ASSESMENT OF ROOFTOP SOLAR NETMETERING 

CONCEPT: 

CONSUMER AND UTILTY POINT OF VIEW 

 

 

 

 

Jayawickrama Gamage Saranga Nalani 

 

(109238H) 

 

 

 

Degree of Master of Science  

 

 

 

Department of Electrical Engineering 

 

University of Moratuwa 

Sri Lanka 

 

 

May 2015 

 



ASSESMENT OF ROOFTOP SOLAR NETMETERING 

CONCEPT: 

CONSUMER AND UTILTY POINT OF VIEW 

 

 

 

 

Jayawickrama Gamage Saranga Nalani 

 

(109238H) 

 

 

Thesis/Dissertation submitted in partial fulfillment of the requirements for the 

Degree Master of Science 

 

 

 

Department of Electrical Engineering 

 

University of Moratuwa 

Sri Lanka 

 

 

 

May 2015 



 



i 
 

                                                                                                                                                                                                                                                                                                                                                                                                                               

DECLARATION 

 

I declare that this is my own work and this thesis does not incorporate without 

acknowledgement any material previously submitted for a Degree or Diploma in 

any other University or institute of higher learning and to the best of my 

knowledge and belief it does not contain any material previously published or 

written by another person except where the acknowledgement is made in the text.  

 Also, I hereby grant to University of Moratuwa the non-exclusive right to 

reproduce and distribute my thesis, in whole or in part in print, electronic or 

other medium. I retain the right to use this content in whole or part in future 

works..   

 

Signature       :............................  Date :.............................. 

The above candidate has carried out research for the Masters thesis under our 

supervision.  

 

  

Prof.H.Y.R Perera    :………………  Date     :…………………  

 

Dr. Asanka Rodrigo :………………  Date      :……………….. 

 

 



ii 
 

ACKNOWLEDGEMENTS 

I would like to begin by thanking my supervisors for guiding me and providing their 

valuable suggestions throughout my thesis work. I would like to thank my supervisors 

Prof.Ranjith Perera and  Dr.Asanka Rodrigo for their Guidance and for supportive 

discussions on the various aspects of Rooftop Net Metering Concept. On the whole I 

would like to thank you for being great mentors and it was my pleasure working with 

you. Secondly I would like to thank entire academic staff of Department of Electrical 

Engineering, University of Moratuwa for their valuable suggestions and inputs during 

the various stages of my thesis work. 

 

At Ceylon Electricity Board, I would like to specifically thank Mr. Bandula Thilakasena 

(AGM-Cooperate Strategy), for providing his critical yet valuable inputs which 

enhanced my research work. And also all the Area Engineers in Ceylon Electricity 

Board and Branch Engineers of Lanka Electricity Company Ltd for providing me with 

data required and for providing the opportunity to meet and discuss with various experts 

of Net Metering. 

 

But above all I would like to thank my parents, my husband, my son and friends. 

Especially my parents for always encouraging me to think and work beyond limits. A 

special thanks to my friends for their numerous feedbacks, critiques and inspiration. 

 

 

 

 

 

 



iii 
 

ABSTRACT 

 
Global energy needs continue to grow, whilst fossil fuels still outstrip renewable energy 

in terms of supportive policies and subsidies. With growing concern towards climate 

change, many countries across the world are rethinking their energy strategy and 

incorporation alternative methods of energy generation. Of all the different modes of 

renewable energy technologies, Solar PV technology has caught the most attention.  

 

With environmental concerns and energy needs increasing, the world is promoting 

renewable energy technologies. Today, the PV systems price is decreasing, which gives 

it a competitive edge. The aim of this study is to research the viability of rooftop solar 

PV systems under certain circumstances. The study performs a cost beneficial analysis 

for the lifetime of the solar PV system making use of economic analysis on residential 

consumer perspectives and avoided cost analysis on utility point of view.  

 

The research concluded with several findings. Basically it concluded that the investment 

on Roof Top Solar is worthwhile when monthly consumption exceed 200 kWhs. 

Therefore, according to the present tariff structure and cost of solar PV Systems, Net 

Metering is not economical for monthly average consumption below 150 units. In utility 

point of view, it has been found that the reduction of avoided cost is rapidly increasing. 

But the rate at which the reduction of avoid cost increasing is decreasing and it becomes 

constant after 20 years. rooftop solar electricity generation cannot replace any marginal 

plant during the period of study concerned. 

 

There is no detailed study has been conducted in Sri Lanka in this particular area of 

study. The outcome of the research provides important and useful information for 

consumers, electricity utilities as well as the policy makers in energy sector. 

 

 



iv 
 

TABLE OF CONTENTS 

Declaration of the candidate &Supervisor      i 

Dedication          ii 

Acknowledgements         iii 

Abstract          iv 

Table of contents         v 

List of Figures                    vii 

List of Tables                                                                                                            viii      

List of abbreviations                   ix 

1. Introduction   

1.1 Global Energy Status and Challengers     1 

1.2 Renewable Energy for Global Energy Demand    1 

1.3 Global Solar Status       2 

1.4 Sri Lanka Energy Picture       4 

1.5 Potential for NCRE  in Sri Lanka      6 

1.6 Solar Resource of Sri Lanka      7 

1.7 Contribution of Solar Power for Sri Lankan Energy Market  11 

1.8 Net Metering        11 

1.9 Rooftop Solar Net Metering      12 

2. Problem Identification       13 

2.1 Research Approach       13 

2.2 Literature Review        14 

2.3 The Problem Statements       17 

2.4 Objectives         19 

2.5 Dissertation Outline       19 

3. Methodology         20 

3.1 Collection of Data        20 

3.2 Data Analysis        21 

3.3 Case Study        21 



v 
 

4. Data Analysis         25 

4.1 Data Prediction        25 

4.2 Solar Irradiance and Insolation      25 

4.3 Efficiency of Solar Panels       27 

4.4 Solar Panel output degradation      32 

4.5 Sunspot         32 

4.6 Tariff Variation        36 

4.7 Lifetime of Solar PV       39 

4.8 System Costs        40 

4.9 Discount Rate        45 

5. Economic Analysis        45 

5.1 Simple Payback Period       45 

5.2 Simple Payback Period calculation     46 

5.3 Net Present Value        55 

5.4 Calculation of NPV       56 

5.5 Calculation of IRR       58 

6. Avoided Cost         60 

6.1 Introduction        56 

6.2 Fuels used in various power plants     61 

6.3 Determination of fraction of time each Power Plant in Margin  61 

6.4 CEB Dispatch Schedule       62 

6.5 Avoided Cost Calculation methodology     63 

6.6 Dispatch Schedule        65 

6.7 Prediction of rooftop solar electricity production    76 

6.8 Future behavior of existing Thermal Plants    79 

7. Discussion         89 

8. Conclusions and Recommendations      91 

References         93 

Appendix A-Sample Data Collection      95 

Appendix B-Manufacturer Data Sheets     99 

 



vi 
 

LIST OF FIGURES 

Figure 1.1 Growth of Global Renewable Power Capacities (Excluding Hydro) 02 

Figure 1.2 Solar PV operating capacity as percentages of leading countries 03 

Figure 1.3 Growth of installed capacity of solar PV    04 

Figure: 1.4  Installed capacities of NCRE Sources     07 

Figure: 1.5  Growth of installed capacity of NCRE Sources    07 

Figure 1.6:  Power Generation of NCRE Sources     08 

Figure: 1.7  Solar resource map developed by the NREL    09 

Figure 1.8 Operation of Solar Net Metering System    12 

Figure 2.1 Grid Connected Solar PV System     14 

Figure 3.1  Monthly Electricity Generation of a particular Customer  22 

Figure 3.2  Comparison Monthly Billing Units with and without Net Metering 24 

Figure 4.1 Variation of monthly Average Solar Insolation   27 

Figure 4.2 Actual Generation of 1 kW Solar Panel    30 

Figure 4.3 Calculated Solar Panel Efficiency     31 

Figure 4.4 Actual Generation of 2.2 kW Solar Panel    31 

Figure 4.5 Calculated Solar Panel Efficiency     30 

Figure 4.6  Actual Generation of 6 kW Solar Panel    32 

Figure 4.7  Calculated Solar Panel Efficiency     33 

Figure 4.8  Calculated Average Efficiency     33 

Figure 4.9 Relationship between Sunspot number and Solar Irradiance  35 

Figure 4.10  Sunspot cycles from 1997 to 2012     36 

Figure 4.11 Daily Totals Solar Irradiance from 1997-2012   36 

Figure 4.12 Variation of sunspot number -from 1997 to 2032   37 

Figure 4.13 Block wise variation of Energy Charge for Domestic Consumers 39 

Figure 4.14 Forecasted Block wise Tariff from 2013 to 2032   41 

Figure 4.15 Price reduction of Crystalline PV Cells    42 

Figure 4.16 Price variation of Chinese Solar Cells    43 

Figure 4.17 Average Cost of installed Solar Systems         44 



vii 
 

  

Figure  5:1  Payback Period of 1kW Solar Panel     49 

Figure  5:2 Payback Period of 2kW Solar Panel     49 

Figure  5.3 Payback Period of 3kW Solar Panel     50 

Figure 6.1 Average Unit Costs of Thermal Power Plants in 2013  65 

Figure 6.2 Load Duration Curve       72 

Figure 6.3 Cumulative installed capacity of Solar PV in GW   77 

Figure 6.4 Forecasted Solar PV growths up to 2033    78 

Figure 6.5  Reduction of total Avoided cost over the lifetime of Solar System 86 

Figure 6.6 Share of energy supply by source     87 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



viii 
 

LIST OF TABLES 

Table 1.1 Availability and Total Production of Electricity by category  5 

Table 1.2 Percentage share of NCRE Sources     8 

Table: 3.1  Solar Net Metering Consumers     21 

Table 3.2 Comparison of Monthly Billing Units     23 

Table 4.1 Monthly Average insolation, Colombo    26 

Table 4.2 Effect of temperature & Dust       29 

Table 4.2 Variation of Solar Irradiance      37 

Table 4.3 Variation of energy charge for domestic consumer category  38 

Table 4.4         Variation of fixed charge for domestic consumer category   39 

Table 4.5 Block wise Tariff escalation rate and % tariff escalation  40 

Table 4.6 Average Cost of installed Solar Systems    44 

Table 5:1 Tariff escalation rate for sensitivity analysis    53 

Table 5:2  Payback Period with different tariffs     54 

Table 5:3  NPV of 1kW Solar Consumer      57 

Table 5.4 IRR of 1kW Solar Panels      58 

Table 6.1 Potential costs avoided due to rooftop solar net metering  60 

Table 6:2 Fuel Prices effect from February 2013    61 

Table 6.3 Generation cost of CEB Thermal Plants    64 

Table 6.4  Dispatch Schedule 2013      67 

Table 6.5  Calculated Plant Factors      69 

Table 6.6  Calculated Plant Factors along with the plant capacity  70 

Table 6.7 Power Plants sorted in the descending order of unit cost  70 

Table 6.8 Fraction of time each plant operate in margin   71 

Table 6.9 Installed rooftop solar capacity as at 31
st
 December 2013  73 

Table 6.10 Total dispatch energy of thermal plants in 2013   74 

Table 6.11 Plant factors of Thermal Plants     74 

Table 6.12   Fraction of margin each plant operates    75 

Table 6.13  Operating costs of marginal power plants    75 



ix 
 

Table 6.14  Global Solar PV installed capacity     76 

Table 6.15 Rooftop Solar Capacity growths     77 

Table 6.16 Forecasted Rooftop Solar PV Capacity    78 

Table 6.17  Additions and Retirements of Thermal Power Plants  79 

Table 6.18  Projected dispatch schedule of Power plants  in GW  81 

Table 6.19   Projected dispatch schedule of Power plants GWh     82 

Table 6.20  Projected total Plant Dispatch capacity of each thermal plant 83 

Table 6.21   Calculated plant factors for projected  Plant Dispatch schedule  84 

Table 6.22   Fraction of margin each plant operates    84 

Table 6.23  Avoid cost of marginal Plants      85 

Table 6.24 Reduction of avoided cost due to rooftop solar net metering  86 

Table 6.25  Expected share of energy supply      87 

 

