UNDERSTANDING THE ECONOMICS BEHIND
OFF-GRID LIGHTING PRODUCTS FOR
SMALL BUSINESSES IN KENYA
By
Kristen Radecsky
A Thesis
Presented to
The Faculty of Humboldt State University
In Partial Fulfillment
Of the Requirements for the Degree
Master of Science
In Environmental Systems:
Energy, Environment, and Society Option
May, 2009
UNDERSTANDING THE ECONOMICS BEHIND OFF-GRID LIGHTING PRODUCTS FOR SMALL BUSINESSES IN KENYA
By
Kristen Radecsky
Approved by the Master's Thesis Committee:
Dr. Arne Jacobson, Major Professor Date
Dr. Charles Chamberlin, Committee Member Date
Dr. Steven Hackett, Committee Member Date
Dr. Christopher Dugaw, Graduate Coordinator Date
Dr. Chris A. Hopper, Dean for Research and Graduate Studies Date
ABSTRACT
UNDERSTANDING THE ECONOMICS BEHIND
OFF-GRID LIGHTING PRODUCTS FOR
SMALL BUSINESSES IN KENYA
Kristen Radecsky
For illumination, many off-grid communities use lighting products such as candles, kerosene-fueled lamps, or dry cell battery-powered lights. Unfortunately, fuel- based and dry cell powered lighting can be expensive, a health hazard and often provides poor quality light. Manufacturers are currently designing rechargeable lighting products using LED technology as an alternative option for lower-income people.
I developed a model to analyze the initial and life cycle costs of 19 off-grid lighting products. With the results, I make design recommendations for manufacturers.
The analysis is based on product prices, laboratory measurements of product performance, and data about lighting cost and use patterns for small, off-grid businesses in Kenya. The field data were collected by Arne Jacobson, Maina Mumbi, Peter
Johnstone and me during 2008.
My results indicate that the economics of off-grid lighting using electric lamps depends on the charging mode. Products that are charged on a fee basis using grid electricity generally have a lower initial cost but a higher life cycle cost than solar- charged products. For grid-charged products, I found that increasing battery size and
iii reducing power consumption strongly influence life cycle costs. For solar-charged products, I found that reducing module size proportionally to a reduction in power consumption influences life cycle costs moderately. I also found that it is best to design grid-charged products with an optional solar component and high-brightness LEDs are the preferred lamp type if available at a reasonable price. Potential design improvements may increase capital costs; manufacturers should consider customers’ willingness-to-pay when making design changes.
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ACKNOWLEDGEMENTS
I am immensely grateful for all the mentoring Arne Jacobson has given me through this thesis and throughout my entire time at Humboldt State University. He is an exceptional role model and friend. I also thank Charles Chamberlin for his impressive attention to detail when reviewing my analysis and Steve Hackett for his help understanding economic concepts embedded in this thesis.
Along with Arne Jacobson, I thank Peter Johnstone and Maina Mumbi for a successful research experience in Kenya gathering data used in this thesis. I also thank
Francis Ngugi, Samwell Elsam, Alice Mumbi, Paul Mwaniki, Gladys Hankins, and Mark
Hankins for their support in Kenya. In addition, our work was dependent on the reliable participation of the small business owners in Maai Mahiu and Karagita.
I am also very grateful to the Schatz Energy Research Center and everyone working there. I thank Stephen Kullman and the lighting lab ladies Patricia Lai and
Jenny Tracy for their collaboration in conducting off-grid product performance tests. I also thank Kyle Palmer, Scott Rommel, Mark Rocheleau, and Andrea Allen for their work on the datalogging LED lamps we employed in Kenya.
I thank the Blum Foundation and the Lighting Africa Project for their funding support and Evan Mills of Lawrence Berkeley National Laboratories for all his support.
Most importantly, I am grateful for the unconditional love provided by my mother, father, and grandmother while I’ve been living far away and throughout my life.
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TABLE OF CONTENTS
Page
ABSTRACT...... iii
ACKNOWLEDGEMENTS...... v
TABLE OF CONTENTS...... vi
LIST OF TABLES ...... xi
LIST OF FIGURES...... xv
LIST OF APPENDICES...... xxi
ACRONYMS AND ABBREVIATIONS...... xxii
CHAPTER 1. INTRODUCTION...... 1
CHAPTER 2. BACKGROUND...... 4
2.1 About Kenya ...... 4
2.2 Introducing Small Businesses in Kenya...... 7
2.3 Current Off-Grid Lighting for Small Businesses in Rural Kenya ...... 8
2.3.1 Fuel-Based Off-Grid Lighting Products in Kenya...... 9
2.3.2 Electric Off-Grid Lighting Products in Kenya...... 12
2.4 Small Businesses in Maai Mahiu and Karagita ...... 14
2.5 Small Business Off-Grid Lighting Use ...... 17
2.6 Current Lighting (Qualms and Options)...... 19
2.7 Progress Towards Improving Off-Grid Lighting Products...... 22
A. Electric Off-Grid Lighting Design Basics...... 23
B. Promising Electric Off-Grid Lighting Products for Kenya...... 36
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CHAPTER 3. LITERATURE REVIEW...... 38
3.1 Consumer Preferences...... 38
3.2 Parameter Values to Support Economic Analyses...... 42
3.3 Economic Analyses...... 45
A. Jones et al. Study ...... 46
B. Peon et al. Study ...... 48
C. Foster and Gómez Study ...... 50
D. Lighting Africa Study ...... 52
CHAPTER 4. METHODOLOGY...... 59
4.1 Methodology 1 – Field procedures while in Kenya ...... 61
4.1.1 Costs associated with lamp ownership ...... 61
4.1.2 Kerosene Fuel Consumption Rates & Lighting Use Patterns ...... 64
4.2 Methodology 2 – Performance testing procedures on battery-powered products...... 69
4.3 Methodology 3 – Procedures to estimate “end of use” lux values...... 72
4.4 Methodology 4 – Process for estimating the Life Cycle Cost values ...... 73
4.4.1 Capital cost...... 74
4.4.2 Maintenance cost ...... 75
4.4.3 Energy cost...... 78
4.4.4 Replacement cost...... 80
4.4.5 Salvage value...... 82
4.4.6 Present worth (pw)...... 82
4.5 Methodology 5 – Procedures for measuring lux values ...... 85
4.6 Methodology 6 – Process for estimating the cost/lux-hr values...... 87
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4.7 Methodology 7 – Design Sensitivity Analysis ...... 88
A. Battery Size ...... 88
B. Battery Chemistry...... 91
C. Charging Options...... 91
D. Optional Solar Module Upon Repurchase ...... 93
E. Power Consumption (Light Output)...... 94
4.8 Methodology 8 – Economic Parameter Sensitivity Analysis ...... 95
CHAPTER 5. RESULTS & DISCUSSION ...... 97
5.1 Results & Discussion Section 1 – Base Case Scenario ...... 100
5.2 Results & Discussion Section 2 – Design Sensitivity Analysis...... 112
A. Battery Size ...... 113
B. Battery Chemistry...... 117
C. Charging Option ...... 121
D. Solar Module Upon Repurchase...... 123
E. Power Consumption (Light Output)...... 125
F. Lamp Type...... 130
G. Color Rendering ...... 130
H. Form Factor...... 132
I. Luminaire...... 133
J. Light Brightness Settings ...... 134
5.3 Results & Discussion Section 3 – Economic Parameter Sensitivity Analysis ...... 137
K. Use Time ...... 138
L. Kerosene Cost ...... 143
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M. Kerosene Fuel Escalation Rate ...... 145
N. Charge Cost ...... 147
O. Battery Life Expectancy...... 152
P. Lighting Product Life Expectancy ...... 155
Q. Real Discount Rate ...... 157
R. Analysis Period...... 160
CHAPTER 6. CONCULSIONS...... 165
REFERENCES...... 168
APPENDICIES ...... 173
Appendix A. What small businesses in Kenya look like: architecture and goods sold ...... 173
Appendix B. Advancements in lamps used for off-grid lighting products ...... 178
B.1 Incandescent ...... 178
B.2 Fluorescent...... 178
B.3 White LED...... 179
Appendix C. Jones et al. 2005 Study ...... 183
Appendix D. Lighting Use Survey Form ...... 184
Appendix E. Lighting Use Time Card ...... 189
Appendix F. Kerosene Measurements Data Sheet...... 190
Appendix G. Performance Testing Procedure Details...... 191
Appendix H. “End of Use” Testing Script ...... 196
Appendix I. “End of Use” Test Setup...... 201
Appendix J. Questions for Torch “End of Use” Test...... 204
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Appendix K. Results from field procedures while in Kenya (Includes results to Methodology 1)...... 205
K.1 Costs associated with lamp ownership...... 205
K.2 Lighting use patterns & fuel consumption rates:...... 207
Appendix L. Results from quality screening testing procedures on electric lighting products & lux-hr/charge values (Includes results to Methodologies 2 and 7)...... 214
Appendix M. Results from estimating “end of use” lux values & measuring single lux values (Includes results to Methodologies 3 and 6) ...... 217
Appendix N. Model input parameters for base case scenario ...... 219
Appendix O. Assumptions ...... 220
O.1 Assumptions used for base case scenario...... 220
O.2 Assumptions used for individual design sensitivity parameters...... 221
O.3 Assumptions used for individual economic parameter sensitivity parameters...... 222
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LIST OF TABLES
Table Page
1. Comparing Lamp Types by Luminous Output...... 28
2. Performance characteristic values for rechargeable batteries...... 29
3. The spread of key design features for samples represented in my analysis. A total of 14 samples were analyzed. Some products are listed twice because they have multiple charging or form factor options...... 37
4. Summary of critical product features in existing solar lanterns in order of importance to the end-user ...... 39
5. Fuel consumption rates measured by members of the University of California, Berkeley with lamps obtained in India (Apte et al., 2007). Each lamp type fuel consumption rate represents measurements from one lamp (Apte, 2009)...... 44
6. Lighting costs, illumination, and payback period for four fuel-based, five electric off-grid, and two electric on-grid lighting products. The payback time is for switching from each source to its corresponding one-watt off-grid LED system, included in the bottom to rows (Jones et al., 2005). The klux-hr (or 1,000 lux-hr) unit represents the area beneath a lux curve while the lamp discharges over time, quantifying the amount of light a product provides throughout a use event. This is useful because lighting products exhibit different lux curves as they discharge...46
7. Lighting costs, illumination, and costs per lighting output values one fuel-based and three electric off-grid lighting products (Peon et al., 2005). The Luxeon K2 is an HBLED...... 48
8. Comparing costs between three lighting system alternatives: one fuel-based and two electric off-grid lighting products (Foster and Gómez, 2005)...... 50
9. Assumptions used for Table 8 results (Foster and Gómez, 2005)...... 50
10. Summary of capital costs for off-grid lighting products presented in the Literature Review economic analyses. The following symbols aside capital cost values signify the following: * only cost of lamp itself, ** Foster and Gómez study electric lighting systems are much larger than those analyzed in the other studies cited, and *** lamp and charging type not specified, from our experiences in Kenya, many off-grid “Light Bulb” systems used incandescent lights and were solar charged...... 54
xi
11. Summary of operation costs for off-grid lighting products presented in the Literature Review economic analyses. The Lighting Africa values were presented in terms of cost per month. I used the operating cost/klux-hr values that Jones et al. provided along with their provided lighting product lux values and discount rate to calculate cost per month operation costs for the lighting products they use in their analysis. I used the total operating costs values over 20 years that Peon et al. provided along with an annual interest rate of 10% to estimate cost per month operation costs for the lighting products they use in their analysis. The reason why I used an annual discount rate of 10% for the Peon et al. study is because they reported an annual discount rate of 10% for a similar but smaller economic study on off-grid lighting products published one year later (Irvine- Halliday et al., 2006)...... 55
12. Summary of LCC values estimated over a 20 year period for off-grid lighting products presented in the Literature Review economic analyses. The Peon et al. LCC values were presented over a 20 year period. I used the total cost/klux-hr values that Jones et al. provided along with their provided lighting product lux values and discount rate to estimate total LCC costs over a 20 year period for the lighting products they use in their analysis. The Foster and Gómez study provides a chart of annual costs over their 24 year analysis. I summed the first 20 years to estimate the Foster and Gómez values below...... 56
13. Price Build-up for Electric Lamps in Kenya (Hankins, 2007) ...... 74
14. Battery cycle values over five sources...... 76
15. Lifespan estimates by Lighting Africa (2008a)...... 81
16. Criteria used to estimate life expectancy for electric lights ...... 81
17. Quantitative base case scenario results analyzed over a four-year period and a 10- year period...... 110
18. Design Sensitivity Analysis Results Summary ...... 135
19. Maximum percentage increase of use hours per day from solar-charged lights after a standard day of solar charging. Base case use is two hours for a task or ambient light and one hour for a torch...... 142
20. Kerosene-fueled lighting LCC increases at 10% escalation rate over four years145
21. Variation of battery cycle lives used for Battery Life Sensitivity. Base case scenario values are highlighted yellow for comparison...... 152
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22. Variation of lighting product lives used for Lighting Product Life Sensitivity. Base case scenario values are highlighted yellow for comparison. Categories 1-4 refer to categories described in Table 16 of Methodology 4...... 156
23. Economic Parameter Sensitivity Results Summary...... 163
24. Kerosene lamp capital and accessory replacement costs. The pressure and hurricane lamp prices were from Naivasha Limited Grocery Store in Naivasha Town, the wick lamp price is from the Karagita market, and the candle price is from the Maai Mahiu market, purchased in a pack of eight...... 206
25. Battery costs per unit of capacity. Prices are averages from batteries found in Kenya...... 206
26. Electric lamp charge Costs in Maai Mahiu and Karagita...... 206
27. Kerosene Fuel Costs in Maai Mahiu and Karagita (Radecsky et al., 2008) ...... 207
28. Quantity of Data Collected for Each Collection Aspect Sorted by Town ...... 208
29. Measured and reported use time values by town (Radecsky et al., 2008) ...... 209
30. Fuel consumption rate and measured use time by lamp type. The hurricane and pressure lamp values were collected in Maai Mahiu and Karagita during summer 2008. *The wick lamp values are taken from a study conducted by Arne Jacobson, Evan Mills and Maina Mumbi during the summer of 2007 in Kisumu and Yala of the Nyanza Province in Kenya (Radecsky et al., 2008)...... 209
31. Datalogger results from Maai Mahiu and Karagita vendor participants between July and December, 2008. Morning use is evaluated as before noon. Evening use is evaluated after 5pm. Afternoon use is evaluated in between morning and evening. The value highlighted in yellow is of most interest to my analysis.....212
32. Lamp discharge results for each electric light used in analysis and lux-hr/charge values...... 215
33. Summary of solar charge and lighting distribution test results used in my economic analysis ...... 216
34. Average "end of use" lux values and respective coefficients of variation. All end of use values are exact averages, except the task lamp which is rounded up from 0.4 lux to 0.5 lux so that it is an easier number to use as a standard...... 217
35. Illuminance values measured for lighting products I use in my economic analysis are highlighted in yellow. The provided lux values for the fuel-based lighting
xiii products are averages from several measurements taken of the same light during one night. The provided lux values for the electric lighting products are medians from their lamp discharge until reaching their end of use lux value. For the electric lighting product values, measurements were taken in one-minute intervals and values reported represent more than one sample of the lighting product model are identified with a star (*)...... 218
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LIST OF FIGURES
Figure Page
1. Political map of Kenya (Photo from http://geology.com/world/kenya-satellite- image.shtml) ...... 6
2. The four main fuel-based lighting products used in Kenya. (Left) kerosene-fueled hurricane lamp; (Center left) kerosene-fueled pressure lamp; (Center right) kerosene-fueled wick lamp; (Right) candle (three left-most photos by Johnstone and Radecsky, candle photo from http://pebblez.com/pictures/art-001/stone- candle-holders/stone-candle-holders-15.jpg) ...... 10
3. Electric off-grid lighting products available in Kenya. (Left) LED grid-charged torches; (Center left) incandescent dry cell torch; (Center right) LED grid-charged torch with array option; (Right) LED dry cell lantern with radio option...... 13
4. (Left) A section of the Maai Mahiu market during the afternoon; (Right) The bustling Karagita market as night falls (Photos by Johnstone) ...... 15
5. Distribution of lighting products used, surveyed from 25 vendors in Karagita and 25 in Maai Mahiu. The top chart focuses on primary lighting, while the bottom includes both primary and secondary lighting (Radecsky et al., 2008)...... 17
6. Lighting Africa 2008a study when asking "What or where in the shop would you like to position lamps?"...... 19
7. Maai Mahiu and Karagita small business owners’ responses to why they were unsatisfied with their current off-grid lighting. Sixty percent of those surveyed indicated they were unsatisfied with their current lighting and were asked to share why they were unsatisfied. The responses were unprovoked and vendors could share more than one reason...... 20
8. Kerosene price trend in Kenya alongside world crude oil price trend. Crude oil prices obtained from the Energy Information Administration (2009)...... 21
9. A charge shop in a Kenyan market (Photo by Mills from Mills and Jacobson, 2007) ...... 22
10. Types of lamps used in off-grid lighting products. (Left) Incandescent Lamp (Photo from http://www.answers.com/topic/incandescent-light-bulb) ; (Center) Fluorescent Lamp (http://www.indiamart.com/svamelectronics/solar- lighting.html); (Right) LED Lamp (www.freewebs.com/otherlights/)...... 24
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11. There are two categories of LEDs used in off-grid lighting products. (Left) Miniature LEDs, sizes 8mm, 5mm, and 3mm from left to right (match included for scale); (Right) HBLEDs from Philips Lumileds Lighting Company mounted on a star shaped heat sink. The diameter of the dome component of the HBLED is approximately ½ cm (Photos from http://en.wikipedia.org/wiki/LED) ...... 25
12. “Luminous output degradation for 5mm and high flux WLEDs after continuous operation" (Peon et al., 2005)...... 25
13. Examples of different color rendering index values (Photo by Javier Ten of the Lighting Research Center)...... 27
14. A comparison of off-grid lighting product form factors. (Left) Torch Light; (Center) Task Right; (Right) Ambient Light...... 31
15. A comparison of off-grid lighting product charging methods. (Top Left) Integrated Solar; (Top Right) Separate Solar; (Bottom Left) Integrated Grid; (Bottom Center) Separate Grid; (Bottom Right) Mechanical Crank ...... 32
16. The best average commercial module efficiencies by technology over time from an NREL study (Roedern, 2008a). The abbreviations Roedern uses are: mono = monocrystalline silicon, multi = multicrystalline silicon, a-Si 3-j = amorphous silicon triple junction, a-Si 1-j = amorphous silicon single junction, a-Si/nc-Si = amorphous silicon nanocrystalline silicon, a-Si/a-Si = amorphous silicon same bandgap double junction (Roedern, 2008b)...... 34
17. Lighting Africa 2008a survey results addressing barriers to improving lighting..41
18. Lighting Africa 2008 study of 400 small business owners, asking "What time does your business usually open and close?" ...... 45
19. Lighting costs and payback periods for four fuel-based, five electric off-grid, and two electric on-grid lighting products (Jones et al., 2005). The grid-connected 60 W incandescent and grid-connected 15 W CFL systems have payback periods of 15 and infinite years, respectively...... 47
20. Lighting costs, illumination, and costs per light output for one fuel-based and three electric off-grid lighting products (Peon et al., 2005)...... 49
21. Lighting costs of one fuel-based and two electric off-grid lighting products (Foster and Gómez, 2005)...... 51
22. Lighting costs of four fuel-based and two electric off-grid lighting products. The numbers above the bars indicate the survey sample for each type of lighting product (Lighting Africa, 2008)...... 52
xvi
23. (Left) Market kerosene vendor filling a plastic container with pump. (Right) Arne Jacobson measuring kerosene sample volume and weight. (Photos by Johnstone) ...... 62
24. Large hurricane lamps and wicks for sale in a store within the Karagita market (Photo by Johnstone)...... 63
25. (Left) Radecsky making measurements of a hurricane lamp to calculate a night fuel consumption value. (Right) Francis Ngugi weighting a pressure lamp the morning after a night of measurements were made (Photos by Johnstone)...... 66
26. Photo of data-logging lamp leased in Kenya. The solar panel was optional. The photo on the right shows a close-up of the lamp head and provided six-foot lamp extension. (Left photo by Johnstone)...... 68
27. Lamp discharge curves for two electric lighting products; the left-side product discharges slowly to zero lux, while the right-side product contains a low-voltage cutoff automatically shutting off the light to prevent battery damage. Lighting products with low-voltage disconnects may occasionally continue to provide a very small amount of light after reaching its low-voltage disconnect voltage value, as seen in the right-side product’s discharge curve...... 71
28. Batteries for sale at Nairobi Nakumatt (Photo by Johnstone) ...... 78
29. Setup for measuring lux values from fuel-based lights...... 86
30. Base case scenario comparing task and ambient lighting products over a four-year period. All lights were able to provide the base case of two hours of useful light per day, except the OB, which could only provide 1.9 hour s of useful light per day on its high ambient setting...... 105
31. Base case scenario comparing task and ambient lighting products over a 10-year period. All lights were able to provide the base case of two hours of useful light per day, except the OB, which could only provide 1.9 hours of useful light per day on its ambient high setting...... 106
32. Base case scenario comparing torch lighting products over a four-year period. All lights were able to provide the base case of one hour of useful light per day.....108
33. Base case scenario comparing torch lighting products over a 10-year period. All lights were able to provide the base case of one hour of useful light per day.....109
34. Comparing products having similar capital costs in terms of their LCC values. The maximum willingness-to-pay is represented as $25 (Mills and Jacobson, 2007). All the fuel-based products are shown as squares...... 111
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35. Sensitivity to energy costs from percent battery enlarged over a four-year period ...... 115
36. Sensitivity to LCC savings from percent battery enlarged over a four-year period ...... 116
37. Comparing YF task lighting products with different battery chemistries; the product designs are identical in every other way. The batteries have very similar capacities – the SLA battery has 2200 Wh and the NiCd battery has 2150 Wh. The YF1 task light increases in lux at the start of its test; this does occur with some lighting products...... 118
38. Comparing grid and solar charging options for seven lighting products over a four-year period. Two of the seven options (the AS and the AN) automatically include both solar and grid charging options, hence there is no difference in the capital costs between the options...... 122
39. LCC savings when solar-charged lighting products are replaced without the repurchasing of a solar module, over a 10-year period...... 124
40. LCC savings with decreased power consumption for grid-charged lights over four years (The LCC savings equals the energy savings in this case.) ...... 126
41. LCC savings with decreased power consumption and solar module size for solar- charged options. (Solar module size is reduced in proportion to reduced power consumption.) ...... 128
42. Capital cost with decreased power consumption and solar module size for solar- charged options. (Solar module size is reduced in proportion to reduced power consumption; see Methodology 7-E for details.)...... 129
43. CIE (x,y) color coordinates for several 5mm and HBLED lighting products tested at the HSU lighting laboratory (The CIE 1931 (x, y) chromaticity diagram photo is from http://www.ledtuning.nl/gallery.php?Name=aboutcolors_EN&month=6&year=19 93, the D65 Daylight CIE (x, y) coordinates are from (CIE, 1998), the Incandescent CIE (x, y) coordinates are from (GE Electric Company, 1997-2008), the Kerosene Lamp CIE (x, y) coordinates are from (Energistic Systems), and the Candle CIE (x, y) coordinates are from (Gigahertz-Optik)) ...... 131
44. LCC values for ambient and task lighting products with varying use times over four years...... 139
45. LCC values for torch lighting products with varying use times over four years.141
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46. Sensitivity to LCC from variation in kerosene cost...... 144
47. Sensitivity to LCC values with increasing kerosene fuel escalation rate over a four-year period ...... 147
48. LCC values for ambient and task lighting products with varying the cost for a full charge over four years...... 149
49. LCC values for torch lighting products with varying the cost for a full charge over four years...... 151
50. Percent saved in LCC from the base-case scenario values from variation in battery cycle life expectancy over four years...... 154
51. Percent saved in LCC from the base-case scenario values from variation in lighting product life expectancy over four years ...... 157
52. LCC values for ambient and task lighting products with varying real discount rates over four years...... 159
53. LCC values for torch lighting products with varying the real discount rate over four years...... 160
54. Sensitivity to LCC values from variation in analysis period...... 161
55. J.M. Kiosk displays produce, some general merchandise and drinks outside a window, but he operates from within the kiosk window (Photo by Johnstone)..174
56. J.’s Vegetable Market displays his family’s produce on a market stall and then prepares and stores it within their attached building (Photo by Johnstone)...... 175
57. A family in Karagita display their houseware merchandise in this small shop (Photo by Johnstone)...... 175
58. The common goods sold surveyed in Maai Mahiu and Karagita markets...... 176
59. Small business owner types through Kenya, study conducted by Lighting Africa (2008a)...... 177
60. How an LED emits light (Kasap, 1999)...... 1810
61. LED light structures (Kasap, 1999) ...... 181
62. High-Brightness LED cross section (Image by Dow Corning)...... 182
63. Comparing data based on lighting type and data collection method...... 210 xix
64. Histograms for Hurricane and Pressure Lamp Burn Rates Measured ...... 211
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LIST OF APPENDICES
Appendix Page
A. What small businesses in Kenya look like: architecture and goods sold...... 173
B. Advancements in lamps used for off-grid lighting products...... 178
C. Jones et al. 2005 Study...... 183
D. Lighting Use Survey Form...... 184
E. Lighting Use Time Card...... 189
F. Kerosene Measurements Data Sheet...... 190
G. Performance Testing Procedure Details...... 191
H. “End of Use” Testing Script ...... 196
I. “End of Use” Test Setup ...... 201
J. Questions for Torch “End of Use” Test ...... 204
K. Results from field procedures while in Kenya (Includes results to Methodology 1) ...... 205
L. Results from quality screening testing procedures on electric lighting products & lux-hr/charge values (Includes results to Methodologies 2 and 7) ...... 214
M. Results from estimating “end of use” lux values & measuring single lux values (Includes results to Methodologies 3 and 6)...... 217
N. Model input parameters for base case scenario ...... 219
O. Assumptions ...... 220
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ACRONYMS AND ABBREVIATIONS
CCT Correlated Color Temperature CIE International Commission on Illumination CFL Compact Fluorescent CRI Color Rendering Index EIA Energy Information Administration HBLED High Brightness Light Emitting Diode HSU Humboldt State University IFC International Finance Corporation KSH Kenyan Shilling LBNL Lawrence Berkeley National Laboratories LED Light Emitting Diode LCC Life Cycle Cost LM Lumen NiCd Nickel Cadmium (Battery) NiMH Nickel Metal Hydride (Battery) NREL National Renewable Energy Laboratory PPS Programmable Power Supply SERC Schatz Energy Research Center SLA Sealed Lead Acid (Battery) UNDP United Nations Development Programme VAT Value Added Tax WBG World Bank Group WLED White Light Emitting Diode
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CHAPTER 1.
INTRODUCTION
1.6 billion people in the world live off-grid – 30% of the world’s population
(UNDP, 2004). For illumination, off-grid communities often use lighting products such as candles, kerosene-fueled lamps, or dry cell battery-powered lights. Unfortunately, fuel-based lighting and dry cell powered lights can be expensive, a health hazard and often provide poor quality light. Many pay up to 20-30% of their annual cash income on the initial purchase, maintenance, and operating costs of their lighting products (Lighting
Africa, 2007). Access to affordable, quality lighting is a crucial step for communities around the globe to become more prosperous.
In addressing this issue, manufacturers world-wide are working to design new electric off-grid lighting products with the goal of making them more affordable for lower-income communities. For example, over 500 global players from the lighting industry and supporting sectors attended the 2008 Lighting Africa conference in Ghana to
“develop appropriate and viable business models for delivering modern, clean and safe non-fuel based off-grid lighting solutions” (Lighting Africa, 2008c). Some off-grid lighting products are very promising, but others are not suitable for lower-income customers and can cause market spoiling. Market spoiling can occur when consumers do not have enough information to distinguish between low and high quality products. In this circumstance, those that buy low quality goods may assume that all products perform poorly (Mills and Jacobson, 2008). Informing customers about product quality is
1 2 important; informing manufacturers about appropriate design is also important.
Manufacturers can greatly benefit from information on how lower-income consumers actually use off-grid lighting products so that they can design more appropriate products.
The purpose of my thesis is to provide recommendations for appropriate design of off-grid lighting products. This work will in turn benefit off-grid customers. Having experience in Kenya, my analysis is relative to customers like rural Kenyan small business owners. Research International1 asked 260 small business owners in Kenya,
“What are the barriers to improving the lighting for your business?” Seventy-six percent
responded that they did not have enough money to purchase or fuel/energize an improved
product (Lighting Africa, 2008a).
My design recommendations focus on how to decrease costs associated with
lighting for off-grid customers while maintaining acceptable quality. I conducted several
economic analyses on 19 off-grid lighting products – five fuel-based and 14 electric. I
use parameter values based on data Arne Jacobson, Peter Johnstone, Maina Mumbi and I
collected from small business owners in Kenya nighttime markets during 2008.
I structure this thesis by first providing background on Kenya, its small business
owners, the current off-grid lighting used, and the basic design components of an electric
off-grid lighting product. Also in the background, I introduce the electric off-grid
lighting products I later use in my economic analysis. After the background, I provide a
1 Research International is a market research firm that was contracted by Lighting Africa, a joint project of the World Bank and International Finance Corporation, to conduct market research on household and small business off-grid lighting uses in five African countries: Kenya, Ethiopia, Uganda, Ghana, and Tanzania. The firm’s research is the primary source of information for the Lighting Africa pieces referred to in this thesis as Lighting Africa, 2008a and Lighting Africa, 2008b.
3 literature review presenting previous study results on Kenyan small business owner preferences, parameter values to support economic analyses, and economic analyses comparing lighting products designed for places like Kenya. Next, I describe all the methodologies used to both collect and create my economic analysis. Lastely, I present my economic analysis in the Discussion and Results section. The Discussion and Results section includes a base case scenario comparing fuel-based and electric off-grid lighting products, design sensitivity analyses to explore relationships between design component characteristics and product costs, and economic parameter sensitivity analyses investigating how results are effected by parameter variations.
From the design sensitivity analysis, I found that increasing battery size and reducing power consumption have the strongest potential for reducing the life cycle cost
(LCC) of electric off-grid lighting systems. Both possible design improvements may increase capital costs; manufacturers must make sure not to exceed its target customer’s willingness-to-pay value. I also found that it is best to design grid-charging lights with an optional solar component and that high brightness light emitting diodes (HBLEDs) are the preferred lamp type if available at a reasonable capital cost.
From my analysis, I do not claim there to be any single best design solution, but instead provide general suggestions about product design elements in the context of use by Kenyan small business owners. My intention is to support all off-grid lighting manufacturers in producing more promising products for off-grid customers around the globe.
CHAPTER 2.
BACKGROUND
To understand the economics behind off-grid lighting products for small businesses in Kenya, it is important to first learn about each component in this endeavor.
In the background I introduce the country of Kenya and its small business owners. I next discuss the types of off-grid lighting products available in Kenya. Since my analysis draws from the two Kenyan towns of Maai Mahiu and Karagita, I discuss the character of each town, focusing on the small business owners and their relationships with off-grid lighting products.
As my analysis evaluates the design of electric off-grid lighting products, I provide an ample background describing key design elements which most influence operating costs. I introduce the lighting products used in my analysis. And finally, I provide background on progress towards making promising off-grid lighting products more available in locations like rural Kenya.
2.1 About Kenya
Kenya is a young republic, receiving its independence from the United Kingdom in 1963. Located in Eastern Africa, Kenya borders the Indian Ocean, Somalia, Ethiopia,
Sudan, Uganda and Tanzania (Figure 1). It is 582,650 km2, approximately twice the size of Nevada. Kenya’s climate is tropical along the coast and becomes more arid in the interior. It is composed of low plains rising to the central highlands with a fertile plateau in the west and is bisected by the Great Rift Valley through its center. Kenya’s 4 5 population is estimated to be 37,953,840 and is growing at the rate of 2.8% per year (CIA
World Factbook, 2009).
Kenya is known to be one of the most stable countries within the continent of
Africa, but has recently engaged in political violence in January, 2008. Many Kenyans identify with a tribe; there are over 42 tribes in Kenya. The 2008 election candidates were of two different tribal origins. The close election results instigated suspicion of cheating, which led to violent hate crimes between tribes within the country (Doyle,
2008). Since then the country has returned to peace; however, during our data collection in June and July of 2008, memories of the violence were still fresh in people’s minds.
