Applied Energy

Applied Energy 87 (2010) 380–389

Contents lists available at ScienceDirect

Applied Energy

journal homepage: www.elsevier .com/ locate/apenergy

Current status of research on optimum sizing of stand-alone hybridsolar–wind power generation systems

Wei Zhou a,*, Chengzhi Lou b, Zhongshi Li a, Lin Lu a, Hongxing Yang a

a Renewable Energy Research Group (RERG), Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kongb School of Environment Science and Technology, Tianjin University, Tianjin, China

a r t i c l e i n f o

Article history:Received 22 April 2009Received in revised form 11 August 2009Accepted 11 August 2009Available online 3 September 2009

Keywords:Hybrid solar–wind energy systemFeasibility studyModellingOptimization

0306-2619/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.apenergy.2009.08.012

* Corresponding author. Tel.: +852 2766 4559; fax:E-mail address: [email protected] (W. Zhou).

a b s t r a c t

Solar and wind energy systems are omnipresent, freely available, environmental friendly, and they areconsidered as promising power generating sources due to their availability and topological advantagesfor local power generations. Hybrid solar–wind energy systems, uses two renewable energy sources,allow improving the system efficiency and power reliability and reduce the energy storage requirementsfor stand-alone applications. The hybrid solar–wind systems are becoming popular in remote area powergeneration applications due to advancements in renewable energy technologies and substantial rise inprices of petroleum products. This paper is to review the current state of the simulation, optimizationand control technologies for the stand-alone hybrid solar–wind energy systems with battery storage. Itis found that continued research and development effort in this area is still needed for improving the sys-tems’ performance, establishing techniques for accurately predicting their output and reliably integratingthem with other renewable or conventional power generation sources.

� 2009 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3812. Meteorological data generation for feasibility study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381

2.1. Time-series meteorological data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3812.2. Statistical meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381

3. Simulation modelling of hybrid solar–wind system components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382
3.1. Modelling of photovoltaic system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3823.2. Modelling of wind energy system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3833.3. Modelling of battery storage system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383
4. Criteria for hybrid solar–wind system optimizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384
4.1. Power reliability analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3844.2. System cost analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384
5. Optimum sizing methods for hybrid solar–wind system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384
5.1. Simulation and optimization software. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3845.2. Optimization techniques for hybrid solar–wind system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385
5.2.1. Optimization scenarios based on different meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3855.2.2. Optimization techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385

5.3. Brief summary of the optimization techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387
6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388

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W. Zhou et al. / Applied Energy 87 (2010) 380–389 381

1. Introduction

Rapid depletion of fossil fuel resources on a worldwide basis hasnecessitated an urgent search for alternative energy sources to ca-ter to the present days’ demand. Another key reason to reduce ourreliance on fossil fuels is the growing evidence of the global warm-ing phenomena. Therefore, it is imperative to find alternative en-ergy sources to cover the continuously increasing demand ofenergy while minimise the negative environmental impacts. Solarand wind energy systems are being considered as promising powergenerating sources due to their availability and topological advan-tages for local power generations in remote areas. Utilization of so-lar and wind energy has become increasingly significant, attractiveand cost-effective, since the oil crises of early 1970s.

However, a drawback, common to solar and wind options, istheir unpredictable nature and dependence on weather and cli-matic changes, and the variations of solar and wind energy maynot match with the time distribution of load demand. This short-coming not only affects the system’s energy performance, but alsoresults in batteries being discarded too early. Generally, the inde-pendent use of both energy resources may result in considerableover-sizing, which in turn makes the design costly. It is prudentthat neither a stand-alone solar energy system nor a wind energysystem can provide a continuous power supply due to seasonaland periodical variations [1] for stand-alone systems.

Fortunately, the problems caused by the variable nature ofthese resources can be partially or wholly overcome by integratingthese two energy resources in a proper combination, using thestrengths of one source to overcome the weakness of the other.The use of different energy sources allows improving the systemefficiency and reliability of the energy supply and reduces the en-ergy storage requirements compared to systems comprising onlyone single renewable energy source. With the complementarycharacteristics between solar energy and wind energy for certainlocations, the hybrid solar–wind power generation systems withstorage banks offer a highly reliable source of power [2], which issuitable to electrical loads that need higher reliability [3].

In the past, the hybrid systems have been considered as pre-ferred for remote systems like radio telecommunication, satelliteearth stations, or at sites far away from a conventional power sys-tem [4–7]. Today, there is a trend to update the existing one sourcesystem (PV, wind or hydro) into hybrid system for grid-connectionapplications [8].

Of course, with increased complexity in comparison with singleenergy systems, the optimum design of a hybrid system becomescomplicated through uncertain renewable energy supplies andload demand, non-linear characteristics of the components, highnumber of variables and parameters that have to be consideredfor the optimum design, and the fact that the optimum configura-tion and optimum control strategy of the system are interdepen-dent. This complexity makes the hybrid systems more difficult tobe designed and analyzed.

In order to efficiently and economically utilize the renewableenergy resources, one optimum sizing method is necessary. Theoptimum sizing method can help to guarantee the lowest invest-ment with full use of the PV array, wind turbine and battery bank,so that the hybrid system can work at the optimum conditions interms of investment and system power reliability. This type ofoptimization includes economical objectives, and it requires theassessment of the system’s long-term performance in order toreach the best compromise for both reliability and cost.

Different sizing methods, such as graphical construction meth-od, probabilistic approach, iterative approach and artificial intelli-gence method, can be applied to reach a techno-economicallyoptimum hybrid renewable energy system. Whichever sizing andoptimization technique are used, they must ultimately search for

an optimum combination of the following parameters: the systemreliability and the system cost. While the expected reliability froma stand-alone hybrid system constitutes an important criterion inoptimization, the cost of the system is the governing factor, unlessan unlimited budget is available. Therefore, relationship betweenthe system reliability and cost should be closely studied so thatan optimum solution can be attained. This paper will concentrateon reviewing the current state of the local meteorological data gen-eration, optimization and control technologies for the stand-alonehybrid solar–wind energy systems with battery storage and try tofind what further work is needed.

2. Meteorological data generation for feasibility study

Climatic conditions determine the availability and magnitude ofsolar and wind energy at a particular site. For different districts andlocations, climatic conditions, including solar radiation, windspeed, air temperature, and so forth, are always changing. For bet-ter utilization of the solar and wind energy resources, an analysisof the characteristics of solar radiation and wind conditions at apotential site should be made at the stage of inception.

2.1. Time-series meteorological data

The long-term system performance is one of the most impor-tant design criteria for stand-alone hybrid solar–wind energy sys-tems. Some researchers used time-series meterological data forfeasibility study and design of the hybrid systems. Weather datacontaining hourly solar radiation, wind speed, and ambient tem-perature are required in the performance simulation of thesesystems.

The global whether data could be obtained from internet [9]and other sources like local meterological station. A hybrid solarand wind system can be evaluated with the help of these globalweather patterns, but for deciding the best feasible solution, thesite-to-site basis weather data is usually needed. Until now, a lotof researches have been done on solar and wind energy resourceanalysis, the possibilities of utilizing solar and wind energy re-sources in many regions or countries have been reported [10–14].

