Optimization of grid independent hybrid PV–diesel–battery system for power generation in remote villages of Uttar Pradesh, India

Energy for Sustainable Development 17 (2013) 210–219

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Energy for Sustainable Development

Optimization of grid independent hybrid PV–diesel–battery system for power generation in remote villages of Uttar Pradesh, India Nitin Agarwal a,⁎, Anoop Kumar b, Varun b a b

Deptt. of Mechanical Engineering, Moradabad Institute of Technology, Moradabad, Uttar Pradesh, India, 244001 Deptt. of Mechanical Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India, 177005

a r t i c l e

i n f o

Article history: Received 8 July 2012 Revised 8 February 2013 Accepted 8 February 2013 Available online 7 March 2013 Keywords: Multi objective Optimization Hybrid system CO2 emissions Uttar Pradesh

a b s t r a c t In this paper a multi-objective optimization model is developed to determine the best size of grid independent solar–diesel–battery based hybrid energy system. The primary objective is to minimize life cycle cost and secondary objective is to minimize CO2 emissions from the system. These objective functions are subjected to the constraints imposed by the power generated by the system components, reliability of the system and state of charge of the battery bank. The decision variables included in the optimization process are the total area of PV arrays, number of PV modules of 600 Wp, number of batteries of 24 V and 150 Ah, diesel generator power and fuel consumption per year. A computer program is build up in C programming language to determine the specifications of hybrid system components. The proposed method has been applied to an un-electrified remote village in Moradabad district of Uttar Pradesh, India. Results shows that the optimal configuration of an autonomous system is PV area of 300 m2, 60 PV modules of 600 Wp, 160 batteries of 24 V and 150 Ah and a diesel generator power of 5 kW. This system involves PV penetration of 86% and a diesel fraction of 14% having LCC of $110,546 for 25 years, fuel consumption of 1150 l/year and CO2 emissions of 0.019 tCO2/capita/year. © 2013 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

Introduction Rural electrification is very important for social and economical development of rural areas. It can improve the quality of life of rural people by providing electricity for lighting of homes, shops, community centers and public places in villages. More than 18,000 villages in India are still un-electrified and use kerosene for lighting. Many others, who receive electricity, face constant blackouts and uncertainty of a steady energy supply. India is blessed with high solar radiation levels. The daily global radiation is approximately 5 kWh/m 2/day with sunshine ranging between 2300 and 3200 h per year in most parts of the country. But standalone photovoltaic systems cannot generate electricity for substantial proportion of time throughout a day. Moreover, PV systems require large storage arrangements which make them a costly option. Consequently, solar PV system integrating diesel generator with battery storage can be a good solutions to these problems. Such systems formed by integrating non-conventional energy sources with the conventional sources are called hybrid energy system (HES) as shown in Fig. 1. HES can be a major source of electricity for rural areas where electricity grid connection is not possible.

⁎ Corresponding author. Tel.: +91 9359397806; fax: +91 591 2452327. E-mail address: [email protected] (N. Agarwal).

Massive research has been carried out on solar based hybrid energy systems with respect to performance and optimization, and other related parameters of implication. Said and El-Hefnawi (1998) developed a computer program in FORTRAN language to determine the optimum configuration of hybrid PV–diesel generator power system. The objective is to minimize the cost of the system as a function of PV array area and number of storage batteries. Habib et al. (1999) presented an optimization method of a hybrid PV–wind energy system which can be used to satisfy the electricity requirements of a given load distribution. The model is applied to satisfy a constant load of 5 kW required for cathodic protection in offshore platforms. Lazou and Papatsoris (2000) performed the life cycle cost analysis of PV system for residential houses in different Mediterranean and European countries. Techno-economic analysis of a PV–diesel hybrid power generation system installed in a bungalow complex in Elounda, Crete is presented by Bakos and Soursos (2002). Kamel and Dahl (2005) assessed the economics of hybrid power systems versus the present diesel generation technology in a desert agricultural area in Egypt by using optimization software. Singal et al. (2007) predicted the potential of available renewable energy sources in Neil Island. A 50 kW solar power plant and 400 kW diesel generating plant are the available supply of electricity in the island. They recommended that the existing sources can be substituted by 100 kW biogas power plants, 150 kW biomass gasification plant and 200 kW solar PV systems, with no adverse effect on the environment and socio-economic life of the habitants. Lagorse et al. (2009)

