The present paper gives a detailed analysis of the electricity market in Nepal and estimates the potential of DSM in the country for two sectors – the domestic and industrial sector. The work will involve merit rating and cost benefit analysis of the measures with the help of Conservation Supply Curves. It determines the economic achievable energy savings of different measures like power factor correction, efficient lighting, time of day tariffs etc. through DSM interventions. Recommendations from this study will provide insight and data for utility and energy experts, as well as policy makers, in terms of electrical energy that could be saved through DSM interventions. Additionally, the outcomes are relevant for similar cases of countries and regions of emerging and regulated electricity markets with comparable challenges of energy shortages.
The electricity sector in Nepal, a slowly rising economy, can be classified as a small electricity system, facing a wide variety of challenges today, like power deficit with an average of 12 hours of load shedding every day throughout the year. Nepal’s vested interest lies in building a reliable electricity surplus network, to realise its vision of graduating from the category of least developed countries (LDC). One approach which the country’s utility has missed so far to make its network reliable is demand side management (DSM). DSM in Nepal is in a dismal state with almost no activity carried out by Nepal electricity authority, the government owned utility. The technical & economic potential of end use energy efficiency in Nepal is huge and is yet to be realized. DSM and end use energy efficiency can have a significant impact on energy savings and bringing down the peak demand.
Contents
Acknowledgement
Abstract
List of Figures
List of Tables
List of Abbreviation
1. Introduction
1.1 Background of the research
1.1. Structure of the report
2. Electricity sector in Nepal
2.1. Background of the Nepal’s electricity sector
2.1.1. State of generation in Nepal
2.1.2. State of transmission in Nepal Electricity Authority
2.1.3. State of distribution in Nepal electricity authority
2.2. Peak demand and energy balance for Nepal
2.3. Stand-alone diesel generation in Nepal
2.4. Load shedding
3. Demand Side Management (DSM)
3.1. DSM historical development
3.2. Concept of DSM
3.3. Generic load shapes changes
4. DSM in Nepal
4.1. Studies and researches done in the past
4.2. The current state of DSM in Nepal
4.3. The way forward and the study
5. Research methodology
5.1. Data collection
5.2. Questionnaire
5.3. Expert opinion/ Interviews
5.4. Energy audit reports compilation
5.5. Project total resource cost test analysis
6. Load research and characterization of Nepal electricity authority’s service
6.1. Load research findings
6.2. Structure of service and tariff
6.3. Electricity use indicator
6.3.1. Connected load
6.3.2. Energy sales
6.3.3. Revenue
6.3.4. Connected load per service
6.4. Growth of Nepal’s electricity sector
7. Model formulation
7.1. Energy conservation supply curve
7.1.1. Economic analysis of energy efficiency measures for domestic sector
7.1.2. Economic analysis of energy efficiency measure for industrial sector
8. Result & Discussion
8.1. Domestic sector
8.2. Industrial sector
8.3. Impact of DSM technologies intervention
9. Barriers and perceived issues in relation to development and implementation of DSM programs
9.1. DSM program capabilities at NEA
9.2. Lack of consumer and information
9.3. Load research and consumer indexing
9.4. Revenue/tariff
9.5. Policy barrier
9.6. Market barrier
9.7. Technological barrier
9.8. Financial barrier
10. Policy recommendation & program concept
10.1. Ministerial level ownership and commitment
10.2. Developing an institutional framework for energy efficiency
10.3. Passing of legislation and regulation
10.4. Incentivizing energy efficiency
11. Proposed DSM action plan and suggested DSM program pre-scope
12. Limitation of the study and conclusion
12.1. Limitations of the study
12.2. Conclusion
13. References
14. Appendix
14.1. Appendix A: Nepali BS calendar to English AD calendar
14.2. Appendix B: Installed capacity and peak demand for Nepal
14.3. Appendix C: Energy generation and import for last ten years
14.4. Appendix D: Sample system load curve data
14.5. Appendix E: Monthly peak demand for Nepal electricity authority 2014-15
14.6. Appendix F: Nepal electricity authority monthly sample system load curve for 2014 -15
14.7. Appendix G: Characterization of Nepal electricity authority service by electricity use Indicators
14.8. Appendix H: DSM measures for the domestic sector
14.9. Appendix I: DSM measure quantified for the Industrial sector
14.10. Appendix J: Penetration of technologies in domestic sector
14.10.1. Saturation of technologies tariff meter class wise
14.10.2. Average power rating of DSM measures for domestic measure
14.11. Appendix K: Conservation supply curve for domestic sector
14.11.1. Saved energy
14.11.2. Saved capacity
14.12. Appendix L: List of People who were Interviewed
14.13. Appendix M: Questionnaire used for Household Electricity use
Acknowledgement
This Study on Demand Side Management and end use energy efficiency is the result of my work for the master’s degree at the Chair of energy economics, at Brandenburg University of Technology, Cottbus-Senftenberg in cooperation with the Nepal Energy Efficiency Programme (NEEP). NEEP is implemented by Ministry of Energy, Government of Nepal with technical assistance provided by Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), acting on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ).
First of all, I would like to acknowledge Prof. Dr. Felix Müsgens for giving me this opportunity to write my master thesis under his supervision. In the same line I would like to express my gratitude to Mr. Frank Boemer, Chief Technical advisor, NEEP for giving me this opportunity and his continuous support and direction throughout the study. I would like to thank Mr. D. Pawan Kumar for his technical guidance and consultation throughout the study, this study would not have been possible without his support. I am also indebted of Dipl. -Vw. Sebastian Kreuz for his continuous guidance throughout the study. This acknowledgment cannot be complete without thanking Dr. Narayan Chaulagain for his guidance and his policy inputs. Finally, I am grateful to my family for providing me support and inspiration.
Shardul Tiwari
Cottbus, September 30, 2016
Abstract
Electricity sector in Nepal can be classified as a small electricity system, facing wide variety of challenges today in a slowly rising economy. One of the major challenges it faces is power deficit in the electricity system, with an average of 12 hours of load shedding every day throughout the year. Nepal’s vested interest lies in building a reliable electricity surplus network, to realize its vision of graduating from the category of least developed countries (LDC) by 2022. One approach which the country’s utility has missed so far, to make its network reliable, is demand side management (DSM). DSM in Nepal is in a dismal state with almost no activity carried out by Nepal electricity authority (NEA), which is the government owned utility. The technical & economic potential of end use energy efficiency (EE) in Nepal is huge and is yet to be realized. DSM and end use EE can have a significant impact on energy savings and bringing down the peak demand. In the future, it can also play a major role in the planning of future electricity generation, which currently is only unidirectional from the supply side. This paves the way for a detailed analysis of electricity market in Nepal and estimating the potential of DSM in the country.
This master thesis attempts to determine the achievable energy savings by different measures like power factor correction, efficient lighting, time of day tariffs etc. through DSM interventions in Nepal. The research is done to bring out the sector-wise DSM intervention and electricity savings, that could be achieved using different measures for domestic and industrial sectors. The work will involve merit rating and cost benefit analysis of the measures. Recommendations from this study will provide insight and data to the policy makers, in terms of electrical energy that could be saved through DSM interventions. The study, in the end, will also provide a brief overview of the barriers for DSM programs in Nepal and policy recommendations to counter the same.
List of Figures
Figure 1 Primary energy consumption of Nepal
Figure 2 Nepal electricity authority total energy available and peak energy demand
Figure 3 Peak demand vs installed capacity Nepal
Figure 4 Installed capacities of power plants in Nepa
Figure 5 Contribution of major electricity suppliers in Nepal
Figure 6 A comparison of Nepal electricity authority T&D losses to selected south Asian countries losses
Figure 7 Weekly load shedding profile for Nepal
Figure 8 DSM planning framework
Figure 9 Generic load shapes
Figure 10 Contribution of different supply side to system peak
Figure 11 Sector wise energy sales for NEA
Figure 12 Each sector contribution to NEA's revenue
Figure 13 Cummulative growth rate of Nepal's different energy sector
Figure 14 Sample of conservation supply curve
Figure 15 Conservation supply curve for energy savings in domestic sector
Figure 16 Conservation supply curve for demand saving in domestic sector
Figure 17 Conservation supply curve for energy savings in industrial sector
Figure 18 Conservation supply curve for peak demand saved in industrial sector
Figure 19 Energy savings achieved by implementing DSM measures in both sectors
Figure 20 Proposed action plan for DSM program implementation
Figure 21 Sample load curves for Nepal Electricity authority
Figure 22 Conservation supply of all technologies assessed for saved energy in domestic sector
Figure 23 Conservation supply curve for all technologies assessed for demand saving in domestic sector
List of Tables
Table 1 Descriptive table of DSM measures for domestic sector
Table 2 Descriptive table of DSM measure for industrial sector
Table 3 Supplement information for conservation supply curve in figure 14
Table 4 Supplement information for conservation supply curve in figure 15
Table 5 Supplement information for figure 15
Table 6 Supplement information for figure 16
Table 7 Nepali (BS) Calendar to English (AD) Calendar
Table 8 Installed capacity and peak demand of last ten years for Nepal
Table 9 Energy generation and imports for last ten years (NEA,2016-2015)
Table 10 Sample load curve data
Table 11 Monthly peak demand for 2014 - 15
Table 12 Characterization of Nepal’s Electricity Authority’s service by electricity use
Table 13 DSM measure and measure key for domestic sector
Table 14 DSM measure and measure key for industrial sector
Table 15 survey result of penetration of technologies domestic sector
Table 16 Power rating of various appliance in domestic sector
List of Abbreviation
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1. Introduction
1.1 Background of the research
Nepal is a landlocked country in Asia surrounded by the two emerging global economies China and India, both yearning for energy. However, Nepal’s energy scenario is very different and underdeveloped when compared to its neighbors. Though the country’s energy needs and usage patterns, have changed over the years, in the global context of the 21st century is still very rudimentary. Nepal’s population relies largely on traditional biomass for energy use. Fuelwood constitutes 71.1 % of country’s primary energy consumption and biomass in total constitutes roughly 80 % of the total energy consumption (consultant, 2013, pp. 1-9). Most of the households still use traditional three stone fire for cooking and heating in their kitchens, which is not only inefficient but also hazardous to health. To improve this scenario, a number of programs promoting improved biomass cook stove (ICS) have been implemented but with little to no success (shrestha, et al., 2007).
Nepal is endowed with rich natural resources with about 40 % of the forest and shrubland cover (consultant, 2013). Nepal also has a high solar energy potential with average annual direct normal radiation of 4.98 kWh/m[2]/day and estimated 300 sunny days in a year (Centre, 2008). The country is equally rich in water resources with one of the highest glaciers in the world, with the glacial cover of 9.6 % and relatively good monsoon (Matambo, et al., 2011). The country has one of the best natural river water systems in the world, with perennial rivers flowing from north to south providing yearlong water supply.
Having endowed with these many natural resources it’s an irony that the state is staggering under an acute modern energy crisis. It has not been able to convert these renewable energy potentials into modern forms of energy[1]. There are many reasons across the spectrum for Nepal not being able to harness the potential of its renewable energy resource and one of them is the global reliance on fossil fuels which Nepal has out rightly rejected. The country has also decided not to set up any major coal-fired power plants and hence coal is not imported for the same. This led Nepal not to join in the global race of polluting the environment for electricity production[2] and this has come at a cost of the scarcity of electricity in the country. Nepal is relatively poor in its fossil fuel reserves with some 19 coal mines and a production rate of 100 - 200 tons/day and does not have any oil well yet (Rahman, 2013).
Given the vast hydropower potential, Nepal has decided to harness it for electricity trading. Country’s theoretical hydro power potential is estimated to be 83,000 MW with technical, economic harnessing potential estimated to be 42,133 MW (Surendra, et al., 2011). There are studies estimating much higher potential than the figure mentioned (Mathema, et al., 2013), but this is the widely used figure and is used by the Government of Nepal (GoN). With this huge potential of hydropower in the country and the terrain to build the hydropower plants, it’s desolating to see the country’s electricity scenario and that only 1.8 % of it is realized by the nation.
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Figure 1 Primary energy consumption of Nepal (consultant, 2013)
The country is in the list of least developed countries (LDC) and its vested interest lies in making a reliable energy surplus network to realize its vision of graduating from the list of LDC by 2022 (UNCDP, 2016). One of the reasons holding the country back is the unreliable supply of electricity, which affects the industrial production and in turn, is reflected in the economic growth of the country (Nexant, 2003, p. 8).
Nepal is not able to harness its hydropower potential and the state of electricity in the country is such that planned rolling blackouts or as called in Nepal “load shedding” is part of a person’s daily life. The load shedding is a yearlong phenomenon where people do not get electricity for some hours every day which can extend up to 16 hrs a day during winters (Giri, 2015). There is a paucity of electricity supply in the country with only 79 % of people having access to electricity and estimated 60 % are connected to the national grid[3]. Those consumers, connected to the grid receive poor and unreliable supply of electricity (NEA, 2015-16). The per capita electricity consumption Nepal stands the lowest in the South Asia and is amongst the lowest in the world at 128 kWh. This is menial in comparison to China’s 3,297 kWh/ capita for a year.
Nepal electricity authority (NEA) is the state owned utility responsible for generation, transmission and distribution of electricity with unbundled generation side. NEA at present is in a dismal state, unable to fulfill the need for a reliable supply of electricity to its consumers and having a poor state of infrastructure. This is mainly because of the amalgamation of two major factors. The first reason is the dearth of the supply side scenarios in the country which is governed by energy policies and investments in the sector and is not in a preeminent state. The second reason for the poor state of the utility, are the system inefficiencies which are present within NEA. NEA today has one of the highest system losses globally, which is in the range of 25.78 % (NEA, 2015-16). It is incurring financial losses year after year and is not able to cash in the opportunity of growing demand for electricity in the country. NEA in 2011 was bailed out by the GoN by incurring all its losses, but even after that the financial state of utility remains the same (NEA, 2011-12). These energy losses hurt NEA most as the energy losses directly transform into financial losses. The losses of NEA are also high pertaining to the fact that it is a small electrical system[4].
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Figure 2 Nepal electricity authority total energy available and peak energy demand (NEA, 2015-16, p. 137)
The current state of the inefficiencies of NEA can be attributed to a number of reasons. One reason which stands out and reflects the state of NEA is that utility does not look into the demand side inefficiencies. NEA has barely made the use of integrated resource planning (IRP) for planning its system. Though NEA has taken steps to improve the system efficiencies, very little is being done to coherently put together a strategy to improve the end use energy efficiency. NEA with the support of donor organizations along with the world bank (WB) worked on demand side management (DSM) and end use energy efficiency, but those were baby steps in a system which holds tremendous potential. In the industrial sector end use EE is in dismal state. Comparing the energy audit results shows that industries in Nepal lies in the bottom of the global industrial efficiency benchmarking developed by UNID for the industries present in the country (Saygin, et al., 2010).
