- Email: [email protected]
Economic Comparison of Microgrid Systems for Rural Electrification in Myanmar
Available online online at at www.sciencedirect.com www.sciencedirect.com Available
ScienceDirect Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Energy Procedia Procedia 00 00 (2018) (2018) 000–000 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2019) 000–000 309–314 EnergyProcedia Procedia159 00 (2017) www.elsevier.com/locate/procedia
Applied with Mini/Microgrids, Mini/Microgrids, Applied Energy Energy Symposium Symposium and and Forum, Forum, Renewable Renewable Energy Energy Integration Integration with REM 2018, 29–30 September 2018, Rhodes, Greece REM 2018, 29–30 September 2018, Rhodes, Greece
Economic of Microgrid Rural The 15thComparison International Symposium on DistrictSystems Heating andfor Cooling Electrification in Myanmar Assessing the feasibility of using the heat demand-outdoor a bb , Gento Mogiaa Duanxia Duanxia Xu Xu Masako Mumata , Gento Mogi temperature function fora ,, aMasako Mumata long-term district heat demand forecast a aSocial-strategic Engineering Laboratory, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan Social-strategic Engineering Laboratory, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan b a,b,c Research Institute, a a of Tokyo, 7-3-1 Hongo, b bPolicy Alternatives The University Bunkyo-ku, Tokyo,c113-8656, Japan Policy Alternatives Research Institute, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
I. Andrić
a
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Correc
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract Abstract
Myanmar Myanmar has has one one of of the the lowest lowest rural rural electrification electrification rates rates in in the the world, world, with with only only approximately approximately 16% 16% of of its its rural rural population population having having access to electricity. Promoting rural electrification is considered to be the key for inclusive socioeconomic access to electricity. Promoting rural electrification is considered to be the key for inclusive socioeconomic development development in in Myanmar. Myanmar. However, However, grid grid extension extension faces faces huge huge challenge challenge in in rural rural areas areas of of Myanmar Myanmar due due to to the the low low population population density density and and electricity electricity Abstract demand, demand, which which creates creates opportunities opportunities for for microgrid microgrid systems. systems. This This study study seeks seeks to to provide provide an an economic economic comparison comparison of of various various microgrid economically efficient efficient microgrid microgrid system microgrid systems systems in in order order to to discover discover the the most most economically system for for rural rural electrification electrification in in each each district district of of District heating networks are commonly addressed in the solar literature as one(SMG), of the diesel most effective solutions for decreasing the Myanmar in different different time periods. periods. Five microgrid systems, Myanmar in time Five microgrid systems, solar microgrid microgrid (SMG), diesel microgrid microgrid (DMG), (DMG), biogas biogas microgrid microgrid greenhouse gas emissions from the building sector. These systems require high investments which arepaper. returned through the heat (BMG), solar & diesel microgrid (SDMG) and solar & biogas microgrid (SBMG), are studied in this Models of demand (BMG), solar & diesel microgrid (SDMG) and solar & biogas microgrid (SBMG), are studied in this paper. Models of demand sales. Due tocost the changed climate conditionsreflect and building renovation policies, heat demand costs in the future could decrease, projection projection and and cost estimation estimation are are established established to to reflect the the dynamics dynamics of of energy energy demands demands and and system system costs in in different different regions regions over over prolonging the investment return period.are carried out to demonstrate the ideal economically efficient microgrid system for each time. Moreover, simulations by HOMER time. Moreover, simulations by HOMER are carried out to demonstrate the ideal economically efficient microgrid system for each The mainMyanmar scope of thisdifferent paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand district district of of Myanmar in in different time time periods. periods. forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 that vary in both construction period ©buildings 2019 The Published by Elsevierreserved. Ltd. and typology. Three weather scenarios (low, medium, high) and three district Copyright ©Authors. 2018 Elsevier Ltd. All Copyright 2018 Elsevier Ltd. All rights rights reserved.intermediate, This is an © open access article under the (shallow, CC-BY-NC-ND licensedeep). (https://creativecommons.org/licenses/by-nc-nd/4.0/) renovation scenarios were developed To estimate the error, obtained heat demand values were Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium Forum, Selection responsibility of scientific committee of Applied Energy Symposium and and Forum, Selection and peer-review under responsibility of the the scientific committee of the the Symposium and Forum, comparedand withpeer-review results fromunder a dynamic heat demand model, previously developed andApplied validatedEnergy by the authors. Renewable Energy Integration with Mini/Microgrids, REM 2018. Renewable Energy Integration with Mini/Microgrids, REM 2018. Renewable Energy Integration with Mini/Microgrids, REM 2018. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the errorMicrogrid; in annualRural demand was lower than 20% for allEconomic weathercomparison scenarios considered). However, after introducing renovation Keywords: electrification; Myanmar; Myanmar; HOMER; HOMER; Economic Keywords: Microgrid; comparison scenarios, the errorRural valueelectrification; increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and 1.decrease Introduction 1. Introduction renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and World leaders adopted 2030 Development Worldthe leaders adopted the 2030estimations. Development Agenda, Agenda, which which outlines outlines the the Sustainable Sustainable Development Development Goals, Goals, aa improve accuracy of heatthe demand
collection collection of of 17 17 global global goals goals including including ensuring ensuring access access to to affordable, affordable, reliable, reliable, sustainable sustainable and and modern modern energy energy for for all all at United Nations Sustainable Development 2017 The Authors. by Elsevier Ltd. Summit at©the the United NationsPublished Sustainable Development Summit on on 25 25 September September 2015. 2015. However, However, studies studies have have shown shown that that an an estimated 1.1 billion people, of the population, are still lacking to where 95% and of them them Peer-review responsibility of the Committee of The Symposium on District Heating estimated 1.1under billion people, 14% 14% of Scientific the global global population, are15th stillInternational lacking access access to electricity electricity where 95% of Cooling.
