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Optimal hybrid renewable energysystems for energy security: acomparative studyAnis Afzal a , Mohibullah Mohibullah a & Virendra Kumar Sharma ba Faculty of Engineering and Technology , Aligarh MuslimUniversity , Aligarh , Indiab Bhagwant Institute of Technology , Muzaffarnagar , IndiaPublished online: 15 Oct 2009.

To cite this article: Anis Afzal , Mohibullah Mohibullah & Virendra Kumar Sharma (2010) Optimalhybrid renewable energy systems for energy security: a comparative study, International Journal ofSustainable Energy, 29:1, 48-58, DOI: 10.1080/14786460903337241

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International Journal of Sustainable EnergyVol. 29, No. 1, March 2009, 48–58

Optimal hybrid renewable energy systems for energy security: acomparative study

Anis Afzala*, Mohibullah Mohibullaha and Virendra Kumar Sharmab

aFaculty of Engineering and Technology, Aligarh Muslim University, Aligarh, India; bBhagwant Instituteof Technology, Muzaffarnagar, India

(Received 30 August 2009; final version received 14 September 2009 )

A hybrid power system may be used to reduce dependency on either conventional energy or renewablesystems. This article deals with the sizing, generator running hours, sensitivity analysis, optimisation, andgreenhouse gas emission analysis of hybrid renewable energy systems (HRES). Two locations have beenselected where the feasibility of using different hybrid systems is studied for the same load demand. Onesite is the small remote community of Amini in the Lakshadweep Islands, located in southern India in theArabian Sea, where solar and/or wind energy is always available throughout the year to provide energysecurity. Another place is the rural township of Hathras, in the northern Indian state of Uttar Pradesh, whereagricultural biomass is found in abundance for the whole year. A comparative study has been made forthe two locations for the same load demand by simulating HRES. To achieve the goal of simulation, thehybrid optimisation model for electric renewables (HOMER) software of the National Renewable EnergyLaboratory, USA, is used. An optimisation model of a hybrid renewable system has been prepared whichsimplifies the task of evaluating the design of an off-grid/standalone system. After simulating all possiblesystem equipment with their sizes, a list of many possible configurations may be evaluated and sorted bynet present cost to compare the design options. An elaborate sensitivity analysis has been used for eachinput variable; the whole optimisation process is repeated to get simulated system configurations

Keywords: emission; energy security; hybrid energy; optimisation technique; renewable energy integra-tion; sensitivity analysis

1. Introduction

Energy is the ultimate factor responsible for industrial, agricultural, and living-standard growth.Its consumption is a parameter for judging the living standard and prosperity of a communityor country, which depend upon different factors, namely, access to energy sources, prices, cli-mate, income, and urbanisation level (Jiang and O’Neill 2004). The use of renewable energy (RE)technology has been rapidly increasing to meet growing energy demand. However, the main disad-vantage associated with standalone RE systems (RES) is their inability to provide energy securityand reliability due to their unpredictable, seasonal, and time-dependant natures. A standalonesolar photovoltaic (SPV) system cannot provide reliable power during non-sunny days, whereas

*Corresponding author. Email: anis_afzal@hotmail.com

ISSN 1478-6451 print/ISSN 1478-646X online© 2009 Taylor & FrancisDOI: 10.1080/14786460903337241http://www.informaworld.com

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a standalone wind system cannot deliver power at a constant load due to significant fluctuation inwind speed magnitude for high cut-in speed ranging from 3.5 to 4.5 m/s (Elhadidy and Shahid2004). Therefore, oversizing of the system becomes necessary to achieve reliability, which causesthe design to become expensive (Elhadidy and Shahid 2000). Although the initial cost of a solaror wind energy system (WES) is costlier than a diesel generator (DG) set for the same capacity,the operation and maintenance (O&M) costs are always lower than a DG set (Notton et al. 2006).