0973-0826/$ – see front matter © 2013 International Energy Initiative. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.esd.2013.02.002

N. Agarwal et al. / Energy for Sustainable Development 17 (2013) 210–219

211

Solar Panels Charge controller

Inverter

Battery Bank Diesel Generator

Fig. 1. Photovoltaic–diesel–battery based hybrid energy system.

recommended a hybrid system integrating a PV, a battery and a fuel cell to improve commercialized stand alone street lightening system consisting of PV cells and a battery. The objective is to determine the optimal size of the system at minimum cost. Bala and Siddique (2009) used genetic algorithm technique to obtain an optimal design of a PV–diesel based hybrid mini-grid system for supplying electricity to a fishing community in an isolated island Sandwip in Bangladesh. Mbaka et al. (2010) compared hybrid PV–diesel system, standalone PV and standalone diesel generator using net present value technique. A case study of small village in north Cameroon is considered for the computation of energy costs and break even grid distance. They concluded that the most optimized dimension of hybrid system is obtained at renewable energy fraction in the range of 82.6–95.5%. Rehman and Al-Hadhrami (2010) studied a PV–diesel hybrid power system with battery backup for a village in Saudi Arabia. The village is being supplied with diesel generated electricity only. The sensitivity analysis indicates that at a diesel price less 0.6$/l, the hybrid system become more economical than the diesel only system. An optimal sizing model based on iterative technique is suggested by Kaabechea et al. (2011) to determine the optimal size of different components of hybrid PV–wind power generation system using a battery bank. The model takes into account the sub-models of the hybrid system, the Deficiency of Power Supply Probability (DPSP) and the Levelised Unit Electricity Cost (LUEC). Rajkumar et al. (2011) performed the techno-economical optimization of hybrid PV–wind–battery system using Adaptive Neuro-Fuzzy Inference System. The optimized system is also able to supply electricity without any renewable sources for a longer period, while adapting to the desired LPSP. In the literature discussed above, the optimum dimensions of the hybrid energy system are attained by minimizing the system cost function. But, the environmental issues, like GHG emissions from these systems have not been taken into account. Therefore in the paper a simulation model has been developed to optimally design

HES with primary objective to minimize life cycle cost (LCC) and secondary objective to minimize CO2 emissions from the system. Problem description Ahraula is an un-electrified village in Munda Pandey block of Moradabad district in Uttar Pradesh. There are 91 households in the village having total population of 538 persons. 90% of the population lives without electricity and depends on kerosene oil for lighting. Therefore the objective of this paper is to determine the best configuration of a reliable PV–diesel–battery based energy system for the electrification of the remote villages so that an optimum trade-off can be maintained between the LCC and CO2 emissions from the system. The decision variables incorporated in the optimization model are the total area of PV arrays, number of PV modules of 600 Wp, number of batteries of 24 V and 150 Ah, diesel generator power (kW) and annual fuel consumption. Electricity consumption The environmental conditions of Ahraula village are shown in Table 1. A survey is carried out in the village to assume load demand for a day during different months of the year. The questionnaire used for survey is shown in Appendix A. Load assumed for each household is three CFLs bulbs of 15 Watt each and two electric fans of 40 Watt each. Twenty CFLs bulbs of 20 Watt are used for lighting the streets. The primary objective is to minimize LCC, therefore, energy efficient end use devices are all cost effective. This is why CFLs and 40 W fans are the part of the design. The entire year is divided into four groups of months and load demand per day anticipated for each group is shown in Fig. 2. Area under each curve has been determined using Simpson's one third rule to obtain the number of units required per day which is shown in Table 2. The load profile of the village is

Table 1 Environmental conditions of Ahraula village. Environmental factors

Unit

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Average temperature Average morning relative humidity Average evening relative humidity Average precipitation Average dew point

