kampung capacity 21.09.2013 - university of california...

Shirley & Kammen – Kampung Clean Energy Capacity August 20, 2013

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RURAL SUSTAINABLE ENERGY SUPPLY POTENTIAL:

A CASE STUDY OF THE BARAM RIVER BASIN, SARAWAK, MALAYSIA Prepared by

Rebekah Shirley and Daniel M. Kammen

Renewable and Appropriate Energy Laboratory

http://rael.berkeley.edu

University of California, Berkeley

September, 2013


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ABSTRACT In this report, we document the potential for renewable energy resource supply and electricity generation in village communities in Sarawak, Malaysia. This research exercise was done in collaboration with the Sabah-‐based NGO Land, Empowerment, Animals and People (LEAP), Sabah-‐based Friends of Village Development or Tonibung and Oregon based NGO Green Empowerment. These groups are currently engaged in rural development and expanding rural energy access in East Malaysia. There are over 600 rural villages (more than 50km from an urban center) in the state of Sarawak. Amongst other issues including the formalization of native customary lands, logging and palm oil concession infringement and urban migration, the common lack of affordable and reliable energy access poses a barrier to development in many villages.

A majority of rural villages in East Malaysia are not grid connected and rely heavily on diesel fuel for all electricity and transportation needs. Tonibung and Green Empowerment have been installing micro-‐hydro systems in Malaysia for over a decade. They have already installed micro-‐hydro plants in a number of Sarawakian villages including Long Lawen1 and have plans for multiple installations in Baram River villages and the Kelabit Highlands. The results of our current assessment show that Sarawak’s rural economy could benefit greatly from a higher penetration of such renewable energy projects. Using data obtained from literature, surveys, and measurements we estimate load demand and resources available for a sample of prospective villages within the Baram River watershed. Using the Hybrid Optimization Model for Electric Renewables (HOMER)2 we will find the optimal configuration for each surveyed village based on cost and resource availability. The report thus answers three primary questions:

• What level of energy demand exists in village communities? • What local resources are available to individual village communities for energy provision? • What is the optimal and most cost effective system design for rural electrification?

Our energy team at RAEL directed by Professor Daniel Kammen has over 20 years of experience in conducting these types of studies in North America, Europe, Asia, Africa, Latin America and the Caribbean. We have prepared a number of reports and decision support tools focused on island communities3. RAEL researchers played a core role in highlighting viable alternative clean energy scenarios for Sabah in 20104. We seek to expand this feasibility study work into Sarawak to support the work of NGOs and Government Agencies that are dedicated to empowering and strengthen indigenous life in East Malaysia.

1 http://www.greenempowerment.org/countries/8/project/17 2 HOMER is developed by the U.S. National Renewable Energy Laboratory (http://homerenergy.com/) 3 https://rael.berkeley.edu/sustainableislands 4 Pinkowski, TIME Magazine, http://www.time.com/time/health/article/0,8599,2052627,00.html


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EXECUTIVE SUMMARY In this study we explore the potential for rural renewable energy supply through a focus on villages of the Baram River Basin in Sarawak as the basin next scheduled for mega hydroelectricity development by the state government of Sarawak. The Baram Villages do not fall into the category of lacking energy access given transport routes, diesel fuel availability and the introduction of cash-‐based rural economies. However diesel fuel cost, while not entirely prohibitive, is a barrier which creates exclusion. Designing more cost effective ways to meet current electricity demand will relieve an economic burden while simultaneously creating potential for new economic revenue streams. As such we have explored optimal system designs for electricity supply in villages of the Baram and determine that lower cost, higher reliability options are available for the villages given current resource potential. The average village household uses 41 kWh/month compared to 205kWh/month for urban Sarawak households. Currently electricity from diesel effectively costs 2.24 RM/kWh in village communities, compared to a 0.31 RM/kWh domestic electricity tariff for utility (SESCO) customers.

We model three Kenyah villages along the Baram River – Long San, Tanjung Tepalit and Long Anap -‐ representing high, medium and low energy use based on size and village activity. Their size ranges from 50 to 25 houses and total energy demand ranges from 45kW to 14kW. We find these villages to have significant energy resource potential with monthly averaged insolation of 5.34 kWh/m2-‐day, high river flow rates and about 0.2 tonnes rice husk/family per year. We developed a set of inputs to HOMER that cover a number of resource and technology inputs for each village. The study shows that there are significant savings which could come from using renewable technologies for electricity generation. In each village modeled the least cost option was some combination of hydro, biogas generators and accompanying batteries (see Figure 1 as an example). In each village case this least cost option was 20% or less of a diesel base case cost. The Levelized Cost of Electricity (LCOE) of these renewable options was also all less than 20% of their diesel base case scenarios (see Figure 2).

We observe that hydroelectricity is the lowest cost means of electricity production available to each village. Small scale biogasification is financially feasible and profitable for village communities however the technical feasibility of maintaining a biogas system must be considered. Despite the cost of diesel fuel, photovoltaic systems (PV) are not cost effective for the village communities. When employed they do not act as dominant energy sources. Capital and replacement costs of battery packs are often the major cost component for many least cost systems. Despite this, diesel, even at the subsidized government price, is the most expensive form of energy for Baram villages, given the recurrent annual fuel costs that it implies. In fact, we find that the Payback Period on Hydro and Biogas systems can be two years or less compared to 100% Diesel base case scenarios. These findings highlight the potential of villages in rural Sarawak to satisfy their own energy access needs with local and sustainable resources. This conclusion supports a state-‐wide energy development strategy that considers small scale energy solutions and technologies as an important part of providing rural energy access and rural development opportunity.


