Modeling of hybrid energy system for futuristic energy demand of an Indian rural area and their optimal and sensitivity analysis

Accepted Manuscript Modeling of hybrid energy system for futuristic energy demand of an Indian rural area and their optimal and sensitivity analysis

Aditya Kumar Nag, Shibayan Sarkar PII:

S0960-1481(17)31148-5

DOI:

10.1016/j.renene.2017.11.047

Reference:

RENE 9449

To appear in:

Renewable Energy

Received Date:

10 March 2017

Revised Date:

31 October 2017

Accepted Date:

15 November 2017

Please cite this article as: Aditya Kumar Nag, Shibayan Sarkar, Modeling of hybrid energy system for futuristic energy demand of an Indian rural area and their optimal and sensitivity analysis, Renewable Energy (2017), doi: 10.1016/j.renene.2017.11.047

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Highlights 

A hybrid energy model is proposed for different combination of renewable energy.



Optimality and sensitivity of the model is done in HOMER software.



Hybrid energy model consists of solar-wind-hydro-biogas-biomass found best.



Here wind turbine deals peak demand, biodiesel plant run throughout the year.



Analyzed result is anticipated through optimal plot, surface plot and line plot.



Renewable energy combinations are proposed for futuristic energy demand.

ACCEPTED MANUSCRIPT

1 2

Modeling of hybrid energy system for futuristic energy demand of an Indian rural area and their optimal and sensitivity analysis

3

Aditya Kumar Nag1, Shibayan Sarkar2

4 5

1Research

6 7

2Assistant

8

1. Email: [email protected]

9

2. Email: [email protected]

Scholar, Department of Mechanical Engineering, Indian Institute of Technology (ISM) Dhanbad-826004, Jharkhand, India Professor, Department of Mechanical Engineering, Indian Institute of Technology (ISM) Dhanbad-826004, Jharkhand, India

10

Abstract

11

The aim of this paper is to model a hybrid energy system for rural India. In this analysis a hybrid

12

renewable system consists of solar-wind-hydrokinetic-bioenergy is proposed. Overall analysis

13

including Optimization and sensitive analysis are evaluated by HOMER software. Optimized

14

results are evaluated considering total net present cost, cost of energy, annual electric generation,

15

renewable fraction and CO2 emission. The analysis is done in order to increase efficiency of the

16

system with comparatively less cost. From this exercise it is found that, combinations of more

17

varieties of different renewable energy system are better to fulfill futuristic demand. Futuristic

18

demand is calculated by logistic growth model. The value of COEs ($) and total generations

19

(kWh/yr) are 0.3563 and 133045; 0.247 and 170948; 0.242 and 179018; 0.241 and 179303 for

20

the year (2012-2021), (2022-2031), (2032-2041) and (2042-2051) respectively. However some

21

other combinations may produce more power, although in such cases, in order to storage excess

22

power COE will increase. As compared to the year (2012-2021) to (2042-2051) the optimized

23

COE of electricity is minimized, keeping Annual cost nearly constant and amount of electric

24

generation is increased without much effect on environment.

25 26

Keywords: Hybrid renewable energy, cost of energy, HOMER, sensitivity analysis, futuristic energy demand, rural electrification.

27 28

1. Introduction

29

Globalization, due to advancements in technologies and excessive human dependency on gadgets

30

increased the consumption of electric energy. Yet globally 1.2 billion people are living without 1

ACCEPTED MANUSCRIPT

1

access to electricity and more than 2.7 billion people are living without clean cooking facilities

2

who are mainly the resident of rural areas [1, 2]. At present conventional energy like fossil fuel

3

and nuclear energy are mainly used for the electricity generation across the world. But these

4

sources of energy are limited, emits harmful gases and radiation which results pollution in our

5

environment. In this circumstance, renewable energy may be considered as the future of

6

electricity generation as it is environment friendly and replenishable. In countries like Germany,

7

Denmark, Australia and some US states, renewable energy produced electricity like coal and

8

natural gas where high efficiency of HRE is achieved (see Fig. 1) [3]. Study also showed that, in

9

order to reduce environmental temperature, utilization of the renewable energy needs to be

10

increased upto 37% of the power generation by 2040 as compared to present scenario of 23%

11

(Table 1) [4]. This may lead to drastic changes in production and consumption of any form of

12

energy, transportation, electricity generation. This may result in large decrement in carbon and

13

harmful gas emissions from an average of 650 million tonnes per year since 2000 to around 150

14

million tonnes per year in 2040 in order to maintain WEO-2016 pledges to reduce environmental

15

temperature of 2°C [2]. Obama B. (2017) stated decarburizing the energy system, storing of CO2

16

and furnishing clean power sector is priority for United States [39].

17

In 2015, world produced renewable energy resources over more than 10442.6 TW·h/year

18

(terawatt-hours per year). Out of this India produced 160 TW·h/year and on the other hand

19

neighboring country China was leading by producing 1300TW·h/year [5].Study shows that

20

India’s energy sector has grown tremendously in the recent years. India has been responsible for

21

almost 10% of the increase in global energy demand since 2000. According to the Central

22

Electric Authority, Govt. of India report energy requirement in 2015-2016 is 1114408 MU

23

projected growth is 8.7% and actual growth 4.3% for the year 2016-17. The monitored capacity

24

of various electric as well as thermal plants is 261270.48 MW maximum production is

25

177422.40MW. On the reports of CEL grid operation and distribution wing all India renewable

26

energy sources (RES) the generation of total power is 45916.95MW upto the date of 16

27

November 2016 [6]. It is observed that Northern, Western and Southern region have better

28

energy generation capacity compared to Eastern and North Eastern region within India (Fig. 1a).

29

Among these energies, RES has very little contribution approx 2% in eastern region. Share of

30

capacity in RES shows that Hydro, wind, solar PV and conventional RES energy contributes the

31

most approximately 10%, 13%, 17% and 57% respectively (Fig. 1b), whereas share of 2

ACCEPTED MANUSCRIPT

1

generation further decreases the value (Fig. 1c). This fact indicates that there is a huge scope for

2

implementing RES plant in Eastern region for producing environment friendly alternate electric

3

generation plant in order to fulfill increasing electric demand of the area.