2.2. Statistical meteorological data

Knight et al. [15] pointed out that the hourly records of meteo-rological variables for extended periods of time do not exist formany locations. When the measured weather data do not existfor a location, they can be obtained in mainly two ways. Firstly,the necessary weather data may be synthetically generated fromthe monthly-average values of the meteorological data. Some sta-tistical properties of solar radiation and wind speed can be appliedto yield a number of days of weather data representing a wholemonth [16]. Secondly, the weather data may be extrapolated froma nearby site by making necessary adjustments [17].

Synthetically generated weather data can be used when incom-plete weather data set is an advantage to work with because of thereduction in computational effort in simulation studies. Generationof solar radiation, in particular, wind speed and temperature data,has been the objective of several studies. Among these studies,Gordon and Reddy developed a solar radiation generator on anhourly basis [18], and on a daily basis [19]. Baklouktsis et al. [20]made stochastic simulations of hourly and daily-average windspeeds. Knight et al. [15] presented techniques for the generationof hourly solar radiation and ambient temperature data, as wellas suggestions for humidity and wind speed. The algorithm devel-oped by Knight et al. [15] requires the input of monthly-average

Data Collection System

PV Module

Wind Turbine

Dump Load

DC Load

Inverter

Battery

Controller

AC Load

Fig. 1. Block diagram of a hybrid solar–wind power generation system.

382 W. Zhou et al. / Applied Energy 87 (2010) 380–389

solar radiation and generates the hourly solar radiation based onthe cumulative frequency distributions of the daily clearness index.

‘Typical meteorological year’ (TMY) is one of the most commonsynthetic weather data sequences used in solar system simula-tions. Hourly TMY weather data usually consists of 12 months ofhourly data. Each month is selected from long-term weather dataas being the best representative of that particular month or is gen-erated from several years of weather data which would yield thesame statistics (such as the average solar radiation and clearnessindex) as those of several years’ data.

The most popular method for deriving the TMY data, firstlydeveloped by Hall et al. [21], is an empirical approach selectingindividual months from different years using the Filkenstein–Scha-fer statistical method [22]. The final selection involved examiningstatistics and the persistence structure of the daily dry bulb tem-peratures and daily total global solar radiation. Other studies[23,24] derived TMYs for different cities. In these studies, differentweighting factors of meteorological parameters were considered.Yang and Lu [25] developed a local TMY for solar and wind energyapplication and evaluation. Their work also proved that determin-ing proper weather parameters and their weighting factors isimperative for the development of the TMYs for different kindsof renewable energy systems.

Based on statistical meteorological data, various feasibility andperformance studies are reported to evaluate the performance ofvarious hybrid solar–wind energy systems [26–28].

3. Simulation modelling of hybrid solar–wind systemcomponents

A hybrid solar–wind system consists of PV array, wind turbine,battery bank, inverter, controller, and other accessory devices andcables. A schematic diagram of a basic hybrid system is shown inFig. 1. The PV array and wind turbine work together to satisfythe load demand. When the energy sources (solar and wind en-ergy) are abundant, the generated power, after satisfying the loaddemand, will be supplied to feed the battery until it’s fully charged.On the contrary, when energy sources are poor, the battery will re-lease energy to assist the PV array and wind turbine to cover theload requirements until the storage is depleted.

The hybrid solar–wind system design is mainly dependent onthe performance of individual components. In order to predictthe system’s performance, individual components should be mod-eled first and then their combination can be evaluated to meet thedemand reliability. If the power output prediction from these indi-vidual components is accurate enough, the resultant combinationwill deliver power at the least cost.

3.1. Modelling of photovoltaic system

Reliable knowledge and understanding of the PV module per-formance under different operating conditions is of great impor-tance for correct product selection and accurate prediction of itsenergy performance. The performance of a crystalline silicon PVmodule is a function of the physical variables of the PV modulematerial, temperature of PV module and the solar radiance onthe PV module surface.

A lot of work has been done on analysis of the environmentalfactors that influence the PV module/array’s performance [29–31]. Radziemska and Klugmann [30] presented the influence oftemperature on the parameters of silicon photocells. For compari-son, the results of mono-crystalline solar cells and photodiodeswith a large light sensitive area are utilized. Nishioka et al. [31]analyzed the temperature coefficient dependence of system perfor-mance in order to estimate the annual output of a PV system in anactual operating environment. As a result, it is found that the an-nual output energy of the PV system increased about 1% by animprovement of 0.1%/�C of the temperature coefficient.

For engineering application, many researchers have investigatedthe simplified simulation models, such as the power efficiencymodels [32–37], which can predict the time series or average per-formance of a PV array under variable climatic conditions.

Overstraeten and Mertens [32] first introduced the equivalentmodel or circuit of solar cells, which are the fundamentals for anyfurther studies. Kerr and Cuevas [29] presented a new technique,which can determine the current–voltage (I–V) characteristics ofPV modules based on simultaneously measuring the open-circuitvoltage as a function of a slowly varying light intensity. And theyalso have given a detailed theoretical analysis and interpreta-tion of such quasi-steady-state Voc measurements. Borowy andSalameh [33] gave us one simplified model with which the maxi-mum power output could be calculated for one certain PV moduleonce solar radiation on the PV module and ambient temperaturewere found.

Zhou et al. [34] presented a novel simulation model for PV arrayperformance predictions for engineering applications based on theI-V curves of a PV module. Five parameters are introduced to ac-count for the complex dependence of PV module performanceupon solar radiation intensities and PV module temperatures.The author claims that this simulation model is simple and espe-cially useful for engineers to calculate the actual performance ofthe PV modules under operating conditions, with limited dataprovided by the PV module manufacturers. Yang et al. [2] devel-oped one model for calculating the maximum power output ofPV modules according to the theory of equivalent circuit of solar


cells by using eight parameters which can be identified by regres-sion with the Amoeba Subroutine or Downhill Simplex Methodfrom experimental data. Accuracy of this model was validated byexperimental data with good fitness.

Jones and Underwood [37] developed an efficiency model of PVmodule power output based on an adaptation of the established PVfill factor method, and attempts are made to take into account thesolar radiation and temperature characteristics in the establishedtheory in order to make a general PV power efficiency mode. TheAC power output from a PV array was estimated from the productof a single PV module power output, the number of PV modules Nm

in the array, and the inverter efficiency ginv :

PArray ¼ FF � Isco �GGo

� �� Voco �

lnðk1GÞlnðk1GoÞ

� To

Tmodule

� �� Nm � ginv ð1Þ

The simulation model is validated using measured data from a39.5 kW building-integrated PV array. Calculations of the PV mod-ule power output were made for two different sets of climatic con-ditions (clear sky conditions and overcast sky conditions) bycompiling data sets from all periods of the year.

