638 IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 1, MARCH 2013

Economic Analysis and Power Management of aSmall Autonomous Hybrid Power System (SAHPS)Using Biogeography Based Optimization (BBO)

AlgorithmAjay Kumar Bansal, Member, IEEE, Rajesh Kumar, Senior Member, IEEE, and R. A. Gupta, Member, IEEE

Abstract—In this study, Biogeography Based Optimization(BBO) algorithm is developed for the prediction of the optimalsizing coefficient of Small Autonomous Hybrid Power System(SAHPS) in remote areas. BBO algorithm is used to evaluateoptimal component sizing and operational strategy by minimizingthe total cost of SAHPS, while guaranteeing the availability ofenergy. Due to the complexity of the SAHPS design with nonlinearintegral planning, BBO algorithm is used to solve the problem.The developed BBO Algorithm has been applied to design thewind/PV/hydro hybrid energy systems to supply a colony locatedin the area of Jaipur, Rajasthan (India) during the period ofJanuary, 2010 to January 2011. It is clear from the results thatthe proposed BBO method has excellent convergence property,requires less computational time and can avoid the shortcoming ofpremature convergence of other optimization techniques to obtaina better solution.

Index Terms—BBO, optimization, pico hydro power plant, smallautonomous hybrid power system, solar PV system, wind energyconversion system.

I. INTRODUCTION

D UE to the continuously increasing diminution of fossilfuels and the associated environmental problems, all

countries in the world are giving all the efforts to the devel-opment of renewable-energy power generation technologyin recent years [1], [2]. A Small Autonomous Hybrid PowerSystem (SAHPS) is a system that generates electricity in orderto serve low energy demand. Renewable energy sources (RES)are used as a primary source of energy in SAHPS and they areusually located in geographically remote and demographicallysparse areas. However, renewable technologies such as windturbine generators (WTGs), Pico-hydro plants (MHs) andphotovoltaic (PVs) are dependent on a resource, which are notdispatchable, but have low reliability of the electric energy[3]. The problem of optimal sizing of a SAHPS belongs to the

Manuscript received August 29, 2011; revised January 14, 2012; acceptedDecember 11, 2012. Date of publication January 14, 2013; date of current ver-sion February 27, 2013. Paper no. TSG-00416-2011.A. K. Bansal is with the Electrical Engineering Department, Poornima Group

of Colleges, Sitapura, Jaipur 302022, India (e-mail: ajaykb007@gmail.com).R. Kumar and R. A. Gupta are with the Department of Electrical Engi-

neering, Malaviya National Institute of Technology, Jaipur 302017, India(e-mail: rkumar.ee@gmail.com, ragmnit@gmail.com).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TSG.2012.2236112

group of combinatorial optimization problems since the sizesof system’s components, which constitute input variables, canonly take specific values [4].Kellogg et al. [5] and Borowy et al. [6] demonstrated

a straight forward numerical algorithm for unit sizing andeconomic analysis of a stand-alone wind, PV hybrid system.Seeling-Hochmuth [7] suggested that many optimal config-urations for hybrid energy systems should be determined byminimizing the kWh cost. Yamille et al. [8] suggested manyareas in power systems require solving one or more nonlinearoptimization problems. While analytical methods might sufferfrom slow convergence and the curse of dimensionality, heuris-tics-based swarm intelligence can be an efficient alternative.Barley et al. [9] suggested guidelines regarding main opera-

tion strategies, namely frugal discharge, the full-power strategy,load-following and the state of charge (SOC) set point. Belfkiraet al. [10] explained that diesel operating point is adjusted tomatch the net load and SOC set point strategy is used to chargebatteries at the user defined point from the diesel generator.Bernal-Agustin et al. [11] suggested the running time for gen-erators and suggested that the generators are operated at full-power generation and excess power generated is used to chargethe batteries. Koutroulis et al. [12], Daming et al. [13], Guptaet al. [14] and Sopian et al. [15] investigated the application ofgenetic algorithm to solve the optimization problem with var-ious constraints. Dufo-Lopez et al. [16] developed a programbased on genetic algorithm, known as HOGA, for optimizingthe PV-diesel hybrid system control strategy with AC loads andfurther HOGA is modified by Dufo-Lopez et al. Hakimi et al.[17] applied PSO, Wang et al. [18] applied modified PSO formulti-criterion design of the hybrid power generation system.Bansal et al. [19] applied Meta PSO for finding the optimal sizeof the Wind/ PV energy system. Ashok [20] developed a re-liable system operation model based on Hybrid OptimizationModel for Electric Renewable (HOMER) [21], to find an op-timal hybrid system among different renewable-energy combi-nations while minimizing the total life-cycle cost.Venayagamoorthy et al. [22] suggests two energy dispatch

controllers for use in a grid-independent photovoltaic (PV)system. The first, an optimal energy dispatch controller, is basedon a class of Adaptive Critic Designs (ACDs) called ActionDependent Heuristic Dynamic Programming (ADHDP). Thesecond energy dispatch controller is a smart energy dispatchcontroller and is built using knowledge from an expert, codified

