A case study on the combined system of a rooftop photovoltaic system and an electric
vehicle in Sri Lanka
Olivia Zetterberg Ellen Ejnarsson
Bachelor of Science Thesis
KTH School of Industrial Engineering and Management Energy Technology EGI-2018
TRITA-ITM-EX 2018:412 SE-100 44 STOCKHOLM
Bachelor of Science Thesis EGI-2018 TRITA-ITM-EX 2018:412
A case study on the combined system of a rooftop photovoltaic system and an electric vehicle in Sri Lanka
Olivia Zetterberg Ellen Ejnarsson Approved
2018-06-08
Examiner Peter Hagström
Supervisor Per Lundqvist
Commissioner Contact person
Abstract
The battle for a better environment is taking place all over the world. A lot of countries have set their own climate targets of which a large number of nations made contributions during the climate conference in Paris 2015. One nation among these were Sri Lanka.
Sri Lanka is under development to become a middle-class income country and to do this in an environmentally friendly way, one of the nation's goals is to increase the share of renewable energy and reduce dependence on fossil fuels. To achieve these goals, the government has a strategy to increase, among other things, the proportion of solar systems connected to the private sector and incentives for a switch to electric vehicles.
To investigate whether these technologies are financially worth implementing for individual households based on the conditions of today, a case study has been conducted of two Sri Lankan households. One household has a rooftop solar photovoltaic (PV) system connected to the national power grid and possesses an electric car of model Nissan Leaf 2014. The other evaluated household wishes to make this investment of a rooftop PV system instead of purchasing all electricity from the grid and a switch from a gasoline car to an electric car. The purpose of the study is to increase understanding of whether it is financially reasonable to make this investment with both a PV system and an electric vehicle for households in Sri Lanka.
By collecting and analyzing data on electricity bills, solar cell generation, car costs, driven mileage and more, financial calculations have been made. The methods used for the calculations were the payback method and the net present value in which two different life cycles were adopted for the combined system of solar cells and an electric vehicle of nine years respectively 15 years. The longer life cycle includes battery replacement on the electric car opposed to the shorter life cycle. The net present value of the nine year life-cycle was calculated to the range between negative 3 MLKR and negative 4,7 MLKR while the net present value of the 15 year life cycle resulted between negative 3,4 MLKR and negative 5,2 MLKR. The variations come from different energy usage and driving
patterns which proved to have a great impact of the investment’s payoff. The longer life cycle includes battery replacement on the electric car opposed to the shorter life cycle. The fact that both calculated scenarios showed negative results implies that the investment is not recommended financially. The payback period for the investment was estimated to be between 21,2 and 38,2 years.
The sensitivity analysis showed that the chosen parameters that were evaluated had a great impact on the results, which indicates that these needs to be chosen carefully. The conclusion can be drawn that the investment will not be financially favorable for the evaluated private households, since the time value of money today of the investment, according to the net present value analysis, is less than the initial cost. Even if the investment had shown to be financially favorable, because of the high initial costs only a small part of Sri Lanka's population would afford to make the investment. However, with the government's continued promotion work and proposals for introducing beneficial taxes and substitutions while technology is continually becoming cheaper, the situation may look different in the coming future. To investigate Sri Lanka's opportunities for a wider range of households making these investments, further research is required.
Sammanfattning
Kampen för en bättre miljö pågår världen över. Många länder har satt upp egna klimatmål varav ett stort antal nationer stiftade sina bidrag under klimatkonferensen i Paris 2015 och däribland Sri Lanka.
För att fortsätta utvecklingen mot att bli ett medelinkomst-klass-land och göra detta på ett miljösmart sätt är ett av Sri Lankas nationella mål att öka andelen förnybar energi samt minska beroendet av fossila bränslen. För att nå dessa mål har regering en strategi vilken bland annat innebär att öka andelen solcellssystem kopplade till nätet i den privata sektorn. Stategierna består även av incitament för en övergång till elfordon.
Rapporten består av en fallstudie på två Sri Lankesiska hushåll med syfte att undersöka om
solcellspaneler och en elbil är finansiellt värda att investera i för privata hushåll utifrån landets system med befintliga teknologier idag. Det ena hushållet har ett solcellssystem på taket kopplat till det nationella elnätet samt en elbil av modell Nissan Leaf 2014. Det andra analyserade hushållet önskar göra denna investering och således övergå från bensinbil till elbil samt installera solceller istället för att köpa all elektricitet från elnätet.
Genom att samla in och analysera data gällande elräkningar, generering från solcellssystemet,
bilkostnader, antal mil körda med mera har finansiella beräkningar gjorts. Metoderna som använts för beräkningarna är payback metoden samt nuvärdesanalys där två olika livscykler antagits för det kombinerade systemet av solceller samt elbil på nio år respektive 15 år. Den längre livscykeln inkluderar batteribyte på elbilen till skillnad från den kortare livscykeln. Nuvärdet för investeringen med livscykel på nio år ligger i intervallet från negativa 3 MLKR till negativa 4,7 MLKR medan nuvärdet för livscykeln på 15 år ligger mellan negativa 3,4 MLKR och negativa 5,2 MLKR.
Variationen på intervallen beror på olika energianvändning samt körmönster. Då båda scenariona visar negativa resultat indikeras att investeringen ej rekommenderas ur finansiell synpunkt. Payback perioden för investeringen beräknas till mellan 21,2 och 38,2 år. Känslighetsanalyser visade att de undersökta parametrarna hade stor inverkan på resultatet, vilket betyder att de bör bestämmas med bästa möjliga noggrant. Slutsatsen kan dras att investeringen inte kommer vara ekonomiskt gynnsam för de utvärderade hushållen då förtjänsten på investeringen enligt nuvärdesanalysen är mindre än de initiala kostnaderna. Även om investeringen skulle vara finansiellt positiv är de initiala kostnaderna höga vilket innebär att enbart en liten del av Sri Lankas befolkning skulle ha råd att göra
investeringen.
Med regeringens fortsatt främjande arbete och förslag på införande av fördelaktiga skatter och substitut samtidigt som tekniken kontinuerligt blir billigare kan situationen komma att se annorlunda ut i framtiden. För att undersöka Sri Lankas möjligheter till att en större mängd privata hushåll ska kunna göra en liknande investering krävs ytterligare forskning.
Acknowledgements
We would like to show our deepest appreciation to some key people that have helped us in the process of writing our bachelor theses in Kandy, Sri Lanka:
Dr Primal Fernando for accepting us to come to Kandy and helping us through the whole process of the paper. Thank you for kindly taking care of us during our visit and always showing great support and willingness to discuss our work at any time. Our stay in Sri Lanka would not have been the same without you.
Dr Pujitha Dissanayake for letting us come to your home and introducing us to your PV-system.
Thank you for providing us information on your electric vehicle and PV system, and taking your time for discussions on our work.
Finally, we would like to thank our supervisor at the Royal Institute of Technology, Professor Per Lundqvist, for taking his time to discuss our project and giving us feedback and guidance during our work.