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Figure 1 Tanjung Aru Optimal System Types

Figure 2 Difference in LCOE for Least Cost Options and Diesel Base Case Diesel


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TABLE OF CONTENTS

1. RURAL ENERGY ACCESS ........................................................................................................................ 6

1.1. THE VALUE OF ENERGY ACCESS ...................................................................................................... 6

1.2. RURAL SARAWAK ........................................................................................................................... 7

2. THE VILLAGES OF THE BARAM RIVER ...................................................................................................... 9

2.1. DESCRIPTION OF ENERGY USE IN SELECTED VILLAGES .............................................................. 9

2.1.1. GENERAL DESCRIPTION ........................................................................................................ 9

2.1.2. ENERGY USE IN THE VILLAGES ............................................................................................ 10

2.1.3. DESCRIPTION OF ENERGY RESOURCES ................................................................................. 14

3. ENERGY MODELING OF THE BARAM VILLAGES .................................................................................... 18

3.1. OPTIMIZATION MODEL FRAMEWORK ...................................................................................... 18

3.2. GENERAL MODEL RESULTS ..................................................................................................... 19

3.2.1. TANJUNG TEPALIT .............................................................................................................. 19

3.2.2. LONG ANAP ......................................................................................................................... 21

3.2.3. LONG SAN ........................................................................................................................... 22

3.3. RESULTS ANALYSIS: DESIGNING SYSTEMS FOR VILLAGE COMMUNITIES .................................. 24

4. CONCLUSIONS ..................................................................................................................................... 26

5. APPENDICIES ...................................................................................................................................... 28

5.1. HOMER DEFAULT INPUT/ASSUMPTION SUMMARY ....................................................................... 28

5.2. SIMULATION RESULTS .................................................................................................................. 30

5.3. SCENARIO COMPARISON RESULTS ................................................................................................. 32


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1. RURAL ENERGY ACCESS

1.1. THE VALUE OF ENERGY ACCESS Energy is essential to meeting basic human needs including preparing food, providing running water, heat, light and transport. Many international economic development agencies acknowledge that poor access to energy impacts on quality of life, educational chances, economic production and healthcare provision5. According to the International Energy Agency, for instance, 1.4 billion people around the world lack access to electricity – 85% of them in rural areas. 2.7 billion people rely on traditional use of biomass, and that number is expected to rise to 2.8 billion by 2030. In response to such statistics the United Nations established the Sustainable Energy for All Initiative to attract global attention to the importance of energy for development and poverty alleviation. The goals of this initiative are to ensure universal access to modern energy services, to double the rate of improvement in energy efficiency and to double the share of renewable energy in the global energy mix6.

Beyond their share of population, rural communities are widely acknowledged as vital to developing economies, being the foundation of agrarian society and industry. Rural agriculture comprises the largest working population in India, for instance, and since more than two-‐thirds of the population of India lives in rural areas, it has widely been acknowledged that increased rural purchasing power is a valuable stimulus to industrial development at the national scale. According to the Food and Agriculture Organization (FAO), a lack of energy access stifles both small scale cottage activity and larger scale rural agriculture; hampers commercial trading opportunities; and inhibits the provision of basic social services such as health care and education.7

The total installed generation capacity on Sarawak’s SESCO grid was roughly 1,345 MW in 2010 generating over 5,700 GWh in electricity sales. The industrial sector was the main user of electricity with 45.2% of total sales, followed by the commercial sector at 30.2% and the residential sector at 23.3%. Over the entire decade, growth rate in total electricity sales thus averaged 7.3% per annum. Despite a 7.3% average annual growth rate over the past decade, rural electrification has expanded much more slowly. Even with plans to develop large scale dams with high voltage transmission from rural areas in the state, this rarely translates into electricity access for affected or nearby communities (see Figure below).

In this study we focus on villages of the Baram River Basin in Sarawak, as the basin next scheduled for mega hydroelectricity development. The Baram Villages do not fall into the category of lacking energy access. Transport routes are open enough such that fuel and infrastructure can be brought to village communities. Trade in meat and produce creates the economic base which makes modern energy services available. Diesel fuel cost, while not entirely prohibitive, is a barrier which creates exclusion. Most of the villages in the Baram have a significant number of families that do not own generators – and therefore do not have electricity – and a larger number of families that have generators but cannot

5 http://www.gvepinternational.org/en/business/why-‐energy 6 http://www.unfoundation.org/what-‐we-‐do/issues/energy-‐and-‐climate/clean-‐energy-‐development.html 7 Forestry Department, Food and Agriculture Organization (1987). Technical and Economic Aspects of Using Wood Fuels in Rural Industries. Chapter 2: The Importance of Rural Industries to National Economics and Rural Development. http://www.fao.org/docrep/004/ab780e/AB780E02.htm#ch2


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afford a consistent fuel supply over the course of a month. While in some villages such as Long San, the fraction of doors with generators was quite high, the average seems to be roughly 60 – 70% of doors with a generator. There were a number of villages such as Long Liam and Long Keluan where these percentages were less than 50%.

Designing more cost effective ways to meet current electricity demand while extending electricity availability into daytime periods will relieve an economic burden while simultaneously creating potential for new economic revenue streams. Especially as the cash economy becomes more pervasive, economically secure village communities are likely to be more resilient to changes in markets, costs of commodities and government practice. As such we have explored optimal system designs for electricity supply in villages of the Baram. We have determined that lower cost, higher reliability options are available for the villages given current resource potential.

Figure 1 Mundung Abun, showing Transmission Lines for Bakun Dam running overhead while village has no grid

connected Electricity

1.2. RURAL SARAWAK The Malaysian economy was originally very dependent on the rural sector for early development. In the 1970s agricultural exports accounted for 30% national GDP and roughly 70% of the rural population was engaged in agriculture. Malaysia is transitioning to an economy dependent on industry and large-‐scale commercial farming of crops such as palm oil, with significant impact on the livelihoods of the still dominant rural population. The Government of Malaysia acknowledges the importance of stimulating rural economy and through the Ministry of Rural and Regional Development


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has launched a program called ‘the Government Transformation Program’ to increase rural energy access, clean water access and road infrastructure networks across the country.8 Sarawak is the fourth most populous state in Malaysia with a population of 2.3 million people. Comprising fully a third of Malaysian land mass, it also has the lowest population density in the country at 22 people per km2. Sarawak also has one of the lowest population growth rates in the country at 1.2% per annum.9 It is difficult to estimate rural and urban populations in the state given that there is often much flux between them however rural communities, arranged by river basin, represent 47% of total population in the state. This is one of the lowest levels of urbanization in the country.10 Thus Sarawak is a largely rural state. The Government Transformation Program seems to acknowledge the importance of the rural community and its plan in Sarawak calls for over RM 500 million to be spent in rural electrification in 2013 alone.11 The success of top-‐down electrification programs has been challenged by experience of the past. General energy policy studies show that subsidies can distort markets and encourage the uptake of fuels, which are not necessarily least cost, or even best suited to the energy services needed. It is often difficult to encourage utilities to invest in rural electrification schemes, hence the usual role of central government in implementation. However, it should be noted that community involvement and investment is necessary to support longevity of projects.12