1949-3053/$31.00 © 2013 IEEE

BANSAL et al.: ECONOMIC ANALYSIS AND POWER MANAGEMENT OF A SMALL AUTONOMOUS HYBRID POWER SYSTEM (SAHPS) 639

into a series of static rules. Welch et al. [23] has proposed afuzzy logic controller (FLC) is developed to assign priority tothe installed system loads such that all critical loads receive ahigher priority than the non-critical loads, and so when thereexists a shortage of available energy the critical loads are firstmet before attempting to power the non-critical loads.Computational Intelligence (CI) and advanced CI techniques

have been applied to solve the challenging problems today inelectric power systems [24], [25]. Advanced computationalmethods [26] are required for planning and optimization, fastcontrol of power system elements, processing of field data andcoordination across the power system [27]. Very recently, anew optimization concept, based on biogeography has beenproposed by Simon [28]. Biogeography Based Optimization(BBO) is a population-based evolutionary algorithm (EA) [29]and it adopts the migration operator to share information amongsolutions. This feature makes BBO applicable to the majorityof problems, where GA and PSO are applicable [30].SAHPS sizing is a nonlinear integral planning, which is a

complex problem. The objective of this paper is to explore theapplication of the BBO algorithm to the SAHPS problems. Thecombination of components represents the sequence of the suit-ability index variables (SIVs), determines the total cost of thesystem. In BBO, after the migration operation, a SIV in the im-migrated island (a bad solution) accepts the sharing informationfrom the emigrated island (a better solution) [31]. The BBO al-gorithm has certain unique features, which overcome severaldemerits of the conventional methods as explained by [32].This paper is organized as follows. In Section II, the SAHPS

and its components are explained. Section III describes the opti-mization problem of SAHPS, Section IV explains the simplifiedBBO algorithm and Section V describes the BBO algorithm forSAHPS. In Section VI, detail of case study data is presentedand Section VII shows the comparison of Hybrid OptimizationModel for Electric Renewable software (HOMER) [21], Bio-geography Based Optimization (BBO) [26], Genetic Algorithm(GA) [12], particle swarm optimization (PSO) [17], [33], com-prehensive learning particle swarm optimization (CLPSO) [34]and ensemble of mutation and crossover strategies and parame-ters in DE (EPSDE) algorithm [35] algorithms. In Section VIII,the results of proposed BBO algorithm have been explained anddiscussed.

II. SMALL AUTONOMOUS HYBRID POWER SYSTEM (SAHPS)

A typical SAHPS comprises of wind turbine generators(WTG), PV panels (PV), Pico hydro plant (MH), storagebatteries (SB) and Diesel Generator (DG), as shown in Fig. 1.In the SAHPS, components are integrated and complementeach other, in order to meet performance targets of generationsystems and to access the most economic power generation.

A. Wind Turbine Generators (WTG)

The energy and current output of the WTG for each time in-stant are calculated on the basis of local weather conditions andactual installation height of the turbines. Wind turbines are usu-ally connected in parallel, not in a series. Several wind turbines

Fig. 1. Small autonomous hybrid power system (SAHPS).

can be connected in parallel to match the system current require-ments. Using the wind speed at a reference height from thedatabase, the velocity at a hub height for the location is esti-mated on an hourly basis and calculated as

(1)

where is the wind speed at the projected height , is the windspeed at reference height and is the power-law exponent( 1/7 for open land). The power generated by the wind systemat any time ‘ ’ can be expressed as

(2)

where is the wind turbine power output, is efficiencyof wind turbine, is efficiency of generator, is the densityof air, is the power coefficient of wind turbine, and is thewind turbine swept area.

B. Photovoltaic Generation (PV)

The PV sizing variable comprises of size of a PV panel andthe number of strings in a PV array. The necessary number ofPV panels to be connected in series is derived by the numberof panels needed to match the bus operating voltage. Whenmatching the current requirements of the system, several PVstrings which are connected in series, need to be installed in par-allel. The number of parallel PV strings is a design variable thatneeds optimization. The output of PV panels must include theimpact of geographic location, such as solar radiation and tem-perature, etc. The output power of photovoltaic panelsat any time ‘ ’ can be calculated as:

(3)

where is conversion efficiency of PV panel, is thenumber of PV panels in parallel, is number of PV panelsin series, is the operating Voltage of PV panels, andis operating current of PV panels.