NEA in the recent past has shown interest in the development of DSM and end use energy efficiency as a tool to help curb the electricity crisis in the country and have also considered the advantage of DSM in the long run. This is mainly because of the worldwide success of DSM programs and its use by utilities all across the globe (Sarkar & Singh, 2010). As there are limited studies in these lines available for NEA in the country, this particular study tries to look into two perspectives and fill the current research gap in the country. Firstly, identifying the gaps in the NEA for smooth implementation of DSM and end use energy efficiency programs, i.e. the areas those should be targeted within the utility. Second to bring out the sector wise DSM measure that could result in potential electricity savings and peak demand savings. The study will end, by briefly conveying the policy interventions required and the means needed to implement these DSM programs.
1.1. Structure of the report
The study is organized in a series of chapters. chapter 2 discusses the grid based electricity sector in Nepal and explains about the state of utility and the scenario of supply side in Nepal. The next chapter explains about the basics of DSM and its development globally. The chapter explains about DSM concept briefly and its potential pertaining to energy sector. Chapter 4 explains the state of DSM in the country and how it has slowly evolved over the years in Nepal. This chapter will also make the way for the current DSM study. Chapter 5 briefly defines the methodology used for the analysis. Chapter 6 brings out the load research done for the utility along with the findings of the study which also forms the basis for DSM. The chapter further goes into details of characterizing NEA service according to electricity use. The subsequent two chapters are the description of analysis and results respectively. Chapter 10 brings out the barriers and challenges which were faced for the DSM study and challenges which are required to be overcome for implementation of DSM program in the country. Chapters of 10 and 11 propositions for policy recommendations, the outline of proposed action plan and the DSM projects which could be undertaken by the utility. The report ends with a brief conclusion and annexure of the data collected, analysis and the results.
2. Electricity sector in Nepal
2.1. Background of the Nepal’s electricity sector
Nepal electricity authority (NEA), the state-owned utility was established in August 1985. NEA formation was the result of the continuous electricity reforms in Nepal (Nepal & Jamsab, 2015). Nepal today has a total installed capacity of 851 MW, 94 % of which are the hydropower plants. NEA owns 473 MW of the hydropower plants and rests 324.5 MW are owned by the independent power producers (IPP)[5]. IPP have been active in Nepal since 2001, after the revision of the hydropower development policy 1992 to water resource development policy 2001. The objective of the policy was to attract more investment into the capital intensive hydropower plant sector (Nepal & Jamasb, 2011, p. 244). This led to development of IPP, which builds the plants and supplies the electricity to NEA under a power purchase agreement (PPA).
NEA is a vertically integrated utility, which provides electricity to all the classes and segments of consumers in Nepal (Nepal & Jamasb, 2011). Being the only state-owned utility in a small electricity system[6], the NEA has a consumer base of little over 2.9 million consumers (NEA, 2015-16). Electricity from the utility constitutes 3 % of the total energy consumption in Nepal (consultant, 2013). The electricity system in the country is still in its developing state and is constantly growing under various reforms. The major reforms in the Nepal electricity sector were made to attract foreign and domestic private investments, promote efficiency and to strengthen the quality of supply at an affordable cost to the consumers (Nepal & Jamasb, 2011). The aim was to allow the utility to sufficiently recover their cost (which is again a major challenge in Nepal). However, despite all these reforms and being endowed with rich water resources, the country is struggling today to bridge the gap between supply and demand which has widened over the past ten years (see appendix B).
From the figure 3 below it can be articulated that the supply side did not increase at the same pace as the demand side in the last ten years in the country. This can be reflected that peak demand has grown at a CAGR of 8.8 % whereas the supply options have only come at a rate of 4 % CAGR (see appendix B). It is also noticeable that this is the suppressed peak demand[7] and not the real peak of the system. The supply options available are the power plants which were available for the generation in that particular and hence a small drop can also be seen in the year 2015 as a small capacity was out of operation in that year. This also shows the vulnerability of such a small system.
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Figure 3 Peak demand vs installed capacity Nepal (see appendix C)
The Government of Nepal (GoN), ministry of energy (MoEn) recently came up with “the action plan on national energy crisis mitigation and electricity development decade, 2016”. This main objective of the plan is to provide electricity to 100 %
Nepalese people in the next ten years, which stands at 79 % right now. Under the plan, the GoN also plans to add 10,000 MW of hydro generation capacities in next ten years (MoEn, 2016). The GoN is planning a major restructuring program in the electric power sector with the key objectives and the deadlines for the same. The objectives of the action plan relevant to the study includes the following points from the action plan:
- Formulation of new electricity act and national electricity regulatory commission act
- Arrangement in developing electricity projects of 500 and above
- Adopt generation mix in electricity by type of energy source
- Provide electricity to all Nepalese people within the next 10 years
- Forecast actual electricity demand
- Institutionalize energy efficiency in Nepal and carry out electricity conservation programs
- Bring Demand Side Management to improve the financial health of NEA
This action plan targets to end the electricity crisis which has engulfed country for the last ten years
2.1.1. State of generation in Nepal
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Figure 4 Installed capacities of power plants in Nepal (NEA, 2014-15)
The generation of electricity in Nepal comes under the generation directorate of NEA The installed capacity in Nepal is hydro dominant and comprises about 94 % of the total installed capacity connected to the grid. The total installed capacity in the country which is connected to the grid stands at 851.25 MW out of which 768 MW was available for the year 2015-16. Out of the total hydropower plants installed capacity of 92 MW is of the reservoir type hydropower whereas the rest 759 MW are the Run of River (ROR) power plants (NEA, 2015-16). Thermal power plants constitute 53.41 MW constituting 6 % of the total installed capacity. Solar, wind and other renewable energies constitute only a fraction of the total installed capacity in the country.
The NEA has concluded, the power purchase agreement for 83 power plants which will add another 1521 MW to the total installed capacity in the next five to ten years depending on the progress of the construction (NEA, 2014-15).
Though these figures look promising, Nepal has been unable to add enough generation capacities in its supply mix at adequate pace. In the last ten years, the capacity addition in the Nepal electricity system has only taken place at a rate of 4 % CAGR, which is slow considering the amount of supply deficit and low absolute numbers of installed capacity (see appendix C). The contribution of NEA and IPP in the peak demand, supply has also reduced in the past ten years as can be seen in figure 5 above. This, in turn, increases NEA dependency on import of electricity from India and makes NEA vulnerable to the security of its supply.
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Figure 5 Contribution of major electricity suppliers in Nepal (see appendix C)
Looking at the current state of utility in Nepal there is no denial that there is a dearth of generation in Nepal. In the long run, Nepal needs a solution for peak load demand because hydropower plants in Nepal are mainly base load power plants (except few reservoir types) which are run of river (RoR) type hydropower plants with no peak storage capacity. In the case of RoR power plants, the supply becomes worse in winters (dry season) as the power production of these power plants drops to as low as 20 % of the total installed capacity (NEA, 2015-16). This drop in power production is mainly because of the significant drop of the water level in the rivers. The output of RoR plant depends largely on the amount of water in the rivers as it is the only variable factor in energy generation, the yearlong average load factor[8] for RoR plants is 51 % in Nepal (NEA, 2015-16). This is much lower than the load factor for coal or nuclear power plants and hence Nepal needs much more capacity to fulfill its peak energy needs in winter when plant load factor drops to as low as 20 % (DUKES, 2012). To make it worse the peak demand of Nepal occurs in winters due to seasonality. This will be discussed further in the subsequent chapters.
2.1.2. State of transmission in Nepal Electricity Authority
Power transmission in the country comes under the transmission directorate of the utility. Power in the country is supplied at various levels of voltage (132 kV, 66 kV, 11 kV and 415 V, LT) through a network of 41 grid substations with total installed capacity of 2,131.55 MVA (NEA, 2014-15). The length of transmission line supplying the electricity is 2,848.86 Kms. NEA has a small grid network which makes it difficult to control the system under power deficit scenario as any sudden demand change can result in a looped tripping which NEA observes frequently especially during the evening peaks.
NEA plans to expand its existing transmission grid length by adding another 2,317 km of transmission lines. This includes the 1,010 km of 132 KV line, 659 km of 220 KV and 648 km of double circuit 400 KV transmission line. These transmission lines are under construction. NEA is also working to improve its quality and efficiency of supply by adding more substations and increasing the capacity of existing substation. The NEA has a 506.5 MVA capacity of grid substation under construction (NEA, 2014-15).
2.1.3. State of distribution in Nepal electricity authority
The distribution and consumer service directorate (DCSD) works for the distribution of electricity in Nepal. NEA currently serves 2.9 million consumers with a strong domestic consumer base of 94 % which it serves from its eight regional offices in Nepal (NEA, 2015-16). NEA has high distribution losses of the tunes of 12 % to 28 % depending on the distribution divisions. The major losses for NEA at distribution level can be accounted to the commercial losses[9] as acknowledged by the utility.
NEA in the fiscal year 2014-15 generated revenue of NPR 31,958.41 million, with the total operating expense of NPR 32,562.79 million. The NEA has an operating deficit of NPR 604.38 million. The net income losses were of the tunes of NPR 6460 million for the fiscal year 2014-15 (NEA, 2014-15).
2.2. Peak demand and energy balance for Nepal
There is no real demand curve estimated by the utility in Nepal as there is never enough electricity in the system. The peak demand with the utility is the suppressed peak demand[10] as the real demand is never met in the system. NEA has also done very little to establish the real system peak. NPC estimated that by 2030, 10,000 MW of installed capacity would be required to meet the peak demand whereas NEA forecast is only 3,500 MW (NEA, 2014-15). The peak demand deficit has increased over the period of the last ten years, in the year 2014-15 there was a peak demand deficit of more than 50 %, i.e. The utility was only able to supply 50 % of its peak load. Nepal’s peak demand is growing at a CAGR of 9 % for the last ten years and supply side is not able to catch up with this growing demand (see appendix B).
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Figure 6 A comparison of Nepal electricity authority T&D losses to selected south Asian countries losses
In Nepal’s electricity system, only 75 % of the energy generated reaches the consumers, rest 25 % are the losses. This has led to huge energy and financial loss to NEA. The energy losses of utility in the country are amongst highest in South Asia. Of the 25 % loss NEA acknowledges the fact that major share of these losses are the commercial losses[11]. The figure 6 compares the losses of NEA with selected south Asian countries and result clearly show the extent of utility losses. The national planning commission (NPC), GoN targeted to bring down NEA losses by 5 % to 20 % in its three-year plan till 2015 (NPC, 2012). The target was not achieved by NEA but if NEA would reduce the losses it would add NPR 2,270 million[12] in its first year of revenue collection. To come out of the vicious cycle of losses NEA would have to work very effectively to reduce it commercial losses and one of the ways is to improve its demand side efficiencies.
2.3. Stand-alone diesel generation in Nepal
Nepal faces rampant load shedding around the year which reaches more often than not maximum of sixteen hours in a day as of 2015-16. The average load shedding for year is 12 hours per day (NEA, 2014-15) and this has be the trend for last ten years. This shortage of electricity supply from the state utility has led people to resort to other means of electricity which are mainly the battery backups in the domestic sector, but batteries have limited storage capacity and with the grid supply of only 8 hours a day during winters, it becomes difficult to even charge the UPS batteries for load shedding period. This has led consumers to switch to more expensive means of electricity supply one of which is diesel generation (DG) sets.
The DG sets are prominently used by the industrial and commercial sectors for generating electricity during the load shedding period. The fuel for the DG sets is imported from India. Nepal completely relies on India for its diesel import. With the increasing load shedding, year after year Nepal’s reliance on DG sets is at an all-time high with increased diesel imports from India. As per Nepal Oil corporation (NOC) around 30 - 40 % of the country’s total imported diesel is used to generate electricity during load shedding. NOC has estimated that country generates approximately 531 MW of electricity using DG sets (World Bank, 2014). This is highest amongst all the indigenous supply available in the country. Since 2006 there is a sharp rise in diesel import from India and this can be clearly attributed to the fact that load shedding in the country started from 2006.
A recent report from World Bank qualitatively analyses that consumption of the diesel in the country is linked to the number of load shedding hours. The estimated annual diesel consumption for captivated power generation is 72 million liters. The cost of generating electricity using Diesel Generation is estimated to be somewhere between 40-70 NPR/kWh, depending on the efficiency of DG set (World Bank, 2014). This is much higher than NEA tariff for electricity and shows the customer’s willingness to pay for electricity if provided by NEA as they are paying much higher prices for electricity currently with DG sets (survey results). With high running cost of DG sets, it is becoming increasingly difficult for producers/manufacturers in Nepal to compete even in the domestic market which is flooded with imported products from India and China even after heavy taxation from the government (Pearson, 2014).
2.4. Load shedding
Load shedding is a yearlong phenomenon in Nepal. As electricity generated from RoR hydropower plants largely depends on the river discharge (Anagnostopoulos & Papantonis, 2007). The NEA load shedding hour are not evenly distributed throughout the year. The load shedding significantly increases during dry season (December - February) and this is also the time when demand is highest for the year (NEA, 2015-16). The load shedding is least during the wet season (June- august). The load shedding is eating the utility from inside as utility is not able to cash in the potential market of electricity which is growing at a brisk pace in the country.
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Figure 7 Weekly load shedding profile for Nepal (NEA, 2014-15)
3. Demand Side Management (DSM)
Demand side management refers to the actions undertaken by the utility at the customer side of the meter that changes the usage pattern of electricity demand and consumption. DSM involves the implementation of the measures that enables the consumer to reduce electricity cost by using electricity more efficiently and reducing the utility cost, by virtue of changes in the shape of the load curve.
DSM as coined by Clark W. Gellings “Is the planning and implementation of those electric utility activities designed to influence customer uses of electricity in ways that will produce desired changes in the utility’s load shape” (Gellings, 1985) DSM traditionally is seen as a way to reduce peak electricity demand so that utilities can delay building peak electricity capacity. DSM and end use EE has a major role to play to defer investments in generation transmission and distribution segments (Gellings & Chamberlin, 1993). DSM can be applied to consumption of energy in general, but in this particular study we will study, about the DSM in the electricity sector.