6
Figure 1. Political map of Kenya (Photo from http://geology.com/world/kenya-satellite- image.shtml)
7 2.2 Introducing Small Businesses in Kenya
My analysis uses data collected from small businesses in rural Kenya. The results are intended to support off-grid lighting manufacturers in designing better lights that will economically benefit small business owners like those in rural Kenya.
Kenya’s economy is heavily dependent on its small businesses. According to a
World Bank survey of 11,012 enterprises, 30% of all employment in Kenya is in the informal sector. Furthermore, the survey showed that the informal sector has an annual employment growth rate of approximately 13% and absorbs 50% of all new non-farm employment seekers (Riley and Steel, 1995). The informal sector in Kenya includes small businesses in manufacturing, retail and wholesale trade. The businesses range from thriving manufacturing businesses to “men doing repairs on the sidewalk of streets”
(Antoine, 2004).
In addition, small businesses in Kenya generally have few employees and are
short-lived. Ninety-six percent of small businesses in Kenya employ no more than three
workers, and 89% employ no more than two (Lighting Africa, 2008a). During the World
Bank survey, 40% of the small businesses identified had been operating for less than two
years, the rest not much more than five years (Riley and Steel, 1995).
Many Kenyans are employed within the informal sector but make little income.
The mean monthly income from a small business worker in Kenya is $146.50 (Lighting
Africa, 2008a). Of the small businesses identified in the World Bank survey,
approximately 55% were involved in retail sales. Because of highly competitive and
8 saturated market conditions, those in the retail sector have particularly low incomes
(Riley and Steel, 1995).
Many small businesses in Kenya do not have access to the grid. Lighting Africa found that 90% of small businesses in Kenya are not connected to the grid (2008a).
According to the Kenya Power and Lighting Company in 2004, 29.4 million are without electricity in Kenya; this is 86% of the population (Lighting Africa, 2007), which demonstrates an even broader need for off-grid lighting. If any who are not connected to the grid desire illumination, their off-grid lighting options are currently limited and expensive.
2.3 Current Off-Grid Lighting for Small Businesses in Rural Kenya
Affordable, quality off-grid lighting products can yield significant economic benefits for small businesses. With adequate illumination, a small business can extend its working day, “expanding production, enriching income opportunities, improving working conditions, and increasing customers” (Lighting Africa, 2007). A case study from
Bangledesh shows that better lighting for tailors extended business hours into the evenings by four hours, increasing revenue by 30% (Khan, 2001 cited in Kirubi, 2006).
Improved off-grid lighting is very much desired by small business owners.
According to the Lighting Africa 2008a study, when asking 400 small business owners:
“If there was one thing you could do to improve your business or its facilities?,” 21% indicated that they would improve their lighting. Lighting Africa also reported that 40% of businesses regularly operate after dark and another 40% indicated they do not operate
9 after dark due to lack of lighting. Businesses believe that “operating after dark is a very welcome idea as it is thought to increase the number of customers at the shop and hence increase profits” (Lighting Africa, 2008a).
Unfortunately, many off-grid lighting products currently available to lower- income communities in Kenya lack affordability and/or quality. The majority of off-grid small businesses in Kenya choose fuel-based lighting. Some battery-powered lighting options have become available; however, those currently available could be improved.
2.3.1 Fuel-Based Off-Grid Lighting Products in Kenya
There are four main fuel-based lighting products used in Kenya – the hurricane lamp, pressure lamp, wick lamp, and candle (Figure 2). The kerosene-fueled hurricane lamp is the most common primary off-grid lighting product. The hurricane lamp supplies a maximum illumination of four lux (lumens/m2) at one meter distance. The kerosene-
fueled pressure lamp supplies a much higher maximum luminance of 75 lux but
consumes approximately four times as much kerosene fuel, making it the more expensive
kerosene-fueled option. The wick lamp, made from a can, is another kerosene-fueled
option. It is attractive for its minute capital cost but is notorious for its excessive
smoking when burned. It also provides poor light – a maximum of two lux at one meter
distance. While most off-grid small businesses in Kenya use kerosene-fueled lamps,
some business owners use the 1.3 lux-providing candle. In comparison, the standard
illumination recommended in industrialized countries for similar applications is 500 lux
(Mills, 2005).
10
Figure 2. Four common fuel-based lighting products used in Kenya. (Left) kerosene- fueled hurricane lamp; (Center left) kerosene-fueled pressure lamp; (Center right) kerosene-fueled wick lamp; (Right) candle (three left-most photos by Johnstone and Radecsky, Candle photo from http://pebblez.com/pictures/art-001/stone- candle-holders/stone-candle-holders-15.jpg)
Reducing fuel-based lighting use could have a significant global impact.
Lawrence Berkeley National Laboratories (LBNL) estimates that fuel-based lighting consumes 77 billion liters of fuel worldwide, equating to $38 billion/year or “1.3 million barrels of oil per day, on par with the total production of Indonesia, Libya or Quatar, or half that of pre-war Iraq” (Mills, 2005). Russell Sturm, IFC Lighting Africa program manager, estimates that “Africans spend more than 18 billion dollars a year purchasing kerosene” (Leahy, 2008). Although they provide low light output, fuel-based lighting
11 systems come with a surprisingly high cost. Lighting researcher Evan Mills, estimates that in terms of cost per light output (cost/lux-hr), fuel-based lighting is up to 150 times more expensive than premium-efficient fluorescent lighting used in industrial countries
(Mills, 2005).
Not only is fuel-based lighting costly, it is also a health hazard. Humboldt State
University’s air quality research team, lead by Dustin Poppendieck, conducted a study of particulate emissions from four kerosene-fueled lighting products purchased in Kenya: a pressure lamp, a large hurricane lamp, a small hurricane lamp, and a wick lamp. The team measured three size categories of particulate emissions in a laboratory setting that consisted of a kiosk model similar to those used in Kenya (i.e., small business structure).
They found that for general PM2.5 and PM10 emissions (particle matter less than 2.5 µm
and 10 µm in diameter, respectively), the particulate concentrations in the test structure
were below 24-hour EPA standards when pressure and hurricane lamps were in use, but
during tests of the wick lamp the levels were far above the standard. They also measured
ultrafine particulates in the PM0.02 to PM0.1 range and found significant concentrations for
all four lamps tested; however, the EPA has not yet formed a standard for ultrafine
particulates. Particles smaller than 2-5 µm are generally more dangerous than those in
the 2.5 µm to 10 µm range. Particles of PM2.5 may become stuck in a person’s bronchial
system, causing problems such as asthma, bronchitis, heart disease or lung cancer. The
research team estimates that an employee working in a Kenyan small business with a
wick lamp may experience PM2.5 levels of 250 µg/m3 – seven times the EPA 24-hour
limit and 17 times the EPA annual limit (Poppendieck and Jacobson, 2009).
12
In addition, Mills estimates the release of 100 kg of carbon dioxide (CO2) by burning a kerosene lantern for four hours per day over one year. This amount of carbon is equivalent to the amount released to generate the electricity to operate a 70 W lamp for four hours per day for a year or by generating 100 kWh of electricity from a coal-fired power plant (Lighting Africa, 2007). CO2 is a notorious greenhouse gas causing global
warming. LBNL estimates that combined fuel-based lighting use throughout the world
emits 190 million metric tons of CO2 – greater than what is emitted by Australia or the
United Kingdom combined (Foster and Gómez, 2005).
2.3.2 Electric Off-Grid Lighting Products in Kenya
The battery-powered off-grid lighting options currently available to small businesses in Kenya may be a step up from the fuel-based lighting, but still have a ways to go to become appropriate for the Kenyan off-grid lighting market (Figure 3). The most common products are battery-powered torches, also known as flashlights. The torches consist of both incandescent and LED (light emitting diode) designs. Torches are either rechargeable or require dry cell batteries. Some small businesses may own a torch, but use it only for secondary lighting purposes or are only open briefly after sunset. They do not serve the needs of the small businesses that stay open for two or three hours after sunset who need to illuminate their entire spread of merchandise as well as distinguish monies during transactions. While in rural areas of Kenya this past summer, we found two other styles of battery-powered products available. One was a rechargeable torch with an LED array integrated into its side. The array is designed to provide users with a
13 more ambient type of lighting as opposed to the focused lighting provided by the standard torch. The other style we found was a small dry cell, lantern-style LED lamp with an integrated radio. The lantern style is designed to provide a more radial spread of light, mimicking the kerosene-fueled hurricane and pressure lamps. Both the LED torch/array and lantern-style LED lamp we found may be an improvement from the plain torch in respect to lighting small businesses, but after testing, both lamps’ performances were still not impressive. The LED torch/array in its array setting provides 9 lux at a one meter distance when fully charged and drops to 5 lux after 1.3 hours, then drops to 3 lux after three hours. It provides useful light for only a short period of time. The lantern-style lamp provides 0.25 lux at a one meter distance with new Duracell alkaline batteries (the same brand they came with), dropping to 0.1 lux after 15 hours. Its light is radial, but very dim – the user would need to work very close to the light source to carry out a task.
Figure 3. Electric off-grid lighting products available in Kenya. (Left) LED grid-charged torches; (Center left) incandescent dry cell torch; (Center right) LED grid-charged torch with array option; (Right) LED dry cell lantern with radio option
14 2.4 Small Businesses in Maai Mahiu and Karagita
My analysis is based on data collected by Arne Jacobson, Peter Johnstone, Maina
Mumbi, and me in Kenya this past summer of 2008. In our study, we collected lighting use data and employed data-logging LED lamps to determine lighting costs for small businesses in two small towns in the Naivasha District of Kenya’s Rift Valley Province –
Maai Mahiu and Karagita. Each town has an active night market, comparable to many other markets in Kenya. According to residents, both towns have populations on the order of 6,000 to 8,000 people. Maai Mahiu is a crossroads town, located at a junction along a major trucking route. The residents are primarily from the Kikuyu tribe, and the town was not impacted heavily by the post-election violence that swept across the Rift
Valley in January, 2008. Overall, the residents of Maai Mahiu tend to be more prosperous than those in Karagita. The building materials used in Karagita include stone, wood, and mud, in contrast to the primarily stone construction used in Maai Mahiu.
Karagita is a small town outside the larger town of Naivasha Town and is amongst several of what are essentially factory towns for the prominent flower farm industry on the southern shores of Lake Naivasha. The ethnic makeup of Karagita was more diverse than Maai Mahiu prior to 2008 and severe acts of post-election violence occurred in the community, leading to the flight of many people from the Luo tribe away from the town.
Conversely, both towns are now home to many refugees, primarily Kikuyu, who fled from other violent areas, such as Kisumu in west Kenya.2
2 Maai Mahui and Karagita descriptions jointly written by Johnstone and Radecsky
15
Figure 4. (Left) A section of the Maai Mahiu market during the afternoon; (Right) the bustling Karagita market as night falls (Photos by Johnstone)
Vendors in the markets we studied sell a variety of merchandise, including produce, housewares, clothing, stationary supplies, meats and electronics. We observed that vendors with higher priced goods could often afford buildings with more elaborate architecture styles with access to the grid, such as shops within a building as opposed to a stand alone kiosk or market stalls. (Architecture style examples are illustrated in
Appendix A.)
Electric grid lines run through both markets, as seen in the figures above, but we observed that over half of nighttime market venders run their businesses off-grid. Grid lines are commonly run through most Kenyan markets. Lighting Africa indicates that
66% of small business owners in Kenya operate within close proximity to the grid but remain off-grid due to the high connection fee (Lighting Africa, 2008a). No matter how little energy the small business uses, the business owner is required to pay a monthly
16 connection fee of 120 Kenyan Shillings (Ksh) per month on top of the cost per energy used (Mwirichia, 2008). The monthly connection fee is approximately equivalent to the
122 Ksh monthly cost to fuel a large hurricane lamp estimated using data we collected in
Kenya in 2008 (Radecsky et al., 2008). A small business makes on average 356 Ksh per month from sales (Lighting Africa, 2008), suggesting that the combined costs of the connection fee and energy used is likely impossible for many small business owners to pay.
Small businesses are ideal participants for collecting lighting use data and employing data-logging LED lamps for three main reasons. First, since small businesses employ a large proportion of Kenyan workers, markets are a direct avenue for increasing income levels for the rural poor. Second, if a small business owner is impressed by electric off-grid lighting, the business owner is likely to bring the technology home, thus also increasing better lighting in households for tasks like children’s nighttime studying.
And third, vendors in a marketplace are more likely comfortable with research interactions as compared to families in their homes. Small business owners are used to inviting people they do not know into their stores, whereas families may be more reserved to invite people they do not know into their homes. Home lighting, however, is also very important and exhibits different use patterns. Capturing home lighting use in
Kenya or similar location is a recommended area for further research on this topic. In the context of Kenya, small business owners may be more likely to be the early adopters of electric off-grid lighting technology.
17 2.5 Small Business Off-Grid Lighting Use
Between Maai Mahiu and Karagita, we surveyed 50 small off-grid businesses for documenting their lighting uses. We attempted to survey as many off-grid night market vendors as would participate. I estimate that our findings represent over 50% of the off- grid night market vendors in Maai Mahiu and Karagita. We found that the hurricane and pressure kerosene lamps were most common in both towns and that the percentages of vendors who used each lamp type were statistically indistinguishable between the towns
(Figure 5). We did not survey vendors using the grid.
Fractions of Primary Off-Grid Lamp Types Used by Those Surveyed in: Kerosene Wick Kerosene Hurricane Kerosene Pressure 28% 26% Candles 56% 62% 24% 68% Electric (LED Lantern) Elec tric ( LED A r ray ) Electric (CFL Bulb) Maai Mahiu Maai Mahiu & Karagita Karagita Combined
Fractions of All Off-Grid Lamp Types Used by Those Surveyed in: Fractions of All Off-Grid Lamp Types Used by Those Surveyed in: Kerosene Wick Kerosene Hurricane Kerosene Pressure Candles 26% 51% Flashlight (LED) 56% 62% Flashlight (Incand.) 24% 22% Electric (LED Lantern) Electric (LED Array) Karagita Electric (CFL Bulb) Maai Mahiu Maai Mahiu & Karagita Combined Figure 5. Distribution of lighting products used, surveyed from 25 vendors in Karagita and 25 in Maai Mahiu. The top chart focuses on primary lighting, while the bottom includes both primary and secondary lighting (Radecsky et al., 2008).
18 From my observations in Maai Mahiu and Karagita, the small business vendors that stay opened after sunset use their off-grid lighting products primarily to illuminate their merchandise. One vendor who was chopping vegetables at night shared with us the importance of having his merchandise illuminated over making the light available for him to chop. I also observed that when vendors would make a sale, they moved close to their light in order to use the illumination to count monies for the transaction. The Lighting
Africa 2008a study surveyed 337 Kenyan retail small businesses asking where they like to position their lighting product during business. Positioned for collecting money was the most common response, followed by illuminating merchandise and lighting up customers’ faces (Figure 6) (Lighting Africa, 2008a). The study’s results support my observations of small business owners’ desire to use light for collecting monies and illuminating merchandise, and further identifies the importance of seeing customers.
19
Figure 6. Lighting Africa 2008a study when asking "What or where in the shop would you like to position lamps?"
2.6 Current Lighting (Qualms and Options)
Of the 50 small businesses we surveyed, 60% indicated they were unsatisfied with their current lighting. When asking why they were unsatisfied, participants overwhelmingly answered “too dim.” The second most popular response was “too expensive” (Figure 7).
20
25
20
15
10
Number Surveyed 5
0
s r ent u e aks ro zard en re e Oth vi B g Ha Too Dim an h Expensive con D lt In ea H
Figure 7. Maai Mahiu and Karagita small business owners’ responses to why they were unsatisfied with their current off-grid lighting. Sixty percent of those surveyed indicated they were unsatisfied with their current lighting and were asked to share why they were unsatisfied. The responses were unprovoked and vendors could share more than one reason.
We collected historical kerosene price data from petroleum stations in both towns ranging from 2004 to the present. The kerosene data provided a similar trend to the world crude oil prices (Figure 8). Between January, 2004 and August, 2008, kerosene prices quadrupled from 20 Ksh/liter to 85 Ksh/liter; however, recently kerosene prices have dropped along side world crude oil prices. As kerosene prices fluctuate, it becomes more desirable to switch to electric lighting.
21
90 160 Kenya Kerosene Data 80 World Crude Oil Data 140
70 120
60 100 50 80 40
60 Cost ($/Barrel)
Cost (Ksh/Liter) 30 40 20
10 20
0 0
Jan-00 Jan-01 Jan-02 Jan Ja Jan-05 Jan- Jan- Jan-08 Jan- n- -03 04 06 07 09
Date
Figure 8. Kerosene price trend in Kenya alongside world crude oil price trend. Crude oil prices obtained from the Energy Information Administration (2009).
After talking with vendors and others who live in Kenya, we learned that people have three main options for charging an off-grid lighting product with rechargeable batteries. These options include plugging the light into a home grid connection with its provided charger, paying a charge shop to plug in the light with its provided charger, or connecting the light to a solar module to charge. Charge shops are abundant in Kenyan markets and are very popular locations for people to charge cell phones, as illustrated in the photo below (Figure 9).
22
Figure 9. A charge shop in a Kenyan market (Photo by Mills from Mills and Jacobson, 2007)
2.7 Progress Towards Improving Off-Grid Lighting Products
A wide variety of new off-grid lighting products are being manufactured. The products vary in several design features, such as lamp type and battery chemistry. Many of these manufactured products have not yet become commercially available in rural
Kenya, but organizations are working towards increasing product availability. In this section, I first provide some background for understanding electric lighting designs.
Then I introduce the off-grid lighting products I use in my analysis. Finally, I provide a brief explanation of the effort to make more products available.
23 A. Electric Off-Grid Lighting Design Basics
Electric off-grid lighting is a promising alternative to fuel-based lighting. The basic electric off-grid lighting product consists of a lamp (or bulb), batteries, an on/off switch and a chassis. More elaborate electric off-grid lighting designs may include a charging circuit, a charger, solar module, brightness settings, and more. In this section I discuss design options for lamp components which play key roles in off-grid lighting operating costs.
Design Component 1 – Lamps
Three types of lamps are used in electric-based off-grid lighting products: incandescent, fluorescent and LED (Figure 10). The incandescent bulb is what most people think of as a standard light bulb. Consumers enjoy the warm, yellow glow emitted by the incandescent lamp, but it is the least efficient and most costly to operate of lamp options. Because of these drawbacks some off-grid lighting manufacturers have switched to fluorescent lighting, which have higher efficiencies at a lower operating cost.
Consumers have historically been unsatisfied with the quality of fluorescent lighting because of its “bluish” tone, but fluorescent technology has since improved to achieve a
“whiter” light. Fluorescent lamps’ have, however, still not been improved in terms of toxicity; they contain mercury and must be disposed of with hazardous waste. Many off- grid lighting manufacturers are now designing with a safer, even more efficient and durable lamp – the white light emitting diode (WLED).
24
Figure 10. Types of lamps used in off-grid lighting products. (Left) Incandescent Lamp (Photo from http://www.answers.com/topic/incandescent-light-bulb) ; (Center) Fluorescent Lamp (http://www.indiamart.com/svamelectronics/solar- lighting.html); (Right) LED Lamp (www.freewebs.com/otherlights/)
I provide a detailed history and explanation of all three lighting types in Appendix
B. Current off-grid lighting products use all three lamp types, but the most promising products use WLEDs. In this section I focus on WLED lamps.
Most electric-based off-grid lighting products use two categories of WLED lamps
– miniature LEDs and high brightness LEDs (HBLED) (Figure 11). The miniature LEDs come in several sizes, including 3mm, 5mm, and 8mm, where the size is specified as the diameter of the LED’s round cross-sectional area. The 5mm LED is the most common size of miniature LED in off-grid lighting products, often clustered in groups of five to 10 in one product. The HBLEDs have a higher lumen/watt output and are typically far brighter than miniature LEDs. In addition, devices with HBLEDs demonstrate a slower
25 rate of decay in light output than devices with 5mm LEDs (Figure 12) (Peon et al., 2005).
HBLEDs, however, are also more expensive.
Figure 11. There are two categories of LEDs used in off-grid lighting products. (Left) Miniature LEDs, sizes 8mm, 5mm, and 3mm from left to right (match included for scale); (Right) HBLEDs from Philips Lumileds Lighting Company mounted on a star shaped heat sink. The diameter of the dome component of the HBLED is approximately ½ cm (Photos from http://en.wikipedia.org/wiki/LED)
Figure 12. “Luminous output degradation for 5mm and high flux WLEDs after continuous operation" (Peon et al., 2005).
26 But just how much better are WLEDs? The following metrics are used when comparing lighting products.
1. Luminous Efficacy (lumens/watt)
2. Color Rendering Index (CRI)
3. Correlated Color Temperature (CCT)
4. Durability
5. Estimated Life Expectancy
The luminous efficacy measures how much light output is provided for the amount of electric power input, measured in lumens per watt. The color rendering index
(CRI) of a light measures how “natural” colors appear under the light. Figure 13 below illustrates three different CRI values. The correlated color temperature of a light, measured in degrees Kelvin, is related to the temperature at which a blackbody would emit light of the same chromaticity. The “warmer” the light, the lower the temperature and “yellower” the light appears, while the “cooler” the light, the higher the temperatures and “bluer” the light appears (Lighting Research Center, 1995-2009). A high quality
WLED has a CRI of > 70 and a CCT between 2500° and 6500° K (Foster and Gómez,
2005).
27
Figure 13. Examples of different color rendering index values (Photo by Javier Ten of the Lighting Research Center)
Table 1 illustrates ranges of the above parameters for the incandescent, fluorescent and WLED lamps, as well as the kerosene lamp. The ranges encompass a number of literature sources. In terms of luminous efficacy, available WLED products can produce up to 100 lm/W – the highest of the compared lamps. There are WLEDs in the market, but often with lower luminous efficacies. All lamp types have CRI values greater than the standard of 70, except for the fluorescent lamp. The fluorescent and
WLED lamps both have higher (bluer) CCT values, but all fit within the 2500° to 6500°K standard. The WLEDs are notably the most durable and HBLEDs have the longest life expectancy.
28
Table 1. Comparing Lamp Types by Luminous Output
Luminous Lamp Life Expectancy Efficacy CRI CCT (°K) Durability Category (units noted) (lm/watt)
Fragile & Kerosene 0.03 (1) 80 (1) 1,800 (1) 2 yr (5) dangerous (1)
2,652 - Incandescent 5 - 18 (1) 100 (2) Very fragile (1) 1,000 - 2,000 hr (4) 3,000 (1, 3)
2,700 – Fluorescent 30 - 79 (1) 62-70 (1, 2) Very fragile (1) 6,000 - 20,000 hr (4, 6) 4,200 (4)
*35,000 - 50,000 hr (7) (1, 2) (1) 2,500 – (1) WLED 25 - 100 82 (10) Durable 6,000 ** 400 – 2,500 hr (8, 9)
* HBLED life expectancy, ** 5mm WLED life expectancy (both to L70 – when depreciation reaches 70% initial lumen value)
Sources for each value above: (1) Energistic Systems (2) Mills, 2008 (3) Gigahertz-Optik (4) Lighting Research Center of Rensselaer Polytechnic Institute, 1995-2009 (5) Lighting Africa, 2008a (6) be!sharp Project 2008 (7) DOE, 2008 (8) Peon et al., 2005 (9) Bullough, 2003 (10) Foster and Gómez, 2005
Design Component 2 – Batteries
Off-grid lighting products use both dry cell and rechargeable batteries. Dry cell
batteries are one time use only; they are typically of alkaline or zinc-carbon chemistries.
Rechargeable batteries can be used more than once. The most common types of
rechargeable batteries are sealed lead acid (SLA), nickel cadmium (NiCd), nickel metal
29 hydride (NiMH) and lithium ion (Li-ion). Off-grid lighting products designed with rechargeable batteries typically have lower operating costs than those designed with dry cells, providing a more practical lighting solution for consumers. Each rechargeable battery chemistry comes with its benefits and drawbacks; deciding on the most appropriate chemistry depends on the specific application. In this section I provide a basic comparison of the four common rechargeable battery chemistries.
Important performance characteristics for a battery includes its energy density, cycle life, relative price, and toxicity. A battery with a higher energy density will provide more energy for an equal battery mass. The life expectancy of a battery is characterized by its cycle life – how many charge-discharge cycles it goes before its capacity drops to
80% of its original rated capacity (Buchmann, 2006).
Table 2. Performance characteristic values for rechargeable batteries (Buchmann, 2006)
Battery Energy Density Typical Cycle Relative Toxicity Chemistry (Wh/kg) Life* Price
SLA 30 – 50 200 - 300 Lower Toxic
NiCd 45 – 80 1500 Middle High Toxicity
NiMH 60 – 120 300-500 Middle Low Toxicity
Li-ion 90 – 190 300 - >1000 Higher Low Toxicity
*Cycle life testing to 80% of initial capacity (Buchmann, 2006)
SLAs generally can be purchased for lower cost/Ah prices, but with a low energy
density they are heavier than other batteries. In addition, because of the lower cost/Ah,
30 products with SLA batteries are typically designed with larger Ah capacities; with higher mAh capacities the products can provide a longer runtime. But SLA’s low cycle life means they require replacing more often, increasing operating costs. NiCds are notably durable, performing well under harsh environmental and use conditions. They have a higher energy density than SLAs and have the longest cycle life, but they contain toxic cadmium. NiMHs are similar to NiCds but have a low toxicity and a higher energy density, providing a higher mAh capacity and longer runtime. NiMHs, however, have fewer cycles and often cost more than NiCds. Li-ions are a much newer technology with a higher energy density, but currently are expensive and may be dangerous if not used properly. I have yet to see an off-grid lighting product designed with a Li-ion battery.
Design Component 3 – Form Factor
Off-grid lighting products are categorized into several form factors; I focus on three common form factors in this analysis: torch, task, and ambient. A torch, also known as a flashlight, is designed to be focused for applications to distinguish objects far away. Task lights are designed to be less focused than torches, but more focused than ambient lights. They are best used for precision work, such as reading or counting monies. Ambient lights are designed to light up an entire room. The ambient light should be well distributed for users of the light to feel comfortable in a social setting as well as carrying out everyday activities within a room. Figure 14 illustrate these three form factors.
31
Figure 14. A comparison of off-grid lighting product form factors. (Left) Torch Light; (Center) Task Right; (Right) Ambient Light
Design Component 4 – Charging Options
Some off-grid lighting products provide both solar and grid charging options, but many only provide one or the other. Other products are charged using a mechanical crank. For solar charging options, the solar module can be integrated into the chassis of the product or come separately from the lighting unit with a cable for connection. For grid charging options, the charging device also can be integrated into the chassis or come separately from the lighting unit with a cable for connection. Figure 15 illustrate these five different methods of charging. Depending on the charging method(s) designed into the product, an appropriate charging circuit must be designed.
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Figure 15. A comparison of off-grid lighting product charging methods. (Top Left) Integrated Solar; (Top Right) Separate Solar; (Bottom Left) Integrated Grid; (Bottom Center) Separate Grid; (Bottom Right) Mechanical Crank
Off-grid lighting products that provide a solar charging option typically use amorphous or crystalline solar photovoltaic modules. Crystalline solar cells come in both monocrystalline and polycrystalline form. Monocrystalline cells are made from a single crystal of silicon material, whereas polycrystalline cells are made from molten silicon with impurities removed. Some researchers believe polycrystalline cells to be less efficient due to grain boundaries (Komp, 2002). Recent data collected by NREL
33 (National Renewable Energy Laboratory) suggest that average efficiencies of commercially available monocystalline modules are similar to polycrystalline modules, currently at approximately 14% efficient (Figure 16) (Roedern, 2008a). Amorphous modules are made from a non-solid photovoltaic substance sputtered onto a solid substrate. Amorphous modules have on average lower efficiencies than crystalline modules, but overall have a lower cost per watt (Komp, 2002). In addition, amorphous modules degrade over time in sun exposure due to Staebler–Wronski degradation, but eventually stabilize. One study reports performance drops of 20 to 29% before stabilization (Jacobson and Kammen, 2007). NREL has also collected data on several types of commercially available amorphous modules. The data suggest current available amorphous module efficiencies ranging from 5.5 to 8.5% (Figure 16) (Roedern, 2008a).
It is important to note that NREL’s study uses large-scale commercially available solar modules, while off-grid lighting products use much smaller modules. In the context of this report, the NREL data serve as a useful comparison between module types.
Efficiencies from testing seven smaller crystalline solar modules from off-grid lighting samples at the HSU lighting laboratory range between 3 to 8% with an average efficiency of 6%. Efficiencies from testing five smaller amorphous solar modules at the HSU lighting laboratory range between 1 to 4% with an average efficiency of 3%.3
3 At the HSU lighting laboratory, we measure module IV curves. IV curves are plots that provide current and voltage pairs for the solar module in testing while varying the resistance put across the module’s negative and positive terminals. The test is conducted during a cloud-free day when the air mass is close to 1.5. We obtain a maximum power point for each module from its measured IV curve; this is where the product of the voltage and current pairs gives the highest power value. We obtain efficiencies for each module by dividing its maximum power in Watts by the product of the module’s surface area in m2 and the standard test condition value of 1000 W/m2.
34
16
14
12
10
8
6 Efficiency (%) Efficiency
mono 4 multi a-Si 3-j a-Si 1-j 2 a-Si/nc-Si a-Si/a-Si 0 Jan-04 Aug-04 Feb-05 Sep-05 Mar-06 Oct-06 Apr-07 Nov-07 Jun-08 Dec-08 Date
Figure 16. The best average commercial module efficiencies by technology over time from an NREL study (Roedern, 2008a). The abbreviations Roedern uses are: mono = monocrystalline silicon, multi = multicrystalline silicon, a-Si 3-j = amorphous silicon triple junction, a-Si 1-j = amorphous silicon single junction, a- Si/nc-Si = amorphous silicon nanocrystalline silicon, a-Si/a-Si = amorphous silicon same bandgap double junction (Roedern, 2008b).
Design Component 5 – Luminaire
The term luminaire encompasses all parts of the lighting product designed to distribute the light and secure the lamp. The luminous efficiency measures how effective the luminaire is in delivering the lamp’s light output to the desired surface. One-hundred percent luminous efficiency indicates that the entire light output will reach the desired
35 location. A luminaire may include a reflector, lens or diffuser to increase luminous efficiency (Lighting Research Center, 1995-2009).
Modifying the luminaire is one of the easiest ways to improve a product’s lighting quality. According to Craine et al., a lamp shade can double lighting levels in activity areas. “People rarely read near the ceiling, so lighting levels above lamps should be minimal.” For example, “the majority of activity in Nepali households takes place in one room near the stove, so simply providing lighting levels above 25 lux in this area is sufficient for most tasks, while walls may need only five lux. Such a lighting design may only require 10% of the energy of the “bare-bulb” lighting systems which dominate most rural households in developing countries” (2002).
Design Component 6 – Brightness Settings
In some off-grid lighting product designs, the consumer is able to control the brightness of the product’s light output through switch settings. In doing so, he or she can adjust the light level to be most appropriate for his or her specific application.
Having the ability to dim the product’s light output when a brighter level is not required allows the consumer to save on operation costs. Products with brightness settings contain a multistage switch to cycle through “high”, “medium”, “low”, and possibly “bed light” settings. In Nepali culture it is common to keep a light on over night to keep certain spirits away. A lamp with a bed light setting may save Nepali families significant costs compared to a lamp with only one medium setting (Craine et al., 2002).
36 B. Promising Electric Off-Grid Lighting Products for Kenya
A wide range of electric off-grid lighting products are being designed for places like rural Kenya. In my analysis I aim to represent promising samples that vary in the design elements which most influence operating costs. The 14 samples I analyze range in
Kenyan retail price from 330 Ksh to 4,000 Ksh (or $5 to $60 with $1US = 67 Ksh) and battery capacity from 400 mAh to 7,500 mAh. All samples, except the first listed, use
WLED technology. The first listed is an incandescent torch that is powered by dry cell batteries. Table 3 below provides the key features of each electric off-grid lighting product I analyze. Some lights are listed twice because they have multiple charging or form factor options, making a total of 23 combinations of products and options. The products are named with codes to maintain the anonymity of the product manufacturers.
The names, however, include the product’s form factor and charging method for ease of understanding in results. From my economic analysis, I am able to compare all these products as well as evaluate how changes in each design element influence each product’s operating costs.