Table of content
1. Introduction 17
1.2 Research question and Goal 17
1.2.1 Purpose 17
1.2.2 Research question 17
To help local research about two of Sri Lanka’s major barriers, which is high cost of renewables and lack of local renewable capacity development, the goal of the study is to give a ground of greater understanding for the possibilities private households have to invest in a renewable system to increase the participation of the private sector in the country’s sustainable
development. 18
2. Literature Study 19
2.1 Climate change and renewable energy 19
2.2 The future of renewables 19
2.3 Energy Mix in Sri Lanka 20
2.4 Grid connected PV Rooftop system 22
2.5 Transport in Sri Lanka 22
2.6 Electric vehicles in Sri Lanka 23
2.6.1 Electric Vehicle Charging Stations 23
2.6.3 EV model - Nissan Leaf 23
2.6.4 Fuel Economy 24
2.7 Economic and social development in Sri Lanka 25
3. Method 28
3.1 Limitations 29
3.2 System Description 30
3.2.1 Grid-connected PV Systems 30
3.2.2 Net Metering 31
3.3 Case Study 32
3.3.1 Scenario one - Dissanayake household 32
3.3.2 Scenario two - Fernando household 33
3.4 Parameter Variation 33
3.4.1 Driving distance evaluation 34
3.4.1 Life-cycle evaluation 34
4. Models 35
4.1 Explanation of Models 35
4.1.1 Net Present Value 35
4.1.2 Simple Payback 35
4.1.3 Sensitivity analysis 36
4.2 Assumptions for data and calculations needed 36
4.2.1 Discount rate 36
4.2.2 Data interval for calculations 37
4.2.3 Electricity Price 37
4.2.4 Fuel price 37
4.2.5 Vehicle Expenditures 37
4.3.1 Implementation of Net Present Value 38
4.3.2 Implementation of Simple Payback 38
4.3.3 Implementation of Sensitivity Analysis 39
5. Results 41
5.1 Results from the case study 41
5.1.1 Net Present Value on the grid-connected PV system and the electric vehicle 41 5.1.2 Simple Payback on the grid-connected PV system and the electric vehicle 42
5.2 Results of the Sensitivity Analysis on the case study 42
5.2.1 How the discount rate had an effect on the NPV of the case study 42 5.2.2 How the gasoline price had an effect on the case study 44 5.2.3 How the electricity price had an effect on the case study 45
6. Discussion 49
6.1 Discussion on the Results of the Net Present Value 49
6.2 Discussion on the Results of the Payback Period 50
6.3 Limitations of method and possible sources of error 51
6.5 Discussion on sustainable aspects of the project 52
6.6 Discussion of today’s system in Sri Lanka 53
6.6.1 Infrastructure and standards 53
6.1.2 Grid Capacity 53
6.1.3 Households economical possibilities of purchasing the PV and EV system 53
7. Suggestions for further research 55
7.1 Further research to improve accuracy of results 55
7.2 External factors that might have an impact on the results 55
8. Conclusion 57
9. References 59
10. Appendix 65
10.1 Appendix 1: Electricity Bill from Case Study scenario one 65 10.2 Appendix 2: Car Usage data from Case Study scenario one 66 10.3 Appendix 3: Electricity Bill from Case Study scenario two 67 10.4 Appendix 4: The grid connected PV system connected to the rooftop of the household in
scenario one. 68
List of figures
Figure 1: Global average annual net capacity additions by type (IEA 2017). ... 20
Figure 3: Major Challenges in the energy sector of Sri Lanka (Ministry of Power and Energy 2015). ... 21
Figure 4: CO2 Emissions over Vehicle Lifecycle for Nissan Leaf (Nissan Motor Corporation 2015). ... 24
Figure 5: Mean and median of monthly household income by sector 2016 (DCS 2017, 7). ... 26
Figure 6: Share of household income by household income quantiles 2016 (HIES 2016) ... 26
Figure 7: Electricity sales 2009-2015 (Ceylon Electricity Board, 2015) ... 27
Figure 8: Comprehensive structure of case study ... 29
Figure 9: Illustration of a Grid-Connected PV system. ... 31
Figure 10: Net Present Value based on variations of driving patterns and life-cycle of the system. ... 41
Figure 11: Payback period for the different variations in mileage traveled. ... 42
Figure 12: Net Present Value of the 9 year life-cycle with a variation of the discount rate. ... 43
Figure 13: Net Present Value of the 15 year life-cycle with a variation of the discount rate. ... 43
Figure 14: How variations of the gasoline price had an effect on the NPV and different driving distances during a 9 year life-cycle. ... 44
Figure 15: How variations of the gasoline price had an effect on the NPV and different driving distances during a 15 year life-cycle. ... 44
Figure 16: How variations of the gasoline price had an effect on the SPB and different driving distances. ... 45
Figure 17: How variations of the electricity price had an effect on the NPV and different driving distances during a 9 year life-cycle. ... 46
Figure 18: How variations of the electricity price had an effect on the NPV and different driving distances during a 15 year life-cycle. ... 46
Figure 19: How variations of the electricity price had an effect on the SPB and different driving distances. .. 47
Figure 20: Solar Panels implemented at the household of the Dissanayake family, scenario one in the case study. ... 68
List of tables
Table 1: Motor Traffic Vehicle Fleet 2017………23
Table 2: Estimated monthly average income per household in Sri Lanka………..…...25
Table 3: CEB and LECO electricity rates for domestic purpose with consumption under 60 kWh...32
Table 4: CEB and LECO electricity rates for domestic purpose with consumption over 60 kWh..…..32
Table 5: Variables that were evaluated in the case study for the two scenarios...34
Table 6: Change in span of essential parameters of the result from the case study...39
Table 7: The Sensitivity analysis value change for each parameter...39
Table 8: Dissanayake’s electricity bills 4th April 2016-4th April 2018...65
Table 9: Dissanayake’s car usage data between April 4th 2016-April 4th 2018...66
Table 10: Electricity bill from the Fernando family………..……67
Nomenclature
List of Abbreviations
CEB Ceylon Electricity Board
CO2 Carbon Dioxide
EV Electric Vehicle
EVCS Electric Vehicle charging Station
EPA United Environmental Protection Agency
IEA International Energy Agency
k Thousand
LCA Life-cycle Assessment
M Million
MPGe Miles per gallon gasoline equivalent
NDC National Determined Contribution
NPV Net Present Value
SPB Simple Payback Period
PV Solar Photovoltaic
SLSEA Sri Lanka Sustainable Energy Authority
LKR Sri Lankan Rupee
SEK Swedish Crowns
UNFCCC United Nations Framework Convention on Climate Change List of Units
𝑚2 Square Meters
J Energy (E)
J/s Power (W)
1. Introduction
Sri Lanka has made great progress in human development and is continuing in that direction. Between 2007 and 2013 their national poverty ratio declined from 15.3 percent to 6.7 percent along with an aging population (The World Bank 2017). In 2015 Sri Lanka scored 0.766 on the Human
Development Index (HDI) which left them on HDI rank 73 (United Nations Development Program 2016). The economic and social development enhances the increasing demand of energy which is forecasted to get doubled by the year 2020 compared to 2010 (IEA 2017).
The transportation sector is an important element in Sri Lanka’s economy. Studies show that the transportation sector is the highest contributor of greenhouse gas, with a proportion of 48 percent emitted CO2 from fossil fuel combustion systems. Sri Lanka’s electricity- and transportation sector primarily uses fossil fuels (Ministry of Power and Energy 2015, 12). To handle this expected increase of vehicles and electricity demand in a sustainable way, the government set a goal for the nation to be electrified completely by renewable energy sources in 2050.