3.1. DSM historical development
In the pre-DSM era of 1960s utilities use to build capacities to meet the growing demand, relying on energy forecasts. There were few classic load management programs to preserve the system reliability, apart from that there was no specific term called DSM (Eto, 1996).
The term Demand Side Management (DSM) was coined as an outcome of the planning process used by utilities in US mid-1970’s after the oil crisis of 1973. The industrialized nations globally were pushed to reduce their energy consumption and hence mandating to work towards end use energy efficiency measures. One of the early examples of this was the National energy conservation policy act, 1978 in the US which mandated utilities to work toward energy efficiency and DSM. US government was amongst the first ones to push for DSM and EE activities (Gellings & Chamberlin, 1993).
The DSM programs of the mid 1970s and 80s were largely pilot and testing projects. These were mainly, pushed by state regulators and were the driving force to mitigate energy crisis. After the initial research and success of DSM and end use EE the utilities started pushing DSM aggressively. In the era from the mid-1980s to 1990s, the first generation programs were implemented (Faruqui, 2012). These programs emphasized on cash rebates and low-interest financing to buy efficient appliances and build more efficient buildings. California developed a standard practice methodology for assessing the cost effectiveness of DSM which was later widely used by other states in America (Faruqui, 2012).
Though successful in that period these programs were very cumbersome and not sustainable over the long haul. This in turn gave rise to the second generation DSM programs in the U.S. after the power crisis hit western U.S in 2000/01. It was estimated that if real time pricing is introduced in the commercial and industrial sector, peak demand would fall by 2.5 %, resulting in a drop of 18 % in wholesale electricity prices (Faruqui, 2012). This turned the definite model of a second generation DSM program which emphasized on reduction in customer load during critical times of low reliability.
Currently, the DSM programs are the third generation programs which call for dynamic pricing, behavioral change programs and the likes of on - bill financing programs. Learning from past international experiences of DSM, Nepal could leapfrog into the third generation of DSM programs (Lampropoulos, et al., 2013).
3.2. Concept of DSM
DSM today is a major element of utility planning to accomplish the goal of integrated resource planning (IRP). DSM provides a three side perspective. It pays benefits to the consumers by reducing the energy cost of the production. The advantage of utility is that they can better manage their supply (Thakur & Chakraborty, 2016). This is particularly true for Nepal as it does not have enough generation capacity. This can be a very prominent business model as the energy which is saved can be catered by the utility to the dedicated consumers which pay much higher price for the electricity. In Nepal’s case, this can translate into higher profits which are not necessarily the case for all the utilities[13]. The reliability of supply will also be increased with a flat load curve (Setlhaolo & Xia, 2016), which is again what NEA is striving hard to achieve. DSM in Nepal is not only a means to save energy, but is a provider of electricity itself.
The concept of DSM is well developed globally and at the least to a level at which work in Nepal is to be executed in the current scenario. Classic DSM concepts are carried out and applied globally. The DSM measures have achieved the energy savings and are successful if implemented with the right and transparent approach (Baker & Battle, 1992, pp. 31-98, 229-248).
The overall DSM planning is a very complex procedure and is primarily employed by the utility for the planning of the electricity system. The DSM planning framework is a very integrated process. The image below presents the overview of the DSM planning framework.
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Figure 8 DSM planning framework (developed from (Gellings & Chamberlin, 1993))
The DSM planning process is a very intricate process, requiring the modeling of the complete scenario and is generally done by the utility. The compass of this subject would be to concentrate on the DSM evaluation and selection of measures for particular sectors. One of the aims of the DSM is the generic load shape objectives which utility wants to achieve using DSM. This is likewise one of the most important objectives of DSM which helps carry out the other aims of the IRP structure
3.3. Generic load shapes changes
In the rules of order to understand what DSM is and what utility is trying to achieve using DSM, it is demanded that we interpret the load shape changing objective (Lampropoulos, et al., 2013). Though, there are infinitive combinations of load shape change possibilities we will look into few which are of relevance to Nepal.
Peak clipping: This is one of the most classic forms of load management. This is the reduction of the peak load by direct load control. Most utilities use this to reducing peaking capacity or capacity purchase and do it on the most probable days of the system (Gellings, 1985). NEA is currently using the extreme form of peak clipping which is load shedding.
Valley filling: Valley filling is the second classical approach of DSM this is generally done when the long run incremental cost is less than the average price of electricity. If we add properly priced off-peak load it can diminish the median cost of electricity (Gellings, 1985). One of the most popular ways of valley filling is using the thermal energy storage.
Load shifting: This is another classic form of load management. It involves the building of off-peak loads by shifting the peak load to the time of the off-peak loads. It helps utilities to flatten its load curve and avoid the peak generation capacity (Gellings, 1985).
The other form of load shape changes is Strategic conservation, strategic load growth, flexible load shape, flexible reliability. It is interesting to observe here that these are interesting from a theoretical tip of view, but vary largely depending on the type of utility.
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Figure 9 Generic load shapes (Gellings & Chamberlin, 1993)
DSM is aimed at addressing the issues of cost reduction, environmental and social improvement, reliability and network issues and to in some context to increase the access of electricity.
4. DSM in Nepal
4.1. Studies and researches done in the past
Nepal now has more than 25 years of engagement with DSM and end use energy efficiency from a theoretical context. The first work on DSM in Nepal was conducted by eletricite de France (EdF) which was “A study on load curve analysis”. The study tried to identify the potential for load management in the country. The research took the role of classic DSM methodologies of utility test, participant test, and total resource cost test. The estimated potential for load reduction from the study was calculated to be 8 MW. EdF developed a structure of the load profiles. The night time load for domestic sector came to about 60 MW while for the industry, it was 20 MW (Yang, 2006, p. 2678) The load for households was considered to be an exaggerated figure and the study did not lead to the positive development of DSM in Nepal.
A research was also delivered in the early 1990s regarding the impact of selected residential sector DSM measure in the electric utility of Nepal. The research targeted the right sector and with the right approach to studying the societal and participant perspective. The DSM study proposed replacing the incandescent bulbs with the CFL bulbs. The study made clear recommendations on the economic attractiveness of DSM technologies even in the 1990s, considering the capital required for hydropower plants (Shrestha & Bhattarai, 1993).
The Water Energy and Commission Secretariat (WECS) in 1996 developed the energy sector perspective concept. The paper presented the concept of DSM within the governmental context. The study reviewed the country’s electricity consumption, forecast electrical loads and took cost-benefit analysis of replacing incandescent with CFL in the household sector. This was the second attempt to appraise the cost effectiveness of replacing incandescent lamps with CFL and was introduced in the 1990s (Yang, 2006, p. 2677). The work took in its restriction as there was a very limited literature review acknowledged by the authors. There were limited follow up activities done after that in the DSM.
A detailed study on the energy efficiency potential of the industrial sector of Nepal was conducted under the Environmental Sector Program support (ESPS) funded by the DANIDA (Kunwar, 2011). Under this program, the energy audit was conducted across the industrial sector in Nepal and the study came out with the healthy recommendation of the energy efficiency activities that could be carried out in the industry. The program was the first one to train the energy auditor in Nepal (ESPS, 2004). Due to political instability in the country, the program results were not implemented successfully. The program also mainly focused on the industrial efficiency and not core DSM in the sector but was a form of DSM (ESPS, 2004).
A project in 2003 by Nexant for USAID/SARI program was done for the development of an action plan ballast[14] standard and labeling program. The report described in detail the ballast market globally, and also studied the Nepal’s ballast market and its potential (Nexant, 2003). The project came out with the impact assessment of standards and labeling programs in Nepal. The result of it showcased that a focused five (5) year plan could result in the cumulative energy saving in the range of 27 - 43 GWh and reduction in peak demand of 7- 12 MW (Nexant, 2003). This was a big number only for one technology with the peak demand of Nepal is 2006 only 603 MW (NEA, 2006-2007). After the conclusion of the study, not very concrete steps were taken to push forward the ballast technology in Nepal and the project came to an abrupt end.
One of the most noticeable studies on DSM potential in Nepal was done by Mr. Ming Yang (Yang, 2006). The study assessed and summarized the potential of DSM technologies in Nepal and their fiscal viability. The paper mentioned in brief the nature of power systems in the country and clearly pointed out the gaps in the sector. He selected the technologies for assessment which mainly included power factor (PF) correction, efficient lighting, and intelligent motor control. Though the study is one of the best studies available on a public platform in Nepal and clearly depicts the need of DSM in the country, it did not take into account the various appliances and their usage in different sectors. The results and discussion were majorly limited to lighting. One major thing which was lacking from the study was load profiling of the rural area along with the load shedding profiling for the different sector, which could have had a direct impact on the approach to DSM. Nevertheless, in today’s context, though the principles of study are still relevant, the major drawback of the study is that it was done in 2005 and to study the use of CFL as EE appliance (Yang, 2006, p. 2677). While with today’s technological advancement CFLs have become archaic and are being replaced by LEDs.
A very elaborate DSM study was done by the Asian development bank on DSM in Nepal (Bank, 2005). The study did load research in the country across different end users and came out with a detailed load profile of different sectors in Nepal. The work of this study was taken to the next step by the project of World bank (Consult, 2010). The study of world bank not only identified the potential of DSM in Nepal but also gave the policy recommendations on how DSM programs can be implemented in the country. This project was first of its kind in Nepal which partly looked into consumer indexing and a consumer wise load profile of the country. It also looked into the appliance wise load across the country. The report came out with a very detailed load analysis across several sectors. The report was then submitted to the NEA and under the program 750,000 CFL was disseminated across the country (Consult, 2009). NEA then established a DSM cell in its division and distribution under the consumer service directorate which is in a dormant state at present.
For many years now DSM is unsuccessfully trying to be part of electrical utility in Nepal and come into system planning of an electric utility in Nepal. The study of lighting is completed quite a few times in the last decade but no real actions took place as there was no umbrella institution to push DSM. Over the years the technological advancement has taken place, in the lighting sector, it is LEDs which are more efficient than CFL (Mills & Schleich, 2014). The star rated appliances has gone at a higher valuation and thus the old studies cannot help today’s policy makers to measure the impact. It is time for studies to look beyond simply the lighting appliance and come out with more detailed macro DSM measures assessment targeting the relevant specific consumer group.
4.2. The current state of DSM in Nepal
Nepal currently is lacking far behind in the DSM activities both from regional and international standards with only a handful of DSM activities (NEA, 2014-15). This is the cumulative effect of a number of reasons which include political will, lack of institutional framework /body to promote end use energy efficiency and also because of the lack of quality research and study in showing the humungous potential of DSM in the country. The deficiency of information in the DSM sector has made it difficult to convince policy makers to see the need of DSM in the country under the current state of the energy crisis. People have started to believe that when we do not get enough energy it is more adept to use as much as we want whenever there is supply (which is also a need). This leads to not understating the repercussions of inefficient use of energy or to say advantages of using electricity efficiently.
To recapitulate, the concept of DSM is new in Nepal. It will be an isolated approach to work in DSM and end use energy efficiency in Nepal without policies, regulations and institutional framework towards end use energy efficiency which country is lagging[15]. DSM is a concept in the wider scope of energy efficiency. Though DSM does not always target energy efficiency, simply put, it’s a constituent in the broad scope of energy efficiency. It is not potential to read and implement DSM in isolation and out of the scope from energy efficiency and end use energy efficiency cannot be implemented without DSM.
There is no denying that the state of DSM in the country is really primitive. The potential work which is executed in the DSM and end use energy efficiency is only the distribution of 750,000 CFL lamps under the ADB’s, DCSD program (Consult, 2009). The DSM, though now is coming under the wider scope of energy efficiency and government has also taken Institutionalizing on its agenda under its recently rolled “Nepal Energy Crisis Prevention and Electricity Development Decade Related Action Plan”
There are major initiatives currently carried out by MoEn with the support from GIZ in the field of energy efficiency. The joint program called NEEP is trying to support in creating an institution for energy efficiency in the country by bringing energy efficiency strategy for Nepal (NEEP, 2016). This is one positive step towards EE as what Nepal needs is an institution for EE which will act as an umbrella to implement DSM measures. In parallel to the action plan, the program has also started to look into the potential of DSM in Nepal in detail.
4.3. The way forward and the study
There is an urgent need to ascertain cost-effective areas for DSM interventions which will be able to help mitigate Nepal’s electricity crisis. Historically, all the studies of DSM in Nepal were desk studies and very little translated into the on-site working programs. This work will now investigate the DSM within the wider scope of energy efficiency. It will try to fill in the gap between the policy maker and the implementer of DSM and attempt to show that DSM can be a potential energy provider and act as virtual power plant.
5. Research methodology
The research methodology adopted for the analysis of DSM measures involves extensive data collection, data mining, and data analysis. This includes expert interviews, case study, sample surveys and cost benefit assessments. The research methodology here represents the work carried out for the utility’s assessment, unearthing the most relevant DSM measures and technologies for Nepal’s utility and the analysis of these technologies. The chapter describes the process of data collection and applicability of data for the DSM study.
5.1. Data collection
Data aggregation is one of the most significant functions of the DSM study in relevance to Nepal. There is little to no research that has been done in the area of load research and consumer indexing in Nepal which is a prerequisite for DSM study. Hence to fill this gap the generation, transmission and distribution data was collected from the utility’s load dispatch center and respective directorates. The data was collected by visiting the utility’s department which are spread all over Kathmandu. The data was then used for the analysis done in chapter 6 to understand the utility’s service. One-year’s hourly generation data, weekly load shedding schedules, weekly load for substation and feeder level and NEA’s annual reports are the major data sets those were gathered[16].
5.2. Questionnaire
A set of questionnaires were prepared to collect the information about the usage pattern of electricity for the domestic consumers in Nepal (see appendix M). These questionnaires along with the previous load research done by the world bank was used to estimate the usage and penetration of various appliances in the domestic sector. One of the limitations of using the questionnaire for this particular study was the resource and time constraint. As the study was only limited to five months’ period and primarily a macro level study it was difficult to collect the representative sample for the whole consumer base. These were only sample questionnaire give to 30 people. However, the questionnaire prepared could be used for load research in the future for detailed load research with larger sample size.
5.3. Expert opinion/ Interviews
Expert opinions and interviews was one of the pivotal ways of collecting information for this study. The experts from the utility, ministry, and private sector were interview based on their area of expertise (see appendix M). The expert interviews were carried out to ascertain the state of utility and how it could be improved. The barriers and policy recommendations partly have evolved from these interview. The other major relevance of interviews was to freeze the DSM measures which were selected based on the literature review, and questionnaires. The DSM measure for both Industry and domestic sector were discussed in the interviews and based on these result the measures were frozen. The measure for domestic and industrial sector are discussed in detail in chapter 7
One set of interview was also held with the private sector to know their perspective of energy efficiency, this is articulated in the policy recommendation later on how DSM programs can be done in Industrial and commercial sector.