Keywords: Heat demand; Forecast; Climate change 1876-6102 1876-6102 Copyright Copyright © © 2018 2018 Elsevier Elsevier Ltd. Ltd. All All rights rights reserved. reserved. Selection and and peer-review peer-review under under responsibility responsibility of of the the scientific scientific committee committee of of the the Applied Applied Energy Energy Symposium Symposium and and Forum, Forum, Renewable Renewable Energy Energy Selection Integration Integration with with Mini/Microgrids, Mini/Microgrids, REM REM 2018. 2018.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. This is an open access article under the CC-BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids, REM 2018. 10.1016/j.egypro.2019.01.010
310 2
Duanxia Xu et al. / Energy Procedia 159 (2019) 309–314 Duanxia Xu/ Energy Procedia 00 (2018) 000–000
reside in countries in sub-Saharan Africa and developing Asia [1]. This paper particularly seeks to review Myanmar, which is a southeast Asian country, as a case study. Myanmar has one of the lowest electrification rates in the world, with only approximately 37% of its population having access to a modern form of electricity in 2016. The electrification rate is especially low in rural areas, with only approximately 16% of rural population having access to electricity [2]. Promoting rural electrification is considered to be the key for inclusive socioeconomic development in Myanmar. The government of Myanmar has set a comprehensive and ambitious plan with the target of achieving universal electricity by the year of 2030. The approaches mainly include grid extension, pre-electrification in rural areas prior to the arrival of the national grid, permanent micro-grid and off-grid connections in remote areas [3]. Although over the long-term grid extension is the most cost-effective option for the overwhelming majority of households, it also faces huge challenges due to low population density and electricity demand. The inapplicability of grid extension has created demand for an alternative solution, micro-grid system, which uses a mix of technologies to provide energy supply to a community. There are several studies regarding to rural electrification in Myanmar. Ramchandra Pode once had a focus on exploring the self-sustaining energy service model to provide grid quality power to rural populations without the need of subsides. The result shows that the rice husk biomass power system installed and operated by rice millers is not only the sustainable and affordable option to rural electrification but also the financially viable business model to provide the grid quality power to rural population without grant or subsidy [2,4]. H. Sasaki and M. Seino conducted demand projection, cost estimation, and scenario preparation for a rural electrification plan in Myanmar. The result shows micro-hydropower could be provided at lower generation cost than diesel, photovoltaic, or biomass [5]. Haein Kim and Tae Yong Jung demonstrated the economic competiveness of Energy Storage Systems (ESS) and solar energy in enhancing rural energy access in Myanmar. They compared performance among various energy configurations using HOMER and examines economic aspects of each option [6]. Masako Numata calculated the levelised cost of electricity of microgrids in Myanmar with the data collected through interviews and field surveys and compared the cost of solar PV microgrid with traditional diesel microgrid [7]. Although the above literatures are very valuable for understanding microgrid systems for rural electrification in Myanmar, there are two reasons why further work is required. Firstly, regional difference of microgrid costs in Myanmar is under-researched. Studies down into a regional level is preferred. Due to the geographical, climate and demand differences, the optimal microgrid system for different areas of Myanmar could be different. Secondly, cost change of microgrid systems with time is under-researched. With the change of electricity demand and technical cost of microgrid component, the optimal microgrid system is likely to shift over time. Here, we seek to provide an economic comparison of various microgrid systems in order to discover the most economically efficient microgrid system for rural electrification in each district of Myanmar in different time periods. 2. System modeling 2.1. Subjects of study Myanmar is consisted of 14 states and regions, 74 districts. Since the Government of Myanmar is aiming at achieving universal electricity by the year of 2030, four time periods are studied, which are 2018, 2022, 2016 and 2030. Based on the village size data of 2014 Population and Housing Census of Myanmar, a generic Myanmar village with 1,440 people in 300 households in each district during each time period is set as the subject of this study [8]. 2.2. Primary energy sources Myanmar has abundant solar radiation, making solar energy a suitable resource for microgrid in most regions. It has a tropical monsoon climate, with dry season lasts from November to April and wet season from May to October. During the dry season, the average solar radiation is more than 5 kW/h/m0 /day and is available for 7-10 hours per day, while during the wet season, average solar radiation is only about 3-4 hours per day [2]. An assumption that solar energy is available in all districts in Myanmar is made in this study. Apart from solar energy, Myanmar also has abundant biomass resource as the agriculture dominates the economy of Myanmar. Rice is the most important crop and rice husk is the the major sources of agricultural residues in Myanmar
Duanxia Xu et al. / Energy Procedia 159 (2019) 309–314 Duanxia Xu/ Energy Procedia 00 (2018) 000–000
311 3
[2]. Therefore, in this study, rice husk is considered as the sources of biomass microgrid projects. It is noteworthy that biomass is only suitable for microgrid projects at a site with a sufficiently constant supply of biomass feedstock, such as a rice mill. Residues from harvesting are considered to be less commercially feasible because high transportation cost is needed to collect from a widespread area. Therefore, rice husk from rice mills are thought to be biomass resource in this study. According to the quantities of rice mills in Myanmar, four regions, Sagaing, Bago, Yangon, and Ayeyawady are assumed to have biomass potential for microgrid projects [9]. Additionally, Myanmar has a number of rivers and streams, which makes hydro a suitable resource for power generation in those areas with rivers and streams. However, the study of micro-hydro system is considerably difficult to be done in a regional level because of the following reasons. First, hydropower highly depends on the site location and the possible sites scatter in the whole country. It is hard to define regions with or without micro-hydro potential. Second, the number of possible hydro sits in each region are far less than the corresponding number of villages, which means that hydro could not be saw as an available energy source for most villages [2,8]. Previous studies about the economic assessment of micro-grid in Myanmar suggest that hydro features the lowest unit price of power [5]. Therefore, considering the features of hydro power as well as its economic efficiency, hydro micro-grid system is supposed to be given the priority in villages with micro hydro potential and is not studied in this paper. Wind speed in most regions of Myanmar is fairly low, ranging from 1 to 3 m/s, which is not fast enough for wind mill to operate [2]. Therefore, in this study, wind power is not considered as a desirable energy source for micro-grid projects in Myanmar. 