European Biomass Conference and Exhibition 2016 ...

24th European Biomass Conference and Exhibition, 6-9 June 2016, Amsterdam, The Netherlands

BIOMASS GASIFIER BASED HYBRID ENERGY SYSTEM OPTIMIZATION FOR ENERGY ACCESS BY USING HOMER 1

Rumi Rajbongshi1,2, Pranab Deb2, Sadhan Mahapatra2,3, S Dasappa3 Department of Electrical Engineering, National Institute of Technology, Silchar, Assam, India 2 Department of Energy, Tezpur University, Tezpur, 784028, Assam, India 3 Centre for Sustainable Technologies, Indian Institute of Science, Bangalore 560012, India

ABSTRACT: This study presents biomass gasifier base hybrid energy systems optimization for energy access to a nonelectrified village by using HOMER simulation tool. Hybrid energy system consists of two or more renewable or nonrenewable source of energy and improves the reliability of energy supply. The case study is considered here for analysis is an un-electrified village Jhawani in Assam to meet the energy needs of the village based on the locally available energy resources. The domestic load requirements includes lighting, fans, television, mobile charging point, water pump for drinking water, etc; community load includes street lights in the village, fans in the community hall and computer for school and commercial loads like lighting in the shop and small scale rural enterprise like rice hulling machine and irrigation for cultivation as livelihood activities in the village. The hybrid energy system uses biomass gasification system, solar photovoltaic system, battery as energy storage to provide electricity when load demand is low and diesel generator as a backup to improve the reliability of the system. The primary objective of the hybrid energy system to provide reliable electricity based on the load and energy requirement of the village. In this study, both off-grid (when grid is not available; present scenario) and grid connected (when grid will be available but not reliable; future scenario of the non-electrified village) is considered. The study presents the analysis of optimal system configurations for different energy and load profiles and for various grid availabilities. The analysis found that the cost of energy generation primarily depends on the load factor. Hence, load scheduling or proper load management is crucial to reduce the cost of energy generation. The optimization result shows that biomass gasifier base grid connected hybrid energy system can provide reliable energy supply to un-electrified villages. Keywords: Energy access; Hybrid energy systems; Biomass gasification; HOMER

1

INTRODUCTION

systems on the livelihood activities of the villages. Banerjee (2006) compared different options of distributed generation in India and assessed them based on cost of generation [3]. It is found that biomass gasification and bagasse based cogeneration are the most cost efficient DG options. Mahapatra and Dasappa (2012) studied off-grid solar photovoltaic, biomass gasifier based power generation and grid extension system for rural electrification [4]. This study provides a relation between renewable energy system capacity and economical distance limit from the existing grid point, based on the life cycle cost analysis. Grid availability and operating hours of renewable energy systems are considered in the analysis. This study concludes that the biomass gasifier is preferable option compared to solar photovoltaic or even compared to grid extension for villages which are far away from the existing grid. Sen and Bhattacharyya (2014) identified the optimal off-grid option and compared that with conventional grid extension using HOMER and found out that hybrid renewable energy system can be cost effective alternative compared to grid extension at an offgrid location [5]. Hybrid renewable system is found to be sustainable, techno-economically viable and environment friendly. Balamurugan et al. (2011) proposed optimal sizing and operation of a wind-biomass gasifier based hybrid system [6]. This study compares the wind-biomass system with the wind-diesel system and found that windbiomass system is economically attractive option than wind-diesel system. Munuswamy et al. (2011) studied the cost of powering a rural health centre with decentralized renewable energy system in India and compared the cost of electricity from grid. This study concludes that decentralized system is cost effective and feasible [7]. Dorji et al. (2012) studied different off-grid options for electrification of rural households of Bhutan and simulated the hybrid energy systems to find the optimal one and compared it with conventional grid extension using HOMER [8]. This study found that distributed generation