37
Table 3. The spread of key design features for samples represented in my analysis. A total of 14 samples were analyzed. Some products are listed twice because they have multiple charging or form factor options.
Estimated Rated Rated Product Form Factor Retail Solar No. Battery Battery Lamp Code and Charging Cost in Module of Type Capacity Type Name Option Kenya Power LEDs (mAh) ($US) (W)
Carbon Incande- YE Torch (batt) $1 4000* -- 1 Zinc scent OC Torch (grid) $5 SLA 1000 -- 5mm 3 Torch (solar) $30 NiMH 800 0.5* 5mm 6 OB Ambient (solar) $30 NiMH 800 0.5* HBLED 3 Torch (solar) $42 NiMH 2000 2* HBLED 1 SC Ambient (solar) $42 NiMH 2000 2* HBLED 1 XS Ambient (solar) $59 NiMH 1800 1.5* HBLED 1 Not ET Ambient (solar) $35 SLA 4000 HBLED 1 available WL Ambient (solar) $51 SLA 7500 5 HBLED 1 Task (grid) $15 NiMH 1200 -- 5mm 12 YF1 Task (solar) $18 NiMH 1200 1 5mm 12 Task (grid) $13 NiCd 600 -- 5mm 12 YF2 Task (solar) $16 NiCd 600 1 5mm 12 Task (grid) $11 SLA 800 -- 5mm 12 YF3 Task (solar) $15 SLA 800 1* 5mm 12 Ambient (grid) $31 SLA 4000 -- 5mm 12 TB Ambient (solar) $42 SLA 4000 2.5 5mm 12 Task (grid) $10 NiMH 1300 -- 5mm 12 TM Task (solar) $22 NiMH 1300 1 5mm 12 Task (grid) $17 NiCd 400 -- HBLED 1 AS Task (solar) $17 NiCd 400 0.6 HBLED 1 Ambient (grid) $35 SLA 4500 -- HBLED 1 AN Ambient (solar) $35 SLA 4500 1.3 HBLED 1
* Battery capacity or solar module power is not rated; the value reported is an estimated rated capacity or power based on measurements taken through the HSU lighting lab.
CHAPTER 3.
LITERATURE REVIEW
As manufacturers around the world are working to improve off-grid lighting designs, researchers are working in parallel to understand what makes an off-grid lighting design better – specifically in terms of costs, quality and general preferences. As the goal of my thesis is to generate design recommendations for manufacturers based on economic analyses so they can produce products more desirable to lower-income customers, my literature review addresses the following subjects:
• Consumer preferences
• Parameter values to support economic analyses
• Economic analyses
3.1 Consumer Preferences
Before designing any product, it is wise to investigate what those who will be using the product actually want. From an early market survey performed in Kenya,
Lambert et al. summarize lighting preferences by rural households and small businesses in the ranked Table 4 below (Lambert et al., 1999). Reduced lighting cost was surveyed as the most important feature, while light quality was the second most. Lighting quality includes the lighting product’s brightness, color, and distribution.
38 39
Table 4. Summary of critical product features in existing solar lanterns in order of importance to the end-user (Lambert et al., 1999)
Ranking Product feature 1 Reduced lighting cost 2 Light quality 3 Lantern portability 4 Appearance 5 Ease of use 6 Safety 7 Clean, environmentally friendly 8 Allows radio use
More recent research was conducted by The Lumina Project to investigate
consumer preference of off-grid lighting products. This project is funded by the Blum
Foundation and the research I participated in this past summer is part of The Lumina
Project. The year prior to my participation, during the summer of 2007, researchers Arne
Jacobson, Evan Mills and Maina Mumbi conducted one-to-one and focus-group meetings
with 80 potential users of off-grid lighting in the towns of Sauri, Yala, and Kisumu in
Kenya. They obtained end-user feedback on lighting performance and ownership costs
while showing up to 15 commercially available off-grid lighting designs. Their main
findings, published by Mills and Jacobson in 2007, include the following:
40 1. User willingness-to-pay for purchasing an off-grid lighting product is $25
retail4 or less.
2. A considerable desire exists for solar-powered options, as to avoid battery
charging costs and problems with intermittent grid availability. However, the
higher cost for solar can be a barrier.
3. The torch form factor creates an expectation for low price.
4. Some users are willing to spend more on superior lighting.
5. Users are reluctant to pay less for a product that does not produce ample light
or that will break prematurely.
6. Users appreciate a large battery for AC charged lamps; less frequent charging
means lower operating cost.
7. The ability to obtain replacement batteries for the products is important.
The Lighting Africa 2008a study conducted 400 surveys of small business owners throughout Kenya with interesting results suggesting consumer needs. When asked:
“What are the barriers to improving the lighting for your business?,” 59% of small business owners indicated they are hindered by capital costs. Seventeen percent indicated they do not have the money to fuel/energize an improved light (Figure 17).
4 The retail price accounts for build-up at the location sold. Price build-up increases a manufacturer’s wholesale price by including shipping costs, value added tax (VAT), import duties, distribution markup and retail markup (Hankins, 2007).
41
a
Figure 17. Lighting Africa 2008a survey results addressing barriers to improving lighting
Lighting Africa’s 2008 work also included obtaining consumer responses to commercially available off-grid lighting products. The researchers conducted price sensitivities using seven products on 1,000 small business owners, and they obtained consumer opinions from 55 Kenyan participants on 10 products. Their main findings, published by Lighting Africa (2008a, 2008b) include the following:
1. Small business owners are not willing to spend more than $18.18 for a lantern,
$3.79 for a torch, $14.40 for a task light, and $16.67 for a flood light (using
66Ksh = $1US conversion).
2. Solar rechargeable lanterns are the favored lighting device.
42 3. Consumers desire products with sufficient light that are easy to use and
durable.
4. The electric light should lower consumers’ lighting operating costs.
5. Consumers avoid products that look expensive, being afraid they may be
stolen.
6. Integrated solar is avoided because light may be stolen if set out to charge.
7. Consumers are concerned that products with only a solar charge option may
not be reliable under bad weather.
8. An indicator to show when the battery is fully charged is strongly preferred.
Combined user preference studies show that off-grid lighting consumers are willing to pay more for a product that is of good quality, maintainable, reliable – and most importantly – will lower their operating costs. Products should cost no more than
$25, depending on the style. Learning user preferences is an important step when analyzing products.
3.2 Parameter Values to Support Economic Analyses
Another important step when analyzing products is to learn how products are used. Obtaining realistic parameter values is crucial to providing confident economic analysis results. Some researchers have conducted specific studies focused on obtaining these parameters. The following studies have found important figures to support further studies.
43 One supporting study was conducted by students and faculty at the University of
California, Berkeley to determine the consumption rates of fuel-based lights. During the spring of 2007, students collected baseline lighting use and needs data for communities in
India, similar to the study we preformed in Kenya, as discussed in Methodology 1. The students conducted tests determining fuel consumption rates on the fuel-based lights they found to be most common in India, which happen to be the same ones we found to be most common in Kenya. Table 5 below shows their measured fuel consumption rates; the wick lamp was field tested in India while all others were tested upon return to the US under still conditions, sheltered from the wind. Like what we did in Kenya, the Berkeley team used the “mass-balance technique, weighting a lantern before and after use, and dividing the mass of fuel consumed by the time elapsed” (Apte et al., 2007). The fuel consumption rate observed for the chimney (wick) lamp is notably high compared to the results we obtained in Kenya. Additional information provided by the author Apte suggests that the higher consumption rate is due to the large wick size of the lamp he had tested in India. Apte describes the chimney (wick) lamp he tested as having a wick diameter of approximately 3-4 times the size of those we tested in Kenya (Apte, 2009).
(Refer to Table 30 in Appendix K for 2008 Kenya study fuel consumption rates.)
44
Table 5. Fuel consumption rates measured by members of the University of California, Berkeley with lamps obtained in India (Apte et al., 2007). Each lamp type fuel consumption rate represents measurements from one lamp (Apte, 2009).
Fuel Consumption Lamp Type Rate (g/hr)
Hurricane Lantern (medium flame) 12
Hurricane Lantern (high flame, sooty) 20
Petromax 62 Chimney (windy) 80 Notes: “medium flame” is at a "typical wick height" “high flame” is an “upper bounds for the fuel consumption” ”Petromax” is what I refer to as pressure lamp “Chimney” is what I refer to as a wick lamp *Number of tests conducted or standard deviation values are not provided in the literature
Lighting Africa sought to find when Kenyan small business owners use light.
Asking 400 small business owners at what time they open and close their businesses, they
obtained results shown in Figure 18 (Lighting Africa, 2008a). Kenya’s sunrise is
between 6AM and 6:30AM and its sunset is between 6:30PM and 7PM throughout the
year. The time does not vary much, as the equator runs through Kenya, which allows for
a more consistent use pattern all year long. Lighting Africa concludes that businesses use
light for one hour in the morning and two to three hours at night, seven days a week.
Businesses would stay open longer, but “due to insecurity, lack of customers, poor
lighting and the high cost of paraffin, they are forced to close early” (Lighting Africa,
2008b).
45
Opening Time Closing Time 5AM 4%
10AM 9PM 7% 6PM 8:30PM 9AM 21% 19% 11% 6AM 5% 28% 6:30PM 9% 8AM 25% 8PM 7PM 7AM 19% 25% 19%
7:30PM 8% Figure 18. Lighting Africa 2008 study of 400 small business owners, asking "What time does your business usually open and close?"
Kerosene consumption rates and use times are two key parameters required to produce an accurate economic analysis. In the following sub-section, Economic
Analyses, additional parameters are provided, but many are estimates.
3.3 Economic Analyses
Several groups have conducted economic analysis on off-grid lighting products.
Some analyses are intended for the general world’s population of off-grid lighting users,
and some are for more specific case studies. In general, the studies show that the LED
options with rechargeable batteries and a solar charging option are the most cost-effective
off-grid lighting option.
46 A. Jones et al. Study
The University of California, Berkeley and LBNL collaborated to compare
“‘competing’ on- and off-grid lighting alternatives ranging from fuel-based to traditional grid-connected incandescent lamps to portable solar lanterns using CFLs or LEDs”
(Jones et al., 2005). Table 6 below outlines the systems compared. They estimated both operating costs per unit of service as well as total life cycle costs per unit of service and payback.
Table 6. Lighting costs, illumination, and payback period for four fuel-based, five electric off-grid, and two electric on-grid lighting products. The payback time is for switching from each source to its corresponding one-watt off-grid LED system, included in the bottom two rows (Jones et al., 2005). The klux-hr (or 1,000 lux-hr) unit represents the area beneath a lux curve while the lamp discharges over time, quantifying the amount of light a product provides throughout a use event. This is useful because lighting products exhibit different lux curves as they discharge.
Capital Costs Useful Operating Costs System Payback (yrs) ($) Illumination (lux) ($/klux-hr) 60 W Incandescent $ 5.00 111 $ 0.04 15.2 (grid-connected) 0.74W Incandescent Flashlight $ 5.00 2.4 $ 59.25 0.1 (alkaline battery)
15W CFL Lamp $ 10.00 122 $ 0.01 Never (grid-connected)
6W CFL Lantern(alkaline battery) $ 15.00 18 $ 6.89 0.1
5W CFL Lantern $ 75.00 30 $ 0.63 Immediate (solar/NiMH battery)
Candle $ 0.10 1.1 $ 36.63 0.5
Simple Kerosene Lamp (wick) $ 1.00 1.1 $ 5.60 5.3
Hurricane Kerosene Lamp (wick) $ 3.00 6.3 $ 2.78 1
Pressurized Kerosene Lamp $ 10.00 182 $ 0.21 0.3 (mantle)
3x0.1W LED Flashlight $ 10.00 8 $ 0.13 n/a (solar/NiMH battery)
1W LED with Optics $ 25.00 320 $ 0.01 n/a (solar/NiMH battery)
47 The complete set of assumptions and notes for the Jones et al. study are included in Appendix C. Most notably, they assumed use for four hours/day, a fuel price of
$0.50/liter, and a NiMH battery life of 500 cycles. A graphical representation of their study is shown in Figure 19 below. When comparing the systems analyzed, the grid- connected CFL system has the lowest cost per light output followed by the LED battery/solar system. The payback shown is in terms of how many years it takes to be paid back after switching to a one-watt LED system that includes an optics system, solar charging, and NiMH rechargeable batteries.
Figure 19. Lighting costs and payback periods for four fuel-based, five electric off-grid, and two electric on-grid lighting products (Jones et al., 2005). The grid- connected 60 W incandescent and grid-connected 15 W CFL systems have payback periods of 15 and infinite years, respectively.
48 B. Peon et al. Study
A second study was published in 2005 by Peon et al. from the University of
Calgary in Canada, comparing costs associated with a Light Up The World (LUTW) brand off-grid lighting system design while varying lamp types for the system. Each option’s values were calculated over 50,000 hours of operation. Assuming six hours of use per day, 50,000 hours is approximately 20 years. Also assumed was a cost to charge at a charge shop of $1 per kWh and a $0.50 per liter cost for kerosene. Additional economic values were not given in the literature. The study’s results are presented in
Table 7 below.
Table 7. Lighting costs, illumination, and costs per lighting output values one fuel-based and three electric off-grid lighting products (Peon et al., 2005). The Luxeon K2 is an HBLED.
Compact Luxeon Kerosene Parameter Incandescent Fluorescent K2 WLED Wick Lamp Lamp Consumption * 25 W 7 W 1 W 0.05 L/h Lamp Cost (USD) $1 $3 $10 $1 Lamp Luminous Output (lm) 250 250 60 10 Lamp Lifetime (hours) 1,000 6,000 + 50,000 5,000 Lamp Lifetime Lumen-hours / $ 250,000 500,000 300,000 50,000 Lamp Lifetime $ / 10,000 $0.04 $0.02 $0.03 $0.20 Lumen-hours Lifetime Cost of Lamps $50 $25 $10 $10 Lifetime Energy Consumption 1250 kWh 350 kWh 50 kWh 2500 L Lifetime Energy Costs ** $1,250 $350 $50 $1,250 Total System Operating Cost $1,300 $375 $60 $1,260 System Lumen-hours / $ 9,615 33,333 50,000 396.82 Total System Cost per Lumen $ 5.2 / lm $ 1.5 / lm $ 1 / lm $ 126 / lm Lumens per Dollar 0.2 lm / $ 0.66 lm / $ 1 lm / $ 0.008 lm / $
* Consumption given in Watts (W) for electric lamps and in liters (L/hr) for kerosene lamps. ** Based on field data, the price of kerosene is estimated at US $0.5 per liter and the grid-independent energy at $1 per kWh.
49 Below is a graphical representation of selected results. The results show that the
WLED alternative has the lowest lifetime cost and lowest total system cost per lumen output. Notably, the kerosene wick lamp has a much higher cost/lumen output value. An important observation is that while the WLED alternative has the lowest life cycle cost and longest lifetime, its capital cost exceeds all other alternatives in the comparison by at least three times. It also gives a smaller luminous output than the other electric-based lights.
$1,400 $0.25
Non-Energy Life Cycle Costs $1,200 Lifetime Energy Costs $0.20 Lamp Lifetime $ / 10,000 Lumen-hours $1,000 Lamp Luminous Output (lm) $0.15 $800
$600 $0.10 Luminous Output (lm) Output Luminous Life Cycle Costs (US$) Costs Cycle Life $400
$0.05 Lifetim e C ost / 10,000 Lum en-hrs $200
$0 $0.00 Incandescent Compact Fluorescent Luxeon K2 WLED Kerosene Wick Lamp
Figure 20. Lighting costs, illumination, and costs per light output for one fuel-based and three electric off-grid lighting products (Peon et al., 2005)
50 C. Foster and Gómez Study
A third economic analysis was conducted in 2005 by the Southwest Technology
Development Institute within New Mexico State University for Sandia National
Laboratories. The analysis compares three systems that are estimated to provide similar service for users in rural areas. Table 8 below summarizes the analysis’ results. Table 9 includes stated assumptions they used in their economic analysis.
Table 8. Comparing costs between three lighting system alternatives: one fuel-based and two electric off-grid lighting products (Foster and Gómez, 2005)
Lighting Kerosene Lighting System Fluorescent Lighting System LED Lighting System System 2 Kerosene Lamps $24 Fluorescent lamp (7W) $25 LED lamp (0.7W) $27 Components PV module (30W, 12V) $240 PV module (5W, 12V) $98 & Costs Charge Controller (6A) $48 Charge Controller (6A) $48 Battery (38 AH) $65 Battery (10 AH) $25 System $24 $403 $279 Capital Cost System Life $1,139 $710 $330 Cycle Cost
Table 9. Assumptions used for Table 8 results (Foster and Gómez, 2005)
Analysis period n (years) 24 Annual discount rate i (%) 3 Fuel cost $0.528/liter Lifetime fluorescent lamps (yr) 5 Lifetime LED lamps (yr) >24 Lifetime kerosene lamp (yr) >24 Lifetime batteries (yr) 6 Depth of discharge for batteries 15% Kerosene consumption rate (liters/hr) 0.05 Days in use per year 365 Hour of use per day 4
51 The results are shown graphically in Figure 21 below. Over the 24-year analysis period, the LED system costs less than a third of the price of the kerosene lamp lighting; however, both electric lighting systems have high capital costs compared to the kerosene lamp lighting system. It is important to note that the level of service provided by the systems are not equal.
$1,200
System Capital Cost $1,000 System Life Cycle Cost
$800
$600
$400 Lighting Costs (US$) Costs Lighting
$200
$0 Kerosene Lighting Fluorescent Lighting LED Lighting System System System
Figure 21. Lighting costs of one fuel-based and two electric off-grid lighting products (Foster and Gómez, 2005)
52 D. Lighting Africa Study
Lighting Africa has also conducted an economic analysis focused on current lighting products available in Kenya. Economic values were generated through a 2008 survey performed throughout Kenya. Questions asked were: “How many of each type of light do you use at the business currently?”, “How much does it cost you per month to run?”, and “What is the cost of buying one of this light now?”
As seen in Figure 22 below, the pressure lamp was reported to have the highest capital cost and monthly operating cost. The electric light bulb system and the hurricane lamp were reported to have similar operation costs, but the hurricane lamp has approximately half the initial cost. The type of light bulb is not specified.
30 26 Capital Cost 25 Operation US$/Month
20
30 15 183 10 21 Lighting Costs (US$) Lighting 5 46 53 0 Hurricane Wick Lamp Pressure Light Bulb Candles Torch Lamp Lamp
Figure 22. Lighting costs of four fuel-based and two electric off-grid lighting products. The numbers above the bars indicate the survey sample for each type of lighting product (Lighting Africa, 2008).
53 Tables 10 through 12 below compare the four economic analyses results for capital cost, operation cost, and LCC in a quantitative format. Some values presented are slightly different from originally stated in their above respective tables. The slight modifications are in terms of time and units in order to easily compare the economic analysis results. Specific modifications are described in each table’s caption. Also while comparing, consider that the Jones et al. and Foster and Gómez studies assumed four hours of use per day, while the Peon et al. study assumed six hours of use per day.
54
Table 10. Summary of capital costs for off-grid lighting products presented in the Literature Review economic analyses. The following symbols aside capital cost values signify the following: * only cost of lamp itself, ** Foster and Gómez study electric lighting systems are much larger than those analyzed in the other studies cited, and *** lamp and charging type not specified, from our experiences in Kenya, many off-grid “Light Bulb” systems used incandescent lights and were solar charged.
Economic Analysis Study Results Summary Off-Grid Lighting Foster and Lighting Metric Jones et al. Peon et al. Product Gómez Africa (2005) (2005) (2005) (2008) Pressure lamp $ 10.00 -- -- $ 20.59 Hurricane lamp $ 3.00 $ 12.00 $ 7.36 Wick lamp $ 1.00 $ 1.00 * -- $ 1.06 Candle $ 0.10 -- -- $ 0.29 Incandescent torch with $ 5.00 -- -- $ 2.13 dry cell batteries LED rechargeable torch $ 10.00 ------with solar LED rechargeable torch ------$ 2.13 without solar Incandescent light with -- $ 1.00* -- $ 14.63 *** Solar
Capital Cost ($) ($) Cost Capital CFL light with dry cell $ 15.00 ------batteries CFL rechargeable light $ 75.00 -- $ 403.00 ** -- with solar CFL rechargeable light -- $ 3.00* -- -- without solar LED rechargeable light $ 25.00 $ 10.00* $ 278.80 ** -- with solar
55
Table 11. Summary of operation costs for off-grid lighting products presented in the Literature Review economic analyses. The Lighting Africa values were presented in terms of cost per month. I used the operating cost/klux-hr values that Jones et al. provided along with their provided lighting product lux values and discount rate to calculate cost per month operation costs for the lighting products they use in their analysis. I used the total operating costs values over 20 years that Peon et al. provided along with an annual interest rate of 10% to estimate monthly operation costs for the lighting products they use in their analysis. The reason why I used an annual discount rate of 10% for the Peon et al. study is because they reported an annual discount rate of 10% for a similar but smaller economic study on off-grid lighting products published one year later (Irvine- Halliday et al., 2006).
Economic Analysis Study Results Summary Off-Grid Lighting Foster and Lighting Metric Jones et al. Peon et al. Product Gómez Africa (2005) (2005) (2005) (2008) Pressure Lamp $ 5.40 -- -- $ 27.06 Hurricane Lamp $ 2.48 -- -- $ 10.12 Wick Lamp $ 0.87 $ 12.16 -- $ 6.32 Candle $ 5.69 -- -- $ 1.29 Incandescent Torch with $ 20.10 -- -- $ 2.08 Dry Cell Batteries LED rechargeable torch $ 0.15 ------with solar LED rechargeable torch ------$ 2.08 without solar Incandescent light with -- $ 12.55 -- $ 10.50 Solar CFL light with dry cell $ 17.53 ------batteries
Operation Cost per ($) Month CFL rechargeable light $ 2.67 ------with solar CFL rechargeable light -- $ 3.62 -- -- without solar LED rechargeable light $ 0.45 $ 0.58 -- -- with solar
56
Table 12. Summary of LCC values estimated over a 20 year period for off-grid lighting products presented in the Literature Review economic analyses. The Peon et al. LCC values were presented over a 20 year period. I used the total cost/klux-hr values that Jones et al. provided along with their provided lighting product lux values and discount rate to estimate total LCC costs over a 20 year period for the lighting products they use in their analysis. The Foster and Gómez study provides a chart of annual costs over their 24 year analysis. I summed costs from the first 20 years to estimate the Foster and Gómez values below.
Economic Analysis Study Results Summary Off-Grid Lighting Foster and Lighting Metric Jones et al. Peon et al. Product Gómez Africa (2005) (2005) (2005) (2008) Pressure Lamp $ 627.67 ------Hurricane Lamp $ 273.01 -- $ 783.40 -- Wick Lamp $ 95.83 $ 1,270.00 -- -- Candle $ 604.51 ------Incandescent Torch with $ 2,149.15 ------Dry Cell Batteries LED rechargeable torch $ 49.18 ------with solar LED rechargeable torch ------without solar Incandescent light with -- $ 1,350.00 -- -- Solar CFL light with dry cell $ 1,910.91 ------batteries CFL rechargeable light $ 539.81 -- $ 682.92 --
LCC *estimated ($) over 20 years with solar CFL rechargeable light -- $ 400.00 -- -- without solar LED rechargeable light $ 143.95 $ 70.00 $ 330.41 -- with solar
57 The four economic analyses described above support the following conclusions:
1. Currently used fuel-based lighting is more costly than the electric alternatives
analyzed over the duration of each study, with the exception of the Lighting
Africa survey study – which does not estimate electric lighting costs.
2. The fuel-based lighting has a lower lighting output than the electric lighting,
with the exception of the pressure lamp.
3. The WLED lamp is the most cost effective lamp over the duration of each
study it is in.
4. The WLED systems in the Jones et al. and Peon et al. studies both have the
greatest capital costs in each comparison (both including a fluorescent
system), while the WLED system in the Foster and Gómez study has a capital
cost less than the fluorescent system. Although all three studies were
published in 2005, the quick progress made on WLEDs in decreasing WLED
costs could have led to the discrepancy. Also, the levels of service by the
various lighting systems are not the same. Thus, it is possible to have WLED
systems that are either more or less expensive than fluorescent systems.
5. The WLED systems have a lower cost per lux-hr output than competing
systems, but they also have a lower luminous output in general. The WLED
system cost per lux-hr is comparable to the CFL and incandescent systems,
but its luminous output is lower.
58 6. Grid connection, when possible, proves to be the most cost-effective lighting
alternative. Connecting to the grid for many Kenyan vendors, however, is not
an option.
In general, the literature presented suggests WLED systems are the most cost effective over the duration of the studies performed,5 but come with the highest capital
costs, especially when a solar module is included. An important question is: Where is the
balance between long-term cost-effectiveness and upfront capital willingness-to-pay?
In addition, economic studies are only estimated models based on many
assumptions; the key to an accurate model is to use as few assumptions as possible and
conduct sensitivity analyses on values the researcher has less confidence behind. Some
parameter values used in the above analyses were assumptions and ranged widely from
one another. In my analysis, I attempt to fill many of these gaps with real data collected
in Kenya as well as in the Humboldt State University (HSU) lighting laboratory. The
procedures I used to obtain these data are detailed in the following Methodologies section
of this thesis.
5 Our Kenyan 2008 study results obtained suggest that LED off-grid lighting systems are not always less expensive than kerosene lighting systems (Radecsky et al.). (Also see Results and Discussion.)
CHAPTER 4.
METHODOLOGY
Through my thesis, I aim to provide design recommendations for off-grid lighting manufacturers. My methodologies describe how I generate the findings to support design recommendations. I base the findings on product capital cost and life cycle cost (LCC).
In order to obtain capital costs and LCCs for products, I must have real data on how off- grid lighting products are used, values associated with lighting operation such as fuel and replacement costs, and data on how lighting products perform. I describe the procedures used to obtain the above information as well as the processes used to perform economic analyses. I first perform a base-case scenario economic analysis, which compares off- grid lighting products with no design changes. Then I perform economic analyses varying certain design components of the electric lights in order to understand potential cost improvements associated with design changes. Finally I conduct a sensitivity analysis around the base-case scenario to understand which parameters in my economic analysis are most influential.
I present the methodologies independently for clarity in reading. The data collection methods in Kenya were carried out jointly with Arne Jacobson, Maina Mumbi, and Peter Johnstone. Some electric lighting product data collection methods were carried out jointly with Stephen Kullmann and Patricia Lai at Humboldt State University. The methodologies are as follows:
59 60 1. Kenya Field Procedures: These procedures were used to collect costs associated
with lamp ownership, kerosene fuel consumption rates, and lighting use patterns.
2. Electric Lighting Product Performance: Tests conducted on electric off-grid
lighting products followed performance testing procedures to measure lamp use
hours, lighting distribution patterns, and color rendering.
3. “End of Use” Lux Values: I developed procedures to estimate the light brightness
levels at which a lighting product becomes too dim, triggering the user to stop
use. I designed unique tests for the torch, task, and ambient form factors.
4. Life Cycle Cost: LCC values were estimated for each off-grid lighting product
analyzed over a given period of time following standard equations; the LCC
includes product capital, fuel/electricity, maintenance, and repair costs.
5. Lighting Product Brightness: Procedures for measuring single lux values for both
electric and fuel-based lighting products were conducted in order to compare the
lamps in terms of brightness alongside cost.
6. Cost/lux-hr: I estimate the cost per hours of brightness associated with each off-
grid lighting product analyzed following standard equations; this value compares
lighting products based on both cost and brightness.
7. Design Sensitivity Analysis: I describe my procedures for evaluating product costs
while varying design components for each chosen electric off-grid lighting
product. This provides information as to potential cost improvements associated
with design changes.
61 8. Economic Parameter Sensitivity Analysis: I describe my procedures for evaluating
product costs while varying analysis parameters which may vary between
locations within Kenya and elsewhere in the world. This provides information as
to how influential each economic parameter is to the economic analysis results.
4.1 Methodology 1 – Field procedures while in Kenya
The most important element in my analysis is its realism. In my model I use actual data on how small business owners in Kenya use off-grid lighting products. Arne
Jacobson, Peter Johnstone, Maina Mumbi and I collected data within Maai Mahiu and
Karagita markets in Kenya during the summer of 2008. (The towns are described in the
Background section of this thesis.) The data we collected consists of the following:
Costs associated with lamp ownership
Kerosene fuel consumption rates
Lighting use patterns
We were able to collect costs associated with lamp ownership independently throughout
the data collection process. Collecting kerosene fuel consumption rates and lighting use
patterns, however, required a careful series of steps.
4.1.1 Costs associated with lamp ownership
Lamp ownership costs consist of energy, capital and ongoing accessory costs. We
first collected kerosene fuel prices from a number of sources used by market vendors:
petroleum stations on the main highways, pump vendors located within the market, and
vendors selling by soda bottles also within the market. For each fuel source, we
62 purchased samples and subsequently measured them to obtain a true cost per liter of fuel.
In doing so, we recorded each sample’s cost, weight and volume. We calculated each sample’s volume per unit cost in Ksh/liter and specific volume in mL/gram, using the following two equations.
Equation 1
Price Paid Volume Unit Cost = Volume
Equation 2
Volume Specific Volume = Mass
Figure 23. (Left) Market kerosene vendor filling a plastic container with pump. (Right) Arne Jacobson measuring kerosene sample volume and weight. (Photos by Johnstone)
63
Second we collected information about the cost to charge lamps with AC electricity from grid charge shops in both markets. We did this by showing the charge shops a lamp with its charger and inquiring the price for a charge. Third, we obtained initial costs for kerosene-fueled lamps, wicks, mantels, candles and batteries by purchasing them from various sources. And fourth, we conducted informal surveys to find the estimated time periods for replacing wicks and mantels. We asked for these values from each vendor participating in our fuel consumption measurements during the end of the testing process, once establishing a relationship.
Figure 24. Large hurricane lamps and wicks for sale in a store within the Karagita market (Photo by Johnstone)
64 4.1.2 Kerosene Fuel Consumption Rates & Lighting Use Patterns
Collecting fuel consumption rate and lighting use pattern data involved long-term studies with vendors. We approached the collection following four steps:
1. Conduct a brief, 15-minute lighting survey with night market vendors.
2. With consent from 25 vendors, measure lamp fuel consumption rates and collect
use-times logged by vendors over five to seven nights.
3. Sell data-logging LED lamps to interested participants from step 2.
4. Download use patterns from data loggers and collect charging methods and costs
over six months.
Step 1:
We surveyed 50 small businesses to obtain information about each participant’s type of lighting used, operating costs, use patterns, and business characteristics. Surveys were conducted by Maina Mumbi, our team member native to Kenya. A blank survey is attached in Appendix D. The data collection could have stopped with the survey, but we aimed to verify the survey data through long-term measurements with a number of night market vendors.
Step 2:
Of those surveyed in step 1, approximately half agreed to participate in our measurements of their lamp’s fuel consumption and collection of their nightly lamp use duration. Francis Ngugi and Paul Mwaniki Wambui assisted in carrying out this step.
The measurement consisted of weighing each lamp two to three times a night for five to
65 seven nights with a my-weigh i5500 electric digital balance (maximum capacity of 5500g with accuracy of ±0.1g) (Figure 25). By taking the difference of the weights over the time between the collections, we obtained consumption rate values for each lamp
(Equation 3). We calculated several consumption rates per participant and took an average of each participant’s set of rates to use as that participant’s single average consumption rate. As we are ultimately interested in an average consumption rate for each fuel-based lighting type, we then separately averaged the collection of participant’s single consumption rates for the pressure lamp participants, for the large hurricane lamp participants, and for the small hurricane lamp participants to obtain final consumption rates for each light type.
Equation 3
Initial Lamp Weight − Final Lamp Weight Consumption Rate = Final Time − Initial Time
In addition, we distributed a card for participants to fill out with the times they
turned their lamps on and off each night. Throughout this thesis, I refer to the nightly
time duration between when the user turns on and off his or her lamp as his or her “use
time.” Similar to calculating the ultimate consumption rates, we first averaged the
nightly use times for each participant singularly, then took a median of the single values
for all those in Maai Mahiu separately from Karagita, and finally took the median of the
66 Maai Mahiu and Karagita values to achieve an ultimate nightly use time. The reason why we take the median of the collection of participant use time averages is because the vendors’ use times are dependent on several factors such as lamp type and good sold.