To reach the renewable electrification goal one target is to increase participation of PV systems in the private sector (Ministry of Mahaweli Development and Environment 2016). A large barrier which prevents this implementation among households is high technology & electricity costs (Ministry of Power and Energy 2015). However, during the past years, there has been large reductions in PV- technology prices. To calculate today's possibilities and evaluate the cost worthiness of private households of rooftop solar panels combined with an electric vehicle in Sri Lanka, this study will contain financial analysis such as simple payback period (SPB) and net present value (NPV) of these investments.
If this implementation is financially possible it would lead to a more sustainable system which would contribute to a better socio-economic standard while minimizing air pollution, increasing rural connectivity and overall reduce the negative climate and environmental effects (Ministry of Power and Energy 2015, 11)
1.2 Research question and Goal
1.2.1 Purpose
The purpose of this study is to give the citizens in Sri Lanka a ground for greater understanding of how an implemented Solar Photovoltaic (PV) System, along with an investment of an electric vehicle (EV), has an effect on the household from a financial point of view.
1.2.2 Research question
To fulfill the purpose of the study the following research question will be answered:
• What is the Net Present Value and the Payback Period of a PV system along with an electric vehicle that gets charged by it?
To help local research about two of Sri Lanka’s major barriers, which is high cost of renewables and lack of local renewable capacity development, the goal of the study is to give a ground of greater understanding for the possibilities private households have to invest in a renewable system to increase the participation of the private sector in the country’s sustainable development.
2. Literature Study
This section presents relevant data and previous research on the subjects of this study. It will address renewable energy with a focus on PV systems, the socio-economic conditions and the transport sector with emphasis on use of electric vehicles (EV) in Sri Lanka. To create accordance, the report deals with costs solely in Sri Lankan Rupees (LKR) where a fixed exchange rate has been determined. 1 LKR corresponds to 0,01 USD and 0,056 SEK 2018-05-08 (Yahoo Finance 2018).
2.1 Climate change and renewable energy
Climate change will affect big parts of the world essentially. Because of this, focus on renewable energy has increased rapidly during the past decades trying to prevent disastrous consequences on the environment. This common issue has gathered several countries to fight for a better environment.
Back in 1997 the Kyoto Protocol gathered the first 37 states with highly industrialized countries to commit to the work towards stabilizing greenhouse gas concentration the atmosphere to a level that is not harming to the world population. This work continued and today the United Nations Framework Convention On Climate Change (UNFCCC) consists of 197 parties that has a common objective. The Paris Agreement were held in 5 October 2015 and states that all 197 parties have to work towards keeping the global temperature from rising more that 2°C by 2020 (United Nations Climate Change 2016: UNFCCC 2016).
2.2 The future of renewables
To gain a sustainable energy system worldwide with less carbon, renewable energy is the center of that transition. The growth of renewables is expected to be more than one-third by 2020 according to the International Energy Agency (IEA). The dominant energy source will be renewables in 2040, since it will be the least-cost source to most countries. 2016 was the first year that PV additions grew faster than the net growth in coal and also any other fuel of energy. Around the world PV capacity grew by 50 percent that year, and accounted for almost two-thirds of the net new power capacity. This was a result of sharp cost reductions and policy support around the world. (IEA 2017). Solar
photovoltaics are expected to grow rapidly with China’s and India's deployment in the front line. By 2040 it is forecasted to make up to 40 percent in total of all renewable sources (IEA2017). Since cost and payback periods are two of many barriers with renewables today, one of the main focuses in the World Energy Outlook 2017 were “the rapid deployment and falling costs of clean energy
technologies”. This will help major developing countries to accomplish what is foreseen with renewable energy within the energy sector (IEA 2017).
Figure 1: Global average annual net capacity additions by type (IEA 2017).
As demonstrated in Figure 1, renewables are today and will continue to be the largest net capacity addition by 2040. Meanwhile coal and gas will reduce its share.
2.3 Energy Mix in Sri Lanka
Sri Lanka’s electricity- and transportation sector primarily uses fossil fuels (Ministry of Mahaweli Development and Environment Sri Lanka 2016). Due to the rapidly increasing demand in these two sectors, along with its dependence of imported fossil fuels, Sri Lanka’s greenhouse gas emissions
demonstrates a steadily growth during the past decade (Asian Development Bank and the United Nations Development Program 2017). The energy mix in Sri Lanka year 2017 was divided as shown below.
Figure 2: Energy generation mix in Sri Lanka during the first 8 months of year 2017 (Ministry of Power and Renewable Energy 2018)
Hydro 16%
Thermal - CEB 18%
Coal 40%
IPP (Thermal) 19%
NCRE 7%
As presented in in Figure 2, coal power plants generate the majority of Sri Lanka’s electricity. The country has large capacity for hydropower but problems with drought affected the possible hydro generation the past year. Of the seven percent NCRE (Non-Conventional Renewable Energy) about 3,7 percent is generated from solar power (Ministry of Power and Renewable Energy 2018). The Thermal energy generation is divided into IPP “Independent Power Producers” and CEB, which is a governmental
authority. Even though most of Sri Lanka is electrified, one of the major challenges that has been faced by Sri Lanka’s power sector, has been high cost of electricity from renewable resources, which hamper further development. Another key challenge is the lack of local research to promote local capacity development (Ministry of Power and Energy 2015).
Figure 3: Major Challenges in the energy sector of Sri Lanka (Ministry of Power and Energy 2015).
In figure 3 above, the major challenges in the energy sector of Sri Lanka is demonstrated. These challenges can be reduced as the technology and development of renewable energy enhances. Today it is still often more beneficial, considering the average cost, to generate electricity from conventional technologies than the renewable alternatives. This is despite the significant cost reduction that has been made for renewables, and especially PV systems the past few years (Ministry of Power and Renewable Energy 2018). The price of solar PV systems has been reduced with more than 40 percent between 2010 and 2016. Underlying factors for this decrease is mainly reduction in technology prices, growing networks of solar installers, innovative financing and financial partners (PUCSL 2017).
One of the strategy programs of Sri Lanka’s Ministry of Power & Energy (2015, 25) is to promote grid
Lanka’s National Determined Contribution (NDC) to the UNFCCC (NDC Sri Lanka 2016). Sri Lanka also strives to be 100 percent driven by renewable electricity in 2050 (Ministry of Power & Energy 2015).
Due to Sri Lanka’s geo-climatic conditions they have great possibilities to build systems for renewable energy sources. The southwest and northeast monsoons cause generous rainfalls that make hydropower generation applicable and also creates high plant density that can be used as biomass. Its geographic location along with its high temperature and high solar irradiation throughout the year creates strong wind that can be used as wind power and great opportunity for solar energy in Sri Lanka (Sri Lanka Sustainable Energy Authority 2010b).