Energy is one of the major inputs of any industry and for socio-economic development of any country. Rural electrification has been regarded as the vital requirement for development of any developing country. It is essential for economic growth, elimination of poverty, generation of employment etc. According to Census 2011, more than 77 million households still use kerosene for lighting in India [1]. In rural India, more than 44 % of the households do not have access to grid electricity [2]. The villages are mainly dependent on energy source like firewood, animal dung, kerosene and diesel. Kerosene based lighting is predominantly used in the villages. Grid based electricity in the villages lacks in quality of supply and also the availability when it is required. For any off-grid location, the delivered cost of electricity mainly depends on the load factor, transmission and distribution losses and cost of transmission and distribution lines. Rural villages have lower energy demand and lower capacity utilization rate. Hence, rural electrification thorough grid extension is economically unattractive option in most of the time. Distributed generation (DG) is an economically viable option for rural electrification in developing countries [3]. DG system can employ a range of technological options from renewable to non-renewable sources and can operate either in a stand alone, hybrid, off-grid or grid connected mode. Decentralized electricity generation based on renewable energy technology is one of the viable solutions to provide electricity to the rural areas. This alternative solution is environmental friendly. It can provide reliable electricity near the point of use and reduce the use of large infrastructure. The quality and reliability of power in this process is higher compared to the centralized energy generation. Various authors have reported different options of distributed energy systems, cost of energy generation and impact of energy generation from renewable energy

1612


is cost effective and the optimal hybrid system configurations varied from location to location depending on the availability of resources. Bhattacharjee and Dey (2014) investigated the feasibility of harnessing rice husk potential for power generation in rice mills in Tripura with grid connected solar PV-biomass gasifier hybrid system [9]. This study found that the cost of energy is US$ 0.143/kWh with renewable fraction of 0.91 in the case of optimal configuration of the hybrid system. Hybrid energy system is a good option for rural electrification. A hybrid energy system consists of two or more renewable or non-renewable sources of energy, which provides increased system efficiency. Due to the availability of many sources of energy to support each other in a cost effective way, this type of system can improve the reliability of energy supply [10]. Due to varying natural condition, different energy sources are integrated with each others. Some additional equipments like battery, converter are used to meet the energy demand without any failure in hybrid energy system. Battery is used to store the energy and this energy can be used when the other renewable energy supply is not available or load demand is very low. To maintain the grid quality power output, controller and power conditioning units are also required. Diesel generator is used as a backup option in case of decentralized system and grid is not available [11]. In this study, solar photovoltaic and biomass gasifier are used as renewable energy source, diesel generator as conventional energy source as backup, battery as energy storage to meet the low load demand. Biomass gasifier and photovoltaic are the primary energy sources to meet the energy demands. Alone, solar photovoltaic or biomass gasifier base systems are capable to meet the energy demand. However, solar energy intensity varied in a day and also varies in the different seasons of the year, which makes it unreliable or system capacity or battery size increases. As the load profile varies over the day, operation of gasifier for low load profile reduces the overall system efficiency. It is found from the analysis that for 19 kW load with 178 kWh/day energy demand, the cost of energy from biomass gasifier is US$ 0.110/kWh and from photovoltaic it is US$ 0.243/kWh. Hence, to improve the system overall efficiency and reliability, hybrid base system is considered for this village. In this study, grid is also connected with the hybrid system. Grid connected hybrid system make the use of grid for purchasing electricity, when demand is higher than the generation from the distributed hybrid energy systems located at the village or sell back the excess generation from the distributed hybrid systems, when the load demand in the village is lower than the generation. This kind of systems actually improves the availability of energy and also improves the plant overall load factor. Different hybrid system configurations can be formed based on the availability of the energy sources at the site. HOMER (Micro power Optimization Model) is a simulation tool for designing and analyzing hybrid power system, which is developed by the National Renewable Energy Laboratory (NREL) [12]. HOMER models the design of energy system and calculates its annual levelized cost (ALC). ALC includes the total installation and operation cost of the system over its life time and then coverts it yearly basis. HOMER also helps to determine how variable energy resources can be optimally integrated into hybrid system for either grid connected or off-grid environments. HOMER is used to run simulations of different energy systems configurations and compare the results based on levelized cost of energy.