4 5 6 7

Fig.1 (a) Regional total Installed energy generation capacity in India (b) Capacity of different renewable energy generation plant installed in India (c) Share of actual renewable energy production in India [6]

8

Several literatures show that among different RES, solar-wind energy system is a good resource

9

of hybrid renewable energy. Solar energy is basically utilized in two ways (i) either to use it

10

directly without using an intermediate electric circuit. Generally solar flat plate collectors are

11

used to generate thermal energy by heating fluid (either air or water). Further this thermal energy

12

may be converted into electrical energy [7]. (ii) To convert it into electrical energy by using

13

photovoltaic (PV) modules. Direct conversion of solar radiation into electrical energy is the most

14

convenient way of utilizing solar energy [8]. In addition, wind turbine also generates electricity

15

without creating pollution and it is well suited for isolated places with no connections to the

16

outside grid [9].Yang et al. tried to optimize the capacity sizes of different components of hybrid

17

solar-wind power generation systems employing a battery bank [10]. Similar exercise has been

18

performed by Xing et al. by calculating the system optimum combinations by considering some

19

decision variables included in the optimization process [11]. Daud and Ismail proposed a hybrid

20

renewable system to solve power-supply problem for remote and isolated areas far from the grids

21

[13]. This system consists of wind-solar system along with diesel engine to accomplish or to

22

recover the load requirements for peak time. Similar exercise is done by Ismail et al., where

23

sensitivity analysis was undertaken to evaluate the effect of change of some parameters on the

24

cost of energy [12]. The results indicated that the optimal scenario is the one that consists of a 3

ACCEPTED MANUSCRIPT

1

combination of the PV panels, battery bank and a diesel generator and powering a rural house

2

using this hybrid system. Mainly, wind energy was used at the peak demand time. Some of non

3

renewable energy like bio fuel or bio mass also has been used to form hybrid renewable energy

4

[14, 15]. Fodder (residual of crop) may be used to produce bio fuel. Sometimes bi-product of

5

sugar cane is also used to extract ethanol from it. Blottnitz and Curran conducted a literature

6

survey on bio-ethanol as a transportation fuel from a net energy, greenhouse gas, and

7

environmental life cycle perspective [16].

8

Piergiorgio et al. discussed about the storing capacity of various types of energies in

9

different types of storage devices. The various types of storage devices which are discussed

10

through this research are constantly improving for more compact and cheaper system. This

11

would probably reduce the cost of hybrid renewable energy sources [32]. Masoud et al. proposed

12

a hybrid renewable system for a small locality load demand (considering Heating load, cooling

13

load, hot water load, electrical load, transportation load) using energies viz. solar, wind and bio-

14

mass [33]. This model is implemented using algorithm multi-objective particle swarm

15

optimization algorithm (DMOPSO) to evaluate optimization and increase the renewable energy

16

usage. Makbul et al. analyzes for the system combination considering solar energy and diesel

17

generator with the capacity storage using Battery and flywheel [34]. This research is focused on

18

power generation, cost of energy (COE), net present cost (NPC) and reduction in carbon

19

emission with or without utilization of flywheel. Similar case studies are presented in other

20

studies as well. For example, Bekele et al. [35] present a study of an Ethiopia, Himri et al. [36]

21

studied in Algeria village and Nandi et al. [37] in Bangladesh. Bahramara et al. studied about the

22

variety of cases considering renewable energy, conventional or either mixture of both the

23

energies [38]. This research validates the implementation of hybrid renewable energy system and

24

its applicability in every variety of load demand, by using HOMER software.

25

On concluding all the literature, it is clearly depicted that the technical and economic feasibility

26

as well as optimality of hybrid energy resources system (HRES). Though the use of renewable

27

energies are in trend, but the combinations of renewable energies i.e. hybrid energy system are

28

limited. Our main concern is to implement HRES is rural areas to fulfill the load requirement at

29

lowest cost. In our research various aspects have motivated viz. utilizing all the available

30

renewable energy resources, optimization of cost variables, minimizing emission of CO2, 4

ACCEPTED MANUSCRIPT

1

fulfilling electric load demand and capacity to store energy. These issues are concerned in this

2

study fill the knowledge gap. Thus following objectives have been set for this study: (i) modeling

3

RES for the rural area in Eastern India with different combinations (ii) optimality and sensitivity

4

analysis of the same in order to find the cost of electricity and generation capacity (iii) Optimized

5

cost for implementation of HRES in rural area followed by very low impact on environmental

6

aspects.

7

2. Methodology

8

Physical concept of the RES including solar, hydro, wind, biomass and biogas energy are

9

depicted in Fig. 2. Here Hydrokinetic turbines (HKT) are considered for harnessing hydro energy

10

whose working principle is analogues to wind turbine. This HKT is capable of producing energy

11

from very low head channel, tidal energy and ocean energy [17]. Therefore it is very effective to

12

use in remote areas in hilly region, near coastal region or water scared region. For solar energy a

13

PV module array system is considered. On the PV panel a glass film may be attached, which

14

uses direct incident radiation from the sun rays and also uses diffused radiation of solar rays to

15

heat up the fluid (like air or water) trapped within the glass film. Henceforth, the heated fluid

16

may further be used as a catalyst in the chemical reaction of bio gas to maintain a temperature of

17

32-35 ̊C (Fig. 2) [18]. Literature showed that the PV panel alone converts solar energy into the

18

electrical energy, which could be stored in battery storage cells with an efficiency of 25-38%

19

[19]. Proposed HES consist of wind turbine also, that converts wind energy into the mechanical

20

energy which further transmitted to gear box in order to enhance the speed ratio. This enhanced

21

speed will be transmitted to generator that converts mechanical energy into electrical energy.

22

This electrical energy will be further transmitted through the step up transformer to the storage

23

cells (Fig. 2). This storage cell fulfills the peak load or fluctuating load demand having

24

approximate efficiency of 27-48% [20, 26]. The mechanical energy which is developed by wind

25

energy will also be extracted from the gear box for making micro granular form of crop residue

26

and also further can be used for mixing and blending of the animal waste and crop residue.

27

Generally, in the bio-mass plant cattle dung like cow dung, poultry or piggery dropping and crop

28

residue are used for the generation of the gas. Further, crop residues are mixed and blend with

29

water to form the slurry by the help of mechanical energy, which is extracted from the wind

30

energy. This slurry is kept for fermentation. In the proposed hybrid system, biomass and bio fuel 5

ACCEPTED MANUSCRIPT

1

will also be included for performance investigation in combination with others. Mechanical

2

energy extracted from wind turbine is further used for converting crop into micro granular form,

3

which is either directly used as a replacement of coal or used after conversion towards.

4

Fig. 2 Description of energy concept through flowchart

5 6

2.1 Governing equations

7

The governing equations of all components used in RES are stated as follows.

8

2.1.1 Solar Energy

9

For finding the maximum power output the equation is shown in Eq (1).

10

  Voc-ln(Voc+0.72)  RS  VOC 0 T0  G . 1 . .   .ISC 0   Pmax =FF. V oc. Isc = 1  voc  VOC  1   ln G 0  T   G0  ISC   G

(1)

11

Where Pmax is the maximum Power generated, FF is the fill factor of the PV module, Voc is open

12

circuit voltage, Isc is the short circuit current, RS is the series resistance of the PV module, T is

13

the PV module temperature (K), G is the solar irradiance (W/m2), G0 is the standard solar

14

irradiance (W/m2) α, β, λ is the exponent responsible for all the non-linear effects that the

15

photocurrent depends on [21].

16

2.1.2 Wind energy

17

The maximum power output is shown by Eq. (2).