C. Pico Hydro Unit (MH)

Pico hydro is a term used for hydroelectric power genera-tion of under 5 kW. The Pico Hydro unit plays a crucial roleto cater for the power uncertainty and helps to secure, the reli-able and profitable operation of power system. The Pico hydroturbine is a device which converts the power of falling waterinto AC or DC electricity at a constant efficiency, but it does not

640 IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 1, MARCH 2013

have the ability to store the water or modulate the power output.The power in falling water is proportional to the product of thestream flow and head, which is the vertical distance throughwhich the water falls. The head loss occurs in the intake pipedue to friction. The net head or effective head is calculated as

(4)

where is available head and is pipe head loss.The flow through the turbine is expressed as

(5)

where is stream flow, is residual flow, is tur-bine design flow rate, and and are turbine’s min-imum and maximum flow ratios. The turbine does not operate,if the stream flow is below the minimum level. The flow ratethrough turbine cannot exceed the maximum level.In each time step, the electric power output of the Pico hydro

turbine at any time ‘t’ can be calculated as:

(6)

where is efficiency of Pico hydro turbine, is thedensity of water, is acceleration due to gravity, is effectivehead and is Pico hydro turbine flow rate.

D. Storage Batteries (SB)

The batteries are used to store the excess energy generatedby hybrid system and supply energy during the low generationperiod. The power input to the battery bank is calculated as

(7)

where is the total power produced by the renewableresources (PV panels and wind turbines) at hour ,

, where is the power demanded by the load athour , is inverter efficiency, and is the total powerproduced by the Diesel generator at hour . For the chargingprocess and discharging process ofthe battery bank, the state of charge (SOC) can be calculated as

(8)

where is equal to the round-trip efficiency in the chargingprocess and is equal to the 100% in the discharging process,

is the DC bus voltage, and is the time step which isgenerally one hour.The power generated fromWTG’s,MH’s and PV’s at the time

‘t’ i.e., total renewable power is expressed as:

(9)

where , , are the total number of wind generators,photovoltaic panels and Pico hydro turbines.

III. DESCRIPTION OF THE PROBLEM

For SHAPS system design, the objective of optimum designis to minimize cost function ,subject to constraint’s explained in (18) to (20). The designparameters that should be derived must include WTG capacity

, PV panel capacity , hydro power system capacity, total battery capacity , and Diesel generator

capacity .

(10)

where is total cost of the system,are the total cost of wind turbine systems, photovoltaic panels,Pico-hydro plants, batteries, diesel generators and the total costof considering the power-supply reliability, respectively.

A. The Total Cost of Wind Turbines

(11)

B. Total Cost of Photovoltaic Panels

(12)

C. Total Cost of Pico Hydro Plants

(13)

D. Total Cost of Batteries

(14)

E. Total Cost of Diesel Generator

(15)

where are the number of windgenerators, photovoltaic panels, Pico hydro plant, bat-teries, diesel generators; are the unit cost(Rs /kW); is the power capacity;

are the main-tenance and operating costs;

BANSAL et al.: ECONOMIC ANALYSIS AND POWER MANAGEMENT OF A SMALL AUTONOMOUS HYBRID POWER SYSTEM (SAHPS) 641

are the replacement costs corresponding toth wind turbine, th photovoltaic panels, th Pico hydro plant,th battery, th diesel generator; is cost of fuel usedin th Diesel Generator; m is life span of the project and isinterest rate.

F. The Total Cost of Other Parameters

The total cost of considering power supply reliability is:

(16)

where is the Compensation Coefficient and EENS is the Ex-pected Energy Not Served. In the calculation of , time seriesand the Monte Carlo methods are used. The time series is di-vided into many terms i.e., wind speed, light, load etc. and thenMonte Carlo method is used, to calculate the reliability of therandomly selected sample. Within the run-time T (8760 hours),the EENS (kWh/year) is calculated as

(17)

where is a step function, which is zero when the supply ex-ceeds or equals to demand and one if there is insufficient powerduring hour , is the state of charge (SOC) of storagebatteries during hour , is the minimum permissiblestorage level of the battery and

is surplus power during hour .

G. Design Constraints

Due to the physical or operational limits of the target system,there is a set of constraints that should be satisfied throughoutsystem operations for any feasible solution.1) For any period t, the total power supplied from the hybridenergy system must supply the total demand with acertain reliability criterion. This relation can be representedas:

(18)

where are the windpower, solar power, Pico hydro plant power, charged/dis-charged battery power, diesel generator power, dumpedpower and total load demand respectively, R is the ratio ofthe maximum permissible unmet power with respect to thetotal load demand at each time instant. The transmissionlosses are not considered because the system is consideredas the remotely located isolated system and do not havesubstantial transmission lines. The dump power is the ex-cess power generated by the system which is not utilizedfor either supply the load or supplied to charge the battery.

2) The state of charge (SOC) of storage batteriesshould not exceed the capacity of storage batteriesand must be larger than minimum permissible storagelevel . The total storage battery capacity shouldnot exceed the allowed storage capacity . Thehourly charge or discharge power should not exceed

the hourly inverter capacity . These constraints areexpressed as

(19)

3) The number of wind power generation, photovoltaicpanels, batteries and Pico hydro plants are subjected tofollowing constraints:

(20)

where is maximum capacity of Photovoltaicpanel, is the maximum capacity of Wind turbine,

is the maximum capacity of the battery paneland is the maximum capacity of the Pico hydroplants.