2

METHODOLOGY

2.1 Study area The hybrid system is designed for the electrification of Jhawani village (longitude 26.5 N and latitude 92.7 E), which is 17 km from district headquarter Tezpur in the state of Assam, India. The village is located on the north bank of mighty Brahmaputra River. The village Jhawani has 31 households with a population of 133. The village is un-electrified. The primary source of income and livelihood is agriculture. The livestock or animal rearing is the subsidiary activity along with agriculture. The total geographical area of the village is 329 acres. The cultivable land is 65 acres and the rest are waste land, grazing land, and community land etc. There is only one lower primary school in the village and the literacy rate is 85.5 percent. Both the traditional and modern energy resources are being used in the village. The traditional energy sources are fuel wood, cow dung, and crop residue for cooking. Kerosene is the major source for lighting. In few houses, solar home lighting systems are used for television and lighting. In few houses LPG is used for cooking purpose. Nearly 94% of the energy needs of the village are derived from fuel wood, which is used in low efficient and highly polluting traditional cook stoves. Kerosene is provided 4 liters for each family per month from public distribution mechanisms and used for lighting. It is found from the primary survey that the amount of kerosene availability is not sufficient to meet their demand especially in monsoon season. The shortage of kerosene affected the study at night time of the school going children. Ranking exercise has carried out to ascertain the energy concerns of the villagers for five different energy needs namely electricity, biogas, photovoltaic, improved stoves and fuel wood. The results showed that electricity is the top priority of the villagers. A micro-grid based distributed generation of electricity based on locally available energy sources could be the most viable solution given its remote location and low energy demand. The typical energy end uses considered for determining the energy access are cooking and lighting. However, the ranking exercise also brings out that the entertainment and mobile charging as the top two electricity end uses. In this study, energy access has look beyond the basic energy needs and targets at addressing the modern energy services needs for the villagers to enhance the quality of life and productive end use to improve the livelihood activities. Various income generation activities like milk processing, bakery, irrigation for agriculture, agro product value addition (potato chips, pickles, oil rearing etc.) could be started if electricity is available in the village. Biogas is the second priority as this can be used for cooking, and also can reduce the burden of women for collection of fuel wood. 2.2 Village load assessment The energy requirement in the village is assessed through primary survey data. The loads include domestic, community, commercial and livelihood activities. The domestic load includes lighting, fans, television, mobile charging point, water pump for drinking water, etc. Community load includes street lights in the village, fans in the community hall and computer for school. In case of commercial loads, lighting is considered for the shops that run in the evening in the village. Livelihood load includes agro product processing mills and irrigation for crops cultivation in winter months. The irrigation pump sets are

1613


required for the winter crops. Table I presents the typical load analysis of the village for two different seasons of the year based on the survey data. Depending on the load requirement of the village at present and the possible future growth in energy demand keeping the peak load constant, three different energy profiles are considered (option A, B and C) for this study. The peak load, energy demand and the corresponding load factor of the load profiles of the village are presented in Table II. It can be observed that the load factor for the village is very low.

The energy demand for option A is approximately 162 kWh/day. However, considering 10% losses in transmission and distribution, the actual energy demand at the generation point for option A becomes 178 kWh/day and the peak load of 19 kW. It has a load factor of 0.386. The daily hourly load profile of option A is obtained after the load analysis. Likewise, the load profile for option B and C are also evaluated considering the various load demand of the village.