  

 

 

 

 

 

 

 

 

 

 

 

 



x 
 

LIST OF ABBREVIATIONS 

 

IEA   International Energy Agency 

HFO   Heavy Fuel Oil 

NREL   National Renewable Energy Laboratory 

SEA   Sustainable Energy Authority 

NOAA   National Oceanic and Atomic Administration 

NGDC   National Geographical Data Centre 

NPV   Net Present Value 

IRR   Internal Rate of Return 

DCF   Discounted Cash Flow 

IPP   Independent Power Producers 

LNG   Liquid Natural Gas 

PUCSL  Public Utility Commission Sri Lanka 

EPIA   European Photovoltaic Industry Association 

NCRE   Non Conventional Renewable Energy 

CEB   Ceylon Electricity Board 

LECO   Lanka Electricity Company 

PV   Photo Voltaic 

LTGEP  Long Term Generation Expansion Plan 

GWh   Gigawatt Hour 

kWh   Kilowatt Hour 

MW   Megawatt 

 



Page 1 of 102 
 

     Chapter 1 

INTRODUCTION 

 

1.1 Global Energy Status and Challenges 

Many countries depend on coal, oil and natural gas to supply most of their energy 

needs, but reliance on fossil fuels presents a big problem. Fossil fuels are a finite 

resource. Eventually, the world will run out of fossil fuels. It is becoming too 

expensive to retrieve the remaining. Fossil fuels also cause air, water and soil 

pollution, and produce greenhouse gases that contribute to global warming.  

Due to the present situation of increasing energy demand, rising energy prices, and 

global warming, Non Conventional Renewable Energy (NCRE) sources playing a 

major role in global energy supply. NCRE sources, such as Wind, Solar and Hydro 

power are clean alternatives to fossil fuels. They produce little or no pollution or 

greenhouse gases, and they will never run out.  

1.2 Renewable Energy for Global Energy Demand 

Renewable energy comes from natural resources such as sunlight, wind, rain, tides, 

and geothermal heat. During 2013, modern renewables continued to grow strongly in 

all end-use sectors: power, heating and cooling, and transport. In the Power Sector, 

renewable sources accounted for almost half of the electricity capacity added 

globally during 2013.They have supplied 18% of global energy consumption in 2013 

with 16% of global electricity coming from hydro electricity and 2% from new 

renewable sources [1]. 

 

Wind and Solar Photo Voltaic (PV) accounted for almost 40% and 30% of new 

renewable capacity, respectively. Hydro Power provided nearly 25% of the global 

energy requirement [1]. By the end of 2013, total renewable power capacity 

worldwide exceeded 1,370 GW, including Hydro Power. In summary, NCRE 

comprised more than 25% of total global power-generating capacity [1]. 

 

http://en.wikipedia.org/wiki/Natural_resource
http://en.wikipedia.org/wiki/Sunlight
http://en.wikipedia.org/wiki/Tidal_energy
http://en.wikipedia.org/wiki/Geothermal_energy


Page 2 of 102 
 

According to the International Energy Agency (IEA), Photovoltaic and solar-thermal 

plants may meet most of the world's demand for electricity by 2060 .It is half of all 

energy demand. Meantime, Wind, hydropower and biomass plants will supply much 

of the remaining generation. Photovoltaic and concentrated solar power together can 

become the major source of electricity. 

 

Figure1.1 exhibits the Renewable Power Capacity (GW) added globally since 2004. 

 

 
 

Figure 1.1: Growth of Global Renewable Power Capacities (Excluding Hydro) 

Source: REN 21, Renewable Global Status Report (2006-2013) [2] 

 

1.3 Global Solar Status 

The sun is our most powerful source of energy. Sunlight, or solar energy, can be used 

for heating, lighting, generating electricity and a variety of industrial processes. Most 

forms of NCRE come either directly or indirectly from the sun. For example, heat 

from the sun causes the wind to blow, contributes to the growth of trees and other 

plants that are used for biomass energy, and plays an essential role in the cycle of 

evaporation and precipitation that makes hydropower possible. 

0

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2004 2006 2008 2010 2012

R
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ew
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(G
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)

Year

Renewable Power 
excluding Hydro

Wind

Biomass

Solar PV

Geothermal



Page 3 of 102 
 

In Solar market, very large-scale ground-mounted systems and building integrated 

(rooftop) small-scale systems continued to play an important role. European Union is 

dominated the global solar market, led by Italy and Germany. Germany is currently 

the global leader in solar power. Germany has a goal to discontinue all nuclear power 

by the year 2020 and replace it with renewable resources. There are major PV feed-

in-tariff programs in Italy, Japan and China. It can be seen that Solar Market has 

been expanded in other regions. China has rapidly emerged as the dominant player in 

Asia. 

The following Figure shows Solar PV operating capacity of leading countries in the 

world as percentage.  

 

 

 

Figure 1.2: Solar PV operating capacity as percentages 

Source: REN 21, Renewable Global Status Report (2006-2013) [2] 

 

During 2013, Solar PV experienced extraordinary market growth. The capacity was 

increased by 30 GW, increasing total global capacity by 74%.The total installed 

Solar PV capacity at the end of 2012 is about 98 GW. The seven year growth rate 

from 2007 to 2013 was approximately 70% per year [2]. 

 

 

35.60%

18.30%6.90%

4.10%
1.90%

2.80%
2.90%

4.10%

4.40%

5.70%

6.50%
7.10%

Germany

Italy

Rest of the World

Other EU

Australia

Czech Republic

Belgium

France

China

USA

Spain



Page 4 of 102 
 

The Figure 1.3 shows the actual annual installed capacity of solar PV  systems 

worldwide from 1995. 

 
 

Figure 1.3: Growth of installed capacity of solar PV 

Source: REN 21, Renewable Global Status Report (2006-2013) [2] 

 

1.4 Sri Lanka Energy Picture 

With steady economic growth, the demand for energy is of paramount importance 

especially for a developing country like Sri Lanka.  

Sri Lanka’s energy sources consist primarily of biomass, hydro-electricity and 

petroleum that contribute to 47%, 8% and 45% of total energy respectively [2]. In the 

power sector, the installed capacity for electricity generation receives from hydro, 

thermal and wind power. 

Electricity generation increased by 7.5% to 11,521 GWh in 2013 reflecting the dry 

weather conditions which prevailed during the second half of the year. As a result, 

thermal power generation increased by 36% to 6,785 GWh. The Coal power plant at 

Norechchole provides 300MW to the electricity demand of Sri Lanka since 2012. 

The second phase of coal power plant at Norachchole added  600 MW to the system 

0

20

40

60

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120

1995 2000 2005 2010

In
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al
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d
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ap
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G
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Page 5 of 102 
 

capacity in 2014. The Coal fired plant in  Sampur will add 500 MW to the installed 

capacity [2]. 

 

At present the annual electricity requirement in Sri Lanka is about 11,000 GWh and 

the installed power plant capacity is about 3141 MW. This installed capacity consists 

of 1401 MW of hydro power, 1690 MW of thermal power and 50 MW from other 

renewables. The thermal power plants generate electricity by firing coal, Heavy Fuel 

Oil (HFO) and diesel. The cheapest option for power generation is hydropower as it 

has no fuel cost involved. The most expensive option is diesel and the costs of HFO 

& coal are in between these two. 

  

The total electricity requirement in the country is about 33 GWh/day at present. The 

power requirement varies during the day time (from 6.30 am – 6.30 pm).The day 

time demand is about 1200-1400 MW. The demand increases and reaches a peak of 

about 2000 MW during the night from 6.30 pm to  9.30 pm. Again it drops to about 

800 MW from 9.30 pm  to6.30 am. Ceylon Electricity Board (CEB) has the 

responsibility of providing uninterrupted power supply to this varying daily demand. 

The hydro power generation is mainly dependent on the water in reservoirs basically 

under the multipurpose Mahaweli project. The following table shows the availability 

and total production of Energy in 2013.  

Table 1.1: Availability and total production of Electricity by category 

Source: Generation Performance in Sri Lanka-2013 prepared by Public Utilities 

Commission of Sri Lanka [3] 

Source MW GWh 

Hydro power 1,401 4,622 

Thermal 1,690 6,785 

Other Renewables 50 121 

Total 3,141 11,528 

 

97% of the households are enjoying the grid connected electricity while another 2% 

of households are provided with basic electricity connection through off-grid 



Page 6 of 102 
 

systems. The demand for electricity is estimated to rise at an annual rate of 8% - 

10%. Per capita consumption of electricity meanwhile reflected 460 kWh / person 

per annum in 2013. 

 

1.5 Potential for NCRE in Sri Lanka 

In order to meet the increasing demand the electricity, generation capacity needs to 

be doubled every ten years. This exponential growth cannot be sustained forever as 

the fossil fuel era has reached its ultimate dead-end. Therefore NCRE is emerging as 

the energy supply solution for the 21
st
 century. Sri Lanka, a small island located 

south of the Indian subcontinent, has embraced NCRE  in electricity generation.  

Renewable sources of energy including solar power, small scale hydro power and 

wind power have emerged as an economical and sustainable alternative source to 

promote medium term electricity generation in Sri Lanka. NCRE resources have 

received great attention in recent times for generating electricity in Sri Lanka for 

meeting the targets of 100% electrification. Electrification of rural areas is a 

challenge due to high capital investment, operational costs and the difficulties 

associated with extending grid connected electricity lines to remote areas.  

 

The National Energy Policy of Sri Lanka clearly highlights the importance of 

promoting indigenous energy resources. And the government has the target to reach a 

minimum level of 10% of the gird electricity using non-conventional NCRE by 2016.  

 

On the way to achieve this target, power generation through NCRE sources has 

contributed 7.9% of the total electricity generation of the national grid in 2013. In the 

Sri Lankan power sector, the grid connected installed capacity for electricity 

generation from NCRE sources was 320.628 MW. However, when comparing 

electricity generation from conventional energy sources, the total contribution from 

the NCRE sector to the national grid still remains small. 

 



Page 7 of 102 
 

The Figure: 1.4 shows the existed installed capacity in MW of each type of 

Renewable Energy power plants by the end of 2013. 

 

Figure: 1.4 Installed capacities of NCRE Sources in Sri Lanka 

Source: Generation Performance in Sri Lanka-2013 prepared by Public Utilities 

Commission of Sri Lanka [3]. 

 

The growth of the grid connected installed capacity of NCRE power plants from year 

1999 is depicted in Figure: 1.5. 

 

Figure: 1.5 Growth of installed capacity of NCRE sources 

Source: Generation Performance in Sri Lanka-2013 prepared by Public Utilities 

Commission of Sri Lanka [3]. 

234.1

11.5
1.378

73.65

Mini Hydro

Biomass

Solar

Wind

0

50

100

150

200

250

300

350

19
99

20
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Page 8 of 102 
 

The percentage share of NCRE sources from total Energy has been increased 

exponentially over last 13 years. 