3.2. Modelling of wind energy system

A literature survey undertaken for reviewing the system perfor-mance assessments for wind energy systems has shown that lim-ited work is available in this specific field. The publications onwind energy have been mostly concentrated on regional wind en-ergy assessment [38], wind speed distribution functions [39], eco-nomic aspects of wind energy [40] and regional wind energypolicies [41].

Different wind generators have different power output perfor-mance curves. Therefore, the model used to describe the perfor-mance of wind generators is expected to be different. Choosing asuitable model is very important for wind turbine power simula-tions, it is a pre-requisite for the successful planning and imple-mentation of wind power generation projects.

The hour-by-hour simulation programs have been the maintools to determine the long-term performance of wind energy sys-tems. Based on the hourly wind speed data, the long-term perfor-mance of the wind system can be obtained. Generally, for a typicalwind turbine, the power output characteristic can be assumed insuch a way that it starts power generation at the cut-in windspeed, then the power output increases linearly as the wind speedincreases from the cut-in wind speed to the rated wind speed, andthe rated power is produced when the wind speed varies from therated wind speed to the cut-out wind speed at which the wind tur-bine will be shut down for safety considerations. Based on theabove assumptions, the most simplified model to simulate thepower output of a wind turbine is described by [42]. In other casestudies [33,43,44], a similar form model is applied regarding theWeibull shape parameter k. Additionally, there are other types ofmodels to describe the power output of wind turbines, where thequadratic expressions are applied for the simulation [45,46].

However, it is generally acknowledged that the hour-by-hoursimulation programs require hour-by-hour wind speed data, whichmay not be available for many locations. Therefore, some simpli-fied design algorithms [47,48] have been developed as alternativesto simulation programs to determine the long-term performance ofrenewable energy systems. However, it is generally acknowledgedthat if the simulation model is more general it is usually lessaccurate.

In some other researches, calculation of wind turbine power isbased on electrical load, average wind speed and power curve ofthe wind turbine [49]. Since the calculation based on actual windspeed and direction is time-consuming and sometimes impossible,

average wind speed can be used. Sometimes, the wind turbinepower curves cannot exactly represent wind turbine power outputbecause the curves can only give the power output of the wind tur-bine as a function of the average wind speed ignoring instanta-neous wind speed variations, and thereby will, to some extent,undermine the performance of the wind turbine [50]. Therefore,considering the effect of instantaneous variations of wind speedfor a hybrid system can improve the accuracy whereas consideringactual wind speed for a hybrid system is almost impossible. Zama-ni and Riahy [51] presented a new method for calculating thepower of a wind turbine by considering wind speed variations.The rate of wind speed variations is assessed by the energy patternfactor (EPF) of actual wind, and the performance of rotor speed andpitch angle controllers is evaluated by a new factor, named windturbine controllability (Ca). By using the EPF and Ca, the powercurve is modified by considering the extra power that is capturedby the controllers.

3.3. Modelling of battery storage system

The harnessing of renewable energies presents, however, a fur-ther set of technical and economic problems. Unlike fossil and nu-clear fuels, which are concentrated sources of energy that can beeasily stored and transported, renewable forms of energy arehighly dilute and diffuse. Moreover, their supply can be extremelyintermittent and unreliable. So, batteries are required to even outirregularities in the solar and wind power distributions.

The development of battery behavioural models has been thefocus of researchers for many years. Based on the model given byGu et al. [52] and incorporation of the diffusion–precipitationmechanism studied by Ekdunge and Simonsson [53] in the reactionkinetics of the negative electrode, Kim and Hong [54] analyzed thedischarge performance of a flooded lead–acid battery cell usingmathematical modelling. Bernardi and Carpenter [55] developeda mathematical model of lead–acid batteries by adding the oxygenrecombination reaction. Nguyen et al. [56] presented a model anal-ogous to the flooded type and examined the dynamic behaviour ofthe cell during discharge with respect to cold cranking amperageand reserve capacity.

In general, these models are complex in terms of the expres-sions and number of parameters employed. Furthermore, manyof the parameters are determined through measurement of inter-nal components or processes or by extensive experimentation.Consequently, these models tend to be used to assess the theoret-ical performance of battery designs and are not practical for simu-lating the performance of an arbitrary battery at arbitraryoperating conditions.

Another common modelling approach is to develop an electricalcircuit that is designed to be functionally equivalent to the battery[57,58]. The components of the circuit can represent the internalcomponents of the battery, e.g. electrode and electrolyte resis-tances. The accuracy of these models depends upon the numberof characterisation tests performed to identify the values of the cir-cuit elements [57]. In some cases, compensation factors are re-quired to eliminate the influence of temperature. Furthermore,re-characterisation has been recommended to cater to changesdue to battery ageing [58].

Other battery behavioural prediction approaches include chargeaccumulation and empirical models. Yang et al. [2] states that alead–acid battery is characterized by two indexes, i.e. the state ofcharge (SOC) and the floating charge voltage (or the terminal volt-age). Extensive SOC determination methods have been introducedby Sabine Piller et al. [59]. It concluded that the most used tech-nique at this time for all systems is ampere-hour counting methodbecause it is the most direct and transparent method and quiteeasily implemented with satisfyingly accurate results for short-


time applications, especially if used in the range of low to mediumSOC. Morgan et al. [60] have studied the performance of batteryunits in an autonomous hybrid energy system at various tempera-tures by considering the state of voltage (SOV) instead of state ofcharge (SOC). For floating charge voltage simulations, models areavailable in the literature [61] that describes the relationship be-tween the floating charge voltage, the current rate and the batterystate of charge.

However, battery charge is a complex function of the battery’soperating conditions. Therefore, experimentally determined cor-rection factors are required [62]. Empirical models are establishedand verified by means of observation and experimentation [63].These include models that relate measurable battery parametersto the status of the battery, employing, for example, curve fittingtechniques [64]. These models also require considerable experi-mentation to obtain the parameters that characterise the targetedbattery’s behaviour.

4. Criteria for hybrid solar–wind system optimizations

In order to select an optimum combination for a hybrid systemto meet the load demand, evaluation must be carried out on thebasis of power reliability and system life-cycle cost. An optimumcombination for hybrid system can make the best compromise be-tween the two considered objectives: power reliability and systemcost.

4.1. Power reliability analysis

Because of the intermittent solar radiation and wind speedcharacteristics, which highly influence the energy production fromthe hybrid system, power reliability analysis is usually consideredas an important step in any such system design process.

There are a number of methods used to calculate the reliabilityof the hybrid systems. The most popular method is the loss ofpower supply probability (LPSP) [65] method. The LPSP is the prob-ability that an insufficient power supply results when the hybridsystem (PV, wind power and energy storage) is not able to satisfythe load demand [66]. The design of a reliable stand-alone hybridsolar–wind system can be pursued by using the LPSP as the key de-sign parameter.

Two approaches exist for the application of the LPSP in design-ing a stand-alone hybrid solar–wind system. The first one is basedon chronological simulations. This approach is computationallyburdensome and requires the availability of data spanning a cer-tain period of time. The second approach uses probabilistic tech-niques to incorporate the fluctuating nature of the resource andthe load, thus eliminating the need for time-series data.