Table I : Typical load analysis for the village Load pattern

Domestic load Light Fan Television Mobile charger Commercial load Light (Shop) Community load Water pump for drinking water Fan (school) Street light Livelihood load Agro product processing unit Water pump for irrigation Domestic load Light Fan Television Mobile charger Commercial load Light (Shop) Community load Water pump for drinking water Fan (school) Street light Livelihood load Agro product processing unit

Number Rating Load of systems (W) (kW) in the village Month: October-March

Load operation time (hr)

Energy demand (kWh/hr)

124 0 31 31

15 60 75 25

1.86 0 2.33 0.78

5PM-11PM 5PM-11PM 5PM-11PM

11.16 0 13.98 4.68

4

15

0.06

5PM-10PM

0.30

2

373

0.75

4 15

60 18

1

10000

0.24 0.27

8AM-10AM; 3PM-5PM 10AM-3PM 6PM-5AM

1.20 2.97

10

11AM-3PM

40

5 3730 18.65 Month: April-September

7AM-11AM

74.6

124 62 31 31

15 60 75 25

1.86 3.72 2.33 0.78

5PM-11PM 8PM-5PM 5PM-11PM 5PM-11PM

11.16 44.64 13.98 4.68

4

15

0.06

5PM-10PM

0.30

2

373

0.75

4 15

60 18

1

10000

Renewable energy base hybrid system is the best viable option for electrification to such kind of villages. In this hybrid system, apart from biomass gasification system, solar photovoltaic system as renewable energy sources, battery is used for energy storage, converter for conversion purpose (DC-AC) and diesel generator as conventional energy source. Fig 1 presents the hybrid energy system configuration using biomass gasifier, solar photovoltaic system, diesel generator, converter and battery. Biomass resources are abundantly available in the village. The optimal hybrid system configuration is assessed based on the lowest annual levelized cost of generation through HOMER simulation tool. As the

3.0

0.24 0.27

8AM-10AM; 3PM-5PM 10AM-3PM 6PM-5AM

1.20 2.97

10

11AM-3PM

40

3.0

energy demand increases, the loading percentage also increases. In case of constant peak load and energy demand, only the distribution of the load profile changes. Table II: Details of different peak load and energy demand options

1614

Option

Peak load (kW)

A B C

19 25 41

Energy demand (kWh/d) 178 169 286

Load factor (%) 0.386 0.279 0.294


The village considered for the study is non-electrified in this moment. However, the possible electrification due to the government initatives towards power for all, we also have considered the grid-connected hybrid system where the conventional grid is combined with the renewable energy base hybrid energy system (biomass gasifier, photovoltaic, battery, converter, and diesel generator). This type of hybrid energy systems can be designed for electricity supply to the villages where the grid is also available. Fig 2 presents the grid connected hybrid energy system configuration.

HOMER software is by default 100% reliable. In a real situation, most of the villages of India which are grid connected show most of the time grid is not available. However, the unreliable grid can also be designed in HOMER by using scheduling method [12]. Details about the gasification technology and its end use is as in [13], [14] and [15]. So, for this purpose the grid component is used for assigning the electricity purchase price rate and sell back rate. The hybrid system is designed with both reliable and unreliable grid. Here, the purchase rate and sell back rate of electricity considered as US$ 0.08/kWh and US$ 0.05/kWh respectively.

3

RESULTS AND DISCUSSION

3.1 Off-grid hybrid system The hybrid system is comprised of solar photovoltaic, biomass gasifier, diesel generator, battery and converter for off-grid system. In case of grid connected hybrid system, conventional grid is connected with the hybrid system. The annual levelized cost of energy for different configurations is assessed in this study by using HOMER simulation tool. The cost of energy is the average annualized cost of the system per kWh of useful electricity produced by the system. The off-grid hybrid system is designed by considering the input parameters like peak load, energy demand, locally available energy resources like solar energy and biomass etc. The simulation tool optimized the result based on net present cost (NPC) and cost of energy (COE). This analysis is carried out considering three different load profiles for off-grid hybrid system. This has done to analyze the effect of load profiles on per unit cost of energy generation. Table III presents the energy demand considered for various options with fixed peak load. The values of energy demand are considered based on percentage of loading. As percentage of loading increase the energy demand also increases. HOMER simulates the hybrid system for these all options and optimized the result based on NPC.