2.4 Grid connected PV Rooftop system
Together with the Sri Lanka Sustainable Energy Authority (SLSEA), Ceylon Electricity Board (CEB) and Lanka Electricity Company the Ministry of Power and Renewable Energy has launched a new community based power generation project called “Soorya Bala Sangramaya” (Battle for Solar Energy). This project is predicted to generate 200 MW of solar electricity to the national grid by 2020 and will continue to grow to 1000 MW by 2025. Small solar power plants will be placed on households, hotels, industries, commercial facilities and religious areas. Observations show that electricity day peak demand is rising faster than the night peak (PUCSL 2017-09). A successful implementation of “Soorya Bala Sangramaya” will be helpful for the community in terms of expanded capacity, for consumers by means of lower electricity bills, and for the environment by reduced emissions. A PV system is expected to have a life-cycle of 20-30 years and there are already programs developed to recycle the system (Solar Energy Industries Association 2018).
The users of rooftop PV systems can be connected to the grid through a net metering system1 and that is how they generate electricity. This means that the customer pays for the net amount of the total electricity that is used (PUCSL 2017). There are three different schemes for the usage of the net metering system established as incentive to investing in a private household PV system by the utility providers CEB and Lanka Electricity Company (LECO) (PUCSL 2017). These schemes are called net-metering, net- accounting and net-plus (also referred to as feed-in-tariff) (PUSCL 2017). The net metering scheme implies that the customer if producing more electricity than the use, the net units can be carried forward as credits for future use in up to ten years. The net plus alternative means that the customer gets paid for all units generated by the PV system according to feed-in-tariffs and pays for the electricity used from the grid as normal. The feed-in tariffs for rooftop solar PV is 22 LKR/kWh in the first seven years and 15.50 LKR/kWh year 8 to 20 (PUCSL 2017). These tariffs are also used in the net accounting scheme were the customer gets paid for the excess of electricity if generation greater than usage. If the electricity
consumption is greater than the generation, the consumer will pay for the net amount excessed. To be granted any of the three schemes, the installed capacity of the PV system cannot exceed the contact demand of the producer (PUCSL 2017).
2.5 Transport in Sri Lanka
In the integrated national energy policy, the government’s strategy includes promoting a transition regarding preferred fuel choice in the transport sector from Liquid Petroleum to electricity and gas.
The intention is to reduce the dependence on imported petroleum products, which constitutes a large cost for the country and raise the emissions (Ministry of Power and Energy 2015, 21).
1Net metering system is a policy that permits customers to sell their generated units from their own generating system to the utility grid. This is furthered explained in section 3.2.2.
In 2017, the total amount of registered motor traffic road vehicles was 7 247 122. This amount consists of different vehicle classes presented in table 1 below.
Table 1: Motor Traffic Vehicle Fleet 2017 (Department of Motor Transport 2018)
Vehicle Motor Cars
Motor Tricycle
Motor Cycles
Buses Dual Purpose Vehicles
Motor Lorries
Tractors Trailers
Amount 756 856
1 139 524 4 044 010 107 435 408 630 352 275 362 445 75 947
Table 1 show that motor cars represent a little over ten percent of the total vehicle fleet as the third largest vehicle class except from motor tricycles and motorcycles.
2.6 Electric vehicles in Sri Lanka
The global market of EVs has increased heavily the past years, which is a trend that also reached Sri Lanka. The registration of EVs in the country has increased from 90 to 3238 between 2014 and 2015 (Public Utilities Commission of Sri Lanka 2017). The total fleet of EVs registered from year 2011 to 2016 is about 4349. This amount consists mostly of electric motor cars but also of three wheelers and
motorcycles (Public Utilities Commission of Sri Lanka 2017).
2.6.1 Electric Vehicle Charging Stations
A part of The Ministry of Energy & Power (2015, 21) strategy to increase the amount of electric vehicles in Sri Lanka is to institute a special off-peak tariff for domestic customers when charging electric vehicles.
Alongside with the increasing amount of EV, the amount of public charging stations has also increased (PUCSL 2017). The need for more widely distributed public charging stations is still growing and
expected to continue that way (PUCSL 2017). It is also required to develop some stations efficiency for the possibility of faster charging at higher voltages and currents than conceivably from charging at home. At this time, there are no governmental insurances, regulations or guidelines for the Electric Vehicle Charging Stations (EVCS) regarding charging meter accuracy, level of quality or inspections (PUCSL 2017). A necessity to succeed with further development is to build a well-functioning EV battery-charging network.
According to PUCSL (2017) the range of a typical electric car is between 100 and 130 km before it needs to recharge. At the time of the consultation about 50 privately owned EVCS existed in the bigger towns in Sri Lanka but there are probably more which are unknown due to lack of legislation (PUCSL 2017).
2.6.3 EV model - Nissan Leaf
One of Sri Lanka’s biggest import sectors is the import of vehicles (World’s Top Exports 2017). Even though this trend has decreased recent years due to new regulations, Sri Lanka was the biggest
importer of used Japanese Vehicles by value in year 2015 and is still high up on the list (Sunday Times 2016). One of the early recognizable electric vehicles imported to Sri Lanka were the Nissan Leaf with a 24 kWh battery pack (Fernando 2018). The Battery was manufactured in Japan by the Automotive Energy Supply Corporation (AESC) as a joint venture between Nissan and NEC (AESC 2011). This Nissan Leaf model won the 2011 World Car of the Year award and has a rated driving range of about 80 miles (128,7 kilometers) (AESC 2011).
An incentive to change to an EV instead of a fossil fuel driven car is the reduced environmental effects. To examine the differences of having a Nissan Leaf compared to a same class gasoline powered car, Nissan analyzed the environmental effects throughout the vehicles whole lifecycle (Nissan Global 2015). This Life-cycle Assessment (LCA) evaluates the environmental impact in all stages regarding the vehicle from resource extraction to manufacturing processes, transport, customer use and the vehicle disposal. The result of the analysis showed that Nissan Leaf reduces the carbon emission by up to 40 percent compared to the same class gas powered car (Nissan Global 2015). This is demonstrated in Figure 4 below.
Figure 4: CO2 Emissions over Vehicle Lifecycle for Nissan Leaf (Nissan Motor Corporation 2015).
Figure 4 shows the emissions over the vehicle lifecycle divided in different stages. As the figure presents, the manufacturing process of specific parts for the EV creates more emissions compared to equivalent parts in the gas powered car, but seen to the entire lifecycle and usage of the complete car, the EV has far less environmental impact than the gas-powered car (Nissan Global 2015).
2.6.4 Fuel Economy
Fuel economy is expressed by the United States Environmental Protection Agency (EPA) through a measurement of miles per gallon (MPG)2. Car-companies use this measurement for their vehicles as the consumers then get an understanding the average distance traveled per unit of energy consumed by the vehicle. It can be used for comparing alternative fuel vehicles, hybrid vehicles and electric vehicles. Most vehicles are labeled with highway, city and combined MPG. Combined fuel economy is the weighted average MPG of city and highway. In some cases, another way to express MPG for electric- and hybrid vehicles is MPGe, which refers to miles per gallon equivalent, and is used to examine how much fuel the same distance traveled would have consumed if it was run by gasoline instead of electricity (United States Environmental Agency 2016).