Year Energy 

(GWh) 

Percentage from 

Total Energy 

2000 43.3 0.6 

2001 64.8 1 

2002 103 1.5 

2003 120 1.6 

2004 206 2.6 

2005 280 3.2 

2006 346 3.7 

2007 344 3.5 

2008 433 4.4 

2009 546 5.5 

2010 724 6.8 

2011 722 6.3 

2012 730 6.9 

 

Table 1.2: Percentage share of NCRE sources 

Source: Generation Performance in Sri Lanka-2013 prepared by Public Utilities 

Commission of Sri Lanka [3]. 

 

The growth of energy generated by the grid connected NCRE plants over past 12 

years is depicted below. 

 

 
 

Figure 1.6: Generation from  NCRE  Sources 

Source: Generation Performance in Sri Lanka-2013 prepared by Public Utilities 

Commission of Sri Lanka [3]. 

0

100

200

300

400

500

600

700

800

2
0

0
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2
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Page 9 of 102 
 

1.6 Solar Resource of Sri Lanka 

Sri Lanka is located near the equator and has a great potential for solar radiation year 

around. The average radiation is 4-6 kWh/m
2
/day. The Solar radiation over the island 

does not show a seasonal variation. According to the solar resource map developed by 

the National Renewable Energy Laboratory (NREL) of  USA, solar radiation over the 

flat dry zone of Sri Lanka varies from 4.0 – 4.5 kWh/m
2
/day. Solar radiation levels are 

low as 2.0 – 3.5 kWh/m
2
/day over the central mountains of the country. The following 

Figure shows yearly sum of irradiance received by countries in the world. Accordingly, 

Sri Lanka receives about 2000 kWh/m
2
 of irradiance per year. Therefore, potential for 

harnessing solar energy is very high. 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1.7:  Solar resource map 

Source: REN 21, Renewable Global Status Report (2006-2013) [2] 

 

 

 

 



Page 10 of 102 
 

1.7 Contribution of Solar Power for Sri Lankan Energy Market 

Solar energy may prove to be the best choice for Sri Lanka's future. It is one of the best 

alternatives to meet the high target set to increase NCRE share  to 10% in 2018 from a 

current level of 4% [4]. 

The population in Sri Lanka is currently 21 Million with an annual growth of 0.7%. The 

electrification rate is 98 % at national level. The electricity demand is growing by 5-8% 

p.a [3].  Off-grid commercial and institutional PV markets play an important role in pre-

electrification of areas not reached by the Sri Lanka power grid.  

The first ever grid connected solar energy park was commissioned in Baruthakanda of 

Hambantota, the first solar energy park in Asia. This is constructed with the grants of the 

Government of Japan and the Government of Korea. The total capacity of the system is 

900 kW and amounts to USD 14.5 million. The goal of the project is to provide clean 

energy through solar power generation. The solar energy park generates 5.6 GWh per 

annum and this is capable of offsetting 860 tons of CO2  [5]. 

 

1.8 Net Metering 

Net metering facilitates import and export of Electricity through a single, bidirectional 

meter. Customers can bank the excess electricity generated and trade the same against  

future consumption. Net metering is an electricity policy for consumers who own 

(generally small) renewable energy facilities, such as wind, solar power or home fuel 

cells. 

Use of NCRE to produce electricity in Sri Lanka is open to all electricity customers. 

They can participate in producing electricity using NCRE in whatever small way they 

can afford. 



Page 11 of 102 
 

The normal energy meter is replaced with a two-way meter. The meter have two 

registers: the "import" register and the "export" register. The consumer can produce 

electricity using a renewable source of energy, and first use that electricity for their own 

requirements, and send the surplus back to the grid. Such "exported" electricity units 

will be registered in the "export" register of the meter. During certain times of the day, 

the consumers own electricity production may not be adequate for their requirements. 

Then the consumption will be recorded in the "import" register. When the electricity 

meter is read once a month, consumer pays only for the difference between the "import" 

and the "export". If in any month the consumer have exported more than what he 

imported, the bill will only carry the monthly fixed charge (no charge for the units of 

electricity), and the excess exported units will be credited to the next month’s bill. 

1.9 Rooftop Solar Net Metering 

With the introduction of Net Metering for NCRE systems, the energy generated during 

the sunshine hours of the day can be used or pumped back into the grid and the house 

meter will run in the opposite direction and reduce the consumers’ monthly bill. Without 

a costly storage battery system this will produce reduction in the monthly bill. 

Maximum solar output is available only for 4.5 hours a day or one-sixth of the time. The 

level of utilization is only 16%.The economic life is only 20 years for a particular Solar 

Panel. Wherever a solar panel is installed, there's a subsidy coming to the user. The Sri 

Lankan government is trying to promote use of solar energy as alternative energy 

technologies by introducing net metering for roof top Solar Systems to cope with the 

energy crisis.  

CEB and Lanka Electricity Company (LECO) has introduced roof top solar net metering 

in Sri Lanka, that is a roof top system where people who can afford solar power system 

on their roof tops can export energy during the day time to the national grid and 

consume some of it during the night and CEB or LECO will act as energy banks. There 



Page 12 of 102 
 

is no money transaction associated. Net metering is a program that provides rooftop 

solar customers with utility bill credits for the surplus clean energy that their solar 

systems feed onto the grid. 

The utility energy meter will be replaced with an Import/Export meter. The electrical 

energy consumed from the grid is considered as import energy and electrical energy 

generated by the Solar Panel and supplied to the grid is considered as export energy. At 

the end of each billing period, CEB/ LECO will read the consumer’s export energy 

meter reading and the import meter reading. The electricity bill will be prepared giving 

credit to the export, and charging the consumer for the difference between the import 

and the export.  

 

 

 

 

 

Figure 1.8: Operation of Solar Net Metering System 

                             Source: www.nrel.gov 

When someone decides to put solar panels on their roof, they not only generate clean 

power, but also reduce strain on the grid while offering financial benefits to all 

electricity consumers. In addition to the bill-saving rooftop solar net metering avoids 

certain costs to the utility. It also provides environmental, public health and economic 

benefits.  



Page 13 of 102 
 

  Chapter 2 

PROBLEM IDENTIFICATION 

 

2.1 Research Approach 

 

For the past couple of hundred years power generation rely more and more on fossil 

fuels. However it is becoming increasingly obvious that reliance on fossil fuels is 

causing problems. The fact is that, world is running out of fossil fuels. Fossil fuels are 

depleting rapidly. The demand for fossil fuels is increasing rapidly and it tends to price 

soaring. The severe problem with fossil fuels is the damage to the environment. The 

burning of fossil fuels increases the green house gases and leads to global warming. 

Therefore reliance on fossil fuels only is not a wise decision. 

Unless we have a plan in place to address these issues, we will have severe problems in 

the future. Therefore, alternative technologies for producing electricity have received 

greater attention. There are verities of renewable energy sources used for electricity 

generation in the world. Hydro, Wind, Solar, Biomass and solar thermal are most 

popular renewable energy sources in Sri Lanka.  

 

Among them, my attention extended towards installation of Solar Panels in Rooftops to 

generate electricity in the day time. Net metering enables consumers to use their own 

generation from roof top solar systems to offset their consumption. When the customer 

generates electricity in excess of their demand, enables to feed the grid and receive 

credits for the excess electricity they generate. Solar Energy credits help to offset the 

electricity consumption of the customer. This method helps to reduce the overall burden 

on electric utilities during daytime peak hours by feeding power into the grid. It provides 

benefits not only to individual consumers, but also to the utility by increasing the 

avoided cost.  

 

 



Page 14 of 102 
 

As similar to other renewable resources the main problem associated with roof top Solar 

PV is its high initial capital cost. But, Electricity rates are rapidly increasing while the 

cost of solar PV installation is rapidly decreasing. Therefore, the future of rooftop Solar 

is profitable option for electricity consumers. However the consumers as well as the 

utility must have a long-term perspective in order to justify an investment in Solar PV. 

Therefore, my aim is to conduct an assessment of Roof top Solar Net Metering 

Concept from Consumer and Utility Point of View. 

 

 

Figure 2.1: Grid Connected Solar PV System 

 
2.2 Literature Review 

 

Several studies have been conducted on Solar PV in world wide. Out of them, following 

studies are found as the most relevant resources to the research on Rooftop Solar Net 

Metering concept on consumer and utility point of view. 

 

A research on Modeling Adoption of Solar Photovoltaic and Analysis of Net Metering in 

the City of Austin has been conducted by Siva Kiran Josyula, MA from University of 



Page 15 of 102 
 

Texas at Austin in 2011.The trends in costs and adoption of solar PV by residential and 

commercial customers in the city of Austin have been analyzed. It has been  

accomplished by tabular and graphical analysis of data on PV installations from 2004 to 

2010. Technology diffusion models has been used to analyze and forecast the diffusion 

of residential PV systems in Austin. The net metering tariff mechanism in Austin has 

been described and the difference between the current and an alternative tariff has been 

explained in the literature.  

 

Difference in revenue for Austin Energy with the alternative tariff has been calculated 

using simulated PV generation data. The results indicate that the alternative tariff adds 

little revenue to Austin Energy‟s energy charge revenues at the current level of 

penetration of solar PV. However, at a higher penetration level of PV, the alternative 

tariffs might result in significant additional revenue for the utility. The thesis concludes 

with a discussion on the possible rationale for the alternative tariff and directions for 

future research. The methodology used in this thesis for analyzing adoption of solar PV 

by residential customers has been referred for my research. 

 

A research on Economic Value of PV and Net Metering to residential customers in 

California has been conducted by Naim Darghouth, Galen Barbose and Ryan from 

Wiser Environmental Energy Technologies Division in California. 

 

The bill savings from PV for residential customers of the California has been analyzed 

in this research. The bill saving has been calculated according to the existing net 

metering tariffs as well as under several alternative compensation mechanisms. It has 

been found that economic value of PV to the customer is dependent on the retail 

electricity rate. It can vary quite significantly from one customer to another. In addition, 

it has been found that value of the bill savings from PV generally declines with PV 

penetration level, as increased PV generation tends to offset lower-priced usage. The 

method used to analyze the bill saving has been incorporated to my research. 



Page 16 of 102 
 

 

A Technical and Economical Assessment of Net-Metering in Kenya has been conducted 

by Georg Hilleet al and Michael Franz in 2012. 

This research analyses the technical and economical feasibility for grid connection of 

solar photovoltaic systems through net-metering in Kenya. It assesses the technical, 

economical and social feasibility of solar net metering as an incentive policy in Kenya. 

All required data has been collected from consumers and local stakeholders including 

Ministry of Energy and Kenya Power Limited. Data received from the existing power 

plant structure and electric network as well as future extension plans also considered.  

Furthermore, the economic viability of the grid operations, as well as potential economic 

implications of investments under net-metering has been considered. It has been 

assessed whether many decentralized small scale PV installations interfere with the grid, 

impact to the Grid stability by the fluctuating generation, the avoided cost of  net 

metering at the customer level and  the  impact of net metering on Kenya Power  

revenues has been studied in this research. 

 

Methodological considerations for the economic analysis comprise of determining the 

advisability of small scale grid connected PV market in Kenya. The discounted cash-

flows mechanism has been used. The three essential elements have been determined :(i) 

discount factor, (ii) cost and (iii) benefits. The avoided cost to the utility due to solar net 

metering has been evaluated. The methodology used for calculation of above parameters 

has been referred for my research.  

 

Another research has been conducted on Evaluation of Net Energy Metering Cost 

Effectiveness by the Energy Division of California Public Utilities Commission. 

It provides a measure of the total net costs to ratepayers from solar customers 

participating in the solar net metering tariffs. This analysis also measures the overall 

cost-effectiveness of solar PV as an energy resource. The direct costs and benefits of net 



Page 17 of 102 
 

metering have been evaluated by calculating economic Net present value and Internal 

Rate of Return. 