2.3. Microgrid systems There are five micro-grid systems studied in this paper, including two hybrid systems, showed in Table 1. Table 1. Alternative microgrid systems Systems
Components
Feasible Areas
Solar PV Microgrid (SMG)
PV/Converter/Battery
All districts
Diesel Microgrid (DMG)
Diesel engine gen-set/Converter/Battery
All districts
Biogas Microgrid (BMG)
Biogas engine gen-set/Converter/Battery
Sagaing/Bago/Yangon/Ayeyawady regions
Solar PV/Diesel Microgrid (SDMG)
PV/Diesel engine gen-set/Converter/Battery
All districts
Solar PV/Biogas Microgrid (SBMG)
PV/Biogas engine gen-set/Converter/Battery
Sagaing/Bago/Yangon/Ayeyawady regions
2.4. Electricity Demand In this study, electricity demand is estimated by calculating hourly load profiles of a generic Myanmar village. In 2017, Nan Wang studied on Myanmar residential electricity demand with a bottom-up research. He projected household appliance occupation probability of each region in Myanmar from 2017 to 2030 [10]. Based on Nan Wang's research, hourly load profile of a village over time can be calculated by the following equation, where i stands for the regions, j represents hour of a day, t represents year, k represents each appliance, 𝑊𝑊2 is the power of each appliance, 𝑃𝑃4556789:4;,2,:,9 is the probability of owning each appliance for a household, 𝑁𝑁2 is the number of each appliance for a household, 𝑃𝑃47>?89:4;,2,@ is the probability of operating each appliance for a household at hourly intervals, 𝑆𝑆: stands for the number of households in a village. 𝐷𝐷>C>59?:59:D (𝑖𝑖, 𝑗𝑗, 𝑡𝑡) =
2.5. Diesel price
2 𝑊𝑊2
𝑃𝑃4556789:4;,2,:,9 𝑁𝑁2 𝑃𝑃47>?89:4;,2,@ 𝑆𝑆:
(1)
The operation cost of diesel generators is highly dependent on diesel fuel price. In rural and remote areas of Myanmar, this effect is even more prominent as diesel prices increase with distance to distribution centers. Besides, with the change of interaction between supply and demand for oil on international markets, world crude oil price is
Duanxia Xu et al. / Energy Procedia 159 (2019) 309–314 Duanxia Xu/ Energy Procedia 00 (2018) 000–000
312 4
expected to fluctuate over time. Hence, diesel price in this study is assumed to be effected by two factors, locations and time. In this study, Low Oil Price Scenario of World Energy Outlook 2015 is referred to calculate the basis diesel price [11]. The calculation equation of basis diesel price is shown below. 𝑃𝑃K:>L>CM8L:L (𝑡𝑡) = 𝑃𝑃K:>L>CM8L:L (𝑡𝑡N ) + 48.45744208×(1 − 𝑒𝑒 [N.NNN\]N^^_×(9[0N`a) )
(2)
𝑃𝑃K:>L>C,: (𝑡𝑡) = 𝑃𝑃K:>L>CM8L:L (𝑡𝑡) ∗ (1 + 0.00007199𝐷𝐷: )
(3)
According to the data from GIZ international fuel price database, domestic production of diesel can only meet approximately 25% of its entire demand in Myanmar. The Port of Yangon, which located along the Yangon River, is the premier port in Myanmar and handles about 90 percent of the country's exports and imports [12]. Thus, local diesel price is assumed to be effected by the distance from the Port of Yangon. Diesel prices of several locations of Myanmar are found on the website of Max Energy, one of the largest energy companies in Myanmar [13]. Regression analysis is carried out to figure out the relationship between local diesel price and distance from the port of Yangon. According to the regression analysis, the local diesel price can be calculated by following equation, where t stands for the year, i stands for each district, 𝐷𝐷: is the distance from the port of Yangon for each district. 2.6. Component cost
In this study, both PV and battery costs are assumed to decrease with time, along with the development of technology and increasing total production volume, while the cost of other components like generators or inverters are assumed to be constant as their techniques are already mature. The projection results of Enegiewende and IRENA are used to decide the cost of PV and lithium ion batteries [14,15]. 3. Simulation Simulations are carried out by a software, HOMER, to figure out the optimal microgrid system in each district of Myanmar during different time periods. HOMER (Hybrid Optimization of Multiple Energy Resources) is an optimization tool for micro-grid system design, developed by National Renewable Energy Laboratory, USA [16]. 3.1. System Input Input required to simulate a micro-grid system in HOMER can be divided into four categories, energy resources, load profile, system components and cost data. In HOMER, solar radiation data and temperature data can be directly downloaded from database of the National Aeronautics and Space Administration (NASA). Hourly electric load profile that reflect local electricity demands can be input into HOMER. In this study, results of the electricity demand estimation model are used as load profile. Random variability inputs including day-to-day and time-step variability are added into the load profile. Cost data of components are collected from previous studies [6,17]. 3.2. System output Output of HOMER can be divided into two categories, optimized capacity and economic output. The sizes of generator, PV and battery are optimized automatically by HOMER to meet the load. The capacity of generator is the smallest that will produce no capacity shortage in all sensitivity cases and future years. The quantities of PV and battery are the feasible ones with the lowest NPC. The total NPC is main economic output in HOMER, the value by which it ranks all system configurations in the optimization results [16]. Another economic output in HOMER is the levelized cost of energy (COE) as the average cost per kWh of useful electrical energy produced by the system. In this study, NPC and COE are both used as economic metric to evaluate systems. NPC is used to compare the economic viability between different system options for a particular district with the same load profile. COE is used to compare the cost of each system in different districts or during different time period.