Some other power reliability criteria also exist, such as the Lossof Load Probability (LOLP), System Performance Level (SPL) andLoss of Load Hours (LOLH).

The LOLP is a measure of the probability that a system demandwill exceed the system’s power supply capacity in a given timeperiod, often expressed as the estimated number of days over along period. Al-Ashwal and Moghram [67] presented a methodfor assessment on the basis of the LOLR (loss of load risk) to decidean optimum proportion for the solar and wind energy in a hybridsystem. The SPL is defined as the probability that the load cannotbe satisfied [68]. Both the SPL and the LOLH [69] methods are alsowidely used.

4.2. System cost analysis

Generally speaking, several economic criteria exist, such as theNet Present Cost, Levelised Cost of Energy and life-cycle cost. The

Net Present Cost is defined as the total present value of a time ser-ies of cash flows, which includes the initial cost of all the systemcomponents, the cost of any component replacements that occurwithin the project lifetime and the cost of maintenance. The sys-tem lifetime is usually considered to be the life of the PV modules,which are the elements that have a longer lifespan. A more detaileddescription of its calculation can be found [70,71], and some costsmay depend on the control strategy selected amongst those possi-bilities [70]. The HOMER (Hybrid Optimization Model for ElectricRenewable) uses the total Net Present Cost to represent the life-cy-cle cost of the system, assumes that all prices escalate at the samerate and takes the ‘‘annual real interest rate” rather than the ‘‘nom-inal interest rate”. This method allows inflation to be factored outof the analysis [72]. The Net Present Cost also takes into accountany salvage costs, which is the value remained in a component ofthe system at the end of the project lifetime. The HOMER assumesa linear depreciation of components, meaning that the salvage va-lue of a component is directly proportional to its remaining life. Italso assumes that the salvage value is based on the replacementcost rather than the initial capital cost.

The Levelised Cost of Energy is defined as the ratio of the totalannualized cost of the system to the annual electricity deliveredby the system [2]. It has been extensively used as an objective termto evaluate the hybrid solar–wind system configurations [73].Other economical approaches, such as the Levelised Cost of System[1] and life-cycle cost are also widely used [74].

5. Optimum sizing methods for hybrid solar–wind system

5.1. Simulation and optimization software

Simulation programs are the most common tools for evaluatingperformance of the hybrid solar–wind systems. By using computersimulation, the optimum configuration can be found by comparingthe performance and energy production cost of different systemconfigurations. Several software tools are available for designingof hybrid systems, such as HOMER, HYBRID2, HOGA and HYBRIDS.

The Hybrid Optimization Model for Electric Renewables(HOMER), public domain software produced by National Renew-able Energy Laboratory, uses hourly simulations for arriving atoptimum target. It is a time-step simulator using hourly load andenvironmental data inputs for renewable energy system assess-ment; it facilitates the optimization of renewable energy systemsbased on Net Present Cost for a given set of constraints and sensi-tivity variables.

HOMER has been used extensively in previous renewable en-ergy system case studies [75,76] and in renewable energy systemvalidation tests [70]. Although simulations can take a long time,depending on the number of variables used, its operation is simpleand straightforward. The program’s limitation is that it does notenable the user to intuitively select the appropriate componentsfor a system, as algorithms and calculations are not visible oraccessible.

HYBRID2 was developed by the Renewable Energy ResearchLaboratory (RERL) of the University of Massachusetts. It is hybridsystem simulation software, the simulation is very precise, as itcan define time intervals from 10 min to 1 h. National RenewableEnergy Laboratory recommends optimizing the system withHOMER and then, once the optimum system is obtained, improvingthe design using HYBRID2.

HOGA is a hybrid system optimization program developed bythe Electric Engineering Department of the University of Zaragoza(Spain). The optimization is carried out by means of Genetic Algo-rithms, and can be mono-objective or multi-objective. The simula-tion is carried out using 1-h intervals, during which all of the


parameters remained constant. The control strategies were alsooptimized using Genetic Algorithms.

HYBRIDS, a commercially available application produced bySolaris Homes, assess the technical potential of renewable energysystem for a given configuration, determining the potential renew-able fraction and evaluating economic viability based on Net Pres-ent Cost. HYBRIDS is a Microsoft Excel spreadsheet-basedrenewable energy system assessment application and design tool,requiring daily-average load and environmental data estimatedfor each month of the year. Unlike HOMER, HYBRIDS can only sim-ulate one configuration at a time, and is not designed to provide anoptimised configuration. HYBRIDS is comprehensive in terms ofrenewable energy system variables and the level of detail requiredand necessitates a higher level of knowledge of renewable energysystem configurations than HOMER. It is designed so that the userimproves their renewable energy system design skills through itsapplication.

5.2. Optimization techniques for hybrid solar–wind system

In order to efficiently and economically utilize the renewableenergy resources, one optimum sizing method is necessary. Theoptimum sizing method can help to guarantee the lowest invest-ment with full use of the system component, so that the hybridsystem can work at the optimum conditions in terms of invest-ment and system power reliability requirement.

5.2.1. Optimization scenarios based on different meteorological dataSome research use Typical Meteorological Year data [23–25] or

long period meteorological data [77] for the hybrid system optimi-zations. Also many optimum sizing methods were developed basedon the worst month scenario [78–80]. Protogeropoulos et al. [79]present two sizing methods for stand-alone hybrid wind-solar en-ergy systems. The first method is the ‘‘yearly average monthlymethod” in which the size of PV panel and wind turbine is derivedfrom the yearly averaged monthly values. Similarly, the load is rep-resented by the yearly mean monthly value. The second method istermed the ‘‘worst months” method, it choose the worst monthsfor solar and wind energy system separately. A similar sizing meth-od, developed by Morgan [80], is the ‘‘worst month” method. Con-trary to ‘‘worst months” method, this method chooses the worstmonth as the one in which the largest total area of PV moduleand wind turbine occurs.

The time-series simulation method is the most commonly usedrenewable energy system optimization routine. Generally, most ofthe researchers used time-series meterological station data for fea-sibility study and design of hybrid systems. The hybrid system’sbehaviour is calculated based on the time-series meteorological in-put data, which usually have a resolution of 1-h intervals. Borowyand Salameh [43] developed an algorithm to optimize hybrid so-lar–wind system; the model proposed was based on a long-termhourly solar radiation and peak load demand data of the site cho-sen. Other applications which also use time-series simulationmethod include Baring-Gould et al. [81] and Notton et al. [82],which use incremental time-scales of 1 h and 1 min, respectively.Notton et al. [82] also studied the effect of time step, input and out-put power profile on the sizing result of stand-alone solar energysystems based on a simulation procedure.