 

The report estimates that on a lifecycle basis, all PV generation on net metering tariffs 

(386 MW installed through 2008) will result in a net present value cost to ratepayers of 

approximately $230 million over the next 20 years, or approximately $20 million per 

year. Net metering as a policy is one small part of the utility’s demand side efforts, 

which overall represent 7% of the average residential bill and provide a net savings to 

ratepayers.  

 

The report estimates that the average net cost of net metering  is $0.12 per kWh 

exported, which is relatively high on a cents per kWh basis .The report includes several 

sensitivity analyses that indicate potential areas for further policy study, including the 

costs associated with net metering billing and interconnection. The report uses a robust 

methodology for estimating the costs and benefits of the net metering mechanism. The 

report highlights a number of research and policy issues that merit further study and 

possible Commission action. The analysis used in the above literature was incorporated 

to my research. 

 

 

2.3 The Problem Statements 

The initial capital cost of Solar Panel installation is very high. It prevented the wide-

spread adoption of Solar PV. Electricity rates are rapidly increased while cost of solar 

PV installation is rapidly decreasing. Consumers must have a long-term perspective in 

order to justify an investment in Rooftop Solar PV with net metering. When people 

invest huge money on it, they should have a clear understanding whether it is 

worthwhile or not. Therefore it is important to assess the benefit to the consumer by roof 

top Solar Net Metering Concept. Solar Energy credits help to offset the electricity 

consumption of the customer. Furthermore it is required to determine the consumer 



Page 18 of 102 
 

category which receives the maximum benefit by installing rooftop solar PV.Rooftop 

solar net metering provides benefits not only to individual consumers, but also to the 

utility. When considering the utility, the impact (pros & cons) due to rooftop Solar Net 

metering should be discussed.  It reduces the overall burden on electric utilities during 

peak daytime hours by feeding power into the grid. Hence, the avoided cost of the utility 

can be optimized. On the other hand rooftop solar net metering would lead to reduction 

of the revenue of utility. Therefore, it is very important to assess the impact to the utility. 

 

2.4 Objectives 

The main objective of the research is to conduct an economic evaluation on Solar Net 

Metering concept on residential consumers and determine the consumers (based on the 

units of consumption) who receives the maximum benefit. 

 

The other objective is to determine the avoided cost to the utility due to implementation 

of rooftop solar net metering concept. 

 

Final objective is to assess the shortcomings of existing methodology and provide 

proposals to promote rooftop Solar PV with Net Metering. 

 

2.5 Dissertation Outline 

 

The dissertation reflects the research approach discussed above. Chapter 1 provides an 

introduction to the thesis topic. It discusses the global and local market of renewable 

energy and emphasis the importance of rooftop solar net metering concept to the 

consumer and the utility. Chapter 2 is dedicated to discuss the back ground for the thesis 

and describe the problem statement. In Chapter 3, the research methodology is 

discussed. Further a case study will be explained in the same Chapter. The data analysis 

required for the thesis has been presented in Chapter 4. Economic indices are calculated 

and summarized in Chapter 5.Chapter 6 discusses the procedure used for avoided cost 



Page 19 of 102 
 

calculation. Chapter 7 is dedicated for the discussion. In Chapter 8, shortcomings of the 

existing methodology of rooftop solar have been discussed and proposals are provided to 

promote net metering Sri Lanka. The conclusions and the topics for further research are 

also indicated in Chapter 8. 

 

 

 



Page 20 of 102 
 

  Chapter 3 

METHODOLOGY 

 

3.1 Collection of Data 

As the first step, a set of data was collected from the domestic consumers who have 

already installed Solar Panels on their rooftops under the Net Metering facility provided 

by CEB or LECO. The data used for the analysis   was up to 31
st
 May 2014. 

The following information from each consumer was obtained and a database was 

prepared. 

1. Capacity of Solar Panels 

2. Cost of installation 

3. Monthly Generation 

4. Monthly Consumption 

 

Daily Solar Irradiance data was obtained from Department of Metrological. The data on 

Electricity tariff over last ten years has been obtained from CEB to predict the future 

behavior of electricity tariff. The dispatch schedule prepared by System Control centre 

for last few years and that the data on actual dispatch of each plant has been obtained. 

The fuel price data using last ten years has been collected from Ceylon Petroleum 

Corporation. 

 

The sources of data obtained from are listed below. 

1. Utility 

i. Ceylon Electricity Board 

ii. Lanka Electricity Company Ltd 

iii. Ceylon Petroleum Corporation 

2. Solar Panel Manufacturers and Solution Providers 

i. J Lanka Technologies (Pvt) Ltd 



Page 21 of 102 
 

ii. Eco Solar (Pvt) Ltd  

iii. Aceess Solar (Pvt) Ltd  

iv. Nikini Automation Systems 

3. General Public  

4. Department of Meteorological, Sri Lanka 

 

3.2 Data Analysis 

Most of the net metering customers are located in Colombo and suburbs. As at 31
st
 May 

2014, there are 2400 Nos of CEB consumers accounting to 5.2 MW and 540 Nos of 

LECO consumers accounting to 1.4 MW were registered as Rooftop Solar Net metering 

consumers which sums to 2640 Nos of consumers. 

 

Table: 3.1 -Solar Net Metering consumers as at 31st May 2014 

Utility No of Consumers Installed 

Capacity 

CEB 2400 5.2 MW 

LECO 540 1.4 MW 

Source: Data obtained from CEB, LECO and SEA 

   

3.3 Case Study 

A customer who has large span of data with successful continuous operation since year 

2012 has been selected for the detailed  analysis. 

• Category   :Domestic- 30A/1 Phase 

• Area       : Colombo South 

• Installed Capacity    : 6.1kW 

• Date of Installation   : 09/07/2012 

• Period of Data Collection : August 2012 to December 2013 

 



Page 22 of 102 
 

The following Figure shows the Monthly Electricity Generation by solar PV of the 

selected customer. 

 

 

Figure 3.1 Monthly Electricity Generations  

 

The monthly average generation by 6.1 kW Solar Panel is about 700kWh per month. 

The maximum generation occurs in the months of March and April. 

 

According to Table: 3.3, Net metering significantly reduces the amount of monthly 

Billing Units. 

 

0

100

200

300

400

500

600

700

800

900
M

o
n

th
ly

 G
e

n
e

ra
ti

o
n

 k
W

h

Month

Generation



Page 23 of 102 
 

Table 3.2: Comparison of Monthly Billing Units with and without Net Metering 

 

Billing Period 

Net 
Generation 

kWh 
Net Import 

kWh 

Net 
Export 
kWh 

Energy 
Credit 
kWh 

Consumption 
from Solar 

kWh 

Billing Units 
with Net 
Metering 

kWh 

Billing Units 
without Net 

Metering 
kWh 

 

2012 Aug  784.78 568 511 0 273.78 57 841.78 

2012 Sep  770.10 512 534 22 236.10 0 748.10 

2012 Oct 760.37 721 469 0 291.37 230 1,012.37 

2012 Nov 631.91 690 349 0 282.91 341 972.91 

2012 Dec  670.87 622 380 0 290.87 242 912.87 

2013 Jan  774.31 832 451 0 323.31 381 1,155.31 

2013 Feb 820.16 626 547 0 273.16 79 899.16 

2013 March 810.90 775 537 0 273.90 238 1,048.90 

2013 April  665.24 674 441 0 224.24 233 898.24 

2013 May  671.50 834 505 0 166.50 329 1,000.50 

2013 June  689.25 600 374 0 315.25 226 915.25 

2013 July  701.50 555 412 0 289.50 143 844.50 

2013 Aug  734.50 601 445 0 289.50 156 890.50 

2013 Sep  653.57 560 395 0 258.57 165 818.57 

2013 Oct 612.48 730 352 0 260.48 378 990.48 

2013 Nov 645.00 713 401 0 244.00 312 957.00 

 

 

 



Page 24 of 102 
 

The Figure 3.2 shows the reduction of units of Electricity Consumption due to Solar Net 

metering. 

 

Figure 3.2 Comparison of Monthly Billing Units  

Monthly Bill Saving for each consumer was calculated using the Tariff prevailed during 

the period of concern. 

Example calculation was performed for a particular customer using the actual data. 

Generation of the Solar Panel for the month of August   : 770 kWh 

Monthly consumption        : 748 kWh 

Net Metering Credits (Carry forward)    : -22 kWh 

Monthly Bill (for 748 units) in case of Net Metering is not present   : Rs: 38,837.00 

Monthly Bill with Net Metering      : Rs: 0 

Net Saving per month       : Rs: 38,837.00 

The calculated annual saving for this consumer is Rs: 505,060.00. 

The above example calculation emphasis that Net Metering is very cost effective for 

consumers.   

-200.00

0.00

200.00

400.00

600.00

800.00

1,000.00

1,200.00

1,400.00

Se
p

O
ct

N
o

v

D
e

c

Ja
n

Fe
b

M
ar

A
p

r

M
ay Ju
n

Ju
l

A
u

g

Se
p

O
ct

N
o

v

D
e

c

K
W

h
s

Month

Billing Units 
without Net 
Metering

Billing Units 
with Net 
Metering



Page 25 of 102 
 

Chapter 4 

DATA ANALYSIS 

 

4.1 Data Prediction 

The available Solar Panel Generation data is from 5 months to 27 months periods. This 

data has to be predicted over 20 years period since the average life time of a particular 

Solar Panel is about 20 years. The Electricity generated by Solar Panel is proportional to 

Solar Irradiance (Isolation).Solar Irradiance varies with Meteorological parameters such 

as precipitation, temperature and wind. However the average variation of irradiance per 

year due to above mentioned meteorological parameters is considered as constant.  

 

4.2 Solar Irradiance and Insolation 

Irradiance is a measurement of solar power and is defined as the rate at which solar 

energy falls onto a surface. In the case of solar irradiance, power per unit area is 

measured. Irradiance is typically quoted as W/m² - that is Watts per square meter. The 

irradiance falling on a surface does vary from moment to moment. Because, irradiance is 

a measure of power - the rate that energy is falling, not the total amount of energy. 

 

The total amount of solar energy that falls over a given time is called the insolation. 

Insolation is a measure of energy. It is the power of the sun added up over some time 

period. If the sun shines at a constant 1000 W/m² for one hour,  it has delivered 1 

kWh/m² of energy. Accordingly, Solar Irradiance is 1000 W/m² and Insolation is 1 

kWh/m². The irradiance varies throughout the year depending on the season. It also 

varies throughout the day, depending on the position of the sun in the sky, and the 

weather. Solar Isolation is a measure of solar irradiance over a period of time - typically 

over the period of a single day. 

  



Page 26 of 102 
 

4.2.1 Colombo Average Solar Insolation 

Colombo is situated in 6.9167° N, 79.8333° E coordinates. Monthly Average Isolation 

data for Colombo for ten years has been obtained from Meteorological Department. The 

average isolation for each month was calculated and presented in Table: 4.1 

 

Table 4.1: Monthly Average insolation, Colombo. 

 

Month 

Monthly Average Isolation 

kWh/m²/day 

Jan 5.44 

Feb 6.20 

Mar 6.59 

Apr 5.87 

May 5.20 

Jun 5.21 

Jul 5.31 

Aug 5.56 

Sep 5.57 

Oct 5.23 

Nov 4.88 

Dec 5.11 

 

Source: Department of Meterological, Colombo,Sri Lanka 

 

Monthly average Solar Insolation of Colombo is plotted to observe the pattern of 

variation. According to Figure 4.1, the highest average insolation of 6.59kWh/m²/day is 

received in month of March whereas as the minimum occurs in November. 