Figure 2 River Basins of Sarawak 8 Ministry of Rural and Regional Development (2012). Improving Rural Development, http://www.pemandu.gov.my/gtp/upload/GTP2_ENG_Cp8.pdf 9 Sarawak Government Facts and Figures 2012. (2012) http://www.spu.sarawak.gov.my/downloads/Facts%20&%20Figures/Sarawak%202011%20Facts%20&%20Figures.pdf 10 http://www.statistics.gov.my/portal/index.php?option=com_content&id=1215 11 http://kklw.bernama.com/en/news.php?id=496379 12 Natural Resources Management and the Environment Department, Food and Agriculture Organization (2000). The Energy and Agriculture Nexus. Chapter 3: Rural Energy Supply (Experience of Rural Energy Programmes) http://www.fao.org/docrep/003/x8054e/x8054e06.htm


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2. THE VILLAGES OF THE BARAM RIVER 2.1. DESCRIPTION OF ENERGY USE IN SELECTED VILLAGES

In this Report we model the energy resource potential of individual villages along the Baram River. The Baram Dam seems to be the next project to be developed under the SCORE schedule and would involve the displacement of over thirteen villages and thousands of families. There are over 20 villages in the Baram region that would be lost in flooding the reservoir. In addition to traditional indigenous lands and primary forest cover lost, this represents the displacement of some 20,000 people. The Baram originates in the Kelabit Highlands, a watershed demarcated by the Mountains of East Kalimantan, which form a natural border with Sarawak. The Baram, which is the second longest river in Malaysia, flows westwards through tropical rainforest to the South China Sea and has a catchment area of 22,930 km2. In preparing this report our researchers conducted site visits to a number of villages along the Baram River. Through interviewing, observation and data measurement and recording we collected information on energy use and energy resource availability in various Baram villages. There are over 20 villages on the Baram River. Here we model three Kenyah villages along the Baram River – Long San, Tanjung Tepalit and Long Anap. These three villages represent high, medium and low energy use based on size and village activity.

Figure 3 Villages Visited along Baram River, March 2013 (get better image from GW)

2.1.1. GENERAL DESCRIPTION The Kenyah settlement of Long San is one of the largest Baram villages and is one of the villages closest to the site proposed for the Baram Dam. It is roughly 150 km south east of Miri and can be accessed by 5 hours driving along logging tracks. Long San is comprised of 160 families representing roughly 800 people. The Long San Long House was destroyed by fire in 2006 and while a Long House is being rebuilt, families live together in individual houses. There are 80 such houses or ‘doors’. There


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is also a community-‐based Guest House and nearby convenience store, a landing strip, a church, a clinic, a primary school of about 400 students built by St. Paul missionaries in the 1940s and a secondary school built by the federal government. The government is also building a community hall. Being one of only villages with a primary and secondary school along the Baram, children from many neighbouring villages are sent to board in Long San for education. Being a major base for trading goods from the city, Long San has become a hub of the Baram community. Long San would be the first village flooded and destroyed by the Baram Dam if it is built.

Tanjung Tepalit is a much smaller Kenyah village community located about 22 km south along the river from Long San. It comprises of a single long house with 25 doors. Currently a new 60 door long house is being constructed for the housing of extended families. This is being entirely financed by village families. In addition to the long house there is a store house for boat engines, a padi store house, a generator store house, a church building and a community hall which has no lights or fans (no generator). There is no school in this village so children are sent to Long San for primary and secondary schooling where they board for two week periods. Produce (fruit, vegetables and meat) are also taken to Long San along the river for trading. Both Long Anap and Tanjung Tepalit are rice farming based villages with similar farm sizes and yields per family.

Long Anap is the furthest village from the Baram Dam site and is 34.5km from Long San. Another Kenyah village, Long Anap is medium sized with two long houses comprised of 54 doors total. Aside from the long houses there is a community church (with generator) and a primary and secondary school. There is no clinic in this village. In this village a number of doors do not have generators. Like Long San, timber companies using traditional village lands for logging provide two barrels of oil a year at Christmas. Once these are used, there is often little diesel for running the church generator. Many villagers explained that families will often go without lights in the evening as purchasing diesel can be prohibitive. In this village we noted that on a monthly basis many families feel they use just as much diesel for electricity as they do for fueling boats for travel.

2.1.2. ENERGY USE IN THE VILLAGES Based on interviews and site visits we were able to record the number and type of generators that are operational within each village to estimate current energy supply. Before this discussion we should note that the local state department supplies diesel to meet electricity demand for public buildings. In

From left to right: Goverment Secondary School, a rice mill house and view of Long San River


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Long San, for instance, there are a number of 20 KW generators for the school buildings which are maintained and fueled by the government. The government has leased the primary school from the missionary group for the past 60 years. Generators are used to provide electricity to the school building during the day and to power the boarding halls and teacher dormitories during the night. The clinics also have their own generators. We do not include these loads in our model as they are satisfied by government funds.

In these villages, the average ‘door’, which houses 2-‐3 families operates generators from ~6pm – 11pm or midnight, consuming about 2 gallons a night. Based on reports of daily fuel usage it appears that most generators are operating at almost half of maximum load. Electricity is used mainly for lighting and for fans. Most households also have refrigerators and washing machines while well off families also own televisions, DVD players and other electronic devices. In some of the villages we visited televisions are housed in the common gallery so that individual households do not need to purchase sets.

Of the 80 doors in the Long San village, almost all of them have a small diesel generator (small 3kW Skandong generators imported from China are most common). However we estimate 85% of doors in Tanjung Tepalit and 70% of doors in Long Anap have generators. Our assumptions for calculating typical electricity use during evening time are explained in Table 4 below. Based on an assessment of energy use in typical houses, we estimate loads in the hypothetical case that all doors could have a generator. Long San has a load of 45kW, Long Anap 28 kW and Tanjung Tepalit 14kW. Diesel generators have a high maintenance cost since this involves transporting the generator to Miri. The Chinese generators last about 3 years before requiring replacement and often if a problem cannot be fixed, the generator is quicker retired than transported to Miri for maintenance.