Duanxia Xu et al. / Energy Procedia 159 (2019) 309–314 Duanxia Xu/ Energy Procedia 00 (2018) 000–000
313 5
4. Results and discussion 4.1. Optimal solutions Simulation results show a clear consistency in the cost competiveness of systems during different time periods. In all time periods, SBMG system is demonstrated to have the lowest united price of power, followed by BMG system. Nevertheless, SBMG system and BMG system can only be constructed in areas where biomass resources are available, districts in Sagaing, Bago, Yangon, and Ayeyawady regions. For regions without biomass potential, SDMG system is demonstrated to be optimal in the year of 2018, 2022 and 2026. In 2030, SMG system shows its superiority in some areas with abundant solar radiation, mainly located in the central Myanmar. The Myanmar map of optimal microgrid solutions are shown in Figure 1. COE of microgrid systems is shown in Figure 2. Additionally, simulation of HMG system is not conducted in this study because of the reasons mentioned earlier. Previous studies about the economic assessment of micro-grid in Myanmar suggest that hydro features the lowest unit price of power, even compared with SBMG system [5]. Therefore, considering the features of hydro power as well as its economic efficiency, hydro microgrid system is supposed to be given the priority in villages with micro hydro potential. Some possible sites for HMG system in Magway, Mandalay and Sagaing regions are also marked in Figure 1 [18]. 4.2. Discussions From the simulation result, there are several findings. Firstly, it is observed that SMG system has the most remarkable regional difference. The major costs of SMG system come from PV and battery. The central regions with abundant and stable solar radiation have the lowest COE of SMG system as less PV and batteries are required compared to other regions. However, in 2030, the regional different has been eliminated into a relatively low level, which means that technology development is of great significance for the reduction of regional cost gap of SMG system. Secondly, hybrid systems including SDMG system and SBMG system show relatively low regional and time difference compared to other systems. This is owing to the adjustability of generation mix. In most regions, diesel or biogas generator contributes to more than half of total electricity generation in 2018 and 2022, while in 2030, PV contributes to most of the electricity generation, with generator as a backup mainly used at the rainy season to cover shortages in electricity supply. Thirdly, the SMG system have the most excess electricity, with more than 30% of the total electricity production being dumped because it cannot be used to serve a load or charge batteries in most cases. In this study, excess electricity occurs mainly because the variability of solar radiation. The average solar radiation during the dry season and the rainy season makes an enormous variation in most areas of Myanmar, leading to the need of a larger-size battery during the rainy season, as well as the occurrence of excess electricity during the dry season.
Fig. 1. Myanmar map of optimal microgrid solutions
Fig. 2. COE of microgrid systems
314 6
Duanxia Xu et al. / Energy Procedia 159 (2019) 309–314 Duanxia Xu/ Energy Procedia 00 (2018) 000–000
Despite the fact that the most economically efficient micro-grid system is figured out for rural electrification in each district of Myanmar, this result has some limitations. First of all, this study is limited by the lack of reliable data. Most of the cost data are collected from previous studies, some of which come from investigation in other developing countries. Moreover, service quality is not considered in this study. In general, both the cost of system and the quality of service should be looked at when assessing the performance of a system. Furthermore, environmental influence is not evaluated in this study. Environmental influence could be reflected to the economic output by setting the penalty for emissions. 5. Conclusion This research provides economic comparison of several micro-grid solutions for the rural electrification in Myanmar. Firstly, the background of rural electrification in Myanmar is introduced. Five microgrid systems, including solar microgrid (SMG), diesel microgrid (DMG), biogas microgrid (BMG), solar & diesel microgrid (SDMG) and solar & biogas microgrid (SBMG), are studied in the case of Myanmar. Then, models of demand projection and cost estimation are established to reflect the dynamics of energy demands and system costs in different regions over time. Moreover, simulations by HOMER are carried out to demonstrate the ideal economically efficient microgrid system for each district of Myanmar in different time periods. Results show hybrid microgrid systems, including SDMG and SBMG system, are more competitive than other solutions. For future research, the combination of performance assessment and economic assessment of micro-grid systems by adding factors like penalty for unmet load, emissions could be effective for a more desirable outcome. Acknowledgements This research was supported by Social-strategic Engineering Laboratory, the University of Tokyo. We express our appreciation to the Nan Wang and Kyohei Shibano who shared their expertise that greatly assisted this research. We are also grateful for assistance with HOMER, a useful tool for microgrid simulations. References [1] International Energy Agency. Energy Access Outlook 2017: From Poverty to Prosperity. 2017 [2] Pode R, Pode G, Diouf B. Solution to sustainable rural electrification in Myanmar. Renewable and Sustainable Energy Reviews. 2016 Jun 1;59:107-18. [3] Word Bank. Myanmar: Towards Universal Access to Electricity by 2030. 2014 [4] Pode R. Potential applications of rice husk ash waste from rice husk biomass power plant. Renewable and Sustainable Energy Reviews. 2016 Jan 1;53:1468-85. [5] Sasaki H, Seino M, Hashimoto N, Sakata I. Off-grid electrification scenarios for rural electrification in Myanmar. [6] Kim H, Jung TY. Independent solar photovoltaic with Energy Storage Systems (ESS) for rural electrification in Myanmar. Renewable and Sustainable Energy Reviews. 2018 Feb 28;82:1187-94. [7] Numata M, Sugiyama M, Mogi G, Swe W, Anbumozhi V. Technoeconomic Assessment of Microgrids in Myanmar. [8] Department of Population Ministry of Labor, Immigration and Population. THE 2014 MYANMAR POPULATION AND HOUSING CENSUS. 2014 [9] World Bank. Myanmar - Capitalizing on rice export opportunities. 2014 [10] N. Wang, J. Oda, K. Akimoto. A Bottom-up End-use Model for Myanmar Regional Residential Electricity Demand. The 34th Conference on Energy, Economy, and Environment. 2018 [11] International Energy Agency. World energy outlook 2015. 2015 [12] Øverland I, Vakulchuk R, Hlaing KK, Naing EZ, Suryadi B, Velautham S. Myanmars Attractiveness for Investment in the Energy Sector: A Comparative International Perspective. [13] Max Energy. https://maxenergy.com.mm [14] Energiewende A, Mayer JN, Philipps S, Saad N, Hussein D, Schlegl T, Senkpiel C. Current and future cost of photovoltaics. Berlin: Agora Energiewende. 2015. [15] IRENA. ELECTRICITY STORAGE AND RENEWABLES: COSTS AND MARKETS TO 2030 [16] HOMER. http://www.homerenergy.com/ [17] Sigarchian SG, Paleta R, Malmquist A, Pina A. Feasibility study of using a biogas engine as backup in a decentralized hybrid (PV/wind/battery) power generation system–Case study Kenya. Energy. 2015 Oct 1;90:1830-41. [18] ADB. Myanmar Off-grid Analytics Website. http://adb-myanmar.integration.org