The consumption rate and use time values combined with unit fuel cost enabled us to estimate the actual total fuel costs for each vendor. (The calculation is detailed in
Methodology 4.) Our lighting use time card and kerosene measurement data sheet are attached as Appendices E and F.
Figure 25. (Left) Radecsky making measurements of a hurricane lamp to calculate a night fuel consumption value. (Right) Francis Ngugi weighting a pressure lamp the morning after a night of measurements was made (Photos by Johnstone).
67
Step 3:
We offered to sell/lease data-logging LED lamps with NiMH rechargeable batteries to participants from step 2 (Figure 26). The data logger records the lamp battery voltage and current, informing us of when the light is being used and when it is being charged. The datalogger was designed by SERC technicians Kyle Palmer and Scott
Rommel. The agreement with the participating vendors was to lease the lamp for six months while we download data every two weeks. We leased with the intention of preventing participants from re-selling the research lamps. After the six months, participants were given the opportunity to return the lamp for a full refund or keep the lamp sans data loggers. All participants decided to keep the lamps. The vendors acquired the LED lamps for 700 Ksh (or $10.50 with $1US = 67 Ksh), which is their estimated retail cost. Each LED lamp was equipped with a grid charger that vendors can use to charge at their homes, a friend’s home, or a charge shop in market. A one-watt solar module for charging was offered for an additional 800 Ksh (or $12 with $1US = 67
Ksh), but there were no purchases.
68
Figure 26. Photo of data-logging lamp leased in Kenya. The solar panel was optional. The photo on the right shows a close-up of the lamp head and provided six-foot lamp extension. (Left photo by Johnstone)
Step 4:
Maina Mumbi downloaded data from the data-logging lamps from July, 2008 until December, 2008. This was done by connecting a DB9 serial cable from each lamp to a laptop equipped with custom data transfer software. When downloading, Maina interviewed participants as to how they had charged their lamps recently and what they paid. From step 2 we were able to calculate actual operating costs for kerosene lamps; from the data downloaded in step 4, we were be able to estimate the operating costs for
LED lamps. (Calculations are detailed in Methodology 4.)
69 4.2 Methodology 2 – Performance testing procedures on battery-powered products
For the electric lamps in my analysis, I use several values from the quality screening tests we conduct at the HSU lighting laboratory. The test results I use for each lamp include its:
Lamp discharge
Solar charge
Lighting distribution
Color rendering
Detailed procedures for each test are attached in Appendix G. In general, the lamp discharge test measures how long the light will provide acceptable light starting with a fully charged battery. The solar charge test measures how long the light will provide acceptable light after one day of standard solar charge – 5 kWh/m2 of solar input on the
solar module (PV GAP, 2005). The lighting distribution test quantifies the distribution of
illumination on a one square meter surface. The value I use to describe the distribution is
the interpolated area, which is the area that contains 50% of a lamp’s illuminance
measured in lux on a one square meter surface. The color rendering test quantifies the
color of light in terms of its CIE color coordinates.
The lamp discharge values are used to calculate operation costs for the electric
lighting products. For the grid charged products, by knowing how long each electric light
will provide acceptable light with the lamp discharge test, the frequency of charging can
be obtained. From this, it is possible to estimate the operating cost over a period of time
70 (Equations 4 and 5). More detailed calculations are revisited in Methodology 4 to estimate operating costs for grid charging products.
Equation 4
Cost Charges Operating Cost = * Charge Period
Equation 5
where: Average Hours Used Charges Nights Night = * Period Period Lamp Discharge Hours Charge
As an electric lamp discharges, we record its current, voltage, and lux at one meter within a “dark box.” As it discharges, it decreases in brightness, as seen in Figure
27 below. Some products will discharge slowly until their battery is fully discharged; however, fully discharging a sealed lead acid battery decreases its lifetime. To ameliorate this problem, some manufacturers incorporate a low-voltage disconnect into their product’s electronics, enabling the light to automatically shut off when reaching a given voltage. For the products containing a low-voltage disconnect, the length of time the light will provide acceptable light is governed by the point at which it is set to automatically shut off. For the products not containing a low-voltage disconnect, deciding when the light becomes unacceptable is unclear. The cutoff is estimated using procedures detailed in Methodology 3.
71
180 12 250 20 Current Draw (mA) Current Draw (mA) 18 160 Battery Voltage (V) 10 Battery Voltage (V) Illuminance (Lux) 200 16 140 Illuminance (Lux) 14 120 8 150 12 100 10 6 80 100 8
60 4 (Lux) Illuminance Battery Voltage(V) Illuminance (Lux) Illuminance 6 (mA) Draw Current Battery V oltage (V) Current Draw (mA) Draw Current 50 4 40 Low-voltage disconnect 2 2 20
0 0 0 0 0 100 200 300 400 500 600 700 800 0 100 200 300 400 500 600 Time (minutes) Time (minutes)
Figure 27. Lamp discharge curves for two electric lighting products; the left-side product discharges slowly to zero lux, while the right-side product contains a low-voltage cutoff automatically shutting off the light to prevent battery damage. Lighting products with low-voltage disconnects may occasionally continue to provide a very small amount of light after reaching its low-voltage disconnect voltage value, as seen in the right-side product’s discharge curve.
The solar charge test estimates the amount of light a lamp will provide after one day of standard solar charge. For solar-charged options, some lights may not be receiving enough charge during a standard solar day with their given solar module.
Hence, if the solar charge test shows the light lasting less than my model’s given nightly use hours, I carried out the analysis with the maximum use time possible per day under a standard solar day’s charging. This result does not affect the operating cost for the light, as the operating cost is zero with a solar module.
The CIE color coordinates and lighting distribution values are also not used in calculations but appear graphically in results for qualitative comparison purposes only.
72 4.3 Methodology 3 – Procedures to estimate “end of use” lux values
As mentioned in Methodology 2, we are interested to know at what level a light becomes too dim and unacceptable for a user to carry out a desired activity. I designed a test to obtain the “end of use” lux values for lights of different form factors. Lux is a measurement of lumens per square meter, where a lumen is the unit of total light output from a source.
The test involves a participant performing an appropriate activity in a room while a light decreases in brightness over time. As the participant performs the activity, the illuminance from the light in testing decreases. The participant indicates when the light becomes too dim to comfortably carry out the assigned activity. Each of the eleven participants tested five lights of three different form factors: two torch, one task, and two ambient. The torch light activity consisted of finding items in an open shelve eight feet away. The task light activity was to read from a book. The ambient lights were used to carry out two different activities – sorting money and socializing. The entire test ran approximately 30 minutes per participant. The methodology testing script and test set up are detailed in Appendices H and I.
In order to decrease a light’s brightness, I used a programmable power supply
(PPS). In doing so, the positive and negative power inputs to the light in testing must be connected to the positive and negative outputs of the PPS. Throughout the session with a participant, I manually switched the power inputs between the lights as the PPS would run through all the pre-programmed current-voltage pairs required to provide the desired decreasing lux levels. Prior to the testing, I obtained these current-voltage pairs by
73 setting each light up in our testing “dark box” and adjusting the current-voltage pairs on the PPS until observing the desired decreasing lux readings on a light meter set up within the box at a one-meter distance from the light. The PPS program consisted of all six sets of current-voltage pairs for all six light tests, with one-minute dark intervals (zero current, zero voltage) between tests to allow for preparation of the proceeding test.
While each participant was in testing, he or she would indicate to me when each light’s level was too dim. I used all values indicated by participants’ to calculate averages for each of the three form factors: torch, task, and ambient. These are the values
I used as the unacceptable brightness level in determining the lamp discharge hours for each light in Methodology 2.6
4.4 Methodology 4 – Process for estimating the Life Cycle Cost values
To economically compare off-grid lighting options, I estimate the life cycle cost
(LCC) for each product over a given period of months using the equation below (Sandia
National Laboratories, 1995).
= + + + − LCCpw Cpw E pw M pw R pw Spw Equation 6 where: C = capital cost M = sum of monthly maintenance costs E = sum of monthly energy costs (e.g. fuel) R = sum of all anticipated equipment repair and replacement costs S = salvage value of system equipment at the end of the life cycle period pw = the “pw” subscript indicates the present worth of each factor
6 Johnstone and Mumbi use Methodology 3 ideas to determine acceptable brightness levels for Kenyan small business owners in January 2009 (Johnstone et al., 2009). Their values were very close to those I obtained.
74 Below I discuss each component of the LCC equation, finishing with the concept of present worth and showing how each component’s value is adjusted under present worth.
4.4.1 Capital cost
Capital costs for the fuel-based lights were obtained from procedures in
Methodology 1 when collecting costs associated with lamp ownership. For the electric lamps selected for my analysis, wholesale prices were obtained and required a price-build up analysis. Table 13 shows the price build-up components for electric lamps in Kenya used to estimate each lamp’s retail price. In Kenya, solar products are relieved of value added tax (VAT) and import duties. Hence, VAT and import duties are not included in the price build-up for electric lamps with a solar charging feature. My analysis uses products’ retail prices as their capital costs.
Table 13. Price build-up for electric lamps in Kenya (Hankins, 2007)
Build-up Component % Built-up Insurance & Freight 10% VAT 16% Import Duties 25% VAT (solar products) 0% Import Duties (solar products) 0% Distribution Markup 22% Retail Markup 20%
75 4.4.2 Maintenance cost
I have organized my analysis to include two types of maintenance costs: a typical habitual maintenance cost and sporadic maintenance costs for replacing lamp components.
The habitual monthly maintenance consists of fixing typical wear and tear issues that may arise, such as unexpectedly replacing components of the lighting product. For example, some lamp owners of the LED lamps we employed in Kenya bent the flexible lamp head necks a bit too much, causing breakage; our team member Maina Mumbi was available to sell them new lamp heads. The habitual maintenance cost will be different for each light depending on its complexity. The best way to estimate a habitual maintenance cost is to base it on the product’s capital cost – typically more expensive items are more complex with more expensive replacement parts. This is not always the case, but will serve as an assumption in my model. Specifically, I use a habitual maintenance cost of 10% of the product’s capital cost per year.
Maintenance costs for the fuel-based lights were also calculated by adding the cost of an accessory to its LCC when needing to be changed. For example, the hurricane lamp’s wick must be changed every three months for 10 Ksh; for an analysis period of one year, the cost for four wick changes is added into the product’s LCC. Kerosene lamp accessory costs and changing intervals were obtained from procedures in Methodology 1.
For electric lights, maintenance costs consist of battery changes. Several published sources show a wide range in battery lifetime values, given as the number of cycles a battery provides over its use (Table 14). These values include batteries of all
76 sizes. From our experience in the HSU lighting laboratory, normal use of lighting products can dramatically decrease a battery’s lifetime; SLAs are especially sensitive to use conditions. Using our Cadex battery analyzer, we have conducted cycle testing on a
4.5V, 800mAh SLA battery from a sample off-grid lighting product. The battery lasted for approximately 80 cycles. SLAs are, however, much less rugged than nickel-based batteries, making our test results show a far lower cycle life for SLAs. These characteristics are reflected in the values I used in the analysis, shown in the last column of Table 14 below. I used the cycle life value for the SLA battery we tested in the HSU lighting laboratory. For the nickel-based batteries, I used the smallest of the cycle life values reported in the sources cited to one significant figure.
Table 14. Battery cycle values over five sources.
Number of Cycles Provided Through Battery Lifetime Sources: Battery Type Van den Practical Electronics Values Buchmann Maxell Bossche et Photovoltaics Handbook Used (2006) Brand AA al. (2006) (2001) (1996)
SLA 500 500 200-300 -- 200-300 80 NiCd 1350 2000 1500 750 1500 800 NiMH 1350 1000 300-500 1000 500 500
The provided number of cycles for each battery type is used to determine how often batteries need to be changed (Equation 7). The use time per cycle is determined through the lamp discharge test in Methodology 2. The use time per month is a given
77 parameter in the model, which for the base case scenario, I use a combination value between use times observed from data-logging lamps and the kerosene-fuel measurement use times collected in Kenya, both described in Methodology 1.
Equation 7
Use Time ∗ Cycles Cycle Battery Life Battery Replacement Period (months) = Use Time Month
While in Kenya we found several battery costs, also described in Methodology 1
(Appendix K). With the prices collected, I calculated an average cost/mAh capacity value for each battery type to estimate the cost for equal replacement batteries. Some lights use a 4V SLA battery; however, these are not available in Kenya. For lights requiring a 4V SLA replacement, I assumed that the entire lamp would be replaced.
78
Figure 28. Batteries for sale at Nairobi Nakumatt (Photo by Johnstone)
After calculating an electric light’s battery replacement period and cost for replacement, its maintenance cost is calculated by adding the replacement battery cost to the light’s LCC when requiring a battery change.
4.4.3 Energy cost
The monthly energy costs for the kerosene-based lights are calculated using
Equation 1 and the data collected in Methodology 1. To obtain the kerosene cost, I took the average of the volume unit costs calculated using Equation 1 for the market kerosene pump vendors and petrol stations in Maai Mahiu and Karagita. The kerosene specific volume value I used was the average of all those sampled and calculated by Equation 2.
79 A fuel consumption rate was calculated for the pressure, large hurricane and small
hurricane kerosene-fueled lights using averages from Equation 3.7 Likewise, a use time
value was calculated as described in Methodology 1, step 2. From our surveys and long-
term data collection, we found that vendors are open approximately seven nights per
week.
Equation 8
Ekerosene (Ksh/month)= Kerosene Cost (Ksh/liter) * Kerosene Specific Volume (liters/gram) * Fuel Consumption Rate (g/hr) * Light Use (hrs/night) * Nights Open Per Week/7 * 30.5 Days/Month
where:
Kerosene Specific Volume = 0.00126 liters/gram
To find the candle’s consumption rate, we burned a candle in a room in Maai
Mahiu near an open door and timed how long it took to burn. The monthly energy cost
for the candle was calculated using the equation below.
Equation 9
Ecandle (Ksh/month)= Candle Cost (Ksh/candle) * Candle Consumption Rate (candle/hr) * Light Use (hrs/night) * Nights Open Per Week/7 * 30.5 Days/Month
For electric lights, the monthly energy costs are calculated using Equation 10 and
also data collected in Methodology 1. I used the cost of 20 Ksh/charge for my baseline,
as was found to be the cost to charge a lamp in both Maai Mahiu and Karagita with little
7 Wick lamp consumption rate data was taken from a previous study done by Arne Jacobson, Evan Mills and Maina Mumbi in Kisumu, Kenya during one night in June, 2007 for 10 market vendors.
80 variability according to the datalogger study results. For light use hours per night, I use the average of the daily use times observed from data-logging lamps and the kerosene- fuel measurement daily use times collected in Kenya. As was true in the case of the fuel- based lighting, I assumed that vendors are open seven nights per week. The light hours per charge values are determined through the lamp discharge test in Methodology 2.
Equation 10
Echarging (Ksh/month) = [Charge Cost (Ksh/charge) * Light Use(hrs/night) * Nights Open Per Week/7 * 30.5 Days/Month] ÷ Light Hours Per Charge (hr/charge)
4.4.4 Replacement cost
The replacement costs for all lighting products were calculated by adding the light’s capital cost to its LCC when finishing its life expectancy. This addition may be more than once depending on the analysis period modeled. Lighting Africa provides life expectancy estimates of some lighting products in its 2008a report, shown in Table 15 below.
81
Table 15. Lifespan estimates by Lighting Africa (2008a)
Lifespan Estimated by Lifespan Used Light Type Lighting Africa (yr) in Model (yr)
Paraffin lamp with glass cover8 2.6-3 2.8
Simple paraffin lamp with wick and no cover (often could be <1 1 home made)9
Pressure lamp 2.6-3 2.8
Flash light/torch <1 1
I estimated life expectancies for the remaining lighting products using the following criteria in Table 16. Having tested all the electric lights used in my analysis provides some confidence behind the categorization, but admittingly the given life expectancies for these products are rough estimates.
Table 16. Criteria used to estimate life expectancy for electric lights
Estimated Category Lifespan (yr)
Body sealed poorly, body material not sturdy 1
Body sealed satisfactory, body sturdy, body parts 2 may break easily, electronics poorly assembled
Body sealed satisfactory, body sturdy, body parts 3 may break easily, electronics assembled well
Body sealed well, body sturdy, electronics 4 assembled well
8 What Lighting Africa refers to as a “Paraffin lamp with glass cover,” I refer to as a hurricane lamp. 9 What Lighting Africa refers to as a “Simple paraffin lamp with wick and no cover,” I refer to as a wick lamp.
82 4.4.5 Salvage value
Because it is not common for off-grid lighting products to be salvaged for money in Kenya, I have made this component of the LCC equal to zero.
4.4.6 Present worth (pw)
Present worth takes into account the time value of money. For example, if an individual will purchase an item 10 years into the future, the cost at the present time is actually less, as technically the individual could invest the present worth of the item today such that the investment will be worth the cost of the item 10 years into the future.
The most challenging piece to present worth analysis is estimating at what rate an investment will increase; this value is known as the interest rate. The interest rate varies greatly between individuals based on location, class, personality, etc. In order to account for the concept of present worth in my model, I use three different types of equations: one for purchasing an item in the future, one for making a uniform series of purchases over time, and one for making a uniform series of purchases over time such that the purchases are increasing with a differential escalation rate (Rubin et al., 2001).
Equation 11 will be used to make present worth adjustments for purchasing items in the future. I use the equation for adjusting the maintenance, repair, and salvage values obtained through the processes described above (Rubin et al., 2001).
Future Value Present Value = Equation 11 (1+ I)N where: N = months of life cycle I = nominal monthly discount rate
83 Specifically, for each item changed in maintenance and item replaced in repair throughout the analysis, I divide its future cost (value obtained through above processes) by (1+I) raised to the month the action takes place. The result is what I incorporate into the light’s LCC. Unique equations for each type of cost are shown below.
= Cost For Item Changed Equation 12 M pw + NR (1 I) Equation 13
= Replacement Cost R pw (1+ I)NR where: M = sum of monthly maintenance costs R = sum of all anticipated equipment replacement costs pw = the “pw” subscript indicates the present worth of each factor NR = month of repair I = nominal monthly discount rate
The nominal discount rate (I) is the sum of the location’s inflation rate (ir) and the individual’s real discount rate (r). A location’s inflation rate is generally reported per country, while an individual’s real discount rate is very difficult to gauge. In this methodology I present procedures using only the nominal discount rate, but when performing the analysis, I divide the two in order to perform a sensitivity analysis on the real discount rate.
I used the second type of equation to calculate the estimated habitual maintenance
(Equation 14). My analysis assumes that 10% of the lighting product’s capital cost will serve as the yearly maintenance. The monthly maintenance cost would be 1/12th of this.
84 Equation 14
= + -N M pw Monthly Maintenance Cost *[1- (1 I) ]/I where: M = sum of monthly maintenance costs pw = the “pw” subscript indicates the present worth of each factor N = months of life cycle I = nominal monthly discount rate
The third type of equation I use to estimate the present worth for a uniform series of energy purchases, possibly increasing at a differential fuel inflation rate (Equation 15)
(Sandia National Laboratories, 1995). I applied this equation to the energy purchases for kerosene-fueled lighting products. The equation includes the interest rate as well as an escalation rate. The escalation rate can be found by calculating the rate at which an energy source increases with historic price data over a period of time and subtracting out inflation. For example, I estimated a fuel escalation rate for kerosene by finding the rate at which historic oil prices have increased and subtracting out Kenya’s inflation rate, as the kerosene trend follows alongside the oil price trend (Figure 8). The escalation rate obtained was very high, perhaps due to the spike in prices during the summer of 2008.
For my base case scenario, I assume an escalation rate of zero, but use the escalation rate as a sensitivity parameter.
For each kerosene-fueled and electric lighting product, I use the monthly energy values obtained through the processes described above and corresponding escalation rates to achieve the present worth of the series of energy purchases over the modeled period of months.
85 Equation 15
= + + + N E pw Monthly Energy Cost *{(1 e)/(I - e) *[1 - [ (1 e)/(1 I)] ]} where: E = sum of monthly energy costs (e.g. fuel) pw = the “pw” subscript indicates the present worth of each factor N = months of life cycle I = nominal monthly discount rate e = monthly escalation rate (differential fuel inflation)
4.5 Methodology 5 – Procedures for measuring lux values
An upgraded light may be an improvement based on its LCC, but its brightness is also a factor. My analysis focuses on reduced cost, but also considers how lights compare based on brightness. I use single lux values to represent the brightness of each light for comparison.
Electric lights do not emit a constant lux value, as their brightness decreases as their batteries discharge. To obtain single lux values, I use the median lux measured before reaching the “end of use” cutoff for each electric light’s lamp discharge test, described in Methodology 2.
The fuel-based lights emit an approximate constant lux value, but because we do not typically test fuel-based lighting in the HSU lighting lab, a special test was designed to measure the fuel-based lights’ lux values. Peter Johnstone and I designed a test similar to the lamp discharge test described in Methodology 2 while also measuring each light’s fuel consumption rate as described in Methodology 1. The setup consisted of a well- ventilated kiosk building located outdoors with an electronic balance placed one meter
86 from a light meter. With help from researchers Annie Yarberry, Nathan Lohse, Ryan
Vicente and Dustin Poppendieck, we were able to setup fuel-based lights we purchased in
Kenya to the appropriate average consumption rates we had collected using Methodology
2. We set each light on the balance while reading the lux measurements one meter away.
We took seven rounds of measurements while adjusting light settings in between until reaching desired consumption rates. Then we averaged the lux measurements occurring when each light was set to its desired consumption rate. Approximately five to 10 minutes separated each light’s readings. We also started the tests with clean pressure and hurricane lamp globe covers. Below is a photo of our test setup.
Figure 29. Setup for measuring lux values from fuel-based lights
87 4.6 Methodology 6 – Process for estimating the cost/lux-hr values
Brightness and lighting costs have acted as separate metrics in the lighting product comparison methodologies described thus far, but the two components are in fact closely linked. For example, a higher priced light may be more costly because it provides a higher quality light. The cost per lux-hr is a metric combining these two important components that allows for a money-based comparison of lighting services (Mills, 2005).
For electric lighting products, I use data from their lamp discharge tests described in Methodology 2. By integrating over the recorded lamp discharge lux curve over time, from start until the “end of use” cutoff, I obtain the number of lux-hours the product will provide after a full charge. To obtain the light’s cost/lux-hr value, the product’s total
LCC is divided by the product of the lux-hours per charge and the total charges over the analysis period, as shown in Equation 16 below.
Equation 16
⋅ = LCC Cost Per Lux hrElectric Lux ⋅ hrs ∗ Total Charges Charge
For the fuel-based lighting products, I use the single lux values obtained in
Methodology 6. The cost/lux-hour is calculated by dividing each light’s LCC by the product of the single lux value and the number of hours it runs over the analysis period.
Equation 17
LCC Cost Per Lux ⋅ hr = Fuel Constant Lux ∗ Total Use Time
88 4.7 Methodology 7 – Design Sensitivity Analysis
The purpose of this thesis is to provide manufacturers with recommendations for improving their products. The design sensitivity analysis examines costs while varying component characteristics of each electric off-grid lighting products.
I performed sensitivity analyses on the following design elements in order to determine the effect of changing each parameter on the LCC of the lamp. I discuss methods used to conduct these sensitivity analyses below. I also explored additional design sensitivities that are more straightforward and therefore do not require further explanation. Results for all of the sensitivity analyses are presented in the Results and
Discussion section.
A. Battery size
B. Battery chemistry
C. Charging option
D. Optional solar module upon repurchase
E. Power consumption (light output)
A. Battery Size
Larger batteries allow users to operate their lamps for more hours between charge events. For users who charge at charge shops, a larger battery could save a fair amount of money, as charge shops charge a flat fee that is relatively independent of battery capacity. In this analysis I calculate how much a customer would save with an increased battery size. The analysis is based on present worth.
89 To conduct this analysis, I keep all base case scenario values constant (values obtained from Methodologies 1-7) while varying the battery sizes of each light by a percentage of its initial size. Ultimately I calculate how each lighting product’s LCC and energy costs change with increased battery size.
Analysis components requiring modifications from the base case scenario are listed and explained below.
Monthly energy cost (Epw): With fewer visits to the charge shop, the monthly
energy cost decreases for users who must charge at a charge shop (Equation 18).
This value is the most significant modified value in this sensitivity analysis in
terms of influencing the product’s LCC for users who charge in a shop for a fee.
For solar-charged products, the monthly energy cost remains constant as the
battery size increases. Also, for users who are able to charge at home or a friend’s
home (i.e., without paying a fee), the battery size does not affect energy costs.
Equation 18
Original E Modified E = pw pw (1+ % Batt Size Increase)
90 Charges required over the analysis period: As the battery size increases, the
number of charges decreases. This figure is used to determine the cost per lux-hr
metric.
Equation 19 Original Charges Per Period Modified Charges Per Period = (1+ % Batt Size Increase)
Capital cost for lighting product (Cpw): Having a larger sized battery, the lighting
product itself will cost more. Using the cost/mAh values provided in Appendix
K, Table 26, a modified capital cost for each light can be obtained using Equation
20 given a mAh capacity addition.
Equation 20
= + Marginal Cost Modified Cpw Original Cpw mAh Addition * mAh Addition
Cost for battery replacement: Just like the capital cost for a light, the replacement
cost must be increased in a similar manner.
Equation 21
= + Modified Batt Replacement pw Original Batt Replacement pw Marginal Cost mAh Addition * mAh Addition
91 B. Battery Chemistry
Manufacturers use a variety of battery chemistries in off-grid lighting products.
Each has its drawbacks and benefits, but some are more economical for customers than others. In this sensitivity analysis, I discuss batteries in terms of their influence on the product’s LCC in the context of Kenyan small businesses.
In order to compare different battery chemistries in the context of off-grid lighting products, the batteries must be of equal energy capacity within identical light designs. I use Equation 22 to estimate a battery’s energy capacity. Measured mAh battery capacities are obtained using Methodology 2’s battery capacity test.
Equation 22
Energy Capacity (Wh) = Nominal Batt Voltage∗ Measured Ah Capacity
C. Charging Options
Most off-grid lighting options can be charged with solar and/or with the grid.
Solar-charged options often require a larger capital cost than grid-charged options due to the expense of a solar module; however, over time the solar-charged option often pays itself back, as it does not require an energy fee.
Just like in comparing battery chemistries, it is best to compare charging options for off-grid lighting products identical in all aspects accept for their charging method.
Ideally, the product could be purchased with a setup for either one method or the other.
Many products come with both grid and solar options, making it more difficult to properly compare one charging method from the other; however, being able to charge
92 using more than one method may be preferred by customers. My analysis does not economically compare products as combination methods, although this is a good area for future research.
My analysis compares solar and grid charging options as if each can only be charged with solar or the grid. Ultimately I estimate how much the customer can save after four years from purchasing a solar-charged option as opposed to its counterpart grid-charged option. I use four years because the highest lighting product life expectancy value I use is four years.
In comparing charging methods, I use the following two metrics:
Difference in product capital cost: Solar options sell at a higher cost due to the
high cost of the solar module. The difference between the capital costs for the
solar and grid options is a useful metric in comparing charging methods.
Equation 23
∆ Capital = Solar Option Capital Cost − Grid Option Capital Cost
Percent LCC savings with solar option: More often the solar option will have a
lower LCC than the grid option. The savings one gains by purchasing the solar
option can be calculated with the equation below.
Equation 24
()Solar LCC − Grid LCC %LCC Savings with Solar = Grid LCC
93 D. Optional Solar Module Upon Repurchase
Solar-charged off-grid lighting products typically are more expensive due to the cost of the solar module. In many cases, when the lighting product reaches its life expectancy and requires replacing, its solar modules may still be working fine. This is because solar modules generally have longer life expectancies than the lighting components of a system. I use the equation below to calculate how much the customer would save when replacing his or her lamp with an option not including the solar module.
I then compare the LCC values for lights affected by this option.
Equation 25
= Modified Replacement Cost Modified R pw (1+ I)N R where: R = sum of all anticipated equipment replacement costs pw = the “pw” subscript indicates the present worth of each factor NR = month of repair I = nominal discount rate
For the products where customers are able to purchase the lighting product without a solar module, I use a modified replacement cost based as prices set by the manufacturers. For all other solar products with external solar modules, I calculate a modified replacement cost with an estimated cost per kW for a solar module (Equation
26). I estimate the cost per kW by averaging the cost per kW price differences of the products that have an optional solar module. Designs with integrated solar module are not eligible for this sensitivity analysis.
94 Equation 26
Modified Replacement Cost = Original Capital Cost − (Cost/kW * Module Size)
I also calculate the percent LCC savings when replacing the light with a non-solar repurchase. This metric is similar to the percent LCC savings with the solar option used in the Charging Options Sensitivity Analysis.
Equation 27
()Original LCC − Modified LCC %LCC Savings with Optional Solar Purchase = Original LCC
E. Power Consumption (Light Output)
One method to increase a lighting product’s runtime is to decrease its power consumption. For shop-charging customers, a longer runtime will save in charge shop fees. Also, for products charged with solar modules, having a longer run time could decrease the size of the product’s solar module used, thus saving on the capital cost of the product.
For grid-charged lighting products, the monthly energy cost and charges/period parameters are both reduced with less power consumption.
Equation 28
= ∗ − Modified E pw Original E pw (1 % Less Power Consumption)
95
Equation 29
Modified Charges Per Period = Original Charges Per Period ∗
(1− % Less Power Consumption)
For the solar-charged lighting products, I explore reducing the solar module size in proportion to the reduction in power consumption. For example, if reducing the power consumption by 20%, I estimate a modified capital cost for the lighting product using a solar module 80% of the original size. This method keeps the same proportions used by the manufacturer. I use the same cost/kW price estimates used in the Optional Solar
Module Upon Repurchase Sensitivity, and I also calculate the modified capital cost in a similar manner.
Equation 30
= − Modified Cpw Original Cpw (Cost/kW * Size Reduction)
4.8 Methodology 8 – Economic Parameter Sensitivity Analysis
It is important and useful to gauge how my model’s results change when changing its parameters. In the economic parameter sensitivity analysis, I examine how specific parameters affect the base case scenario results while varying each parameter independently.
I examine variation of the following economic parameters. The list begins with K to keep consistent with the organization structure of the Results and Discussion section.
96 K. Use time
L. Kerosene cost
M. Kerosene fuel escalation rate
N. Charge cost
O. Battery life expectancy
P. Lighting product life expectancy
Q. Real discount rate
R. Analysis period
I conduct each economic parameter’s sensitivity analysis in a similar manner. For each parameter listed above I develop a single evaluation where I hold all other parameters constant to base case scenario parameters, and I then evaluate the changed
LCC values for all analyzed lighting products over the variations of the economic parameter in the analysis. Ultimately for each parameter’s evaluation, I provide a plot of all the lighting product’s LCC values against the variation of the economic parameter. In some cases, the plot axis must be modified for easier understanding of the results. From these plots, I am able to discover which parameters are most sensitive, how economical comparisons between lighting products change, and at what point certain products become more cost-effective than others.
CHAPTER 5.
RESULTS & DISCUSSION
In this section, I focus on the economic comparisons between five fuel-based lighting products and 14 electric off-grid lighting products. I first conduct a base case scenario between the products based on their capital and operation costs, as well as their lighting quality and performance. Considering the base case scenario results, I analyze electric lights by varying certain design elements with potential to improve lighting costs.
And finally I conduct a sensitivity analysis around the economic parameters I use in my model.
From the design sensitivity analysis, I find that increasing battery size and reducing power consumption have the strongest influence on electric lighting LCC. Both potential design improvements may increase capital costs; manufacturers must make sure not to exceed its target customer’s willingness-to-pay value. From the analysis, I also find that it is best to design grid-charging lights with an optional solar component, that battery chemistries with higher cycle lives decrease operating costs but toxicity is an important consideration, and that HBLEDs are the preferred lamp type if available at a reasonable capital cost.
From the economic parameter sensitivity analysis, I find that daily use time, kerosene cost, and the cost per full charge influence economic analysis results the most.
Lamp life expectancy, real discount rate, and period analyzed also notably influence
97 98 results, but to a lesser degree. I find that an analysis period of four years is the best for economic comparison purposes for the set of lights I analyze.
In order to obtain confident economic results, I used economic parameters drawn from several sets of data, as outlined in the numerous above methodologies. These values are important, but are not the focus of my results. I place the economic parameters found in the following appendices:
Appendix K. Results from Kenya data collection
Lamp ownership costs
Use time and consumption rates
Datalogging LED lamp results
Appendix L. Quality screening test results & lux-hr/charge values
Appendix M. “End of use” lux values & single lux values
General parameter values and assumptions used for the analysis are given in
Appendices N and O, respectively.