Table 1 Evening Energy Use in Long San Households

These villages are based on subsistence agriculture, with each family owning land used for hill paddy planting. A large family might own 6-‐7 acres of land within the village bounds while a smaller family might own 2-‐3 acres. The average family owns roughly 5 acres of land. This report is consistent across the villages visited. Fields are cleared and planted with rice padi starting in September and rice is harvested in February and March of the following year. Rice (including husks) is stored in 50kg bags. A harvest of 20 bags/acre is considered a successful season, while 10 bags/acre would be a poor year. After harvest, women of the village winnow the rice to separate the grain from the stock. The grains

Household Loads Wattage (W) Number Hours/Night Nights/mthFraction of Doors

Total (kWh/mth)

Light Bulb (CFL) 18 6 5 30 1 1,296Light Bulb (Tube) 40 5 5 30 1 2,400Electric Fan 40 2 5 30 1 960Television 50 1 4 20 0.2 64DVD Player 30 1 2 5 0.2 5Ice Box 115 1 5 30 0.7 966Washing Machine 445 1 4 10 0.7 997

No. Doors 80 Total 6,688Average kW 45


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are left to dry and are stored in rice huts. They are pounded by hand and foot to separate rice from the husk of the grain every month for domestic rice needs.

Alternatively, a rice mill will be used if it can be afforded. There are 10 family owned rice mills in Long San village. There are about five family rice mills each in Tanjung Tepalit and Long Anap. Other families will pay the owners to use the mill at a rate of RM 2/tin. The 8.8 kW Yenmar Rice Mill from China is common. Along These rice mills are primarily run during the two months of the harvest season but also through the year as rice is needed. We do not model this load given its irregular pattern of use through the year. Other loads might include the village convenience store, if applicable, on weekdays and a 5 kW load on Sunday mornings for four hours at the community church. Long San also has a guest house though it is not used frequently and is not factored directly into the model. A standard 3 kW generator costs US$1,100 (or US $400/kWinstalled) and diesel fuel costs 12RM/gal (US$3.96/gal). This is a standard retail price based on a government subsidy.

Figure showing villager with Fire wood for cooking. They do not use LPG cook stoves but rather fuel wood collected from within the village, which is cheaper and which also adds to the taste of the food.

Picture of old dam and potential new Microhydro intake and Community Guest House in Long San


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With this diesel price the average electricity price in the Baram villages is 2.24RM/month ($0.67 US/gal). The current domestic rate in Sarawak is 0.31 RM/kWh ($0.09 US/kWh). This is more expensive than TNB and SESB but much lower than other ASEAN countries13. Also, the average Sarawakian domestic demand was 205 kWh/month/household in 201014, much lower than the national average. Thus the average Sarawakian household is spending 63 RM/month ($19 US/month) on electricity, emphasizing how much more rural village communities pay for energy access.

13 (Suruhanjaya Tenaga (Energy Commission) (2012) Electricity Supply Industry in Malaysia: Performance and Statistical Information 2010 14 Calculated from SESCO Annual Performance Statistics (2010) and Sarawak Govt Facts and Figures (2011)

From Left: (a) Tanjung Tepalit new Long House, (b) interviewing villagers, (c) Long Anap villager with standard rice mill, (d) Long Anap Secondary School


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2.1.3. DESCRIPTION OF ENERGY RESOURCES Hydro Potential: Our team visited potential microhydro sites near to each of the Baram villages. In Long San for instance, the most appropriate site is an old dam that was constructed in the 1940s by the missionaries. A major storm destroyed the power house and it has been non-‐operational since. It is 25m net head above the village intake, which is equivalent to the gross head minus the friction loss in the penstock, valves, and fittings. We also measured stream flow at each site. These measurements were correlated with precipitation data to estimate monthly average flow rates (see figure below). Based on previous Tonibung and Green Empowerment installations in other villages we estimate the complete capital cost of microhydro infrastructure to be US$ 1,300/kWinstalled with minimal operation costs. Assumptions on hydro turbine specifics are in Appendix 2.

Figure 4 Monthly Averaged Stream Flow

Solar Resource: Using NASA Surface Solar Energy data and the coordinates of the villages we determine solar potential for the region. Given their proximity to each other they fall under the same resolution pixel and data is the same for each village. That annual averaged insolation is 5.34 kWh/m2-‐day, peaking at 6 kWh/m2-‐day in March. Average annual radiation is 0.4W/m2. The clearness index is a measure of the clearness of the atmosphere and is the fraction of the solar radiation that is transmitted through the atmosphere to strike the surface of the Earth. The clearness index for the region is fairly high with a 0.53 average. Assuming an area of 100m2 (panels placed on the newly constructed long house comprising a 50m by 2m area) and a 23% efficiency, the long house could support a 15kW solar installation which could generate about 45 MWh/year for day time use. From surveying we find that villagers can purchase individual solar panel systems at a rate of $8.25/Winstalled. However for larger scale systems we estimate cost at US$ 3.85/Winstalled. Photovoltaic technology has been introduced into some village communities through telecommunication companies that run satelittes using solar cells (see picture below).


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Figure 5 Monthly averaged Daily Insolation Levels in Baram Region

Figure 6 Averaged Radiation in Baram Region

Figure 7 Solar System for Cellular Communication Satelite in Tanjung Tepalit

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Biomass Resource: We also estimate the potential for biogasification using rice husk as a feedstock. Assuming that the average family plants 5 acres of hill paddi rice a year with a conservative average yield of 10 bags of rice per acre every year and a residual ratio for rice straw of 1:0.3 for rice husk, there are at least 20 tonnes of husk residue every harvest season in each village. We can approximate how this rice husk is distributed throughout the year based on monthly rice consumption (see figure below). The higher heating value (HHV) of rice straw and husk are 15.09 and 15.84 MJ/kg respectively, while the LHV of their produced gas is approximately 7 MJ/kg15,16. Literature shows gas yield rate is between 1.63~1.84m3/kg and gasification efficiency is between 80.8%~84.6%17. Assuming 30% electrical conversion efficiency, a standard biogas generator would require 1.2kg/hr-‐KWoutput. This depends strongly on the gasification ratio assumed. The gasification ratio is the amount of biogas produced for a given amount of biomass residue (m3 biogas/kg biomass residue). To be conservative we assume 1.7m3/kg biomass residue but experiment with this value through sensitivity analysis. We assume a biomass gasifier and generator cost US$3,000/kWinstalled with maintenance costs similar to diesel generators.

Figure 8 Village women of Tanjung Tepalit standing with a typical family’s padi yield in a good harvest

15 Yi and Yonghao, “Experiment on Rice Straw Gasification in a Two-‐Stage Gasifier.” 16 Lim et al., “A Review on Utilisation of Biomass from Rice Industry as a Source of Renewable Energy.” 17 Yi and Yonghao, “Experiment on Rice Straw Gasification in a Two-‐Stage Gasifier.”