6

ACCEPTED MANUSCRIPT

3

3

V  R   3 3 P MAX  0.5  ACp  ………….(2)  0.5  ACp      m m   TSRTARGET   m 

(3)

1

PMAX

2

Where Pmax is the maximum Power generated by wind turbine (kW), ρ is density of the air

3

(kg/m3), A is the swept area, CP is the coefficient of wind turbine, ωm is the rotor speed

4

(mechanical rad/s), TSRTARGET is tip speed ratio, R is the radius of the blade (m), V is the linear

5

speed of the wind (m/s) [22].

6

2.1.3 Hydro kinetic energy

7

Power developed (P) by hydro kinetic turbine is as follows.

8

P  1  AU 3 4a(1  a) 2 a  (U  V1 ) / U 2

9

Where P is the power developed A, ρ, U, V1 are turbine swept area, water density, free stream

(4)

10

velocity and water velocity at plane of rotor respectively.

11

2.1.4 Biomass energy

12

Conversion of methane from slurry through fermentation is as follows.

13 14

15



mesophilic temp (22 38 C ) Slurry (inkg/(20-40)days)   CH 4 (kg/(20-40)days)  CO2

(5)

Energy generated from biomass generator is calculated as follows.  tone  Total bilfuel available  1000  BM  CVBM year   Total available energy  kWh / year   CF  operating hours/day

(6)

16

Where ηBM, CVBM, CF are efficiency, calorific value and conversion factor of biomass [14].

17

2.1.5 Biogas

18

Conversion of methane from excreted manure in the presence of yeast through fermentation is as

19

follows.

20

Hydrolysis Pr esence of yeast Excreted manure  Water  Decomposition(actetic acid , H 2 , CO2 )   CH 4  CO2 at 22 38 C

21

Energy generated from biogas generator is calculated as follows.

22

 m3   kg  Total gas yield  m3   Gas yield per kg of wet dung    Total dung availability   (8)  day   kg 

23

3  kWh  Total gas yield  m   CVBM  Diesel engine Generator Energy yield   CF  day 

7

(7)

(9)

ACCEPTED MANUSCRIPT

1

Where ηDieselengine and ηGenerator are efficiency of diesel engine and generator respectively.

2

Calorific value of biogas is considered as 4700 kcal/m3, ηDiesel engine is assumed as 28%, ηGenerator

3

is 95%, CF is Conversion factor which is taken as 860 [15, 16].

4

2.2 Implementation of the proposed model in HOMER

5

The above said concept has been implemented through Hybrid Optimization of Multiple Electric

6

Renewable (HOMER) software. HOMER evaluates designs of both off-grid and grid-connected

7

power systems for a variety of applications. It simulates the operation of a system by making

8

energy balance calculations in each time step of the year. For each time step, HOMER can

9

compare the electric demand in that time step to the energy that the system can supply in that

10

time step. Further it calculates the flows of energy to and from each component of the system.

11

In this study different renewable resources is combined to form following five HRES: (a)

12

solar-wind-hydrokinetic-bioenergy (b) solar- hydrokinetic-bioenergy (c) solar-bioenergy (d)

13

bioenergy and (e) solar-wind- bioenergy (Fig. 3). These combinations are selected according to

14

the cost of energy, net present cost highest renewable fraction along with lowest emission of

15

CO2. The availability of renewable energy resources, its economy and effect on environment are

16

considered while simulating the power production in HOMER software. For this purpose, an

17

area of Eastern India is considered as study area, which is described as follows.

8

ACCEPTED MANUSCRIPT

1 2 3 4

Fig. 3 Implementation of proposed hybrid energy model through different combination of non conventional energy in HOMER

9

ACCEPTED MANUSCRIPT

1

HOMER performs the simulation of various combinations of the hybrid models

2

considering various sensitive parameters. It determines technical feasibility to simulate long term

3

analysis, performs the feasibility solution. On that basis it finds out the optimization solution,

4

where cycle charging strategy is considered as priority. During this cyclic charging it serves the

5

load demand and store in battery unit. 3. Study Area

6 7

The proposed model is considered to be tested in the rural areas of State of Jharkhand located in

8

Eastern India.

9

3.1 Description of study area

10

Precisely some rural areas viz. Pastmara, Tukipur, Bhurahi, Ganduba, Rangamatia villages,

11

under Phatehpur Gram panchayat, Tundi Block, Dhanbad District located within the extent

12

between 23°44ˈ26.35ˈˈ to 24°28ˈ06.28ˈˈN and 86°15ˈ03.58ˈˈ to 87°07ˈ01.78ˈˈ E are considered

13

as study area. As per census of 2011, 1008 number of people resides here in 188 household over

14

an area of approximately 303 ha [23]. Reason for choosing this area is theses villages are located

15

in remote area and having huge amount of landmass area including dispersed households,

16

cultivated area, forest area, availability of various resource considering micro hydro resource,

17

huge variety of crops and animals. Based on census population data from 1991 to 2011 an

18

estimation of energy is done for this area and the model is proposed [28].

19

3.2 Logistic growth model for predicting future population

20

The logistic law of growth states that system always grows exponentially until carrying

21

capacity characteristic in the system is approximated. Then the rate of growth slows down

22

and finally saturates, and generates the characteristic curve of S-shape having following

23

equation structure [29].

24

P t  

25

Where P(t) is the population, t is year of interest , α is the rate parameter, β is the location

26

parameter and κ is the asymptotic value that limits the function value and provides the specific

27

level of the growth process. When the population of P(t) reaches κ, this leads to sluggish

28

growth rate upto zero after being saturated [30].

29

In equation (10), β may be considered as − toα, in order to replace location parameter β by to. In



(10)

1  e  t  

10

ACCEPTED MANUSCRIPT

1

population growth it is important to define a parameter Δt as the length between 10 to 90

2

percent of the saturation level κ of growth process. From equation (10), when P(t) is equal to

3

0.1 κ and 0.9 κ, Δt = (ln81)/α is obtained. With the background of time-series data the 3

4

parameter logistic model can then be define as follows [31].

5

P t  

6

Where r  ln  81 / t . In this paper, curve fitting toolbox of MATLAB is used to find out the

7

three-parameter values based on population data available. Further, using those parameter

8

values, prediction of population growth in study area is made. It is found that total population

9

will be approx 1200 at the year 2040 (Fig. 4.a)



(11)

r  to  t 

1 e

10

3.3 Model inputs to HOMER corresponding to study area resources

11

The load profile of the study area is estimated corresponding to power consumed by

12

kWh/day/capita in Jharkhand state. As per 2011 census, presently 31.9 million people of

13

Jharkhand consume power is 835 kWh/day [23]. Accordingly total estimated population energy

14

to be consumed by the community is considered as 335 kWh/day with a 42.71 kW peak demand

15

for this analysis. This load demand is predicted considering two major season summer season

16

(April – Sept) and winter season (Oct-March). This electrical load is taken from the village data

17

resource management and self generated HOMER software by the help of national renewable

18

energy sources. This process is the area of research of pre HOMER analysis for demand

19

assessments (Fig. 4.b).