IV. BIOGEOGRAPHY-BASED OPTIMIZATION (BBO)

In the science of biogeography, a habitat is an ecologicalarea that is inhabited by particular plant or animal species andgeographically isolated from other habitats. Each habitat is clas-sified by Habitat Suitability Index (HSI). Geographical areas,which are well suited as residences for biological species aresaid to have a high HSI. Features that correlate with HIS in-clude rainfall, diversity of vegetation, diversity of topographicfeatures, land area, temperature, etc. If each of the features is as-signed a value, HSI is a function of these values. Each of thesefeatures that characterize habitability is known as SuitabilityIndex Variables (SIV). SIVs are the independent variables whileHSI are the dependent variables.Habitats with high HSI has the large population and have high

emigration rate , simply by virtue of a large number of speciesthat migrate to other habitats. The immigration rate is lowfor those habitats which are already saturated with species. Onthe other hand, habitats with low HSI has high immigration rate, low emigration rate due to sparse population. The value ofHSI, for lowHSI habitat, may increase with the influx of speciesfrom other habitats as suitability of a habitat is the function ofits biological diversity. However, if HSI does not increase andremains low, species in that habitat go extinct and this leads toadditional immigration. For the sake of simplicity, it is safe toassume a linear relationship between habitats HSI, its immigra-tion and emigration rate. These rates are same for all the habitatsand depend upon the number of species in the habitats.Fig. 2 shows the relationships between fitness of habitats

(number of species), emigration rate and immigration rate .E is the possible maximum value of emigration rate and I is thepossible maximum value for immigration rate. S is the numberof species in the habitat, which corresponds to fitness. isthe maximum number of species the habitat can support.is the equilibrium value. When , the emigration rateis equal to the immigration rate . From Fig. 2, it is clear

that island which has outstanding performance like has ahigh emigration rate and a low immigration rate. On the otherhand, island which has poor performance like has a highimmigration rate and a low emigration rate.After calculating HSI for each solution , the immigration

rate and the emigration rate can be evaluated as (21) and

642 IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 1, MARCH 2013

Fig. 2. The model of immigration rate and emigration rate of biology.

(22), respectively. These two rates are the functions of fitnessor HSI of the solution. Since, according to the biogeography,the SIVs of a high-HSI solution tend to emigrate to low-HSIsolutions, a high- HSI solution has a relatively high and low, while in a poor solution, a relatively low and a high

are expected. The values of emigration and immigration ratesare given as

(21)

(22)

where is the maximum possible immigration rate; is themaximum possible emigration rate; is the number of speciesof the th individual; and is the maximum number of species.In (21) and (22), represents the rank of the th habitat aftersorting all habitats according to their HSIs and represents thesize of the population. It is clear that since more HSI representsa better solution, more represents the better solution.Mathematically, the concept of emigration and immigration

can be represented by a probabilistic model. The probability ,in which habitat contains exactly species at . For each SIV inone solution, it is decided probabilistically, whether or not to im-migrate. If immigration is selected for a given solution feature,emigrating habitat is selected for a given solution probabilisti-cally, using the roulette wheel normalized by . The mutationoperator is probabilistically applied to the habitat, which tendsto increase the biological diversity of the population. The muta-tion rate is inversely proportional to the solution probability,which is expressed as

(23)

where is a user-defined parameter.The BBO algorithm has certain unique features, which over-

come several demerits of the Computational Intelligence (CI)methods as mentioned below [33]:1) In BBO and other CImethods, the solutions survive foreveralthough their characteristics change as the optimizationprocess progresses. However, solutions of evolutionary-based algorithms like GA, DE etc. “die” at the end of eachgeneration. Due to the presence of crossover operation inevolutionary based algorithms, many solutions, whose fit-ness is initially favorable, sometimes lose their quality in

later stage of the process. In BBO, there is no crossoverlike operation as the solution gets fine-tuned gradually asthe process goes on through migration operation. Elitismoperation has made the algorithm more efficient in this as-pect and gives an edge to BBO over other techniques.

2) In conventional CI methods, solutions are more likely toclump together in similar groups. While in the case ofBBO, solutions do not have the tendency to cluster due toits new mutation operation.

3) BBO involves fewer computational steps per iteration ascompared to other algorithms like GA, PSO, DE etc. Dueto this, BBO results are faster in convergence.

4) In BBO, poor solutions accept a lot of new features fromgood ones, which may improve the quality of solutions.This is a unique feature of BBO algorithm compared toother CI techniques. At the same time, this makes con-straint satisfaction to be much easier, compared to otheralgorithms.