5.4. Energy audit reports compilation
NEEP during its phase1 conducted industrial grade energy audits (IGEAs) of energy intensive industries and came out with the energy efficiency potential of different industries. A thorough study of these IGEA reports was done to prepare a matrix of the measures which were attractive to the industry on a macro scale. These measures were summarized based on their penetration and saving potential. These summarized measure for industrial sector were shared in the interview as mentioned above and based on that the DSM measures were quantified as discussed in chapter 7.
5.5. Project total resource cost test analysis
The classical approach of DSM is used for assessing the strengths and weaknesses of DSM measures and the DSM program (Gellings & Chamberlin, 1993). To determine the level of DSM activity by the utility has always been the bottleneck for the DSM planning and analysis. The utility has to work on the complexity in choosing the DSM programs. This is determined mainly by some of the classical measures of DSM which have developed in the literature. These mainly include the Lovins’ test, the ratepayer tax, the total resource cost test and the societal test (Baker & Battle, 1992, pp. 11-29).The total resource cost test analysis is complimented with the conservation supply curve (CSC) (Meier, 1982)to help policy makers in giving a summarized picture of DSM measures and associated cost with these measures.
6. Load research and characterization of Nepal electricity authority’s service
One of the focal areas and first thing that is required for conducting the study of DSM and end-use energy efficiency is the load research to establish the core components of the utility’s load profile. The load research helps utility to identify their customer use of electricity both in total and sector wise. Load research is defined as “an activity embracing the measurement and study of the characteristics of electrical load to provide a thorough and reliable knowledge of trend and general behaviors of the load characteristics of the customer service by electricity industry” (AEIC). The load research is particularly helpful in identifying the end uses electrical patterns of the country from the utility perspective. The research assists in identifying sector wise end use technologies contribution to the system peak. The primary drivers of the peak demand in different sectors are placed using the load research. It is of prime importance for successful implementations of DSM plans to read the peak demand scenario in the country and load curve analysis helps in identifying the role of electricity over the track of the twelvemonth.
6.1. Load research findings
Referable to the limitation of duration and resources an extensive sampling was not conducted for load research for the study. Instead, the load research involved studying the load research and sampling done by World Bank (WB) for NEA in 2009 (Consult, 2009). The hourly energy generation for the year of 2014 - 15 provided by Load Dispatch Centre (LDC), NEA was also used for establishing and analyzing the load curve (available with author)[17]. NEA does not prepare load curves for each day or month and hence compilation of hourly load data for the whole year was done and monthly load curves were prepared. The sample load curves are part of the appendix E and G.
The analysis of load research shows that NEA is a distinctly evening peaking system. The summer peak starts are around 19:00 hrs. ends by 21:00 hrs. gradually tapering off by 22:00 hrs. The peak occurs around 20:00 hrs. The winter peak is two hours earlier than the summer peak at 18:00 hrs. following the same pattern (see appendix D). The averaged peak demand for the year was 1,200 MW. It is seen that there is no major difference in the average peak and the system peak because of the suppressed demand, which NEA predicts based on the load shedding data.
The maximum peak demand of 1,291 MW was observed in Poush 8, 2071 BS (Dec 12, 2014 AD) (see appendix E). Only 706.8 MW of this demand was served and the rest was resorted to load shedding. The peak was observed at 18:00 hrs. Kathmandu valley division of NEA is the highest consumer of electricity amongst all six grid divisions of NEA in Nepal and it abides by a morning peak as well. Though morning estimated peak is not as high as the evening peak but is still significant. The morning peak is only observed in Kathmandu and not in the whole country.
The estimated peak demand swing is of mere 200 MW, this is mainly because it is estimated peak demand and is not the actual peak. The average demand swing between the peak demand and minimum demand for the year is 590 MW. The maximum demand swing was observed in the month of January- February as the demand is much higher during the winter months.
There is always a gap between supply and demand in the NEA system. The load shedding is a yearlong phenomenon and there are very few days in a year when there is no load shedding. In the last eight years, there was an average load shedding of 12 hrs every day if we calculate the yearly average. The major contributor to the system peak last year was load shedding, which goes up to 50 % of the contribution to the total load. This is the situation wherein any sector resulting in the reduction in kW demand would lead directly to a reduction in system demand curve.
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Figure 10 Contribution of different supply side to system peak (developed from (NEA, 2014-15))[18]
If we look into the demand curve[19] of NEA without load shedding the maximum peak was observed on October 23, 2014 at 830 MW. This is the energy supplied by NEA and hence becomes the highest unshedded demand or the maximum energy supplied by NEA in a fiscal year.
It is also observed that during winters the IPP contribution to the peak load is significantly lowered to a mere 8 % during the dry season from as high as 22 % during the wet season. This is mainly because of less water in the river and all the IPP power plants are ROR power plants. NEA contribution is also lowered from 33.5 % to 23.50 % from wet to dry season respectively. The decrease is not as high as IPP because NEA has reservoir type hydropower plants. Import from India contributes 20 % of the supply during the dry season (See appendix E). This is 40 % of the total served load during the dry season. The import from India is minimum during monsoon because of energy generation within Nepal.
The peak load observed during the evening is chiefly influenced by the domestic and commercial consumers. The major contributor to the system peak is the lighting load both for the domestic sector and the commercial sector. The commercial sector with only 0.8 % of the market share is not big in absolute numbers and hence the evening peak is on accounts of a substantial increase in domestic lighting load (Consult, 2009).
The residential sector contributes 66 % of the system peak, followed by commercial at 18 % and industrial at 7 %. For the end use technologies, the highest contributor to system peak is lighting with 28 % share of domestic lighting. Lighting contribution to system peak as a combined measure for all major four sectors is 39 % (Consult, 2009).
A summary of penetration of various technologies and their contribution is attached in appendix K
6.2. Structure of service and tariff
The service of NEA is categorized broadly into eleven sectors and the tariff structure is broadly classified into three consumer categories of domestic consumers, other consumers and time of day (ToD) tariff consumers. The domestic consumers are subcategorized into low voltage and medium voltage consumers. The minimum energy charge from each category depends on the installed meter capacity. The energy charges are same for all the single phase meter depending on which consumption block are they falling.
The other consumers’ category has the same low voltage and medium voltage segments with different electricity fixed rates depending on the sector for example whether it is industrial, commercial, noncommercial or Temple etc.
The third category of consumers is the ToD consumers which are classified into High voltage 66 KV, medium voltage 33 KV, medium voltage 11 KV and community wholesale consumers. The first three are mainly the industrial consumers. The time of day tariff is classified into three categories by NEA which is peak time, off peak and normal period. The time is fixed for these categories for all the set of consumers.
All the consumers above 66 kV are high voltage consumers, medium voltage consumers fall in two categories of 66 kV and 33 KV and low voltage is 400/230 V. This is common for all the three tariff categories (NEA, 2014-15).
The service categories are:
- Domestic
- Non commercial
- Commercial
- Industrial
- Water supply & Irrigation
- Street light
- Temporary supply
- Transport
- Temple
- Community sales
The details of the categorization and tariff details could be found in the annual NEA’s report.
6.3. Electricity use indicator
6.3.1. Connected load
Connected load is the wattage of load that is connected to the grid. For example, any consumer’s connected load will be the total maximum peak given to the meter based on all the appliances connected to the meter. Connected load is not the actual energy consumption of the meter. NEA does not keep the centralized information on the connected load. It is available in the individual distribution station office in hard copy and there is no online database for the same. This is one of the major bottlenecks to find the electricity use indicators. The connected load is one of the most important parameters for the planning of feeders and substations as it helps the utility in estimating the capacity for its transformers and feeders.
NEA’s poor structure of database and missing data for connected load across its entire consumer directorate and lack of centralized database is one of the barrier to the study which needs to be addressed for starting any substantial DSM program in the country. To estimate the connected load for one substation, the data collection was tried from one of the substation of NEA in Kathmandu valley. It is unfortunate to say that the data obtained for the thesis was not of the international standards for a national utility as acknowledged by the substation head. Without availability of data it was very difficult to estimate the connected load NEA electricity system and is deliberately mentioned here in the core of the report as it is one of the major challenges for NEA and DSM study.
6.3.2. Energy sales
The total energy sales for NEA in fiscal year 2014-15 was 3,743.75 GWh (NEA, 2014-15). Of the total energy consumption, the domestic sector accounted for 45 % with 1,676 GWh, Industrial sector constituted of the 36% of the total energy consumed in the year with 1,359.34 GWh. The commercial sector is the third largest sector with 8 % of the total electricity sales and consumption of 302.10 GWh. The other eight sectors as segregated according to the NEA’s billing system together constitute only 10 % of the energy sales.
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Figure 11 Sector wise energy sales for NEA (developed from (NEA, 2014-15))
The average energy consumption per service is highest for the community sales but this is mainly because there is no distinction in NEA report as to how many households are there in the community. On the per service basis the highest average consumption per service is of industrial sector with 32,053 kWh/service. The commercial sector follows with 18,815 kWh/service and this reflects that with only 0.55 % the commercial sector constitutes 8 % of the total energy consumption (see appendix G).
The domestic per service electricity consumption for connected consumers is 619.57 kWh which is the lowest amongst all the consumers’ category (see appendix G). This is understood because of the dominance of number of domestic consumers in the country.
6.3.3. Revenue
NEA has total gross revenue of NPR 30,483 million for the year 2014-15 (NEA, 2014-15). This includes NEA’s revenue only from the energy sales and income from other means is not considered in this figure. NEA yields most of its revenue from the domestic sector of NPR 13.266 million which we have seen is the largest base of consumer in Nepal. The industrial sector follows with the highest per consumer consumption where 1.5 % of consumer generates 35 % of NEA revenue. The commercial sector with only 0.8 % of the consumer base constitutes 8 % of the energy consumption which leads to 12 % of the revenue.
The average per unit electricity charge ranges from lowest of 3.9 NPR/kWh from community sales to highest of 11.85 NPR/kWh for commercial sector (see appendix G). The average price of electricity for all the sector combined is 8.56 NPR/kWh with the average price of domestic and Industrial sector lower than the average price of electricity for NEA.
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Figure 12 Each sector contribution to NEA's revenue (NEA, 2014-15)
The Domestic, Industrial and commercial sector together constitute the 90 % of the total revenue of the NEA and hence are the most prominent sector to target commercially to kick start the DSM program by NEA.
6.3.4. Connected load per service
NEA does not keep a centralized connected load for its users as is the case for energy consumption and number of consumers. Hence a sample data was taken from one of the NEA’s feeders for one distribution center and the connected load compilation for that was done.
The connected load for these 3,800 consumers is estimated to be 4,000 KW this gives the connected load per service of 1.05 KW/Consumer. This value cannot be extrapolated for the whole nation but in case of Nepal the majority of the consumers are household consumers. This estimated figure should also be used with caution as the reliability of connected data from NEA is questionable as mentioned earlier.
6.4. Growth of Nepal’s electricity sector
The peak demand is rising in the country at 10 % per year and the energy consumption is rising at the rate of 8 % PA (NEA, 2014-15). This growth rate by NEA is fairly linear and does not take into consideration the suppressed peak demand. If we look into sector wise growth rate of last ten years. The street light has grown maximum with a CAGR of 31 % though in absolute terms the number is low. This showcase the government policy to push for the street lights and public lighting which is lacking greatly in the country.
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Figure 13cummulative growth rate of Nepal's different energy sector
The domestic sector is growing at a healthy CAGR of 9 % with more and more households getting connected to the grid each year. Though the reliability of supply is a question, NEA is striving hard to connect each household in the country to the grid electricity, which is also one of the goals of recent energy crisis plan of the government. Commercial and Industrial sector are also growing at rate of 11 % and 7 % respectively. Though the growth rate is fairly high for all the sectors averaging around 10 % in absolute numbers there is vast difference in absolute numbers in growth rate of 9 % for households and 14 % of water supply & irrigation.
The other factors which the study was not able to quantify are the utilization factors, energy intensity, and demand intensity due to either unavailability of data or unreliable data with NEA. This is also considered as one the limitation of the study and challenges to be overcome by NEA. This shall be discussed in the later chapters of the report.
7. Model formulation
7.1. Energy conservation supply curve
The energy conservation supply curve is a tool developed to help identify and assess the energy efficient measures, considering the economic and technological perspectives. The supply curves were first developed and used by Meier and colleagues at Lawrence Berkley laboratory (Meier, 1982). The conservation supply curve (CSC) shows the conservation measures in a graph, in ascending order of their cost i.e. the conservation measure which has the lowest running cost will come first followed by the more expensive one. This continues till the last measure is represented in the curve (Meier, 1982).
The CSC helps policy makers compare the cost of conservation of various measures represented in the curve and identifying the significant measures. The curve also helps in identifying the current optimal use of energy (Meier, 1982, pp. 33-36). CSC differs from other curves in a way that CSC shows the potential of energy efficient measures and not the forecasts for energy savings. CSC were successfully used in the past to depict the energy savings (Fleiter, et al., 2009).
DSM is a two-way approach and weighs the benefits for both the utility and consumers (Shakti, 2014). In the current case, the CSC is also modified to take into account economic advantage of both the aspects. Traditionally CSC was used for the saved energy but here we have also used it to represent the potential impact of the saved capacity (Meier, et al., 1982).
The graph below shows a sample of CSC (Gellings, 1985). The CSC can be drawn from two perspectives one from the utility perspective and other from consumer perspective. In utility the cost of capacity saved (CCS) and saved capacity are considered whereas in case of consumer it is saved energy and cost of saved energy (CSE).
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Figure 14 sample of conservation supply curve
This is because consumer pays electricity bill based on the energy consumed and utility invests in power plants based on the capacity. In CSC the X axis can be represented by saved capacity or saved energy. The Y axis is represented by cost of capacity saved or cost of saved energy respectively. The graph rises in order of magnitude of increasing CCS or CSE with the least one coming first and the expensive one shown in the last. Any technology which falls below the “brown line” in the graph is considered as qualified for DSM programs. The brown line represents the cost of capacity addition in case of saved capacity and price of electricity in case of CSC for saved energy.