20

21 22

Fig. 4 (a) Average daily load profile of Jharkhand, India in hourly basis (b) Futuristic population

23

estimation using Logistic growth model. 11

ACCEPTED MANUSCRIPT

1

3.3.1 Solar energy

2

Daily average radiation and monthly total radiation of Jharkhand, obtained through National

3

renewable energy laboratory of India is considered as the solar data of the study area and used as

4

input to HOMER (Table 1) [24]. It is observed that average solar radiation is 4-7.8 kWh/m2/day

5

throughout the year. Due to the clear weather, direct and diffuse radiation on this region is

6

appropriate for solar Photovoltaic cells. The capital, replacement and operating and maintenance

7

(O&M) cost is considered as $1667, $1667 and $19.38/year for the search space of 0 to 50kW in

8

HOMER analysis. The cost and detailed dimension of photovoltaic cells is taken from local

9

manufacturers and distributors on September 2016.

10

Table 1 Daily average radiation and monthly total radiation in Jharkhand, India [24] Month January February March April May June July August September October November December TOTAL

Clearness index 0.398 0.443 0.562 0.736 0.809 0.753 0.604 0.554 0.551 0.56 0.417 0.384

Daily Average Radiation (kWh/m2/day) 4 4.6 5.9 7.5 7.8 7 5.7 5.5 5.7 5.8 4.2 3.8

Monthly total radiation (kWh/m2/day) 120 138 177 225 234 210 171 165 171 174 126 114 2025

11 12

3.3.2 Wind energy

13

The wind speed profile of study area is considered as 3.8-7 m/s (Fig. 5a). Average monthly wind

14

speed and corresponding power generation in Jharkhand, India is plotted in Fig. 5b. Wind data

15

for this region is obtained from national institute of wind energy [25, 27]. Due to the moderate

16

climate and hilly area stagnant flow of 4 m/s is considered. Wind turbine capital cost and

17

replacement costs include shipping, tariffs, installation, and dealer mark-ups are considered as

18

$1200, $1200 and $18/ year. The hub height of wind turbine is assumed as 17 m for the search

19

space quantity of 0 to 5. The cost of each component and dimensions is taken from the Ministry

20

of new and renewable energy of India [25].

12

ACCEPTED MANUSCRIPT

1 2

Fig. 5 (a) Monthly wind speed of Jharkhand, India (b) Average monthly wind speed plot and

3

power generation in Jharkhand, India [25, 27]

4 5

3.3.3 Hydro kinetic energy

6

In this region the hydro kinetic plant requires heavy components and suitable water supply which

7

is available in half of year and merely less in other months in these rained hilly areas. The initial

8

capital cost for microhydro (i.e. hydrokinetic) system, is considered as $1283.33/kW and O&M

9

cost as $44.05/kW. Life time is considered as 25years. The components and equipment costs are

10

interpolated from the manufacturers’ quotations on September 2016.

11

3.3.4 Biomass

12

Availability of huge amount of forest area as well as cattle fields and animals habitat in study

13

area, a variety of crop and its residue is available in this rural area at a low cost at approximately

14

a rate of $0.55/kg which is being further processed in biomass generator plant as shown in

15

Eq.(5). The biomass generator of 1kwh is considered for the system for the size 9kW having

16

capital cost, replacement cost, O&M cost as $917,$917 and $0.020 respectively and for the size

17

6kW having capital cost, replacement cost, O&M cost is $742, $742 and $0.015 respectively.

18

Search Size of the biodiesel generator is 6kW & 9kW. The cost of bio-fuel generator is taken as

19

per the standard resources and estimates from local contractors on September 2016.

20

3.3.5 Biogas

21

Accordingly, very ease to get to animal manure nearly 600-700tonnes/day is available in the

22

study area. To assess this energy animal manure is processed as shown in the Eq.(7),

23

decomposition in biogas plant produces energy. Methane gas generic generator is added for the 13

ACCEPTED MANUSCRIPT

1

system having predefined fuel curve of fuel cost $0.25/litre. Search space for the biogas

2

generator is 6kW & 9kW.

3

3.3.6 Convertor

4

A device needed to convert AC & DC components for energy flow to grid is converter. The

5

capital cost, replacement cost & O&M cost is $465, $465 and $9.00. Operational hours is 15year,

6

input search space is 0 to 50kW. From the previous literature survey and HOMER library

7

resources the cost is incorporated according to the study area, accordingly cost is estimated for

8

battery on September 2016.

9

3.3.7 Battery

10

Li-ion 1kwh battery is used of 7.4V, capital cost, replacement cost & O&M cost is $80, $80 and

11

$8. The range of the number of batteries in the string is 2 and search space is 0, 50, 100 and 150.

12

3.3.8 Sensitive Parameter

13

The key variables for the input of sensitive parameter is bio-diesel (methane and biomass) price

14

$0.95, $1, $1.5 and $2, wind speed 4, 5, 6 and 7m/s. These parameters are considered for the

15

sensitive analysis, which are the input to search space.

16

4.

17

In this study, proposed HERS combination is simulated and optimized in HOMER

18

corresponding to the study area of Jharkhand, India. Model optimization and sensitive outcomes

19

are discussed in following sections. In this analysis, economic aspect and electricity generation

20

with cost parameter is also considered including environmental aspects.

Result and Discussion

21

Generally, during analysis HOMER performs numbers of simulation. In this study, out of

22

these, HOMER displays 12 optimal outcomes for final assessment in Table 2 considering

23

sensitive parameters. These 12 cases are basically generated and simulated according to the input

24

sensitive parameter considering biodiesel fuel price and wind speed. Outcome shows that the

25

architecture of PV array is 10kW, wind turbine is 3kW, biomass/biogas generator is 16kW, Li-

26

ion battery is 300kW and converter with cyclic charging is 30kW. Above combinations are

27

obtained corresponding to the wind speed range of 4 to 4.5m/sec, biodiesel price range of

28

$0.95/liter to $2/liter, levelised cost of energy (COE) range of $0.36/kWh to $0.68/kWh, total net

29

present cost (NPC) range of $1067361 to $561148 (Table 2).