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Figure 9 Monthly Averaged Daily Rice Husk Available

Wind Resource: Roughly 50% of the year wind speeds are below 2m/s because of the interior location and rugged geography of the region. Given the low wind speed patterns in the region we assume that wind turbine are not a feasible energy option and they are not considered in the study (see Figure below).

Figure 10 Monthly Averaged Percent Of Time The Wind Speed At 50 m Is Within The Indicated Range


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3. ENERGY MODELING OF THE BARAM VILLAGES 3.1. OPTIMIZATION MODEL FRAMEWORK

Our project used NREL’s HOMER resource optimization model to determine the most economically feasible electricity fuel mix for Long San. In order to find the least cost combination of components that meet electrical and thermal loads, HOMER simulates thousands of system configurations, optimizes for lifecycle costs, and generates results of sensitivity analyses on most inputs.

HOMER simulates the operation of each technology being examined by making energy balance calculations for each of the 8,760 hours in a year. For each hour, HOMER compares the electric and thermal load in the hour to the energy that the system can supply in that hour. For systems that include batteries or fuel-‐powered generators, HOMER also decides for each hour how to operate the generators and whether to charge or discharge the batteries. If the system meets the loads for the entire year, HOMER estimates the lifecycle cost of the system, accounting for the capital, replacement, operation and maintenance, fuel, and interest costs18.

Thus, the HOMER model uses a number of inputs, along with optimization and sensitivity analysis algorithms, to simulate different system configurations, or combinations of components, and generate a list of feasible configurations sorted by net present cost in terms of cost of electricity per kilowatt hour. HOMER’s Cost of Electricity output is synonymous with LCOE and provides us with information needed to analyze the potential for adoption of renewable energy generation in the Baram region.

We have developed a set of inputs to HOMER that cover a number of resource and technology inputs for each village. We provide monthly biomass residue availability, daily solar insolation and monthly averaged flow rates as described in the section above. We also provide data on hydro turbine design flow rate, expected PV efficiencies, biomass gasification ratio and feed rates for the biogas generator. We provide data on converter size and give the model a number of battery options. Finally, we input capital, replacement and operation/maintenance costs for each technology. These cost figures are based on data directly from groups such as Green Power and Sungevity, which specialize in installations for small scale and remote applications. In HOMER we assume a maximum annual capacity shortage of 10%, an interest rate of 7% and a system lifetime of 25 years. All assumptions and data inputs can be seen in Appendix 4.1.

18 Renne, David. "Decision Support System for Assessing Hybrid Renewable Energy Systems." U.S. Climate Change Science Program, 2008.


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Table 2 Energy Demand and Resource Characteristics of the Baram Villages modeled

3.2. GENERAL MODEL RESULTS HOMER delivers results which show the optimal system for each possible technology configuration ranked according to Total Net Present Cost (NPC). For our analysis of all three villages there are a number of major conclusions. Here we discuss the least cost options in terms of NPC and LCOE. We describe the least cost system configuration given each village’s resource availability. We also describe the results of sensitivity analysis on stream flow, biomass resource and diesel cost. Summaries of simulation results can be found in Appendix 2.

3.2.1. TANJUNG TEPALIT Tanjung Tepalit has a low level of demand but a large hydro potential given the head available and steady annual stream flow patterns. The least cost system for the village is a single 7KW Hydro plant, a 90 12V battery pack and a complementary 10kW inverter. The battery maintains more than 80% state of charge all year except for July, when the stream flow is lowest. The system’s NPC would be $57,228, the major cost being the battery. The LCOE is $0.196/kWh. Above $0.5/L diesel is too expensive and does not figure into any optimal systems. Diesel consumption declines gradually as the average stream flow available increases. Once diesel costs more than $1.00/L then total diesel consumption can drop to less than 800L/year.

Biomass Resource (averaged at 0.0275tonnes Rice Husk per day) is the lowest in this village, given its population. Above 0.05 tonnes rice husk per day (double the assumed current capacity) biogas becomes viable. The optimal system then changes to 7kW hydro and 10kW biogas generator. In this system no battery is required. The hydro provides 84% and the biogas 16% of annual generation. This system has NPC of $49,358 and has a LCOE of $0.167/kWh. We also note that photovoltaics do not figure into any optimal systems at the current cost of $2300/kW. Batteries, biogas generators and diesel generators are all selected before photovoltaic (PV) panels. The lowest cost PV hybrid system costs $85,240.

N Lat. E Long.Long San Kenyah 3 17 888 114 46 855 80 160 800 45 6,688 80,256 13,777 54,557 Yes Yes Yes Yes Long Anap Kenyah 3 03 747 114 49 140 54 108 540 28 4,180 50,160 8,610 34,096 No Yes Yes No Tanjung Tepalit Kenyah 3 14 396 114 48 975 25 50 250 14 2,090 25,080 4,305 17,048 No No Yes No

Location

N Lat. E Long. Max Min Avg Max MinLong San Kenyah 3 17 888 114 46 855 320 33.6 32,552 25 98 31 65 13.3 5.34 0.43 0.37Long Anap Kenyah 3 03 747 114 49 140 216 15.5 15,016 70 23 11 17 8 5.34 0.43 0.37Tanjung Tepalit Kenyah 3 14 396 114 48 975 100 10.5 10,172 20 103 25 62 5.8 5.34 0.43 0.37a. assumes Residue Ratio Rice:Husk is 1:1.3b. Assumes Diesel Generator Efficiency is 16% in the villagesc. Assumes Hydro Turbine Efficiency is 60%d. Assumes roughly 2 families living per door of a long housee. Solar data available from NASA Surface meteorology and Solar Energy groups all three villages within the same resolution pixel

Average Radiation (W/m2)Mthly Avg

Insolation (kWh/m2-‐day)

Padi Acreage

Rice Husk (tonnes/yr)

Monthly Averaged Flow Rates (L/s) Annual Potential (kWh)

Annual Potential (kWh) Head (m)

Capacity (KW)

Non-‐Household Loads

Hydro Resource Solar Resource

Coordinates Community HallkWh/year School Church

Location Size

Clinic Diesel Expense Gen Cap (KW) kWh/month

Estimated Household Demand Indigenous Group

Diesel Consumption

Village NameIndigenous Group

Coordinates

Village Name No. DoorsNo. Families No. People

Biomass Resource


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Figure 11 Tanjung Aru Optimal System Types