°C % % mm °C

15 83 41 22.8 7.0

17 78 35 20.3 8.1

24 71 30 15.2 10.2

30 55 21 10.1 10.7

34 49 24 15.2 13.9

34 61 36 68.5 18.9

32 82 61 200.6 23.9

31 85 64 200.6 23.9

30 81 51 121.9 21.7

27 76 33 17.7 15.0

22 78 31 2.54 10.6

17 82 38 10.1 6.7

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N. Agarwal et al. / Energy for Sustainable Development 17 (2013) 210–219 Feb & Nov March, April, Sept & Oct

Ahraula village

Average daily load demand (kWh/day)

Jan & Dec May, June, July & Aug

Load demand (kW)

11 9 7 5 3 1 1

3

5

7

9

11

13

15

17

19

21

Agwanpur village

250 200 150 100 50 0 Jan

Feb

Mar Apr

23

May Jun

Jul

Aug Sep

Oct

Nov Dec

Months

Hours of the day Fig. 3. Electricity consumption pattern of Ahraula and Agwanpur village. Fig. 2. Typical daily load demand of the village.

also compared with a nearby electrified village named Agwanpur, having total population of 562 persons. It has been observed that the average daily load demand of Agwanpur is more than Ahraula village because of the use of electrical appliances like TV, radio etc for entertainment. However, the electricity consumption pattern of both the villages is almost similar. It is shown in Fig. 3. Mathematical model In order to determine the optimum dimensions of HES, the individual system components are to be modeled first and accordingly their combination can be estimated to meet the electricity load demand. The design parameters of different components of HES are given below.

Battery bank

Cbat DOD Ibat Nu SOC n nb ηb ηinv ηC

Battery nominal capacity (Ah) Depth of discharge of the Battery Battery charging current (A) Number of units required per day (kWh/day). State of charge of the battery Number of battery backup days. Life cycle period of battery bank Battery efficiency Inverter efficiency Combined efficiency of inverter and battery.

Solar PV system Diesel generator

Am Gh Gdh Gd Gbh Gb Gr Gt NOCT Ppv tcell ta ηpv θZ θ α β ψ ω ρ

Total area of PV modules (m 2) Solar radiation on the horizontal surface (kWh/m 2/day) Diffuse radiation on horizontal surface (kWh/m 2/day) Diffuse radiation on tilt surface (kWh/m 2/day) Beam radiation on horizontal surface (kWh/m 2/day) Beam radiation on tilt surface (kWh/m 2/day) Reflected radiation on tilt surface (kWh/m 2/day) Total radiation on tilt surface (kWh/m 2/day) Normal operating cell temperature (°C) Hourly power output of solar PV modules (kWh) PV cell temperature (°C) Ambient temperature (°C) PV system efficiency Zenith angle (degree) Angle of incidence (degree) Latitude of the location (degree) Surface tilt angle (taken as 28 degree) Surface azimuth angle (degree) Hour angle (degree) Ground reflectivity

AD PDG

Annual diesel consumption Diesel generator power (kW)

Solar radiation modeling The PV modules are generally tilted at an angle to enhance the amount of radiation captured and to minimize the reflection losses. The radiation data are normally available for horizontal surface and must be converted into tilt global radiation. The total solar radiation on the inclined surface is obtained by adding the components of diffuse, beam and reflected radiation on an inclined surface: Gt ¼ Gd þ Gb þ Gr

ð1Þ

Diffuse radiation Diffuse solar radiation on an inclined plane is calculated by using Perez model (Perez et al., 1986; Perez et al., 1987; Perez et al., 1990).

Table 2 Electricity consumption (kWh per day) during different months of the year. Month

Jan.

Feb.