14

1

Table 2 Optimal result obtained in HOMER considering sensitive parameter of proposed HRES model Sensitivity Architecture Cost Bio Wind velocity PV G3 Gen5 LI Converter COE Operating Fuel Dispatch NPC ($) Price (kW) cost ($) Average (kW) (Nos) (kW) ASM ($) (m/s) ($/L) 0.95 4 10 3 16 300 30 CC 0.355 561147.6 38846.3 0.95 5 -5 16 300 40 CC 0.358 564980.4 39886.9 0.95 6 -3 16 300 30 CC 0.352 556741.4 39794.9 0.95 7 -3 16 300 30 CC 0.335 528910.6 37642.1 1 4 10 -16 300 30 CC 0.383 604444.7 42473.9 1 5 -5 16 300 40 CC 0.373 589074.9 41750.7 1 6 -3 16 300 30 CC 0.368 580889.3 41662.9 1 7 -3 16 300 30 CC 0.349 551752.4 39409.0 1.5 4 10 3 16 300 30 CC 0.518 818267.8 58735.6 1.5 5 -5 16 300 30 CC 0.521 822467.1 60164.3 1.5 6 -3 16 300 30 CC 0.521 822929.1 60385.7 1.5 7 -3 16 300 30 CC 0.493 779193.1 57002.5 CC- cyclic charging, G3- 3kW wind turbine, Gen5 indicates biodiesel generator, Li ASM is Li-ion battery

15

Electricity Production (kWh) Initial capital ($)

Gen5

PV

G3

LI ASM

58962 49342 42292 42292 55362 49342 42292 42292 58962 44692 42292 42292

108620 111604 112172 105472.8 114260 111604 112156 105488.8 108492 111576.3 112140 105440.8

18186.2 ---18186.2 ---18186.2 ----

6238.5 21573.2 20664.9 28034.1 -21573.2 20664.9 28034.1 6238.5 21573.2 20664.9 28034.1

33393.3 31605.3 30238.7 32415.4 31123.1 31685.1 30203.0 32434.5 33079.9 31514.2 30081.6 32225.8

ACCEPTED MANUSCRIPT

1

From the Table 2 it is clear that with increase in wind speed (in a range of 4-6 m/s), NPC

2

decreases and annual electric generation increases. However, at 7m/s win speed electric

3

generation decreases to minimum. On the other hand, if wind speed is kept constant by varying

4

biodiesel price, it is observed that with the increase in biodiesel, NPC and COE also increase

5

(Fig. 6a & 6b). Optimal solution is found out within search space and by considering sensitive

6

variable (i.e. biodiesel fuel price and wind speed) along with other parameters like COE, fuel

7

cost, and renewable fraction (RF). It is observed that minimum value of COE is obtained as

8

$0.33 at biodiesel/biogas price of $0.95 and 7 m/sec wind speed at a RF value of 14.Whereas,

9

maximum value of COE is obtained as $0.68 at biodiesel /biogas price of $2.0and 4m/sec wind

10

speed at a RF value of 10 (Fig. 6b & 6d). Therefore, it is observed that system having wind

11

turbine-biodiesel-battery combination produce optimal result in higher wind speed (4.5 – 7.0

12

m/s), whereas solar-biodiesel-battery combination able to produces optimal result in lower wind

13

speed. Moreover, in solar-biodiesel-battery combination if total footprint value increases with

14

decrease in NPC, whereas RF and COE shows moderate values.

15 16 17 18

Fig. 6 Optimal system (a) based on NPC ($) (b) based on COE ($) (c) based on total footprint (d) based on RF.

16

ACCEPTED MANUSCRIPT

1

Sensitive variables are further represented through surface plotted along with one primary

2

and one secondary variable (Fig. 7a). Here NPC is considered as primary (represented as colored

3

band) and capacity storage (discrete points) is considered as secondary variable. From this plot it

4

is found that at 4 m/sec wind speed and $1.48/liter biodiesel price the capacity storage reached

5

its maximum value as 116 kW. This shows that at moderate sensitivity parameters and at an

6

average value of load demand (i.e. electrical load input by the user to HOMER) the excess

7

generated power is stored in the battery. In Fig. 7b, annual electric generation is considered in

8

the surface plot and superimposed by NPC. At the wind speed of 5.0 m/sec, the electric

9

generation values are comparatively higher with a NPC range of $39887 to $78777. Whereas, at

10

wind speed is 7 m/sec, electric generation values reaches its highest value and NPC varies within

11

$37642 to $74602 (Fig. 7b). This phenomenon indicates maximum cash expenditure towards the

12

biodiesel generator. Annual electric consumption surface plot superimposed by COE shows that

13

consumption of electricity at the peak demand is severed by wind energy when wind speed is

14

more than 5.0 m/sec. At this region superimposed values of COE are nearly reaches its minimum

15

value (Fig. 7c). In the surface plot of CO2 emission superimposed by total footprint, a huge plot

16

area is covered with very less amount of CO2 emission. At 4 m/sec wind speed total footprint is

17

found maximum as 11000 and for rest of the wind speed values the total footprint value is found

18

minimum as 2000 (Fig. 7d). Therefore, analysis shows that in order to produce more power,

19

higher cost biodiesel should be used which may increase CO2 emission. In addition, battery

20

storage also shows moderate value corresponding to the higher price biodiesel. However, battery

21

storage capacity found more corresponding to the biodiesel of moderate price. On the other hand,

22

with the increase in wind speed, electricity generation and its consumption during peak time is

23

more. At the same time there is less CO2 emission which is suitable for environment.

17

ACCEPTED MANUSCRIPT

1 2 3 4

Fig. 7 Surface plot (a) based on NPC ($) and capacity storage (b) based on annual electric production (kWh) and total operating cost ($) (c) based on annual electric consumption and COE ($) (d) based on CO2 emission (kg/year) and total footprint.

5

In Fig. 8, variation of COE and RF for different Biodiesel Fuel price are shown

6

corresponding to different wind speeds. It is observed that in general COE and RF have

7

increasing trend with increase in biodiesel fuel price except the case for wind speed equal to 4

8

m/s. In this case, RF showed quite dispersed value. It may happen due to low value of wind

9

speed. As a result, generated power was not sufficient to fulfill the load requirement. In

10

consequence to that HOMER intercepted the rest of the energy components in order to meet the

11

requirement. As a result COE increased, and RF showed dispersed value corresponding to the

12

biodiesel fuel price.

18

ACCEPTED MANUSCRIPT

1 2 3

Fig. 8 Variation of COE and RF for different Biodiesel Fuel price ($/L) corresponding to (a) 4m/s wind speed (b) 5m/s wind speed (c) 6m/s wind speed (d) 7m/s wind speed

4

At the beginning of this section it is mentioned that generally, during analysis HOMER

5

performs numbers of simulation. Further, using different combination of architecture optimal

6

values obtained in Table 2 are used for five different cases as mentioned in methodology section

7

viz. case (a) through case (e), and analyzed considering sensitive parameters. Out of these best

8

sensitive results corresponding to case (a) through case (e) is depicted in Table 3. It is found that

9

case produces best result having combination of 10 kW of PV array, three numbers of 3kW wind

10

turbine, biodiesel generator of 16kW, one number of hydrokinetic turbine, 150 numbers of

11

battery string size and 30kW of convertor having cyclic charging. The total electric generation is

12

found maximum in case (a) as 133045 kWh/yr. Total NPC and CO2 emission are found

13

minimum as $563000.4 and -370.6 kg/yr respectively in case (a) due to lesser hours of operation

14

of biodiesel generator. In this group, case (d) is performed as the second best case having 19

ACCEPTED MANUSCRIPT

1

combination of 10 kW of PV array, three numbers of 3kW wind turbine, biodiesel generator of

2

16kW, 150 numbers of battery string size and 30kW of convertor having cyclic charging. Here,

3

total NPC, electric generation and CO2 emission are found as $561147.6, 133045 kWh/yr and -

4

370.6 kg/year respectively. Case (c) is the least effective case having combination of 16kW

5

biodiesel generator, 150 numbers of battery string size and 30kW of convertor having cyclic

6

charging. In this case, total NPC is found maximum among all the cases as $633739.6. It also

7

produces least electric among all the cases as 131766 kWh/yr. This signifies that stand alone

8

electric generation plant using bio diesel fuel is not economically cost effective, as well as it is

9

not able to fulfill electric load demand completely.