Over the last two decades, researchers have started looking for integration of different energysources, that is, combining a conventional generator powered by diesel/conventional fuel with arenewable source such as SPV, wind, or SPV/wind, known as hybrid renewable energy systems(HRES) (Lazarov et al. 2005). HRESs have become feasible alternatives for power production asthe strengths of both conventional and RES are considered. For isolated places like islands, HRESsare reliable and an economical way of power generation. Combination of wind and SPV into aHRES reduces fluctuation of power production, therefore greatly reducing energy requirements(Notton et al. 1996, Elhadidy and Shahid 2000). However, the HRESs are invariably installedwith storage batteries to meet peak demand and energy security in the absence of RE. To ensurethe energy security of an island, an artificial neural network model may also be useful to forecastenergy output from a DG grid-connected SPV system (Ashraf and Chandra 2004). In another study,the support vector machine regression algorithm has been suggested, which provided accuratepredictions of wind power and wind speed at 10-min intervals up to 1 h into the future, whilethe multilayer perception algorithm has been found accurate in predicting power over hour-longintervals up to 4 h ahead (Kusiak et al. 2009).

For study and proposal, a remote location is chosen, namely Amini Island, which is one ofthe clusters of 36 Lakshadweep Islands, located in southern India in the Arabian Sea at latitude11.1 ◦N and longitude 72.7 ◦E at 1 m above sea level (NASA website, 2008; http://www.nasa.gov).

There are DG grids of installed capacity 1034 kW and grid-interactive SPVs of 100 kW inAmini, which are insufficient for the future planned load (MNES 2004). In Amini Island, anyone of the RE sources is available throughout the year (i.e. either sufficient solar radiation orwind speed is available for the whole year). In June and July, if solar radiation is not sufficient,high wind speed is available to run the wind turbine (Table 1). The future plan is to construct

Table 1. Weather data.

Lakshadweep Islands Hathras

Amini Kavaratti Minicoy

Solar Wind Solar Wind Solar Wind Solarradiation speed radiation speed radiation speed radiation

Months (kWh/m2/day) (m/s) (kWh/m2/day) (m/s) (kWh/m2/day) (m/s) (kWh/m2/day)

January 5.68 3.1 5.72 3.2 5.8 3.8 3.77February 6.51 3.1 6.54 3 6.44 3 4.64March 7.05 3.5 7.03 3.2 6.9 2.9 5.49April 7.12 3.7 6.98 3.5 6.53 3.2 6.02May 6.17 4.3 6.01 4.2 5.65 4.6 6.26June 4.63 7.8 4.65 7.7 4.62 6.9 5.98July 4.9 7.2 4.9 6.8 4.99 5.7 4.97August 5.55 6.8 5.49 6.4 5.49 5.6 4.57September 5.93 5.3 5.91 5.2 5.95 4.9 4.83October 5.49 3.6 5.54 3.6 5.65 3.9 4.66November 5.44 3.2 5.44 3.2 5.31 3.2 4.05December 5.43 3.7 5.44 3.7 5.48 3.9 3.54Average 5.82 4.61 5.80 4.47 5.73 4.3 4.90

Source: NASA, USA.

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a wind–diesel hybrid system in Amini and other prominent islands of Lakhshadweep to ensureenergy security by 2010.

A different location, Hathras, is chosen at latitude 27.5 ◦N, longitude 77 ◦E, and elevation224 m near the cities of Mathura and Aligarh in the northern state of Uttar Pradesh, India. Majorcrops of the area are rice, wheat, pearl millet (botanical name: Pennisetum glaucum), sweetsorghum (botanical name: Sorghum vulgare), pulses, potatoes, etc. Being a big wholesale mar-ket of food stocks (paddy, rice, pulses, etc.), different types of biomass residue and husk arefound in abundance in and around Hathras. The average husk production from the rice mill isfound to be 187 kg/tonne of paddy (Ahiduzzaman 2007). Therefore, a large biomass generatormay be operative without any shortage of biomass supply with low transportation costs. It isalso economical to run a biomass generator at all capacities from 5 kW onwards (Kirubakaranet al. 2009).

Section 2 of this article contains the justification of ‘Selection of software’, Section 3 for‘Methodology’ used, Section 4 for ‘Control strategy of HRES’, Section 5 for ‘HRES simulation’,Section 6 for ‘Simulation results, Section 7 for ‘Discussion on results’, and finally Section 8 for‘Conclusion’.