Figure 12 Changes in Diesel Consumption with Stream Flow

Figure 13 Least Cost System Design for Tanjung Aru (7kW Hydro and Battery System)


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3.2.2. LONG ANAP Long Anap has 54 doors and an estimated demand of 28kW. However the annual average stream flow is 17L/s. However with a large head the village can still have a 6kW Hydro system. Note that we do not have access to many stream flow data points for the region. However sensitivity analysis on stream flow shows that as average stream flow increases, the hydro production increases steadily from 62,000 kWh/yr at 17L/s to 80,000 kWh/yr at 30 L/s. Since biomass resource is higher in Long San, biogas generators factor in to optimal design at lower cost than in Tanjung Tepalit. Unlike Tanjung Tepalit, biogas is selected in the least cost option, which is a 6.2kW Hydro, 20kW biogas generator, converter and a 90 piece 12V battery pack system. This system has a NPC of $129,386 and a LCOE of $0.225/kWh. The Hydro turbine and biogas generator are 72% and 28% of annual production (kWh/yr) respectively.

Diesel generators drop out of optimal system types at a diesel price of $0.33/L. After this price point the Hydro, Biogas generator and Battery system described above dominates. In this configuration the biogas generator capital cost is the biggest cost over the lifetime of the project. Long Anap is the only of the three villages where PV can be factored into an optimal system design. Only where diesel is less than $0.15/L (a tenth of its current price) a hydro and PV hybrid is cost effective. These PV systems cost much more because of the additional price of the battery packs that would be needed. At $1.5/L a 30 kW, a system of diesel generators only costs $53,332 NPC and has an LCOE of $0.995/kWh. A hydro and diesel system (without battery) would have a LCOE of $0.84/kWh and an operating cost of $44,743/year due to diesel fuel costs.

Figure 14 Optimal System Types for Long Anap


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Figure 15 Optimal System Type given PV Capital Costs, Long Anap

Figure 16 Least Cost System Design for Long Anap

3.2.3. LONG SAN Long San has the largest population with 80 doors and an estimated demand of 45kW. Like Tanjung Tepalit, Long San has high average stream flow. Like the other two villages assessed, the least cost system includes a hydroturbine (of 8.83 kW) and like Long Anap, because of the biomass resource available to the village, the least cost system also includes a 20 kW biogas generator and battery pack. The NPC is $193,423 and the LCOE is $0.225. In fact, above stream flow of 75 L/s and above a rice husk availability of 0.18 tonnes/day, the optimal system is a hydro turbine and biogas generator system without the need for battery. This system has a NPC of $145,463. A biogas generator is selected along with a hydro turbine for the ten least cost systems for Long San.

At a price of $0.4/L a hydro, biogas, battery and diesel system is optimal. However beyond a price point of $0.8/L, diesel fuel drops out and the optimal system is the Hydro turbine, biogas generator and battery system described above. The PV panels are not selected in any least cost systems at its current cost. A hydro turbine and diesel system (without battery) would have a NPC of $783,057, a LCOE of $0.819/kWh and a operating cost of $65,573/year due to the expense of diesel.


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Figure 17 Optimal System Type for Long San

Figure 18 Diesel Consumption for Long San

Figure 19 Least Cost System for Long San


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3.3. RESULTS ANALYSIS: DESIGNING SYSTEMS FOR VILLAGE COMMUNITIES The study shows that there are significant savings which could come from using renewable technologies for electricity generation. In each village modeled the least cost option was some combination of hydro, biogas generators and accompanying batteries. In each case this least cost option was 20% or less of base case (diesel) NPC. The LCOE of these renewable options were also all less than 20% of their diesel base case scenarios (see figures below). The biggest percentage difference is in Long Anap,

Figure 20 Differences in NPC and LCOE for Least Cost Options and Base Case (Diesel)

• Hydroelectricity is selected in every simulation, meaning that it is the lowest cost means of electricity production available to each village. Based on its low upfront and O&M costs the hydro search space is actually insufficient in the models, meaning that villages would benefit from even more hydro capacity than our head and flow measurements show is available. In each village and under almost every scenario modeled, hydro provides at least 50% of the total electricity supply. Thus the benefit of cheap and dispatchable electricity will exceed the upfront cost of expanding existing systems, designing larger (or multiple) systems.

• Small scale biogasification is financially feasible and profitable for village communities. Even in Tanjung Tepalit where rice husk availability is the lowest, biogas substitutes for diesel during the three months that biomass is available. This is because the cost of installation and maintenance of the biogasification unit is similar to that of a diesel generator however the fuel is virtually free. It should be noted that this does not include the devaluation of systems over time. Given low usage, it would seem that the biogasifier would devalue more quickly and have to be replaced more often. We approach this by performing sensitivity analysis on the lifetime (number of operating hours) of a biogasifer which affects how often it has to be replaced. Even when the number of operating hours is reduced to one third, which is significantly lower than a diesel engine, the gasifier is chosen in the least cost scenario. There are a number of technical barriers to the deployment of small scale biomass technologies as described above and these need to be considered alongside cost.


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• Despite the cost of diesel fuel, photovoltaic systems (PV) are not cost effective for the

village communities. In every village modeled the cheapest PV scenario is at least 12%% more expensive than the lowest NPC scenario. In Tanjung Tepalit, after hydro and biogas resources are maximized, it is more actually cost effective to add additional diesel capacity than to add solar. Even when a 10 kW PV system is introduced it does not produce more than 15% of total electricity supplied. This trend only changes under sensitivity analysis when the cost of diesel is very high and we select prices for PV that are below current market prices. Nevertheless in each village PV scenarios are still cheaper than 100% diesel only scenarios.

• Capital and replacement costs of battery packs are often the major cost component for Least Cost systems. Battery packs of 90-‐180 12V batteries are often selected in the lowest cost options but represent most of the cost. For each of the villages modeled a battery pack was selected for the least NPC and least LCOE scenario and cost at least 40% of total net present cost. In Tanjung Tepalit where there the most optimal system is a hydro turbine and battery pack alone, the battery is 90% of the total system cost. This is due to high upfront cost and the replacement cost of batteries.

• Diesel, even at the subsidized government price, is the most expensive form of energy for Baram Villages, given the recurrent annual fuel costs that it implies. A 100% diesel generator system in Long San, the largest village demand modeled, would require 60 kW of diesel capacity and though having one of the lowest capital costs (US $26,400), the annual fuel cost cause it to be one of the most expensive NPC scenarios. To meet smaller loads the generators will often be under capacity leading to increased fuel consumption and a lower overall mean electrical efficiency. In every village modeled the diesel only scenario was at least five times more expensive than the least NPC scenario. This means that the current system of 100% diesel generation is the most expensive option that village communities could design.