10

Total production and total cost values of each components of case (a) through case (e)

11

considering sensitive parameters are depicted in Table 4. It is found that in case (a) contribution

12

towards the electricity generation of biodiesel generator is 65%, PV array is 10.9%, wind turbine

13

generator is 3.7% and battery generation is 20% which signifies that Biodiesel generator is

14

fulfilling the maximum electrical load.

20

1 2

Table 3 Best sensitive results obtained using different combination of architecture optimal values obtained in Table 2 and analysed for case (a) through case (e) considering sensitive parameters Com binat ion

PV Array (kW)

Wind turbine (kW)

Bio generato r (kW)

(i)

(ii)

(iii)

(iv)

Nos. of Hydro kinetic turbine (v)

Batter y (String size)

Converter (kW)

Levelised COE ($/kWh)

(vi)

(vii)

(viii)

CO2 emissio n (kg/year ) (ix)

Total Cost ($)

Annualize d Cost ($)

Total Generatio n (kWh/yr)

Total Consump tion (kWh/yr)

(x) (xi) (xii) (xiii) 563000. a 10 3 16 1 150 30 0.3563 -370.6 43550.5 133045 122231 4 581696. b 10 -16 1 150 30 0.3681 -389.9 44966.7 132462 122234 0 633739. c --16 -150 30 0.4011 -449.8 49022.5 131766 122209 6 561147. d 10 3 16 -150 30 0.3551 -370.6 43407.2 133045 122231 6 579843. e 10 -16 -150 30 0.3669 -389.9 44853.4 132462 122234 2 * Negative value in CO2 indicates the less amount of CO2 emission, when the same capacity of conventional energy plant was supposed to emit. ** Levelised COE indicated the COE including paying Govt. tarrif

3

Table 4 Total production and total cost values of each components of case (a) through case (e) considering sensitive parameters Combina tion

(a)

(b)

(c)

(d)

(e)

compone nts

PV array

Wind Turbine

Hydroki netic

Biogas/ Bio mass

PV array

Hydro kinetic

Biogas/ Bio mass

PV array

Biogas/ Bio mass

Biogas/ Bio mass

PV array

Wind Turbine

Biogas/ Bio mass

Total productio n (kWh/yr)

18186.2

6238.5

112.0

180620

18186.2

112

114276

18186.2

114276

131766

18186.2

6238

108620

Total cost

19175.3

4799.0

1852.8

451636

19175.3

1852.8

475130. 6

19175.3

475130. 6

548202.3

19175.3 3

4799.0

451635.9

Hours of operation (hrs/yr)

4384

5983

37

6790

4384

37

7143

4384

7143

8275

4384

5983

6790

4 21

ACCEPTED MANUSCRIPT

1

Accordingly as per the futuristic growth curve, incremental population leads to increase in

2

electricity load demand which are calculated and simulated on HOMER software and tabulated

3

in Table 5 in consolidated form. Due to the incremental demand requirement, the combination

4

changes with the parameter of each component to accomplish the electric demand in the year

5

(2022-2031). Here PV of 25kW, 5 numbers of 3kW of wind turbine, 6 numbers of hydrokinetic

6

turbines and bio fuel generator of 32kW capacity are found optimum. With the incremental value

7

170948 kWh/yr in total renewable energy generation the total cost increases to $589089, the

8

decrease of COE is $0.247 and annual cost is $33648 with respect to previous year.

9

The years (2032-2041) are also following same trend. The COE, total cost, annual cost for

10

(2032-2041) are found $0.242, $604375 and $34413 respectively, which are achieved in

11

combination consists of 25kW PV, 8 numbers of 3kW wind turbine, 6 numbers of hydrokinetic

12

turbine, 32kW of bio-fuel generator.

13

The COE, total cost, annual cost for (2042-2051) are found $0.241, $605539 and $34503

14

respectively, which are achieved in combination consists of 25kW PV, 8 numbers of 3kW wind

15

turbine, 6 numbers of hydrokinetic turbine, 32kW of bio-fuel generator. Out of these, it is found

16

that combination of year (2042-2051) produces lowest COE.

17

From the above exercise it is found that, combination of more varieties of different renewable

18

energy system are better to fulfill future energy demand. In general, combination having PV-

19

wind-hydrokinetic-biofuel generator produces optimal power corresponding to future demands

20

with less cost of energy to be paid by the consumer. However some other combinations are

21

producing more power, although in such cases, power storage will increase COE. It is also found

22

that even if Annual cost is remains same for different years, these combinations are able to

23

produce more energy.

24 25

Table 5 Total cost, annual cost and total generation of each combination respective to futuristic electric load demand Electric load 335 kWh/day (2012-2021) Combination

PV

Wind

HK

Bio

COE ($/kWh)

Total Cost ($)

Annual cost($)

10 10 10 --

3 --3

1 1 ---

16 16 16 16

0.3563 0.3681 0.4011 0.3551

563000.4 581696.0 633739.6 561147.6

43550 44966.7 49022.5 43407.5

22

Total generation (kWh/yr) 133045 132462 137166 133045

ACCEPTED MANUSCRIPT

10 25 --25 25 25 25 -25 25 25 25 -25 25

--

-16 0.3669 579843.2 Electric load 506.19 kWh/day (2022-2031) 5 6 32 0.247 589089 10 8 32 0.264 630418 -8 32 0.272 648489 10 -32 0.361 862889 --32 0.376 897695 Electric load 528.9 kWh/day (2032-2041) 8 6 32 0.242 604375 -6 32 0.248 620415 -8 32 0.266 665365 8 -32 0.358 894003 --32 0.373 932861 Electric load 531.1 kWh/day (2042-2051 predicted) 8 6 32 0.241 605539 -6 32 0.248 622008 -8 32 0.266 666496 8 -32 0.357 896252 --32 0.373 935534

44853.4

132462

33648 40017 42420 55292 59377

170948 153827 156152 207764 199340

34413 36768 43726 57977 62097

179018 170792 160350 195672 207805

34503 36891 43813 58151 62304

179303 171105 160597 212849 208309

1 2

5

3

In this study, a hybrid energy renewable system combination is proposed for simulation and

4

optimization in HOMER corresponding to a rural area of Jharkhand, India, where large amount

5

of electric energy is required and huge potential of resources for hybrid energy renewable system

6

is available. In this analysis, economic aspect and electricity generation with cost parameter are

7

also considered including environmental aspects. Detailed findings of the same are discussed as

8

follows.