Consumer perspective: DSM measure selection and quantification from consumer perspective relies on the basic understanding of electricity use. An electricity consumer does not require the electricity itself but the service of that electricity and hence there is always a device converting the energy into some form of useful service required by the consumer (Meier, et al., 1982, p. 347). The efficiency of the use of this energy depends on the device itself and research from the past has shown that it can vary a lot even with the devices doing the same service. For example, an incandescent bulb for the same output in lumens[20] consumes much more energy than a LED bulb for the same output (Star, 2016). Hence with less energy usage for consumers, it transforms to less electricity use and increased electricity availability in the system. CSC represents these different DSM measure with their cost in a single graph to assess the economic benefit of DSM measures.
The two major factors, which determine the drawing of CSC from consumer’s perspective, are the cost of saved energy (CSE) in NPR per kWh and the amount of saved energy in kWh (Meier, 1982). The saved energy can be calculated for any duration of time but in our case we will work on the annual energy savings. The CSE is plotted on the y axis and saved energy is plotted on the x axis in a CSC. The DSM measures in this CSC are ranked in the ascending order of their CSE starting from the cheapest measure and ending with the most expensive measure. This ranking is irrespective of the amount of energy an individual measure saves.
As seen one of the most important parameter for CSC is CSE. The CSE[21] transmutes the data of investment cost of energy efficient measure to the cost of saving one unit of energy as NPR/kWh (Koomey, et al., 1993). The cost of saved energy is given as:
Abbildung in dieser Leseprobe nicht enthalten
Equation 1 is the basic equation for calculation of CSE and all the other equation below in this sub chapter are to support the calculation parameters in equation 1. In equation 1, ܫ is the total measure cost in NPR, which is the life cycle cost of the device. ܫ is the sum of the initial cost and the discounted cost of all the replacement required over the duration of the measure life and is given as:
Abbildung in dieser Leseprobe nicht enthalten
In the equation (2) m is the device life in years and the series includes all the terms where the exponent is less than the measure life or plant life. The m can vary from individual measure or technologies. In the equation 2, d is the discount rate. The real discount rate d taken here is 10 % based on the figures provided by the utility and as previously used for the studies in Nepal
Thus for a device with initial cost of NPR 1,000 a device life of five years and utility discount rate of 9 % with measure life (m) of 25 years, number of replacements required are 4 (each at the end of 5th,10th, 15th and 20th Years). The measure life is the life of a power plant which supplies electricity. It is not an integer multiple of the device life i.e. the last replacement of device outlasts the measure life. Hence the savings from the device after the measure life are free, the pro - rata cost of the last replacement taken for the number of years from the year of the last replacement to the last year of measure life.
The second factor in the equation 1 is the capital recovery factor (CRF). CRF is the ratio of a constant annuity to the present value of receiving that annuity for a given length of time. Using a discount rate d CRF is given as:
Abbildung in dieser Leseprobe nicht enthalten
In equation [3], d is same as equation [2] and n is the measure life in years. It is the life of a power plant which supplies electricity. In Nepal’s the power purchase agreement between the utility and IPP is [30] years hence the measure life n is taken as [30] yrs. (Consult, [2009]).
The third and final direct factor of equation [1] is ∆E which is the saved energy in kWh by the energy efficient measures or technology. ∆E is the amount of energy that can be saved using the EE measure. The value of ∆E changes from each technology depending on their potential. The total saved energy is represented by ∆E. The details of calculating ∆E will be explained in the subsequent subchapters while dealing with the sectors and specific technologies of DSM.
The CSE provides a simple and straight means to compare the different energy efficient measures of varying degree of investments and lifetime (Meier, et al., [1982]). A conservation measure is attractive to the consumers if the CSE is less than the price of the electricity which utility charges.
In case of Nepal there is always a shortage of supply in the electricity system and electricity saved is electricity available and hence is also profitable to utility especially in case of Nepal.
Utility perspective: Electric utilities across the world face the challenge of keeping the system frequencies as stable as possible and NEA is no exception to it (Horvat, 2007). It is even more difficult for small electricity systems like Nepal to maintain the system frequency with inadequate supply as any increase in sudden peak demand can lower the system frequency considerably triggering the looped tripping and the opposite case with higher system frequency is also true. The RoR based system of Nepal has long faced investment problems in the generation sector (Sharma & Awal, 2013) and hence any additional capacity which is saved on the consumer end will reduce the additional amount of installed capacity required by the utility (Gellings & Chamberlin, 1993). This can be articulated in a CSC for the utility which will now reflect the cost of capacity saved (CCS) instead of CSE.
The CCS morphs the data of investment cost of energy efficient measure to the cost of saving one unit of capacity as NPR/kW. All the measures which cost less than the cost of one kW of capacity addition are considered successful for the utility. The CCS is given as:
Abbildung in dieser Leseprobe nicht enthalten
Here I is the total cost calculated for each measure and is the same as in equation 1, peak coincidence factor (PCF) is the probability of an appliance coming online during the period of peak demand. The PCF is used because only the coincident peak demand savings can shift the load curve. Also, only at the time of system peak do energy savings improve the system reliability (Koomey, et al., 1993). The demand curve shift, which is one of the generic load shape change, under DSM is for the peak demand as then only utility can avoid the additional generation which corresponds to the peak demand savings (Gellings & Chamberlin, 1993). The PCF is calculated either by using the simulations or on the basis of real-time load analysis (Koomey, et al., 1993). In our case, the utility does not use any software and does not have connected load data available. For the study, the peak coincidence factor for different appliance were assumed based on the load surveys done previously by the world bank (Consult, 2009), market survey and expert opinions.
TDLF is the transmission and distribution loss factor excluding the commercial losses of the system. For this study we have taken TDLF as 15 % which is a lower value than the total system losses of NEA which stands at about 25 % (NEA, 2014-15). CUF is the capacity utilization factor and is the plant load factor of the power plants. The NEA is hydro dominated hence the CUF for it is 51 % which is the average load factor for all the hydro power plants in Nepal (NEA, 2014-15).
The CCS is a means to calculate the economic impact of DSM on utility and how much investment would be required from utility to save the capacity using DSM. The costs are calculated for the individual measures and for all the prequalified DSM measures. The cost will only be the investment required for the DSM measure and will exclude the program cost. Program costs are another necessary element for DSM program implementation. These are the costs associated with implementing a DSM program by the utility and includes the appliance cost along with bureaucratic costs and the overheads. (program cost and details of other cost are described briefly in chapter 10). As mentioned above the DSM and end use energy efficiency measures will only be attractive to the utility if it costs less than the cost of per kW of capacity addition (Gellings, 1985).
The x-axis of the CSC for the utility represents the total capacity saved or the total potential of bringing down the peak demand which is represented by Abbildung in dieser Leseprobe nicht enthalten in kW in equation 4. Abbildung in dieser Leseprobe nicht enthalten is the coincident peak demand saving which could be achieved using the DSM measures implementation. The analysis of sector wise peak demand [22] savings (Abbildung in dieser Leseprobe nicht enthalten will be further discussed in the next sub chapters.
One such example for use of CSC is in Thailand’s cement industry which is an excellent study of DSM measure through CSC (Hasanbeigi, et al., 2010). It should be highlighted that the tool developed is a way to screen the DSM technologies both on the technological front and on an economical basis. However, there are limitations to the study in the wake of reliable data from the utility for connected load and under the condition of suppressed peak demand. With the availability of more reliable data, the saving potential might be different potentially being more than what is estimated in the study. Hence the limited research which is available in Nepal and uncertainties in the input data, the result should be interpreted carefully from the study.
7.1.1. Economic analysis of energy efficiency measures for domestic sector
Domestic sector is the most predominant sector for electricity consumption amongst the entire sectors in Nepal where biomass still dominates the total energy consumption (consultant, 2013). The CSC constructed for the domestic sector in the study, presents to policy makers a concise and consolidated information regarding DSM measures. It represents the CCE, annualized cost of electricity, total investment required, annualized investment required, annualized achievable energy savings by individual technology. The details of the calculation of CCE have already explained above. Here we will talk about the annually saved energy and the cost of investments for each measure. The rationale for end use energy efficiency improvement is that the appliance used currently in the domestic sector can be replaced by more energy efficient appliances for same or better useful service. One of the criteria for implementation of end use EE measure is that the quality of service should not be compromised for saving energy.
For preparing the CSC for the domestic sector the first step was to select the measures which will feature in the CSC. A list of measure was first prepared based on the literature review and previous programs of DSM which were implemented in a developing economy (Yang, 2006). From the detailed first list of measure twelve of them were shortlisted based on the expert opinion of people from utility and private (see appendix M) sector. The people involved for the expert interviews had previous experience in the field of energy efficiency and DSM[23]. The list of twelve DSM measure for domestic sector which were shortlisted for the quantification are:
Abbildung in dieser Leseprobe nicht enthalten [24]
Table 1 Descriptive table of DSM measures for domestic sector
The measure number in the table 1 above are the dedicated measure numbers for that particular DSM measure in domestic sector. As the name of the measure are long and descriptive in graphs the measure numbers will be used instead of the measure name. In most of the measure the term more energy efficient EE appliance is used in the table, the EE appliance is taken according to Indian standards of energy efficiency[25]. However, for practical purposes any international standards could be considered provided it is in the same line of electricity consumption as specified by Indian standards.
When we go into the quantification of annualized energy savings using a particular measure it depends on the difference of energy consumption of an old appliance and the new energy efficient measure. Along with this the other major factor that needs to be addressed is the number of hours of operation. To explain the concept more clearly we shall continue our example of measure 1 which is replacing the inefficient bulbs with LEDS:
In the case of lighting in the domestic sector of Nepal, there are different categories of lighting appliances are in use like the incandescent bulbs of 100 W, 60 W, the CFL of 20 W, 218 W etc. Based on the load survey conducted by world bank (see appendix K) the weighted average for each bulb was calculated and taken as 35 W. The proposed measure for replacing this appliance is a 7 W LED. Again there are a lot of appliance in the market from 3 W to 15 W LED and even more but the selection of 7 W LED was done on the basis of equivalent lumens of the LED bulb and weighted wattage. As the quality of the service of electricity should not be reduced and lumens from 7 W LED are same as the weighted average of already existing bulbs and hence they shall be replaced by 7 W LED. The load change (LC) for one consumer which could be achieved is nothing but the difference of previously connected load (PWCL) and the new connected load (NCL) and is given as:
Abbildung in dieser Leseprobe nicht enthalten [26]
Here LC is in kW for one bulb for one consumer. The LC represent the capacity of only one appliance for one consumer and not for the whole sector. This in the current case comes to be 28 W. This LC is of significance but it does not reflect the capacity which could be saved by the utility as this load has to come online to save the reduced generation capacity. This is taken care by peak coincidence factor (PCF).
PCF is gives us the probability of certain load coming online during peak load. The utility first operates dispatchable supply option with low fixed cost and high operating cost for those few hours and coincidence for these capacities is therefore implicit from the supply side, but from the demand side, the technology solely depends on the time of peak. In the case of Nepal, it is an evening peaking system with domestic and commercial sector in line and the load research clearly shows that lighting contributes significantly to the evening peak system. Though this signifies that evening load is the lighting load it does not tell us how much and what is the probability of entire applicable load coming in line during the evening peak at one particular instant. It would be accurate to use the loss of load probability model (LOLP[27] ) in this case to estimate the probability of a load in our case lighting contributing to the peak load.
LOLP is difficult to implement in NEA as the system is always under load shedding and supply side is never able to fulfill the demand. According to this, the system is always under demand peak as supply is never met. Here we will consider peak demand as estimated by the utility. Hence an approximation is taken for each appliance depending on the load research and averaging the load savings over the 200 highest residential hourly loads. The PCF for lighting case is taken as 0.6 and per consumer capacity saved (οݏ) in kW for utility is given as;
Abbildung in dieser Leseprobe nicht enthalten
The delicate thing here is to calculate saved capacity or peak demand savings Abbildung in dieser Leseprobe nicht enthaltenLED, domestic for all the connected consumers considering the fact that it is difficult to quantify the connected lighting load in the whole domestic sector in Nepal, this is again because of the unreliable database of consumer connected load kept by the utility.
To find a solution for this particular study a factor is considered for each appliance which is applicability factor. Applicability tells us how much percentage penetration of an EE appliance is possible in an existing market. In this case it would be the percentage of households that could be penetrated using LED technology in existing scenario. The applicability of a particular appliance depends on the market penetration of that appliance and quality of supply available amongst other minor factors (see appendix K). In this case. In this case we have considered 50 % applicability for the 7 W LED on the number of consumer basis, with each consumer using one LED.
Multiplying the applicability with the target households in our case domestic households we get the applicability number. In our case we assume that each household has only once bulb because we are taking a weighted wattage hence the applicability number gives us the number of bulbs. It should be noted that applicability of each measure varies and is not constant from one measure to other. For example, the applicability factor for lighting could be same or different from the applicability factor for fans in domestic sector.
Abbildung in dieser Leseprobe nicht enthalten
The capacity saved for the utility will be the capacity which comes under the peak load and the measure reduces the peak load (theoretically) or shift the peak load. For this, we need to estimate how much of the connected load will actually fall during the peak load hours. This is termed as coincident peak demand savings (Abbildung in dieser Leseprobe nicht enthalten) in kW or MW. In our case the appliance is LED and sector is domestic sector. Hence the demand saving is given as:
Abbildung in dieser Leseprobe nicht enthalten
The equation 7 gives us the capacity that would be saved using LED as a DSM measure in domestic sector[28]. The value from equation 7 now can be put into equation 4 to calculate the CCS.
For the calculation of total saved energy, per consumer saved energy (PCSE) in kWh is calculated on the basis of yearly hours of operations of that appliance and multiplying it with the LC from equation 5 as shown in equation 8.
Abbildung in dieser Leseprobe nicht enthalten [29]
Multiplying PCSE by the applicable number of consumers we quantify the total estimated saved energy in a year for the domestic sector for that particular appliance. In case of LED it will be given as
Abbildung in dieser Leseprobe nicht enthalten
The Abbildung in dieser Leseprobe nicht enthalten from equation 9 then could be substituted in the equation 1 to calculate the CSE.
The total domestic sector cost for each measure is calculated by multiplying total energy saved to the cost of saved energy calculated in equation 1. The same principal is followed for calculating investment required for capacity savings. The cost of saved capacity is multiplied by the total capacity saved. Here the investment differs depending on the priority whether the utility is looking to invest into saving energy or saving capacity. Though investing into one will by default save the other.