March

April

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

kWh per day

70

82

156

156

206

206

206

206

156

156

82

70

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This model corresponds to a comprehensive analysis of the isotropic diffuse, circumsolar and horizon brightening radiation by means of empirically derived coefficients. Then, the diffuse radiation on the PV plane is given by 2

Gd ¼ Gdh: ð1−F1 Þ: cos ðβ=2Þ þ Gdh : F1 : ða=bÞ þ Gdh : F2 : sinðβÞ

ð2Þ

The terms ‘a’ and ‘b’ account for the incidence angle of the sun on the considered inclination. F1 and F2 are circumsolar and horizon brightness coefficient respectively depending upon the sky clearness and brightness. Beam radiation Beam radiation on an inclined surface can be calculated by the following equation: Gb ¼ Gbh : ðcosθ=cosθZ Þ

cosθ ¼ sinδ:sinα:cosβ−sinδ:cosα:sinβ:cosψ þ cosδ:cosα:cosβ:cosω ð4Þ þ cosδ:sinα:sinβ:cosψ þ cosδ:sinβ:sinψ:sinω Here δ is solar declination whose value lies between − 23.45° and 23.45° and θZ is the zenith angle given by cosθZ ¼ cosδ:cosα:cosω þ sinδ:sinα

  Battery bank power ¼ ðn  N u Þ= DOD  ηc

ð10Þ

Where, ηc = ηb × ηinv Most batteries used in hybrid systems are of the deep-cycle lead– acid type. The selection of appropriate dimension of the battery bank requires an inclusive analysis of the battery's charge and discharge requirements, including load, output and pattern of the solar or alternative energy sources. If the power produced from the PV system is greater than the load during a given time step, then the excess electricity is stored in the battery. The battery's state of charge (SOC) is simulated during the charging process by Yang et al. (2007): SOCðt þ 1Þ ¼ SOCðt Þ:ð1−σ ðt ÞÞ þ

Ibat ðt Þ:Δt:ηch C bat

ð11Þ

σ(t) is hourly self discharge rate taken as 0.02%. ηch is battery charging efficiency taken as 65–85%. Here Ibat is the charging current calculated by

P pv − Ibat ðt Þ ¼ V bat

P load

. ηinv

ð12Þ

V bat

If the power produced by the PV system cannot meet the load demand, then the battery is discharging. Battery SOC during discharging is given by ð6Þ SOCðt þ 1Þ ¼ SOCðt Þ:ð1−σ ðt ÞÞ−

Where ρ is the ground reflectivity taken as 0.2.

Ibat ðt Þ:Δt C bat

ð13Þ

Battery charging current is given by

Photovoltaic module efficiency The hourly power output of solar PV modules depends on the solar irradiance, the ambient temperature and the PV module tilt angle. It is expressed as (Deshmukh and Deshmukh, 2008): P pv ¼ ηpv :Am :Gt

The battery bank power can be computed as follows (Agarwal et al., 2012)

ð5Þ

Reflected radiation The hourly solar radiation reflected by the ground is given by Gr ¼ ð1=2Þ: ρ: Gh : ð1–cosβÞ

Battery bank model

ð3Þ

Angle of incidence can be computed by the equation given by Duffie and Beckman (1980)

213

P load

Ibat ðt Þ ¼

. ηinv

V bat

−

P pv V bat

ð14Þ

ð7Þ

Where ηPV is PV module efficiency and Am is total area of PV modules. PV module efficiency is calculated by the following equation (Notton et al., 2010):

In a physical battery, there are energy losses due to various reasons. However, this model considers all energy losses while the battery is charging; the discharge efficiency is set to be 100%.

h

i ηpv ¼ ηref 1−λ t cell –t cell;ref þ μ log Gt =Gt;ref

DC/AC inverter

ð8Þ

Where ηref is reference PV module efficiency, tcell is the PV cell temperature, tcell,ref is reference cell temperature (25 °C), Gt,ref is the reference solar irradiance (1000 W/m 2), λ is temperature coefficient and μ is solar irradiance coefficient. tcell,ref, ηref, λ and μ are the data given by solar PV manufacturer. λ and μ depend on the material of PV module. Evans (1981) took λ = 0.0048/°C and μ = 0.12 for silicon. On the basis of the study carried out by Bergene and Lovik (1995), Hegazy (2000) considered λ and μ as 0.004/°C and 0 respectively. tcell can be calculated by the following equation. t cell ¼ t a þ ½ðNOCT−20Þ=800:Gt

ð9Þ

where NOCT is the normal operating cell temperature given by PV cell manufacturer.