21 MPG equals to 0,43 km/l.
Fuel economy depends on driving patterns and conditions of roads and traffic, and the MPG that are labeled on cars is in ideal circumstances. Examples of factors that affect the fuel economy of a light duty vehicle are (T. Sugathapala 2015):
● Vehicle type/size
● Vehicle age and accumulated distance travelled
● Fuel used
● Tire type and maintenance
● Maintenance condition of the vehicle
● Traffic conditions (or driving cycle) and how the vehicle is driven
● Road conditions
● Ambient weather conditions
2.7 Economic and social development in Sri Lanka
Sri Lanka has gone through some tough years during the past decades. In 2004 they got hit by a tsunami and between 1983 and 2009 they suffered through a civil war where the Sri Lankan
government were in conflict with the Liberation Tigers of Tamil Eelam (BBC News 2018). After the war the country’s economy has grown by 6.2% per year on average and had a GDP of 3,8 dollars in 2016, which grew by 3,9% in 2017. During the past few years they have also suffered from natural disasters as in the worst droughts in four decades and one of the worst floods in 14 years. This has left the agriculture affected, which led to a 53% drop in rice production and also reduced tea and rubber exports. Despite this, their macroeconomic performance has been broadly satisfactory. (Asian Development Bank 2016:The World Bank 2016).
According to a Household income and expenditure survey (HIES) conducted by Sri Lanka’s Ministry of National Policies and Economic Affairs (2017) the mean income per household each month shows a steady increase from year to year during the past decade. The mean monthly income per capita in Sri Lanka year 2016 was 16 377 LKR while the median income per capita was 11 307 LKR. This can be compared to monthly income by income receivers which gives a mean income of 33 894 LKR and mean income of 23 260 LKR. The average number of income receivers per household is 1,8 while the average household size is 3,8. The number representing income per capita is calculated by dividing persons in the household by the household income (DCS 2017).
Table 2: Estimated monthly average income per household in Sri Lanka (Department of Census and Statistics, DCS, 2017)
Variable Survey Period (12 months)
2016 2012/2013 2006/2007 2002
Mean income [LKR] 62 237 45 878 26 286 12 803
Median income [LKR] 43 511 30 814 16 735 8 482
Table 2 presents the average mean and median monthly income per household during past years to show how the household income has increased along with Sri Lanka’s development.
Each sector is divided into ten equal size decile groups. The first decile group holds 10 percent of the total population and evaluates the lowest values for the analyzed attributes and the higher decile groups income level increase in ascending order. (DCS 2017)
Figure 5: Mean and median of monthly household income by sector 2016 (DCS 2017, 7).
The distribution of share income per sector of the total household income displays that the urban sector holds a 38,6 percent share while the rural and estate holds a share of 33,7 percent respectively 27,7 percent. When breaking down each share to total number of households evaluated, the numbers show that 18,3 percent of the urban sectors households receive 51,3 percent of the total urban sector household income. This majority earns over 115 648 LKR per household per month. In the estate sector, 64,6 percent of the households among the poorest 40 percent households of the whole country.
20 percent of the Estate sector earns less than or equal to 15 321 LKR per household per month. The middle 60 percent of the population earns average 46 097 LKR per household per month while the richest 20 percent of the populations has an average monthly household income of 158 072 LKR (DSC 2017).
Figure 6: Share of household income by household income quantiles 2016 (HIES 2016)
As illustrated in Figure 6, the rich quintile stands for 20 percent of the populations but makes up 44 percent of the income share per household. The poorest quintiles also stand for 20 percent of the population but only covers 4 percent of the total income. This is an indication of how segregated the country’s economy is. However, as mentioned earlier the electricity demand is increasing throughout the populations, which is indicated by the electricity sales, see figure 7 below.
Figure 7: Electricity sales 2009-2015 (Ceylon Electricity Board, 2015)
As shown in the latest annual report, and Figure 7, from the national electricity supplier Ceylon Electricity Board (CEB) the increasing demand is continuously making the the electricity usage go up in the country (Ceylon Electricity Board 2015).
3. Method
The research question was answered through conducting a case study with two different scenarios:
1. The household of the Dissanayake family has solar panels installed on its household and an electric vehicle is charged by electricity from the grid.
2. The household of the Fernando family with no solar panels is considering to implement a PV system on the rooftop and replacing its gasoline car with an EV.
The location of the case studies was both in Peradeniya, Kandy, at the two different households. The comprehensive structure for the method used to address the case study is visualized as in figure 8.
Figure 8: Comprehensive structure of case study
As figure 8 shows, the process started with collecting relevant data from the case study. Based on this, assumptions and limitations were set for being able to process the data and proceed with financial calculations. To evaluate the accurately and the sensitivity of the calculations a sensitivity analysis where made. All the above stages resulted in a discussion and conclusion, which opened for further analysis.
3.1 Limitations
The case study is limited to an analysis of two specific households in Kandy, Sri Lanka with
household-individual user patterns. Because of this, the results are limited to Sri Lanka’s systems and the calculations cannot be used as a general investment basis since other households will have other preconditions. However, the results can give a greater understanding for the investment needed and be used as a reference value when comparing to other situations.
The study is focused on a combined system of rooftop PV-panels and an EV and does not consider whether the techniques are beneficial when separately analyzed. This makes it difficult to compare
The core of this case study is the financial aspect of the investments and even though some social-, technical-, and political-barriers is mentioned in the report they are not deeper evaluated or taken into decisive account.
Due to lack of data, parts of the calculations has been made with qualified assumptions and the study does not evaluate currency fluctuations of the Sri Lankan Rupee.
3.2 System Description
The system analyzed for this case study is limited to the use of a rooftop PV system on private household connected to an electric vehicle. This system is evaluated with two scenarios that address the same limitations but with differences in the existing parameters. By comparing the two scenarios within the same system a greater understanding is given for how the parameters result in different outcomes. The first scenario evaluates a system that is already installed while the second scenario evaluates a future investment in a Solar PV and EV system. Because of this, the financial analysis of the scenarios will part while the study in scenario one consider the profitability and payback period of an existing system while the case in scenario two evaluates the profitability of installing a Solar PV and EV system compared to the financials of the household’s present system using electricity from the grid and a gasoline vehicle.
This section describes the grid connected rooftop PV system and Sri Lanka’s Net Metering system which is the system evaluated for both cases. It is also described further how the PV system is connected to the electric vehicle.
3.2.1 Grid-connected PV Systems
In a grid-connected PV system the photovoltaic cells convert the sunlight to a direct current (DC) of electricity followed by converting it to an alternating current (AC) through an inverter. After the AC is synchronized to the grid’s AC it is fed into the utility network as shown in Figure 9 (Public Utilities Commission of Sri Lanka 2016, 4).
Figure 9: Illustration of a Grid-Connected PV system.
The bidirectional inverter energy meter registers import as well as the export (Public Utilities Commission of Sri Lanka, 2016 4).
3.2.2 Net Metering
Both cases in this study will be evaluated by using the net metering system. The term net metering refers to the fact that it measures the flow of electricity both ways. Net Metering is a policy that permits customers to sell their generated electricity units from their own generation system to the utility grid. If the production of renewable energy exceeds their electricity consumption it is exported to the grid and they receive credits on their coming electricity bills. If the user consumes more than what the on-site generating system generates they will import electricity from the grid. Hence,
consumers can stock the power that they generate by exporting excess power to the grid and move the units on to next month’s electricity bill. Net Metering is ideal for PV systems since it only generates energy during the day time (Public Utilities Commission of Sri Lanka, 2016, 1).