All the above mentioned calculation for saved energy, saved capacity along with CSE and CCS are iterated for the measures shown in table 1. All the calculations were done separately for each of the measures both for the CCS and CSE. It shall be noted that this is macro analysis for the domestic sector of Nepal and hence all the analysis is only for the domestic sector. The analysis presented here is the basis for the results and policy analysis explained in the subsequent chapters.
The list of all the domestic measures along with the saving potential and CSE and CCS is attached in the appendix H
7.1.2. Economic analysis of energy efficiency measure for industrial sector
The average per consumer electricity consumption for the industrial sector is highest amongst all the sectors excluding the electric sales to the community (see appendix H). This makes it one of the most potential sectors for DSM and end use energy efficiency programs with the small target population and high potential dividends. The basic principle for calculating the saved energy and saved capacity for the industrial sector remains the same as for the domestic sector but the market approach varies largely from the domestic sector. The industrial sector has much more varied use of technologies than the domestic sector which in the case of Nepal has almost the same use of technologies[30]. Hence it becomes much more challenging to have an integrated EE approach for the industries.
The macro analysis of DSM and end use energy efficiency appliance for the industrial sector was dependent on the set of data that was collected using the reports of industrial grade energy audits (IGEAs) conducted by Nepal Energy Efficiency Programme (NEEP) (NEEP, 2016) and environmental sector programme support (ESPS) (ESPS, 2004). Energy audit of the 36 most energy intensive industries was conducted by NEEP and these IGEAs came out with the energy saving potential for the industries along with the measure that could be implemented to achieve these energy savings.
The IGEA reports were studied and compiled for the different energy saving measure and their potential savings across the whole industrial sectors. It was concluded that the standard measures for EE are common for all the industries. The standard electrical equipment service is common all the industries and largely remains the same apart from the size of the service. For example, electrical motors and air compressors are common for almost all the electricity consuming industries. This was also verified by the data analyzed from the energy audits report[31].
The first requirement for analyzing the DSM and EE potential was to quantify the technologies or electrical appliances which are most common for the industries having electrical appliance. To quantify the penetration of electrical appliance in the industrial sector a matrix was developed which included the penetration of different electrical technology (measure) and also the energy savings potential of that measure, this was done for the all the potential saving measure which were identified in the energy audits of different industries. The matrix helped in identifying what is the penetration potential for various technologies. All the technologies which had penetration potential of more than 75% based on the IGEA reports were considered for the macro analysis.
After identifying the penetration of technologies, the EE measures to replace the existing technology were identified and the percentage energy savings was calculated. The technologies not achieving significant number of savings were further filtered out of the DSM measure list. The technologies which have significant energy savings were analyzed further for the DSM options. The table below shows the EE measures which were selected to quantify for the industrial sector.
Abbildung in dieser Leseprobe nicht enthalten
Table 2 Descriptive table of DSM measure for industrial sector
For each measure in the table 2, the percentage savings of the appliance were estimated. This was based on the recommendation of the energy audits. In Nepal, the technologies used by industries in most of the cases are barely of the international standards[32] and there is huge efficiency potential in the industrial sector. In the industrial sector the potential savings those could be achieved are based on the energy audit reports every appliance has a deemed saving potential. Once the percentage savings for the measure is estimated, per consumer saved capacity is calculated which is given as:
Abbildung in dieser Leseprobe nicht enthalten
The PCSC is per consumer saved capacity in kW and is then extrapolated to the applicable consumers of the Industrial sector. These applicable consumers are calculated on the basis of penetration of that particular appliance which was calculated above. Hence the total saved capacity is the additional demand savings or saved generation capacity.
To calculate the energy savings for the industrial sector, same methodology is used as in the domestic sector but this time percentage energy savings for each appliance is estimated based on the energy audits conducted. From the percentage energy savings, the energy savings per consumer is calculated based on the contribution of that appliance in the total energy consumption. This is then multiplied by the applicable population and hence total energy saving potential is calculated.
The list of all the measures and the cost of the measures for industrial sector is attached in the appendix I
8. Result & Discussion
This chapter focuses on the result of the study and the analysis done for the study. The result of sector wise analysis will be presented first and then combined impact of both the sectors results on the electricity system in Nepal will be reported. Based on the data collected and methodology explained above the conservation supply curve were created.
8.1. Domestic sector
Domestic sector as we know by now is one of the most lucrative sectors for DSM and end use energy efficiency projects. Based on the methodology explained in chapter 8, for all the measures, potential savings which could be achieved was calculated. The conservational supply curve (CSC) for cost of saved energy (CSE) and for cost of capacity saved (CCS) were drawn. In case of Nepal electricity generation is through hydropower which are carbon neutral and also CO2 emission from thermal power plants is a very small figure in terms of absolute number, hence is not considered anywhere in the study.
Conservation supply curve for cost of saved energy: The analysis for the saved energy and CSE for the domestic sector was done for a total of twelve technologies mentioned in chapter 7 and are also part of appendix H. These technologies were shortlisted for the domestic sector to estimate the saving potential of conserved energy. Out of these 12 measures, 5 measures qualified for the energy saving potential under the current average electricity tariff of NPR 8.14 / kWh (see appendix H). In figure 14 below the brown line in the CSC is the line of average electricity tariff in CSC of saved energy. This implies that all measures which cost less than the current price of electricity are attractive to the consumers. To explain it further for all these five energy efficiency measures to save 1 kWh of electricity is cheaper than purchasing 1 kWh of electricity. This implies the cost effectiveness of the energy efficiency measure for the consumer.
In the figure below the x-axis represents the total saving potential of all the measures and y-axis denotes the per kWh cost for each measure. The area under each line aggregates the total investment required for that particular technology to achieve the savings. The figure is supported with the table 1 below with the qualified measures. The table is arranged in the ascending order of CSE and is representative of the graph. The measure numbers which forms the first column of table is given to index the measures and is not the serial number
Abbildung in dieser Leseprobe nicht enthalten
Figure 15 Conservation supply curve for energy savings in domestic sector
The cost effective electricity saving potential in the domestic sector of the Nepal under current tariff structure is 400 GWh per year. This is about 24 % of the total sector consumption in 2014-15. The NEA is recently coming up with the revised tariff structure (NEA, 2015-16). Under the new structure eight measure qualify for the technical economic potential of the implementation and the saving in the tune of 509 GWh could be achieved. This is 29 % of the total domestic sector consumption in 2014-15. The total technical energy saving potential of all the twelve technologies which were quantified for the domestic sector is 806 GWh per year. This is about the 46 % of the domestic sector’s consumption and 22 % of the total electricity consumption of Nepal.
Abbildung in dieser Leseprobe nicht enthalten [33]
Table 3 Supplement information for conservation supply curve in figure 14
The EE measures which are not economically lucrative today like measure 5, 6 can potentially qualify in the long run with the amalgamation of technological advancement and rising electricity tariff in the country bringing their cost down.
The implementation of all the DSM measures is difficult to be done all at one go as the fair amount of investment and incentive is required to be given by the utility. The implementation is done under the different penetration scenarios over a period of time. The cost which we have taken is the current cost of electricity which is bound to increase as electricity is subsided in Nepal for different consumers in the household sector. Hence the measures which are not attractive now might become attractive over the course of time.
As can be seen from the table and supply curve most economical measure to implement from DSM is induction stoves (measure 7) which cost only NPR 1/kWh. It is followed by electric water heaters (measure 9), rice cooker (measure 8), and LEDs (measure 1). The technological investment which goes in achieving the energy savings from these technologies is about NPR 1,350 million which translates to EUR 11.20 million. This is the technological cost of investment for all the qualified measure. The lighting sector which includes measures 1 and 2 can together save up to 87 GWh of electricity each year.
Conservation supply curve for cost of capacity saved: The technologies remain the same as for the CSE, though the technologies which qualified for CSE may not qualify here. The CCS is from the utility perspective of avoided additional generation. Here the price of electricity is replaced by the cost of installing one kW of hydropower plant which is represented by the straight brown line in the figure 5. The present average cost of installing one additional kW of hydropower in Nepal is NPR 180,000 (EUR 1500). Therefore, all the measures which have CCS less than the cost of installing additional 1 kW of generation capacity are attractive to the utility. This is because the cost of investing in demand saving measure is less than investing in bringing additional generation capacity into the system for the utility.
The cost effective avoided additional generation potential for NEA from domestic perspective is 166.19 MW. This is the peak demand saving that could be achieved as the avoided generation can only be achieved by the demand shift and is done by peak demand saving. This is 12.5 % of the peak demand of 2014-15. If we consider the range of the cost of installing per kW of power plant it goes up to NPR 220,000 (which we will consider for this study). This will bring the peak demand savings up to 207 MW with eight measures now qualifying. This is 16 % of the current peak demand of the country. The potential of reducing the peak demand in total is by 284.7 MW which is 17 % of the peak load. This is the potential if all measure quantified are implemented
Abbildung in dieser Leseprobe nicht enthalten
Figure 16 Conservation supply curve for demand saving in domestic sector
The table below shows the capacity saving potential along with the cost associated with it. The table is arranged in the same order as the graph to compliment the CCS drawn in figure 14. The table is prepared in the ascending order of CCS which is same as in the figure 15.
Abbildung in dieser Leseprobe nicht enthalten
Table 4 Supplement information for conservation supply curve in figure 15
In the table above it can be seen that though measure 9 is the most economical in term of CCS but the maximum amount of capacity is saved by measure 12 which is water pumping which is around 54 MW. The lighting sector together contributes to 35 MW of saved capacity and is one of the measures which could be pushed by the utility as lighting load contributes significantly to the peak load.
8.2. Industrial sector
As mentioned before the industrial sector has one of the highest average per service electricity consumption amongst all the sectors in the country. NEEP programme came out with the Nepal energy efficiency strategy (EEST) in which it was mentioned that the modern energy growth rate of Nepal was 1.04 % whereas for industries it was in the line of 5.5 % (Gurung, et al., 2015).
In total nine measures were shortlisted for the cost benefit analysis for industrial sector as explained in the chapter 7 and these will be considered in later stages for implementation programs. The CSC curve for saved energy and saved capacity was constructed from the industrial sector (consumer) and from the utility perspective respectively.
The CSC for the saved energy and cost of saved energy was built for the EE measure of the industrial sector. There are a few measures which can only be considered for the saved capacity and not for the saved energy as these measures are not giving direct savings to consumers. One example of this is the power factor improvement (measure 1)[34]. The supply curve (figure 15) below shows the measure along with the energy savings those could be achieved using the DSM measure.
Abbildung in dieser Leseprobe nicht enthalten
Figure 17 Conservation supply curve for energy savings in industrial sector (own calculation)
In the CSC of energy saving for industrial sector, no cost line can be seen as represented in figure 16 as the cost of these EE measures is much lower than the average price of the electricity. This clearly shows lucrativeness of the EE measures for energy saving in the industries. The total energy saving potential for the industrial sector is 522 GWh. This is 14 % of 2014-2015 electricity consumption from the utility. The electricity savings which are achieved here are only based on the utility electricity supply and not on the total electricity used by the industries which will also include the electricity generated from DG sets or by inverters in some cases.
The table below compliments the CSC with the measure and the energy saving potential for each measure and the amount of investment required for an individual measure. The table is arranged in the same order as the graph. The first column of the table shows the measure which is fixed for each measure and is used throughout the study to represents that measure for industrial sector.
Abbildung in dieser Leseprobe nicht enthalten [35]
Table 5 Supplement information for figure 15
The maximum electricity savings can be achieved using measure 5 which is lighting. The achievable savings are of 187 GWh. The cost of saved energy for any measure does not cost above 0.40 NPR and is well below the average cost of electricity in the country which is NPR 8.14. The energy savings of this magnitude are the actually the additional energy supply that will be available. Multiplying these energy savings of 522 GWh with the average price of electricity for the industry which is NPR 7.78, NEA can generate additional revenue of NPR 4,064 million in a year. Hence DSM measure can very well be considered as virtual power plants for energy supply generating revenue for NEA.
Saved capacity and cost of capacity saved: The industrial sector can potentially contribute in avoiding the additional generation capacity. The avoided capacity is estimated to be 88 MW in a year using the DSM program for the industrial sector. This avoided generation in industrial sector is much lower than the domestic sector for almost the same amount of energy savings that is achieved using DSM measure. This is because there is less probability of industrial load coming in line during the peak duration, when the actual load curve shift takes place. Hence industrial DSM measures save less in terms of saved capacity. This will change drastically when the utility starts supplying electricity to the industrial consumer during peak load hours which is currently not the case. The peak demand saving curve is shown below
Abbildung in dieser Leseprobe nicht enthalten
Figure 18 Conservation supply curve for peak demand saved in industrial sector (own calculation)
As can be ascertained from the curve the cost of saved capacity is much lower than the cost of an additional capacity addition. The table below compliments the CSC for demand savings.
Abbildung in dieser Leseprobe nicht enthalten
Table 6 Supplement information for figure 16 (own calculations)
8.3. Impact of DSM technologies intervention
The impact for utility in total is the combined effect of all the DSM measure if implemented and potential energy that could be saved using all the qualified measures. The measures considered in this section include only the qualified measures for the DSM programs from both the sectors.
The total energy saving potential using the qualified DSM measures is 1031 GWh. This is the additional amount of energy which will be available in the system if DSM and end use energy efficiency programs are implemented. This is 19 % of the total electricity generated by the utility in 2014-15 and 26.5 % of the total electricity supplied. DSM if implemented will act as virtual power plant supplying a quarter of electricity available in the system today. If we consider the average power tariff of NPR 8.14/kWh, it will give NEA an additional revenue of NPR 8,009.76 million in one year. The combine investment required in technologies for these measures will be NPR 1,400 million. The real investment figures will be higher considering the program costs and other additional cost for setting up the institution.
Abbildung in dieser Leseprobe nicht enthalten
Figure 19 Energy savings achieved by implementing DSM measures in both sectors (own calculations)
This is theoretical perspective and is to showcase the potential that DSM measures hold. This inefficiency of the system is shown under the current scenario with not enough supply in the system. As the supply of electricity increases so will the potential of DSM technologies. It would be interesting to look with how much percentage the DSM impact increase with the increase of electricity supply. Though this is out of the present scope of the study.
9. Barriers and perceived issues in relation to development and implementation of DSM programs
DSM and end use energy efficiency always comes under the wider scope of energy efficiency. The energy efficiency cannot be institutionalized without incorporating DSM, as it is one of the key aspect of institutionalizing energy efficiency (Consult, 2010). DSM in Nepal is in its infancy stage, this study attempts to fill the information and policy recommendation vacuum present in the area of DSM in Nepal
Prior to the DSM program development and then going into full-scale implementation by the utility, there are certain issues in institutionalizing context which needs to be clearly appreciated (NPC, 1998). In this chapter we study the most perceived barriers in the DSM program development and how can they be tackled by the utility.