2. Selection of software

The HRES chosen has been modelled for components and factors affecting its function. Bymeans of simulation and modelling, performance of a system has been analysed under differentoperating conditions. Methodologies are adopted for modelling system components of the HRESwith special reference to solar and wind energy (Deshmukh and Deshmukh 2008).

Some specialised software, available for simulation and optimisation hybrid systems, aredescribed below.

The hybrid optimisation model for electric renewables (HOMER) is one of the world’s mostpowerful and most widely used tools for designing hybrid renewable power systems. HOMERsimulates and optimises stand-alone and grid-connected power systems comprising any combina-tion of wind turbines, PV arrays, run-of-river hydro power, biomass power, internal combustionengine generators, microturbines, fuel cells, batteries, and hydrogen storage, serving both electricand thermal loads.

Since 1997, Mistaya Engineering Inc., Canada, has provided software engineering, documenta-tion, and technical support for HOMER, first for the US National Renewable Energy Laboratory,and now for HOMER Energy, a new company created to commercialise HOMER. The presentversion 2.19 of size 4 KB has been available for users since 14 June 2004; its licence expiresevery 6 months and can be renewed freely for an unlimited number of times (NREL, 2008; http://www.nrel/gov/homer). Mean annual and monthly relative error of 3% and 10%, respectively, arefound in its simulation results (Sheriff and Ross 2003).

It simulates the operation of a system by making energy balance calculations for each of the8760 hours in a year. For each hour, it compares the electric and thermal demand in the hour withthe energy that the system can supply in that hour, and calculates the flow of energy to and fromeach component of the system. For systems that include batteries or fuel-powered generators,the software also decides, for each hour, how to operate the generators and whether to charge ordischarge the batteries. It performs energy balance calculations for each system configuration thatit is required to consider. It then determines whether a configuration is feasible, that is, whetherit can meet the electric demand under the specified conditions, and estimate the cost of installingand operating the system over the lifetime of the project. The system cost calculations accountfor costs such as capital, replacement, O&M, fuel, and interest.

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After simulating all the possible system configurations, it displays a list of configurations, sortedby net present cost (also called lifecycle cost), that one can use to compare system design options.

When sensitivity variables are defined as inputs, the software repeats the optimisation processfor each sensitivity variable that is specified. For example, if wind speed is defined as a sensitivityvariable, it will simulate system configurations for the range of wind speeds that are specified.

The renewable energy technology software (RETScreen) was developed by the InternationalClean Energy Decision Support Centre, Canada. The software can be used to evaluate the energyproduction and savings, costs, emission reductions, financial viability, and risk for various typesof RE and energy-efficient technologies. A non-optimised SPV system may be simulated to findtechnical status, environmental impact, and financial viability by using the software (Afzal et al.2008).

The long-range energy alternatives planning (LEAP) software is a scenario-based energy–environment modelling tool which accounts for how energy is produced, converted, and consumedin a given region or economy under a range of alternative assumptions on population, economicdevelopment, technology, price, and so on.

Design, optimisation, and analysis of energy projects (energy PRO3) software may be used fordesigning, calculating conversion in a specific year, and operational economics. It also provides aplanning strategy for more years, investment details, and finance aspects. The software is designedfor conditions in European/western countries.

Softwares other than HOMER do not provide simulation with sensitivity and optimisation anal-yses. The data and simulation provided by other software packages are not applicable universally.Therefore, this software package is used for simulation in this study.

3. Methodology

In this analysis, HOMER provides a micro-optimisation model of off-grid power HRES withsensitivity analysis according to different criteria based on each component of the system and thetotal integrated system. The parameters of investment, cost of energy, consumption of energy/fuel,and system sustainability are considered as parameters (Notton et al. 2006, Rao 2007).

Cost of energy (COE) is the average cost per kilowatt hour of useful electrical energy producedby the system, which is given by the following equation:

COE = (Cann,tot − CboilerEthermal)/(Eprim,AC + Eprim,DC + Edef + Egrid,sales), (1)

where Cann,tot is the total annualised cost of the system ($/year), Cboiler the boiler marginal cost($/kWh) = 0, Ethermal the total thermal load served (kWh/year) = 0, Eprim,AC theAC primary loadserved (kWh/year), Eprim,DC the DC primary load served (kWh/year) = 0, Edef the deferrableload served (kWh/year) = 0, and Egrid,sales the total grid sales (kWh/year) = 0

The second term in the numerator is the portion of the annualised cost that results from servingthe thermal load. In systems that do not serve a thermal load (Ethermal = 0), this term will be equalto zero. Other terms Cboiler, Eprim,DC, Edef , and Egrid,sales are not applicable, hence their values arealso zero.