9

(1)

Conclusion

A hybrid energy renewable system model is proposed by combination of non

10

conventional energies viz. solar, wind, hydro kinetic, biomass and bio gas which are

11

analyzed in HOMER software.

12

(2)

Simulation of this model is performed for the present condition (2012-2021) first.

13

HOMER produces 12 optimal outcomes for final assessment by considering sensitive

14

parameters viz. biodiesel fuel price ($0.95/liter, $1/liter, $1.5/liter, $2/liter) and wind

15

speed (4m/sec, 5m/sec, 6m/sec, 7m/sec).

16 17

(3)

Optimized results are also evaluated considering total net present cost, cost of energy, annual electric generation, renewable fraction and CO2 emission. 23

ACCEPTED MANUSCRIPT

1

(4)

It is observed that proposed system for the present year (2012-2021) including wind

2

turbine-biodiesel-battery combination produces optimal result at higher wind speed (4.5

3

– 7.0 m/s), whereas solar-biodiesel-battery combination is able to produce optimal result

4

at lower wind speed (< 4.5 m/s). Moreover, in solar-biodiesel-battery combination, total

5

footprint value increases with decrease in net present cost. However, renewable fraction

6

and cost of energy shows moderate values.

7

(5)

Therefore, analysis shows that in order to produce more power, higher cost of biodiesel

8

should be a good option, but it may increase CO2 emission. In addition, in this situation

9

battery storage also shows moderate value. However, battery storage capacity is found

10

more corresponding to the moderate price of biodiesel. On the other hand, with the

11

increase in wind speed, electricity generation and its consumption during peak time is

12

more. At the same time there is less CO2 emission which is suitable for environment.

13

(6)

It is observed that renewable fraction value is less at lower wind speed, which indicates

14

wind speed has less contribution towards hybrid energy renewable system during base

15

load demand. However, it is better to be use for peak demand.

16

(7)

Further, using different combination of architecture obtained from numbers of

17

simulation performed by HOMER, five different cases are considered for further

18

analysis viz. (a) solar-wind-hydrokinetic-bioenergy (b) solar- hydrokinetic-bioenergy

19

(c) solar-bioenergy (d) bioenergy and (e) solar-wind- bioenergy.

20

(8)

Case (a) produces best result having combination of 10 kW of PV array, three numbers

21

of 3kW wind turbine, biodiesel generator of 16kW, one number of hydrokinetic turbine,

22

150 numbers of battery string size and 30kW of convertor, having cyclic charging.

23

Therefore, it can be concluded that Case (a) is able to meet the electric load requirement

24

of the study area.

25

(9)

On the other hand Case (c) produces comparatively ineffective result. This signifies that

26

stand alone electric generation plant using bio diesel fuel is not economically cost

27

effective, as well as it is not able to fulfill electric load demand completely.

28

(10) On accounting environmental factor case (a) and (d) emits very low amount of carbon

29

emission. These results might be helpful to reach 2°C temperature phenomenon targeted

30

to protect environmental degradation.

24

ACCEPTED MANUSCRIPT

1

The above exercise is performed corresponding to the projected electric load demand for the

2

future upto year 2051. In order to increased efficiency of the system with comparatively less cost

3

for different combinations of hybrid renewable energy system is simulated. From this exercise it

4

is found that, combinations of more varieties of different renewable energy system are better to

5

fulfill future energy demand. In general, combination having PV-wind-hydrokinetic-biofuel

6

generator produces optimal power corresponding to future demands with less cost of energy to

7

be paid by the consumer. The value of COEs ($) and Total Generations (kWh/yr) are 0.3563 and

8

133045; 0.247 and 170948; 0.242 and 179018; 0.241 and 179303 for the year (2012-2021),

9

(2022-2031), (2032-2041) and (2042-2051) respectively. However some other combinations are

10

producing more power, although in such cases, due to power storage COE will increase. It is also

11

found that even if Annual cost is remains same for different years, these combinations are able to

12

produce more energy. As compared to the year (2012-2021) to (2042-2051) the optimized

13

combination cost of generation of electricity is respectively minimized and amount of electric

14

generation is increased without much effect on environment. Thus hybrid renewable energy

15

systems in the above mentioned combinations are preferable for implementation in rural part of

16

India.

17

Acknowledgement

18

Authors would like to acknowledge the authority of IIT (ISM), Dhanbad, Jharkhand, India for

19

carry out this study. Authors would like to acknowledge the authority of Dhanbad Municipality,

20

Jharkhand, India for sharing the Census data. Authors would like to acknowledge to all vendors

21

and suppliers for sharing cost of different components required for this research.

22

References

23

[1]

Accessed on 17 Nov 2016.

24 25

[2]

28

International Energy Agency World energy outlook 2015. https://www.iea.org. Accessed on 17 Nov 2016.

26 27

REN21 Renewable energy policy network for 21st century. http://www.ren21.net.

[3]

Renewable energy from Wikipedia, the free encyclopedia. https://en.wikipedia.org/ wiki. Accessed on 17 Nov 2016.

25

ACCEPTED MANUSCRIPT

1

[4]

on 17 Nov 2016.

2 3

International Energy Agency Key World Energy Statistics. http://www.iea.org. Accessed

[5]

Renewable energy from Wikipedia, the free encyclopedia. https://en.wikipedia.org/ wiki/

4

List_of_countries_by_electricity_production_from_renewabe_sources. Accessed on 17

5

Nov 2016.

6

[6]

reports.html. Assessed on 17 Nov 2016.

7 8

[7]

Sweeka Meshram, Ganga Agnihotri and Sushma Gupta. Modeling of grid connected dc linked PV/Hydro hybrid system. Electrical and Electronics Engineering: An International

9

Journal (ELELIJ) 2013; Vol 2, No 3.

10 11

Govt. of India Ministry of power central electric authority. http://www.cea.nic.in/

[8]

V.V. Tyagi, S.C. Kaushik, S.K. Tyagi. Advancement in solar photovoltaic/thermal

12

(PV/T) hybrid collector technology. Renewable and Sustainable Energy Reviews 2012;

13

16: 1383– 1398

14

[9]

Yang Hong xing, Zhou Wei, Lou Chengzhi. Optimal design and techno-economic

15

analysis of a hybrid solar–wind power generation system. Applied Energy 2009; 86: 163–

16

169

17

[10]

Hong xing Yang, Wei Zhou a, Lin Lu, Zhao Hong Fang. Optimal sizing method for

18

stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm.

