# Presentation: 357644

## Business Unusual:Methods to Develop a Clean Energy Economy

**E**nergy and
**R**esources
**G**roup

**Business Unusual:**Methods to Develop a Clean Energy Economy

- Daniel M. Kammen
- Energy and Resources Group & Goldman School of Public Policy
- Director, Renewable and Appropriate Energy Laboratory (RAEL)
- University of California, Berkeley
*National Renewable Energy Laboratory, Washington, DC, September 20, 2004*

## Director, 2 junior faculty, 3 post-doctoral fellows, 20+ students; visiting scientists & students

Solar, biomass and wind science and technology
Amorphous silicon PV
Biomass combustion & gassification
Distributed energy technologies and networks
CHP/Solar/Wind/Biomass integration (DoE CHP Center)
H2 in stationary, vehicle, vehicle-to-grid applications
Dispatchability & economics of renewable-intensive systems
Deep Cuts in Greenhouse Gas Emissions
State, national and global energy assessments
Energy Policy
Innovation and R&D Policy

**Solar, biomass and wind science and technology**
**Amorphous silicon PV**
**Biomass combustion & gassification**
**Distributed energy technologies and networks**
**CHP/Solar/Wind/Biomass integration (DoE CHP Center)**
**H****2 ****in stationary, vehicle, vehicle-to-grid applications**
**Dispatchability & economics of renewable-intensive systems**
**Deep Cuts in Greenhouse Gas Emissions**
**State, national and global energy assessments**
**Energy Policy**
**Innovation and R&D Policy**

**A Bit About RAEL**http://socrates.berkeley.edu/~rael

## Carbon Emissions and Innovation

Developing solar energy markets
Learning lessons of market support
The case of Kenya

Energy and Economic Development
Lorentz curves for energy

A R&D Update

A Comment on Hydrogen

**Developing solar energy markets**
**Learning lessons of market support**
**The case of Kenya**
**Energy and Economic Development**
**Lorentz curves for energy**
**A R&D Update**
**A Comment on Hydrogen**

**Overview**

## U.S. Energy Projections:Differences in Technology Assumptions

***Source: AEO 2004, EPA estimates 2004, and 1980 DOE and EPA Policy Analysis***

**Historical Consumption**

**High-Innovation Low-Carbon Future**
**Based Upon 1980 DOE Analysis**

**AEO 2004 Forecast**

**Typical Forecasts**
**Pre-1980**

## Performance Beyond Experience Can Appear In A Few Years

- Source: J. McMahon, LBL

**Notes:**
REPLACE with chart showing 1990, 1993 and 2001 levels.

## Standards Shift Markets 

**Standards Shift Markets**

**Energy Use Factor (ft****3****/kWh)**

**Percentage of Models**

**Notes:**
1. This is an example of a Minimum Energy Performance Standard.
2. The chart shows how energy efficiency standards transformed the clothes washer market in the U.S.
3. It plots the fraction of products at different levels of efficiency (the farther to the right, the higher the efficiency)
4. Note the shift in product availability after standards were introduced in 1994.

## World PV Module Shipments (Megawatts)

- 20+% annual growth for over a decade; 2004 forecast > 700 MW

## Photovoltaics Experience Curve

- 0

- 1

- 10

- 100

- 0

- 1

- 10

- 100

- 1,000

- 10,000

- 100,000

**Cumulative MWp**

**$/Wp**

- 2000

- 1976

- Projected 2020:
- ~ $1 - 1.50/watt

- Learning rate: 22%

**Photovoltaics Experience Curve**

## Kenya Solar Market Growth (largest LDC market per/capita)

**SHS market grew out of donor aid market in 1980s**
**Infrastructure & capacity building support key for sustained growth**

**14 W panel**

- Duke, Richard. D, Jacobson, Arne, and Daniel M. Kammen (2002) “Product quality in the Kenyan solar home industry”, *Energy Policy, ***30 (6),** 477-499

## Intra-Household Energy Allocation for Large and Small Solar Home Systems

- SHS > 25 Wp
- (avg. = 40 Wp)

- SHS < 25 Wp
- (avg. = 16 Wp)

- A. Jacobson, Ph.D. (2004)

**Notes:**
Connective Uses (and especially TV) dominate energy allocation for small systems (TV is top priority)

As system size increases the amount of energy allocated to TV remains relatively constant - the excess is allocated to lighting

Therefore, lighting uses only become prevalent after demand for TV is met.