Throughout my analyses, I present cost values in Kenyan Shillings (Ksh). When in Kenya during the summer of 2008, the exchange rate was $1US to 67 Ksh. Also, I recommend referring to Table 3 in the Background section for analyzed electric lighting product characteristics while examining the following results.
I divide the Results & Discussion section into three sections. In the first section, I present comparative results using a base case scenario. The base case scenario evaluates the lights based on their actual design and performance. It also uses economic
99 parameters reflecting light use by Maai Mahiu and Karagita night market small business owners to the closest degree possible. I present the base case scenario life cycle costs over both a four-year period and a 10-year period. From the base case scenario results, I can identify trends and relationships between off-grid lighting products. In addition, I can also gauge the most useful ways to perform design sensitivities. The second Results
& Discussion section is the heart of my thesis, focusing on the electric lighting designs and how to improve them economically to benefit customers like Kenyan small business owners. In the design sensitivities, I vary design elements independently while holding all other parameters constant. I also theoretically discuss some important design components for which I am unable to perform formal analyses. Through the design sensitivities, I make recommendations to manufacturers that could significantly change how they design their lighting products. The third Results & Discussion section focuses on the economic parameter sensitivity analysis. The parameters I use in the base case scenarios are mostly from data collected in Kenya during the summer of 2008, but these parameters may vary based on location within Kenya, time of year, political influences, the kerosene market, etc. In the economic parameter sensitivities, I vary parameters of lesser confidence that may easily change within a given setting. From the economic parameters sensitivities, I can evaluate how significantly off-grid lighting economics change under different parameter settings.
100 5.1 Results & Discussion Section 1 – Base Case Scenario
Using the results achieved within Methodologies 1-7, I generated base case scenarios for off-grid lighting products over four-year and 10-year periods. (Refer to
Appendices N and O for analysis parameter values and assumptions.) Using the base case scenario, I compare the lighting options in terms of costs and quality, making several points about the benefits and drawbacks of the options.
I generated four detailed graphs: two comparing the task/ambient lighting products over four-year and 10-year periods (Figures 30 and 31) and two for comparing the torch lighting products over four-year and 10-year periods (Figures 32 and 33).
These graphs illustrate the lighting products based on costs, light distribution, brightness and cost/lux-hr. Results are divided between torches and task/ambient lights because the two categories provide very different uses. Specifically, it is unlikely that a consumer would purchase an LED torch in place of a hurricane lamp, as the light output from a torch is much more focused than that of a hurricane lamp and thus better suited for different activities. It is typical for a consumer to replace the standard incandescent electric torch with an LED torch. On the same note, consumers are more likely to replace the common fuel-based lighting products for electric lights categorized as a task or ambient light. I choose to generate graphs for both four-year and 10-year periods to show how the comparisons change when investing in new technologies over shorter and longer time periods.
In the comparison graphs, I order the lighting products in approximate decreasing brightness. In the task/ambient comparison, I also bunch the solar-charged, grid-charged,
101 and fuel-based options separately. From the results, no single recommendation can be made to the consumer. Instead, I discuss the lighting designs in general, identifying benefits and drawbacks of design elements. For example, certain aspects in a lighting product design may make the product less expensive, but it may reduce the level of lighting service delivered.
I include quantitative values for the lighting product costs in Table 17, presented after the comparison graphs. Values include LCC components over a four-year and a 10- year period.
The following points address the task/ambient comparison (Figures 30 and 31):
1. In both analysis periods, the fuel-based lighting products (excluding the pressure
lamp) are cost competitive or less expensive than most electric lighting products,
but provide poorer lighting quality. The hurricane, wick, and candle lights
provide the lowest light output, shown in the orange line. Such low brightness is
not suited for carrying out precision activities such as reading, distinguishing
monies, or chopping vegetables. The cost/lux values for these fuel-based lights
show that they have among the highest cost for the quantity of light output. If a
vendor is willing to pay a similar or slightly higher price for a better quality light,
most electric lights analyzed are promising. From experience selling/leasing the
datalogging electric lights in Kenya, most vendors were eager to spend 700 Ksh
on the grid-charging option after experiencing its higher light quality.
102 2. The capital cost of most electric lights could hinder the purchasing of electric
lights. According to Mills and Jacobson (2007), focus groups suggested a
maximum retail cost of $25 for a lighting product, equivalent to 1,675 Ksh ($1US
= 67 Ksh). Eleven of the twenty-three electric lighting options analyzed have
capital costs exceeding this limit. With a pressure lamp capital cost of 1,500 Ksh,
some users may prefer purchasing the very bright pressure lamp even with its
higher fuel cost.
3. The pressure lamp is notably the brightest, as well as the second most costly. For
current vendors who use a pressure lamp, none of the electric lamps analyzed
could compare to the brightness these vendors are used to. The high costs to
operate pressure lamps, however, could inspire vendors who use a pressure lamp
to switch to an electric lamp.
4. The fuel-based lighting products are radially dispersed, having the highest
interpolated area shown in the blue line. Most customers may be accustomed to
the radial dispersion of light from a lantern, but many of the electric lamps if
mounted to the ceiling give a similar effect, plus add direction.
5. Of the electric lights, the solar lighting options in general have a lower LCC in
both the four-year and 10-year period analyses. The solar options look more
promising in the 10-year analysis; while the energy and maintenance costs
accumulate for other options, only maintenance costs accumulate for the solar
options (i.e., the solar option does not involve charging or fuel costs). With the
103 solar options, however, a high capital cost may act as a barrier for its purchase.
Nine of the 14 solar options have a retail cost exceeding $25.
6. Of the solar lighting options, those with SLA batteries have higher maintenance
costs than those with nickel-based batteries. Because the SLA batteries have a
considerably shorter cycle life, their accumulated battery changing costs are
significant. The consequences of the lower cycle life are more noticeable in the
10-year analysis, as the accumulation is over a longer period. The SLA batteries
typically have larger Ah storage capacities as well. If only a solar charging option
is available for the lighting product, the batteries may only occasionally receive a
full charge. Fully charging a large battery may take several days, a day or two
extra that a customer may not want to wait if desiring to use the light that night.
This behavior may run the battery constantly in a lower state of charge, possibly
decreasing the cycle life of the battery further and thus increasing maintenance
costs.
7. Maintenance costs for solar lighting options also include lighting product
replacements. Life expectancy estimates for the lights analyzed range from 1 to 4
years. Replacement costs for solar lighting options are generally high, like their
capital costs, raising the product’s LCC considerably. These high replacement
costs are especially significant in the 10-year analysis. If manufacturers were to
provide a less expensive replacement option excluding a solar module, the
product’s LCC could lessen. The life expectancy for a large commercial solar
104 module can exceed 20 years (Link, 2008); I assume a smaller solar module,
appropriate for an off-grid lighting product, has a life expectancy of 10 years.
8. Of the grid-charging lighting options, those with SLA batteries typically have
lower energy costs than those with nickel-based batteries. Because SLA batteries
tend to be larger in mAh capacity, they will provide a longer run time after
receiving a full charge from a charge shop. This requires fewer visits and
payments to the charge shop.
9. The base case scenario uses a charge cost of 20 Ksh/charge for the grid-charging
light options. This is the cost we found for a charge in Maai Mahiu and in
Karagita. Those who have grid at home can charge for approximately 0.04 Ksh.
Having the ability to charge at home or at a friend’s house makes the grid-
charging lighting options more promising.
105
25,000 125
Other O&M Costs
Fuel & Charging Costs
Initial Cost
% of 1 m2m2 containing 50% max illuminance
20,000 Light Output (from Lux Measurement) 100
Cost of Lighting Service (LCC/10,000 Lux-hr)
15,000 75
10,000 50 Measured Illuminance at One Meter Four-Year Cost ofOwnership (Ksh) containing 50% illuminance max of light (Lux) . 2 % of m % of
Four-YearCost per Unit Lighting (Ksh Service 10,000 Lux-hr) / 5,000 25
0 0
) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) r ) r r ) ) ) e r r r r r r r r r d d d e k l a i d d id d i i r g l a a a a a a a a i i i r r m c d l l l l l l l la la la l r r r r r u L i g ( S n o o o o o o o o o g g g g g g s o o o ( ( ( ( W s s s s s s s ( ( ( ( s a ( s s s s s t t e ( ( ( ( ( ( ( ( ( ( ( e k k k k k e n C t t t t t t n n r t k k s s s s s n n n n n n n k k k e e a n s s a a i i P a e s s s a a a c i e e e e e e b i c i i i i i i a a a a a T T T T T b r i b b b r r b b b b T T T T T - - - - - m r m u m - - - - - u m m m m m m A A M 2 1 S 3 H A - A A A A A A 1 2 3 S F F F - H M T A ------F F F T A Y Y Y B N Y Y Y N L B C S T B T A A X E T W O S Solar-Charged Grid-Charged Fuel-Based
Figure 30. Base case scenario comparing task and ambient lighting products over a four- year period. All lights were able to provide the base case of two hours of useful light per day, except the OB, which could only provide 1.9 hour s of useful light per day on its high ambient setting.
106
25,000 125
Other O&M Costs
Fuel & Charging Costs
Initial Cost
% of 1 mm22 containing 50% max illuminance
20,000 Light Output (from Lux Measurement) 100
Cost of Lighting Service (LCC/10,000 Lux-hr)
15,000 75
10,000 50 Measured Illuminance at One Meter 10-Year Cost10-Year of Ownership (Ksh) containing 50% max illuminanceof light (Lux) . 2 % of m % of 10-Year Cost10-Year per Unit Lighting (Ksh Service Lux-hr) 10,000 / 5,000 25
0 0
) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) r r r ) ) ) e r r r r r r r r r d d d e k l a i d d id d i i r g a a a a a a a a i i i r m c d l l l l l l l l la la la l r r r r r r u L i g ( S n o o o o o o o o o g g g g g g s o o o ( ( ( W s s s s s s ( ( ( ( ( s a ( s s s s s s t t e ( ( ( ( ( ( ( ( ( ( ( e k k k k k e n C t t t t t t n n r t k k s s s s s n n n n n k k k e e a n n n a i i P a e s s s s s a a a a c i e e e e e e b i c i i i i i i a a a a a T T T T T b r i b b b r r b b b b T T T T T - - - - - m r m u m - - - - - u m m m m m m A A M 2 1 S 3 H A A A A A A 1 2 3 - - H A M S F F A F - T ------F F F T A Y Y Y B N Y Y Y N L B C S T B T A A S X E T W O Solar-Charged Grid-Charged Fuel-Based
Figure 31. Base case scenario comparing task and ambient lighting products over a 10- year period. All lights were able to provide the base case of two hours of useful light per day, except the OB, which could only provide 1.9 hours of useful light per day on its ambient high setting.
107 The following points address the torch comparison (Figures 32 and 33):
1. All three LED torches have a lower LCC than the dry cell incandescent torch.
2. Of the three LED torches, the grid-charged torch has the lowest LCC and highest
light output. Its cost/lux values in both the four-year and 10-year analyses are by
far the lowest of all four torch options. Its interpolated area, however, is smaller
than the two solar torches.
3. The solar torch options have a considerably higher LCC than the grid-charged
option. Coming with a solar module, the solar torch options have a higher capital
cost.
4. The solar torch options, however, also provide non-torch settings, as seen in the
task/ambient graphs. One of these particular models compares well under its
ambient form factor option, but the other is able to provide only half of the
required two hours of ambient light output per night.
108
7,000 500 Other O&M Costs
Fuel & Charging Costs 450 Initial Cost 6,000 x % of 11 mm22 Ccontaining 50% max illuminance 400 Light Output (from Lux Measurement)
5,000 Cost of Lighting Service (LCC/10,000 Lux-hr) 350
300 4,000
250
3,000 200 Measured Illuminance at One Meter at Illuminance Measured
150 (Lu light of max illuminance 50% containing 2 Four-Year Cost of Ownership (Ksh) 2,000
100 m % of
1,000 Four-Year Cost per Unit Lighting Service (Ksh / 10,000 Lux-hr) / 10,000 (Ksh Service Unit Lighting per Cost Four-Year 50
0 0
) ) t ) ) d t r r i a a r a l l g b o o ( ( s s ( ( h h c c h h r r c c o o r r T T o o
- T T - - - C E Y C B O S O Figure 32. Base case scenario comparing torch lighting products over a four-year period. All lights were able to provide the base case of one hour of useful light per day.
109
10,000 500 Other O&M Costs
Fuel & Charging Costs 9,000 450 Initi al C ost x % of 11m m22 C containing 50% max illuminance 8,000 400 Light Output (from Lux Measurement)
Cost of Lighting Service (LCC/10,000 Lux-hr) 7,000 350
6,000 300
5,000 250
4,000 200 Measured Illuminance at One Meter 10-YearOwnership Cost of (Ksh)
3,000 150 containing50% max illuminance of light (Lu 2
2,000 100 m % of 10-YearCost per UnitLighting Service (Ksh /10,000 Lux-hr)
1,000 50
0 0
) ) ) ) t r r d t i a a r a l l g b o o ( ( s s ( ( h h c c h h r r c c o o r r T T o o
- - T T
- - E C Y C B O S O Figure 33. Base case scenario comparing torch lighting products over a 10-year period. All lights were able to provide the base case of one hour of useful light per day.
Table 17. Quantitative base case scenario results analyzed over a four-year period and a 10-year period
Analysis over 4 Years Analysis over 10 Years Capital Energy Habitual Energy Habitual Lamp Name Cost LCC Replacement LCC Replacement Cost Maintenance Cost Maintenance (Ksh) (Ksh) Cost (Ksh) (Ksh) Cost (Ksh) (Ksh) Cost (Ksh) (Ksh) Cost (Ksh) YE - Torch (batt) 100 6,616 6,327 23 167 8,885 8,522 31 233 OC - Torch (grid) 350 1,803 788 80 586 2,337 1,061 107 818 OB - Torch (solar) 2,033 3,,301 0 462 807 3,909 0 622 1254 OB - Ambient (solar) 2,033 3,301 0 462 807 3,909 0 622 1254 SC - Torch (solar) 2,823 3,464 0 641 0 4,751 0 863 1064 SC - Ambient (solar) 2,823 4,045 0 641 581 5,500 0 863 1814 XS - Ambient (solar) 3,953 4,850 0 897 0 6,651 0 1209 1489 ET - Ambient (solar) 2,372 4,115 0 538 1205 4,970 0 725 1874 WL - Ambient (solar) 11,294 19,348 0 2564 5491 23,282 0 3453 8535 YF1 - Task (grid) 983 4,230 2,634 223 390 5,437 3,548 300 606 YF1 - Task (solar) 1,186 1,926 0 269 471 2,280 0 363 732 YF2 - Task (grid) 901 6,823 5,361 204 357 8,952 7,220 275 556 YF2 - Task (solar) 1,073 1,742 0 244 426 2,063 0 328 662 YF3 - Task (grid) 737 5,447 3,311 167 1232 7,143 4,460 225 1721 YF3 - Task (solar) 1,016 2,,947 0 231 1699 3,701 0 311 2374 TB - Ambient (grid) 2,047 4,919 1,302 465 1106 6,625 1,753 626 2199 TB - Ambient (solar) 2,823 4,989 0 641 1525 6,720 0 863 3034 TM - Task (grid) 700 3,596 2,459 159 278 4,658 3,312 214 432 TM - Task (solar) 1,500 2,436 0 340 595 2,884 0 459 925 AS - Task (grid) 1,129 5,382 3,548 256 448 6,,951 4,779 345 697 AS - Task (solar) 1,129 1,834 0 256 448 2,172 0 345 697 AN - Ambient (grid) 2,375 4,802 1,559 539 329 6,521 2,100 726 1320 AN - Ambient (solar) 2,375 3,243 0 539 329 4,421 0 726 1320 Pressure 1,495 16,271 13,641 339 795 21,554 18,374 457 1228 Hurricane (Lg) 459 4,530 3,690 104 277 5,991 4,970 140 422 Hurricane (Sm) 235 3,155 2,683 53 183 4,194 3,615 72 273 Wick 10 2,831 2,795 2.3 24 3,813 3,765 3.1 35 Candle 4.4 2,648 2,644 -- -- 3,565 3,561 -- --
110 111 Figure 34 below illustrates the results in terms of their capital costs verses their four-year LCC values. The majority of the lighting products have capital costs below the maximum willingness-to-pay value of $25 (or 1,675 Ksh), shown as a red line (Mills and
Jacobson, 2007). Note that the pressure lamp has a higher LCC value, but costs less than
1,675 Ksh. Those with low LCC values and low capital costs are most promising for small business owners in Kenya.
20,000 18,000 YE - Torch (batt) OC - Torch (grid) 16,000 OB - Torch (solar) SC - Torch (solar) SC - Ambient (solar) XS - Ambient (solar) 14,000 ET - Ambient (solar) WL - Ambient (solar) 12,000 Maximum YF1 - Task (grid) YF1 - Task (solar) Willingness to YF2 - Task (grid) YF2 - Task (solar) Pay 10,000 YF3 - Task (grid) YF3 - Task (solar) TB - Ambient (grid) TB - Ambient (solar) 8,000 TM - Task (grid) TM - Task (solar) AS - Task (grid) AS - Task (solar) 6,000 AN - Ambient (grid) AN - Ambient (solar) Pressure Hurricane (Lg) 4,000 Hurricane (Sm) Wick Four-Year LCC (Ksh) Candle 2,000 0 0 2,000 4,000 6,000 8,000 10,000 12,000
Capital Cost (Ksh)
Figure 34. Comparing products having similar capital costs in terms of their LCC values. The maximum willingness-to-pay is represented as $25 (Mills and Jacobson, 2007). All the fuel-based products are shown as squares.
112 5.2 Results & Discussion Section 2 – Design Sensitivity Analysis
The design sensitivity analysis serves as the heart of my analysis. Manufacturers want to make products more attractive to customers, and customers want to purchase products of higher quality at lower costs. In this analysis I explore the several points I addressed in the above base case scenario comparison initiating investigation in off-grid lighting design elements. Most sensitivity analyses are conducted over a four-year period. I choose a four-year period because from the Analysis Period Sensitivity
Analysis, shown later in the Results and Discussion section, after a four-year period results were fairly stable. Four years is also the longest life expectancy I use for the lighting products analyzed.
I include a summary chart as Table 18 at the end of this section for concise review and for comparing between the potential design improvements analyzed.
The design elements I perform sensitivity analyses on include:
A. Battery size
B. Battery chemistry
C. Charging option
D. Optional solar module upon repurchase
E. Power consumption (light output)
113 I further theoretically discuss the following design influences:
F. Lamp type
G. Color rendering
H. Form factor
I. Luminaire design
J. Light brightness settings
A. Battery Size
Battery size is one of the most important design elements in terms of operating costs for most Kenyan small business owners. If the lamp owners are paying the same price to charge their lamps, no matter how much energy they require to receive a full charge, then a larger battery is preferred as long as the larger battery does not increase the product’s initial cost substantially. A larger battery provides a longer run time per charge, requiring fewer charges and saving the lamp owner in charging fees.
These charge savings are only relevant for lamps that are grid-charged and lamp owners who use charge shops as opposed to those who may charge at home or at a friend’s house. From our experience in Kenya, we found that the majority of those participating in our LED lamp study use charge shops.
Using Methodology 7-A, I generated Figure 35 below to show how the energy costs for the grid-charged options decrease when increasing the battery size. Notably, some lights decrease in energy costs more drastically with increased battery size. These
114 are generally those with smaller original battery capacities. LCC values are less influenced by increasing the battery size of products with larger SLA batteries. Related, as the percent augmentation of battery size increases, the slower the rate at which energy costs decrease. This suggests that there is an optimal battery size for each grid-charged lighting product.
It is important to note, however, that the curves shown within this analysis are influenced by parameters such as the lamp owner’s discount rate and battery costs.
Differences in discount rate between lamp owners imply optimal battery sizes specific to each lamp owner. Also, I assume an increased product capital cost with increased battery size based on battery prices found in Kenya. Manufacturers may very likely be able to obtain increased battery sizes for lower prices. Such differences would shift the curves shown within this analysis, thus shifting the optimal battery size. With this, I cannot claim that the values which appear to be optimal from the curves shown are the exact optimal battery sizes for each lighting product if used by small business owners in Kenya.
Instead, I present them as possible optimal battery sizes and use this analysis to show that a strong correlation exists between increased battery size for grid-charged products and reduced LCC values.
115
6,000 OC - Torch (grid)
5,000 YF1 - Task (grid)
4,000 YF2 - Task (grid)
YF3 - Task (grid) 3,000
TB - Ambient (grid) 2,000 TM - Task (grid)
1,000 AS - Task (grid) Energy Costs over Four Years Years (Ksh) Costs Four over Energy
0 AN - Ambient (grid) 0% 20% 40% 60% 80% 100%
% Battery Enlarged
Figure 35. Sensitivity to energy costs from percent battery enlarged over a four-year period
To show possible optimal battery sizes for some lighting products, I plot the product’s LCC savings from increasing its battery size against the increased battery size up to 200% (Figure 36). As each product has a unique design, each will respond differently in this model. The YF2, YF3, and XS products appear to have an optimal battery size when enlarged to over 200%; however, more drastic savings can occur with enlargements less than 100%. For example, by just increasing battery size by 50%, the
LCC savings for these two lights is more than half what they would be if increasing the size by 200%. The TM and YF1 products are shown to achieve optimal sizes around a
100% increase. The AN product’s initial battery size is shown as almost optimal; as its size increases in the model, the capital costs and maintenance costs associated with having a larger battery out weight the LCC cost savings.
116
50% OC - Torch (grid)
40% YF1 - Task (grid)
30% YF2 - Task (grid)
YF3 - Task (grid) 20% TB - Ambient (grid)
10% TM - Task (grid) over Four Years
% LCC Savings (Ksh) (Ksh) Savings LCC % 0% AS - Task (grid)
0% 50% 100% 150% 200% AN - Ambient (grid) -10% % Battery Enlarged
Figure 36. Sensitivity to LCC savings from percent battery enlarged over a four-year period
From the Battery Size Sensitivity Analysis, increasing the battery size for most grid-charged designs lowers the light’s LCC – to an extent. At a point, the capital and maintenance costs associated with the increased battery size outweigh the energy savings by having the increased battery size. This optimal point, however, may not necessarily indicate the best battery size to use if by increasing the battery size the capital cost exceeds the customer’s willingness-to-pay. The curves I present within this analysis are specific to the particular economic parameters used. Manufacturers are likely to obtain shifted curves when repeating the analysis with different economic parameters.
117 B. Battery Chemistry
As discussed in the background, there are several types of battery chemistries, but three have been dominate in off-grid lighting products: SLA, NiCd, and NiMH. All three have benefits and drawbacks. Here I compare the battery chemistries using four of their characteristics: lamp discharge performance, cost per energy capacity, cycle life, and toxicity.
Conveniently, one line of off-grid products I use in the analysis includes two lighting designs identical in all aspects but their battery chemistries. One design has a
2200 Wh SLA battery and one has a 2150 Wh NiCd battery. The Wh energy capacities are determined by multiplying the product’s rated mAh capacity by its rated voltage.
SLA batteries typically discharge linearly, decreasing at a constant rate in illuminance over time. Nickel-based batteries, however, typically have a flatter discharge curve, providing a more constant lighting output during its use. Lighting product users may prefer a more constant light output over one that is continually decreasing in light output.
This is because the constant light output may provide a more acceptable illuminance level for a longer period of time. Figure 37 illustrates the differences between the discharge curves of the YF task lighting product with an SLA battery and with a NiCd battery. The two lighting products provide the same lux-hours, but the NiCd design’s has a higher median illuminance than the SLA – the NiCd design provides a median of 10 lux while the SLA design provides a median of 6 lux.
118
20 YF1 - Task (SLA) 18 YF2 - Task (NiCd) 16 14 12 10 8
Illuminance (Lux) Illuminance 6 4 2 0 0 50 100 150 200 250 300 350 400 Time (minutes)
Figure 37. Comparing YF task lighting products with different battery chemistries; the product designs are identical in every other way. The batteries have very similar capacities – the SLA battery has 2200 Wh and the NiCd battery has 2150 Wh. The YF1 task light increases in lux at the start of its test; this does occur with some lighting products.
Based on price data collected in Kenya in 2008 and price differences between the
YF NiCd and YF SLA products described above, the SLA battery chemistry is less expensive in terms of cost per energy capacity. It is important to note that different manufacturers may be able to obtain batteries at different costs than those used in YF products as well as those we obtained in Kenya. This may led to a different comparison in the context of product capital cost. The battery prices collected in Kenya are more relevant in the context of battery replacements in Kenya. According to the price differences between the YF products, the NiCd design’s retail price is 20% more than the
119 SLA design’s. According to the price data collected in Kenya, NiCds and NiMHs cost on average 0.06 Ksh/mWh, SLA-12Vs cost on average 0.018 Ksh/mWh, and an SLA-6Vs cost on average 0.025 Ksh/mWh. SLA batteries smaller than 6V are not sold in Kenya.
(Refer to Table 25 in Appendix K for battery prices in Kenya.) These costs per unit of energy suggest that the SLA-12Vs are the least expensive, followed by the SLA-6Vs, and lastly the nickel-based batteries.
Cycle life is another battery characteristic influencing battery chemistry preference. The battery cycle life controls when it is replaced, which is important when calculating a product’s LCC value. In some cases, the user may not know how to physically replace the battery, which in turn would increase the frequency of lamp replacement costs. The battery information I have reviewed suggests that NiCds have the longest cycle life, followed by NiMHs, and lastly by SLAs. Depending on the lighting product’s life expectancy, a product’s battery may or may not even require replacement within the product’s lifetime. With the battery and lighting product life expectancies I used in my base case scenario economic analysis, four of the five LED options that required battery replacing had SLAs; the fifth had a NiMH. All but one product with a nickel-based battery did not require replacement; this suggests that even though NiCds may have a longer cycle life than NiMHs, they may have an equally long lifetime in the context of off-grid lighting products.
Toxicity is another important consideration when choosing which battery chemistry to design with. The battery information I have reviewed suggests that of the battery chemistries discussed, NiMHs are the least toxic, followed by the SLA, and lastly
120 the NiCd. It may be very hazardous to design NiCd or SLA products for locations lacking NiCd or SLA recycling facilities or many proper sanitary landfills. In many locations within Sub-Saharan Africa, “trash is agglomerated in unlined garbage pits”
(Lai, 2009). NiCds are specifically worrisome because they contain toxic cadmium.
Cadmium, found in NiCd batteries, is known to cause prostate and kidney cancer, reproductive damage, and can cause severe respiratory illness if inhaled when burned
(Australian Government, 2005). NiMH batteries may not provide as many cycles as
NiCds or be as low-cost as SLAs, but their lower toxicity is a very important quality.
SLAs may have a lower cycle life and lesser preferred lamp discharge curve, but their lower cost per Wh energy capacity allows manufacturers to design products with larger battery capacities. If manufacturers target a specific purchase price for a product, they can use a larger SLA than nickel-based battery for the same price. Larger capacities provide longer runtimes, which decrease charge costs for grid-charged options and increase reliability for solar-charged options under scattered periods of sunshine.
When selecting the best battery chemistry to use, its lamp discharge curve, cost per energy capacity, cycle life, and toxicity should all be considered. SLAs have the lowest cost per energy capacity, NiCds have the longest cycle life, and NiMHs are the least toxic. A manufacturer should weight all battery chemistry characteristics with the best interest of the customers in mind.
121 C. Charging Option
In my analysis, lights can be charged via grid or solar. Some have both charging options, but I have not explored LCCs combining the two charging methods, as it is more complicated to estimate the percentage of time that each method is used in the field. My results comparing grid and solar charging methods serve as a good start in the discussion of charging options.
The best way to evaluate grid and solar charging methods is to compare designs that offer both grid and solar options that can be purchased separately. Of the products I analyze, seven offer both charging options. Five of the seven automatically come with a grid charger and offer solar as an additional expense. Two of the seven automatically come with both solar and grid charging options. Results illustrate a wide range of how the products economically compare between their charging options (Figure 38).
To read Figure 38, the blue represents the additional expense of purchasing a solar module for charging the product, and the red represents the energy costs required to grid charge the product over four years. The line represents the how much a customer would save over four years of using the product if having initially purchased a solar module.
122
80% 8,000
70% 7,000
60% 6,000
50% 5,000
LCC/Grid LCC) LCC/Grid 40% 4,000 ∆
30% 3,000
20% 2,000
10% 1,000 Costs (Ksh) Over Four Years Four Over (Ksh) Costs
0% 0 ∆
-10% -1,000 % Savings with Solar = ( TB AS AN TM -20% YF1 YF2 YF3 -2,000
∆ Capital Cost ∆ Energy Cost % LCC Savings with Solar
Figure 38. Comparing grid and solar charging options for seven lighting products over a four-year period. Two of the seven options (the AS and the AN) automatically include both solar and grid charging options, hence there is no difference in the capital costs between the options.
Three solar options yield savings over 50% while one solar option actually does not yield any savings. Those with smaller or negative savings appear to have a smaller grid energy costs while those with the highest savings have the highest grid energy costs.
The design having a negative savings with solar includes a large-sized battery. This suggests that for some models, it may be more cost-effective to increase the battery size than to purchase a solar module in the context of Kenyan small businesses.
Reliability and capital cost are additional concerns for customers. Having a grid- charged option allows for increased reliability for when the sun does not shine. Most market vendors in Kenya are in close proximity to the grid (Lighting Africa, 2008a), and
123 thus most likely are close to a charge shop. Also, most solar-charged products come with a high capital cost which many lower-income customers can not pay for upfront.
D. Solar Module Upon Repurchase
Many solar off-grid lighting products include a separate solar module in their purchase. The module is external and is plugged into the lighting product when charging.
The lighting products, however, typically do not have as long a life expectancy as their companion solar module. Large commercial solar modules have a life expectancy of at least 20 years (Link 2008); I assume a smaller solar module, appropriate for an off-grid lighting product, has a life expectancy of 10 years. In my base case scenario, I estimate lighting product life expectancies of four years and less. When a customer’s solar lighting product reaches its life expectancy and must be replaced, it is likely that the customer’s solar module is still good. If the product was available for replacing without the repurchasing of the solar module, the customer would save a fair amount of money.
This analysis explores how much influence a non-solar replacement would impact the lighting product’s LCC. It is conducted over a 10-year period because I assume 10 years as the life expectancy of the product’s solar module.
124
25,000 20%
18%
20,000 16%
14%
15,000 12%
10%
10,000 8%
6% Savings LCC %
LCC (Ksh) Over 10 Years 10 Over (Ksh) LCC 5,000 4%
2%
0 0% SC - Torch AN - WL - SC - XS - YF2 - TaskYF1 - Task AS - Task TB - TM - Task YF3 - Task (solar) Ambient Ambient Ambient Ambient (solar) (solar) (solar) Ambient (solar) (solar) (solar) (solar) (solar) (solar) (solar)
Optional Solar Purchase LCC (Ksh) Original LCC (Ksh) % LCC Savings
Figure 39. LCC savings when solar-charged lighting products are replaced without the repurchasing of a solar module, over a 10-year period
By comparing the solar light LCC values between replacing with and without the repurchasing of a solar module, Figure 39 illustrates that adding the option does not make a significant impact for most lighting products. All but three examples in the above
Figure would save 6% or less over a 10-year period. Three of the products would save over 12% of their LCC value by replacing with a non-solar option. The product with the highest LCC savings is designed with a 4V SLA battery, a battery not available for replacement in Kenya. Hence, when the battery’s life is over, so is the light’s. By replacing the light with a non-solar option, the customer would save a fair amount of money.
125 However, the main reason to offer solar and non-solar options is to allow customers to purchase the solar-charging option incrementally over time. For example, a customer may be able to buy only the lighting product without solar first and then purchase the solar module later after having saved enough money. Aside from improving a product’s LCC, the optional solar module makes it possible for many customers to afford a solar-charged lighting product in the first place.
In general, the LCC savings in offering a non-solar option for replacement purposes is not a significant savings for most lighting products, but the option for every product does indeed save money. Furthermore, providing a solar and non-solar option can make the product more affordable because the customer can purchase the lamp and solar components incrementally.