ScienceDirect Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Energy Procedia Procedia 00 00 (2018) (2018) 000–000 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2019) 000–000 309–314 EnergyProcedia Procedia159 00 (2017) www.elsevier.com/locate/procedia
Applied with Mini/Microgrids, Mini/Microgrids, Applied Energy Energy Symposium Symposium and and Forum, Forum, Renewable Renewable Energy Energy Integration Integration with REM 2018, 29–30 September 2018, Rhodes, Greece REM 2018, 29–30 September 2018, Rhodes, Greece
Economic of Microgrid Rural The 15thComparison International Symposium on DistrictSystems Heating andfor Cooling Electrification in Myanmar Assessing the feasibility of using the heat demand-outdoor a bb , Gento Mogiaa Duanxia Duanxia Xu Xu Masako Mumata , Gento Mogi temperature function fora ,, aMasako Mumata long-term district heat demand forecast a aSocial-strategic Engineering Laboratory, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan Social-strategic Engineering Laboratory, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan b a,b,c Research Institute, a a of Tokyo, 7-3-1 Hongo, b bPolicy Alternatives The University Bunkyo-ku, Tokyo,c113-8656, Japan Policy Alternatives Research Institute, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
I. Andrić
a
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Correc
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract Abstract
Myanmar Myanmar has has one one of of the the lowest lowest rural rural electrification electrification rates rates in in the the world, world, with with only only approximately approximately 16% 16% of of its its rural rural population population having having access to electricity. Promoting rural electrification is considered to be the key for inclusive socioeconomic access to electricity. Promoting rural electrification is considered to be the key for inclusive socioeconomic development development in in Myanmar. Myanmar. However, However, grid grid extension extension faces faces huge huge challenge challenge in in rural rural areas areas of of Myanmar Myanmar due due to to the the low low population population density density and and electricity electricity Abstract demand, demand, which which creates creates opportunities opportunities for for microgrid microgrid systems. systems. This This study study seeks seeks to to provide provide an an economic economic comparison comparison of of various various microgrid economically efficient efficient microgrid microgrid system microgrid systems systems in in order order to to discover discover the the most most economically system for for rural rural electrification electrification in in each each district district of of District heating networks are commonly addressed in the solar literature as one(SMG), of the diesel most effective solutions for decreasing the Myanmar in different different time periods. periods. Five microgrid systems, Myanmar in time Five microgrid systems, solar microgrid microgrid (SMG), diesel microgrid microgrid (DMG), (DMG), biogas biogas microgrid microgrid greenhouse gas emissions from the building sector. These systems require high investments which arepaper. returned through the heat (BMG), solar & diesel microgrid (SDMG) and solar & biogas microgrid (SBMG), are studied in this Models of demand (BMG), solar & diesel microgrid (SDMG) and solar & biogas microgrid (SBMG), are studied in this paper. Models of demand sales. Due tocost the changed climate conditionsreflect and building renovation policies, heat demand costs in the future could decrease, projection projection and and cost estimation estimation are are established established to to reflect the the dynamics dynamics of of energy energy demands demands and and system system costs in in different different regions regions over over prolonging the investment return period.are carried out to demonstrate the ideal economically efficient microgrid system for each time. Moreover, simulations by HOMER time. Moreover, simulations by HOMER are carried out to demonstrate the ideal economically efficient microgrid system for each The mainMyanmar scope of thisdifferent paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand district district of of Myanmar in in different time time periods. periods. forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 that vary in both construction period ©buildings 2019 The Published by Elsevierreserved. Ltd. and typology. Three weather scenarios (low, medium, high) and three district Copyright ©Authors. 2018 Elsevier Ltd. All Copyright 2018 Elsevier Ltd. All rights rights reserved.intermediate, This is an © open access article under the (shallow, CC-BY-NC-ND licensedeep). (https://creativecommons.org/licenses/by-nc-nd/4.0/) renovation scenarios were developed To estimate the error, obtained heat demand values were Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium Forum, Selection responsibility of scientific committee of Applied Energy Symposium and and Forum, Selection and peer-review under responsibility of the the scientific committee of the the Symposium and Forum, comparedand withpeer-review results fromunder a dynamic heat demand model, previously developed andApplied validatedEnergy by the authors. Renewable Energy Integration with Mini/Microgrids, REM 2018. Renewable Energy Integration with Mini/Microgrids, REM 2018. Renewable Energy Integration with Mini/Microgrids, REM 2018. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the errorMicrogrid; in annualRural demand was lower than 20% for allEconomic weathercomparison scenarios considered). However, after introducing renovation Keywords: electrification; Myanmar; Myanmar; HOMER; HOMER; Economic Keywords: Microgrid; comparison scenarios, the errorRural valueelectrification; increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and 1.decrease Introduction 1. Introduction renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and World leaders adopted 2030 Development Worldthe leaders adopted the 2030estimations. Development Agenda, Agenda, which which outlines outlines the the Sustainable Sustainable Development Development Goals, Goals, aa improve accuracy of heatthe demand
collection collection of of 17 17 global global goals goals including including ensuring ensuring access access to to affordable, affordable, reliable, reliable, sustainable sustainable and and modern modern energy energy for for all all at United Nations Sustainable Development 2017 The Authors. by Elsevier Ltd. Summit at©the the United NationsPublished Sustainable Development Summit on on 25 25 September September 2015. 2015. However, However, studies studies have have shown shown that that an an estimated 1.1 billion people, of the population, are still lacking to where 95% and of them them Peer-review responsibility of the Committee of The Symposium on District Heating estimated 1.1under billion people, 14% 14% of Scientific the global global population, are15th stillInternational lacking access access to electricity electricity where 95% of Cooling.