Figure 1: Hybrid energy system configurations

Table III: Energy demand at various loading Option

Option A (Peak load: 19 kW) Option B (Peak load: 25 kW) Option C (Peak load: 41 kW)

Figure 2: Grid connected hybrid system configurations Grid also can be used both for selling and purchasing the electricity. Grid is used to purchase electricity depending on the load profile. If the load demand cannot meet by hybrid energy system, then the certain amount of electricity can be purchased from the grid at purchase price rate. If there is excess electricity available due to low energy consumption at a particular time at the village that can be sold back to the grid at sell back price rate to get extra revenue. In this type of system, the village will get uninterrupted power supply and also become an energy generation hub. The grid component that is available in

1615

Loading (%) 1 2 3

Energy demand (kWh/d) 1 2 3

25

30

40

117

141

186

25

30

40

152

182

242

25

30

40

248

297

392


COE ($/kWh)

0,15

increases for fixed energy demand. It also important to note that for same energy demand the average load is fixed. Hence, load factor is mainly dependent on peak load. As peak load increases the capacity of the generating system such as solar photovoltaic system, biomass gasifier system, diesel generator etc. are also increases to meet the peak demand. Since the capacity of the system increases the fixed cost of the hybrid system increases. So the unit cost of energy generation increases for fixed energy demand. Hence load rescheduling or shifting of load from peak to off peak time is important to reduce the peak load which leads to the reduction of cost of energy generation. However, in case of fixed peak load and energy demand, the cost of energy generation changes with the variations in the load profile. Depending on the load profile, HOMER chose the energy generating system by itself. It is found that when energy requirement is less for less period of time, diesel generator or solar photovoltaic or battery is used. However, when the energy requirement is relatively higher for longer period of duration, biomass gasifier is used. It is also observed from the simulation that even if the capacity of the generating systems is same for two different cases, the cost of electricity generation is not same. This is mainly due to the operation hour of the individual component of the system.

19 kW 25 kW 41 kW

0,13

0,11

0,09 20

25

30 35 Load factor (%)

40

45

Figure 3: Variations of cost of energy with load factor at constant peak load Fig 3 represents the relationship between load factor and cost of energy for constant peak load for various options. It is observed from the Fig 3 that the load factors influence the cost of electricity generation. As the load factor increases, the average load also increases consequently. Hence, for constant peak load the number of units generated for a given period of time will be higher as the energy demand is also high. Therefore overall per unit cost of electricity (COE) decreases due to distribution of fixed costs, which are proportional to the peak demand and independent of units generated. In all the cases, the COE decreases as the load factor increases, since energy demand increases. Hence higher value of load factor means the higher percentage loading is important for reduction of COE.

0,19

COE ($/kWh)

0,17

Table IV: Peak load at various loading Option

Peak load (kW) 1 2 3

Option A Energy demand:178 kWh/day Option B Energy demand:169 kWh/day Option C Energy demand:286 kWh/day

15

25

35

20

30

40

30

40

50

0

10 20 30 BG capital cost increment (%) Biomass price=40$/t Biomass price=60$/t Biomass price=80$/t Biomass price=100$/t Figure 5: Variations of cost of energy with gasifier capital cost increment

COE ($/kWh)

178 kWh/d 169 kWh/d 286 kWh/d

The effect of biomass gasifier capital cost on the cost of electricity at different biomass price is shown in Fig 5. The global solar radiation, diesel price, PV capital cost, diesel generator capital cost are fixed at 4.77 kWh/m2/d, US$ 1/l, US$ 2800/kW and US$ 370/kW respectively. Fig 5 shows that the cost of energy increases linearly with the increase of biomass gasifier capital for all biomass prices. When the gasifier capital cost increases from 0% to 30%, the cost of electricity increases from US$ 0.119/kWh to US$ 0.125/kWh at biomass price of US$ 40/t. As the biomass price increases, the cost of electricity also increases at fixed gasifier capital cost. In case of 10% increase in gasifier capital cost, the cost of energy increases from US$ 0.121/kWh to US$ 0.174/kWh with the increase in biomass price from US$ 40/t to US$ 100/t.