Main disadvantage of the time-step simulation method is that itrequires significant computational effort. Furthermore, time-seriesenvironmental input data, especially wind data, may not be avail-able for many locations. To improve the performance of hybrid sys-tem optimizations, many efforts have been conducted to decreasethe simulation time and/or reduce the number of variables used.Celik [83] developed a predictive algorithm requiring monthly-average values of wind speed distribution parameters and solar

radiations, enabling the estimation of system performance usingsimple wind distribution parameters and thus eliminating thenecessity for time-series hourly data. Protogeropoulos et al. [79]simplified this process further by using an annual average method.Muselli et al. [84] and Kaye [85] developed these predictive algo-rithms further in the form of stochastic and dynamic optimizationmodels, incorporating uncertainties in demand, component failureand weather behaviour in the estimation of renewable energy sys-tem potentials.

5.2.2. Optimization techniquesAs the number of optimization variables increase, the number

of simulations also increases exponentially, with a consequent in-crease in time and effort required. It is therefore very important fordesigners to find a feasible optimization technique to select theoptimum system configurations quickly and accurately.

Various optimization techniques for hybrid solar–wind systemhave been reported in the literature such as graphic constructionmethods, probabilistic approach, iterative technique, artificialintelligence methods, multi-objective design. Using feasible opti-mization method, optimum configurations which meet the loadrequirement can be obtained [1,2].

5.2.2.1. Graphic construction method. A graphical construction tech-nique for figuring the optimum combination of PV array and bat-tery for a stand-alone hybrid solar–wind system has beenpresented by Borowy and Salameh [33] based on using long-termdata of solar radiation and wind speed recorded for every hour ofthe day for 30 years. Load consumption of a typical house in Mas-sachusetts was used as the load demand for the hybrid system. Fora given load and a desired LPSP, the optimum configuration ornumber of batteries and PV modules was calculated based on theminimum cost of the system.

Borowy and Salameh [33] assumed that the total cost of the sys-tem is linearly related to both the number of PV modules and thenumber of batteries. The minimum cost will be at the point of tan-gency of the curve that represents the relationship between thenumber of PV modules and the number of batteries. Then the opti-mum sizing of the battery bank and the PV array can be achieved.

Another graphical technique has been given by Markvart [86] tooptimally design a hybrid solar–wind power generation system byconsidering the monthly-average solar and wind energy values.

However, in both graphical methods, only two parameters(either PV and battery, or PV and wind turbine) were included inthe optimization process, some important factors (such as the PVmodule slope angle and the wind turbine installation height.) werecompletely neglected.

5.2.2.2. Probabilistic approach. Probabilistic approaches of sizinghybrid solar–wind system account the effect of the solar radiationand wind speed variability’s in the system design.

Bucciarelli [87] proposed a sizing method treating storage en-ergy variation as a random walk. The probability density for dailyincrement or decrement of storage level was approximated by atwo-event probability distribution [88]. The method was furtherextended to account for the effect of correlation between day today radiation values [89]. Bucciarelli’s method was modified byGordon [90] and Bagul et al. [88] where the storage energy transi-tions were approximated by three-event probabilistic approach toovercome the limitations of conventional two-event approach inmatching the actual distribution of the energy generated by hybridsystems.

Tina et al. [91] presented a probabilistic approach based on theconvolution technique [92] to incorporate the fluctuating nature ofthe resources and the load, thus eliminating the need for time-ser-ies data, to assess the long-term performance of a hybrid solar–


wind system for both stand-alone and grid-connected applications.Performance of the hybrid system under study is assessed byemploying probabilistic models for both PV array and wind tur-bines. Finally, a numerical example application was included toillustrate the validity of the developed probabilistic model: the re-sults are compared to those resulting from time-series simulations.

Disadvantage of this probabilistic approach is that it cannot rep-resent the dynamic changing performance of the hybrid system.

5.2.2.3. Iterative technique. Yang et al. [2] proposed a Hybrid Solar–wind System Optimization (HSWSO) model, which utilizes theiterative optimization technique following the LPSP model andLevelised Cost of Energy model for power reliability and systemcost respectively. Three sizing parameters are considered in thesimulation, i.e. the capacity of PV system, rated power of windsystem, and capacity of the battery bank. For the desired LPSPvalue, the optimum configuration can be identified finally byiteratively searching all the possible sets of configurations toachieve the lowest Levelised Cost of Energy.

Similarly, an iterative optimization method was presented byKellogg et al. [93] to select the wind turbine size and PV modulenumber using an iterative procedure to make the difference be-tween the generated and demanded power (DP) as close to zeroas possible over a period of time. From this iterative procedure,several possible combinations of solar–wind generation capacitieswere obtained. The total annual cost for each configuration is thencalculated and the combination with the lowest cost is selected torepresent the optimum mixture.

For iterative optimization method, minimization of the systemcost was implemented either by linearly changing the values ofthe corresponding decision variables or employing linear program-ming techniques, resulting in suboptimal solutions and increasedcomputational effort requirements. Furthermore, it usually doesnot optimize the PV module slope angle and wind turbine installa-tion heights which also highly affect both, the resulting energy pro-duction and system costs.

5.2.2.4. Artificial intelligence methods. Artificial intelligence is aterm that in its broadest sense would mean the ability of a machineor artefact to perform similar kinds of functions that characterisehuman thought [94]. Artificial intelligence methods, such as Genet-ic Algorithms, Artificial Neural Networks and Fuzzy Logic, arewidely used to optimize a hybrid system in order to maximize itseconomic benefits.

Genetic Algorithms are selected because they have shown to behighly applicable to cases of non-linear systems, where the loca-tion of the global optimum is a difficult task. Due to the probabilis-tic development of solutions, Genetic Algorithms are not restrictedby local optimum; it can find the global optimum system configu-ration with relative computational simplicity compared to conven-tional optimization methods such as dynamic programming andgradient techniques.

Koutroulis et al. [77] proposed a methodology for optimum de-sign of a hybrid solar–wind system. Purpose of the proposed meth-odology is to suggest, among a list of commercially availablesystem devices, the optimum number and type of units ensuringthat the 20-year round total system cost is minimized by GeneticAlgorithms subject to the constraint that the load energy require-

Table 1Detailed design parameters of the pilot hybrid solar–wind power generation project.

Load PV array

Design parameters 1500 W (+24 V) MBFP100100 W � 78 =(29.5� inclina

ments are completely covered, resulting in zero load rejection.Yang et al. [1] proposed one optimum sizing method based on Ge-netic Algorithms by using the Typical Meteorological Year data.This optimization model is proposed to calculate the system opti-mum configuration which can achieve the desired LPSP with min-imum Annualized Cost of System. The author has brought intopicture two optimization variables that are not commonly seen,PV array slope angle and turbine installation height. Dufo-López[70] and Seeling [12] used Genetic Algorithms reducing simulationtime significantly, addressing the problems of uncertain renewableenergy supplies, load demand and the non-linear characteristics ofsome components by incorporating past and future demand. Ge-netic Algorithms are also widely used in the design of large powerdistribution systems [95] and the solution of power economic dis-patch problems [96] because of their ability to handle complexproblems with linear or non-linear cost functions both, accuratelyand efficiently.