Keywords: Heat demand; Forecast; Climate change 1876-6102 1876-6102 Copyright Copyright © © 2018 2018 Elsevier Elsevier Ltd. Ltd. All All rights rights reserved. reserved. Selection and and peer-review peer-review under under responsibility responsibility of of the the scientific scientific committee committee of of the the Applied Applied Energy Energy Symposium Symposium and and Forum, Forum, Renewable Renewable Energy Energy Selection Integration Integration with with Mini/Microgrids, Mini/Microgrids, REM REM 2018. 2018.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. This is an open access article under the CC-BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids, REM 2018. 10.1016/j.egypro.2019.01.010
310 2
Duanxia Xu et al. / Energy Procedia 159 (2019) 309–314 Duanxia Xu/ Energy Procedia 00 (2018) 000–000
reside in countries in sub-Saharan Africa and developing Asia [1]. This paper particularly seeks to review Myanmar, which is a southeast Asian country, as a case study. Myanmar has one of the lowest electrification rates in the world, with only approximately 37% of its population having access to a modern form of electricity in 2016. The electrification rate is especially low in rural areas, with only approximately 16% of rural population having access to electricity [2]. Promoting rural electrification is considered to be the key for inclusive socioeconomic development in Myanmar. The government of Myanmar has set a comprehensive and ambitious plan with the target of achieving universal electricity by the year of 2030. The approaches mainly include grid extension, pre-electrification in rural areas prior to the arrival of the national grid, permanent micro-grid and off-grid connections in remote areas [3]. Although over the long-term grid extension is the most cost-effective option for the overwhelming majority of households, it also faces huge challenges due to low population density and electricity demand. The inapplicability of grid extension has created demand for an alternative solution, micro-grid system, which uses a mix of technologies to provide energy supply to a community. There are several studies regarding to rural electrification in Myanmar. Ramchandra Pode once had a focus on exploring the self-sustaining energy service model to provide grid quality power to rural populations without the need of subsides. The result shows that the rice husk biomass power system installed and operated by rice millers is not only the sustainable and affordable option to rural electrification but also the financially viable business model to provide the grid quality power to rural population without grant or subsidy [2,4]. H. Sasaki and M. Seino conducted demand projection, cost estimation, and scenario preparation for a rural electrification plan in Myanmar. The result shows micro-hydropower could be provided at lower generation cost than diesel, photovoltaic, or biomass [5]. Haein Kim and Tae Yong Jung demonstrated the economic competiveness of Energy Storage Systems (ESS) and solar energy in enhancing rural energy access in Myanmar. They compared performance among various energy configurations using HOMER and examines economic aspects of each option [6]. Masako Numata calculated the levelised cost of electricity of microgrids in Myanmar with the data collected through interviews and field surveys and compared the cost of solar PV microgrid with traditional diesel microgrid [7]. Although the above literatures are very valuable for understanding microgrid systems for rural electrification in Myanmar, there are two reasons why further work is required. Firstly, regional difference of microgrid costs in Myanmar is under-researched. Studies down into a regional level is preferred. Due to the geographical, climate and demand differences, the optimal microgrid system for different areas of Myanmar could be different. Secondly, cost change of microgrid systems with time is under-researched. With the change of electricity demand and technical cost of microgrid component, the optimal microgrid system is likely to shift over time. Here, we seek to provide an economic comparison of various microgrid systems in order to discover the most economically efficient microgrid system for rural electrification in each district of Myanmar in different time periods. 2. System modeling 2.1. Subjects of study Myanmar is consisted of 14 states and regions, 74 districts. Since the Government of Myanmar is aiming at achieving universal electricity by the year of 2030, four time periods are studied, which are 2018, 2022, 2016 and 2030. Based on the village size data of 2014 Population and Housing Census of Myanmar, a generic Myanmar village with 1,440 people in 300 households in each district during each time period is set as the subject of this study [8]. 2.2. Primary energy sources Myanmar has abundant solar radiation, making solar energy a suitable resource for microgrid in most regions. It has a tropical monsoon climate, with dry season lasts from November to April and wet season from May to October. During the dry season, the average solar radiation is more than 5 kW/h/m0 /day and is available for 7-10 hours per day, while during the wet season, average solar radiation is only about 3-4 hours per day [2]. An assumption that solar energy is available in all districts in Myanmar is made in this study. Apart from solar energy, Myanmar also has abundant biomass resource as the agriculture dominates the economy of Myanmar. Rice is the most important crop and rice husk is the the major sources of agricultural residues in Myanmar
Duanxia Xu et al. / Energy Procedia 159 (2019) 309–314 Duanxia Xu/ Energy Procedia 00 (2018) 000–000
311 3
[2]. Therefore, in this study, rice husk is considered as the sources of biomass microgrid projects. It is noteworthy that biomass is only suitable for microgrid projects at a site with a sufficiently constant supply of biomass feedstock, such as a rice mill. Residues from harvesting are considered to be less commercially feasible because high transportation cost is needed to collect from a widespread area. Therefore, rice husk from rice mills are thought to be biomass resource in this study. According to the quantities of rice mills in Myanmar, four regions, Sagaing, Bago, Yangon, and Ayeyawady are assumed to have biomass potential for microgrid projects [9]. Additionally, Myanmar has a number of rivers and streams, which makes hydro a suitable resource for power generation in those areas with rivers and streams. However, the study of micro-hydro system is considerably difficult to be done in a regional level because of the following reasons. First, hydropower highly depends on the site location and the possible sites scatter in the whole country. It is hard to define regions with or without micro-hydro potential. Second, the number of possible hydro sits in each region are far less than the corresponding number of villages, which means that hydro could not be saw as an available energy source for most villages [2,8]. Previous studies about the economic assessment of micro-grid in Myanmar suggest that hydro features the lowest unit price of power [5]. Therefore, considering the features of hydro power as well as its economic efficiency, hydro micro-grid system is supposed to be given the priority in villages with micro hydro potential and is not studied in this paper. Wind speed in most regions of Myanmar is fairly low, ranging from 1 to 3 m/s, which is not fast enough for wind mill to operate [2]. Therefore, in this study, wind power is not considered as a desirable energy source for micro-grid projects in Myanmar. 