Hence,

COE = Cann,tot/Eprim,AC (2)

To get optimal configuration and control of HRES, an analysis of the multifactor function isconducted:

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• COE is to be kept minimum, which may be given by

min(COE = f (L, CUi , ni, COFi , QFi , RE, Ti, Pi, . . .)), (3)

where L is the energy consumption, CUi the overall cost of each unit, ni the number of unitsof the same type, COFi the cost of fuel, QFi the fuel consumption of the unit, RE the availablerenewable energy, Ti the lifetime of the unit, and Pi the rated power of the unit.

• System sustainability is the maximum stability or minimum number of supply interruption,which is given by

min(Err = f (Li, RE, Pi, SFmin, SFmax, . . .)), (4)

where Err is the number of supply interruptions, Li the load with its own priority, and SFmin,SFmax the minimum/maximum stored energy.

• If use of RE (REU) is maximum, the fuel consumption (FC) is minimum. The objective maybe represented by

max(REU = f (L, RE, Pi, SFmin, SFmax, . . .)), (5)

min(FC = f (L, RE, Pi, SFmin, SFmax, . . .)). (6)

From Equations (2) and (3), optimisation criterion combined to get a target function isgiven by

opt(min(COE), min(Err)). (7)

4. Control strategy of HRES

The main control strategy of a hybrid system is based on sizing optimisation of the system wherea part of the input cannot be controlled. The control may be centralised, scattered on the units, orcombined for the entire HRES. Generally, control is not meant for RE sources; it is applied to thegenerators with conventional fuel, batteries, and converter (combination of rectifier and inverter)independently or jointly.

A control system may be determined as passive or active according to its course of action. Mostof a passive system works on the ‘on/off’ principle. It is used with a simple HRES. On the otherhand, an active control system is used with a large HRES using a large number of components.The active system measures and calculates input data (energy flow, solar insolation, wind flow,etc.). The system is flexible and can work in different modes.

A programmable controller or software is suggested, which may be used to execute the functionof a control system. It may follow a control algorithm to keep the system maintaining activities insequence as shown in Figure 1. An SPV–WES–DG system is considered for the analysis. Powerproduced by a WES is required for monitoring, that is, if the voltage of wind generator is greaterthan a minimum set voltage, the DG set is stopped. If the voltage from the wind generator is lessthan the minimum set voltage, then voltage received by the converter is the sum of voltage fromthe WES and the DG set. The effects of tower shadow, wind shear, yaw error, and turbulencein a stand-alone wind–diesel system utilising a fixed-speed WES are required to consider powerquality (Fadaeinedjad et al. 2009).

The characteristic of lead–acid batteries used in hybrid solar–wind power generation systemsworks under very specific conditions. Sometimes it is very difficult to forecast when the energy willbe extracted from or supplied to the battery. The behaviour of a battery depends upon differentfactors like current rate, the charging efficiency, the self-discharge rate, as well as the batterycapacity. State of charge (SoC) of the battery may be statically analysed considering the hourly

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WES

PV

Charger

Battery

Is BatteryFull ?

TrickleCharge

Is State ofCharge<25% ?

Converter(Inverter)DC to AC

Load

Timer

Converter(Rectifier)AC to DC

Sum

Is voltage>min. volt?