 

 



Page 27 of 102 
 

 

Figure 4.1: Variation of monthly Average Solar Insolation 

 

4.3 Efficiency of Solar Cells 

Solar cell efficiency is the ratio of the electrical output of a solar panel to the incident 

energy in the form of sunlight. The energy conversion efficiency (η) of a solar cell  is the 

percentage of the solar energy to which the panel is exposed that is converted into 

electrical energy. This is calculated by the following formula. 

Solar cell Efficiency (η) = Power output at  maximum power point (W) 

          Input light (W/m
2
)  * Surface area of the solar cell (m

2
). 

Manufacturers mention the solar cell efficiencies measured under standard test 

conditions . Standard test conditions specify a temperature of 25 °C and an irradiance of 

1000 W/m
2
 with an air mass 1.5 (AM1.5) spectrum. Under these test conditions 

theoretically average efficiency of solar panel is about 20% [11]. 

4.3.1 Factors affecting the efficiency of Solar Panels: 

The Solar Panel efficiency values quoted by the manufacturer that have been tested 

under Standard Test Condition are not matched with the module efficiency measured on 

site under real climate conditions. There are several factors affecting the efficiency of 

4

4.5

5

5.5

6

6.5

7

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

M
o

n
th

ly
 A

ve
ra

ge
 In

so
la

ti
o

n
 

kW
h

/m
2 /

d
ay

 

Month Monthly Avg Insolation

http://en.wikipedia.org/wiki/Energy_conversion_efficiency
http://en.wikipedia.org/wiki/Solar_energy
http://en.wikipedia.org/wiki/Electricity
http://en.wikipedia.org/wiki/Irradiance
http://en.wikipedia.org/wiki/Surface_area


Page 28 of 102 
 

Solar systems. Basically Temperature, Dust and shade directly affected to the 

performance of Solar panels resulting reducing the efficiency.  

 

The temperature of PV surface rises with longer exposure period to sunlight and high 

ambient temperature. The temperature of the solar panel directly impact the PV 

efficiency. When the temperature increases, Atom vibrations in the p-n junction is 

increased. Therefore, it obstructs the charge carrier movement and decreases the  

efficiency. EIA has conducted a study to analyze data from 18 grid connected PV plants 

located on different geographic locations and it showed a direct relation between 

temperature and PV module efficiency. The plants were located in Austria, German, 

Italy, Japan, India, China and Switzerland. The study concluded that 17 out of the 18 

systems showed annual losses in efficiency due to temperature changes by 1.7% to 

11.3%.The average efficiency reduction in South Asian countries is about 7.5% [16]. A 

study was conducted on a ploy crystaline PV module with solar tracker on  Saudi Arabia 

showed similar temperature effect. The data were compared based on daily peak power 

output. PV module efficiency decreased from 5% to 8% when module temperature 

increased from 35°C to 45°C [17].  

 

On a typical sunney day, it is not uncommon for a solar cell to reach an average 

temperature of 40 °C. The efficiency of so solar cells can decrease more than 0.5% for 

every 1 °C above 25 °C. When the temperature increase beyond 25 degree Celcius, the 

efficiency of solar panel reduces. Because sunlight consists invisible infrared radiation, 

which carries heat. The  solar panel will perform great if it gets a lot of light, but as it 

gets hotter, its performance degrades.It has been identified that for each degree over 

25˚C, the maximum power of the panel is reduced by 0.5% [18].  

 

The accumulation of dust on solar panels (Soiling) can have a significant impact on 

performance of PV systems. The performance reduction due to dust depends on several 

factors such as the location of the PV system, orientation, rainfall and wind. There are 



Page 29 of 102 
 

several studies has been conducted to identify the effect of dust on efficiency of solar 

panels. There are several studies has been conducted to assess the impact of dust 

accumulation on solar panels to their performance. A study has been conducted to 

concentrate on the effects of settling dust on photovoltaic solar panels in Israel [19]. It 

was concluded that regular dust accumulation decreases the efficiency of solar 

photovoltaic panels by about five to six percent. A study conducted on “Effects of Dust 

on the Performance of PV Panels” [20] has been concluded that it causes  6% decrease 

in the solar panel efficiency due to dust in urban areas expose to normal environment.  

 

By summarizing above values obtained from different literatures, the actual efficiency of 

Solar Panel is considered as 9% after accounting the effect of temperature and dust. 

 

Table 4.2: Effect of temperature and dust for the efficiency of the solar panel 

Manufacturer 

Quoted Efficiency 

under Standard 

Test Conditions 

Efficiency reduction 

due to the effect  of 

Temperature rise 

(40
o
C) 

Efficiency 

reduction due to 

the Effect of Dust 

Actual 

Efficiency 

20% 7.5% 6% 9% 

 

Considering the effect of Temperature rise and the accumulated dust on the solar panel 

the actual efficiency of the solar system is considered as 9%.The actual data obtained 

from several solar systems has been used to examine the accuracy of considering 9% as 

the solar panel efficiency for the analysis. 

 

In practical case, following formular has been used to calculate the Solar Panel 

Efficiency(η). 

• Solar Panel Efficiency(η)  =Actual Generation Output  kWh/m
2
/Day *100% 

                                             Average Solar Insolation  kWh/m
2
/Day 



Page 30 of 102 
 

Solar Panel efficiency (η) is the ratio of the electrical output of a solar Panel to the 

incident energy in the form of sunlight. It is calculated by dividing a panel's power 

output (in kWh/m
2
/Day) by the Average input Solar Insolation  (in kWh/m

2
/Day) 

 

To calculate the Solar Panel Efficiency, the actual data obtained from each customer is 

plotted to obtain a comparison between the meteorological data and the actual data. 

Monthly actual solar panel output of randomly selected customers and their efficiencies 

are tabulated below. 

 

Customer 1: 

 Installed Capacity:  1 kW 

Area of the Solar Panel: 8 m
2
 

 

 

 

Figure 4.2: Actual Generation of 1 kW Solar Panel 

 

0.35

0.4

0.45

0.5

0.55

0.6

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

A
ct

u
a

l G
e

n
e

ra
ti

o
n

 
kW

h
/m

2
/d

a
y

Month Actual Generation



Page 31 of 102 
 

 

Figure 4.3: Calculated Solar Panel Efficiency 

Customer 2: 

 Installed Capacity:  2.2 kW 

Area of the Solar Panel: 17.5 m
2 

 

 

Figure 4.4: Actual Generation of 2.2 kW Solar Panel 

 

6.5

7

7.5

8

8.5

9

9.5

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ef
fi

ci
e

n
cy

 %

Month Calculated Efficiency

0.35

0.4

0.45

0.5

0.55

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

A
ct

u
al

 G
en

er
a

ti
o

n
kW

h
/m

2 /
d

ay

Month
Actual Generation



Page 32 of 102 
 

 

Figure 4.5: Calculated Solar Panel Efficiency 

Customer 3 

Installed Capacity:  6 kW 

Area of the Solar Panel: 49 m
2
 

 

 
Figure 4.6: Actual Generation of 6 kW Solar Panel 

6.5

7

7.5

8

8.5

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ef
fi

ci
e

n
cy

 %

Month

Calculated Efficiency

0.4

0.45

0.5

0.55

0.6

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

A
ct

u
al

 G
en

e
ra

ti
o

n
 k

W
h

/m
2/

d
ay

  

Month Actual Generation



Page 33 of 102 
 

 

 

Figure 4.7: Calculated Solar Panel Efficiency 

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                

Figure 4.8: Calculated Average Efficiency 

7.5

8

8.5

9

9.5

10

10.5

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ef
fi

ci
e

n
cy

 %

Month
Calculated …

7

7.2

7.4

7.6

7.8

8

8.2

8.4

8.6

8.8

9

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ef
fi

ci
en

cy
 %

Month Calculated Average Efficiency

Average Efficiency



Page 34 of 102 
 

The calculated average Efficiency of solar system is 8.18 % for the actual situation. 

However it is close to the findings of researches explained above. Therefore the 

efficiency of solar systems is considered as 9% for the analysis. 

 

4.4 Solar Panel output degradation 

It is very important to predict the power delivery by a solar panel over the lifetime. The 

rate, at which power generation decline over the time is called as degradation rate. It is 

an essential factor to utility companies, integrators, investors, and researchers. Solar 

panels degrade naturally over time because they are designed to react to photons that 

strike the surface. A good quality and well looked after solar panel can last around 20 

years with good output. Technically, degradation mechanisms are important to 

understand because they may eventually lead to failure. Financially, degradation of a PV 

module is important, because a higher degradation rate leads to less power produced 

and, therefore, reduces future cash flows. 

 

According to a Research [1] conducted by NREL, the average degradation rate for PV 

Solar Panel Modules is 1% per year. Therefore panel degradation per year is assumed as 

1% throughout the analysis. 

 

4.5 Sunspot 

Sunspots are cooler regions of the Sun's surface that appear dark against their brighter, 

hotter surroundings. The amount of sunspots on the Sun varies from a minimum to a 

maximum over an eleven year cycle. The reason for this variation has not been found 

yet. Solar irradiance is proportional to sunspot number according to National Oceanic 

and Atmospheric Administration’s (NOAA's) National Geophysical Data Center 

(NGDC). The following graph shows the relationship between Sunspot Number and 

Solar Irradiance. 

 



Page 35 of 102 
 

 

 

Figure 4.9: Relationship between Sunspot number and Solar Irradiance 

  Source: National Geophysical Data Center (NGDC) http://www.ngdc.noaa.gov 

 

Sunspot Numbers and Solar Irradiance from 1997 to 2012 have been studied and the 

graph of Sunspot Cycles and graph of Solar Irradiance were plotted to obtain the 

relationship. 

 



Page 36 of 102 
 

 

 

Figure 4.10: Sunspot cycles from 1997 to 2012 

 

 

 

Figure 4.11: Daily Total Solar Irradiance from 1997-2012 

 

0

20

40

60

80

100

120

140

JA
N

SE
P

T

M
A

Y

JA
N

SE
P

T

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A

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N

SE
P

T

M
A

Y

JA
N

SE
P

T

M
A

Y

JA
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SE
P

T

M
A

Y

JA
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P

T

M
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Y

JA
N

SE
P

T

M
A

Y

JA
N

SE
P

T

M
A

Y

1997199819992000200120022003200420052006200720082009201020112012

N
o

 o
f 

Su
n

sp
o

ts

Year

1364.00

1364.50

1365.00

1365.50

1366.00

1366.50

1367.00

Ja
n

Se
p

M
ay Ja
n

Se
p

M
ay Ja
n

Se
p

M
ay Ja
n

Se
p

M
ay Ja
n

Se
p

M
ay Ja
n

Se
p

M
ay Ja
n

Se
p

M
ay Ja
n

Se
p

M
ay

1997199819992000200120022003200420052006200720082009201020112012

D
ai

ly
 T

o
ta

l S
o

la
r 

Ir
ra

d
ia

n
ce

 (
W

/m
2)

Year



Page 37 of 102 
 

Table 4.3: Variation of Solar Irradiance with the at maximum and minimum Sunspot 

number. 

 

 
Sunspot 

Number 
Solar Irradiance 

Worst year within the sunspot Cycle 1.70 1364.3 W/m
2
 

Best year within the sunspot Cycle 120.80 1366.85 W/m
2
 

 

The total variation of solar irradiances during one sunspot cycle 

                                = (1366.85-1364.3) x100% 

      (120.80-1.70) 

  = 2.14 % 

 

NGDC has predicted Sunspot Numbers expected in future. The data from 1997 to 2032 

(period considered for the research) has been obtained and plotted the following graph. 

