Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system

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Sizing optimization of grid-independent hybrid photovoltaic/wind powergeneration system

A. Kaabeche a,*, M. Belhamel a, R. Ibtiouen b

a Centre de Dveloppement des Energies Renouvelables, B.P. 62 16340 Bouzareah, Algiers, Algeriab Ecole Nationale Suprieure Polytechnique dEl Harrach, Algiers, Algeria

a r t i c l e i n f o

Article history:

Received 20 April 2010Received in revised form15 November 2010Accepted 17 November 2010Available online 23 December 2010

Keywords:

Hybrid PV/wind systemUnit sizingOptimizationEconomic evaluation

a b s t r a c t

To allow a real penetration of the huge dispersed naturally renewable resources (wind, sun, etc.)intermittent and more or less easily predictable, optimal sizing of hybrid renewable power generationsystems prove to be essential. This paper recommends an optimal sizing model based on iterativetechnique, to optimize the capacity sizes of different components of hybrid photovoltaic/wind powergeneration system using a battery bank. The recommended model takes into account the submodels ofthe hybrid system, the Deciency of Power Supply Probability (DPSP) and the Levelised Unit ElectricityCost (LUEC). The ow chart of the hybrid optimal sizing model is also illustrated. With this incorporatedmodel, the sizing optimization of grid-independent hybrid PV/wind power generation system can beaccomplished technically and economically according to the system reliability requirements. A case studyis conducted to analyze one hybrid project, which is designed to supply residential household located inthe area of the CDER (Center for Renewable Energy Development) situated in Bouzarah, Algeria (36

480N, 3

10E, 345 m).2010 Elsevier Ltd. All rights reserved.

1. Introduction

Energy consumption in the last century has considerablyincreased due to massive industrialization. The forecast of energyneeds for years to come only conrm this trend, especially givendemographic trends and development in some world regions,particularlyin Asia. On the one hand, theelds of traditional energyresources of fossil origin can be exploited for several decades,which suggests a situation of energy shortage globally which willbe imminent. On the other hand, nuclear waste poses furtherproblems in terms of pollution of radioactive waste, decom-missioning of old plants and industrial hazard. To meet the energyneeds of todays society, it is necessary to nd solutions and todiversify them.

Alternative energy resources such as solar and wind haveattracted energy sectorsto generate poweron a large scale. However,commondrawbackwithsolarandwindenergyistheirunpredictablenature and dependence on weather and climatic changes. Stand-alone photovoltaic (PV) or wind energy systems do not produceusable energy for considerable portion of time during the year.

In order to efciently and economically utilize the renewableenergy resources, one optimum match design sizing method is

essential. The sizing optimization method can help to endorse thelowest investment with adequate and full use of the solar system,wind system and battery bank, so that the hybrid system can workat optimum conditions in terms of investment and system powerreliability requirement.

Different optimization techniques for hybrid PV/wind systemssizing have been reported in the literature such as dynamic pro-gramming, graphical construction technique, probabilistic app-roach, articial intelligence methods, multi-objective design, linearprogramming and iterative technique[1].

Thus, Musgrove[2]presented a dynamic programming model,RAPSODY, which is designed to determine optimal operating strat-egies for a hybrid wind power system incorporating battery storageand an auxiliary diesel generator. The model takescapital, operatingand maintenance, and fuel costs into account to calculate theaverage daily cost of satisfying an electrical load prole. The devel-oped model is provided with an efcient optimizing routine whichallows the user to obtain optimal component sizes for a particularload prole and wind or solar resource. A graphical constructiontechnique to follow the optimum combination of PV array andbattery for a hybrid solarewind system has been presented byBorowy and Salameh[3]. For a given load and a desired LPSP, thenumber of batteries and PV modules were calculated based on theminimum cost of the system. The minimum cost will be at the pointof tangency of the curve that represents the relationship between

* Corresponding author. Tel.: 213 21 90 15 03; fax: 213 21 90 15.E-mail address:[email protected](A. Kaabeche).