JLanka Technologies is Sri Lanka’s premier solar solution provider with an electricity capacity of 20 MW and it is a part of the Sooray Bala Sangrama project along with CEB. JLanka takes care of the whole installation process including inspections of the property, complement of an engineering assessment and a consultation for the customer (JLanka Technologies 2018). CEB has a billing scheme for the Net Metering system that is based on the amount of units you buy from the grid. The more units you consume from the grid the higher the price of each unit becomes (Ceylon Electricity Board 2015).
3.2.2.1 Electricity rate
The electricity expenditures do not only depend on the units (kWh) consumed, but also the rate of consumption. The same consumption will be more expensive if consumed within 10 days than it
The net metering system in Sri Lanka is made up of an exponential increase in price for the users. For the domestic purpose tariff, the prices for a 31-day billing block have the following prices (Lanka Electricity Company 2014):
Table 3: CEB and LECO electricity rates for domestic purpose with consumption under 60 kWh Consumption per month
(kWh)
Unit Charge (LKR/kWh) Fixed Charge (LKR/month)
0-30 2,50 30
31-60 4,85 60
Table 4: CEB and LECO electricity rates for domestic purpose with consumption over 60 kWh Consumption per month
(kWh)
Unit Charge (LKR/kWh) Fixed Charge (LKR/month)
0-60 7,85 N/A
61-90 10,00 90
91-120 27,75 480
121-180 32,00 480
>180 45,00 540
3.3 Case Study
In this section the two different scenarios of the case study is described. Scenario one is of a household that is already in possession of a PV system and an EV and is set to the standard distance boundary. This is the scenario that the other variation, which will later be explained in section 3.4, is based on. Scenario two is based on a household that is considering an investment of a PV system along with an EV, in order to replace its gasoline car.
3.3.1 Scenario one - Dissanayake household
In scenario one, the Dissanayake family has twelve solar panels installed on its rooftop within the company Solar Edge’s framework, see Appendix 4 for a picture of the system. In total the whole PV system has 3,12 kW capacity and the data for the production and consumption is accessed through the owner’s private login to the company’s online service. The initial investment was of eight solar panels on December 2015, and in the last week of march 2016 four additional panels was purchased to fulfill the system. The whole system was purchased for 900 kLKR along with an installation cost of 900
kLKR (Dissanayake 2018). The PV system is assumed to have a life cycle of 20-30 years and the Nissan Leaf 2014 battery is, according to Nissan, lasting for 160,000 km (Solar Energy Industries Association 2018; Nissan 2013).
The 280 𝑚2 double story household uses electricity for electric equipment such as air conditioner, washing machine, lightning and freezer etc. The house has a solar hot water system on the rooftop as well that is used for water heating. Hence the water heating is not taking electricity from the grid.
CEB is the national electricity supplier that the household is connected to (Dissanayake 2018).
The electric vehicle that the family is using is a Nissan Leaf 2014, G-Grade Model, and was
purchased for 3.9 MLKR in early December 2015. However, the electric vehicle was not used much within the first month since they had two cars at that time (Dissanayake 2018).
3.3.2 Scenario two - Fernando household
Scenario two is based on the Fernando family that lives in a household in Kandy, Sri Lanka, just as the Dissanayake’s from scenario one. The Fernando family is currently in no possession of a PV system or an electric vehicle, but is interested in investing in such system.
Today the family owns a Suzuki Swift Beetle 2007 and they use it for work and common errands such as driving their daughter to school or going to the grocery store. Approximately once per month they go on longer trips which is about 400 km, but apart from that they just use the car in the city
(Fernando 2018).
The Fernando family has documented all the data for the car since they bought it. Hence these users’
costs and mileage usage will be used in the calculations for this scenario. Their electricity bills were also collected from the family, however, since they do not have a PV system yet the export from scenario one will be used in scenario two. Assuming they will have the same generation since the households is based in the same city with the same climate. The cost of the entire investment will also be assumed to be the same for scenario two as in scenario one.
3.4 Parameter Variation
The financial benefits of owning a system with solar panels and an electric vehicle will differ depending on the car usage. A consumer that drives a lot will thereby consume more electricity from the grid and the electricity bill will be more expensive. Because of this, three different driving patterns will be analyzed. The life-cycle of the electric vehicle will also have an impact which is why this will be the second variation in the calculations. This is further explained in section 3.4.1 and section 3.4.2.
The following table demonstrates the variations in distance traveled and life-cycle that will be examined.
Table 5: Parameters that were evaluated in the case study for the two scenarios Parameter Variation
Travel distance High distance: Corresponding to an increase in 50% of the mileage traveled by the user in the case study, scenario one.
Standard distance: The mileage traveled by the user with an electric vehicle from the case study, scenario one, is set to the standard distance traveled.
Scenario two: The mileage traveled by the user in the case study, scenario two, that currently has a petrol vehicle.
Low distance: Corresponding to a decrease of 50% of the mileage traveled by the user in the case study, scenario one.
Life-cycle 15 year life-cycle: If an exchange of battery is depleted the life-cycle of the system is assumed to last for 15 years before the owner will sell the car.
9 year life-cycle: According to Nissan the car can drive for 160000 km during its lifetime, based on the driving pattern of the user this will take approximately 9 years.
3.4.1 Driving distance evaluation
Due to lack of data on the average distance traveled by private vehicle owners, an increase of 50%
compared to the standard distance traveled will be used as the high distance boundary. For the low distance boundary, a decrease of 50% compared to the standard distance traveled will be used. The standard distance traveled is based on the data from the user in the case study, scenario one. The data from the user in scenario two shows a decrease in mileage usage by 34% compared to scenario one.
Scenario two will be compared to the high, low and standard distance boundaries as well, but will not alone be evaluated with a ±50% distance variation. What has to be kept in mind is that the standard-, high- and low-distance boundary is based on the same data, meanwhile scenario two is somewhat separated from those since it is based on the data from the Fernando family that does not possess a system yet.
3.4.1 Life-cycle evaluation
The variations in the life-cycle variable where chosen as the values that represents what Nissan stated the lifetime to be along with what the owner estimated the lifetime would be with his usage-pattern.
The car has a lifetime of about 9 years according to Nissan, while Dissayanake (2018) counted on using the car for about 15 years. In Sri Lanka people often use the car for as long time as possible and exchange parts and restore the car instead of buying a new one when the present one starts to break. If Dissayanake keeps the car for 15 years a battery exchange will have to be done. The cost of this exchange will be included in the calculations of the 15 year life-cycle, which will be further explained in section 4.2.5 of the report. The establishment of an official battery exchange is not yet made in Sri Lanka, but there are some local shops that does it for the electric vehicle owners today. The discussion and process of starting up a company that performs official battery exchanging is already on going.
Hence, in a couple of years there might be a proper way to do battery exchanges (Dissayanake 2018;
Daily news 2017).
4. Models
In this part of the paper the models of the Net Present Value, Simple Payback Period and the Sensitivity Analysis are labeled. Followed by how these calculations were accomplished.
Suppositions that were required for the sensitivity analysis in the case study as well as the result are also portrayed in this chapter.