V. BBO ALGORITHM FOR SAHPS PROBLEMS

In this section, a new approach to implement the BBO algo-rithm is described for solving the SAHPS problems. The processof BBO algorithm can be summarized as follows:1) Representation of the SIV: Since the decision variables forthe SAHPS problems are sizing vector of different com-ponents, they are used to represent individual habitat. Thesizes of different components are represented as the SIVin a habitat. For initialization, choose the number of SIVof BBO algorithm (m), number of habitat (N). The com-plete habitat set is represented in the form of the followingmatrix:

(24)where is the position vector of the habitat as

where is number of PV panels in parallel, is type ofPV panel, is number of wind turbines, is type of windturbine, is number of batteries in parallel, is type of bat-tery, is number of DC generator and is size of inverter.Each habitat is one of the possible solutions for the problemand size of the habitat is equivalent to the population size.The element of is the th position component ofhabitat or in other words is the th SIV of the thhabitat. represents the size of component of theth habitat set.

2) Initialization of the SIV: Each element of the Habitat ma-trix, i.e., each SIV of a given habitat set H, is initializedrandomly within the effective sizing limits. The initializa-tion is based upon (20) for component size. The steps ofthe algorithm to solve SAHPS problem are as follows.Step 1: For initialization, choose the number of compo-

nent units, i.e., number of SIV is m, number ofhabitat N. Specify maximum and minimum ca-pacity of each generator, power demand, power

BANSAL et al.: ECONOMIC ANALYSIS AND POWER MANAGEMENT OF A SMALL AUTONOMOUS HYBRID POWER SYSTEM (SAHPS) 643

generation by each unit. Also, initialize the BBOparameters.

Step 2: Each SIV of a given habitat matrix is initialized.Each habitat set of matrix must satisfy qualityconstraint (18) to (20). Each habitat represents apotential solution to the given problem.

Step 3: Calculate the HSI for each habitat set of the totalhabitat set for given emigration rate , immigra-tion rate . HSI represent the capital and oper-ating cost of the units in the power system for apower demand. Here, indicates the capitaland operating cost due to the th set of generationvalue (i.e., th set of habitat matrix) in cost/KWh.

Step 4: Based on the HSI (cost/ KWh), identify the valueof elite habitats. Here, elite term is used to indi-cate those habitat sets of renewable power output,which gives best cost of energy. Top “p” habitatsets are kept as it is, after individual iterationwithout making any modification on it. Thosehabitats, whose fitness values, i.e., HSI values arefinite, considered as valid species in problem.

Step 5: Probabilistically perform migration operation onthose SIVs of each non-elite habitats, selected formigration. How to select any SIV for migrationoperation is described as:1) First select lower and upper value of immi-gration rate and , respectively.

2) Then calculate value of and for eachhabitat set.

3) Next calculate from which habitat and whichSIV to be selected for newly generated habitatafter migration.

After migration operation, new habitat set is gen-erated. In SAHPS problems, these represent newmodified unit values of various components.

Step 6: Species count probability of each habitat is up-dated. Mutation operation is performed on thenon-elite habitat. If the mutation rate, as calcu-lated using (23) of any habitat is greater than arandomly generated number, habitat is selectedfor mutation. In mutation operation, habitat set,which is selected for mutation, is simply replacedby another randomly generated new habitat setthat satisfies constraints of problems. HSI valueof each new habitat set is recomputed, i.e., cost/KWh of each unit.

Step 7: Go to step 3) for the next iteration. This loop canbe terminated after a predefined number of itera-tions. After each habitat is modified (steps 5 and6), its feasibility as a problem solution shouldbe verified, i.e., each SIV should satisfy differentconstraints of SAHPS problem.

VI. CASE STUDY

The developed methodology for BBO Algorithm has beenapplied to design the Small Autonomous Hybrid Power Systemhaving wind/PV/hydro systems to supply a varying load located

Fig. 3. Typical hourly load profile of a day.

Fig. 4. Average solar radiation monthly data for One year (kWh/m2/d).

TABLE ISAHPS COMPONENTS COST AND LIFE TIME

in the area of Jaipur in Rajasthan (India) with geographical coor-dinates defined as: latitude: , longitude: and al-titude: 431 m above sea level. The wind speed, solar irradiance,sunshine duration and ambient temperature recorded for everyhour, during the period of 1 January, 2010 to 30 December,2010. The wind speed was measured at a 30 meters height. Inthis application, PV panels, wind turbines, hydro power system,battery, diesel generator and inverter have been used.The cluster of colonies is assumed to be located in a remote

area with adequate sunshine, moderate to high wind speeds.The average daily load profile of the study area is shown inFig. 3. The daily energy consumption of load is 16 kWh/daywith 1.4 kW peak demand. Day to day variation of 30% is in-troduced in the load profile. The total power requirement ofthe load is 5766 kWh/year. The optimal solution is verified byshowing the energy profile during the period from 1 January,2011 to 7 January, 2011.The monthly solar radiation in Jaipur, Rajasthan is between

4-7 kWh/m /d, with the monthly sunshine duration rangingfrom 5 hr/day to 8 hr/day as shown in Table II. The sunshinehour has been taken for the same duration as for the global solarradiation. These values are essential for sizing of solar-energysystems. The monthly solar radiation patterns are shown inFig. 4. The technical, economical data and study assumptionsare given in Table III.