The inverter must be sufficiently capable to seize the maximum power of AC loads (Agarwal et al., 2012). Therefore it is selected 10% higher than the total AC load. Since the total AC load of the village is 11.78 kW, therefore thirteen inverters of rated power 1000 W are sufficient to serve the purpose. Therefore the inverter specifications will be 1000 W, 24 VDC and 220 VAC. Diesel generator model A quadratic equation is used to compute the rate of fuel consumption ‘RF’ by the diesel generated of rated power ‘PDG’. The equation is given by: 2

RF ¼ A:P DG þ B:P DG þ C

ð15Þ

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Where A, B and C are constants, determined from the manufacturer's data for fuel consumption at zero load, half load and full load conditions. System reliability model In the present analysis, the reliability of the system is expressed in terms of Loss of Load Probability (LLP) and System Autonomy (SA). LLP is defined by the following equation (Ekren et al., 2009):

LLP ¼ ∑PSt =∑PC t

ð16Þ

four times during the life of a PV system. Usually, these costs occur in specific years and the entire cost is included in those years. The salvage value (S) of HES is its net worth in the final year of the life-cycle period. In the present analysis, salvage value is taken as 20% of original cost of the equipment. However, this rate can be modified depending on other factors such as obsolescence and condition of equipment. Formulas used for computing present worth under different conditions are given below: (i) The formula for the single present worth (P) of a future sum of money (F) in a given year (N) at a given discount rate (I) is N

P ¼ F=ð1 þ I Þ Here PSt is the total power shortage because of not meeting the demand, PCt is the total power consumption and t is the simulation time period. System Autonomy is computed by the following equation (Celik, 2003):

ð19Þ

(ii) The formula for the uniform present worth (P) of an annual sum (A) received over a period of years (N) at a given discount rate (I) is h i −N =I: P ¼ A 1−ð1 þ IÞ

SA ¼ 1−ðH LOL =H T Þ

Here HLOL is the total number of hours during which loss of load occurs and HT is the total hours of operation. Using the above equations, a set of system configurations that satisfies the reliability requirements can be obtained and the optimal one is consequently selected on the basis of minimum LCC and CO2 emissions. Cost analysis based on life cycle cost (LCC) concept

(iii) The formula for the modified uniform present worth of an annual sum (A) that rises at a rate (E) over a period of years (N) at a given discount rate (I) is n h io N P ¼ A ð1 þ EÞ=ðI−EÞ  1−ð1 þ EÞ=ð1 þ IÞ

ð21Þ

Data for size and cost analysis of the system is presented in Table 3.

The concept of LCC is developed as the benchmark of system cost analysis in this study. Many researchers have used LCC as major criterion for cost analysis of hybrid energy systems (Gupta et al., 2007; Muralikrishna and Lakshminarayana, 2008). LCC gives the total cost of system — including all expenses incurred over the life of the system. There are two reasons to perform an LCC analysis. First reason is to compare different power options, and second is to determine the most cost-effective system designs. An LCC analysis permits the designer to analyze the effect of using different components with different reliabilities and lifetimes. The LCC analysis consists of finding the net present worth of all the expense expected to occur over the reasonable life of the system. The life-cycle cost of HES can be calculated using the formula

LCCHES ¼ C þ OMnpw þ Rnpw –Snpw

ð20Þ

ð17Þ

ð18Þ

Here OM is the operation and maintenance cost, R is the replacement cost and S is the salvage value. The subscript ‘npw’ indicates the net present worth of each factor. The capital cost (C) of a hybrid system consists of the initial capital expenditure for equipments, the system design, engineering, and installation. This cost is taken as a single payment occurring in the initial year of the project, regardless of how the project is financed. Operation and maintenance cost (O & M) includes the sum of all yearly scheduled operation and maintenance costs. O & M costs take account of such items as an operator's salary, inspections, property tax, insurance, and all scheduled maintenance. Annual fuel consumption cost of diesel generator is included in O & M cost. Replacement cost (R) is the sum of all repair and equipment replacement cost expected over the life of the system. The replacement of a battery is a good example of such a cost that may occur three to