Based on Genetic Algorithms, one pilot hybrid solar–windpower generation project designed by Yang et al. was built to sup-ply power for a telecommunication relay station from renewableenergy sources on a remote island (Dalajia Island) along thesouth-east coast of China [1,4]. The electric use for the normaloperation of the telecommunication station includes 1300 WGSM base station RBS2206 consumption (24 V AC) and 200 W formicrowave communication (24 V DC). According to the projectrequirement and technical considerations, a continuous 1500 Wenergy consumption is chosen as the demand load, and the de-tailed design parameters are shown in Table 1. Furthermore, basedon the one year time-series field data of the pilot project, Zhouet al. [5] studied the system behaviours and good performanceobserved.

Artificial Neural Network, often just called ‘‘Neural Network”, isa mathematical model or computational model based on biologicalneural networks. It consists of an interconnected group of artificialneurons and processes information using a connectionist approachto computation. Kalogirou [97] proposed an optimization model ofsolar systems by using Artificial Neural Networks and GeneticAlgorithms. The system is modeled using a TRNSYS computer pro-gram and the climatic conditions of Cyprus, included in a TypicalMeteorological Year file. The Artificial Neural Network is trainedusing the results of a small number of TRNSYS simulations. Subse-quently, a Genetic Algorithm is employed to estimate the optimumconfigurations, for maximizing life-cycle savings: thus the designtime is reduced substantially.

5.2.2.5. System control for energy flow and management. One mainproblem for the hybrid solar–wind system is related to the controland supervision of the energy distribution. The dynamic interac-tion between the renewable energy sources and the load demandcan lead to, critical problems of stability and power quality, thatare not very common in conventional power systems. Managingflow of energy throughout the proposed hybrid system to assurecontinuous power supply for the load demand is essential.

Conventional approach that controlling power supply to theload requirement according to the demand was used in various hy-brid systems. In the conventional approach, power electronicsbased DC–DC converter are used for maximum energy extract fromsolar and wind energy resources and control the complete hybrid

Wind turbine Battery capacity

WT6000/024 GFM-1000 (2 V)7.8 kW 6 kW � 2 = 12 kW 5000 Ah (24 V)

tion)


system. Some researchers have used different conventional con-trolling technique [98] for different combination of hybrid energysystems. Park et al. [99] presented the power compensation systemfor controlling energy flow through hybrid energy system accord-ing to load demand. Valenciaga and Puleston [100] and Onaret al. [101] developed controller for hybrid power systems.Valenciaga and Puleston [100] developed three modes of operationand they used sliding mode control methods [102] for controllingthe hybrid system.

Beside the conventional approaches, some advanced controllingtechniques exist, which can remove the power fluctuations causedby the variability of the renewable energy sources that may affectthe quality of the power delivered to the load.

El-Shater et al. [103] discussed the energy flow and manage-ment of a hybrid solar–wind–fuel system. Each of the three energysources is controlled so as to deliver energy at optimum efficiencyby Fuzzy Logic control technique which is employed to achievemaximum power tracking for both solar and wind energies andto deliver is maximum power to a fixed DC voltage bus. Chedidand Rahman [104] presented controller design that monitors theoperation of the stand-alone or grid-connected systems. The con-troller determines the energy available from each of the systemcomponents and environmental credit of the system. The modeldeveloped can give production cost, unmet and spilled energies,and battery charged and discharged losses. Some new approachesbased on Fuzzy Logic and Genetic Algorithm techniques [105,106]are also proposed for the scheduling of the battery and the dieselgenerator of a hybrid solar–wind–diesel system.

5.2.2.6. Multi-objective design. Whenever we wish, in any engineer-ing field, to carry out a design, it is likely that we wish to have inmind several objectives simultaneously, being typical that someof them conflict with each other [107]. In the optimum sizing ofhybrid solar–wind–diesel systems, we wish to carry out the designconsidering simultaneously at least two objectives (costs and pol-lutant emissions). These two objectives are in conflict, since areduction in design costs implies a rise in pollutant emissionsand vice versa.

Therefore, the task of getting good results in problems of thiskind (multi-objective) is complicated. Given the complexity of thiskind of problems, because of the large number of variables that areusually considered and of the mathematical models applied, classicoptimization techniques may consume excessive CPU time or evenbeing incapable of taking into account all the characteristics asso-ciated to the posed problem. In the specialised technical literature[27,70,108] the design of these systems is usually done by search-

Table 2Simple summary of the relative merits and demerits of different optimization methodolog

Merits

Software tools HOMER

HOGA Carried out by genetic algorithms, can bHYBRIDS Comprehensive in terms of optimization

level knowledge of system configurationOptimization

techniquesGraphicconstructionmethodProbabilisticapproach

Eliminate the need of time-series data

Iterative technique

Artificialintelligencemethods

Find the global optimum system configucomputational simplicity

Multi-objectivedesign

Can optimize simultaneously at least tw

ing the configuration and/or control that yields the lowest totalcost through the useful life of the installation. However, the envi-ronmental issues associated to this type of installations should alsobe taken into account during the design process. Until now, usu-ally, the pollutant emissions have been calculated after obtainingthe design that minimises costs. In some cases, as in the HOMERprogram, it is possible to consider the pollutant emissions by eco-nomically valuating them, and therefore becoming a part of thecosts objective function. This mapping of costs to emissions is sub-jective, and decisively influences the results of the design. Themethod that HOMER uses for the multi-objective design is knownas the method of the weights [1].

Multi-Objective Evolutionary Algorithms (MOEAs) stand out inthe multi-objective design task, being applied in numerous papers.Pelet et al. [109] carried out an application of MOEAs for the opti-mization of system cost and CO2 emissions for a stand-alone hy-brid system in which three hotels and a town in the TunisianSahara were thermally and electrically supplied. Bernal-Agustínet al. [71] present a multi-objective optimization (NPC versusCO2 emissions) for hybrid a solar–wind–diesel system with batterystorage based on MOEAs. Dufo-López and Bernal-Agustín [110]presented a triple multi-objective optimization to minimise simul-taneously the total cost throughout the useful life of the installa-tion, pollutant emissions (CO2) and unmet load. For this task, aMOEAs and a Genetic Algorithm have been used in order to findthe best combination of components and control strategies forthe hybrid system.

Strength Pareto Evolutionary Algorithm was also applied to themulti-objective design of hybrid systems. The design is posed as anoptimization problem whose solution allows obtaining the config-uration of the system as well as the control strategy that simulta-neously minimises both the total cost through the useful life of theinstallation and the pollutant emissions.

5.3. Brief summary of the optimization techniques

Based on the detailed illustration given above, Table 2 shows asummary of the relative merits and demerits of different optimiza-tion software and techniques for better identification.

6. Conclusion

The stand-alone hybrid solar–wind power generation system isrecognized as a viable alternative to grid supply or conventionalfuel-based remote area power supplies all over the world. It is gen-erally more suitable than systems that only have one energy source

ies.

Demerits

Cannot enable the user to intuitively selectappropriate system components

e mono or multi objectivevariables, and require highers

Only simulate one configuration at a time

Only two parameters can be included in theoptimization process

Cannot represent the dynamic changingperformance of the systemUsually result in increased computationalefforts and suboptimal solutions

ration with relative

o conflict objectives


for supply of electricity to off-grid applications. However, the de-sign, control, and optimization of the hybrid systems are usuallyvery complex tasks.