No

DG

DGOff

No

Yes

No

Yes

Yes

DGOn

Figure 1. Control algorithm of the suggested HRES.

and the monthly variations as well as the probability distributions (Zhou et al. 2008). It is ameasure of the available capacity expressed as a percentage in reference to rated capacity inkilowatt hour and current (i.e. at the latest charge–discharge cycle) capacity. Knowing the amountof energy left in a battery compared with the energy it had when it was full gives the user anindication of how much longer a battery will continue to perform before it needs recharging(www.mpoweruk.com/soc.htm, 2009). The harmful effect of deep discharging of batteries isavoided by a setpoint SoC. When the batteries are fully charged, the absolute SoC is equal tothe maximum capacity of the battery bank. The SoC is constantly monitored; if the batteriesare discharged below a prescribed limit of SoC at 25%, the DG set starts. The timer is used tocount time for the measurement of SoC, as indicated in Table 2 for a 12V battery. The charger isgiven a signal between SoC ≥25% and <100% (batteries not fully charged) to restart the chargingprocess. The battery bank is also protected from overcharging by stopping the DG set and keepingit on trickle charge, rather than dissipating excess generated/stored energy in a dump load (Nottonet al. 2006). The changes in the control algorithm will be required if more than one DG set isused for the purpose of energy security, that is, a peak shaving unit may be used along with thepower transfer unit as used in Parc de la Verendrye, Quebec, Canada (Lautier et al. 2007). Thistechnique provides energy with high efficiency at a cheaper rate.

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Table 2. State of charge of a 12V battery.

SoC (%) 100 75 50 25 DischargedOpen circuit voltage 12.65 12.45 12.24 12.06 11.89 or less

Courtesy: Cadex Electronics Inc. Data 2007, Vancouver, Canada.

5. HRES simulation

The proposed HRES is to be installed for light and power loads in different schools located atAmini Island (Table 3). The equipment used in the hybrid system are selected for simulation asshown in Figure 2. For the sake of optimisation, the software HOMER chooses equipment andsizes of different systems such as SPV, WES, DG set, battery bank, and converter.

SPV and batteries are connected to a DC bus, whereas wind induction generator and DG areconnected to an AC bus. A converter combination acts as a coupling between the two buses to keepthe batteries always in charging mode directly through the DC bus or to supply the load through theAC bus. Different input variables are chosen for ‘solar resource inputs’ (Table 1): ‘wind resourceinputs’ (Table 1) and ‘diesel inputs’ (Price 0.6, 0.8, 1 $/l). Other variables are ‘economic inputs’(annual real interest rate 6%, project lifetime 25 years), ‘general control inputs’ (SoC 25, 75,

Table 3. Daily average load profile.

Hour Load (kW) Hour Load (kW)

00:00–01:00 5.240 12:00–13:00 83.61001:00–02:00 5.240 13:00–14:00 80.36002:00–03:00 5.240 14:00–15:00 78.04003:00–04:00 5.240 15:00–16:00 67.26004:00–05:00 6.860 16:00–17:00 42.12005:00–06:00 8.370 17:00–18:00 32.94006:00–07:00 25.630 18:00–19:00 28.71007:00–08:00 53.060 19:00–20:00 16.58008:00–09:00 68.750 20:00–21:00 12.45009:00–10:00 76.370 21:00–22:00 9.53010:00–11:00 79.280 22:00–23:00 8.51011:00–12:00 82.780 23:00–00:00 6.230

Source: TERI, India.Notes: Annual average: 880 kWh/day, annual peak: 148.9 kW, load factor: 0.246.

WindGeneratorWES 18

GeneratorDG

Primary Load880 kWh/d

149 kW peakSPV

_ Battery

ConverterAC Bus DC Bus

Figure 2. Equipments of HRES.

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100%), ‘emission inputs’ (emission penalty 0 $/tonne), and ‘constraints’ (as percentage of load:hourly load 10%; as percentage of renewable output: solar power output 25% and wind poweroutput 50%).

Similarly, in the second case of Hathras, as shown in Figure 2, the wind generator is replacedby a biomass generator and the simulation is carried out using suitable inputs and sensitivityvariables.

6. Simulation results

The simulation result of the HOMER software provides important details of the optimal hybridsystem, like size of the system component, total net present cost, COE, and GHG emission.