Life-cycle carbon dioxide emissions Carbon dioxide (CO2) is the principal greenhouse gas, mostly produced by the combustion of fossil fuels. The quantity of CO2 produced per liter of the fuel used depends upon the carbon content of fuel. For diesel, this value usually falls in the range of 2.4 to 2.8 kg/l (Bernal-Agustín et al., 2006). In the case of PV system, no CO2 is emitted during use, since no fossil fuels are burnt. However, there

Table 3 Data for sizing and cost analysis of hybrid energy system. Description

Value

PV system lifetime (N) Battery life time Diesel generator life time Initial cost of 600 Wp PV module PV system installation cost O & M cost of PV system per year Inflation rate Discount rate Cost of 24 V & 150 Ah battery Annual O & M cost of battery bank Number of battery backup days Maximum battery SOC Minimum battery SOC Cost of charge controller Inverter cost 5 kW diesel generator cost Diesel generator service period Diesel generator service cost Annual O & M cost of diesel generator Diesel cost per liter Desired loss of load probability Desired system autonomy

25 years 5 years 15 years $1000 10% of initial capital cost 2% of initial capital cost 5% 6% $200 2% of initial capital cost 2 1 0.35 $80 $80 $1000 4 years 12% of initial cost 5% of initial capital cost $0.9/l 0.2 0.8

N. Agarwal et al. / Energy for Sustainable Development 17 (2013) 210–219

215

Start

Input parameters for solar PV: θ, θz, β, δ, ρ, NOCT, tcell,ref , µ, Gt,ref Input the average solar radiation data of the location (kWhm -2day-2) Calculate PV system efficiency (ηpv) Calculate the average solar radiation data on the tilted surface Initialize the PV area, A=0 m2 Input the load demand, D(n) Calculate power supplied from Solar PV of given area, Ppv(n) (Kwhday-1)

Select the configuration with minimum LCC and CO2 emissions

Y Ppv(n).apv + Pdg(n).adg >= D(n)

N Calculate power required from diesel generator to met the load demand, Pdg(n) (kWhday-1) Determine the system configuration 1) Number of mono crystalline PV modules required. 2) Number of batteries of 24V and 150 Ah 3) Diesel generator power (kW). Stop

N

LLP ≤ (LLP)Desired Y

N

SA ≥ (SA)Desired

Y N

(SOC)min≤ SOC ≤ (SOC)max Increase the PV area by 100 m2: A = A+20

Y Calculate 1) CO2 emissions 2) LCC of HPGS

Fig. 4. Flow chart of the optimization process.

are CO2 emissions in the manufacture and transport of the PV system to the point of use and for disposing it at the end of use. Of these emissions, most originate in manufacturing and depend on the parameters like type of solar cell material, quality and grade of silicon, PV module efficiency as well as irradiation conditions. In the current analysis mono-crystalline silicon solar cells are considered whose CO2 emissions are taken as 30 g per kWh (Krauter and Rüther, 2004). Different chemicals can be combined to make

batteries. “Deep-discharge” lead–acid batteries offer the best balance of capacity and cost for PV systems, and they are the most common batteries used in HESs (Lagorse et al., 2008). CO2 emitted by lead–acid battery is high during manufacturing but low during operation and maintenance in comparison to other batteries (Denholm and Kulcinski, 2003). A 24 V, 150 Ah lead acid battery weighs 24 kg and emits 4 kg of CO2 per kg of battery weight during its lifespan (Sullivan and Gaines, 2010).

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N. Agarwal et al. / Energy for Sustainable Development 17 (2013) 210–219

300 Demand(kWh/day) PV Area=140 m²

Average daily energy (kWh/day)

250

PV Area=160 m² PV Area=180 m² PV Area=200 m²

200

PV Area=220 m² PV Area=240 m²

150

PV Area=260 m² PV Area=280 m² PV Area=300 m²

100

PV Area=320 m² PV Area=340 m²

50

PV Area=360 m² PV Area=400 m²

Dec

Nov

Oct

Sep

Aug

Jul

Jun

May

Apr

Mar

Feb

Jan

0

Months Fig. 5. Electricity produced by photovoltaic arrays of different sizes versus demand.