2.3. Microgrid systems There are five micro-grid systems studied in this paper, including two hybrid systems, showed in Table 1. Table 1. Alternative microgrid systems Systems
Components
Feasible Areas
Solar PV Microgrid (SMG)
PV/Converter/Battery
All districts
Diesel Microgrid (DMG)
Diesel engine gen-set/Converter/Battery
All districts
Biogas Microgrid (BMG)
Biogas engine gen-set/Converter/Battery
Sagaing/Bago/Yangon/Ayeyawady regions
Solar PV/Diesel Microgrid (SDMG)
PV/Diesel engine gen-set/Converter/Battery
All districts
Solar PV/Biogas Microgrid (SBMG)
PV/Biogas engine gen-set/Converter/Battery
Sagaing/Bago/Yangon/Ayeyawady regions
2.4. Electricity Demand In this study, electricity demand is estimated by calculating hourly load profiles of a generic Myanmar village. In 2017, Nan Wang studied on Myanmar residential electricity demand with a bottom-up research. He projected household appliance occupation probability of each region in Myanmar from 2017 to 2030 [10]. Based on Nan Wang's research, hourly load profile of a village over time can be calculated by the following equation, where i stands for the regions, j represents hour of a day, t represents year, k represents each appliance, 𝑊𝑊2 is the power of each appliance, 𝑃𝑃4556789:4;,2,:,9 is the probability of owning each appliance for a household, 𝑁𝑁2 is the number of each appliance for a household, 𝑃𝑃47>?89:4;,2,@ is the probability of operating each appliance for a household at hourly intervals, 𝑆𝑆: stands for the number of households in a village. 𝐷𝐷>C>59?:59:D (𝑖𝑖, 𝑗𝑗, 𝑡𝑡) =
2.5. Diesel price
2 𝑊𝑊2
𝑃𝑃4556789:4;,2,:,9 𝑁𝑁2 𝑃𝑃47>?89:4;,2,@ 𝑆𝑆:
(1)
The operation cost of diesel generators is highly dependent on diesel fuel price. In rural and remote areas of Myanmar, this effect is even more prominent as diesel prices increase with distance to distribution centers. Besides, with the change of interaction between supply and demand for oil on international markets, world crude oil price is
Duanxia Xu et al. / Energy Procedia 159 (2019) 309–314 Duanxia Xu/ Energy Procedia 00 (2018) 000–000
312 4
expected to fluctuate over time. Hence, diesel price in this study is assumed to be effected by two factors, locations and time. In this study, Low Oil Price Scenario of World Energy Outlook 2015 is referred to calculate the basis diesel price [11]. The calculation equation of basis diesel price is shown below. 𝑃𝑃K:>L>CM8L:L (𝑡𝑡) = 𝑃𝑃K:>L>CM8L:L (𝑡𝑡N ) + 48.45744208×(1 − 𝑒𝑒 [N.NNN\]N^^_×(9[0N`a) )
(2)
𝑃𝑃K:>L>C,: (𝑡𝑡) = 𝑃𝑃K:>L>CM8L:L (𝑡𝑡) ∗ (1 + 0.00007199𝐷𝐷: )
(3)
According to the data from GIZ international fuel price database, domestic production of diesel can only meet approximately 25% of its entire demand in Myanmar. The Port of Yangon, which located along the Yangon River, is the premier port in Myanmar and handles about 90 percent of the country's exports and imports [12]. Thus, local diesel price is assumed to be effected by the distance from the Port of Yangon. Diesel prices of several locations of Myanmar are found on the website of Max Energy, one of the largest energy companies in Myanmar [13]. Regression analysis is carried out to figure out the relationship between local diesel price and distance from the port of Yangon. According to the regression analysis, the local diesel price can be calculated by following equation, where t stands for the year, i stands for each district, 𝐷𝐷: is the distance from the port of Yangon for each district. 2.6. Component cost
In this study, both PV and battery costs are assumed to decrease with time, along with the development of technology and increasing total production volume, while the cost of other components like generators or inverters are assumed to be constant as their techniques are already mature. The projection results of Enegiewende and IRENA are used to decide the cost of PV and lithium ion batteries [14,15]. 3. Simulation Simulations are carried out by a software, HOMER, to figure out the optimal microgrid system in each district of Myanmar during different time periods. HOMER (Hybrid Optimization of Multiple Energy Resources) is an optimization tool for micro-grid system design, developed by National Renewable Energy Laboratory, USA [16]. 3.1. System Input Input required to simulate a micro-grid system in HOMER can be divided into four categories, energy resources, load profile, system components and cost data. In HOMER, solar radiation data and temperature data can be directly downloaded from database of the National Aeronautics and Space Administration (NASA). Hourly electric load profile that reflect local electricity demands can be input into HOMER. In this study, results of the electricity demand estimation model are used as load profile. Random variability inputs including day-to-day and time-step variability are added into the load profile. Cost data of components are collected from previous studies [6,17]. 3.2. System output Output of HOMER can be divided into two categories, optimized capacity and economic output. The sizes of generator, PV and battery are optimized automatically by HOMER to meet the load. The capacity of generator is the smallest that will produce no capacity shortage in all sensitivity cases and future years. The quantities of PV and battery are the feasible ones with the lowest NPC. The total NPC is main economic output in HOMER, the value by which it ranks all system configurations in the optimization results [16]. Another economic output in HOMER is the levelized cost of energy (COE) as the average cost per kWh of useful electrical energy produced by the system. In this study, NPC and COE are both used as economic metric to evaluate systems. NPC is used to compare the economic viability between different system options for a particular district with the same load profile. COE is used to compare the cost of each system in different districts or during different time period.