E. Power Consumption (Light Output)
Decreasing the power consumption increases the lighting product’s runtime before having to recharge. A manufacturer can decrease the power consumption in several ways, such as employing a more efficient LED driver circuit, decreasing the lighting output, or upgrading to more efficient LEDs. Ideally, the manufacturer can use more efficient driver circuits and LEDs, but decreasing the light output in cases where the initial brightness is more than adequate is also a potential avenue for economic improvement.
126 In this analysis, I encompass all three possible improvements into one sensitivity parameter – power consumption. For all three methods described, as power consumption decreases by a percentage of the original power consumption, energy costs will decrease by a similar percentage. My analysis, however, does not take into account potential increases in product capital costs. For example, if power consumption were decreased by upgrading to a more efficient, but more expensive LED, the increased capital cost is not captured in my model. Although, at the rate in which LEDs are becoming more efficient and more cost-effective, assuming a zero capital cost increase may not be reasonable.
For the method of reducing power consumption through decreasing light output, no capital cost increase is required.
50% OC - Torch (grid) 45% 40% YF1 - Task (grid)
35% YF2 - Task (grid) 30% YF3 - Task (grid) 25% TB - Ambient (grid) 20% TM - Task (grid)
over FourYears 15% % LCC Savings (Ksh) Savings LCC % 10% AS - Task (grid) 5% AN - Ambient (grid) 0% 0% 20% 40% 60%
% Reduction in Power Consumption
Figure 40. LCC savings with decreased power consumption for grid-charged lights over four years. (The LCC savings equals the energy savings in this case.)
127 The energy cost savings for all grid-charged options is the main parameter affected in my model when reducing power consumption. As expected, as the power consumption is reduced, the energy savings increases. The energy savings is reflected as the LCC savings in Figure 40 above. I chose to vary reductions up to 60% because from my knowledge of lighting product components, 60% is approximately a maximum power reduction without potentially decreasing the brightness of the product to a point where it is no longer useful. In Figure 40 above, grid-charged products with higher capital costs increase in percent LCC savings at a slower rate, such as the BT ambient light. In addition, the grid-charged options that increase in percent LCC savings at a faster rate are designed with smaller-sized batteries. Like in the Battery Size Sensitivity Analysis results, the larger the battery size, the fewer visits and fees a customer has to pay to the charge shop. For example, reducing the number of charging events in a year from 100 to
50 saves twice as much money as reducing from 50 to 25.
The energy cost to charge is zero for solar-charged options; however, by having a longer runtime from reducing power consumption, solar-charged lighting products can be designed with smaller-wattage solar modules. This could reduce the capital cost of the product, making solar-charged options more affordable for customers. But from the analysis, it appears that the capital cost does not decrease very much (Figure 41).
128 Products with larger solar modules reduce in capital cost more and at a faster rate as the power consumption is reduced; however, none of the products that had an initial price that was originally above $25 (1,675 Ksh) reduced the price to under $25, even when no solar module is included. The recommended maximum willingness-to-pay price is $25 given by focus groups in Kenya, as described in the Literature Review (Mills and
Jacobson, 2007).
16% OB - Torch (solar)
14% OB - Ambient (solar)
SC - Torch (solar) 12% SC - Ambient (solar) 10% XS - Ambient (solar)
8% WL - Ambient (solar)
YF1 - Task (solar) 6% YF2 - Task (solar) 4% YF3 - Task (solar) % LCC Savings (Ksh) Savings LCC % Over Four Years 2% TB - Ambient (solar)
TM - Task (solar) 0% 0% 10% 20% 30% 40% 50% 60% AS - Task (solar)
% Reduction in Power Consumption & Reduced Solar Module Size AN - Ambient (solar)
Figure 41. LCC savings with decreased power consumption and solar module size for solar-charged options. (Solar module size is reduced in proportion to reduced power consumption.)
129
4,000 1,675 Ksh (or $25) Capital Cost OB - Torch (solar) 3,500 OB - Ambient (solar)
3,000 SC - Torch (solar)
SC - Ambient (solar) 2,500 XS - Ambient (solar)
2,000 YF1 - Task (solar) YF2 - Task (solar)
1,500 YF3 - Task (solar) Capital Cost (Ksh) Cost Capital TB - Ambient (solar) 1,000 TM - Task (solar)
500 AS - Task (solar)
AN - Ambient (solar) 0 0% 20% 40% 60%
% Less Reduction in Consumption & Reduced Solar Module Size
Figure 42. Capital cost with decreased power consumption and solar module size for solar-charged options. (Solar module size is reduced in proportion to reduced power consumption; see Methodology 7-E for details.)
In comparison, the LCC savings from reduced power consumption is greater for grid-charged options than solar-charged options. For example, of the products analyzed, the average cost savings over four years from a 20% power reduction for the grid-charged options is 624 Ksh and for the solar-charged option (with reduced module size) is 118
Ksh.
130 F. Lamp Type
In my model, I focus on WLED lighting products because they are the most economically promising for off-grid lighting products. Based on all economic off-grid lighting analyses I reviewed, the LED lamps hands-down are generally more cost effective than off-grid lighting products based on incandescent and fluorescent technologies. (Refer to Jones et al.,2005, Peon et al., 2005, and Foster and Gómez, 2005 studies in the Literature Review section of this thesis.)
I highly recommend designing with the white LED lamp type, not only for economical reasons, but also environmental and convenience reasons. They require less energy per output, have low toxicity, are quite durable, and the HBLEDs have a very long life expectancy. (Refer to “Lamps” in the Background section of this thesis.)
As there are several WLED designs available, from 5mm to HBLEDs, one area of further study includes economically comparing available WLEDs suitable for off-grid lighting. The study includes many challenges, such as obtaining realistic WLED prices available to manufacturers, measuring the true efficacies of the WLEDs, and acquiring a large enough sample size to reach valid conclusions.
G. Color Rendering
In addition to economical variation between lamp types, lamps vary in their color quality. The standard CIE coordinate spectrum is one tool used to quantify color quality.
From measuring the CIE (x, y) coordinates for several off-grid lighting products we have
131 in our lab, it is clear that products with HBLEDs consistently provide a “whiter” light output than products with 5mm WLEDs (Figure 43).
0.9 5mm WLED ▲ HBLED 0.8 ♦ D65 Daylight * Incandescent 0.7 * Kerosene Lamp * Candle
0.6 y 0.5
0.4
0.3
0.2
0.1
0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 x
Figure 43. CIE (x, y) color coordinates for several 5mm and HBLED lighting products tested at the HSU lighting laboratory (The CIE 1931 (x, y) chromaticity diagram photo is from http://www.ledtuning.nl/gallery.php?Name=aboutcolors_EN&month=6&year=19 93, the D65 Daylight CIE (x, y) coordinates are from (CIE, 1998), the Incandescent CIE (x, y) coordinates are from (GE Electric Company, 1997-2008), the Kerosene Lamp CIE (x, y) coordinates are from (Energistic Systems), and the Candle CIE (x, y) coordinates are from (Gigahertz-Optik))
132 H. Form Factor
In manufacturing off-grid lighting products for Kenyan small businesses, each form factor has its economic and use benefits; of the three discussed in this thesis, the ambient form factor can provide lighting service more in line with small business owner use demands but can also be more expensive. According to the Lighting Africa 2008 study, small business owners desire lighting products for money transactions, lighting up customers’ faces, and illuminating merchandise. These desired uses were approximately equal in weight (Lighting Africa, 2008a). A task light may serve the desired uses, but most likely only one use at a time – when a task lamp is used to illuminate merchandise, often the light does not spread enough to light up a customer’s face. The torch may also provide for only one desired use at a time, plus the torch’s focused output may not be ideal for lighting up a customer’s face or for capturing a small business’ spread of merchandise.
The available task lamps, however, generally have lower LCCs. With light output, there is a cost balance between lighting distribution, or spread, and brightness provided. For example, if a given task lamp design were modified to supply a more distributed light, it would not be as bright, unless more WLEDs were added. Adding more WLEDs, in turn however, may increase both capital and energy costs. Further study is recommended on the economics between the two form factors subject to small business owners’ willingness-to-pay for a brighter, or more spread, light output. In general, however, although a bright ambient light appears ideal for Kenyan small business, a task lamp may be preferred since it has a lower cost.
133 With respect to the torch form factor, it appears least ideal for Kenyan small businesses; however, the torch may be the only affordable option for many. When in
Kenya we observed several small business employees use torches to stay open briefly after dark. They used the torches not only in the context of sales, but also for gathering their merchandise at closing time and for illumination as they left the marketplace. For such purposes, a torch serves as an appropriate investment. The torch may not be the ideal lighting product for Kenyan small businesses, but by providing the option, many small businesses that cannot afford an ambient or task light may greatly benefit for the service provided by a torch.
I. Luminaire
Modifying a lighting product’s luminaire can be one of the easiest ways to improve a product’s lighting quality, as described under “Luminaire” in the Background section of this thesis. Manufacturers may consider improving product luminaire design using a reflector, lens, or diffuser to increase luminous efficiency – the percentage of light output reaching the desired location. Further study is recommended to experiment with luminaire styles for most cost-effective solutions.
134
J. Light Brightness Settings
With variable brightness settings, a user can adjust the light’s brightness output to the strength required. Specifically, if he or she does not need very bright light for a desired activity, a lower setting can be selected to lower the product’s power consumption. The design addition has the potential to save a customer a fair amount of money. Adding brightness settings, however, may increase the capital cost of the lighting product.
From the Power Consumption Sensitivity Analysis results, reducing power consumption provides a higher cost savings for grid-charged options compared to solar- charged options; I suspect a similar relationship for incorporating brightness settings. A similar study to the Power Consumption Sensitivity is recommended to delve into this topic further. Price information for incorporating brightness settings into lighting products, average brightness setting distributions, and a large sample size of lighting products with brightness settings would be required for the study.
Table 18. Design Sensitivity Analysis Results Summary
Effects on Effects on Design Sensitivity Analysis Grid-Charged Solar-Charged Noted Results Recommendation Component Products Products Increases lamp For grid-charged options, choose a For grid-charged options, there exists operation time battery size that decreases LCC but an optimal battery size that will between charge Increases capital does not increase the product's capital Battery size enlarged provide the greatest LCC savings events. Increases cost, no effect on cost to exceed customer affordability. Battery Size High from 0 to 100% of unique to each lighting product. capital cost, operating cost, For solar-charged options, use a battery original size Optimal sizes are dependent on the decreases increases LCC size slightly larger than required to customer parameters modeled, e.g., operating cost, satisfy typical hours of use per day as a Kenyan small businesses. decreases LCC factor of safety for non-sunny days.
Comparing products identical in all A high cycle life aspects except for lowers operating The economical preference of battery Effects lamp NiMH are recommended. Even though their batteries, as costs, having a chemistry is NiCd, NiMH, SLA-12V, discharge NiCds are more cost-effective, they Battery well as theoretically high energy SLA-6V. SLAs have lower capital Medium performance, but contain toxic cadmium. In locations Chemistry comparing density increases costs per Wh of energy capacity, but no effect on this without hazardous disposal facilities a chemistries based on capital cost per have a much shorter cycle life, analysis battery low in toxicity is more suitable. cost per Wh energy Wh of energy strongly influencing their LCC. capacity and cycle capacity life Comparing products identical except for Design with grid-charge and include their charging solar as an optional non-integrated Grid-charged products with relatively method. Calculating Removes energy Increases energy feature. A grid-charged option Charging small batteries can save up to 74% of Medium how much a cost, increases cost, decreases increases reliability for non-sunny days. Option LCC with the addition of an customer would save capital cost capital cost An optional solar component is appropriately sized solar module. if having initially important for locations with no grid purchased a solar access. module Altering lamp Decreases Make solar optional for decreasing LCC Optional Solar 3 of the 11 products analyzed over replacement costs to maintenance and for making products affordable to Module Upon Low No effect 10 years obtain a savings over 12% not include the solar costs, decreases customers who need to purchase the Repurchase of their LCC value, the rest under 6% module cost LCC solar component incrementally.
135
Table 18 Continued. Design Sensitivity Analysis Results Summary
Effects on Effects on Design Sensitivity Analysis Grid-Charged Solar-Charged Noted Results Recommendation Component Products Products Decreases Power consumption For the grid charge options, products operating costs, For grid-charged options, do not provide Power decreased from 0 to Decreases battery with smaller batteries have a greater High decreases LCC, more light than what is needed, plus a Consumption 60% of original replacement costs savings from reduction in power decreases battery factor of safety. consumption consumption replacement cost The savings are lower than the grid- Power consumption For solar-charged options, the LCC Power Decreases capital charged reduced power consumption decreased from 0 to Decreases savings may not be great enough to Consumption cost, decreases savings. The average cost savings 60% of original operating costs, reduce light output. However, Reduction battery and lamp over 4 years from a 20% power Medium consumption and decreases battery depending on available solar module with Solar replacement reduction for the grid-charged options decreased solar replacement cost, options, a reduction in product capital Module Size costs, decreases is 624 Ksh and solar-charged option module size in decreases LCC cost could achieve wider customer Reduction LCC (with reduced module size) is 118 proportion affordability. Ksh. As the luminous As the luminous efficacy increases, WLEDs often have a high luminous Theoretical and efficacy increases, capital cost efficacy (lumen/watt), but a higher reference to capital cost increases from the capital cost. According to three Lamp Type High Design with the WLED lamp type. Literature Review increases, and lamp component, economic analyses presented in the analyses. the energy cost but decreases Literature Review, WLEDs are the decreases from the solar most cost-effective lamp option. component Measuring and Consider designing with HBLEDs. They plotting CIE (x,y) A "whiter" light will closer resemble Color HBLEDs generally provide a "whiter" may currently be more costly, but N/A color coordinates for daylight, which is preferable over bluish Rendering light than 5mm WLEDs provide much better light and their different lighting light for many customers. prices are dropping quickly. products Incorporating a reflector, lens, or Providing better direction for a light will Requires further study for economic Luminaire High Theoretical diffuser can be one of the easiest ways increase its luminous efficiency. results. to improve lighting quality. Increases Reducing power consumption when Increases capital cost, capital cost, not needed could greatly reduce Light decreases operation Design with brightness settings as long decreases several life cycle costs while only Brightness High Theoretical costs, decreases as the electronic additions are low-cost battery increasing capital cost slightly. Settings battery replacement and efficient. replacement Requires further study for economic cost cost results.
136 137
5.3 Results & Discussion Section 3 – Economic Parameter Sensitivity Analysis
Model results are only as accurate as their parameters and assumptions. In my model for Kenyan small business owners, I used as many real parameters and as few assumptions as possible. The parameters I used are based on data collected in the summer of 2008 from small business owners within the towns of Maai Mahiu and
Karagita in Kenya. These parameters may change upon time of year, location within
Kenya, political influences, the kerosene market, and more. To best estimate how the model’s results are influenced by changing parameters, I vary each parameter independently over a reasonable range and report how the model’s results are affected.
I perform this method by independently varying the following economic parameters in my base case scenario. I also include a summary chart as Table 23 at the end of this section for concise review and comparison of the affects from each economic parameter analyzed.
K. Use time
L. Kerosene cost
M. Kerosene fuel escalation rate
N. Charge cost
O. Battery life expectancy
P. Lighting product life expectancy
Q. Real discount rate
R. Analysis period
138 K. Use Time
In the base case scenario, I used two hours as the length of time a Kenyan small business owner requires ambient or task light for his or her business per night. I use one hour for a torch light. Some vendors may stay open longer than others, changing the analyzed lighting products’ LCC values for each vendor. In addition, small business owners in other locations within Kenya or elsewhere in the world may operate for longer or shorter periods during the night. By performing a sensitivity analysis on how long small businesses require light, I am able to report to what degree the model results change when the use time parameter is varied.
In Figures 44 and 45 below I vary the use time from -80% to 200% of base case use time values for both ambient/task lighting and torches, respectively. This is from 0.4 to 6 hours per day for ambient/task and 0.2 to 3 hours per day for torches. I choose these values based on my field experience in Kenya. The analysis period is over four years for proper comparison with base case scenario results. To read the graphs, the base case scenario LCC values are in bright yellow; higher and lower use time percentages correspond to the colors identified in the legend. LCC values for solar options do not change with use time; hence, only their base case LCC is shown. Solar options, however, may not be able to provide up to 200% of use time. Table 19 indicates the maximum percentage increase of use hours per day from base case for each solar option after one standard day of solar charging. (Procedures for these values are described in
Methodology 2.)
139
130,000 200% 120,000 160% 120% 110,000 80% 40% 100,000 0% -40% 90,000 -80%
80,000
70,000
60,000
50,000 LCC (Ksh) over Four Years Four over LCC (Ksh) 40,000
30,000
20,000
10,000
0 ) ) ) ) ) re ) ck ar lar ar) ar) ar) id) id) id) id d) ol lar) l ol r Lg Wi s so s (gr (gr (gr (gri (grid) ssu ( ( so (solar) (so sol ( (g e Candle t (solar) t ( ( sk sk nt r nt nt nt ask ie ask P e e en ask (solarent ask (solarie T Ta Ta b T bient bi ent (solbi ar) bi T T b - - m m m m mbien - mbi m m A A A 1 A A HurricaneHurricane ( (Sm) - A Ambi 3 A F F2 - F3 - - TM AS - Task (grid) - - - TM - TaskAS (solar) - Y Y Y B N - YF1 - TaskYF2 - TaskYF B T A OB - A SC XS ET - WL T AN -
Solar-Charged Grid-Charged Fuel-Based
Figure 44. LCC values for ambient and task lighting products with varying use times over four years
140 For the ambient/task lighting products, some of the lighting products increase in
LCC value more than others over time. The fueled-based lighting products increase in
LCC wildly compared to the grid-charged lighting product increases. This reflects that the fuel cost per hour for the fuel-based lighting is much greater than the charge fees for the electric lighting. In general, with more hours of use per day, the electric lamps are increasingly more economically attractive. Also, given that the solar-charged lights can provide more light in a day with their currently-size modules, they specifically become more attractive economically, as their cost does not change with increased use time.
(Table 19 below provides the maximum percent more use hours per day possible for each solar lighting product.)
141
20,000
200% 18,000 160% 120% 16,000 80% 40% 14,000 0% -40% 12,000 -80%
10,000
8,000
LCC (Ksh) over Four Years 6,000
4,000
2,000
0 YE - Torch (batt) OC - Torch (grid) OB - Torch (solar) SC - Torch (solar)
Figure 45. LCC values for torch lighting products with varying use times over four years
For the torches, as use time increases per day, the incandescent dry cell torch becomes less and less attractive. The grid-charged torch approximately doubles in LCC cost when able to provide 200% more use time, but remains the most cost-effective option of the torches. The OB torch, according to Table 19, cannot provide more than one hour of light per day, and the SC torch remains more expensive than the OC, even at
200% more use time.
142 Table 19. Maximum percentage increase of use hours per day from solar-charged lights after a standard day of solar charging. Base case use is two hours for a task or ambient light and one hour for a torch.
Max % More Use Light Code Name Hours Per Day of Base Case OB - Torch 130% OB - Ambient -5% SC - Torch 250% SC - Ambient 130% XS - Ambient 170% ET - Ambient Not tested WL - Ambient 0% YF1 - Task 635% YF2 - Task 103% YF3 - Task 402% TB - Ambient 491% TM - Task 586% AS - Task 220% AN - Ambient 386%
Overall, the greater the use time, the more attractive the LED electric lights are economically compared to the fuel-based and incandescent lighting. The solar options also become more attractive economically with increased use time, granted they can provide the increased use hours and their capital costs are not too high of a barrier.
143
L. Kerosene Cost
The cost for fuel is one of the most volatile parameters in this analysis. While in
Kenya during the summer of 2008, kerosene prices were up to 89 Ksh/liter at petrol stations; by December, 2008 the price for kerosene dropped to 72 Ksh/liter. My model uses 89 Ksh/liter, as that was the kerosene price during the time of data collection.
I perform a sensitivity analysis increasing the base case scenario fuel price, 89
Ksh/liter, between -80% and 100%, or 18 and 180 Ksh/liter (Figure 46). I choose these values based on field experience in Kenya. The kerosene-fueled lighting products are bolded in Figure 46 below for easy comparison. Markers inserted for the December,
2008 kerosene price and the kerosene price from while we were collecting data over the summer of 2008.
As seen in Figure 46, the fuel price has a sizeable influence on the lighting product LCC comparisons. Over the four-year period shown, three fewer electric lights have lower LCC values than the large hurricane lamp when varying the kerosene cost from 89 to 72 Ksh/liter. By doubling the cost of kerosene used in my analysis from 89
Ksh/liter to 138 Ksh/liter, all but one electric lamp design has a lower LCC value than the large hurricane lamp.
Many individuals are predicting increased fuel prices in the near future; depending on the extent the prices increase, electric lights will keep looking more and more attractive economically to Kenyan small business owners.
144
YE - Torch (batt) OC - Torch (grid) OB - Torch (solar) OB - Ambient (solar) 30,000 SC - Torch (solar) SC - Ambient (solar) XS - Ambient (solar) ET - Ambient (solar) WL - Ambient (solar) YF1 - Task (grid) YF1 - Task (solar) YF2 - Task (grid) YF2 - Task (solar) YF3 - Task (grid) YF3 - Task (solar) TB - Ambient (solar)
TM - Task (grid) TM - Task (solar) AS - Task (grid) AS - Task (solar)
AN - Ambient (grid) AN - Ambient (solar) Pressure Hurricane (Lg) Hurricane (Sm) Wick Candle 25,000
20,000
72 Ksh/liter
15,000
89 Ksh/liter LCC (Ksh) over Four Years 10,000
5,000
0 0 20 40 60 80 100 120 140 160 180 Kerosene Cost (Ksh/liter) Figure 46. Sensitivity to LCC from variation in kerosene cost
145
M. Kerosene Fuel Escalation Rate
For the base case scenario, I assume that kerosene fuel increases at the nominal discount rate. It is common, however, to model fuel costs increases with an additional fuel escalation rate. In this sensitivity analysis, I explore how LCC values for kerosene- fueled lights change with adding a kerosene fuel escalation rate and how they interact with the constant electric light LCC values.
In Table 20 below, I provide specific values for LCC increases between fuel escalation rates of 0% and 10% for over a four-year period. The lamps with the higher fuel consumption rates exhibit the greatest increases in LCC with increasing escalation rate. The LCC increases are not huge, but are substantial; the increase in LCC for a large hurricane lamp with a 10% escalation rate is 682 Ksh, or $10 – enough to purchase an electric LED light.
Table 20. Kerosene-fueled lighting LCC increases at 10% escalation rate over four years
∆LCC with 10% Lamp Type Escalation Rate (Ksh) Pressure 2522 Hurricane (Lg) 682 Hurricane (Sm) 496 Wick 517
146 I generated Figure 47 to show how increased fuel escalation rates influence LCC relationships between kerosene-fueled and electric lights up to an annual fuel escalation rate of 20%. The variation values were chosen based on field experience in Kenya. The kerosene-fueled lamps are in bold while the electric lamps are shown with finer lines.
The general trend shows that electric lighting products become slightly more economically attractive when increasing the kerosene fuel escalation rate. Over the four- year period shown, four more electric lights have lower LCC values than the larger hurricane when varying the escalation rate from zero to 10%, and over a 10-year period, all the electric lights analyzed have lower LCC values. This analysis suggests that the kerosene fuel escalation rate has a moderate influence in comparing LCC values between off-grid lighting products, and the influence increases quickly as the analysis period increases.
147
25,000
20,000
15,000
10,000 LCC (Ksh) over Four Years 5,000
0 0% 5% 10% 15% 20%
Yearly Kerosene Escalation Rate
YE - Torch (batt) OC - Torch (grid) OB - Torch (solar) OB - Ambient (solar)
SC - Torch (solar) SC - Ambient (solar) XS - Ambient (solar) ET - Ambient (solar) WL - Ambient (solar) YF1 - Task (grid) YF1 - Task (solar) YF2 - Task (grid)
YF2 - Task (solar) YF3 - Task (grid) TB - Ambient (solar) TM - Task (grid)
TM - Task (solar) AS - Task (grid) AS - Task (solar) AN - Ambient (grid)
AN - Ambient (solar) Pressure Hurricane (Lg) Hurricane (Sm)
Wick Candle
Figure 47. Sensitivity to LCC values with increasing kerosene fuel escalation rate over a four-year period
N. Charge Cost
Charge cost can vary for three main reasons – location, market supply/demand, and percent of a full charge received. In both Maai Mahiu and Karagita, charge shops consistently charged 20 Ksh per charge event for LED lamps. This is a common rate in
148 Kenya, but elsewhere in the world the rate may vary. Additionally, not all lighting products receive a full charge during each charge event, as discussed in Datalogger LED
Lamp Results within Appendix K. Only 55% of charge events recorded received a full charge, and the median cost per full charge from datalogger participants was 44 Ksh – over twice what lamp owners may have thought they are paying for a full charge. These values are discussed in further detail within Appendix K’s datalogger LED lamp results section.
For this sensitivity analysis, I vary the cost per full charge from 0.04 Ksh to 90
Ksh. The cost in grid energy required for a full charge in Kenya is 0.04 Ksh10 (Mwirichia,
2008). I choose to vary the cost per full charge up to 90 Ksh because some of the participants in our datalogger LED lamp study were paying close to 90 Ksh per full charge. In our 2008 datalogger study, approximately a third of our participants were able to charge at home or at a friend’s home. I present Charge Cost Sensitivity results below in Figures 48 and 49. Like in the Use Time Sensitivity, the bright yellow bars indicate no change from the base case values. LCC values for non-grid charging options do not change with charge cost; hence, only their base case LCC is shown.
10 We calculated the price to charge directly from the grid, for example from one’s grid-connected home, using electricity costs provided with “Press Statement on Review of Electric Power Tariffs and Tariff Structure” by Eng. Kaburu Mwirichia, Director General of Kenyan Energy Regulatory Commission (2008). We assumed that those who charge at home with the grid are within the second lowest cost domestic tier in Kenya’s power structure. At this tier, energy costs 8.1 Ksh per kWh. The median rated battery energy capacity of the electric lighting products I analyzed is 5.6 Wh; under the rate of 8.1 Ksh per kWh, the cost per full charge at home with the grid is 0.04 Ksh.
149
30,000 Cost Per Full Charge (Ksh) 90 80 70 60 50 40 30 20 10 0.04
25,000
20,000
15,000 LCC overFour (Ksh) Years
10,000
5,000
0
) ) ) ) ) ) ) ) lar lar) ar) lar) id) ick olar ol olar) sure W solar solar (grid) t ( k (grid) t (grid Candle t ( k (so k (s sk (grid) ne (Sm ent (solar sk (so a as ask (gridient (grid) ask (gr Pres cane (Lg) bient (so b ri bient (sbi - T - Task mbien S - T Hur urrica Am 1 - Tas Ambient (s S - Ta TM A H - Ambien - Am - TM - TasA - AmbienYF1 - TYF2 - T YF3 S YF YF2 - TaskYF3 (solar) - Task (solar) N TB - Am AN - A OB - AmbientSC - (solar)X ET WL - Am TB A Solar-Charged Grid-Charged Fuel-Based
Figure 48. LCC values for ambient and task lighting products with varying the cost for a full charge over four years
150 In the results for the task and ambient lighting products, several grid-charged options become the most cost-effective when charged at home as opposed to paying a charge shop. The home-charge LCC values are shown in pink. Because charging at home is practically free for those who have access, grid charge options with home charges have lower LCC values than their solar competitors, as their capital costs do not include the purchase of a solar module.
As the cost for a full charge is increased, the grid-charged options become dramatically more expensive. At 40 Ksh per full charge, represented in Figure 48 by the light blue bars, all the grid-charged options have LCC values well above the large hurricane lamp’s, and two are almost as costly as the pressure lamp. Also at 40 Ksh per full charge, all grid-charged options are more costly than all except one solar-charged option.
151
7,000 Cost Per Full Charge (Ksh) 90 80 70 60 50 6,000 40 30 20 10 0.04
5,000
4,000
3,000
LCC (Ksh) over Four Years 2,000
1,000
0 YE - Torch (batt) OC - Torch (grid) OB - Torch (solar) SC - Torch (solar)
Figure 49. LCC values for torch lighting products with varying the cost for a full charge over four years
For torch cases, the one grid-charged option is still the most cost-effective up to
50 Ksh per full charge. But even at 90 Ksh per full charge, the grid-charged option still has an LCC value that is considerably lower than the incandescent dry cell torch.
In general, the cost per full charge has an enormous influence on the LCC values for grid- charged lighting products and their economic relationships with competing options.
152 O. Battery Life Expectancy
As discussed in Methodology 4, there is a wide range of cycle life estimates for each battery chemistry analyzed. The cycle life values I used were conservative given the range, meaning I erred on the side of requiring more battery replacements than indicated necessary by the literature. It is important to examine how influential these values are in LCC calculations.
In the Battery Life Sensitivity, I vary the base case scenario battery cycle lives from -80% to 280% (Table 21). I focus on the higher end because I used conservative base case cycle values. I only went up to 280% because after approximately a 100% increase in battery cycle life, none of the batteries in my analysis required replacement.
The sensitivity analysis is over four years so that each lighting product in the analysis is replaced at least once. The lighting product replacement period is closely linked to the battery replacement period; if the lighting product is replaced prior to requiring a battery replacement, the battery replacement for the original lighting product is canceled.
Table 21. Variation of battery cycle lives used for the Battery Life Sensitivity. Base case scenario values are highlighted yellow for comparison.
Battery Life % less or -80% -40% 0% 40% 80% 120% 160% 200% 240% 280% more
SLA Cycles 16 48 80 112 144 176 208 240 272 304
NiCd Cycles 160 480 800 1120 1440 1760 2080 2400 2720 3040
NiMH Cycles 100 300 500 700 900 1100 1300 1500 1700 1900
153 As seen in Figure 50 below, the battery cycle life does not greatly influence LCC values over a four-year period. For the ambient and task lighting products, only five of the 23 products’ LCC values change with increased battery cycle life, and almost all of those LCC changes are small. Over four years with an approximate 280% increase in battery life expectancy, the percent saved in LCC from the base-case scenario ranges from zero to 12%. The cycle life affect on LCC is small because most lighting products’ life expectancies are reached before their batteries’ life expectancies are.
From this analysis, it appears that battery life expectancy does not have a significant influence in a lighting product’s LCC value. Those most affected require a battery replacement prior to the expected end of the lighting product’s life. Four of the five that exhibit LCC changes with increased battery life have SLA batteries. This is because SLA batteries have the lowest cycle life.
154
40%
20%
0% -100% -50% 0% 50% 100% 150% 200% 250% 300% -20%
-40%
-60%
-80%
-100%
-120%
-140%
-160% Percent of Base Case LCC Saved(Ksh) Over Four Years Four Over Saved(Ksh) LCC Case Base of Percent
-180% Percent of Battery Cycle Life Increase or Decrease
YE - Torch (batt) OC - Torch (grid) OB - Torch (solar) OB - Ambient (solar) SC - Torch (solar) SC - Ambient (solar) XS - Ambient (solar) ET - Ambient (solar) WL - Ambient (solar)
YF1 - Task (grid) YF1 - Task (solar) YF2 - Task (grid)
YF2 - Task (solar) YF3 - Task (grid) YF3 - Task (solar) TB - Ambient (grid) TB - Ambient (solar) TM - Task (grid) TM - Task (solar) AS - Task (grid) AS - Task (solar) AN - Ambient (grid) AN - Ambient (solar)
Figure 50. Percent saved in LCC from the base-case scenario values from variation in battery cycle life expectancy over four years
155 P. Lighting Product Life Expectancy
The method I used to estimate ambient and task electric lighting product life expectancy provides a rough approximation. For the fuel-based and torch lighting products, I used values reported by the Lighting Africa 2008 study which were determined from numerous Kenyan vendor estimates. For the task and ambient lighting products, I estimate product life based on lighting product quality criteria. I discuss all lighting product life expectancy values in Methodology 4. I have more confidence in the life expectancy values for the fuel-based lighting products from the Lighting Africa study than I have for those I estimated for the LED task and ambient lighting products. In this analysis, I focus on varying the electric lighting products’ life expectancies and hold the fuel-based life expectancies constant.
In the Lighting Product Life Sensitivity, I vary the base case scenario lighting product life expectancy values from 10 months less to 35 months more. These values are based from laboratory experience with off-grid lighting products. These additions and subtractions range product life from two months to almost seven years (Table 22). The sensitivity analysis is over four years.
156
Table 22. Variation of lighting product lives used for the Lighting Product Life Sensitivity. Base case scenario values are highlighted yellow for comparison. Criteria for the base case product life expectancy categories are described in Table 16 of Methodology 4.