4.1 Explanation of Models
4.1.1 Net Present Value
NPV is a common investment method that allows companies to compare the costs and benefits of a project in terms of money value today. The NPV Rule implies that a positive NPV indicates that the investment should be made. When considering different projects, the NPV decision rule points on choosing the project with the highest NPV. This decision will lead to the biggest increase of value for the investor. NPV is calculated through the following formula (J. Berk, P. DeMarzo 2014):
𝑁𝑃𝑉 = 𝑃𝑉(𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠) − 𝑃𝑉(𝐶𝑜𝑠𝑡𝑠) (1)
𝑁𝑃𝑉 = ∑𝑇𝑡=1(1+𝑟)𝐶𝐹𝑡𝑡 (2)
𝑁𝑃𝑉 = 𝑛𝑒𝑡 𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝐶𝐹𝑡 = 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡 𝑟 = 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒
In the calculations the present value is discounted by future cash flows to its cash value today. In the private sector the discount rate is not set and difficult to evaluate. In other cases, the opportunity cost of capital is often used as the discount rate (Short et al. 1995). The initial investment gives a negative cash flow year zero, that year the discount factor is one.
The terminal value will be included as a long-term growth rate for the investment and will affect the year N in the calculations, where year N is the final year of the evaluated life-cycle.
𝑃𝑉𝑁= 𝐶𝐹𝑁× (1+𝑔
𝑟−𝑔) (3)
𝑔 = 𝑙𝑜𝑛𝑔 − 𝑡𝑒𝑟𝑚 𝑔𝑟𝑜𝑤𝑡ℎ 𝑟𝑎𝑡𝑒 𝑃𝑉𝑁= 𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑦𝑒𝑎𝑟 𝑁 𝐶𝐹𝑁= 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑦𝑒𝑎𝑟 𝑁
4.1.2 Simple Payback
Simple Payback (SPB) calculations will be performed since the analyzed case is at a household. SPB
duration at which the investor’s capital is at risk. Tax features and alternative financing are not to be included in the calculations since the feasibility of the method gets lost in that way. SPB does not consider the returns after payback, which is why it is not used when ranking projects (Short et al.
1995).
A disadvantage of the method is that it does not consider the time value of money. Hence, opportunity cost for the investor is ignored. Although an advantage is that if there are uncertainties such as tenure of the technology it is still useful (Short et al. 1995).
4.1.3 Sensitivity analysis
A sensitivity analysis is essential as a variable or parameter can be misleading if it cannot be decided accurately. Such parameters and variables can cause inaccurate results and can vary across groups or populations. It provides a judgement of the range of different outcomes and their tendency (Short et al. 1995). The difference from the uncertainty analysis is that the sensitivity analysis addresses the relative changes in model response from commotion of different model parameters. Hence, not the quantifying the uncertainty ranges of the result of the model output (L. Lee et al. 2016, 3-24).
Generally, the Sensitivity Analysis consists of defining which variables or parameters that has high likelihood on changing the output. After that is completed an interval is determined, that the
parameters are expected to change within. Lastly an analysis of the effect on the output of the model is made where one parameter is changed at a time (P. Jovanovic 1999). There are other ways of conducting a sensitivity analysis but this is the procedure used for this report.
4.2 Assumptions for data and calculations needed
In this part of the report the assumptions for calculations and the development of data processing is presented.
4.2.1 Discount rate
A sensitive parameter in the calculations of the Net Present Value is the discount rate. As mentioned in section 4.1.1 they are difficult to determine when there is no known opportunity cost of capital. It is notably difficult to determine a general discount rate for a household since each individual has
different prerequisites. Studies have shown that the discount rate should be higher for the commercial sector compared to a private investor. For a household the discount rate can be generalized to a range between 3% to 6% and it should reflect the market price of capital (J. Steinbach, D. Staniaszek, 2015).
However, there are sources that claim that a 10% real discount rate should be used when calculating financial metrics of a household and a PV system, including NPV and payback period (Sigrin, 2013;
Short et al. 1995). When there is a lack of investment specific data the 10% real discount rate is recommended to be used, and this is why the discount rate will be set to 10% in this study.
4.2.1.1 Long-term growth rate
The terminal value for the investor is usually included in the calculations of the NPV as the long-term growth rate. This growth rate is estimated by assuming a constant long-term growth rate for the cash flow that is included in the final year of the calculation (Berk and DeMarzo 2014). Hence, year 9 or
year 15 depending on the variation of the NPV calculation. The long-term growth rate that will be used is set to 3%, based on the 3,22% grow rate that is forecasted on the company SolarEdge and the 3,2% GDP annual growth rate of Sri Lanka (Nasdaq 2018; Trading Economics 2018). This is a realistic long-term growth rate to assume since it should be below the country’s estimated GDP growth rate (Berk and DeMarzo 2014).
4.2.2 Data interval for calculations
The electricity bill data that was received was composed in monthly intervals, starting at the 4th of each month. The calculations will be done based on the two-year interval from April 4th 2016 to April 4th 2018 that data is collected from. The PV system was completed with the 4 final solar panels on April 4th 2016, and that is why the year of 2016 will be seen as the initial year in this study.
4.2.3 Electricity Price
When charging the electric vehicle at the household, the electricity usage will raise by one unit per 5,5 km used (Dissanyake 2018). This will increase the price fundamentally because of the tariff system in Sri Lanka. However, with the PV system the export will put the user on a lower block and he would be without the system. The value that is seen as cash flow in the evaluation will therefore be the net cost paid during the months when the owner is in possession of the system subtracted from the initial electric costs before the owner bought the vehicle and the solar panels.
4.2.4 Fuel price
In the calculations of SPB and NPV the gasoline cost of the mileage traveled will be seen as cash flow. The expenditure of gasoline will not occur for the owner of an electric vehicle; therefore this cost will be calculated as an income. Based on the historically documented data from the user in the case study, scenario two, the gasoline price has been steady on 117 LKR/liter since january 2015.
Because of this the set value of 117 LRK/liter will be used in the calculations.
The fuel price will also be affected by the fuel economy of the vehicle. Nissan Leaf 2014 has a combined MPG of 114, which is equivalent to 48,47 km/liter (Nissan, 2013). The fuel economy for motor cars are much lower that what is labeled for electric vehicles. The user in scenario two has a Suzuki Swift Beetle 2007, which has a fuel economy of 24 MPG, which is equivalent to 10,2 km/liter.
Because of this and the conditions of roads and the aggressive driving patterns in Sri Lanka the fuel economy for this study is set to 10,00 liters/km (T. Sugathapala 2015).
4.2.5 Vehicle Expenditures
During the counted timeline for the investment, no parts in the EV generally need to be replaced. If the battery even so is degrading to a large impact of the vehicles driving range, the warranty will cover for a new battery within the first 5 years (Nissan, 2014). If a battery exchange has to be done it will cost about 777,250 kLKR in Sri Lanka today (Dissanyake 2018). In the calculations of the 15 year interval of the NPV this will be performed during the 5th year of the life-cycle, and will be seen as an expenditure.
The cash flow in the calculations will be interpreted as positive if the yearly vehicle expenditures are
same for the two cars, which is why these costs will not be included in the calculations. However, the motor car owner has to pay a yearly emission-test cost of 2 kLRK, which will be seen as an income for the electric vehicle owner (Fernando 2018). The initial registration costs is 15 kLKR more expensive for an electric vehicle, which will be seen as negative cash flow in the calculations
(Dissanyake 2018; Fernando 2018). The insurance cost depends on what the type of the insurance the consumer decides to get, in the calculations these costs will not be included since the same owner will most likely pay for the same insurance undependable of what type of car he invests in.