This paper has reviewed the up-to-date progress of this technol-ogy, which includes the feasibility study, component simulations,system optimization and control technologies of the hybrid sys-tems. The feasibility study is carried out on both time-series mete-orological data bases and statistical meteorological data bases.Most of the commonly used criteria that evaluate the systempower reliability and system cost are investigated. Various optimi-zation techniques have been reviewed including the graphic con-struction methods, probabilistic approach, iterative technique,artificial intelligence methods, multi-objective design etc.

According to the review carried out in this paper, a detailedrenewable energy resource analysis at first stage of the designfor optimum sizing of a hybrid solar–wind energy system and foroptimum resource allocation based on load demand is essentialfor reducing the hybrid system’s initial cost and operation cost.Furthermore, the inclusion of artificial intelligence as part of theenergy management system in the future can definitely help oper-ators reduce the system’s cost further.

Acknowledgements

The work described in this paper is supported by a grant fromthe Sun Hung Kai Properties Group (Project No. ZZ1T) and a re-search grant from The Hong Kong Polytechnic University (ProjectNo. Z02T).

References

[1] Yang HX, Zhou W, Lu L, Fang ZH. Optimal sizing method for stand-alonehybrid solar–wind system with LPSP technology by using genetic algorithm.Solar Energy 2008;82(4):354–67.

[2] Yang HX, Lu L, Zhou W. A novel optimization sizing model for hybrid solar–wind power generation system. Solar energy 2007;81(1):76–84.

[3] Giraud F, Salameh ZM. Steady-state performance of a grid-connected rooftophybrid wind–photovoltaic power system with battery storage. IEEE TransEnergy Convers 2001;16(1):1–7.

[4] Yang HX, Zhou W, Lou CZ. Optimal design and techno-economic analysis of ahybrid solar–wind power generation system. Appl Energy 2009;86:163–9.

[5] Zhou W, Yang HX, Fang ZH. Battery behavior prediction and battery workingstates analysis of a hybrid solar–wind power generation system. RenewEnergy 2008;33(6):1413–23.

[6] Diafa S, Belhamelb M, Haddadic M, Louchea A. Technical and economicassessment of hybrid photovoltaic/wind system with battery storage inCorsica Island. Energy Policy 2008;36(2):743–54.

[7] Celik AN. Techno-economic analysis of autonomous PV–wind hybrid energysystems using different sizing methods. Energy Convers Manage2003;44(12):1951–68.

[8] Bakos GC, Tsagas NF. Technoeconomic assessment of a hybrid solar/windinstallation for electrical energy saving. Energy Build 2003;35(2):139–45.

[9] <http://eosweb.larc.nasa.gov/sse>.[10] Kimura Y, Onai Y, Ushiyama L. A demonstrative study for the wind and solar

hybrid power system. In: Proceedings of world renewable energy congress;1996. p. 895–8.

[11] Beyer GH, Langer C. A method for the identification of configurations of PV/wind hybrid systems for the reliable supply of small loads. Solar Energy1996;57(5):381–91.

[12] Seeling GCH. A combined optimisation concept for the design and operationstrategy of hybrid-PV energy systems. Solar Energy 1997;61(2):77–87.

[13] Behave AG. Hybrid solar–wind domestic power generating system: a casestudy. Renew Energy 1999;17(3):355–8.

[14] Elhadidy MA, Shaahid SM. Parametric study of hybrid (wind + solar + diesel)power generating systems. Renew Energy 2000;21(2):129–39.

[15] Knight KM, Klein SA, Duffie JA. A methodology for the synthesis of hourlyweather data. Solar Energy 1991;46(2):109–20.

[16] Gansler RA, Klein SA, Beckman WA. Assessment of accuracy of generatedmeteorological data for use in solar energy simulation studies. Solar Energy1994;53(3):279–87.

[17] Wahab MA, Essa KSM. Extrapolation of solar irradiation measurements: casestudy over Egypt. Renew Energy 1998;14(1–4):229–39.

[18] Gordon JM, Reddy TA. Time series analysis of hourly global horizontal solarradiation. Solar Energy 1988;41(5):423–9.

[19] Gordon JM, Reddy TA. Time series analysis of daily horizontal solar radiation.Solar Energy 1988;41(3):215–26.

[20] Baklouktsis A, Tsanakas D, Vachtsevanos G. Stochastic simulation of hourlyand daily average wind speed sequences. Wind Energy 1986;10(1):1–11.

[21] Hall IJ, Prairie RR, Anderson HE, Boes EC. Generation of typical meteorologicalyears for 26 Solmet stations. ASHRAE Trans 1979;85(2):507–17.

[22] Filkenstein JM, Schafer RE. Improved goodness to fit tests. Biometrica1971;58:641–5.

[23] Merter U, Arif I. Typical weather data of main Turkish cities for energyapplication. Int J Energy Res 2000;24:727–48.

[24] Zhang Q, Huang J, Lang S. Development of typical year weather data forChinese location. ASHRAE Trans 2002;108(2):1063–75.

[25] Yang HX, Lu L. Study on typical meteorological years and their effect onbuilding energy and renewable energy simulations. ASHRAE Trans2004;110(2):424–31.

[26] Elhadidy MA, Shaahid SM. Promoting applications of hybrid (wind +photovoltaic + diesel + battery) power systems in hot regions. RenewEnergy 2004;29(4):517–28.

[27] Elhadidy MA. Performance evaluation of hybrid (wind/solar/diesel) powersystems. Renew Energy 2002;26(3):401–13.

[28] Celik AN. Optimization and techno-economic analysis of autonomousphotovoltaic–wind hybrid energy systems in comparison to single photovol-taic and wind systems. Energy Convers Manage 2002;43(18):2453–68.

[29] Kerr MJ, Cuevas A. Generalized analysis of the illumination intensity vs. open-circuit voltage of PV modules. Solar Energy 2003;76(1–3):263–7.

[30] Radziemska E, Klugmann E. Thermally affected parameters of the current–voltage characteristics of silicon photocell. Energy Convers Manage2002;43(14):1889–900.

[31] Nishioka K et al. Field-test analysis of PV system output characteristicsfocusing on module temperature. Solar Energy Mater PV Modules2003;75(3–4):665–71.

[32] Overstraeten RJ Van, Mertens RP. Physics, technology and use ofphotovoltaics. Bristol and Boston: Adam Hilger; 1986. p. 187–91.

[33] Borowy BS, Salameh ZM. Methodology for optimally sizing the combinationof a battery bank and PV array in a wind/PV hybrid system. IEEE Trans EnergyConvers 1996;11(2):367–73.

[34] Zhou W, Yang HX, Fang ZH. A novel model for photovoltaic array performanceprediction. Appl Energy 2007;84(12):1187–98.

[35] Mondol JD et al. Long-term validated simulation of a building integratedphotovoltaic system. Solar Energy 2005;78(2):163–76.