644 IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 1, MARCH 2013

TABLE IIAVERAGE DAILY SOLAR RADIATION , SUNSHINE HOURS (HRS.)

AND WIND SPEED (M/S) AT JAIPUR (RAJ.)

TABLE IIIOPTIMIZATION RESULTS FROM VARIOUS OPTIMIZATION ALGORITHMS

Fig. 5. Average of daily wind speed for one year (m/s).

The average wind speed for Jaipur, Rajasthan is between 4to 11 m/s as shown in Table II. To understand the benefits ofwind power generation over the period of a year, the hourly windspeeds of each month were collected. Fig. 5 describes the dailyaverage wind speed, indicating that the wind energy resourceduring the summer season is markedly larger than that duringthe winter season. With the monthly average wind speed of lessthan 6.0 m/s from October to April, the benefit from monthlywind power generation was less. From May to September ofthe following year, the monthly average wind speed exceeded8.4 m/s.Monthly average stream line flow at study area is shownin Fig. 6.

VII. COMPARISON OF BBO WITH OTHER ALGORITHMS

The Small Autonomous Hybrid Power System optimizationis performed on an Intel Core 2 Duo PC with 2.1 GHz processorspeed, 2 GB RAM and Windows 7 operating systems. The ex-periments are performed using the MATLAB R2010b program.The BBO results are compared with the HOMER, GA, PSO,CLPSO and EPSDE.

Fig. 6. Average of daily water availability (L/s) for one year.

TABLE IVCOMPARISON OF OPTIMIZATION ALGORITHMS.

Net present cost (NPC) of an integrated system takes into ac-count the initial capital investment, the present value of oper-ation and maintenance cost, the wind system replacement costand the battery replacement cost. The concept of Net PresentCost is the present value of the cost of installing and operatingthe system over the lifetime of the project.

(25)

where is total annualized cost (Rs/yr), CRF is Cap-ital Recovery Factor, ir is the interest rate (%) and isthe project life-time (yr). The total annualized cost isthe sum of annualized costs of each system componentand annualized replacement cost . The annualized cost ofeach component is the sum of its annualized capital cost, annu-alized replacement cost, annualized O&M cost and annualizedfuel cost (if applicable) [65]. It allows a fair cost comparisonbetween components with low capital and high operating costs(such as diesel generators) and those with high capital and lowoperating costs (such as PV arrays or wind turbines).The Cost of Energy is the average cost per kWh of

useful electrical energy produced by the system. is theratio of the annualized cost of producing electricity to the totaluseful electric energy production.

(26)

where is AC primary load served [kWh/yr] andis DC primary load served [kWh/yr].

For the sake of comparison of performance between the var-ious algorithms, the stopping criterion is set at 100 iterations.Mutation and Cross over are used toget the best results for GA. For PSO, , , Wstarts at 1 and decreases until reaching 0 at the end of the run. InCLPSO, is selected and in EPSDE, ,gives the best results. The mating is performed using single

BANSAL et al.: ECONOMIC ANALYSIS AND POWER MANAGEMENT OF A SMALL AUTONOMOUS HYBRID POWER SYSTEM (SAHPS) 645

TABLE VOPTIMUM COMPONENT SIZE RESULTS AND COMPARISON OF OPTIMIZATION RESULTS RECEIVED THROUGH HOMER AND PROPOSED BBO ALGORITHM

Fig. 7. Convergence characteristics of various optimization algorithms.

point crossover. Fig. 7 depicts convergence of five optimizationalgorithms for the combination of the Small Autonomous Hy-brid Power System in which PV, WTG, Hydro-power system,Diesel generator, battery and inverter are present. For compar-ison of different algorithms, the initial solution points are takenas constant. It can be seen that the fitness value decreases rapidlyin the first 10 generations. During this stage GA, PSO, CLPSO,EPSDE and BBO concentrate mainly on finding feasible so-lutions to the problem. Then the value decreases slowly, andthey have been converged approximately at around 20 itera-tions. Consequently, the total system cost, components size hasbeen almost same in BBO, PSO, CLPSO, EPSDE andGA.Moredetails about convergence and optimal solutions are given inTables III and IV.It can be easily seen from Tables III and IV that HOMER,

PSO, GA, CLPSO, EPSDE and BBO algorithms are able to findoptimum design parameters of stochastic simulation model. Itcan be easily seen from Tables IV and V that Net Present Cost(NPC) and Cost of Energy (COE) of BBO algorithm is lowestdue to less excess energy generated by the system as the energy

required by the system is 5766 kWh/yr. From Fig. 7, it can beeasily seen that BBO algorithm is more rapid and give minimumcost as compared to GA, PSO, CLPSO and EPSDE. Therefore,the proposed BBO-based optimization procedure can comfort-ably, rapidly approach the optimum state for a large-scale com-plex simulation of a hybrid energy system. The total cost of theoptimized hybrid energy system showed that the system can de-liver energy in a stand-alone installation with an acceptable cost.The Homer software with a combination of various compo-

nents and strategies variables of 38 million, would require thecalculation time of approximately 15 hours to evaluate eachcombination. The proposed BBO algorithm not only reduces thedemerits of HOMER but uses only a certain number of combina-tions. The proposed BBO algorithm has reduced 15 hours of cal-culation time (in HOMER) in around 0.73 hours on Intel Core 2Duo PC for complete hybrid energy system optimization.It is observed from the above mentioned tables that HOMER,