• The Payback Period on Hydro and Biogas systems can be two years or less compared to 100% Diesel base case scenarios. In Long San, which requires the largest system capacity, the 8.8 kW Hydro and 20 kW Biogas Scenario has a year simple pay back and beyond a year the cash flow from this system is positive relative to a 100% diesel scenario. In Tanjung Tepalit, the simple pay back on the 7kW Hydro system is 1.30 years. The simple payback on a system with PV would be 2.5 years. In Long Anap, the 6kW Hydro and 20kW biogas system has a simple pay back of 1.35 years. See the economics comparison figures in Appendix 4.3 for more detail.


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4. CONCLUSIONS There are 14 villages along the main Baram river that have been identified to be directly flooded by the reservoir. This excludes the villages in the tributaries of Baram which will also be flooded. There are 19 villages along the main Baram river upstream of the dam and more than 20 villages downstream ranging from 40 to 200 plus doors each. In this report we analyse the potential for these communities to provide local energy service using available resources. This report produces a number of findings on both the current electricity situation and potential system configurations in the Baram kampong.

• Average village door (2-‐3 families) in the Baram uses ~ 3 kWh/night or ~83 kWh/month. Entire villages can use ~130 kWh/night or ~4,000 kWh/month

o In the villages, generators run at lower efficiencies. We assume 16% efficiency for generators (usually 3kW gensets) or 9-‐10 kWh/gal (where generators can normally achieve 25% or higher)

o The average electricity use per household in Sarawak is 205 kWh/month (biggest loads: air conditioner, ceiling fans, refrigerators, lights, water heater)

• A single village door currently spends ~ 50 US$/month and thus over 600 US$/year on diesel

o Entire Long Houses are spending on order of 10,000 US$/year (larger villages like Long San: $50,000/year)

o Average urban household in Sarawak spends ~19 US$/month on electricity.

• Diesel electricity use in the Baram represents a US$0.67/kWh (2.24 RM/kWh) cost of electricity, given a subsidized diesel cost of 12 RM/gal

o Residential rate from SESCO is 0.31sen/kWh, SESB is 0.22 sen/kWh

• Modeling shows designs with microhydro and biomass that can cost as little as US$0.25/kWh (0.82 sen/kWh). This is not as cheap as SESCO rates, which are heavily subsidized, but still represents a 88% reduction from the current effective rate village households pay.

o From flow rate and head measurements, river systems can usually provide 5-‐10KW

o Villages generate on the order of 100 tonnes/year of waste rice husk from milling

o Small biomass gasification can give ~ 900 kWh/tonne (but there are a number of issues with operating biomass systems)

Optimization modeling provides insight into the comparative economics of various rural electrification systems. We observe that hydroelectricity is the lowest cost means of electricity production available to each village. Small scale biogasification is financially feasible and profitable for village communities however the technical feasibility of maintaining a biogas system must be considered. Despite the cost of diesel fuel, photovoltaic systems (PV) are not cost effective for the village communities. When


27

employed they do not act as dominant energy sources. Capital and replacement costs of battery packs are often the major cost component for most least cost systems. Despite this, diesel, even at the subsidized government price, is the most expensive form of energy for Baram villages, given the recurrent annual fuel costs that it implies. In fact, we find that the Payback Period on Hydro and Biogas systems can be two years or less compared to 100% Diesel base case scenarios. These findings highlight the potential of villages in rural Sarawak to satisfy their own energy access needs with local and sustainable resources. This conclusion supports a state-‐wide energy development strategy that considers small scale energy solutions and technologies as an important part of providing rural energy access and rural development opportunity.


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5. APPENDICIES

5.1. HOMER DEFAULT INPUT/ASSUMPTION SUMMARY

Hydro Turbine System

Photovoltaic Panel System

Capital cost: $1,300

Size (kW) Capital ($)

Replacement ($)

O&M ($/yr)

Replacement cost: $1,300

1 2,300 2,300 0

O&M cost: $ 0/yr

Lifetime: 25 yr

Lifetime: 30 yr

Derating factor: 80%

Available head: 25 m

Tracking system:

No Tracking

Design flow rate: 60 L/s

Slope: 3.28 deg

Min. flow ratio: 25%

Azimuth: 0 deg

Max. flow ratio: 150%

Ground reflectance: 20%

Turbine efficiency: 60%

Pipe head loss: 0%

Diesel Generators

Size (kW) Capital ($) Replacement

($) O&M ($/hr)

Fuel: Diesel

1 440 440 0.1

Price: $ 1.5/L

Lifetime: 5,000 hrs

Lower heating value:

43.2 MJ/kg

Min. load ratio: 15%

Density: 820 kg/m3

Heat recovery ratio: 0%

Carbon content: 88.00%

Fuel used: Diesel

Sulfur content: 0.33%

Fuel curve intercept:

0.08 L/hr/kW

Fuel curve slope:

0.45 L/hr/kW


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Rice Husk Biogas Generator


Replacement ($)

O&M ($/hr)

1 1,400 1,400 0.1

Sizes to consider:

0, 5, 10, 20, 30 kW

Lifetime: 15,000 hrs

Min. load ratio: 30%

Heat recovery ratio:

0%

Fuel used: Biomass

Fuel curve intercept:

0.0253 L/hr/kW

Fuel curve slope:

1.2 L/hr/kW

Battery: Hoppecke 6 OPzS 600

Converter

Quantity Capital ($)

Replacement ($)

O&M ($/yr)


Replacement ($)

O&M ($/yr)

1 415 415 0

1 297 297 0

Voltage: 2 V

Lifetime: 15 yr

Nominal capacity: 600 Ah

Inverter efficiency: 90%

Lifetime throughput:

2,083 kWh

Inverter can parallel with AC generator:

Yes

Rectifier relative capacity:

100%

Rectifier efficiency: 85%

Economics

Generator control

Annual real interest rate: 7% Check cycle charging: Yes

Project lifetime: 25 yr Setpoint state of charge: 80%

Capacity shortage penalty: $ 0/kWh Allow systems with multiple generators: Yes

System fixed capital cost: $0 Allow multiple generators to operate simultaneously: Yes

System fixed O&M cost: $ 0/yr Allow systems with generator capacity less than peak load: Yes