Base Case Product Life Expectancy Change (months) Product Life Expectancy -10 -5 0 5 10 15 20 25 30 35 1 Year 2 7 12 17 22 27 32 37 42 47 2 Years 14 19 24 29 34 39 44 49 54 59 3 Years 26 31 36 41 46 51 56 61 66 71 4 Years 38 43 48 53 58 63 68 73 78 83
Figure 51 below suggests that varying lighting product life expectancy influences
LCC values more for some lights than others. Over a four-year analysis with an approximate three-year increase in life expectancy, lamps range in savings from one to
29% of their base-case LCC value. From this analysis, it appears that lighting product life expectancy can have a sizable influence in its LCC value.
157
40%
20%
0% -10-50 5 101520253035
-20%
-40% Over Four Years Over Four
-60% PercentofCase Base Saved LCC (Ksh) -80%
-100% Monthly Addition or Subtraction to Lighting Product Life
YE - Torch (batt) OC - Torch (grid) OB - Torch (solar) OB - Ambient (solar) SC - Torch (solar) SC - Ambient (solar) XS - Ambient (solar) ET - Ambient (solar) WL - Ambient (solar)
YF1 - Task (grid) YF1 - Task (solar) YF2 - Task (grid) YF2 - Task (solar) YF3 - Task (grid) YF3 - Task (solar) TB - Ambient (grid) TB - Ambient (solar) TM - Task (grid) TM - Task (solar) AS - Task (grid) AS - Task (solar) AN - Ambient (grid) AN - Ambient (solar)
Figure 51. Percent saved in LCC from the base-case scenario values from variation in lighting product life expectancy over four years
Q. Real Discount Rate
The nominal discount rate is composed of the real discount rate and an inflation rate, as discussed in Methodology 4. In the base case scenario I use an annual inflation
158 rate of 11%, an average of 2003 to 2007 annual rates provided by the Kenyan National
Bureau of Statistics (2008). The real discount rate is much more challenging to estimate, as it reflects each individual’s relationship with the time value to money. Those with less money may have a higher discount rate, meaning money to them is more valuable right now. For my analysis I use a real discount rate of 20%. Having less confidence in the real discount rate used, performing a sensitivity analysis on the parameter is very important.
Varying the real discount rates does have a notably large affect on LCC values for lighting products which requires habitual purchasing during their lifetimes (Figures 52 and 53). Grid-charged lights and fuel-based products require countless energy purchases, as well as replacements over the four-year analysis. Grid-charged lights’ LCC values are thus heavily influenced by a customer’s real discount rate. Most solar options require replacements but no energy purchases; hence, a customer’s real discount rate has less influence the lights’ LCC values.
The lighting options with LCC values that stay constant over varying real discount rates are shown as a dark blue bar – they do not require additional costs after their initial purchase within four years. For the lighting products analyzed, real discount rate variations between 0% and 100% yield LCC differences from 507 Ksh to 11,945
Ksh, with a median of 2,020 Ksh over a four-year period. I choose to vary the annual discount rate to the high rate of 100% to include a wide range of individuals with unique time values for money.
159
30,000
Annual Real Discount Rate 0% 10% 20% 30% 25,000 40% 50% 60% 70% 80% 90% 100%
20,000
15,000
10,000 LCC (Ksh) overYears Four
5,000
0 ) ) ) ) ) ) r) d) d e ar ar ar ar a ar id) i i r gr sur solar) solar) grid) grid) grid) (g Wick (solar) (sol sol sol (sol (sol ( ( ( e (Sm) (solar) ( ( k n Candle nt ent ( Pres ent ( ent ent (solar)sk sk ent ( as icane (Lg) a a Task bi r bi bi bient Task Task bi bi - Task (gr m - - - T - T Am Am Am Am S Hur Hurrica Ambie - A M TM - A - - - - F3 - Task - Am(solar)T AS - YF1 - TaskYF2 - TaskYF3 - T YF1 YF2 Y TB - Ambient (grid) AN OB SC XS ET - AmbientWL (sol TB AN Solar-Charged Grid-Charged Fuel-Based
Figure 52. LCC values for ambient and task lighting products with varying real discount rates over four years
Varying the real discount rate tells an interesting story about capital cost.
Individuals with a high real discount rate are less likely to invest in an expensive product because their money is more important at that present time than in the future. The torch graph below illustrates a perfect example. The incandescent dry cell torch has a much higher LCC value than its competitor torches in this analysis. However, at higher real discount rates, the incandescent dry cell torch becomes more attractive compared to the solar options. This may explain why less expensive products of poorer quality – such as incandescent torches, candles and wick lamps – tend to be more widely used in lower- income locations.
160
10,000 Annual Real Discount Rate 9,000 0% 10% 20% 30% 40% 50% 60% 70% 8,000 80% 90% 100%
7,000
6,000
5,000
4,000
3,000 LCC (Ksh) over Four Years Four over LCC (Ksh) 2,000
1,000
0 YE - Torch OC - Torch OB - Torch SC - Torch (batt) (grid) (solar) (solar)
Figure 53. LCC values for torch lighting products with varying the real discount rate over four years
R. Analysis Period
The analysis period is the final economic parameter I perform a sensitivity analysis on. In doing so, I calculate the base case scenario results over varying analysis periods from one to 10 years. In order to gauge the sensitivity of the this parameter, I
161 calculate the monthly LCC values by dividing the LCC values for each year varied by the number of analysis months each LCC is over. This provides an average monthly LCC for each device over each analysis period varied.
In most of my model results I report findings over a four-year period. To estimate how much the choice of four years affected my results, I graph the differences between monthly LCC values for a four-year period and each of the varied periods (Figure 54).
2,000
0 10 30 50 70 90 110
-2,000
YE - Torch (batt) OC - Torch (grid) OB - Torch (solar) OB - Ambient (solar) SC - Torch (solar) SC - Ambient (solar) -4,000 XS - Ambient (solar) ET - Ambient (solar) WL - Ambient (solar) YF1 - Task (grid) YF1 - Task (solar) YF2 - Task (grid) YF2 - Task (solar) YF3 - Task (grid) -6,000 YF3 - Task (solar) TB - Ambient (solar) from Four Year LCC per Month per LCC Year Four from
Difference in LCC Month per (Ksh) TM - Task (grid) TM - Task (solar) AS - Task (grid) AS - Task (solar) AN - Ambient (grid) AN - Ambient (solar) -8,000 Pressure Hurricane (Lg) Hurricane (Sm) Wick Candle
-10,000
Analysis Period (Months)
Figure 54. Sensitivity to LCC values from variation in analysis period
162 In Figure 54 above, most lighting product monthly LCC values remain fairly constant for analysis periods greater than four years. There is more variation in the lower analysis years because of variations between lighting product replacement costs. In the base case scenario, products are replaced from one year up to four years. This suggests why after approximately four years, the monthly LCC values appear more stable. With this, a four-year period seems appropriate for the analysis.
Table 23. Economic Parameter Sensitivity Results Summary
Effects on Effects on Effects on Economic Sensitivity Analysis Grid-Charged Solar-Charged Fuel-Based Noted Results Parameter Products Products Products Vary Use Time Increasing use parameter from -80 time, increases Increasing use to 200% of base Increasing use energy cost, time, increases LCC is very sensitive to charging energy costs. case value (0.4 to 6 time, increases Use Time High increases replacement costs, Products with higher energy costs per day hrs/day for energy costs, replacement increases LCC decrease in attractiveness more quickly. ambient/task and 0.2 increases LCC costs, increases slightly to 3 hrs/day for LCC torch) Kerosene-fueled products with higher Vary Kerosene Cost Increasing consumption rates have more drastic LCCs parameter from -80 kerosene cost, Kerosene variations. By doubling the base case cost of High to 100% of base No effect increases energy Cost kerosene, all but one electric lamp design has case value (18 to costs, increases a lower LCC value than the large hurricane 180 Ksh/liter) LCC lamp.
Like the Kerosene Cost Sensitivity, kerosene- Increasing fuel fueled products with higher consumption rates Kerosene Vary Fuel Escalation escalation rate, have more drastic LCCs variations. The Fuel Medium Rates from 0% to No effect increases energy changes in LCC for the products between the Escalation 20% costs, increases variations chosen are moderately high over a Rate LCC four-year period, but would be even more significant over a loner time period.
With a range of reasonable costs for a full Increasing charge, there is extreme variation in grid- Vary Charge Cost charge cost charge LCC costs. At small costs per full parameter from 0.04 Charge Cost Very High increases energy No effect charge, grid-charge options are more attractive to 90 Ksh per full costs, increases than solar-charge options in terms of LCC. At charge LCC large costs per full charge, the lights become almost as expensive as the pressure lamp.
163
Table 24 Continued. Economic Parameter Sensitivity Results Summary
Effects on Effects on Effects on Economic Sensitivity Analysis Grid-Charged Solar-Charged Fuel-Based Effects on Fuel-Based Products Parameter Products Products Products
Base case values for lighting product life Vary Battery Lives expectancy is often lower than battery life from -80 to 280% of expectancy, leaving little room for significant base case value (16 Increasing battery life decreases changes in LCC when varying battery life. Battery Life to 304 cycles for Low battery replacement costs, decreases No effect Lower SLA battery life expectancies accounted Expectancy SLA, 160 to 3040 LCC for the majority of battery replacements prior to cycles for NiCd and reaching lighting product life expectancy. The 100 to 1900 for median savings from increased battery life NiMH) expectancy over 4 years was 0 Ksh.
More lighting product replacements occur Vary Lighting within my model than battery replacements, Lighting Product Lives by Increasing lighting product life hence lighting product replacements influence Product Life Medium subtracting up to 10 decreases lighting product No effect LCC more. With an approximate 3-year Expectancy months and adding replacement costs, decreases LCC increase in lighting product life, the median up to 35 months. savings over 4 years was 360 Ksh.
Products with higher on-going costs are Vary Annual Real Increases all Increases Increases all affected more by real discount rate variations. Real Discount Medium Discount Rate from operating costs, replacement costs operating costs, Between real discount rates of 0 and 100%, Rate 0 to 100% increases LCC slightly increases LCC the median savings over 4 years was 2020 Ksh.
There is more variation for LCC with shorter analysis periods because of variations Analysis Vary Analysis Period All but capital costs increase. Medium between lighting product replacement costs. Period from 1 to 10 years. After approximately 4 years, the differences between analysis period results are small.
164
CHAPTER 6.
CONCULSIONS
There are several ways in which manufacturers can improve their off-grid lighting designs to better serve customers like those owning small businesses in Kenya. I provided recommendations after performing economic analysis and discussions around design components; my top findings include:
Increased battery size reduces operation costs for grid-charged options.
Decreased power consumption will decrease operation costs for grid-charged
options as well as solar-charged options (when reducing solar module size).
Power consumption can be decreased by improving LED or LED driver circuit
efficiencies or by designing to meet only brightness outputs required (such as
incorporating brightness settings).
Providing a grid-charging option with an optional solar component provides the
following benefits: (1) increases charging reliability on non-sunny days, (2)
reduces the LCC for solar-charged options, as the lamp owner would not be
required to repurchase the solar panel component of the lighting product when
the lighting product reaches its life expectancy and must be replaced, (3) makes
products affordable to customers who need to purchase the solar component
incrementally, (4) accommodates customers in locations with no grid access by
providing an optional solar component.
165 166 Using the WLED type lamp reduces LCC, lowers toxicity and increases
durability. Specifically, HBLEDs provide brighter light and better color
rendering.
Designing with a luminaire can increase luminous efficiency.
In addition, I found that NiCds may be the most cost effective in terms of lighting product LCC values, but NiMHs are a close second and are much less toxic. It is important to consider battery toxicity when designing products for locations that lack proper battery recycling facilities or proper sanitary landfills.
Many of these design recommendations will increase the capital cost of the off- grid lighting product. Capital cost increases were calculated for solar and battery changes; however, additional design changes require specific prices which are much easier for the manufacturer to obtain, such as adding brightness settings, improving LED efficacy, and modifying the luminaire. Prior to pursuing design recommendations, manufacturers should verify that the modified design does not exceed the customer’s willingness-to-pay.
My model is specific to small business owners in rural Kenya, but can be applied to similar lighting product users. I use parameters values that reflect data collected in
Kenya during 2008, which include fuel-based lighting consumption rates, hours of use per day, fuel-based lighting capital costs, fuel/energizing costs and replacement costs.
Depending on whom the model is applied to, and other factors, results may change.
166 167 I conducted a sensitivity analysis around the parameters used. I found that the following parameters most significantly influenced results:
Daily use time
Kerosene cost
Cost per full charge
Lamp life expectancy, real discount rate, fuel escalation rate, and period analyzed also notably influenced results, but to a lesser degree.
I intend for manufacturers to use my results as a step to improving their off-grid lighting designs so that they are more desired and more cost-effective for lower-income customers. From this, I hope that more lower-income individuals will be able to enjoy and prosper from the benefits of quality lighting.
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APPENDICIES
Appendix A. What small businesses in Kenya look like: architecture and goods sold
Vendors in the markets sell a variety of merchandise: from staple vegetables to popular electronic goods. We observed that vendors selling pricier merchandise often sold from more elaborate architecture styles. We categorized the architecture styles as:
• Kiosk – A free standing, four-walled structure or attached on one side to a
building. The vendor stays behind a counter but goods are seen from outside and
purchased through a window.
• Market Stall (roof, no walls) – A free standing table display with no walls, but a
roof. Approximately 5 ft2 in size.
• Market Stall with Small Building – A stall as described above attached to a small
building which is also used to either display or prepare merchandise.
• Small Shop – A closed structure with merchandise displayed inside. Less than
100 ft2 in size.
• Large Shop – Same as above, but greater than 100 ft2 in size.
• No Structure – No permanent structure used to display merchandise. Often goods
were laid out on a blanket or upside-down bucket to sell.
• Other – Some vendors had own unique architecture style, such as a butcher.
173 174 Vegetables were most often sold from kiosks, market stalls (with and without small buildings), and no structure styles. House wares were often sold from small and large shops. The photos below provide a picture of what the small businesses in Kenya look like.
Figure 55. J.M. Kiosk displays produce, some general merchandise and drinks outside a window, but he operates from within the kiosk window (Photo by Johnstone)
175
Figure 56. J.’s Vegetable Market displays his family’s produce on a market stall and then prepares and stores it within their attached building (Photo by Johnstone)
Figure 57. A family in Karagita display their houseware merchandise in this small shop (Photo by Johnstone)
176 Of the 25 vendors surveyed in Maai Mahiu and the 25 in Karagita, we observed that the majority of vendors had market stalls and small shops. An overwhelming majority of the vendors surveyed sold raw vegetables (Figure 58).
Fractions of Architecture Style Surveyed in: Kiosk
Market Stall (roof, 10% no walls) 24% Market Stall with 16% 20% Small Building 20% 28% 28% Small Shop 4% 12% 20% Large Shop 20% 30% 28% 40% No Structure Karagita Maai Mahui Other Maai Mahui & Karagita Combined
Raw Foods Fractions of Goods Sold Surveyed in: Packaged Foods or Snacks 19% Prepared Foods
13% 3% General 6% 42% 25% Merchandise 40% Clothes/ Shoes 19% 44% 20% Curios 19% 22% 14% 9% 3% Alcohol 2% Maai Mahui Karagita Other Maai Mahui & Karagita Combined
Figure 588. The common goods sold surveyed in Maai Mahiu and Karagita markets
A study conducted by Research International found similar results throughout
Kenya, suggesting that “retail businesses in Kenya are small Permanent shops and Kiosks mostly selling household commodities, food stuffs, fruits and vegetables” (Figure 59).
The study was done by surveying 400 participants (Lighting Africa, 2008a).
177
Others 11% Small Shop/Permanent Barber shop Shop 4% 27% Small scale producer 4% Large Shop 5%
Green grocers 5%
Boutique/ Tailoring 7% Kiosk 20% Hawker at fixed site 7% Hotel/Restaurant/ Café 10%
Figure 599. Small business owner types through Kenya, study conducted by Lighting Africa (2008a).
178 Appendix B. Advancements in lamps used for off-grid lighting products
B.1 Incandescent
The first type of electric lamps used in off-grid lighting products is the incandescent lamp. The incandescent lamp entered the markets in the early 1900’s, approximately 50 years after Sir Joseph Swan and Thomas Edison’s early works in the
1870’s. The design consists of a negative and positive electrode attached to a tungsten filament within an enclosure of inert gas, often argon. When electricity passes through the tungsten filament, electrons within the metal are excited and jump to a higher energy level, heating up the metal. As the electrons return to their original energy level, light is emitted. This process is called incandescence (Harris, 2001b). Consumers enjoy the warm, yellow glow emitted by the incandescent lamp, but the low efficiency of the incandescent lamp increases operation costs.
B.2 Fluorescent
Manufacturers have improved off-grid electric lighting efficiencies by using the fluorescent lamp. The fluorescent lamp was developed in parallel to the incandescent and marketed shortly after in the early 1900’s. Fluorescent lamps are four to six times more efficient than the incandescent. The design consists of a two electrodes on either end of a phosphor-coated glass tube housing an inert gas, often argon, and a small bit of mercury.
As electricity flows through each electrode, electrons are excited at the electrode and can be released from the electrode into the surrounding gas. A voltage difference between the electrodes influences the electrons to flow across the tube. The increase in energy
179 causes the liquid mercury to become a gas. As the flowing electrons and other atoms travel across the tube, they excite electrons within the mercury vapor to jump to a higher energy level. When the electrons return to their original energy level, ultraviolet light is emitted. The phosphor walls of the lamp absorb the ultraviolet light energy, exciting phosphor electrons to jump to a higher energy level. When phosphor electrons return to their original energy levels, white light is emitted. In the case of the incandescent lamp, white light was emitted along with heat energy. The fluorescent lamp emits much less heat energy, thus providing a more efficient light. In comparison to the incandescent lamp, however, the fluorescent lamp provides a “bluish” toned light often less preferred by consumers. In addition, many consumers are concerned by the hazardous mercury contained within the lamp (Harris, 2001a).
B.3 White LED
Most off-grid lighting manufacturers are now designing with a safer, even more efficient and durable lamp – the white light emitting diode (LED). White LEDs where first introduced by the Japanese company, Nichia in 1996 and soon after became commercially available by other companies (Irvine-Halliday et al., 2005).
The LED is composed of two adjacent semiconductor layers: p-type and n-type layers. The p-type material is a substrate doped with impurities having a valence shell of one-less than the substrate. The n-type material starts with the same substrate material, but is doped with impurities having a valence shell of one-more than the substrate. When the a voltage potential is applied in forward bias across the layers, as in Figure 60,
180 electrons flow from the n-type layer of a higher energy level, to the p-type material of a
lower energy level. In the process, light is emitted, just as it is when electrons move from
higher to lower energy levels in the other two types of lamp designs discussed. The
quality of light emitted depends on the exact characterization of the semiconductor
materials used and how it is structured. Figure 61 illustrates how a higher light output is
achieved by encapsulating the semiconductor junction within a domed transparent plastic
medium, such as epoxy (Kasap, 1999).
Electron energy
p n+ p n+ Ec eVo Ec Eg (a) EF Eg EF (b) υ h - Eg Ev eVo Ev
Distance into device V Electron in CB Hole in VB
(a) The energy band diagram of a p-n+ (heavily n-type doped) junction without any bias. + Built-in potential Vo prevents electrons from diffusing from n to p side. (b) The applied bias reduces Vo and thereby allows electrons to diffuse, be injected, into the p-side. Recombination around the junction and within the diffusion length of the electrons in the p-side leads to photon emission. © 1999 S.O. Kasap, Optoelectronics (Prentice Hall)
Figure 60. How an LED emits light (Kasap, 1999)
181
(c)
Figure 61. LED light structures (Kasap, 1999)
There are two ways to use LEDs to make white light. The first is to combine red,
green, and blue light producing LEDs to obtain white light, often referred to as RGB
LEDs. RGB LEDs are not yet in markets due to unsolved technological problems
hindering long-term use. The second way to create white light from LEDs is to coat an
ultra-violet LED with a phosphor coating. The Japanese company Nichia was the first to
successful use this method (Craine et al., 2002).
182 Of the white LEDs (WLED), there are two main types: miniature and High-
Brightness LEDs (HBLED). HBLEDs are viewed to be an improvement over miniature
WLEDs, providing a brighter light output with higher luminous efficacies (Figure 62).
Special deposition techniques in the solid-state industry have made HBLEDs possible
(Dow Corning, 2000-2009).
Figure 62. High-Brightness LED cross section (Image by Dow Corning)
183
Appendix C. Jones et al. 2005 Study
184
Appendix D. Lighting Use Survey Form
Survey Form Portable Lamps in Kenya June 22, 2008 VENDOR SURVEY
Survey by: Arne Jacobson Humboldt State University Arcata, CA 95521, USA
Section 1: General Information (filled in prior to starting interview):
1.1 Name of person(s) administering survey:
1.2 Date & time of interview:
1.3 Province: District: ______
Town: Village or area:
1.4 Participant # ______
###########################################################################
1.5 What is the name of the business? ______
1.6 What is the name of the person being interviewed?
Name: ______
1.7 How can we contact you later if we need to find you in the next six months? (list all that apply)
At business location: O
At another location: O
Give location: ______
By Mobile Phone: O
Telephone number: ______
In another way: O
Describe: ______
Electric Type Code Section 2: Lamp Use: Lamps 2.1 Lamps and Costs: Torch T 2.1.1 What types of lights and how many of each do you use in your business? Form Lantern L 2.1.2 How much did each initially cost you? Factor Array (or Strip) A 2.1.3 How many hours do you use each per night? (or last night) Gooseneck Lamp G 2.1.4 How much do you spend on powering the lamp each night? LED LED 2.1.5 How else do you use each light? Fuel Based Lamps Code Incandescent INC Bulb Kerosene Wick W Fluorescent FLO Kerosene Hurricane H CFL CFL Kerosene Pressure P Rechargeable R Battery Candles C Dry Cell (mawe) D
Ex: T-LED-D
2.1.1 2.1.2 2.1.3a 2.1.3b 2.1.4 2.1.5
Hours Hours used Lamp uses outside of market Initial Lamp used per per night Nightly Cost ID Lamp Type Cost night (home or (Ksh) General Lighting (Ksh) Lighting in Children Other: (business) elsewhere) Lighting at your way NONE the Kitchen Studying ______Home for walking
1 O O O O O O
2 O O O O O O
3 O O O O O O
4 O O O O O O Note 1: ______Note 2: ______Note 3: ______
185 2.2 Other Lamps Used: 2.2.1 What other types of lights have you used in the last three years? # Lamp Type (code) Why Stopped Using Notes
Broke O
Stolen O
Expensive O
1 Too Dim O
Dangerous O
Other: O
Broke O
Stolen O
Expensive O
2 Too Dim O
Dangerous O
Other: O
Broke O
Stolen O
Expensive O 3 Too Dim O
Dangerous O
Other: O
2.3 Lamp Preferences 2.2.2 Are you satisfied with your current lamps? Yes O No O 2.2.3 If you are not satisfied, why not? (mark all that apply)
Too expensive to operate O Notes: ______
Inconvenient to operate (e.g. wind) O Notes: ______
Breaks often O Notes: ______
Too Dim (not enough light) O Notes: ______
Too Dangerous O Notes: ______
Health Concerns (e.g. smoke) O Notes: ______
Other O Notes: ______2.4 Familiarity with LEDs 2.4.1 Are you familiar with LED lights? Yes O No O 2.4.2 If yes, what types are you familiar with? (mark all that apply)
LED Torch O
LED Lantern O
LED Strip Light O
LED Desk Lamp O
LED Bulb O
Others O
Describe: ______
2.4.3 Are you interested to use LED lights? Yes O No O Why or why not? (please explain):
______
______
______
2.4.4 If you had to charge this LED torch using the grid on a regular basis, where would you go? (select the place person would go most often)
Charge at your home with the grid O
Charge at the home of a family member or friend O
Charge at the workplace (yours or family member) O
Charge at a business (charging shop) O
Charge somewhere else (other) O
Other: ______
Not sure: O 2.4.5 Would you expect to pay a fee for charging at that place? Yes O No O Section 3: Market 3.1 Market Type:
3.1.1 How many days was your business open at night in the last week? ______
3.1.2 Business structure type:
Architecture Style A) Kiosk O B) Market stall (roof, no walls) O C) Market stall with small building O D) Small Shop (<100 ft2) O E) Large Shop O F) Market seller, no structure O G) Other______O
3.1.3 What kinds of goods are being sold? Check all that apply
Good Sold
A) Raw Food O B) Packaged foods or snacks O C) Prepared foods O D) General Merchandise O E) Clothes or Shoes O F) Curios O G) Alcohol O H) Other______O Appendix E. Lighting Use Time Card
Jina (Name): ID#:
Saa ya Saa ya kufunga Saa ya kuzima Tarehe (Date) kuwakisha taa duka (closing taa (Time OFF) (Time ON) shop)
Asante sana kwa kusaidia
Appendix F. Kerosene Measurements Data Sheet
Vendor Lamp ID Market
Day's Fuel Business Business Mass 1 (g); Mass 2 (g); Mass 3 (g); Mass 4 (g); Purchase Date ON Time OFF Time Time Time Time Time NOTES (kSh); (HH:MM) (HH:MM) (HH:MM) (HH:MM) (HH:MM) (HH:MM) Kiosk Source >>> Petrol Station Station Petrol Street Vendor Vendor Street
o o o
o o o
o o o
o o o
o o o
o o o
190 191
Appendix G. Performance Testing Procedure Details
I use results from the lamp discharge test, solar charge test, lighting distribution test, and the color rendering test. Results from the solar IV curve test and battery capacity test are not directly used in my analysis, but support those used as mentioned.
1) Lamp Discharge Test
We measure the illuminance, current, and voltage for each lamp at a distance of one meter until it is discharged. We determine that the lamp is “discharged” when the illuminance at one meter drops below 2 lux.
a. We first “top off” the battery with the Cadex battery analyzer to ensure that the battery is fully charged at the beginning of the test. b. We next clamp the lamp in a dark box one meter from the illuminance meter. c. We connect the lamp to a Hobo datalogger to record its battery voltage and discharge current. d. For lamps with multiple lighting levels, we test all of the samples at least one of the settings (often the highest setting, but this may vary by lamp type on a case by case basis). In some cases, we then test one of the lights at all of the settings. e. We let the test run undisturbed until the illuminance reading is below 2 lux.
2) Solar IV Curve Test
For lamps with solar modules, we obtain each module’s IV curve to determine the quality of the solar charge.
a. We conduct the test only on days that are clear and sunny (no clouds). b. For crystalline PV modules, we measure the IV performance after light soaking for 30 minutes. c. We use the standard outdoor IV curve test procedure (see attachment for a detailed description). In this test, we place the module in a circuit with a variable resistor. We measure the voltage across the module and current flowing through the module as we vary the resistance. Plotting the measured current and voltage yields an IV curve that can be used to find the peak wattage of the module.
192 d. We normalize our results to standard test conditions of 1000 W/m2 and 25°C. e. For amorphous silicon PV modules, we measure the IV performance prior to light soaking (i.e. prior to exposing the module to the sun). The modules are then allowed to light soak for 3-6 months. The IV performance is re-measured periodically until the maximum power output has stabilized. At this point, the above steps for the Solar IV Curve Test is carried out.
3) Solar Charge Test
We determine the how long the light provides acceptable light after charging under a standard solar day.
a. The lamp must be completely discharged prior to running this test. If this test precedes the Lamp Discharge test, it will be completely discharged. Else, we discharge the lamp following the discharging rates specified in the Battery Capacity test. b. We use a Hobo data logging system and appropriate transducers to record the battery voltage and the current delivered to the battery during the charge test. c. We write a solar charging program for each light based on the data obtained from the IV curve test results and the Standard Model for “Day with high solar irradiation (5kWh/m2 Total)” obtained from PV Gap 2005. i. Calculated current-voltage pairs for each specific PV module are based on the Standard Model’s irradiance values: 100, 500, and 700 W/m2. 1. Programmed current values are determined within the operating voltages of the battery. 2. Programmed voltage values correspond to the voltage at the maximum power point of the IV curve. d. The solar charging program is run through a BK Precision programmable power supply (PPS). The total charge duration is approximately 10 hours. e. We conduct a lamp discharge test immediately following the Solar Charge. This provides the number of hours of light the lamp will give after one day of standard solar charge. Refer to Lamp Discharge Test section for full description of procedures.
193 4) Lighting Distribution Test
We measure the distribution of illuminance from each lamp on a 1 m2 surface at a distance of one meter, taking 125 readings within a 10cm-spaced grid.
a. We first “top off” the battery with the Cadex battery analyzer to ensure that the battery is fully charged at the beginning of the test. b. We next set up the lamp such that the center of its bulb is 1 meter from the center of our 1 m2 testing surface. c. We measure illuminance levels from light hitting the testing surface at 10 cm intervals in a dark room. d. For lamps with multiple optical settings (e.g. changeable lenses and/or diffusers), we repeat the test for each setting. i. If a lamp has multiple light levels, we use the “common” setting used in the Lamp Discharge Test. This is often the highest setting, but this may vary from one product line to another on a case by case basis.
5) Battery Capacity Test
We measure the capacity of the storage battery for each light. The battery is isolated electrically from the rest of the system circuit during this test.
a. We charge the battery using a Cadex battery analyzer to ensure that it has a full charge. We use the following charge rates for each battery type: i. Sealed lead acid (SLA): 20 hour charge rate ii. Nickel cadmium (NiCd): 5 hour charge rate iii. Nickel Metal Hydride (NiMH): 5 hour charge rate b. We next discharge the battery using a Cadex battery analyzer while recording the battery voltage and current draw. i. We use the following charge rates for each battery type: 1. Sealed lead acid (SLA): 20 hour discharge rate 2. Nickel cadmium (NiCd): 5 hour discharge rate 3. Nickel Metal Hydride (NiMH): 5 hour discharge rate ii. We set the ‘end of discharge’ voltage to the minimum possible for each battery type. (This is 1.36 V/cell for SLA and 0.76 V/cell for NiCd and NiMH in the Cadex analyzer.) We leave all other parameters in the Cadex battery analyzer in their default settings. c. We recharge the battery after completing this test to maintain the battery’s health. We use the charge rates indicated in step a. of this test.
194 6) Color Rendering Test
Through this test, we eliminate products with poor color rendering, such that the user to can not distinguish between colors appropriately.
a. We clamp the lamp in a dark box one meter from the HCT-99 color meter and record the light’s correlated color temperature and CIE 1931 (x, y) chromaticity diagram values.
Specifications for Instruments Used in Product Performance Tests
Illuminance Measurements from Integrated LED Systems
Illuminance distribution on a 1m2 surface - light mounted 1 meter from measurement surface - illuminance measurements made every 10 cm on a 1 square meter grid - illuminance: Extech Datalogging Light Meter (model 401036), (precision 0.01 Lux; accuracy +/-3% of reading)
Lamp discharge - light mounted in a “dark box” at a distance of 1 meter from illuminated surface - light begins test with a full battery - illuminance, current from the battery to the light, battery voltage at 1 minute intervals during discharge - illuminance at center of beam measured with an Extech Datalogging Light Meter (model 401036; see above for specifications) - current measured with a CR Magnetics DC Current Transducer (model 5210-2) (accuracy +/-1.0%; output signal 0.5 VDC) - voltage and output signal from current transducer measured with a Hobo H08- 006-04 Datalogger (8 bit resolution, accuracy +/-3% of reading)
Tests of Light Quality Color rendering - light mounted in a “dark box” at a distance of 1 meter from illuminated surface - light is tested with a full battery - the light’s correlated color temperature and CIE 1931 (x, y) chromaticity diagram values are recorded using a Gigahertz-Optik HCT-99 Color Meter (Sampling rate: 1ms, Color uncertainty: 0% with CIE standard illuminant A)
Tests of Batteries
Battery storage capacity - measurement made by discharging the battery at a constant current - discharge curves are collected using a Cadex C7200 series battery analyzer (programmable analyzer; records voltage and current information at 1-minute intervals; 100 – 4,000 mA current range; 1.2 – 16 volts voltage range; NiCd, NiMH, SLA, and Li chemistries supported; +/-1% accuracy)
195
Specifications for Instruments Used in Product Performance Tests Continued
Tests of Solar Cells
Solar module peak power at standard test conditions - outdoor performance measurement of module output made on a clear, sunny day - PV module oriented so that it is normal to sun’s beam during test - peak power estimated from a current-voltage (IV) curve normalized to std. test conditions:1000 W/m2 and 25°C - IV measurement collected over 30-40 seconds using a custom data collection system (accuracy +/- 0.5% for voltage and current) - module temperature measured with Type-K thermocouple (accuracy +/-2%) - solar insolation measurement made with Licor LI200-SA pyranometer (accuracy +/-5%; LI200-SA calibrated annually with Eppley PSP pyranometer) - overall accuracy of peak power estimate: +/-10%
Tests of Charging Systems
Solar charging lamp - measurement of charging performance of lamp with solar as primary charging method - programmed current and voltage values into a BK Precision Programmable Power Supply model 1785B (0-18V/0- 5A output) for a standard daily solar day (values based on PVGAP standard and current-voltage values from test#7 results at standard’s specified solar illuminance W/m2 values) (BK PPS programming resolution: Voltage10mV and Current 10mA) (BK PPS programming accuracy: Voltage: ≤0.05% + 10mV and Current: ≤0.02% + 10mA) - voltage and output signal from current transducer measured with a Hobo H08-006-04 Datalogger (8 bit resolution, accuracy +/-3% of reading) - current measured with a CR Magnetics DC Current Transducer (model 5210-2) (accuracy +/-1.0%; output signal 0.5 VDC)
196
Appendix H. “End of Use” Testing Script
Script completed December 2, 2008
Testing conducted 12/2/08 and 12/3/08
Instructions:
1. “You will be testing five lights: two flashlights, one task light (for reading),
and two ambient lights (to light a room). In testing each light, you will be
conducting activities in which the lights were designed for. While doing the
activity, the light will step down in brightness every 20 seconds. Each light
will begin at its full potential and end when very dim.”