In the first case, a hybrid SPV–WES–DG system is proposed for an actual primary load demandof 880 kWh/day, 149 kW peak (Table 3) along with batteries (Vision 6FM200D, 12V, 200Ah,2.4 kWh), and converter forAmini Island. A DG set and battery bank are opted to maintain electricsupply if solar and/or wind energy are not able to supply the load sufficiently. In the secondscenario, An SPV–biomass generator–DG set, converter, and batteries (Surrette 6CS25P, 6V,1156Ah, 6.94 kWh) are considered for the same load demand in the different weather conditionsof Hathras (Table 1) for the sake of comparing the feasibility of the two energy systems. In thiscase also, energy security is given priority by using a DG set and batteries and not relying fullyon the SPV or biomass generator.

There are two choices of optimisation results obtained from the simulation, as mentionedbelow:

• Overall system ranking shows top-ranked system configurations according to net present cost.• Categorised ranking shows least cost system of each type.

The sensitivity results provide output data for several sensitivity cases of each component andvariable. Eight different wind speeds (average value 4.61, 5, 6, 10, 15, 20, 25, and 30 m/s) andthree different diesel prices (0.6, 0.8, and 1 $/l) are selected for sensitivity analysis. The softwareperforms a separate optimisation for each sensitivity case.

These results are indicated either in tabular or graphic form. In the tabular form, sensitivityresults consist of a list showing the least cost system for each sensitivity case. The breakdown ofthe system cost is as follows:

• the production and consumption of electrical energy by the system;• the operation of SPV, wind generator, and DG, if the system contains one;• the use and expected lifetime of the battery;• the quantity of emission of the pollutants; and• the hourly data to analyse those variables that are stored for each hour of the year

The optimisation result obtained from the simulation of the hybrid SPV–WES–DG systemsuggested for Amini Island is shown in Tables 4 and 5. It is also found in the result that electricalenergy production by WES is 1,130,671 kWh/year (93%), whereas it is 87,552 kWh/year (7%)by DG, for a wind speed of 4.61 m/s.

Sensitivity case: wind speed, 4.61 m/s; diesel price, 0.6 $/l; hub height, 25 m; SoC, 25%Sample calculation of COE for wind speed 4.61 m/s:

Primary load, Eprim,AC = 880 kWh/day

= 321, 200 kWh/year.

From Table 5, total annualised cost of the system, Cann,tot = 40, 083 $/year.

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Table 4. Simulation results of HOMER software for different sensitivity values of wind speed.

Optimal system configurationsOptimal

Wind Wind cost of Initial DG GHGspeed SPV generator DG Battery Converter energy capital running emission(m/s) (kW) WES-18 (kW) nos (kW) ($/kWh) ($) hours (kg/year)

4.61 — 10 100 168 100 0.125 159,600 903 76,6615 — 10 100 180 100 0.106 162,000 643 55,2076 — 10 100 156 100 0.076 157,200 310 26,838

10 — 6 100 96 100 0.038 113,200 99 805015 — 4 50 96 50 0.024 74,200 85 300020 — 5 — 96 50 0.022 74,200 0 025 — 4 — 96 50 0.020 66,200 0 030 — 4 — 96 50 0.020 66,200 0 0

Table 5. Cost breakdown calculations by HOMER.

Initial Annualised Annualised Annual Annual Totalcapital capital replacement O&M fuel annualised

Component ($) ($/year) ($/year) ($/year) ($/year) ($/year)

WES 80,000 6258 0 500 0 6758DG 16,000 1252 276 1806 17,467 20,801Battery 33,600 2628 6877 672 0 10,177Converter 30,000 2347 0 0 0 2347Total 159,600 12,485 7153 2978 17,467 40,083

From Equation (2),

COE = Cann,tot/Eprim,AC = 40, 803/321, 200

= 0.12479 $/kWh

≈ 0.125 $/kWh.

In the second case, the total energy produced is 300,161 kWh/year by biomass generator, when thebiomass price is 20 $/tonne. In Table 6, the optimisation result is shown, obtained from the simula-tion of one of the different sensitivity cases of the hybrid SPV–biomass–DG system considered forHathras.

Sensitivity case: biomass feedstock price, 20 $/tonne; diesel price, 0.6 $/l; SoC, 25%.

Table 6. Simulation results by HOMER software for different sensitivity values of biomass price.