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5.2. SIMULATION RESULTS

Table 3 Simulation Results for Tanjung Tepalit

Table 4 Simulation Results for Long Anap

Variables: Stream Flow: 62 L/s Biomass Resource: 0.025 t/day Diesel Cost: $1.5L/day

PV Hydro Diesel Biogas 12V Battery

No. kW kW kW kW

1 0 7.06 0 0 90 41,620 57,228 4,911 0 0.196 1

2 0 7.06 0 5 90 47,135 66,353 5,694 0 0.224 1

3 0 7.06 10 10 90 60,020 72,030 6,181 0 0.242 1

4 0 7.06 10 0 90 47,505 72,940 6,259 683 0.245 0.97

5 10 7.06 0 0 90 64,620 81,017 6,952 0 0.274 1

6 0 7.06 10 10 0 19,700 81,782 7,018 2,384 0.275 0.89

7 10 7.06 10 0 90 70,505 86,916 7,458 0 0.292 1

8 10 7.06 0 5 90 70,135 89,353 7,667 0 0.302 1

9 10 7.06 10 10 90 83,020 95,030 8,155 0 0.319 1

10 10 7.06 10 10 0 44,185 106,551 9,143 2,370 0.358 0.89

11 20 0 0 5 90 93,320 140,972 12,097 0 0.490 1

12 20 0 0 0 180 125,155 155,377 13,333 0 0.564 1

13 0 7.06 10 0 0 5,700 166,087 14,252 10,498 0.561 0.52

14 20 0 10 10 180 143,555 179,475 15,401 719 0.603 0.97

15 10 7.06 10 0 0 30,185 190,929 16,384 10,490 0.645 0.52

16 30 0 10 0 180 152,555 197,559 16,953 814 0.664 0.96

17 0 0 10 5 0 11,400 221,247 18,985 13,277 0.743 0.36

18 10 0 10 5 0 35,885 246,071 21,116 13,268 0.826 0.36

19 0 0 10 5 90 50,235 265,094 22,748 13,279 0.890 0.36

20 0 0 20 0 0 8,800 337,006 28,919 21,627 1.132 0

Renewable Fraction

Total Capital Cost ($)

Total NPC ($)

Total Annual Cost

($)Total Fuel Cost ($/yr)

LCOE ($/kWh)



No. kW kW kW kW

1 0 6.18 0 20 90 71,105 129,386 11,103 0 0.225 1

2 10 6.18 0 20 90 94,105 152,343 13,073 0 0.265 1

3 10 6.18 0 0 180 107,910 180,056 15,451 0 0.302 1

4 0 6.18 30 20 120 101,210 203,649 17,475 2,851 0.319 0.94

5 10 6.18 20 20 120 119,810 207,458 17,802 1,485 0.325 0.97

6 30 0 0 20 120 155,710 248,759 21,346 0 0.420 1

7 20 6.18 20 0 180 139,710 253,543 21,757 2,457 0.398 0.94

8 0 6.18 20 30 0 52,100 280,812 24,097 12,784 0.463 0.69

9 10 6.18 20 30 0 78,070 307,508 26,387 12,768 0.507 0.69

10 0 6.18 20 0 180 93,710 358,094 30,728 13,325 0.565 0.7

11 30 0 30 30 180 207,810 376,722 32,327 6,832 0.590 0.84

12 0 0 30 30 0 55,200 440,992 37,842 24,900 0.691 0.43

13 10 6.18 20 0 0 36,070 451,254 38,722 29,017 0.787 0.26

14 10 0 30 30 0 81,170 467,578 40,123 24,876 0.733 0.43

15 0 0 30 30 90 95,520 486,977 41,788 24,901 0.763 0.43

16 0 6.18 30 0 0 14,500 535,918 45,987 34,941 0.840 0.23

17 0 0 30 0 0 13,200 634,713 54,465 43,527 0.995 0

18 10 0 30 0 0 39,170 661,467 56,761 43,518 1.037 0

19 30 0 20 0 180 155,470 671,355 57,609 34,332 1.052 0.22

20 0 0 30 0 90 53,520 680,585 58,401 43,530 1.067 0


Total NPC ($)

Total Annual Cost


LCOE ($/kWh)

Renewable Fraction


31

Table 5 Simulation Results for Long San



No. kW kW kW kW

1 0 8.83 0 20 90 72,590 193,423 16,598 0 0.225 1

2 10 8.83 0 20 90 95,590 218,260 18,729 0 0.251 1

3 0 8.83 20 30 180 129,770 367,779 31,559 12,013 0.406 0.81

4 10 8.83 20 30 180 152,770 390,779 33,533 12,013 0.431 0.81

5 0 8.83 20 20 0 38,100 451,698 38,760 23,876 0.493 0.63

6 10 8.83 20 20 0 64,070 478,452 41,056 23,867 0.523 0.63

7 20 0 20 20 180 160,470 652,228 55,968 27,291 0.732 0.57

8 0 0 40 30 0 59,600 723,649 62,097 39,202 0.790 0.43

9 0 0 20 20 90 77,120 750,183 64,374 39,698 0.851 0.36

10 10 0 40 30 0 85,570 750,403 64,392 39,193 0.819 0.43

11 0 8.83 20 0 180 90,740 773,367 66,363 40,828 0.809 0.41

12 0 8.83 40 0 0 18,900 783,057 67,195 52,502 0.819 0.21

13 10 8.83 20 0 180 113,740 796,333 68,334 40,828 0.833 0.41

14 10 8.83 40 0 0 44,870 809,811 69,490 52,493 0.847 0.21

15 0 0 60 0 0 26,400 1,054,087 90,452 68,576 1.101 0

16 30 0 40 0 180 164,270 1,059,949 90,955 58,423 1.108 0.15

17 10 0 60 0 0 52,370 1,080,841 92,747 68,566 1.129 0

18 0 0 40 0 120 76,310 1,087,795 93,344 69,549 1.137 0


Total NPC ($)

Total Annual Cost


LCOE ($/kWh)

Renewable Fraction


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5.3. SCENARIO COMPARISON RESULTS

Figure 21 Comparing Economics of Least NPC Scenario and Diesel Scenario for Long San

Figure 22 Comparing Economics of Least NPC Scenario and Diesel Scenario for Tanjung Tepalit

Figure 23 Comparing Economics of Least NPC Scenario and Diesel Scenario for Long Anap

kampung capacity 21.09.2013 - university of california...

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