Duanxia Xu et al. / Energy Procedia 159 (2019) 309–314 Duanxia Xu/ Energy Procedia 00 (2018) 000–000
313 5
4. Results and discussion 4.1. Optimal solutions Simulation results show a clear consistency in the cost competiveness of systems during different time periods. In all time periods, SBMG system is demonstrated to have the lowest united price of power, followed by BMG system. Nevertheless, SBMG system and BMG system can only be constructed in areas where biomass resources are available, districts in Sagaing, Bago, Yangon, and Ayeyawady regions. For regions without biomass potential, SDMG system is demonstrated to be optimal in the year of 2018, 2022 and 2026. In 2030, SMG system shows its superiority in some areas with abundant solar radiation, mainly located in the central Myanmar. The Myanmar map of optimal microgrid solutions are shown in Figure 1. COE of microgrid systems is shown in Figure 2. Additionally, simulation of HMG system is not conducted in this study because of the reasons mentioned earlier. Previous studies about the economic assessment of micro-grid in Myanmar suggest that hydro features the lowest unit price of power, even compared with SBMG system [5]. Therefore, considering the features of hydro power as well as its economic efficiency, hydro microgrid system is supposed to be given the priority in villages with micro hydro potential. Some possible sites for HMG system in Magway, Mandalay and Sagaing regions are also marked in Figure 1 [18]. 4.2. Discussions From the simulation result, there are several findings. Firstly, it is observed that SMG system has the most remarkable regional difference. The major costs of SMG system come from PV and battery. The central regions with abundant and stable solar radiation have the lowest COE of SMG system as less PV and batteries are required compared to other regions. However, in 2030, the regional different has been eliminated into a relatively low level, which means that technology development is of great significance for the reduction of regional cost gap of SMG system. Secondly, hybrid systems including SDMG system and SBMG system show relatively low regional and time difference compared to other systems. This is owing to the adjustability of generation mix. In most regions, diesel or biogas generator contributes to more than half of total electricity generation in 2018 and 2022, while in 2030, PV contributes to most of the electricity generation, with generator as a backup mainly used at the rainy season to cover shortages in electricity supply. Thirdly, the SMG system have the most excess electricity, with more than 30% of the total electricity production being dumped because it cannot be used to serve a load or charge batteries in most cases. In this study, excess electricity occurs mainly because the variability of solar radiation. The average solar radiation during the dry season and the rainy season makes an enormous variation in most areas of Myanmar, leading to the need of a larger-size battery during the rainy season, as well as the occurrence of excess electricity during the dry season.
Fig. 1. Myanmar map of optimal microgrid solutions
Fig. 2. COE of microgrid systems
314 6
Duanxia Xu et al. / Energy Procedia 159 (2019) 309–314 Duanxia Xu/ Energy Procedia 00 (2018) 000–000
Despite the fact that the most economically efficient micro-grid system is figured out for rural electrification in each district of Myanmar, this result has some limitations. First of all, this study is limited by the lack of reliable data. Most of the cost data are collected from previous studies, some of which come from investigation in other developing countries. Moreover, service quality is not considered in this study. In general, both the cost of system and the quality of service should be looked at when assessing the performance of a system. Furthermore, environmental influence is not evaluated in this study. Environmental influence could be reflected to the economic output by setting the penalty for emissions. 5. Conclusion This research provides economic comparison of several micro-grid solutions for the rural electrification in Myanmar. Firstly, the background of rural electrification in Myanmar is introduced. Five microgrid systems, including solar microgrid (SMG), diesel microgrid (DMG), biogas microgrid (BMG), solar & diesel microgrid (SDMG) and solar & biogas microgrid (SBMG), are studied in the case of Myanmar. Then, models of demand projection and cost estimation are established to reflect the dynamics of energy demands and system costs in different regions over time. Moreover, simulations by HOMER are carried out to demonstrate the ideal economically efficient microgrid system for each district of Myanmar in different time periods. Results show hybrid microgrid systems, including SDMG and SBMG system, are more competitive than other solutions. For future research, the combination of performance assessment and economic assessment of micro-grid systems by adding factors like penalty for unmet load, emissions could be effective for a more desirable outcome. Acknowledgements This research was supported by Social-strategic Engineering Laboratory, the University of Tokyo. We express our appreciation to the Nan Wang and Kyohei Shibano who shared their expertise that greatly assisted this research. We are also grateful for assistance with HOMER, a useful tool for microgrid simulations. References [1] International Energy Agency. Energy Access Outlook 2017: From Poverty to Prosperity. 2017 [2] Pode R, Pode G, Diouf B. Solution to sustainable rural electrification in Myanmar. Renewable and Sustainable Energy Reviews. 2016 Jun 1;59:107-18. [3] Word Bank. Myanmar: Towards Universal Access to Electricity by 2030. 2014 [4] Pode R. Potential applications of rice husk ash waste from rice husk biomass power plant. Renewable and Sustainable Energy Reviews. 2016 Jan 1;53:1468-85. [5] Sasaki H, Seino M, Hashimoto N, Sakata I. Off-grid electrification scenarios for rural electrification in Myanmar. [6] Kim H, Jung TY. Independent solar photovoltaic with Energy Storage Systems (ESS) for rural electrification in Myanmar. Renewable and Sustainable Energy Reviews. 2018 Feb 28;82:1187-94. [7] Numata M, Sugiyama M, Mogi G, Swe W, Anbumozhi V. Technoeconomic Assessment of Microgrids in Myanmar. [8] Department of Population Ministry of Labor, Immigration and Population. THE 2014 MYANMAR POPULATION AND HOUSING CENSUS. 2014 [9] World Bank. Myanmar - Capitalizing on rice export opportunities. 2014 [10] N. Wang, J. Oda, K. Akimoto. A Bottom-up End-use Model for Myanmar Regional Residential Electricity Demand. The 34th Conference on Energy, Economy, and Environment. 2018 [11] International Energy Agency. World energy outlook 2015. 2015 [12] Øverland I, Vakulchuk R, Hlaing KK, Naing EZ, Suryadi B, Velautham S. Myanmars Attractiveness for Investment in the Energy Sector: A Comparative International Perspective. [13] Max Energy. https://maxenergy.com.mm [14] Energiewende A, Mayer JN, Philipps S, Saad N, Hussein D, Schlegl T, Senkpiel C. Current and future cost of photovoltaics. Berlin: Agora Energiewende. 2015. [15] IRENA. ELECTRICITY STORAGE AND RENEWABLES: COSTS AND MARKETS TO 2030 [16] HOMER. http://www.homerenergy.com/ [17] Sigarchian SG, Paleta R, Malmquist A, Pina A. Feasibility study of using a biogas engine as backup in a decentralized hybrid (PV/wind/battery) power generation system–Case study Kenya. Energy. 2015 Oct 1;90:1830-41. [18] ADB. Myanmar Off-grid Analytics Website. http://adb-myanmar.integration.org