Contents lists available at ScienceDirect

Energy

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m/ l o c a t e / e n e r g y

0360-5442/$e see front matter 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.energy.2010.11.024

Energy 36 (2011) 1214e1222
mailto:[email protected]://www.sciencedirect.com/science/journal/03605442http://www.elsevier.com/locate/energyhttp://dx.doi.org/10.1016/j.energy.2010.11.024http://dx.doi.org/10.1016/j.energy.2010.11.024http://dx.doi.org/10.1016/j.energy.2010.11.024http://dx.doi.org/10.1016/j.energy.2010.11.024http://dx.doi.org/10.1016/j.energy.2010.11.024http://dx.doi.org/10.1016/j.energy.2010.11.024http://www.elsevier.com/locate/energyhttp://www.sciencedirect.com/science/journal/03605442mailto:[email protected]



the number of PV modules and the number of batteries. Then theoptimum sizing of the battery bank and the PV array can be ach-ieved. Another graphical technique has been given by Bin et al.[4],Kaabeche et al.[5]and Markvart[6], to optimally design a hybridsolarewind power generation system. However, in both graphicalmethods, only two parameters (either PV and battery, or PV andwind turbine) were included in the optimization process.

Tina et al.[7] presented a probabilistic approach based on theconvolution technique[8]to incorporate the uctuating nature oftheresourcesand theload,thuseliminating theneed fortime-seriesdata, to assess the long-term performance of a hybrid solarewindsystem for both stand-alone and grid-connected applications.Disadvantage of this probabilistic approach is that it cannot repre-sent the dynamic changing performance of the hybrid system.

A methodology for optimum design of a hybrid PV/wind systemhas been proposed by Koutroulis et al. [9]. The purpose of theproposed methodology is to suggest, among a list of commerciallyavailable system devices, the optimum number and type of unitsensuring that the 20-year round total system cost is minimized byGenetic Algorithms subject to the constraint that the load energyrequirements are completely covered, resulting in zero load rejec-tion. Yang et al.[10]proposed one optimum sizing method basedon Genetic Algorithms by using the Typical Meteorological Yeardata. This optimization model is proposed to calculate the systemoptimum conguration which can achieve the desired LPSP withminimum Annualized Cost of System. Another Heuristic techniquebased on the evolutionary algorithms have been performed byEkren et al.[11]for optimizing size of a PV/wind integrated hybridenergy system with battery storage. The proposed methodologyuses a stochastic gradient search for the global optimization. In thestudy, the objective function is the minimization of the hybridenergy system total cost.

Bernal-Agustn et al. [12] present a multi-objective optimization(NPC versus CO2 emissions) for a hybrid solar/wind/diesel systemwith battery storage based on Multi-Objective Evolutionary Algo-rithms (MOEAs). A triple multi-objective optimization to minimize

simultaneously the total cost throughout the useful life of theinstallation, pollutant emissions (CO2) and unmet load has beenpresented by Dufo-Lpez and Bernal-Agustn [13]. For this task,a MOEAs and a Genetic Algorithm have been used in order to ndthe best combination of components and control strategies for thehybrid system. According to the methods proposed by Chedid andRahman[14]and Yokoyama et al.[15]the optimal sizes of the PVand wind power sources and the batteries are determined byminimizing the system total cost function using linear program-ming techniques. The total system cost consists of both the initialcost and yearly operation and maintenance costs.

Yang et al. [16,17] have proposed an iterative optimizationtechnique following the loss of power supply probability (LPSP)model for a hybrid solarewind system. The number selection of the

PV module, wind turbine and battery ensures the load demandaccording to the power reliability requirement, and the system costis minimized. Similarly, an iterative optimization method waspresented by Kellogg et al.[18]to select the wind turbine size andPV module numberneeded to make the difference of generated anddemanded power (DP) as close to zero as possible over a period oftime. From this iterative procedure, several possible combinationsof PV/wind generation capacities were obtained. The total annualcost for each conguration is then calculated and the combinationwith the lowest cost is selected to represent the optimal mixture.