0,12

0,10 10

20

0,13 0,11

0,16

0,14

0,15

30 40 Load factor (%)

50

60

Figure 4: Variations of cost of energy with load factor at constant energy demand Table IV presents three different peak loads with constant energy demand. Fig 4 presents the cost of energy at various load factors for fixed energy demand. It can be observed that the cost of energy varies inversely with the load factors. As load factor increases the cost of energy decreases and as peak load increases the load factor also

3.2 Grid connected hybrid system The village where grid is available the grid can also be combined with the hybrid system. The grid component which is available in HOMER simulation tool is 100% reliable. However, most of the grid connected villages in

1616


India experiences that most of the time grid is not available. Hence, unreliable grid is designed in the simulation tool by proxy generator scheduling method. In this study, two different conditions of grid are considered: one is unreliable grid and other one is 100% reliable grid. In case of grid connected hybrid systems, the system is designed to purchase or sell electricity from the grid. Whenever there is excess electricity generated at the grid connected hybrid system configuration, it can be sell beck to the grid and whenever there is higher load demand compared to generation then electricity can be purchased from the grid. The optimization results for option B (25 kW peak load with 169 kWh/day energy demand) is shown in Fig 6. It is found from the optimization results that the costs of energy of all options are same although the load profiles are not same. Depending on the load profile the capacity of the components are selected. However, the ratio of total annualized cost of the system to the total electricity served by the system is almost same for all these options. Hence, the cost of energy of the system for all three options are same. However, it does not represent that the cost of energy of the system is same for any load profile. The detailed analysis of the system with different time of a day is shown in Fig 7 for this case (option B). The hourly data analysis of the optimization system provides the amount of electricity purchased from the grid and amount of electricity sell back to the grid at the specified time. It is clear from this analysis that depending on the AC primary load the gasifier is operated to generate electricity and electricity is purchased from the grid when the load cannot be fulfill by the gasifier. Again, when there is excess generation it is selling back to the grid to get extra revenue.

The grid connected system is also design with unreliable grid means grid is not 100% reliable. In this study, the different values of grid availability are considered at different time interval of the day. The simulation is performed by considering same values of grid availability for each option. Table V represents the optimization results for different grid availability for Option B. For 6 hour of grid availability, the cost of energy is less compared to all other conditions. In this condition the higher amount of electricity is sell-back to the grid. Actually the excess electricity generated by the system is sell-back to the grid to get the revenue. So the cost of energy of the system is less compared to all other cases even if the capacity of gasifier is higher. For 12 hour, 18 hour and 24 hour of grid availability, the capacity of each component, and percentage of generation from gasifier and amount of grid sales are same. However, only difference is in the percentage of generation from unreliable grid and amount of grid purchase, which affects the cost of energy. The total cost difference of unreliable grid in case of 12 hour and 18 hour of grid availability is higher compared to the cost difference of reliable grid for 12 hour and 18 hour of grid availability. So unreliable grid has more influence in the cost of energy of the system. The system generates 2% and 23% of electricity in case of 12 hour and 18 hour of grid availability respectively. Hence, the cost of energy of 12 hour of grid availability is less than 18 hour of grid availability. For 18 hour and 24 hour of grid availability the cost of energy is same. This is because the amount of electricity purchased from the grid is almost same. The total cost of the system for both the cases is almost similar. Hence the cost of energy is same.

Figure 6: Optimization result of grid connected hybrid system for 25 kW peak load with 169 kWh/day energy demand Table V: Optimization results for different grid availability for Option B Grid availability (hr) 6 12 18 24

Optimum system configuration BG UG Grid (kW) (kW) (kW) 15 100 100 10 100 100 10 100 100 10 100 100

COE ($/kWh)

0.064 0.065 0.067 0.067

1617

Electricity generation (%) BG Unreliable grid 90 0 77 2 77 23 77 23

Grid purchase (%)

Grid sales (%)

10 21 1 0

28 7 7 7


[3]

[4]

[5]

[6]

[7] Figure 7: Hourly analysis of option B

4

CONCLUSIONS

[8]