(ii) System reliability constraints

Multi objective optimization

LLPb ¼ ðLLPÞDesired SA >¼ ðSAÞDesired

The optimization model has two objectives viz. minimization of total life cycle cost of the system and minimization of CO2 emissions from the system. So the objective functions are defined by: Mimimize ð1Þ

(iii) Constraint on battery state of charge

n X ðTotal LCCÞi

SOCmin b ¼ SOCðtÞb ¼ SOCmax

i¼1

ð2Þ

n X ðCO2 emissionsÞ i¼1

i

Where ‘n’ is number of components of HES. In the present analysis three components are considered i.e. solar PV modules, diesel generator and battery bank. Both the objective functions are subjected following constraints: (i) Power production per day from HES must be greater than or equal to the daily power demand P pv ðnÞ:apv þ P dg ðnÞ:adg >¼ DðnÞ Where D(n) is the average demand at day ‘n’. Ppv(n) and Pdg(n) are photovoltaic and diesel generator power production at day ‘n’ and apv and adg denotes the size of PV system and diesel generator.

The maximum SOC of the battery is 1 and the minimum SOC is depends on the maximum DOD, SOCmin = 1 − (DOD)max. In the present analysis maximum DOD is taken as 65%. The flow chart of the optimization process is shown in Fig. 4. Operational strategy The following operational strategy is prescribed for the control of HES: (i) If the solar radiation is adequate, PV power can be directly used to supply the load and excessive power can charge the batteries. (ii) If the solar radiation is not adequate, the power is supplied by the batteries. (iii) If solar radiation is not enough and batteries SOC is low, the power is supplied by the diesel generator.

CO2 emission (tCO2/capita/year)

0.16

Average shortage (kWh/day)

100

80

60

40

20

0 100

140

180

220

260

300

340

380

Photovoltaic area (m2) Fig. 6. Average daily electricity shortage for photovoltaic of different areas.

0.12

0.08

0.04

0 100

140

180

220

260

300

340

Photovoltaic area (m2) Fig. 7. Average daily CO2 emissions for photovoltaic of different areas.

380

N. Agarwal et al. / Energy for Sustainable Development 17 (2013) 210–219

200

217

220 LCC (Diesel generator) LCC (PV system) LCC (total)

200

160

Life cycle cost (1000$)

120

80

40

System Autonomy=0.9

180

System Autonomy=0.8

160

140

120

100 0.1

0 100

140

180

220

260

Photovoltaic area

300

340

0.2

0.3

0.6

0.7

0.8

0.9

1

can be seen that minimum LCC is obtained at system autonomy of 0.8 when renewable to total energy ratio is 0.8. Fig. 10 shows solar radiation and battery state of charge in the month of December and January. It illustrates that SOC of the battery bank is within the prescribed limit in the worst month of December and January. It has been determined that Battery SOC is between 0.35 and 1.0 throughout the year when the number of battery backup days are two. Fig. 11 shows the effect of Loss of Load Probability on LCC of the system at different battery backup days. LCC is significantly influenced by the number of battery backup days, but this cannot be considered less than two days, since it will violate the constraint forced by battery SOC. Conclusions The analysis of hybrid energy system identifies that Ahraula village in Uttar Pradesh is a potential candidate for the use of PV–battery–diesel system for electricity generation. LCC analysis using present worth method is carried out to explore the economic behavior of the hybrid system. The objective is to find out the optimum dimensions of hybrid system components such that the LCC and CO2 are minimized. A case study of an un-electrified village having 91 household is considered to examine the applicability of the optimization model. The specifications of the an optimized system (with LLP=0 and SA=1) are 300 m 2 PV area, 60 PV modules of 600 Wp, 160 batteries of 24 V and 150 Ah, 5 kW diesel generator and 1150 l fuel consumption per year. This configuration reduces LCC of the system by 40% and CO2 emissions by 78% Solar radiation

Battery SOC

8

Solar radiation (kWh/m2-day)