Optimal system configurationsOptimal Biomass

Biomass cost of Initial generator GHGprice SPV Biomass DG Battery Converter energy capital running emission($/tonne) (kW) generator (kW) (kW) nos (kW) ($/kWh) ($) hours (kg/year)

20 — 60 — 60 25 0.189 87,500 5894 76.930 — 60 — 72 25 0.232 93,500 5748 76.540 — 60 — 72 25 0.276 93,500 5748 76.550 50 — 50 60 25 0.279 305,417 — 208,697

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7. Discussion on result

(1) In the first case of Amini Island, WES, SPV, DG set, battery, and converter are selected forsimulation purposes but equipment configured after simulation are WES, DG set, battery, andconverter. SPV is not configured at all in this simulation result as shown in Table 4.

(2) The result in Table 4 is found for eight different sensitivity variables of wind speed, dieselprice of 0.6 $/l, hub height 25 m, and setpoint SoC 25%.

(3) In the second case of mainland Hathras, SPV, biomass generator, DG set, battery, and con-verter are selected for simulation but the equipment configured after simulation are biomassgenerator, battery, and converter for biomass price 20, 30, 40, 50 $/tonne as shown in Table 6.SPV is not found at this sensitivity value of biomass price. When biomass price becomes50 $/tonne, SPV, DG set, battery, converter are configured, but biomass generator is notconfigured.

(4) COE in the biomass case is 0.189 $/kWh (shown in Table 6) for a biomass price of 20 $/tonne,which is more than the first case of 0.125 $/kWh (shown in Table 4) for an average value ofwind speed of the island.

(5) GHG emission in the case of the biomass generator is merely 76.9 kg/year (shown in Table 6),whereas in the first case of wind and DG set, its value is 76,661 kg/year (shown in Table 4).

(6) The biomass generator is not configured when its cost rises to 50 $/tonne. SPV and DG set areconfigured. GHG emission jumps to a very high value of 208,697 kg/year (shown in Table 6).

8. Conclusions

• Energy security is important for an island; therefore, a hybrid system is preferred over oneparticular RES, so that the strong points of conventional and RES may be availed.

• In case HRES contains SPV, WES, and DG set, the simulation shows that COE (1) decreasesas wind speed increases, (2) increases with the increase in diesel price, (3) decreases as hubheight increases, and (4) increases with setpoint SoC.

• SPV is not configured in the simulation result shown in Table 4. Hence it is not feasible to useSPV in the prevailing condition.

• At higher wind speed (20 m/s), DG set is not configured, reducing GHG emission to zero(Table 4).

• In the case of HRES containing SPV, biomass generator, and DG set, SPV is configured in thesimulation when the biomass price of 50 $/tonne is too expensive to fuel the biomass generator(Table 6).

• The biomass generator is not configured when its cost rises to 50 $/tonne (as shown in Table 6)and it becomes uneconomical if used; but GHG emission is negligible. Hence CO2 saved canbe traded to compensate some financial loss caused by the high cost of biomass, but greatlygenerating clean energy.

• HRES technology may be used in a wider and international perspective. The technology maybe implemented in any island for energy security at the cheapest rate.

• If biomass is available, it is always beneficial to use biomass energy so that GHG emission maybe avoided. Biomass releases CO2 when it is used for gasification. Because biomass absorbsCO2 during sunlight hours as it grows, the entire process of growing, using, and re-growingbiomass results in very low to zero CO2 emissions. If proper balance is maintained betweengrowing and gasifying of biomass in a particular area, zero emission may be achieved whilegenerating electricity.

• However, wind has more CO2 mitigation potential as compared with biomass, SPV systems,and small hydro in India (Mohibullah et al. 2006).

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• Sometimes optimal configurations simulated by the software are not practically possible toinstall, hence compromise has to be made in favour of the second or third most economicalconfiguration.

Acknowledgements

The authors wish to thank Mr Shahid Hasan, Associate Director, Regulatory Studies & Governance, The Tata Energyand Resources Institute (TERI), Darbari Seth Block, IHC Complex, Lodhi Road, New Delhi 110 003, India (website:www.teriin.org) and Mr P.N. Pandey, Engineer, Non-conventional Energy Development Authority, Government of India,Aligarh, India for their valuable contributions by providing different data for this article. This would have not been possiblewithout their suggestions.

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