 

 

 

Figure 4.12: Variation of sunspot number -from 1997 to 2032 

0

20

40

60

80

100

120

140

JA
N

A
P

R
JU

LY
O

C
T

JA
N

A
P

R
JU

LY
O

C
T

JA
N

A
P

R
JU

LY
O

C
T

JA
N

A
P

R
JU

LY
O

C
T

JA
N

A
P

R
JU

LY
O

C
T

JA
N

A
P

R
JU

LY
O

C
T

JA
N

A
P

R
JU

LY
O

C
T

JA
N

1997 2002 2007 2012 2017 2022 2027 2032

N
o

 o
f 

Su
n

sp
o

ts

Year



Page 38 of 102 
 

There are only two sunspot cycles occurs from 2012 to 2032.According to the above 

finding, the average variation of solar irradiance during a sunspot cycle is about 2%. 

Therefore, effect of Sunspots on Solar Irradiation was neglected and assumed constant 

throughout the study. Solar Irradiance during 20 years period (period of concern for the 

study) is considered as 1366 W/m
2
. 

 

4.6 Tariff Variation 

The electricity tariff of Sri Lanka is increasing rapidly. The following table explains the 

manner it has been increased during last 13 years from year 2000. 

 

Table 4.4: Variation of energy charge for domestic consumer category 

Block 

fr
o

m
 1

-0
6

-2
0

0
0

 

Fr
o

m
 1

-0
4

-2
00

2
 

fr
o

m
 1

-0
8

-2
0

0
2

 

Fr
o

m
 1

-0
2

-2
00

6
 

fr
o

m
 2

0
0

6
-0

9
-0

1
 

fr
o

m
 2

0
0

7
-0

2
-0

1
 

fr
o

m
 2

0
0

8
-0

3
-1

5
 

fr
o

m
 2

0
0

8
-1

1
-0

1
 

fr
o

m
 2

0
1

1
-0

1
-0

1 
 

fr
o

m
 2

0
1

3
-0

4
-2

0
 

0-30 2.40 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 

31-
60 

2.90 
4.00 3.70 3.70 4.70 4.70 4.00 4.70 4.70 4.70 

61-
90 

2.90 
4.40 4.10 4.10 5.10 7.50 5.50 7.50 7.50 12.00 

91-
120 

5.50 10.60 10.60 10.60 12.10 14.00 
10.00 16.00 21.00 26.50 

121-
180 

5.50 10.60 10.60 10.60 12.10 14.00 
11.00 25.00 24.00 30.50 

>180 7.20 15.80 15.80 15.80 17.30 19.80 15.00 30.00 36.00 42.00 

240-
360 

            
18.00 

      

360-
600 

            
21.00 

      

>600             25.00       

 

 



Page 39 of 102 
 

Table 4.5: Variation of fixed charge over years for domestic consumer category 

Block 
from  

2013-

04-20 

from  

2011-

01-01  

from  

2008-

11-01 

from  

2008-

03-15 

from  

2007-02-

01 

from  

2006-09-

01 

from 

 1-02-

2006 

from  

1-08-

2002 

from 

 1-04-

2002 

from  

1-06-

2000 

0-30 30 30 60 60 60 60 60 30 30 30 

31-60 60 60 90 90 90 90 90 30 30 
30 

61-90 90 90 120 90 120 120 120 30 30 

91-120 315 315 180 90 
180 180 180 30 30 30 

121-180 315 315 240 90 

>180 

420 315 240 

90 

240 240 240 30 30 30 
240-360 90 

360-600 90 

>600 
300

0 

 

The following graph describes the block wise tariff variation from year 2000 to 2013 for 

domestic consumers. 

 

Figure 4.13: Block wise variation of Energy Charge for Domestic Consumers 

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

from 
1-06-2000

from
1-02-2006

from 
2008-03-15

from 2013-04-20

En
er

gy
 C

h
ar

ge
 R

s

0-30

31-60

61-90

91-120

121-180

>180



Page 40 of 102 
 

According to the graph 4.13, the tariff of the lowest block remains at Rs: 3.00 for 13 

years. The Tariff up to block 3 has not been changed significantly. However, an 

exponential increase of Tariff from last three blocks can be seen after 2008. 

 

The average tariff escalation rate during period of 13 years (from year 2000 to 2013) is 

calculated using the above information and tabulated below. 

 

Table 4.6: Block wise Tariff escalation rate and % tariff escalation per year 

 

Block 

Tariff 

Escalation 

Rate over 13 

years 

% Tariff 

Escalation 

per year 

0-30 
25.00 1.9230769 

31-60 
62.07 4.7745358 

61-90 
313.79 24.137931 

91-120 
381.82 29.370629 

121-180 
454.55 34.965035 

>180 
483.33 37.179487 

 

Percentage tariff escalation per year has been calculated and forecasted the tariff from 

2014 to 2032. The following graph shows the forecasted tariff for each block up to 2032.  



Page 41 of 102 
 

 

Figure 4.14: Forecasted Block wise Tariff from 2013 to 2032 

The above projected tariff has been used to calculate the financial indices. 

 

4.7 Lifetime of Solar PV 

The financial life for a solar PV system is usually considered to be the manufacturer’s 

guarantee period which is often 20 to 25 years. However, research has shown that the 

life of solar PV panels is well beyond 25 years. Life times of 30 year or more is realistic.  

 

For solar PV, the Operation and maintenance costs are due to replacing inverters 

(usually every 10 years), occasional cleaning and electrical system repairs. Economic 

life of the system depends on the acceptable energy output, which depends on the 

degradation rate (rate at which there is a reduction in output). It has been found that, 

more than 65.7% of panels are below the 1% per year degradation rate. The end of life 

of the system has not been reached once the power output still satisfies the user. Gradual 

degradation occurs due to chemical and material processes associated with weathering, 

oxidation, corrosion, and thermal stresses. Considering above factors, the average 

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

20
13

20
14

20
15

20
16

20
17

20
18

20
19

20
20

20
21

20
22

20
23

20
24

20
25

20
26

20
27

20
28

20
29

20
30

20
31

20
32

En
e

rg
y 

C
h

ar
ge

 R
s 

Year

0-30

31-60

61-90

91-120

121-180

>180



Page 42 of 102 
 

economic life for rooftop solar PV modules are taken as 20 years for economic 

calculations. 

4.8 System Costs 

The cost of Solar Panels has been reduced drastically during last few years. The main 

reason behind that is the introduction of Chinese products to the market. As the demand 

for systems rises and manufacturing volume increases, costs will decrease. It leads to 

reduction of economic payback time of future investments. The following graph shows 

the variation of the cost of Crystalline Silicon PV Cells with time. 

 

 

 

                        Figure 4.15: Price reduction of Crystalline PV Cells 

                        Source: International Energy Agency: www.iea.org 

 

0

10

20

30

40

50

60

70

80

90

1
9

7
7

1
9

7
9

1
9

8
1

1
9

8
3

1
9

8
5

1
9

8
7

1
9

8
9

1
9

9
1

1
9

9
3

1
9

9
5

1
9

9
7

1
9

9
9

2
0

0
1

2
0

0
3

2
0

0
5

2
0

0
7

2
0

0
9

2
0

1
1

2
0

1
3

P
ri

ce
 P

er
 W

at
t 

($
) 

Year
Price of Crystalline Silicon 
Photovoltic Cell $ per Watt



Page 43 of 102 
 

As seen from the above figure, solar cell prices have come down by a factor of 100 over 

the last 35 years. The 2013 average price expected was to be $.0.74. The sharp drop has 

been occurred due over production, especially in China, which has caused prices to 

collapse. The Figure 4.16 shows the Price variation of Chinese Solar Cells during last 

few years. 

 

Figure 4.16: Price variation of Chinese Solar Cells 

Source: International Energy Agency: www.iea.org 

As seen from the Figure 4.16, recent solar cell prices have had a dramatic price 

reduction. From 2006 to 2011, a five year span, Chinese "cell" prices have dropped 80% 

from $4.50 per watt to about $.90 per watt, an incredible drop. The main reason 

crystalline silicon cell prices dropped was because the price of the raw material poly 

silicon, which makes up a very significant part of the total cost, dropped so 

tremendously. 

In addition to the poly silicon issue, the decline is also being driven by the increasing 

efficiency of solar cells and dramatic manufacturing technology improvements.  

The price of a PV system depends on the overall size and complexity of the system.In 

order to begin the calculations, at first, it is required to determine the initial costs. The 

cost of solar panel depends on the capacity, technology and the manufacturer’s identity. 

The initial cost components that make up a residential solar system are: system design, 

0

1

2

3

4

5

2006 2007 2008 2009 2010 2011 2012 2013

C
o

st
 ($

/W
)

Year

$ /W



Page 44 of 102 
 

solar modules, inverter, bi-directional billing meter, connection devices, and installation 

labor. 

 

The total average cost of solar systems installed in Sri Lanka during the period of 

research is shown in Table 4.5.The data has been obtained from Solar System solution 

providers in Sri Lanka and interviewing consumers who has already made the 

investments on Soalr Systems. 

Table 4.7: Average Cost of installed Solar Systems 

Capacity of 

the System 

kW 

Average Cost 

LKRs 

Capacity of 

the System 

kW 

Average Cost 

 LKRs 

1 650,000 4.5 1,225,000 

1.5 700,000 5 1,300,000 

2.2 750,000 5.5 1,450,000 

2.5 850,000 6 1,650,000 

2.8 870,000 6.5 2,000,000 

3 900,000 8 2,700,000 

3.25 950,000 10 3,350,000 

3.7 1,100,000 12 4,200,000 

4 1,130,000 16 5,750,000 

 

 

Figure 4.17: Average Cost of installed Solar Systems 

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

7,000,000

0 2 4 6 8 10 12 14 16 18

A
ve

ra
ge

 C
o

st
/L

K
R

System Capacity/kW



Page 45 of 102 
 

According to Figure No 4.17, the rate at which the module cost increase is sharp when 

the System capacity exceeds 6kW. 

 

The initial cost of installation is the cost for year zero. Normally inverter has to be 

replaced after 10 years of the operation. The electrical installation cost and annual 

maintenance cost should be taken into account for determining the system cost. 

Therefore, following assumptions are incorporated with the system cost calculation. 

System Cost is calculated based on following assumptions. 

• Lifetime of the System is 20 years. 

• The inverter is replaced at the 10
th
 year. The cost of replacement is 10% of the 

initial investment. 

• The annual maintenance cost is 1% of the total initial cost. 

Then, the total cost of the system throughout the life time is calculated. 

 

4.9 Discount Rate  

The discount rate refers to the interest rate used in discounted cash flow (DCF) analysis 

to determine the present value of future cash flows. The discount rate in DCF analysis 

takes into account not just the time value of money, but also the risk or uncertainty of 

future cash flows; the greater the uncertainty of future cash flows, the higher the 

discount rate. For any single project the discount rate will affect whether the NPV is 

greater or less than zero. For comparing projects, the discount rate will affect the NPV 

ranking. For the analysis, the discount rate is considered as 6%.  

  



Page 46 of 102 
 

Chapter 5 

ECONOMIC ANALYSIS 

 

Solar PV systems vary greatly in size and cost. Calculating the economics of a Solar 

System is a key to understanding whether a solar system is right for the investors. It is 

very useful to households and Industries considering the installation of a solar energy 

system as a means of cutting their utility bills.  

 

Standard financial analysis is applied to compute Payback Period, Net Present Value 

(NPV) and Internal Rate of Return (IRR). This study is focused on residential rooftop 

PV Systems in Sri Lanka.  

Economic calculations are done based on following assumptions. 

• Lifetime of the System is 20 years. 

• The inverter is replaced at the 10
th
 year. The cost of replacement is 10% of the 

initial investment. 

• The annual maintenance cost is 1% of the total initial cost. 

• The efficiency of the solar panel is 9%. 

• Electricity tariff escalation is in accordance with Table No: 4.6 & Figure No: 

4.14. 