##  Advertising “Wars” in Kenyan Press:a-Si (‘low price’) vs. x-Si (‘quality’)

**70 Shillings (Ksh)/dollar: 11,985 Ksh = $170 **

**Notes:**
•  The Kenyan solar market is very competitive, and vendors of crystalline and amorphous PV each claim that their technology is the best buy.

•  a-Si ads tend to emphasize low cost and modern conveniences such as watching the World Cup on TV.

•  x-Si ads emphasize quality and long term performance.  Some ads, like the one shown here by Kenital, suggest that a-Si is an inferior technology that can’t measure up to crystalline PV in the long term.

## Staebler-Wronski Degradation for a-Si Module (single junction)

**Staebler-Wronski Degradation ****for a-Si Module (single junction**)

- RAEL/EAA World Bank Report (2001)

## Results of Market Study:> 150 systems surveyed in the field

**Percent of rated power **

## Television and Solar Panel Sales in Kenya, 1970-2000

## Household Allocation of Solar Electricity

- Chinese *Great Wall* DC TV ($US 50)

- • Lighting
- • Television
- • Radio
- • Cellular Phone Charging

## Solar Access in the Kenya Market(i.e. Who can afford to buy solar?)

- _Conclusion_: rural wealthy + rural “middle class”

- Stratified random sample,
- n = 1,512 rural households

## 1999

- 2004

## Power Output for 5 Brands of 14 Watt a-Si Solar Modules in Kenya

**All values are *****Pre-Lighting Soaking***
**Initial testing by Arne Jacobson (UCB/HSU): 9/04**

**Notes:**
eSolar = 9.4 Wp (brand A) 
FEE = 15.6 Wp (Brand B) 
Shenzen Topray = 9.5 Wp (Brand C) 
Solar Cells = 15.5 Wp (Brand D)
ICP-Solar = 18.1 Wp (Brand E)  

## Income Generating Uses of Solar Electricity in Kenya

- Conclusions:
- Income generation uses _*minor*_ except _*school teachers*_
- Rural school teacher use is significant
- Few business uses
- Other home income related uses minor

- Results from: • Household survey/interviews , n = 76 households
- 	        • Supply chain survey, n = 366 recently installed systems

## Lorentz Curves: General Description

**Lorentz Curves: General Description**

## Slide 21

## Gini Calculations

- The Gini coefficient is calculated from unordered size data as the "relative mean difference," i.e., the mean of the difference between every possible pair of individuals, divided by the mean size:
- 	G = 1 - (Yi+1 + Yi)(Xi+1 - Xi)
- Where
- 	Xi is the number of energy users
- 	Yi is the quantity of energy used
- G = 0 	perfect equity
- G = 1	perfect inequity

**Gini Calculations**

## The Energy Gini

## Summary Statistics

**Summary Statistics**

- Jacobson, Milman and Kammen (2004) *Energy Policy*

## Slide 25

##  Sensitivity to Energy efficiency among residential users in the USA

** ****Sensitivity to Energy efficiency among residential users in the USA**

## Commercial Energy Use and Gini Coeff.: California, 1990 - 2000 

**Commercial Energy Use and Gini Coeff.: California, 1990 - 2000 **

## State Renewables Portfolio Standards and Mandates – 16 States

- Renewable energy “goals” established in Illinois and Minnesota
- RPS being considered in many other states (e.g., CO, NY, VT, WA); potentially revised in others (ME, PA, WI); and national RPS has been discussed

**CA: 20% by 2017                              **

**HI: 20% by 2020**

##  Scenarios to Provide 20% of 

**Scenario 1: ****	****20% Renewables - At the Current Mix**
- 	     	biomass 85%; wind energy 14%; solar PV 1%.
**Scenario 2: ****	****20% Renewables - Strong Growth of Wind **
**                   ****	**biomass** **60%; wind energy 37%; solar PV 3%
**Scenario 3:****	****20% Renewables - Strong Growth of Wind **
**	****    ****	**biomass 40%; wind energy 55%; solar PV 5%
**Scenario 4: ****	****No Renewables - Oil/Gas Mix**
**	****    ****	**oil 50%; gas 50%
**Scenario 5: ****	**** No Renewables - Gas Dominates ****	****    **
**	****	**gas = 100%