Rural electrification is an important concern to improve the socio-economic condition of the rural village of any developing country. In this study, different load demand of the village is considered. In later time, if the grid is available to the village, then grid connected hybrid system is used to electrify the village. It is clear from the analysis that the cost of electricity generation mainly depends upon the load factor. Load factor depends on the peak load and average load. So by decreasing the peak load or increasing the energy demand the cost of electricity generation can be reduced. Moreover the cost of electricity generation depends on the load profile. For grid connected hybrid system, both reliable and unreliable conditions of grid are considered. In case of unreliable grid connected hybrid system, the unreliable grid is used as an energy generation source scheme. It is observed from the analysis that depending on the load profile and grid availability the cost of energy has changed. In case of reliable grid connected hybrid system the cost of energy is less compared to the off-grid hybrid system. This is because the excess electricity is sell-back to the grid by the system to earn more revenue. The analysis revealed that the best option scenario for all the conditions is biomass gasification system in comparison to photovoltaic systems. However, as the photovoltaic system capital cost is reducing drastically, in the near future, it will play a crucial role for sharing in the generation mix of any village load. The hybrid system can provide reliable energy supply to the village and it has less emission compared to conventional system.

[9]

[10]

[11]

[12]

[13]

[14]

[15]

6 5 [1]

[2]

REFERENCES

http://powermin.nic.in. R. Banerjee, Comparison of options for distributed generation in India, Energy Policy, 34(1), 101–111, 2006. S. Mahapatra, and S. Dasappa, Rural electrification: optimising the choice between decentralised renewable energy sources and grid extension, Energy for Sustainable Development, 16 (2), 146– 154, 2012. R. Sen, and S. C. Bhattacharyya, Off-grid electricity generation with renewable energy technologies in India: An application of HOMER. Renewable Energy, 62, 388-398, 2014. P. Balamurugan, S. Ashok, and T. L. Jose, An optimal hybrid wind-biomass gasifier system for rural areas, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 33(9), 823832, 2011. S. Munuswamya, K. Nakamuraa, and A. Katta, Comparing the cost of electricity sourced from a fuel cell-based renewable energy system and the national grid to electrify a rural health centre in India: A case study, Renewable Energy, 36(11), 2978-2983, 2011. T. Dorji, T. Urmee, and P. Jennings, Options for offgrid electrification in the Kingdom of Bhutan, Renewable Energy, 45, 51-58, 2012. S. Bhattacharjee, and A. Dey, Techno-economic performance evaluation of grid integrated PVbiomass hybrid power generation for rice mill, Sustainable Energy Technologies and Assessments, 7, 6–16, 2014. J. L. Bernal-Agustín, and R. Dufo-López, Simulation and optimization of stand-alone-hybrid renewable energy systems, Renewable Sustainable Energy Reviews, 13(8), 2111–2118, 2009. P. Nema, R. K. Nema, and S. Rangnekar, A current and future state of art development of hybrid energy system using wind and PV-solar: A review, Renewable and Sustainable Energy Reviews, 13(8), 2096–2103, 2009 Homer Energy Simulation Tool, http://www.homerenergy.com/software.html, accessed on 20/03/2016. S Dasappa, U Shrinivasa, BN Baliga, HS MukundaFive-kilowatt wood gasifier technology: Evolution and field experience, Sadhana 14 (3), 187212. S. Dasappa, On the estimation of power from a diesel engine converted for gas operation–a simple analysis K Sandeep, S Dasappa, Oxy–steam gasification of biomass for hydrogen rich syngas production using downdraft reactor configuration, International Journal of Energy Research 38 (2), 174-188

ACKNOWLEDGEMENT

The authors duly acknowledged the financial support from DST, Government of India for the RHEES project.

Census Report. Registrar General & Census Commissioner, Government of India, 2A Mansingh Road, New Delhi 110 011, India. http://www.censusindia.net. Ministry of Power, Government of India, Shram Shakti Bhavan, New Delhi 110001, India.

1618