• Discount rate is 6%. 

• Panel output degradation rate is 1% per year. 

• Average Solar irradiance is constant over 20 years period. 

 

5.1 Simple Payback Period 

It is essential to determine the ability of consumers to afford solar panels and how long it 

takes to meet their initial investment. With basic information on the system price, and 

the value of the electricity generated, it is possible to calculate the payback time on the 

investment using discounted cash flow analysis. As the demand for system rises and 



Page 47 of 102 
 

manufacturing volume increases, costs of solar panels will decrease and the economic 

payback time will also decrease for future investments. 

The simplest financial metric is simple payback period. This is simply the number of 

years in the future when the sum of the expenses (negative cash flows) is equal to the 

sum of the income/savings (positive cash flows). If the expense is all up-front, and the 

income/savings are consistent year-to-year, payback period can be calculated with 

simple division: 

 

𝐒𝐢𝐦𝐩𝐥𝐞 𝐏𝐚𝐲𝐛𝐚𝐜𝐤  𝐏𝐞𝐫𝐢𝐨𝐝 =
𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭

𝐈𝐧𝐜𝐨𝐦𝐞 𝐨𝐫 𝐒𝐚𝐯𝐢𝐧𝐠𝐬
 

 

This form of the metric is widely used due to its simplicity, despite its limitations. Since 

the future savings generated by a solar system are unlikely to be constant and because it 

ignores the time-value of money, this metric is not exactly accurate for this kind of 

analysis. 

 

5.2 Simple Payback Period calculation 

 

Step 1: Calculating the cost of Investment 

The total investment is the summation of initial cost, maintenance cost and the inverter 

replacement cost. Initial cost includes the cost of purchasing solar panels and the cost of 

installation. 

 

Step 2: Calculating System Savings 

The next step in a payback calculation for a PV system is to figure out how much money 

the system will save each month. The output of a PV system can be estimated with good 

accuracy based on the specifications of the system installation and the solar insolation 

data. 

 



Page 48 of 102 
 

Following data is available with the System 

 Monthly output of the Solar  PV System. (G) 

 Average household consumption (from Solar PV). (C) 

 The import register of the Energy Meter records the number of units have been 

imported (I) 

 The export register records the exported units (E) 

 

Monthly Billing Units with Net Metering  = I-E 

Monthly Billing Units without Net Metering  = C+I 

 

The Energy Bill with Net Metering and without Net Metering can be calculated 

separately. Based on this analysis, annual cost savings for the system can be 

determined.  

 

It is required to know how much energy that solar panels are going to produce during the 

20 years of life time.  For a worst case scenario, 80% of that number is taken. 

The expected monthly electricity bill without Solar Net Metering and with Solar Net 

Metering was calculated separately. The difference shows the amount can be saved per 

month.  

 

Step 3: 

Now for the final step, the cost for initial installation adds discounted annual 

maintenance cost and inverter replacement cost and divides it by the yearly savings. The 

value is the payback period in years. This number represents the number of years it will 

take to recoup the investment.  

 

After this number of years, the monthly saving is the profit to customer. Obviously it 

shows how the customer can save money in the long run by investing in green energy. 



Page 49 of 102 
 

The less expensive the solar system and the higher the regular electricity rate, the faster 

the payback can be achieved on the system. 

 

1kW Solar Panel 

 

Figure: 5:1 Payback Period of 1kW Solar Panel 

 

2kW Solar Panel 

 

Figure 5:2:  Payback Period of 2kW Solar Panel 

0

5

10

15

20

25

30

35

40

45

50

80 130 180 230 280

P
ay

b
ac

k 
P

e
ri

o
d

/Y
e

ar
s

Monthly Consumption /kWh

0

5

10

15

20

25

30

35

40

45

50

70 90 110 130 150 170 190 210 230 250

P
ay

b
ac

k 
P

er
io

d
/Y

e
ar

s

Monthly Consumption /kWh



Page 50 of 102 
 

3kW Solar Panel 

 

 

Figure 5.3: Payback Period of 3kW Solar Panel 

 

According to above graphs, Payback period falls below 20 years when the monthly 

consumption exceeds 170 kWhs. People who consume less than that are unable to 

benefit financially.  

When all the data is plotted in a single graph irrespective of the Solar panel capacity, 

following graph can be obtained. 

0

5

10

15

20

25

30

35

40

45

50

70 90 110 130 150 170 190 210 230 250 270 290

P
ay

b
ac

k 
P

e
ri

o
d

/Y
e

ar
s

Monthly Consumption/kWh



Page 51 of 102 
 

 

 

Figure 5.4:  Payback Period of Solar Panel against monthly consumption 

 

As shown in above graph, payback period varies with the net electricity consumption. 

Consumers who consume fewer units are not likely to get benefits. It means that they are 

unable to meet their investment during the lifetime of the solar panel. Since we consider 

the life time of solar panels is 20 years, very few numbers of consumers are getting the 

benefit. That group is the people who consume more than 170 kWh per month. 

 

5.2.1 Sensitivity Analysis 

Above calculation has been performed assuming that the tariff variation in future is at 

the same rate at which it increased in last 13 years from 2000. During last 13 years, the 

power sector was highly depending on fossil fuel burn thermal plants where the 

operating cost is extremely high. But Sri Lanka is rapidly moving towards Coal fired 

plants  while existing thermal plants are retiring from the system.CEB has already 

commissioned the 900MW Coal power plant in Norochchole. A new coal plant in 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

60 80 100 120 140 160 180 200 220 240 260 280 300

P
ay

b
ac

k 
P

e
ri

o
d

/Y
e

ar
s

Monthly Consumption/kWh



Page 52 of 102 
 

Sampur is under construction. According to the long term Generation expansion plan 

prepared by Generation Planning Division of CEB, there are 11 Nos of Coal fired plants 

in the pipeline which will contribute 3700 MW to the System at the end of year 

2032.Therfore the unit cost of generation will reduce and the tariff variation may deviate 

from the rate at which it varied from Year 2000 to 2013. 

 

Considering above facts, a sensitivity analysis is performed and calculate the Payback 

Period considering two scenarios of Tariff variation. 

1. Tariff is constant from Year 2013. 

2. Tariff escalation rate is half of the rate at which tariff has increased during last 13 

years (Year 2000-Year 2013). 

 

Scenario 1: Tariff is constant from Year 2013 

Payback period has been calculated assuming that the electricity tariff announced for 

year 2013 is remained unchanged throughout the lifetime of the solar systems 

considered. 

 

Figure 5.5:  Payback Period assuming constant tariff 

0

5

10

15

20

25

30

35

40

45

50

60 80 100 120 140 160 180 200 220 240 260 280 300

P
a

yb
a

ck
 P

e
ri

o
d

/Y
e

a
rs

Monthly Consumption/kWh



Page 53 of 102 
 

Scenario 2: Half of the rate at which tariff has increased during last 13 years (Year 

2000-Year 2013). 

Payback period has been calculated assuming that the tariff escalation rate for next 20 

years is half of the rate at which it increased during last 13 years. 

 

Table 5.1 Tariff escalation rate used for the sensitivity analysis 

Block 

% Tariff 

Escalation per 

year 

Half of the % 

Tariff 

Escalation per 

year 

0-30 1.92 0.96 

31-60 4.77 2.38 

61-90 24.13 12.06 

91-120 29.37 14.68 

121-180 34.96 17.48 

>180 37.18 18.59 

 

 



Page 54 of 102 
 

 

Figure 5.6:  Payback Period assuming tariff escalation rate is half of the rate at 

which tariff has increased during last 13 years (Year 2000-Year 2013). 

 

Table No 5:2 summarizes the Payback Period variation with variation of average 

monthly consumption for domestic consumers. 

 

Table 5:2 Payback Period of variation with different tariff scenarios 

 

Average 

monthly 

Consumption 

/kWh 

Payback Period /Years 

Tariff escalation 

rate is as same as 

last 13 years 

Constant tariff 

from year 

2013 

tariff escalation rate is half 

of the rate at which tariff 

has increased during last 13 

years 

90 41 45 43 

120 31 36 34 

150 24 29 26 

180 17 24 22 

0

5

10

15

20

25

30

35

40

45

50

80 100 120 140 160 180 200 220 240 260 280 300

P
ay

b
ac

k 
P

e
ri

o
d

/Y
e

ar
s

Monthly Consumption/kWh



Page 55 of 102 
 

 

Payback 

Period /Years 

 

Average monthly Consumption /kWh 
Tariff 

escalation 

rate is as 

same as last 

13 years 

Constant tariff 

from year 2013 

tariff escalation rate is half of 

the rate at which tariff has 

increased during last 13 years 

10 230 290 260 

20 170 190 180 

 

According to the above table, installing rooftop solar panels with net metering facility is 

marginally profitable for consumers who consume more than 190 kWh per month 

irrespective of the tariff escalation mechanism. A financial project is said to be 

economical when the investment can be recovered within 10 years. Therefore, according 

to above table, rooftop solar net metering is financially beneficial for consumers who 

exceed their monthly consumption 290 kWh even the electricity tariff is remain constant 

for the next 20 years period. 

 

5.3 Net Present Value 

One of the most recognized metric for capital projects such as solar systems is Net 

Present Value (NPV). This is more complex than calculating payback period, but 

provides better information. It may be unclear what payback period is acceptable, but 

NPV provides the actual value of completing a project. NPV also recognizes the time 

value of money. NPV is simply the sum of all cash flows (positive and 

negative),discounting future cash flows for the present. It can be calculated by the 

following formula. 

 

𝑁𝑃𝑉 = 
Ct

(1 + r)t

𝑛

𝑡=0

 

Where, 



Page 56 of 102 
 

t - Time of the cash flow 

n - Total time of the project 

r - Discount rate (the rate of return that could be earned on an investment in the 

financial markets with similar risk.) 

Ct - Net cash flow (the amount of cash) at time t. 

 

Using the above formula, NPV of domestic solar systems has been calculated. The 

savings due to solar PV is calculated for each year of the solar PV system lifetime and 

when discounted, savings at present value can be obtained. This is nothing but the NPV 

of the yearly savings. From the concept of NPV, if the value of NPV is positive then the 

system is making a benefit. Hence the system with higher NPV savings per kWh is the 

best system. The larger the NPV, the greater the total savings can be expected. 

 

5.4 Calculation of NPV  

Step 1: 

In order to calculate the NPV, a spreadsheet was set up displaying the financial 

information. The NPV is calculated using the cumulative cash flows for the 20 years. A 

table was created to show the system cost, annual cash Inflows (bill saving due to solar 

PV Net metering), annual cash Outflows (System maintenance cost, Inverter 

replacement cost, etc),The system cost is a one-time cost in the year zero. 

 

Step 2: 

The future cash flows are discounted to year 0 assuming 6% discount rate using the 

following formula. 

𝑁𝑃𝑉 =
Ct

(1 + r)t
 

The discount rate is an estimate based on the bank interest rate. The current average 20-

year loan rate is approximately 6%, which for this study will be rounded to 6%. 

NPV of the annual cash flows are calculated and hence obtained the cumulative NPV. 



Page 57 of 102 
 

The annual bill saving by installing rooftop solar Panels were calculated using the 2013 

electricity tariff. However, using that number every year for 20 years would be 

unrealistic and thus the electricity tariff escalation rate should be accounted for starting 

in year one. Due to the complexity of the calculation, assume that electricity tariff 

remains constant for entire life of solar Panel. 

 

Step 3: 

Cumulative NPV was calculated and the result is the NPV of the entire project. The 

following table shows a sample NPV calculation. 

