption at customers’ meters
- Hourly loads at various points in transmission grid
- Hourly use and annual consumption by end use for small samples of customers
- Equipment ownership and usage
- Aggregate use derived analytically
- Key uncertainties: end use load shapes, diversity factors, building/equipment operation

**Notes:**
With any analytical results, it’s always good to question the empirical underpinnings and the uncertainty in the final values.
The data presented here are based on four main empirical data sets.
Customer metering data is the one comprehensive data set, albeit at the whole building level.  These meters are being supplemented by interval meters for C&I customers, which could prove to be a very rich data set.
The last two empirical data sets are from research studies on small samples of customers.  The load data are now at least 10 years old.
These empirical data are combined analytically to generate the load shapes and aggregate use data presented earlier.
Building and equipment operation patterns are a key uncertainty for load shapes, since load is driven to a large extent by operating patterns.
Also, diversity is an important determinant of aggregate loads, so understanding variability within the population is important.

## Looking Forward

- Improve baseline estimates
- Update/improve load shape data
- Incorporate data from interval meters
- Reduce peak & make it more price-responsive
- Which end uses/building components to target?
- Big challenge:  population and economic growth swamp efficiency improvements

**Notes:**
Long-term policy perspective was lost during restructuring, but needs to be kept on the policy agenda now.
Issues about data availability in restructured market.
Even in scenario with most optimistic assumptions about demand reductions, CEC still forecasts about 15 GW of demand growth over next decade -- about 30 central station power plants.

## For further information see: http://enduse.lbl.gov/Projects/CAdata.html

## Backup Slides

## Electricity Supply Profile for Typical Hot Summer Day

- Source: CEC

- Source: CEC

## CEC Power Plant Permit Filings

- 11,350 MW