** ****Scenarios to Provide 20% of **
**U. S. Electricity by 2020**

## Job Creation from the 20% Scenarios

**http://socrates.berkeley.edu/~rael/papers.html**

## Rate of Change in Energy/GDP 

**Rate of Change in Energy/GDP **
**for the United States**

** ****International Energy Agency  (IEA) and EIA (Energy Information Agency)**

**-6%**

**-5%**

**-4%**

**-3%**

**-2%**

**-1%**

**0%**

**1%**

**2%**

**1981**

**1982**

**1983**

**1984**

**1985**

**1986**

**1987**

**1988**

**1989**

**1990**

**1991**

**1992**

**1993**

**1994**

**1995**

**1996**

**1997**

**1998**

**1999**

**2000**

**2001**

- IEA data

- EIA data

**- 2.7%**

**Average = - 0.7%**

**- 3.4%**

**Notes:**
The next five figures  illustrate annual rates of change in energy intensity data provided by the United States Energy Information Agency and the International Energy Agency.  Figures are provided for the United States, the state of California, Western Europe, China and the entire World.  Recent changes (from 1997 onward) look to me (and to Joe Romm) to be making a permanent change in the rate of decrease of our energy intensity (E/GDP).  The vertical lines divide the time period (1981 to 2001) into three eras.  Era 1 covers the latter years of high OPEC oil prices.  Era 2 starts soon after the collapse of OPEC and the consequent stagnation of CAFE  (Corporate Automobile Fuel Economy) standards.  Era 3 begins with the significant impact of information technologies on business and the economy.  The numbers written above each era reflect the average rate of change in E/GDP (EIA data) for that era. Compare for example, in the United States trend in Era 2 of only – 0.7% per year to the more sudden – 2.7% per year in Era 3

## Rate of Change in  Energy/Gross State

**Rate of Change in  Energy/Gross State**
**Product for California**

**(Sources: EIA and California Department of Finance)**

**-7.0%**

**-6.0%**

**-5.0%**

**-4.0%**

**-3.0%**

**-2.0%**

**-1.0%**

**0.0%**

**1.0%**

**1981**

**1982**

**1983**

**1984**

**1985**

**1986**

**1987**

**1988**

**1989**

**1990**

**1991**

**1992**

**1993**

**1994**

**1995**

**1996**

**1997**

**1998**

**1999**

**2000**

**Average = -1.0% **

** ****-4.5% **

** ****-3.9% **

**Notes:**
Only one data series, that from EIA, is shown for California.  Both ERA 1 and ERA 3 show significant improvements in energy intensity while ERA 2 shows an average change of just -1.0% per year.

## Federal R&D Policy Can be Very Effective(All sectors of the U. S. Economy)

- Federal R&D Policy Can be Very Effective(All sectors of the U. S. Economy)

**R. Margolis and D. Kammen (1999) *****Science*****, 285, pages 690 - 692 **

**Patents Granted (thousands)**

**R&D Spending (billions)**

## The Same Funding-Patent Correlation ...But Now for Energy Only

- The Same Funding-Patent Correlation ...But Now for Energy Only

**Patents Granted (thousands)**

**R&D Spending (billions)**

**R. Margolis and D. Kammen (1999) *****Science*****, 285, 690 - 692.**

## The Same Funding-Patent Correlation ...But Now for Energy Only

- The Same Funding-Patent Correlation ...But Now for Energy Only

**G. Nemet and D. Kammen (2004) *****Science*****, in review**

## G. Nemet and D. Kammen (2004) Science, unpublished

**G. Nemet and D. Kammen (2004) *****Science*****, unpublished**

##  Conclusions & Directions

-  **Technological Innovation Paired with Economic Opportunity   **
**   ****Moves Markets**
** ****Critical Opportunities Exist for Market Support **
** ****Clean Energy for Productive Economic Uses as well as Basic **
**  ****Services**
** ****Research and Development Support Needed at a Sustained Level**
**Needs:**
** ****Economic and Management understanding of renewable-intensive **
**  ****energy futures (supply dispatchability)**
**Technological innovation to develop new applications**
** ****Policy Tools to build clean energy markets**

** ****Conclusions & Directions**

## Greenhouse Gas Comparison: FCVs vs. ICE/Hybrid Vehicles

- Source: Bevilacqua-Knight, 2001

## Costs for Delivered Hydrogen

- Source: Lipman, 2004