2. “I need you to tell me when, in your opinion, the light becomes too dim to
comfortably carry out the activity and you would decide to go charge it, or
change out its batteries.”
3. “And in between tests there will be no light so your eyes can adjust; I will use
a dim flashlight to help direct you.”
Torches:
1. Ask participant to stand at yellow line.
2. Give participant 1st torch (already connected to the PPS).
3. Explain activity storyline: “You are a detective looking for a killer in the
forest. In order to find him, you must follow his clues by searching for
certain items on these six shelves. [clearly, physically outline the shelves the
197 subject should be looking at] Hence, I will be asking you specific questions
about the items I that will help you find the killer.”
4. Give instructions: “When the test starts, all the lights will be turned off for 30
seconds for your eyes to adjust, then the light will turn on and then I will
begin to ask you to find specific items. Answer the best you can. And most
importantly, state when the light becomes too dim for you to comfortably
carry out the activity.”
5. Now turn off room lights, then start test, then turn off computer monitor.
6. Don’t say anything during the test. Answer questions if appropriate and unbiased.
7. When light hits darkness, say “You can stay right where you are and I am
going to hand you a new flashlight. [Switch out torches] The light will again
turn on in 1 minute and I will continue to ask you questions about the items
you can see in these six shelves. Let me know when the light becomes too dim
for you to comfortably carry out the activity.”
8. Keep a small, dim flashlight in pocket during the test to help switch out the lights.
9. Repeat step 4.
10. When the second torch hits darkness, say “That completes the flashlight testing.
I will now come over with my dim flashlight to direct you to the next test”
Task Light:
11. Walk over to the participant with dim flashlight, direct her/him to the chair for the
reading test and take the testing torch from him/her. Open the Wind Power book
to the book-marked section.
198 12. Explain activity storyline while switching out lights: “It’s Saturday night and
you are camping out in the Trinities. You have a test on Monday in one of
your classes on wind power. The task light here [point to task light] is your
only source of light and you need to study. You do have access to charging
your task light in your cabin.”
13. Give instructions: “So you will now be sitting here reading about wind power
using this task light. The light has a flexible neck. Feel free to adjust it at
any point in the test. The light will turn on in ~30 seconds and let me know
when the light becomes too dim for you to comfortably read the book.”
14. Repeat step 4.
15. When the task light hits darkness, say “That completes the task light testing. I
will now come over with my dim flashlight to direct you to the next test”
Ambient Lights ($):
16. Walk over to the participant with dim flashlight, direct her/him to move his/her
chair over to the money sorting test setup. (right next to the task lighting test
setup) Leave task lighting setup the way it was.
17. Explain activity storyline while switching out lights: “You are a vendor who has
just sold a new car. The customer hands you the pouch of monies in front of
you. You need to sort out the monies in order to know if the customer paid
you correctly.”
18. Give instructions: “So you will now be sitting here sorting the monies in this
pouch [point to first pouch in front of participant] using the ambient lighting
199 which is already mounted to the walls. The light will turn on in 30 minute
and let me know when the light becomes too dim for you to comfortably sort
the monies.”
19. Repeat step 4.
20. When light hits darkness, say “If you could stand to your left side of your chair
for a few seconds, I will use it to switch out the ambient lights. [Switch out
lights, remove task light setup, move chair back over to that side, direct
participant back into chair, give participant new pouch of money] The light will
again turn on in 30 seconds and you will then sort the monies in this second
pouch. [point to second pouch in front of participant] Let me know when the
light becomes too dim for you to comfortably sort the monies.”
21. Repeat step 4.
22. When the task light hits darkness, say “I will now come over with my dim
flashlight to direct you to the next test setup”
Ambient Light (Social):
23. Walk over to the participant with dim flashlight, direct her/him to into the chair
on the other end of the room. Move chair participant was sitting in to the other
corner of the room.
24. Explain activity storyline: “We are now at a party at my house. We are
hanging out in the living room and socializing.”
200 25. Give instructions: “The light will again turn on in 30 seconds and we will
begin chatting. Let me know when the light is too dim and it becomes
uncomfortable for you to socialize.”
26. Speak with participant. Perhaps get feed back on which lights he/she liked best.
27. When the participant specifies the light is to dim, say “Great, that completes the
testing. Thank you.”
201
Appendix I. “End of Use” Test Setup
The “End of Use” testing looked at three types of lighting form factors – Torch,
Task, and Ambient. In this appendix, I describe how I set up each of the tests.
For the torch testing, I had the participant stand eight feet away from a shelf containing myriad items, as seen in the figure below. The eight-foot spot was marked with a yellow piece of electric tape. Items searched for were set easy to see when light by the room’s ceiling lighting. The questions asked during the torch test are listed in
Appendix J. I began the questions when the first torch was tested and continued asking in order from the list until the participant indicated the second torch was too dim. Long alligator clips connected the torch to the PPS with adequate slack.
Torch Light Test Setup
202 The task light test was set up such that the participant was seated in a chair and asked to read the book Wind Power by Paul Gipe. The book was chosen for its colored pictures, non-glossy pages, and interesting content. Each participant began the test reading from the same page in the book. The task light was set adjacent to the book, as in the figure below. The participant was able to adjust the lamp’s flexible gooseneck as well as the lamps position relative to the book.
Task Light Test Setup
203 Two styles of ambient lights were tested, as shown as “A” and “B” in the left-side photo below. They were positioned to give appropriate light for the money counting task, seen in the right-side photo below. The participant was seated in front of a pouch of monies, which included several foreign currencies, and asked to sort the monies. After ambient light A was tested, a new pouch of monies was used to test ambient light B.
After the two lights where tested with the money sorting task, the participant was directed into another chair in the corner of the room while I sat in the opposite corner of the room. We were approximately 10 feet apart; light B sat in between us. I then socialized with the participant until he or she indicated the light was too dim.
B
A
Ambient Light Test Setup
204
Appendix J. Questions for Torch “End of Use” Test
1. How pairs of sunglasses can you find? 2. How many forks can you find? 3. Can you find a bottle of nail polish? 4. Where is it? 5. What color is it? 6. How many apples can you find? 7. Can you find a peacock feather? 8. Where is it? 9. How many hot pink flashlights can you find? 10. How many orange flashlights can you find? 11. Can you find a yellow school bus? 12. How many candles can you find? 13. How many spoons can you find? 14. How many tennis balls can you find? 15. Can you find a ceramic skull? 16. Where is it? 17. What color is it? 18. What is inside it? 19. How many clothes pins can you find? 20. How many pairs of scissors can you find? 21. Can you find a tape dispenser? 22. Where is it? 23. What color is it? 24. Can you find a yellow flashlight? 25. Where is it? 26. Can you find a screw driver? 27. Where is it? 28. What color is its handle? 29. Can you find a tape measurer? 30. How many inches is it measuring to? 31. Can you find a stapler? 32. Where is it? 33. How many blue reading lights with flexible goosenecks can you find? 34. How many red reading lights with flexible goosenecks can you find? 35. How many plastic bags can you find? 36. How many silver flashlights can you find?
205 Appendix K. Results from field procedures while in Kenya (Includes results to Methodology 1)
As described in Methodology 1, our time in Kenya consisted of many tasks. First we collected costs associated with lighting products, then performed surveys, measured consumption rates and use times, and finally employed data-logging rechargeable LED lights and downloaded participants use of the LED lights. Results are presented and discussed below in this order.
K.1 Costs associated with lamp ownership
After purchasing lighting products, inquiring of their accessories and attempting to charge electric lights in Kenya at charge shops, we obtained the data presented in
Tables 24 through 26 below. The fuel lamp and accessory information was consistent between inquiries, as was the charge shop feedback. The majority of charge shops in each town agreed with each other on the price per charge, which generally coincided with the cost to charge a cell phone. In the towns of Maai Mahiu and Karagita, we found only dry cell batteries. The popular Nakumatt department store in Nairobi sold several brands of AA rechargeable NiCd and NiMH batteries. Surprisingly, all but one available nickel- based battery were consistent in cost per rated mAh values regardless of brand, size, and chemistry. Researcher Maina Mumbi provided SLA battery prices and confirmed that batteries under 6V were unavailable in Kenya. Unfortunately the 4V SLA batteries which are popular in many SLA off-grid lighting products cannot be replaced.
206
Table 25. Kerosene lamp capital and accessory replacement costs. The pressure and hurricane lamp prices were from Naivasha Limited Grocery Store in Naivasha Town, the wick lamp price is from the Karagita market, and the candle price is from the Maai Mahiu market, purchased in a pack of eight.
Accessory Retail Cost Wick/Mantel/Candle Type of Light Replacements (Ksh) Cost (Ksh) per Year Pressure 1495 10 8 Hurricane (Lg) 459 10 4 Hurricane (Sm) 235 10 4 Wick 10 1 4 Candle 4.38 4.38 --
Table 26. Battery costs per unit of capacity. Prices are averages from batteries found in Kenya.
Average cost per mAh Battery Type (Ksh) NiCd 0.21 NiMH 0.21 SLA-12V 0.21 SLA-6V 0.15 SLA-4V Not available
Table 27. Electric lamp charge Costs in Maai Mahiu and Karagita
Cost per LED Lamp Charge Charge Source (Ksh/charge) Maai Mahiu Charge Shops 20 Karagita Charge Shops 20 Cost of Electricity to Charge a 1200 mAh Battery11 0.04
11 We calculated the price to charge directly from the grid, for example from one’s grid-connected home, using electricity costs provided with “Press Statement on Review of Electric Power Tariffs and Tariff Structure” by Eng. Kaburu Mwirichia, Director General of Kenyan Energy Regulatory Commission (2008)
207 We analyzed three or more kerosene samples from most kerosene sources and took an average from the samples (Table 27). The kerosene costs varied up to 40% by source. The Karagita kerosene shop was a noticeable outlier, but all three of its samples were consistent, varying by less than 2%.
Table 28. Kerosene Fuel Costs in Maai Mahiu and Karagita (Radecsky et al., 2008)
Kerosene Price Kerosene Source (Ksh/liter) Maai Mahiu Kerosene Shop (pump) 79 Maai Mahiu Kobil Petrol Station 81 Karagita Kerosene Shop (pump) 114 Karagita OiLibya Petrol Station 82 Karagita Street Vendors (sold in soda bottles) 88 Average of all sources 89
K.2 Lighting use patterns & fuel consumption rates:
Table 28 summarizes the quantities of data on fuel consumption rates and lighting use patterns that we collected from small business owners in Kenya. We aimed to evenly divide our collection efforts between the two towns, but were able to spend more time in
Maai Mahiu and thus have more long-term data from Maai Mahiu.
208
Table 29. Quantity of Data Collected for Each Collection Aspect Sorted by Town
Data Maai Mahiu Karagita Total Vendor Surveys 25 25 50 Vendor Participants in Burn 12 11 23 Rate & Use Time Collection Total Burn Rate Measurements 73 43 116 Total Daily Use Time 118 95 213 Measurements Data-logging Lamps Sold 9 4 13 Days of Data-logged Use 234 98 332
We learned a lot from the survey, much of which has been included in the
Background Section, but the main piece from the survey I am concerned with for my analysis is the use time. Table 29 below displays median use time results from both the surveys and participant-logged cards collected simultaneous with measuring their consumption rates. Vendors in Maai Mahiu reported and measured as staying open longer in the night than those in Karagita. Because the January 2008 election violence occurred very near to the Karagita market, vendors were closing earlier than usual during our data collection. The median use time of all participants was 1.9 hours of use per night.
Table 30 displays the median use times and median fuel consumption rates based on lamp type over all data collected in Maai Mahiu and Karagita. It is clear that the pressure lamp consumes a much higher rate of kerosene, over three times more than the large hurricane lamp. Vendors with pressure lamps, however, stay open longer. This
209 may be due to the lamp’s brightness attracting good business, as well as increasing safety.
Figure 64 illustrates these differences between use times over lighting types and data collection method. The pressure lamp, however, is by far the most expensive to operate per night, as seen in the Fuel Cost column of Table 30.
In comparison, from 55 surveys conducted in 2008 throughout Kenya, Research
International Africa estimates that rural businesses in Kenya use light for one hour in the morning and for about two to three hours in the evening (Lighting Africa, 2008b).
Table 30. Measured and reported use time values by town (Radecsky et al., 2008)
Median Measured Median Reported Town St. Dev. St. Dev. Use (hrs/night) Use (hrs/night) Maai Mahiu 2.1 0.97 2.5 1.0 Karagita 1.3 0.22 1.8 0.64
Table 31. Fuel consumption rate and measured use time by lamp type. The hurricane and pressure lamp values were collected in Maai Mahiu and Karagita during summer 2008. *The wick lamp values are taken from a study conducted by Arne Jacobson, Evan Mills and Maina Mumbi during the summer of 2007 in Kisumu and Yala of the Nyanza Province in Kenya (Radecsky et al., 2008).
Mean Fuel Median Kerosene Sample Fuel Cost Consumption St. Dev. Measured Use Lamp Type Size (Ksh/night) Rate (g/hr) (hrs/night) Hurricane Lamp 14 20.5 6.0 1.6 4.0 (Large) Hurricane Lamp 2 14.4 2.6 1.7 2.5 (Small) Pressure Lamp 7 72.3 15.6 2.2 24.4 Wick Lamp* 10 14.9 6.6 2.5 3.2
210
4 Data Collected (KAR) Data Surveyed (KAR) Data Collected (MMU) 3 DataSurveyed (MMU)
2 Hours of Use of Hours 1
0 Hurricane Pressure
Figure 633. Comparing data based on lighting type and data collection method.
For the fuel consumption rate, we had an average of 6 nights of good measurements from each lamp tested. A histogram of the entire data set collected shows sub-histograms for the hurricane and pressure lamp burn rates (Figure 65). The hurricane consumption rate lamp data set has a dominant peak, while the pressure lamp data set is more spread. The spread may be due to the sporadic pressurizing of the lamp throughout the night. While a pressure lamp is burning, the user must maintain its pressure by executing an occasional pumping action. Depending on what point the vendor completes this action throughout the night most likely affected our results. An average of the values collected under the large sample number should balance out the pressurizing in providing us with a fairly accurate pressure lamp consumption rate.
211
20 Maai Mahiu - Hurricane Lamp Maai Mahiu - Pressure Lamp 18
16 Karagita - Hurricane Lamp Karagita - Pressure Lamp
14
12
10
Frequency 8
6
4
2
0
0 5 0 0 5 0 5 0 5 1 15 20 25 30 3 4 45 50 55 6 65 70 75 80 8 90 95 0 2 1 10 11 115 120 1 Consumption Rate (g/hr)
Figure 644. Histograms for Hurricane and Pressure Lamp Burn Rates Measured
Interestingly, the fuel consumption rates that we obtained for pressure and kerosene lamps closely line up with the pressure and “sooty” hurricane lamp measurements obtained by University of California, Berkeley researchers of 62 and 20 g/hr, respectively, presented in Table 5 of the Literature Review (Apte et al., 2007).
K.3 Datalogging LED lamp results:
The final step of our 2008 summer study was to employ datalogging LED lamps to small business owners in Maai Mahiu and Karagita. Team member Maina Mumbi downloaded the lamp data from July through December for analysis. We obtained a total of 332 days of over 14 vendor participants, collecting use patterns at two-minute intervals. The data provided us with a number of results; two results used in my analysis
212 include the vendor’s hours of light use per night and the percentage of a full charge that vendors received from a charge shop.
The median use time was 2.2 hours per night. This value was calculated as the median of each participant’s average hours per night usage. This value is very close to the 1.9 hours per night we found while having vendor participants log their nightly use hours.
One element overlooked in my analysis is the morning use times. According to the data logger results, vendors require light for approximately 35 minutes in the morning. Whether the morning use is for business or for personal-related topics, vendors do use their lighting products in the morning.
Table 32. Datalogger results from Maai Mahiu and Karagita vendor participants between July and December, 2008. Morning use is evaluated as before noon. Evening use is evaluated after 5pm. Afternoon use is evaluated in between morning and evening. The value highlighted in yellow is of most interest to my analysis.
Use Time Results Hours St. dev
Morning 0.6 0.4 Median Values Over All Participant Afternoon 0.1 0.2 Averages Evening 2.2 1.0
Morning 208 --
Events Captured Afternoon 26 --
Evening 365 --
213 The second result of interest in my analysis was that the median cost for a full charge was 44 Ksh – over twice what one would be expecting to pay for a full charge.
55% of charge events recorded received a full charge, while 71% received a charge of
80% or more full. The 53 Ksh cost for a full charge was calculated by first calculating each vendor’s average cost/mAh received over his or her charge events, then evaluating the median of the vendors averages, and finally taking multiplying that median cost/mAh by the mAh capacity of each vendor’s lamp’s battery.
The incomplete charges are due both to the quality of the charges as well as the time of day lamp owners begin charging. We have records of some charge events receiving variable current input; they may start with a typical input, drop drastically for a period of time, and then return to the typical input. This could be due to problems with charge shop equipment or grid reliability issues. I suspect, however, that most incomplete charges are due to lamp owners bringing their lamps to the charge shops too late in the day and retrieving them prematurely in order to use them for that same night’s business. Data show that most lamps brought to charge shops before noon receive a full charge prior to evening use; those brought after noon often do not.
In my analysis, I assume vendors receive a full charge; however, I explore how model results change as vendors receive less than a full charge.
214
Appendix L. Results from quality screening testing procedures on electric lighting products & lux-hr/charge values (Includes results to Methodologies 2 and 7)
The data I use in my thesis from the lighting lab quality screening tests include lamp discharge, solar charge, lighting distribution, and color rendering. The procedures for these tests are discussed in Methodology 2.
The values from the lamp discharge test are used throughout my analysis. Results are in Table 32 below. Criteria for determining the light’s end-of-use value incorporates lux values established using Methodology 3 and presented in Appendix M. The specific criteria are as follows:
1. Low-voltage disconnect, self automated cutoff
2. If ambient, 3 lux cutoff at a distance of one meter
3. If task, 0.5 lux cutoff at a distance of one meter
4. If torch, 30 lux cutoff at a distance of one meter
The end-of-use lux tests may seem biased towards the task and ambient lights; this is because the ways in which the lighting products are used makes them require a charge at different lux values. For example, a task light is used for tasks closer in proximity than tasks in which a torch is used for.
Directly after determining the lamp discharge hours for each light, I calculated the lux-hrs per charge using Method 7 procedures. These values are also shown in Table 32 below. It is interesting to note that by including a low-voltage disconnect, the quantity of lux-hours the product provides is reduced, as products with low-voltage disconnects stop providing light well above a user’s end-of-use lux value. By not fully discharging their
215 battery, however, products with a low-voltage disconnect may increase their batteries’ lifetime.
Table 33. Lamp discharge results for each electric light used in analysis and lux-hr/charge values.
Lamp Electric Light Test End of End of Use Lux-hrs Discharge Code Setting Use Lux Criteria /Charge (Hours) YE - Torch (batt) Normal 30.3 2 9.9 1887 OC - Torch (grid) Normal 30.0 4 21.1 6898 High- OB - Torch (solar) 66.3 7.0 452 Torch 1 High – OB - Ambient (solar) 9.8 6.3 63 Ambient 1 Medium– SC - Torch (solar) 49.1 4.7 324 Focused 1 SC - Ambient (solar) Medium 14.7 1 3.8 107 XS - Ambient (solar) High 3.0 2 8.6 224 ET - Ambient (solar) High 13.9 1 24.5 357 WL - Ambient (solar) Normal 10.2 1 13.6 137 YF1 - Task (grid) Normal 0.5 3 12.6 104 YF1 - Task (solar) Normal 0.5 3 12.6 104 YF2 - Task (grid) Normal 0.5 3 6.2 53 YF2 - Task (solar) Normal 0.5 3 6.2 53 YF3 - Task (grid) Normal 0.5 3 10.0 55 YF3 - Task (solar) Normal 0.5 3 10.0 55 TB - Ambient (grid) Normal 3.0 2 25.5 332 TB - Ambient (solar) Normal 3.0 2 25.5 332 TM - Task (grid) Normal 0.5 3 13.5 98 TM - Task (solar) Normal 0.5 3 13.5 98 AS - Task (grid) High 0.5 3 9.4 70 AS - Task (solar) High 0.5 3 9.4 70 AN - Ambient (grid) High 8.3 1 21.3 279 AN - Ambient (solar) High 8.3 1 21.3 279
216 The solar discharge, lamp discharge and color rendering test results are summarized in Table 33. The same lighting product settings were used for the tests below as in the lamp discharge test above. Some electric lighting products do not come with solar modules or are analyzed without a solar option.
Table 34. Summary of solar charge and lighting distribution test results used in my economic analysis
Max Interpolated Electric Light Code Test Setting UseHours/Day Area (m2) with Solar
YE - Torch (batt) Normal -- 0.02 OC – Torch (grid) Normal -- 0.04 OB – Torch (solar) High- Torch 2.3 0.15 OB - Ambient (solar) High - Ambient 1.9 > 1 SC – Torch (solar) Medium - Focused 3.5 0.08 SC - Ambient (solar) Medium 4.6 0.49 XS - Ambient (solar) High 5.4 0.47 ET - Ambient (solar) High -- 0.46 WL - Ambient (solar) Normal 2.0 > 1 YF1 – Task (grid) Normal -- 0.48 YF1 – Task (solar) Normal 14.7 0.48 YF2 – Task (grid) Normal -- 0.48 YF2 – Task (solar) Normal 4.1 0.48 YF3 – Task (grid) Normal -- 0.48 YF3 – Task (solar) Normal 10.0 0.48 TB - Ambient (grid) Normal -- 0.68 TB - Ambient (solar) Normal 11.8 0.68 TM - Task (grid) Normal -- 0.48 TM - Task (solar) Normal 13.7 0.48 AS - Task (grid) High -- 0.46 AS - Task (solar) High 6.4 0.46 AN - Ambient (grid) High -- > 1 AN - Ambient (solar) High 9.7 > 1
217
Appendix M. Results from estimating “end of use” lux values & measuring single lux values (Includes results to Methodologies 3 and 6)
Methodology 3 discusses procedures for the “end of use” lux values. The lux values that testing participants indicated as too dim were consistent for ambient lights, providing a low coefficient of variation. Values indicated for the torch and task lights were very scattered, as indicated by their larger coefficient of variation. These values were used in the lamp discharge test described in Methodology 2.
Table 35. Average "end of use" lux values and respective coefficients of variation. All end of use values are exact averages, except the task lamp which is rounded up from 0.4 lux to 0.5 lux so that it is an easier number to use as a standard.
Form “End of use” Coefficient Factor lux values of variation Task 0.5 128% Ambient 3.0 71% Torch 30 117%
Methodology 5 discusses measuring lighting product lux values. Procedures were performed for both the fuel-based and electric lamps. Of the fuel-based lighting products, the pressure lamp is noticeably higher in lux output, while all other fuel-based lighting products are similarly low. Of the electric lighting products, the torches are noticeably higher. The ambient lights are much lower in lux than the torches, but slightly higher than the task lights. All the electric lights are higher in lux than the fuel-based lights, with the exception of the pressure lamp.
218 Table 36. Illuminance values measured for lighting products I use in my economic analysis are highlighted in yellow. The provided lux values for the fuel-based lighting products are averages from several measurements taken of the same light during one night. The provided lux values for the electric lighting products are medians from their lamp discharge until reaching their end of use lux value. For the electric lighting product values, measurements were taken in one-minute intervals and values reported represent more than one sample of the lighting product model are identified with a star (*).
Lux at 1 Minimum Maximum Type of Fuel-Based Light or Electric Samples meter Lux at 1 Lux at 1 Light Code w. Form Factor Taken distance meter meter Pressure 75 73 80 4 Hurricane (Lg) 3.8 3.5 4.2 6 Hurricane (Sm) 3.5 2.7 4.1 6 Wick 2.0 1.7 2.5 7 Candle 1.3 1.1 1.5 7 YE – Torch (batt) Normal 188.7 30.3 548.4 595 OC – Torch (grid) Normal 414.0 31.0 509.0 1265 OB – Torch (solar) High- Torch 66.1 66.1 67.2 420 OB - Ambient (solar) High – Ambient 9.9 9.7 10.6 380 SC – Torch (solar) Medium - Focused 69.2 49.1 92.9 280 SC - Ambient (solar) Medium 28.4* 15.0 35.7 456 XS - Ambient (solar) High 28.2 3.0 46.1 515 ET - Ambient (solar) High 13.3* 11.2 17.6 2941 WL – Ambient (solar) Normal 9.5* 9.4 11.5 1630 YF1 – Task (grid) Normal 10.0 0.6 17.8 756 YF1 – Task (solar) Normal 10.0 0.6 17.8 756 YF2 – Task (grid) Normal 10.3 0.6 17.6 371 YF2 – Task (solar) Normal 10.3 0.6 17.6 371 YF3 – Task (grid) Normal 2.1* 0.5 19.0 2409 YF3 – Task (solar) Normal 2.1* 0.5 19.0 2409 TB - Ambient (grid) Normal 13.0 3.0 22.4 1532 TB - Ambient (solar) Normal 13.0 3.0 22.4 1532 TM – Task (grid) Normal 9.7 3.1 13.9 615 TM – Task (solar) Normal 9.7 3.1 13.9 615 AS - Task (grid) High 7.1* 0.5 12.3 1121 AS - Task (solar) High 7.1* 0.5 12.3 1121 AN - Ambient (grid) High 14.3 8.2 20.6 1278 AN - Ambient (solar) High 14.3 8.2 20.6 1278
219
Appendix N. Model input parameters for base case scenario
Time-related Parameters: NOTES: We found in Kenya that most night vendors are open 7 Nights open per week 7 nights a week, according to observation and survey. Days in a month 30.5 Fuel Parameters: Averages of pumps and petrol stations in Maai Mahiu and Kerosene cost (Ksh/liter) 89 Karagita Median use hours per night from the 23 vendors in Maai Mahiu and Karagita participating in our kerosene Use hours per night 2 measurements, combined with hours used per night found from datalogging lamps in Maai Mahiu and Karagita. Monthly maintenance cost as An annual maintenance estimate of 10% of capital 0.83% a percentage of initial cost cost/year Electric Parameters Torch use hours per night 1 A standard value used in several torch analyses Median use hours per night from the 23 vendors in Maai Ambient/Task use hours per Mahiu and Karagita participating in our kerosene 2 night measurements, combined with hours used per night found from datalogging lamps in Maai Mahiu and Karagita. Charge fee (Ksh/Charge) 20 Price charged in Maai Mahiu and Karagita charge shops Assume full charge at shop? Y If "N" the charge duration given below is used. (Y/N) Cost for full charge with 8.1 Ksh/kWh, Source: Eng. Kaburu Mwirichia, Director 0.04 home grid (Ksh/Charge) General of Kenyan Energy Regulatory Commission (2008) Battery cost for the Cost for EverReady Super Heavy Duty D-cell batteries 75 Incandescent Torch (Ksh) purchased in Maai Mahiu It is uncommon in Kenya for lighting products to be % Salvageable 0% salvaged. Monthly maintenance cost as An annual maintenance estimate of 10% of capital 0.83% a percentage of initial cost cost/year Economic Parameters Monthly nominal discount 2.6% i = ir+ r rate (i) Kenya National Bureau of Statistics, an average of 2003- Monthly inflation rate (ir) 0.9% 2007 inflation rates. Monthly real discount rate (r) 1.7% Assuming a real discount rate of 20% per year Analysis period (n) in months 48 Monthly electricity escalation 0.0% rate (e) Monthly kerosene fuel 0.0% escalation rate (ek)
220 Appendix O. Assumptions
I aimed to use as few assumptions as possible in this model, but those I have used are listed below by their Results & Discussion section. The assumptions I used in the base case scenario are relevant to all the sensitivities and are not repeated. For the assumptions used in the design sensitivity analysis and economic parameters sensitivity analysis, I list out by sensitivity analysis.
O.1 Assumptions used for base case scenario
Economic parameter assumptions:
1. Annual nominal discount rate is 20%.
2. Annual energy escalation rates are 0%.
3. Annual maintenance fee of 10% capital cost of lighting product.
4. No salvage value can be obtained from the lights after their lifetime.
Use assumptions:
5. One candle is used at a time in a small business.
6. Two hours of use per night is appropriate for task and ambient lighting
products. (This value is the combination between the 1.9 hours per night
collected during the 2008 summer study in Kenya, and the 2.2 hours per night
calculated from the 2008 datalogger study results.)
7. All LED lighting products analyzed cost the same per charge event.
8. Lamp owners bring their lamps to a charge shop when they can no longer
provide acceptable light and charge shops provide a full charge. (Many lamp
221 owners, however, do not receive a full charge, as discussed in the datalogging
LED lamp results in Appendix K.)
9. If replacement batteries are available in Kenya, the lamp owner can replace
them in the lamp.
10. Lamps provide consistent service throughout their lives, ie. lamp discharge
quality does not degrade.
11. Solar-charged products can receive a standard day of solar charging every day
of the year. A standard day of solar charging is 5 kWh/m2 of solar input on
the solar module per day (PV GAP, 2005).
12. Solar-charged products, when not able to achieve a full charge under one
standard day of solar charging, will provide the lux-hrs per use such that it
runs its possible daily use from a state of full charge.
13. Lamps are only used in businesses during the evenings.
14. Lamp owners will pay 10% of the product’s capital cost in annual
maintenance.
O.2 Assumptions used for individual design sensitivity parameters
Battery size:
1. Increasing the battery size increases the capital cost of the battery at the same
rate batteries of equal chemistry and voltage increases in Kenya (Ksh/mAh).
2. The solar options receive enough charge to supply desired daily use.
222 3. The battery replacement period reflects the original battery size. In reality, as
the battery size increases, the replacement period will decrease, thus
increasing true maintenance costs. This assumption will increase the LCC
values for the electric lamps slightly. (See Battery Life Sensitivity results.)
Battery chemistry, Charging option, and Optional solar module upon repurchase:
No additional assumptions made.
Power consumption:
4. The battery replacement period reflects the original battery size, as in
assumption 3 made for the battery size sensitivity analysis. Likewise in
reality, as the power consumption decreases, the replacement period will
decrease, thus increasing true maintenance costs.
O.3 Assumptions used for individual economic parameter sensitivity parameters
Charge cost:
1. Those who charge at home with the grid are under the second lowest cost
domestic tier in Kenya’s power structure. At this tier, energy costs 8.1 Ksh
per kWh (Mwirichia, 2008). The median rated battery energy capacity of the
electric lighting products I analyze is 5.6 Wh; under the rate of 8.1 Ksh per
kWh, the cost per full charge at home with the grid is 0.04 Ksh.
Use time, Kerosene cost, Kerosene fuel escalation rate, Battery life expectancy,
Lighting product life expectancy, Real discount rate, and Analysis period:
No additional assumptions made.