In scenario one, data on the cost of charging outside the household was collected and used in the calculations. However, in scenario two no such data is available since it is a forecast of the future.
Hence, no charging costs outside the household are included in the calculations for scenario two. The costs of the distance driven by the user will be directly reflected on the electricity bills instead.
4.3 Implementation of models
4.3.1 Implementation of Net Present Value
In this study, the NPV evaluated the cash value today of the PV system and electric car investment.
Savings from using an electric vehicle instead of a gasoline car and the electricity bill reduction by having solar panels on the rooftop was considered as the cash flow of the investment. The initial investment cost includes the PV system of 900 kLKR, the installation cost of 900 kLKR along with the investment of the electric car which was purchased for 3,9 MLKR. The discount rate was set to 10 percent as described in section 4.2.1. The usage of the electric car started fully in january 2016 and that is why 2016 will be considered as year 0 in the analysis. The calculations were made in Excel.
4.3.2 Implementation of Simple Payback
The SPB was used to evaluate how long it would take for the owner to regain the initial investment of the solar panels and an electric vehicle. This was done by using the data for fuel expenditures,
electricity bills and maintenance costs for the vehicle.
The savings per month was calculated by the following formula:
𝑠𝑛= (𝐿𝑛× 𝑝1) + (𝐼𝑛− 𝑁𝑛) − (𝐶𝑛+ 𝑀𝑛) (4)
𝑠𝑛= 𝑠𝑎𝑣𝑖𝑛𝑔𝑠 𝑖𝑛 𝑚𝑜𝑛𝑡ℎ 𝑛
𝐿𝑛= 𝑙𝑖𝑡𝑒𝑟 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 𝑏𝑦 𝑔𝑎𝑠𝑜𝑙𝑖𝑛𝑒 𝑐𝑎𝑟 𝑖𝑛 𝑚𝑜𝑛𝑡ℎ 𝑛
𝐼𝑛= 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑐𝑜𝑠𝑡 𝑏𝑒𝑓𝑜𝑟𝑒 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑜𝑓 𝑎 𝑃𝑉 𝑠𝑦𝑠𝑡𝑒𝑚 𝑎𝑛𝑑 𝑎𝑛 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑚𝑜𝑛𝑡ℎ 𝑛
𝑁𝑛= 𝑛𝑒𝑡 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑐𝑜𝑠𝑡 𝑎𝑓𝑡𝑒𝑟 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑜𝑓 𝑃𝑉 𝑠𝑦𝑠𝑡𝑒𝑚 𝑎𝑛𝑑 𝑎𝑛 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑚𝑜𝑛𝑡ℎ 𝑛 𝐶𝑛= 𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔 𝑐𝑜𝑠𝑡 𝑓𝑜𝑟 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑜𝑢𝑡𝑠𝑖𝑑𝑒 𝑜𝑓 ℎ𝑜𝑚𝑒 𝑚𝑜𝑛𝑡ℎ 𝑛
𝑀𝑛= 𝑀𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑠𝑡 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑚𝑜𝑛𝑡ℎ 𝑛
The SPB was calculated through the following formula:
𝑆𝑃𝐵 =𝑖𝑛𝑖𝑡𝑖𝑎𝑙 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡
∑ 𝑠𝑛 (5)
The price of the electricity that has to be bought back from the grid will vary based on the units required by the electricity consumption as explained in section 3.2.2.1. If there is an occurrence of charging the electric vehicle outside of home that cost will be subtracted from the monthly savings. If the maintenance cost is higher for an electric vehicle than a motor vehicle, the difference will also be subtracted from the savings.
4.3.3 Implementation of Sensitivity Analysis
The Sensitivity Analysis was conducted to get an understanding of how essential parameters would make a difference in the results of the SPB and NPV. The default values were the ones collected from the case study along with a discount rate of 10%. Following parameters with the evaluated span of change is labeled. Later in this section an explanation of why these spans were chosen is identified.
Table 6: Change in span of essential parameters of the result from the case study
Parameter Span of change
Discount rate ±50 %
Gasoline price ±14 %
Electricity rate ±20%
The table above demonstrates the examined parameters span in percentage change.
Table 7: The Sensitivity analysis value change for each parameter
Electricity rates Gasoline price Discount rate
Increased discount rate Tariff price LKR 117 LKR/liter 15%
Reduced discount rate Tariff price LKR 177 LKR/liter 5%
Increased gasoline price Tariff price LKR 133,62 LKR/liter 10%
Reduced gasoline price Tariff price LKR 100,62 LKR/liter 10%
Increased electricity rate Tariff price LKR +20%
117 LKR/liter 10%
Reduced electricity rate Tariff price LKR -20% 117 LKR/liter 10%
Table 7 demonstrates the changes in parameters for the sensitivity analysis. Since the electricity price differs each month there is no set price, the collected values from the case study will be used for all baseline value calculations. On the electricity rate sensitivity analysis calculations, a 20 percent increase and decline will be added on each month’s electricity bill.
4.3.3.1 Discount rate
As stated in section 4.2.1, the discount rate for a household is between 3% to 6%. However, in countries like Sri Lanka higher risks are assumed and this is why the span 3% - 6% is considerably low. The sensitivity analysis of the discount rate will therefore be made with a span of ±50%. Hence, a 5% and a 15% discount rate as the variation.
4.3.3.2 Gasoline price
As mentioned earlier, the gasoline price has been 117 LRK/liter the past two years. Based on the documented data from the user in the case study, scenario two, there has been an average decrease of 14% of the gasoline price between august 2010 and mars 2018. Because of this, the sensitivity analysis will examine the effect on NPV and SPB if the price would increase or decrease 14% in the following years. Hence, the price of 133,38 LRK/liter and 100,62 LRK/liter will be examined.
4.3.3.3 Electricity price
The electricity tariff for domestic purposes was implemented so that the wealthy could subsidize the cost for the poor. The wealthy part of the population who uses excessive amounts of energy will pay a higher rate to recover the loss from selling electricity at a low price to the low-income consumers.
However, since the part of the population who is mostly implementing solar panels is the wealthier one, and thereby reducing their electricity bills, the utility companies will suffer a financial loss eventually (Public Utilities Commission of Sri Lanka 2016). Because of this, it is uncertain how long the current tariff system will last. In order to examine what would happen if the utility companies would change the tariffs, a span of ±20%will be investigated for the change in electricity price.
5. Results
In the following section the results from the NPV, Payback and sensitivity analysis is presented along with some discussion that will later be expanded in section 6.
5.1 Results from the case study
5.1.1 Net Present Value on the grid-connected PV system and the electric vehicle
In this section the NPV of the different variations of distance traveled (explained in section 3.4) and life-cycle are presented.
Figure 10: Net Present Value based on variations of driving patterns and life-cycle of the system.
As demonstrated in Figure 10, a user will get a better NPV with a higher distance traveled with the EV. The higher travel distance with a 9 year life-cycle has the best NPV with a negative value of minus 2,6 MLKR. This shows that even though the electricity bill is more expensive with the high distance traveled, since the car needs to be charged more, the gasoline price that the user saves by driving more covers that cost and makes the system more beneficial to the user.
This also applies to the user in scenario two that saves less money from the electricity bill than in scenario one when comparing the usage before and after implementing the system. The user in scenario two drives less then the user in scenario one and therefore has a lower gasoline usage, which affects his NPV negatively.