The barriers discussed here are both from the utility perspective and from the institutional stand point. In this chapter, we mention the barriers which are hindering the implementation of DSM. DSM programs cannot be implemented in isolation and hence the barriers for DSM are considered in the wider scope of energy efficiency. These barriers here are perceived only at a macro level and more in depth analysis would be required for any of the DSM program implementation.
9.1. DSM program capabilities at NEA
The NEA has an established DSM cell, but it is in a dormant stage (NEA, 2014-15). A DSM cell is a body within NEA which will work specifically towards realizing the goals of DSM and end use energy efficiency The revival of the DSM cell is one of the pre - requisite for the DSM development. Reviving DSM cell at NEA requires the development of skills and experience of program planning and coordinating with relevant interest group while handling the various programs. DSM cell should have adequate expertise, infrastructure budgetary assistance and a significant technical assistance along with external specialist of end-use energy efficiency. This expert should have experience of DSM program development (NPC, 1998). The DSM cell should be able to develop and carry the program to its logical conclusion under the institution of energy efficiency.
9.2. Lack of consumer and information
In the present context there are few user driven energy audits conducted across the industry. Energy audits are mainly conducted under a donor program such as ESPS project by DANIDA (eec, 2016) and NEEP in which technical support was provided to conduct energy audits and capacity development (NEEP, 2016). The bulk of the energy audits in industries are program driven and there are very little to no energy audits conducted for commercial sectors (NEEP, 2014 ). Therefore, most of the consumers are very little aware of the potential of energy efficiency. There is an urgent need for the mandatory energy audits for the energy intensive industries. These energy audits should be put under the umbrella of DSM so as the implementation could be carried out after the audits. Energy audits in growing economies such as China (Shen, et al., 2012) and India has become a prerequisite now to come out with energy efficiency measures at a macro level for all the industries and commercial sector (productivity, 2016). These energy audits are missing in Nepal and therefore there is little awareness about the potential of energy efficiency even in the energy intensive sector.
9.3. Load research and consumer indexing
One of the major barriers today with the utility in Nepal is limited load research which is done by NEA (Tiwari, 2016). Detailed load research for the entire electricity system is not just the basis for DSM study, but also for the utility itself to make it system more comprehensive. NEA today does not have reliable connected load of the consumers and neither there is an accurate data available for load across various transformers, feeders as well as substations. This has led to gaps in system planning and is also affecting NEA perspective of moving towards integrated resource planning (IRP).
NEA is in a grave need of consumer indexing and load research to enhance the reliability of its system. This, in turn, will automatically improve system functioning and NEA would be able to target the distribution losses from the point of its origin. For this to achieve NEA needs to develop an electronic database system which is currently file based[36] and hence not integrated to best of its capabilities. This load research will also facilitate assessment of the impact on the system load curve of various appliances. At most this will also help NEA target its commercial losses directly.
9.4. Revenue/tariff
NEA though, is government owned but is still a utility and hence should charge its consumers based on its cost of generation, transmission, and distribution of electricity. This particular approach is lacking in NEA because of the government policies of subsided electricity for consumers who cannot afford electricity, which is completely justified from the government point of view, but this has led to losses for the utility (Niti, 2012). The NEA also has its own system inefficiency cost and commercial losses which NEA should not pass on to the consumers (NEA, 2014-15).
Electricity is a very different commodity which cannot be stored in large scale and there should be hourly pricing based on the time of use of electricity. The NEA currently lacks the level of sophistication in its system as electricity pricing is a very complex process requiring high-end economic analysis and modeling. Hence NEA should work on other means for improving its revenue.
One of the means is to do kVAh billing instead of kWh billing for its dedicated high end consumers. Currently with the kWh billing there is no motivation on the consumer side to improve their power factor (PF) and hence the system operates under poor PF. NEA can vastly improve its quality of distribution if NEA starts kVAh billing which will motivate consumers to improve the power factor and hence saving the demand and improving the quality of supply.
NEA’s unreliable supply of electricity push consumers to switch to diesel generation. It costs roughly four times to generate electricity through DG sets than the price of grid based electricity (World Bank, 2014). NEA needs to work on the revised tariff structure, provided it improves the quality of supply. In a survey, it was pointed that consumers are willing to pay more price for electricity, if NEA improves the quality of electricity supply (substantiated by results of the survey conducted). NEA also needs a detailed financial analysis on the price of electricity under different consumer category for the time of day use of electricity and providing the subsidy to the lower end consumers and developing a subsidy mechanism for such consumers.
9.5. Policy barrier
A clear government policy toward end use energy efficiency and DSM is a requirement for the DSM to be fully effective and implemented[37]. Firstly, it requires an institutional framework for implementation of DSM. The institution needs to provide a budgetary push from the government for DSM program which is lacking in the current framework of the utility. For instance, if the government reduces taxes on energy efficiency appliance and opened the market for free competition, the EE market will become more attractive than traditional electrical technologies. To check the quality of appliances in the market government can give the authority to already established DSM cell to check for the quality control in the market with power to take necessary action for non-compliance of the import standards.
The DSM cell will also be responsible for developing the importing and manufacturing standard for the equipment’s and conducting energy audits.
The government needs to push the DSM as a “supply resource” as similar to the supply side management and hence collectively focusing on the IRP. In Nepal alternative energy promotion center (AEPC) a government owned institution under the ministry of science and technology promotes renewable energy in the country, the government can look for a body in the same line for promoting energy efficiency in the country but preferably within the utility.
9.6. Market barrier
In Nepal today the majority of the products are imported either from India or from China as country is surrounded by the economies of scale (MoF, 2015-16). This on one hand is good for the country as Nepal has access to the products of both the countries. The downside to this is without any quality check and guidelines the quality of products which are imported is relatively poor. People mostly are looking for cheaper products and hence the energy efficient appliance are not being able to compete with these products as EE products are generally in the higher price range and hence more expensive.
The EE market globally has developed a lot which includes India and China as they already have in place standards for EE equipment’s (BEE, 2016). What is missing in Nepal are the qualified EE electricity products, as there is no incentive for the market to bring energy efficient products in the market which are more expensive than the non-efficient appliances. The market is also driven by the consumers and as there is very little awareness about the EE measures and how they can reduce the energy bills the demand for appliances is not that towering (NEEP, 2016).
To curtail the existing market barrier present in the Nepal energy efficient market, a multifaceted approach is required to strengthen the EE market in Nepal is required. For example, under the scope of EE institution, it should be able to work as a body for testing and does the quality check for the EE appliance. As observed Nepal does not need a mammoth standard and labeling program as it barely manufactures its own product and shall always be at a disadvantage of economies of scale of China and India. Instead, Nepal needs a program to develop the policy and institution to do the quality control of appliances imported into the country. For instance, if Nepal wants to improve efficiency in domestic sector, it need not develop the whole standard and labeling program like China or India rather a program to check the quality of import which allows only star rated appliance import according to international or regional standards. These appliances as mentioned earlier can be given a tax Barriers and perceived issues in relation to development and implementation of DSM programs exemption from the government over non-efficient products to promote the EE in the country. The same institution will be responsible for quality checks and can be done in partnership with the Nepal bureau of standards and metrology (NBSM).
9.7. Technological barrier
Technical barriers can really set the implementation of DSM technologies aback as the market needs to adapt to new technologies. This can be influenced by a lot factor, including the price of the appliance market, penetration, awareness and consciousness about the measures (Bailly, 1994). Nepal’s case could be taken care of by the developed market of its neighbors. Considering the state of EE in Nepal, the country is looking to adopt newer technologies available in China or globally. It can work with the existing market with just an auditing body in place. What the EE institution can focus on is the training and market acquaintance with the new technology.
9.8. Financial barrier
Energy efficiency projects need finances. Fair amount of financial resource is required to implement the program as was quantified for technologies in earlier chapters and there is more to it than just technological cost. The finances are also needed for setting up the institutional body along with them the bureaucratic costs and then the significant investment is required for DSM measures and technologies either in the public or the private sector (NPC, 1998). Getting investment for energy efficiency without any clear EE policy is much more challenging compared to the renewable energy market (NEEP, 2014 ). The financial security and the return of the investment in the energy efficiency market are also very new to market. This is one of the main challenges in developing a financial market for DSM and end use energy efficiency, which is best possible with a proper institution in place.
One of the most debated ways of financing the EE investment is through energy service company (ESCO) sometimes even with the initial support provided by the government. The ESCO finance the implementation of EE measure and return are obtained with the energy savings those are achieved. To implement ESCO government need a policy and a structure in places to assess the energy consumption hence ESCO might be a potential solution to financial barrier, but it is part of the policy barrier in Nepal. The other way of financing is reducing import taxes on the energy efficient appliances those are imported. For this government again needs a very strict implementation body to keep a quality check on the imports. This will also create economies of scale within the country with only EE appliance available for purchase.
10. Policy recommendation & program concept
With IEA running DSM programs, the potentials of DSM programs in EE are accepted worldwide (IEA, 2016). The utilities have invested in DSM and end use energy efficiency to improve the quality of the service, reduce losses or in some cases decrease the electricity deficit. In Nepal’s case, DSM makes a case for each and every perspective. The studies in past in Nepal (though limited) have shown to some extent the scope of DSM and this study is again an effort to show case the potential of DSM in the country in the present context and how it benefits the society in total. The main reason for the success of any DSM program worldwide and why it is not gaining the driving seat in Nepal’s utility is policy initiatives in other countries (Waren, 2015) and dearth of them in Nepal. Any DSM activity cannot reach a wider audience unless there is a policy an institution driving it, especially in the case of a market which is not liberalized and state has a major role to play in all aspects of utility (Waren, 2015).
Nepal today is in a desperate need for an energy efficiency policy under whose wider scope DSM can be implemented. This policy will govern the forming of an institution for EE in Nepal. The introductory policy, facilitating successful implementation of DSM plays a vital role in the implementation of DSM which is, missing in the country today.
Consider the case of standards and labeling there is no such policy of the government to promote the use of labeled or star rated appliance nor is there a big promotional activity undertaken by the government to promote energy efficiency. People barely have knowledge of energy efficiency and how it can be helpful for them to reduce their energy bills. Laymen consumers have very little knowledge of how a marginally expensive good quality LED can improve the quality of light and reduce their electricity consumption. They buy a cheaper low quality LED (as there are no standards, low quality LEDs are available in market) which will not give the same life as star labeled LED and eventually customer will go back to Incandescent bulbs. This consumer approach also has an effect of manufacturer focusing on reducing the price than to improve the quality of the bulb.
To facilitate the implementation of DSM programs and EE initiative government needs to create a policy which facilitates market environment, working on a sustainable basis resulting in the overall economic benefit to the society (Cheng, 2005). The policy initiative required in Nepal can be broadly categorized as follows
10.1. Ministerial level ownership and commitment
A high level ministerial level commitment is the first step required to kick starts the energy efficiency and DSM programs in the country. A formal commitment at the ministerial level ensures that these activities are endorsed by the government and will give confidence to the utility to undertake the tasks for DSM at the organizational level. The government endorsement and recognition of energy efficiency by the government as energy resource can fulfill the purpose of giving DSM an official recognition (ESMAP, 2011).
This study is intended to support the policy makers in recognizing the technical and economic potential of DSM for utility and the consumer and how it can help mitigate to a great extent the grave modern energy crisis in the country. There are previous policy recommendations as well which support the DSM in Nepal along with this there are some isolated projects undertaken by utility to showcase the importance of DSM and its grave requirement in the country.
10.2. Developing an institutional framework for energy efficiency
One of the barriers to EE is the lack of an institutional framework for EE. The modern energy policy in Nepal revolves around the hydro power development and all the institutional framework and policy are mainly cohered towards hydro power plants (Gurung, et al., 2015). This though makes total sense because of lack of supply option in Nepal but is no reason to neglect the other aspects of development of modern energy policy. It is interesting to notify that government already have institution working in different sectors of energy like AEPC for promoting renewable energy in Nepal, WECS and the utility in itself the NEA. Hence it is required that an institution for EE is set up with a clear directive and autonomy to work in the field of energy efficiency.
Apart from the institution, there would be requirement of other institution, one especially under the NEA. As NEA directly come under MoEn with Secretary of MoEn chairman of NEA board even the setup of a DSM cell has to be initiated by creating a body within MoEn working for promoting EE with a scope of DSM. The pilot programs for DSMs of standard, labeling and promotional activities for EE shall be conducted under the same DSM cell.
10.3. Passing of legislation and regulation
The main activity to enable a policy in the country is through apt legislation channels (Consult, 2010). Globally, countries with state owned utility and some of the liberalized power markets as well have enacted laws for energy efficiency, which governs the usage of energy in the country across different end user’s categories. A very successful example of that is the Bureau of Energy efficiency (BEE), India (BEE, 2016). On a wider scope energy efficiency laws generally cover all the aspects of EE within a jurisdiction. The enactment of EE jurisdiction generally comes at the mature stage of the DSM programs. Once the pilot programs of DSM and EE are widely successful and there is a ministerial level commitment for EE, then the introduction of legislation can be done (Bailly, 1994) .
10.4. Incentivizing energy efficiency
In an economy of limited scale like Nepal with a very low income, incentives are one of the best means and most encouraging factor to promote EE in the country. With lack of awareness amongst the domestic sector, which is also the major consumer of electricity, the consumers do not necessarily consider the payback period for an expensive technology or the life cycle cost for the equipment. What is most important from a domestic consumer perspective is the initial investment cost for buying that equipment. To bring down the investment costs incentives can play a major role. In the industrial sector incentives can be given to improve the power factor. This will lead to big industrial consumer bearing the cost of capacitor banks and utility improves its quality of supply.
11. Proposed DSM action plan and suggested DSM program pre-scope
The result of this DSM study clearly states that significant reduction in electricity consumption can be achieved using DSM measure. In the context of Nepal’s utility, a significant amount of addition electricity will be available in the system using DSM measure as DSM activities will act as virtual power plant (S, 2014) and this will be achieved at a much lower cost than to add the supply side resources. Hence energy efficiency measures can be termed as a virtual power plants. This clearly states to the policy makers that DSM and end use energy efficiency is a must for power deficit Nepal’s electricity system. However, DSM measures cannot be implemented using an isolated approach. Introducing the DSM measure require the same (if not more) extent of planning and institutional level intervention as for the supply side interventions in a utility.