PSO, GA, CLPSO, EPSDE and BBO, all are able to find the op-timum design parameters of the stochastic simulation model. Itcan be easily seen that BBO algorithm is more rapid as com-pared to GA, CLPSO and PSO but require slightly more time ascompared to EPSDE but it can be neglected as time required isalmost same. Therefore, the proposed BBO-based optimizationprocedure can comfortably, rapidly approach the optimum statefor a large-scale complex simulation of a hybrid energy system.The total cost of the optimized hybrid system shows the systemcan deliver energy in a stand-alone installation with an accept-able costThe Homer software with a combination of various compo-

nents and strategies variables of 38 million, requires the calcu-lation time of approximately 15 hours to evaluate each combi-nation. The proposed BBO algorithm not only reduces the de-merits of HOMER but uses only a certain number of combina-tions. The proposed BBO has reduced 15 hours, of calculation

646 IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 1, MARCH 2013

TABLE VIOPTIMIZATION RESULTS WITH SENSITIVITY OF DIESEL GENERATOR

IN CASE 22

time in around 0.73 hours on Intel Core 2 Duo PC for completehybrid system optimization.

VIII. RESULTS AND DISCUSSIONS

The mathematical modeling is driven by HOMER, the resultsof HOMER software can be used for comparison and point ofreference. The optimization results using HOMER software andBiogeography-based optimization algorithm results are shownin Table V. The total power generated by renewable sourcesseemed enough, except for its failure to provide the necessarypower at peak time, which requires the support of battery andinverter. Optimization calculations obtained by HOMER areslightly different as compared with BBO. There are three sig-nificant disadvantages of HOMER:1) HOMER requires calculation of every single combinationof sizing and operation strategy.

2) The data for each variation of component needs to be en-tered manually and execute separately.

3) HOMER uses diesel generator more, so hybrid system costincreases due to increase in fuel intake.

The parameters selected in BBO algorithm are No. of habi-tats or population as 50, Generation as 50, Number of SIVs perhabitat as 20, Habitat modification probability as 1, Island Mu-tation probability as 0.05, Elitism parameter as 2, Maximumemigration Rate as 1 and Maximum immigration Rate as 1. InTable V, optimal sizing results consisting of device numbersare presented. The comparison of HOMER and BBO results aregiven in Table V. The reliability of SAHPS is much higher ascompared to other systems & output is not very much affectedby changes in weather conditions.Without much operation reserve, diesel generator can also

supply the load demand independently but at much higher cost,COE of Rs. 18.644/kWh. The generator uses 2,158 liters ofdiesel and operating for 8,760 hours annually. That is almosthalf of generators lifetime operating hours.The global price of the oil is increasing and the Government

of India has indicated that it can no longer provide the oil sub-sidy. It is necessary to note that if the true diesel price is usedin the calculation, the COE is going up by Rs. 0.1 per kWhfor 1 Rs/liter increase in diesel price. The sensitivity results fordiesel price are shown in Table VI. If diesel price is raised to Rs50 per liter, the COE is increased by 3.6%.In order to study the hourly behavior of the power exchange

in the SAHPS, the simulation results were conducted on a pe-riod from 1 to 7 January, 2011 for the case of the optimal con-figuration obtained from BBO algorithm, as given in case 6 ofthe Table V, is shown in Fig. 8. It shows the power suppliesfrom the renewable resources, such as the power demand, theinput/output battery bank power, etc. The diesel generator is

Fig. 8. Power management indicating load power, wind power output, hydroturbine power output, input/output power of the battery bank of the SAHPSsystem in case 5 of Table V.

Fig. 9. Percentage share of components cost in total net present cost for hybridenergy system as given in case 14 of Table V.

used only when the renewable resources and the batteries arenot able to satisfy the load demand.Fig. 9 shows the annualized cost of the case 14, which have