19

Solar energy 2008; 82: 354-367

20

[11]

wind power generation system, Solar Energy 2007; 81: 76–84

21 22

Hong xing Yang, Lin Lu, Wei Zhou. A novel optimization sizing model for hybrid solar-

[12]

M.S. Ismail, M. Moghavvemi, T.M.I. Mahlia. Techno-economic analysis of an optimized

23

photovoltaic and diesel generator hybrid power system for remote houses in a tropical

24

climate. Energy Conversion and Management 2013; 69: 163–173

25

[13]

Karim Daud, M Ismail, Walid Kukhun, Marwan M Mahm. Simulation of a Hybrid Power

26

System Consisting of Wind Turbine, PV, Storage Battery and Diesel Generator: Design,

27

Optimization and Economical Evaluation. International Journal of Energy Engineering

28

2013; 1: 51-61

29 30

[14]

Belum V. S. Reddy, S. Ramesh, A. Ashok Kumar , S. P. Wani, R. Ortiz ,H. Ceballos , T. K. Sreedevi. Bio-Fuel Crops Research for Energy Security and Rural Development in

26

ACCEPTED MANUSCRIPT

1

Developing Countries Bioenergy. Res. 2008; 1:248–258 DOI 10.1007/s12155-008-9022-

2

x

3

[15]

Jessica Hoffmann, Souman Rudra, Saqib S. Toor, Jens Bo Holm-Nielsen, Lasse A.

4

Rosendahl. Conceptual design of an integrated hydrothermal liquefaction and biogas

5

plant for sustainable bio energy production. Bio resource Technology 2013;129: 402–410

6

[16]

Harro von Blottnitz, Mary Ann Curran. A review of assessments conducted on bio-

7

ethanol as a transportation fuel from a net energy, greenhouse gas, and environmental life

8

cycle perspective. Journal of Cleaner Production 2007; 15: 607-619

9

[17]

D. Kumar and S. Sarkar. A review on the Technology, Performance, design optimization,

10

Reliability, Techno-Economics and Environmental impacts of hydrokinetic energy

11

conversion systems. Renewable & Sustainable Energy Reviews, ELSEVIER, 2016;

12

Volume 58: Pages 796–813, DOI: 10.1016/j.rser.2015.12.247

13

[18]

Bioresource Technology 2002; 83: 47–54

14 15

[19]

R. Chenni, M. Makhlouf, T. Kerbache, A. Bouzid. A detailed modeling method for photovoltaic cells. Energy 2007; 32: 1724–1730

16 17

Peter McKendry. Energy production from biomass (part 2): conversion technologies.

[20]

Renugadevi.V, Margaret Amutha. W, Dr. Rajini.V. A Novel Improved Fused Converter

18

Based Hybrid System with MPPT Control for Rural Telephony, International Journal of

19

Emerging Technology and Advanced Engineering, Website: www.ijetae.com (ISSN

20

2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 5, May 2014): 837-843

21

[21]

Yang Hong Xing, Zhou Wei, Lou Chengzhi. Optimal design and techno-economic

22

analysis of a hybrid solar–wind power generation system, Applied Energy, Elsevier 2009;

23

Vol- 86: pp-163–169

24

[22]

Eduard Muljadi, C. P. Butterfield, Pitch-Controlled Variable-Speed Wind Turbine

25

Generation. IEEE Transactions On Industry Applications January/February 2001; Vol-37:

26

NO.1

27

[23]

Directorate of Census Operations Jharkhand, (2014) District Census Handbook Dhanbad,

28

Village and Town Wise Primary Census Abstract (PCA) Census of India 2011, Ranchi,

29

Jharkhand,Series-21PartXIIB,http://www.censusindia.gov.in/DigitalLibrary/

30

publication2011. aspx. Accessed at 10 May 2015

27

ACCEPTED MANUSCRIPT

1

[24]

INDIA SOLAR RESOURCE MAPs,

Solar Energy Centre, Ministry of New and

2

Renewable Energy, Government of India, http://mnre.gov.in/sec/solar-assmnt.htm

3

Accessed 12 July 2016.

4

[25]

Small Wind Energy and Hybrid Systems Programme, Ministry of New & Renewable

5

Energy, Government of India, http://mnre.gov.in/schemes/offgrid/small-wind/, Accessed

6

on 12 July 2016

7

[26]

india.nic.in/ Accessed 12 July 2016.

8 9

[27]

National Institute of Wind Energy Ministry of New and Renewable Energy, Government of India http://niwe.res.in/department_wra_lwms.php. Accessed at 23 July 2016.

10 11

Bureau of Energy efficiency, Government of India, Ministry of power, http://www.bee-

[28]

Directorate of Census Operations Jharkhand, 2014. District Census Handbook Dhanbad,

12

Village and Town Wise Primary Census Abstract (PCA) Census of India 2011, Ranchi,

13

Jharkhand,

14

DigitalLibrary/publication2011.aspx, date of access 10/12/2015.

Series-21

Part

XII-B,

http://

www.

censusindia.

gov.in/

15

[29]

Stone R (1980) Sigmoids, Bulletin in Applied Statistic 7, 59-119

16

[30]

Nakicenovic., N., 1988. U. S. Transport Infrastructures, in Cities and Their Vital Systems, J. Ausubel and R. Herman, eds., National Academy Press, Washington, D. C.,

17 18

[31]

thesis. Lappeenranta University of Technology, Finland.

19 20

Apenteng, O. O., 2009. Demographic Modelling of Human Population Growth. Master’s

[32]

Alotto, Piergiorgio, Massimo Guarnieri, and Federico Moro. Redox flow batteries for the

21

storage of renewable energy: A review. Renewable and Sustainable Energy Reviews

22

2014; 29: 325-335.

23

[33]

Masoud Sharafi, Tarek Y. ElMekkawy, Eric L. Bibeau. Optimal design of hybrid

24

renewable energy systems in buildings with low to high renewable energy ratio.

25

Renewable Energy 2015; 83: 1026-1042.

26

[34]

hybrid system with flywheel energy storage. Renewable Energy 2015; 78: 398-405.

27 28

[35]

31

G Bekele, B Palm. Feasibility study for a sustainable solar-wind-based hybrid energy system for application in Ethiopia. Applied Energy 2010; 87(2): 487-495.

29 30

Makbul A.M. Ramli, Ayong Hiendro, Ssennoga Twaha. Economic analysis of PV/diesel

[36]

Y Himri, A Boudghene Stambouli, B Draoui, S Himri. Techno-economical study of hybrid power system for a remote village in Algeria. Energy 2008; 33(7): 1128-1137. 28

ACCEPTED MANUSCRIPT

1

[37]

alternative to grid extension in Bangladesh. Energy 2010; 35(7): 3040-3047.

2 3

S Nandi, Himangshu Ranjan Ghosh. Prospect of Wind-PV-battery hybrid system as an

[38]

S. Bahramara, M. Parsa Moghaddam, M.R.Haghifam. Optimal planning of hybrid

4

renewable energy systems using HOMER: A review. Renewable and Sustainable Energy

5

Reviews 2016; 62: 609–620.

6 7

[39]

Obama B. The irreversible momentum of clean energy. Science. 2017 Jan 13;355(6321):126-9.

8 9 10 11

29