Moving further from what was mentioned in the previous chapter for policy recommendation and program concept, implementation of DSM program under the wider scope of EE institutional framework is equally extensive and important to look into. In any state the policy is made by the government and the implementation for DSM is not possible without the utility. In our case, assuming NEA would be the implementation agency (as NEA is the state owned utility) for the DSM. For successful implementation of DSM NEA would be needing an action plan and scope of the programs which will be discussed in this chapter. The table below gives an overview of the proposed DSM action plan.
To be specific the DSM and end use energy efficiency action plan proposes the following programs and activities that needed to be carried out by the inputs and support provided by MoEn. DSM programs are identified which are presented below and can form the basis of DSM program implementation in Nepal. Each of these DSM programs is an activity in itself and would require a dedicated planning and sub activities
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Figure 20 Proposed action plan for DSM program implementation (Bailly, 1994)
The DSM programs which were pre scoped in the study are as follows:
Program 1: Reviving the DSM cell at NEA active which is currently in a dormant stage
Program 2: Conducting load research and consumer indexing for Nepal which may include the GIS mapping
Program 3: Introducing computer based data logging system for substations and feeders for identifying the transmission and distribution losses
Program 4: Upgrading of NEA load dispatch center to reach up till feeder level for online monitoring of system which is currently only till substation level
Program 5: Establishing remote metering system for NEA for TOD consumers and overhauling the metering system of NEA from electromechanical meters to digital meters
Program 6: Developing and adopting seasonal tariff mechanism in electricity to balance the cost of supply side.
Program 7: Conducting energy audits of high end industrial and commercial consumers
Program 8: Developing DSM Programs for energy efficiency improvement in Domestic sector
Program 9: Developing DSM Programs for energy efficiency improvement in Industrial sector
Program 10: Developing DSM Programs for energy efficiency improvement in commercial sector
More DSM program can be scoped in a detailed study based on the requirement and resources of the utility. The scoping of DSM plans and preparing a detailed study the detailed action plan while considering a current policy constraint requires more in depth analysis of the DSM and end use energy efficiency at the policy level which is out of the scope of present study.
12. Limitation of the study and conclusion
12.1. Limitations of the study
The study of DSM and end use EE for Nepal is conducted in a period of five months and is to support the framework for institutionalizing EE in Nepal. There are limited studies conducted in the field of EE with the last one being six years ago in 2010. There are data gaps due to unavailability of the data, as very little research was done in the past in the field of DSM in Nepal. This study is trying to fill this research gap in the country. However, due to such a wide scope of DSM in the country and limited duration of the study, there were simplifications and assumption used in the study. The barriers for the study have already been discussed above. The following are the limitations of the study:
- In this study, various parameters to check the service of the utility, for example utilization factor, demand intensity, energy intensity to name few, were not quantified, because of limited availability of data. This has led to limited understanding of the utility services to with respect to its varied consumer base.
- The CSC, which are plotted for different sectors, are made under certain assumption or by using a limited sample survey. This cannot accurately represent the scenario of the whole country.
- This is a macro level study. For implementation of even one DSM program in one particular sector, a thorough study is required to set the target population and to develop an exact program for the same.
- The impact of DSM programs in different penetration scenarios can be more streamlined using model simulation and other extensive methods which are not carried out in this study.
- This study also does not forecast the future savings that could be achieved under dynamic market conditions.
12.2. Conclusion
There is no denying that there are data gaps in the study which require to be fulfilled and the study cannot be considered as the implementation report for DSM in Nepal. Irrespective of this, the study is still relevant for the DSM and energy efficiency market of Nepal. This is a first of its kind study in Nepal’s potential market for energy efficiency, taking a research based approach to assess the market potential of DSM measures in Nepal.
The study is a pioneering step to make the policy makers, economists and the utility understand, the potential of DSM in different sectors in Nepal on a macro scale. The research has bought out some very serious gaps in the utility system which need to be addressed, if GoN is to target & improve the energy famine. These gaps need to be filled with or without DSM program implementation but are a pre requisite for a full scale DSM program. In the energy efficiency front DSM’s economic and technical potential are clearly brought out making a clear case for DSM. These are achievable savings and are not the theoretical potential and hence are more attractive to the utility. This case for DSM is a win - win situation for the utility and for the consumers.
The study’s final target was to bridge the knowledge gap between the policy makers, economists and the utility. To make the case for policy makers, policy recommendations are articulated to show the need of policy level commitment for DSM and how the program cannot move forward without an institution governing the energy efficiency in the country. To further add to the result of the study, proposed DSM programs and action plan are also briefly articulated. This is to give a clear direction on where to go after coming out of successfully assessing the DSM measures.
The study also brings out some of the valuable data which is available in the appendix. Due to the sheer amount of data, this thesis potential will come out with a volume 2 of this report (out of the scope of current academic work.), with the annexures and data sets as these will be helpful in future research work in the field of DSM and end use energy efficiency in Nepal. This thesis is among the first steps towards assessing and implementing DSM and IRP in Nepal’s utility sector, which holds tremendous potential for energy efficiency.
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14. Appendix
14.1. Appendix A: Nepali BS calendar to English AD calendar
The data available from the Nepal’s utility and the government was mainly according to Nepali year. The calendar followed in Nepal is Bikram Samvat (BS) calendar and is different from the English Anno Domini (AD) calendar. Though the number of days remains the same but the Nepali month starts from 15th of English calendar month. These dates vary +- 2 days but for simplicity here we have taken Nepali month from mid of one month to the mid of next English month.
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Table 7 Nepali (BS) Calendar to English (AD) Calendar
The first month of Nepali calendar is “Baisakh”
14.2. Appendix B: Installed capacity and peak demand for Nepal
Abbildung in dieser Leseprobe nicht enthalten
Table 8 Installed capacity and peak demand of last ten years for Nepal (NEA, 2006-2015)
14.3. Appendix C: Energy generation and import for last ten years
Abbildung in dieser Leseprobe nicht enthalten
Table 9 Energy generation and imports for last ten years (NEA,2016-2015) (own calculation)
14.4. Appendix D: Sample system load curve data
Abbildung in dieser Leseprobe nicht enthalten
Please turn over
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Table 10 Sample load curve data (rounded off to zero)
14.5. Appendix E: Monthly peak demand for Nepal electricity authority 2014-15
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Table 11 Monthly peak demand for 2014 - 15 (NEA, own data collection)
14.6. Appendix F: Nepal electricity authority monthly sample system load curve for 2014 -15
Abbildung in dieser Leseprobe nicht enthalten
Figure 21 Sample load curves for Nepal Electricity authority (developed from (data provided by NEA))
14.7. Appendix G: Characterization of Nepal electricity authority service by electricity use Indicators
Abbildung in dieser Leseprobe nicht enthalten
Table 12 Characterization of Nepal’s Electricity Authority’s service by electricity use (NEA, 2014-15) (own calculation)
14.8. Appendix H: DSM measures for the domestic sector
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Table 13 DSM measure and measure key for domestic sector (own calculation)
14.9. Appendix I: DSM measure quantified for the Industrial sector
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Table 14 DSM measure and measure key for industrial sector (own calculation)
14.10. Appendix J: Penetration of technologies in domestic sector
14.10.1. Saturation of technologies tariff meter class wise
Abbildung in dieser Leseprobe nicht enthalten
Table 15 survey result of penetration of technologies domestic sector
14.10.2. Average power rating of DSM measures for domestic measure
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Table 16 Power rating of various appliance in domestic sector
14.11. Appendix K: Conservation supply curve for domestic sector
14.11.1. Saved energy
Abbildung in dieser Leseprobe nicht enthalten
Figure 22 Conservation supply of all technologies assessed for saved energy in domestic sector
14.11.2. Saved capacity
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Figure 23 Conservation supply curve for all technologies assessed for demand saving in domestic sector
14.12. Appendix L: List of People who were Interviewed
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14.13. Appendix M: Questionnaire used for Household Electricity use
Quantify Energy to Save Energy
Questionnaire for Household energy/electricity use
Name of Respondent: .
Age of the Respondent:
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1. Basic Information
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Age of Respondent _______________________
Educational level of the respondent …..
Abbildung in dieser Leseprobe nicht enthalten
Abbildung in dieser Leseprobe nicht enthalten
2 Characteristics of the building
a. Is your building:
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b Is your building a storey? Abbildung in dieser Leseprobe nicht enthalten
c If yes, how many floors do you have in your building?
d How many rooms do you have in your building (including sitting room/hall)?
3 Household Energy Use Pattern
a Please tick where applicable the energy type use for the activities stated in the table below:
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4 Sources of Household Energy and use
4.1 Electricity Supply and Use
5.1.1 What is your main source of electricity?
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4.1.2 Do you have a standby source of electricity? Abbildung in dieser Leseprobe nicht enthalten
4.1.3 If yes, tick as appropriate?
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4.1.4 Please indicate the total electricity used in the last three months
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Other source of electricity apart from the grid)
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5 Household Lighting Equipment Use
5.1 What is your main source of lighting?
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5.2 If your main source of lighting is electricity, provide the information requested in the table below on the use of lighting in your house.
(i) Indoor Lighting (Lights in rooms of the Households)
Abbildung in dieser Leseprobe nicht enthalten
(ii) Outdoor Lighting (lights on the compound of the house)
Abbildung in dieser Leseprobe nicht enthalten
6 Household Refrigeration Equipment Use
6.1 Please complete the table below on the use of refrigeration in your household;
Abbildung in dieser Leseprobe nicht enthalten
7 Household Air-conditioning Equipment Usage
7.1 Do you have any form of air condition in your building? [Abbildung in dieser Leseprobe nicht enthalten]
7.2 If yes, complete the table below on the use of air conditions in your residence.
Abbildung in dieser Leseprobe nicht enthalten
8 Household water heating equipment use
8.1 Please complete the table below on the equipment use for water heating in your household
Abbildung in dieser Leseprobe nicht enthalten
9 Household Washing Machine, Dish Washers and Cloth Dryers Usage
9.1 Complete the table below on the use of washing machines, dish washers and cloth dryers in your household
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10 Household Other Electrical Appliances Use
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11 Household Energy Saving Measures
11.1 Have you introduced any energy saving measure in your household? [Abbildung in dieser Leseprobe nicht enthalten]
11.2 If yes to 12.1, state the kind of energy saving measure(s) you have introduced:
11.3 If yes to 12.1, how did you know about the energy saving measure?
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11.4 If no to 12.1, why? …
12 Effect of Power Outages
12.1 How has power outages (cuts) affected your household?
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12.2 Are you happy with the service being provided by your power utility (NEA)?
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12.3 How would you grade the performance of the utility?
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12.4 Specifically indicate what you are not happy with?
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12.5 Would you be prepared to pay more if this will improve their services to you?
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12.6 If yes, by what percentage?
12.7 If no, why?
Thank you for your cooperation in completing this questionnaire.
[...]
[1] Modern form of energy is considered as electricity
[2] Majority of the electricity production in the world is through coal which constitutes approximately 40 % of the total CO2 emission
[3] Nepal has around 19% of its population connected to stand alone PV system and isolated mini hydro power plants providing Tier 1 electricity.
[4] A small electricity system is the system with installed capacity of less than 1000 MW
[5] IPP are the private producers of electricity in Nepal after unbundling of generation side in 2003
[6] Any system with installed capacity of less than 1000 MW and less than 5000 Km of transmission line
[7] Suppressed peak is the maximum demand which utility estimates and is not the real peak as system is always under electricity deficit
[8] Plant load factor or capacity factor is the actual output of the power plant over a period of time divided the electricity it would have generated under ideal conditions
[9] Commercial losses are the losses because of electricity stealing and are non-technical losses
[10] Suppressed peak demand is the estimate demand by the utility because there is never enough supply in the system to estimate the real peak
[11] Commercial loss is the loss of electricity which is not billed majorly because of electricity theft and faulty meters
[12] One dollar is equivalent to Nepali Rupee (NPR) 100
[13] Theoretically some utilities’ might generate less revenue with energy consumption going down due to DSM activities
[14] An electrical ballast is a device use to limit the current in an electrical circuit for example inductive ballast in fluorescent lamps
[15] There is no policy or act in the country which governs or promotes energy efficiency
[16] All the data sheets are not part of appendix only the major ones and few samples. This is because of sheer quantity of files, but if required are available with the author
[17] These are daily sheets of hourly generation for a year and were not made part of appendix due to large set of data. The results from the data are discussed later
[18] Nepal follows BS calendar and not AD calendar refer to appendix A for detail
[19] NEA does not have a real demand curve as peak demand is never met in the system. Hence supply curve shows us the demand met.
[20] Lumens are a measure of the total amount of visible light to the human eye from a light source.
[21] In a lot of literature, the cost of saved energy (CSE) term is replaced with cost of conserved energy (CCE). They both are the same and should not be confused or mixed.
[22] In south Asian context the use of the total measure cost method is widely used for DSM cost assessment by utility in India (BEE, 2016).
[23] The detail contact and portfolio of these people is available on request from the author
[24] Super fans are the most EE fans available in the Nepali market with their efficiency going beyond what is required by the BEE, India
[25] Indian standards claim that its EE standards for appliances are in line with other country appliance EE standards.
[26] The subscript represents the technology and consumer or sector
[27] LOLP is defined as the probability in any hour of the load exceeding the available generation capacity. It is a nonlinear function concentrated in the highest 100- 500 hour of load. In case of Nepal LOLP is always 1
[28] Capacity saved and peak demand saved are synonym.
[29] The subscript represents the technology and the consumer category
[30] Due to poor electrical supply and low income country according to UN there are limited technologies available in Nepal’s market
[31] These reports are confidential and are available with Nepal Energy Efficiency Programme
[32] This is deduced on the basis of energy audit reports and field visits. The energy audit reports are confidential and cannot be cited here
[33] On an average one dollar is equivalent to 100 NPR
[34] The detail of how PF saved demand and not energy can be found here http://www.wisdompage.com/SEUhtmDOCS/SEU04.htm
[35] One dollar on an average can be taken as NPR 100
[36] NEA currently keeps all the information of connected load in files manually and the database is not available electronically and is scattered
[37] Nepal does not have any Energy efficiency policy or act
- Citar trabajo
- Shardul Tiwari (Autor), 2016, Demand Side Management in Nepal. Assessing the Impact of Selected DSM Measures in Nepal’s Electricity Sector, Múnich, GRIN Verlag, https://www.grin.com/document/368409
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