PV, wind turbine, Pico hydro turbine, diesel generator, batteryand Inverter i.e., all are connected in the system. Wind turbinecontributes 15%, battery & inverter costs about 20%, Pico hydroturbine costs about 39%, PV system contributes 14%, Dieselgenerator costs 3% and fuel costs around 1% of the total annualcost of Rs. 216,403. PV panels, hydro turbine and Wind systemare assumed to last the lifetime of the project i.e., for 25 years,while the Diesel generator, battery and inverter needs to be re-placed after certain hours of operation.To check the sensitivity of the results to variations in average

wind speed from year to year, the SAHPS system in case 14of Table V is run with the wind speeds adjusted upward anddownward by 17.5%, which is the inter-annual variability (onestandard deviation) found in the historical wind measurements.With the wind speed 17.5% lower than the present measurementyear, COE rose by 11.6%. With the wind speed 17.5% higher,COE dropped by 8.06%.For checking the accuracy of the proposed BBO algorithm

for large SHAPS system a higher load profile with same weather

BANSAL et al.: ECONOMIC ANALYSIS AND POWER MANAGEMENT OF A SMALL AUTONOMOUS HYBRID POWER SYSTEM (SAHPS) 647

TABLE VIIOPTIMIZATION RESULTS FROM VARIOUS OPTIMIZATION ALGORITHMS

and economical parameters has been also evaluated. In this ap-plication, PV panels, wind turbines, battery, diesel generator andinverter have been used. The daily energy consumption of loadis 2263 kWh/day with a 261 kW peak demand and day to dayvariation of 30% is introduced in the load profile. Due to variouslimitations, only the comparison of the best result with HOMERand GA has been presented in Table VII.

IX. CONCLUSION

Small Autonomous Hybrid Power Systems (SAHPS) aremore suitable than stand-alone systems which only have oneenergy source for the supply of electricity to off-grid appli-cations, especially in remote areas and that also with difficultaccess. However, the design, control, and optimization of thehybrid energy systems are usually very complex tasks.In order to utilize renewable-energy resources efficiently and

economically, one optimum sizing method is developed in thispaper based on a Biogeography Based Optimization (BBO),which has the ability to attain the global optimum with rela-tive computational simplicity compared to the conventional op-timization methods. Apart from this the system configuration,characteristics of the main components, overall sizing, controland power management strategy for the hybrid energy systemhas also been presented. The wind and PV generation systemsare the main power generation devices, and the battery acts asa storage device for excess power. The developed methodologyis based on the use of long-term data of wind speed, solar ir-radiance and water availability. The BBO algorithm optimizesthe size and the operation strategy for a simple daily load. Fur-thermore, a numerical example (Case study) is used to demon-strate the applicability, power management and usefulness ofthe proposed method. Results clearly show that the total costof the SAHPS is lower than the stand alone systems. The pro-posed BBO algorithm not only reduces the shortcomings ofHOMER but also use only a certain number of combinations.The BBO algorithm is able to optimize small as well as largepower system. The proposed BBO has reduced 15 hours of cal-culation time taken by HOMER in around 0.73 hours on a P4computer system.

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Ajay Kumar Bansal (M’09) received the B.Tech.degree from M.B.M. Engineering College, Jodhpur,India, in 2000, the M.Tech. degree from MalaviyaNational Institute of Technology (MNIT), Jaipur,India, in 2007. He is currently working towards thePh.D. degree at the Department of Electrical Engi-neering, Malaviya National Institute of Technology,Jaipur, India.Since 2000, he has been a Faculty Member in

the Department of Electrical Engineering, PoornimaCollege of Engineering, Jaipur, where he is serving

as an Associate Professor. His research interests lie in the fields of non-con-ventional energy sources, power system and artificial intelligence.Mr. Bansal is a Member of IEEE, Associate Member of UACEE, Associate

Member of IE (INDIA), Life Member of IETE, and Life Member of ISTE.

Rajesh Kumar (M’08–SM’10) received the B.Tech.(Hons.) degree from National Institute of Tech-nology (NIT), Kurukshetra, India, in 1994, the M.E.(Hons.) degree from Malaviya National Instituteof Technology (MNIT), Jaipur, India, in 1997, andthe Ph.D. degree from the University of Rajasthan,India, in 2005.Since 1995, he has been a Faculty Member in the

Department of Electrical Engineering, MNIT, Jaipur,where he is serving as an Associate Professor. He wasPost Doctorate Research Fellow in the Department

of Electrical and Computer Engineering at the National University of Singa-pore (NUS), Singapore, from 2009 to 2011. His field of interest includes theoryand practice of intelligent systems, bio and nature inspired algorithms, compu-tational intelligence and applications to power system, electrical machines anddrives.Dr. Kumar has received the Career Award for Young Teachers in 2002 from

Government of India. He is a Senior Member of IEEE, Member of IE (INDIA),Fellow Member of IETE, and Life Member of ISTE.

R. A. Gupta (M’08) received the B.Tech. and M.E.degrees from M.B.M. Engineering College, Jodhpur,India, in 1980 and 1984, respectively, and the Ph.D.degree from IIT Roorkee, India (formerly Universityof Roorkee), in 1996.He has 28 years of teaching and research experi-

ence. Presently, he is a Professor in the Departmentof Electrical Engineering, MNIT, Jaipur. His field ofinterest includes power electronics, electrical drivesand control and applications to power system andnon-conventional energy sources.

Dr. Gupta is a Member of IEEE, Fellow Member of IE (INDIA), FellowMember of IETE, and Life Member of ISTE.