Another iterative optimization technique for a stand-alonehybrid photovoltaic/wind system (HPWS) with battery storage ispresented by Diaf et al. [19]. The main objective of the presentedstudy is tond the optimum size of system, able to fulll the energy

requirements of a given load distribution, for three sites located at

Corsica island and to analyze the impact of different parameters onthe system size. In the proposed stand-alone system, the supply owind power via an uninterruptible power supply (UPS) is used andtherefore the energy produced by the wind generator can be sentdirectly to the load.

In this paper, a grid-independent hybrid PV/wind system opti-mization model, which utilizes the iterative optimization techniqueto follow the Deciency of Power Supply Probability (DPSP) modeand the Levelised Unit Electricity Cost (LUEC) model for powerreliability and system cost respectively, is presented. At the differ-ence of the system conguration used by Diaf et al.[19]in whichthe wind generator is taken as the primary sourceof energy and thePV generator as the secondary source of energy. This congurationis good especially for high wind potential regions. The conguration adopted in this study corresponds to the hybrid PV/windsystem in which both wind and PV generators present the primarysource of energy thereby represent the best complementaritybetween the two renewable energy sources photovoltaic and windand leads to an energy management strategy different from thapresented in[19]. Also, the algorithm developed in this study doesnot allow the unballasting of the production in the event of surpluenergy: power produced from natural sources intermittent (windsun) greater than the amount of power consumed and themaximum acceptable power storage device. Moreover, this algo-rithm permits the calculation of the excess energy. So, the surplusenergy produced could be used in the production of hydrogen froman electrolyzer for Long-term energy storage, helping to improvethe total efciency of the hybrid system. Using the DPSP objectivefunction, the congurations of a hybrid system which can meet thesystem reliability requirements can be obtained. There are threesizing parameters in the simulation, i.e. the capacity of PV systemthe rated power of wind system and the capacity of the batterybank. The optimum conguration can be identied from the set othe above obtained congurations by reaching the lowest LevelisedUnit Electricity Cost (LUEC). couter Lire phontiquement.

2. Grid-independent system description

A schematic diagram of a stand-alone hybrid PV/wind system isshown inFig. 1.Battery chargers, connected to a common DC busare used to charge the battery bank from the respective PV andwind input power sources. Depending on the battery chargetechnology, the maximum available power can be extracted fromthe PV and wind power sources (Maximum Power Point TrackingMPPT). The battery bank is used to store the energy surplus and tosupply the load in case of low wind speed and/or irradiation

Fig. 1. Block diagram of a hybrid PV/wind system.

A. Kaabeche et al. / Energy 36 (2011) 1214e1222 1215



conditions. A DC/AC inverter is used to interface the DC batteryvoltage to the consumer load AC requirements. The energyproduced from each PV or wind source is transferred to theconsumer load through the battery charger and the DC/AC inverter,while the energy surplus is used to charge the battery bank.

3. Hybrid PV/wind system model

3.1. PV generator model

The hourly output power of the PV generator with an area Apv(m2) at a solar radiation on tilted plane moduleGt(W/m

2), is givenby[20]:

Ppv hpvApvGt (1)

Wherehpvrepresents the PV generator efciency and is given by[21,22]:

hpv hrhpc

h1 b

TcTcref

i (2)

Where hr is the reference module efciency, hpc is the power

conditioning efciency which is equal to 1 if a perfect maximumpower tracker (MPPT) is used. b is the generator efciencytemperature coefcient, it is assumed to be a constant and forsilicon cells the range of b is 0.004e0.006 per (C), Tcref is thereference cell temperature (C) andTcis the cell temperature (C)and can be calculated as follows[23]:

Tc Ta NOCT 20=800Gt (3)

WhereTais the ambient temperature (C) andNOCTis the nominalcell operating temperature (C).hpcb,NOCTandApv, are parametersthat depend upon the type of module used. The data are obtainedfrom the PV module manufacturers.

3.2. Wind turbine system model

The wind speed distribution for selected sites as well as thepower output characteristic of the chosen wind turbine are thefactors that have to be considered to determine the wind energyconversion system power output. Choosing a suitable model is veryimportant for wind turbine power output simulations. The mostsimplied model to simulate the power output of a wind turbinecan be described by[24]:

PwV

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Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system