Example: 

Capacity of the Solar Panel  : 1kW 

Net consumption per month  : 80 Units 

Discount Rate    : 6% 

 

 

It has been considered several consumers who have various amounts of electricity 

consumption during the month. Assuming that each consumer has installed 1kW Solar 

Panel in their rooftops and NPV was calculated. 

 

Table 5:3:  NPV of several consumers after installing 1kW Solar Panel 

 

Monthly Consumption  

kWh NPV 

80 (548,907.40) 

90 (455,037.56) 

120 (345,200.36) 

150 (218,432.60) 

175 (95,937.27) 

180 (75,704.33) 

190 1,511.18 

200 64,274.59 



Page 58 of 102 
 

According to above table, it is obvious that installing rooftop solar panels on net 

metering concept is economical when the consumption is more than 190 kWh per 

month. 

It has been found that, the cumulative NPV becomes positive after the year 15 for the 

consumer who consumes 190kWh per month.. This shows that after 15 years, the solar 

panel system will begin operating as a positive cash flow and no longer a financial 

burden.  

 

5.5 Calculation of Internal Rate of Return (IRR) 

IRR of a series of cash flows is the discount rate that would set the NPV to zero. This 

metric is commonly used for project accept/reject decisions. The advantage of using IRR 

vs. NPV is that the analysis can be done without choosing a specific discount rate.  

 

IRR is the discount rate that makes the net present value of all cash flows from a 

particular project equal to zero. When the IRR of particular project is high, it is more 

desirable to undertake the project. For example, an IRR of 12% means the consumer 

makes a profit of 12% per year on   the investment. 

 

IRR has been calculated for several consumers who have installed 1kW solar panel with 

various monthly consumption patterns. 

Table 5:4:  IRR of 1kW rooftop Solar Panels 

Monthly Consumption /kWh IRR 

80 -5.0% 

90 -4.2% 

120 -3.4% 

150 -1.2% 

175 4.6% 

180 6.2% 

190 6.5% 

200 6.8% 

 



Page 59 of 102 
 

When the average monthly consumption exceeds 175 kWh, the IRR becomes positive 

and the investment becomes profitable. 

Table 5.5 summary of economic calculations 

Monthly 

Consumption/ 

kWh 

Payback Period/Years 

(Assuming Tariff Remains 

Constant) NPV IRR 

80 49 (548,907.40) -5.0% 

90 45 (455,037.56) -4.2% 

120 36 (345,200.36) -3.4% 

150 29 (218,432.60) -1.2% 

175 26 (95,937.27) 4.6% 

180 24 (75,704.33) 6.2% 

190 20 1,511.18 6.5% 

200 19 64,274.59 6.8% 

 

According to Table 5.5, rooftop solar net metering system is financially viable when 

monthly consumption exceeds 190 units where payback period is 20 years while NPV is 

positive and IRR is positive and close to discount rate. 

 

 

 

 

 

 

  



Page 60 of 102 
 

Chapter 6 

AVOIDED COST 

 

6.1 Introduction 

When the power generated from rooftop solar Panels are connected to the grid, some 

costs are avoided elsewhere in the system. The power plants, which would normally 

have produced the power (now being reduced due to solar power), reduces its power 

output. It will be the benefit to the utility. There are several potential costs avoided due 

to rooftop solar net metering. 

Table 6.1: potential costs avoided due to rooftop solar net metering 

 

Avoided Cost is the cost an electric utility would otherwise incur to generate power if it 

did not purchase electricity from another source.  

 

Avoided cost is the incremental cost to the electric utility that the utility would either 

generate itself or purchase from thermal IPPs if it did not purchase from a renewable 

energy producer. In the avoided cost mechanism, when a renewable energy generator is 

connected to the grid, some costs are avoided in the system. First of all, the power plant, 

which would normally have produced the power (now being provided by the renewable 

source), saves some fuel and operation costs because it reduces its power output.  

Avoid Cost Description 

Avoided energy cost All fuel, variable operation and maintenance costs and any 

charges associated with the marginal unit generation costs. 

Avoided Transmission 

and Distribution 

Capacity 

Contribution to deferring the addition of transmission and 

distribution resources needs to serve load points, far reaching 

resources, or elsewhere 

Avoided environmental 

pollution 

Reduction of greenhouse gas emission from operating of the 

marginal units. 

Avoided Outages Costs Estimated cost of power interruptions that may be avoided by 

rooftop solar generation that are still able to operate during 

outages 



Page 61 of 102 
 

Avoided cost of energy represents the maximum value of generation avoided by CEB as 

a result of any purchase of energy from sources outside the CEB system. This value is 

usually equal to the value of one unit of energy (kWh) displaced at the margin by a unit 

of energy purchased from such sources. According to this definition, the avoided cost of 

a unit of electricity comprises fuel and variable O&M costs of generation displaced at 

the margin by a unit purchased at a given instant. This is generally the cost of the most 

expensive unit being generated at that instant.  

 

6.2 Fuel used in various power plants: 

Presently petroleum based fuels and coal are the only few feasible fuel options for 

thermal power generation in Sri Lanka. Other fuel options such as Liquid Natural Gas 

(LNG) and Nuclear are being studied. During past two years, global fuel prices have 

fluctuated drastically. Therefore it is impossible to predict the future fuel prices. It is 

assumed that fuel price remains constant during the period of concern. 

 

Following table shows the price of each fuel (Rs/Liter/kg) which is in effect on February 

2013 which used to generate electricity in CEB owned and IPP thermal power plants. 

Table 6:2 Fuel Prices effect from February 2013 

Fuel Type 
 Fuel Prices 

(Rs/Liter, kg)  

Lanka Heavy Fuel                               90 

Lanka Disstillate Fuel 121 

Naptha 90 

Lanka Furnace Oil 92 

Coal               15.11 

 

 

6.3 Determination of fraction of time each Power Plant in Margin 

To estimate the fraction of time each power plant is operating in margin, it is required to 

find the plant factors of power plants. 



Page 62 of 102 
 

Plant factor is a value used to express the average percentage of full capacity used over a 

given period of time. For example, a power plant which operates at an average of 60% 

of its normal full capacity over a measured period has a plant factor of 0.6 for that 

period. To calculate the plant factor, take the total amount of energy the plant produced 

during a period of time and divide by the amount of energy the plant would have 

produced at full capacity. The plant factor of a power plant is the ratio of the actual 

energy output of the power plant over a period of time to its potential output if it had 

operated at full nameplate capacity the entire time.  

Plant Factors vary greatly depending on the type of power plants and it is calculated 

according to the following formula. 

Plant Factor = Actual Energy Production during the Nominal Period 

Potential Energy Production during the Period 

 

6.4 CEB Dispatch Schedule 

Dispatch schedule is prepared monthly basis based on the expected fuel prices, 

availability of hydro capacities, machine maintenance schedule, and expected System 

Demand. Generators are connected and disconnected or 'dispatched' manually by the 

CEB, based on a 'merit order' which lists plants from the cheapest to the most expensive. 

 

CEB has to follow the power demand in the country by varying its generation 

accordingly, matching consumer demand. During the early morning and the day time, 

CEB has lot of generating options as the demand is much lower than the combined 

generating capability called the installed capacity of the CEB. Thus, the CEB can afford 

to generate from the cheapest sources without resorting to expensive ones. CEB start 

generation from the cheaper plants first and go on adding more and more expensive 

generation as the demand goes up. This is called Merit Order Dispatch. When the 

demand drops, the reverse activity takes place and the CEB reduces generation starting 

from the most expensive power plant. 

http://en.wikipedia.org/wiki/Energy


Page 63 of 102 
 

As it stands in 2013, capacity of total CEB hydro is 1356 MW. Since present generation 

is varying from 2160 MW to 900 MW, theoretically there is a possibility of meeting the 

total demand in certain times of the day only with CEB hydro power plants, However, 

System Control Center dispatches CEB hydro power plants to optimize the available 

CEB hydro generation. Therefore, under normal circumstances System Control Center 

dispatches CEB hydro for its full capacity to meet peak load and keep the hydro 

generation at low level during off peak hours to preserve water in the reservoirs for peak 

operation. In order to reflect the true avoided cost of generation from renewable energy 

sources, summation of fractions of time in margin should be decided for each year 

separately. For current generation mix, this value should be very much close to 1. CEB 

implements a generation dispatch schedule every 6 months before operation. It contains 

the amount of energy to be produced by each power plant for the coming year. Due to 

various reasons the actual dispatch could be deviated from this schedule. 

 

6.5 Avoided Cost Calculation methodology 

Step 1-Calculation of Unit cost of generation in each thermal plants 

The average cost of generation of each thermal plant (CEB owned and IPPs) is 

calculated based on the data on annual generation and the total cost to the CEB. The 

following table summarizes the average Unit cost of Generation of CEB operated and 

Private Thermal Power Plants in 2013. 

 

 

 

 

 

 

 

 

 



Page 64 of 102 
 

Table 6.3: Generation cost of CEB Thermal Plants 

Power Plant Average Unit 

Cost Rs/kWh 

KPS GT - 5 x 17 MW 56.45 

KPS Combined  - 165 MW 31.10 

KPS GT- 115 MW 30.66 

AES Kelanitissa - 165 MW 29.87 

Kerawalapitiya DPP- 270 MW 24.48 

ASIA Power - 51 MW 22.04 

LakdanaviSapu. - 225 MW 19.68 

ACE - Horana - 24.8 MW 18.84 

ACE - Embilipitiya - 99 MW 18.77 

Barge - 60 MW 18.30 

ACE - Matara - 24.8 MW 17.94 

Heladhanavi - 99 MW 16.92 

Sapu Old - 4 x 18 MW 13.97 

Sapu Ext.- 8 x 9 MW 12.75 

Coal Puttalam- 300MW 7.67 

 

After calculating the average unit cost of each thermal plant in the system, thermal 

plants would be sorted in descending order based on their average unit cost. Following 

Graph shows the average unit cost of each thermal plant in descending order. 



Page 65 of 102 
 

 

 

 Figure 6.1:  Average Unit Costs of Thermal Power Plants in 2013 

Source: Generation Performance Report 2013 prepared by PUCSL 

 

6.6 Dispatch Schedule 

When dispatching power plants to meet the demand, System Control dispatches power 

plants based on merit order. Thus, most expensive thermal power plant is to dispatch as 

the last option to meet the demand by keeping hydro generation capacities at optimum 

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Page 66 of 102 
 

level. Dispatching and backing off power plants to meet the demand is a complex real 

time exercise done by System Control engineers.  

 

The Systems Control Centre uses the short term planning model called the METRO 

model, which provides estimates of energy expected to be delivered from each power 

plant during each month of the particular year. It is implemented every 6 months prior 

operation. While estimating the energy expected to be delivered by a particular plant, the 

model optimizes various power plants based on the generation cost along with other 

constraints and inputs in the model. Due to numerous reasons the actual dispatch is 

deviated from this schedule. The dispatch schedule of CEB for the year 2013 is as 

shown in Table 6.4. 



Page 67 of 102 
 

Table 6.4 Dispatch Schedule 2013 

No Plant Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total 

1 Heladhanavi - Pul - 99 MW 59.1 62.1 70 59.7 60.1 54.9 60 60 58.1 60 58.1 60 722.1 

2 ACE - Embilipitiya - 99 MW 59.3 53 58.5 48.4 63.7 56.1 54.5 54.5 52.6 54.5 52.5 54.5 662.1 

3 Barge - 60 MW 42.8 40.1 43.1 41.3 44 40.9 42 42 40.6 42 40.6 42 501.4 

4 ACE - Horana - 20 MW 17.7 16.6 17 13.8 16.9 17.2 17 17 16.4 17 16.4 11.5 194.5 

5 ACE - Matara - 20 MW 17.1 15.8 10 0 0 0 0 0 0 0 0 0