A multi-objective optimization model is developed to determine the optimum size of the solar–diesel–battery based hybrid power generation system. The primary objective is to minimize LCC and secondary objective is to minimize CO2 emissions. A case study of an un-electrified village having 91 household is considered to test the applicability of the model. The average load demand per day is shown in Table 2. The demand of electricity is higher in the months of May, June, July and August. Fig. 5 shows electricity produced by photovoltaic arrays of different sizes versus demand. It clearly shows that the PV area less than 160 m 2 does not satisfy the load demands for all the months of the year. However, with PV area of 340 m 2, the load demand is assured for about 10 months. Standalone PV system without any electricity shortage is acquired at an area of 400 m 2. Fig. 6 shows average daily electricity shortage for photovoltaic of different areas. It indicates that the electricity shortage decreases as the penetration of PV system is raised; hence the diesel consumption for electricity generation is reduced. Therefore with the increase in incursion of PV array, the emissions of CO2 in the atmosphere decrease (Fig. 7). LCC analysis of the HES is presented in Fig. 8. It can be seen that the total life cycle cost of the system reduces as the PV array area increases from 100 m 2 to 300 m 2. At an area of 300 m 2 and diesel generator power of 5 kW, total LCC is minimum ($110,546) and average CO2 emissions are 0.019 tCO2/capita/year. This arrangement of HES saves $89,627 during the life cycle and reduces CO2 emissions by 0.24 tCO2/capita/year in comparison to standalone diesel generator system. If the area of PV system is further increased, the CO2 emissions is decreased but there is significant increase in lifecycle cost of the system. Since the villages have low income, so the configuration having minimum LCC is more significant than the one having minimum CO2 emissions. In India, about 56% of households use electricity for lighting, the rural–urban inequality is quite prominent. Merely 43.5% of rural households have access to electricity while the rest rely on kerosene while about 87.6% of the urban households use electricity for lighting (Purohit, 2009). The average kerosene consumption among household earning less than $80 per month is assumed to be 10 l per month (Rao, 2012). Therefore the CO2 emissions avoided from kerosene being replaced by electricity in Ahraula village is 27.3 tCO2/year. It is obvious that the drop in system autonomy will decrease the LCC of the system. This drift in reduction of LCC is shown in Fig. 9. It

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Fig. 9. Life cycle cost of the system at different system autonomy.

Fig. 8. Life cycle cost of the system (at SA = 1 and LLP = 0) for different areas of photovoltaic arrays.

Results and discussion

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Renewable to total energy ratio

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1 0.8

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Hours of the day Fig. 10. Solar radiation and battery state of charge in the month of December and January.

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3 day battery backup 2.5 day battery backup 2 day battery backup

100 1.5 day battery backup 1 day battery backup 0.5 day battery backup

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60 0%

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Percentage loss of load probability Fig. 11. Effect of loss of load probability on life cycle cost at different battery backup days.

when compared with standalone diesel generator. If cost is not the major limitation than one can select the configurations with higher incursion of PV system because in that case CO2 emissions will be significantly lower. The computer program developed for the analysis of the hybrid system can be used for sizing of PV–battery–diesel system for other locations having similar climatic conditions. Our future work in HES design optimization will include introducing the effect of PV array tilt angle on the system efficiency during different seasons of the year. It would also be an appealing research subject to consider the probabilistic performance analysis of the HES in conjunction with a system reliability assessment. The authors are also looking into new approaches for the optimization method. Techniques like Particle Swam Optimization and Simulated Annealing could significantly reduce the computing time and efforts. Acknowledgment Authors are thankful to all the villagers of Agwanpur and Ahraula village for their cooperation and participation in the survey. Appendix A Survey questions Village: Ahraula Block: Munda Pandey District: Moradabad 1) 2) 3) 4) 5) 6) 7) 8) 9) 10) 11) 12) 13)

Name of Family Head……………………………………………… Number of members in the family……………………………… Number of children below 5 years of age……………………………… Number of children above 5 years of age……………………………… Number of children going to school……………………………… Number of persons above 60 years of age……………………………… Source of income of the family……………………………… Monthly income of the family……………………………… Number of earning members in the family and their occupation……………………………… Normal wake up time……………………………… Normal time of going to work……………………………… Time to take lunch……………………………… How many members take lunch at home………………………………

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