[36] Stamenic L, Smiley E, Karim K. Low light conditions modeling for buildingintegrated photovoltaic (BIPV) systems. Solar Energy 2004;77(1):37–45.

[37] Jones AD, Underwood CP. A modeling method for building-integratedphotovoltaic power supply. Build Serv Eng Res Technol 2002;23(3):167–77.

[38] Rosen K, Buskirk RV, Garbesi K. Wind energy potential of coastal Eritrea: ananalysis of sparse wind data. Solar Energy 1999;66(3):201–13.

[39] Sfetsos A. A comparison of various forecasting techniques applied to meanhourly wind speed time series. Renew Energy 2000;21(1):23–35.

[40] Neij L. Cost dynamics of wind power. Energy 1999;24(5):375–89.[41] Lew DJ. Alternatives to coal and candles: wind power in China. Energy Policy

2000;28(4):271–86.[42] Fadia MAG et al. Simulation and analysis of hybrid systems using

probabilistic techniques. In: Power conversion conference-Nagaoka, vol. 2;1997. p. 831–5.

[43] Borowy BS, Salameh ZM. Optimum photovoltaic array size for a hybrid wind/PV system. IEEE Trans Energy Convers 1994;9(3):482–8.

[44] Karaki SH, Chedid RB, Ramadan R. Probabilistic performance assessment ofwind energy conversion systems. IEEE Trans Energy Convers1999;14(2):217–24.

[45] Alhusein MA, Abu-Leiyah O, Inayatullah G. A combined system of renewableenergy for grid-connected advanced communities. Renew Energy1993;3:563–6.

[46] Lu L, Yang HX. Investigation on wind power potential on Hong Kong Islands –an analysis of wind power and wind turbine characteristics. Renew Energy2002;27(1):1–12.

[47] Siegel MD, Klein SA, Beckman WA. A simplified method for estimating theyearly-mean performance of PV systems. Solar Energy 1981;26:413–8.

[48] Clark DR, Klein SA, Beckman WA. A method for estimating the performance ofPV systems. Solar Energy 1984;33(6):551–5.

[49] Nehrir MH et al. An approach to evaluate the general performance of stand-alone wind/photovoltaic generating systems. IEEE Trans Energy Convers2000;15(4):433–9.

[50] Muljadi E, Butterfield CP. Pitch-controlled variable-speed wind turbinegeneration. IEEE Trans Ind Appl 2001;37(1):240–6.

[51] Zamani MH, Riahy GH. Introducing a new method for optimal sizing of ahybrid (wind/PV/battery) system considering instantaneous wind speedvariations. Energy for Sustain Develop 2008;12(2):27–33.

[52] Gu H, Nguyen TV, White RE. A Mathematical model of a lead–acid cell:discharge, rest, and charge. J Electrochem Soc 1987;134(12):2953–60.

[53] Ekdunge P, Simonsson D. The discharge behaviour of the porous leadelectrode in the lead–acid battery. I. Experimental investigations. J ApplElectrochem 1989;19(2):127–35.

[54] Kim SC, Hong WH. Analysis of the discharge performance of a floodedlead/acid cell using mathematical modeling. J Power Sources 1999;77(1):74–82.

[55] Bernardi DM, Carpenter MK. A mathematical model of the oxygen-recombination lead–acid cell. J Electrochem Soc 1995;142(8):2631–41.

http://eosweb.larc.nasa.gov/sse


[56] Nguyen TV, White RE, Gu H. The effects of separator design on the dischargeperformance of a starved lead–acid cell. J Electrochem Soc1990;137(10):2998–3004.

[57] Bernieri A, Noviello EI. A method for lead–acid battery performanceprediction. In: Proceedings of the 10th IASTED international symposium onmodelling identification and control; 1991. p. 436–41.

[58] Cun JP, Fiorina JN, Fraisse M, Mabboux H. The experience of UPS company inadvanced battery monitoring. In: INTELEC, Boston, USA; 1996. p. P22–5.

[59] Piller S, Perrin M, Jossen A. Methods for state-of-charge determination andtheir applications. J Power Sources 2001;96(1):113–20.

[60] Morgan TR, Marshall RH, Brinkworth BJ. ARES-a refined simulationprogramme for the sizing and optimization of autonomous hybrid energysystems. Solar Energy 1997;59(4–6):205–15.

[61] Chaurey C, Deambi S. Battery storage for PV power systems: an overview.Renew Energy 1992;2(3):227–35.

[62] Kozaki M, Yamazaki T. Remaining battery capacity meter and method forcomputing remaining capacity. United States Patent No 5691078, November1997.

[63] Baert D, Veraet A. Lead–acid battery model for the derivation of Peukert’s law.Electrochim Acta 1999;44(20):3491–504.

[64] Murase M, Yoshida M, Sekiya K, Yamashita N, Yamashita T, Hirai T. A functionof the battery capacity evaluation in telecommunications power systems. In:INTELEC, vol. 19, Melbourne, Australia; 1997.

[65] Diaf S, Notton G, Belhamel M, Haddadi M, Louche A. Design and techno-economical optimization for hybrid PV/wind system under variousmeteorological conditions. Appl Energy 2008;85(10):968–87.

[66] Yang HX, Burnett J, Lu L. Weather data and probability analysis of hybridphotovoltaic–wind power generation systems in Hong Kong. Renew Energy2003;28(11):1813–24.

[67] Al-Ashwal AM, Moghram IS. Proportion assessment of combined PV–windgenerating systems. Renew Energy 1997;10(1):43–51.

[68] Maghraby HAM, Shwehdi MH, Al-Bassam GK. Probabilistic assessment ofphotovoltaic (PV) generation systems. IEEE Trans Power Syst2002;17(1):205–8.

[69] Shrestha GB, Goel L. A study on optimal sizing of stand-alone photovoltaicstations. IEEE Trans Energy Convers 1998;13(4):373–8.

[70] Dufo-López R, Bernal-Agustín JL. Design and control strategies of PV–dieselsystems using genetic algorithms. Solar Energy 2005;79(1):33–46.

[71] Bernal-Agustín JL, Dufo-López R, Rivas-Ascaso DM. Design of isolated hybridsystems minimizing costs and pollutant emissions. Renew Energy2006;31(14):2227–44.

[72] Dalton GJ, Lockington DA, Baldock TE. Feasibility analysis of stand-alonerenewable energy supply options for a large hotel. Renew Energy2008;33(7):1475–90.

[73] Deshmukh ML, Deshmukh SS. Modeling of hybrid renewable energy systems.Renew Sustain Energy Rev 2008;12(1):235–49.

[74] Valente LCG, Almeida SCAD. Economic analysis of a diesel/photovoltaichybrid system for decentralized power generation in northern Brazil. Energy1998;23(4):317–23.

[75] Khan MJ, Iqbal MT. Pre-feasibility study of stand-alone hybrid energy systemsfor applications in Newfoundland. Renew Energy 2005;30(6):835–54.

[76] Zoulias EI, Lymberopoulos N. Techno-economic analysis of the integration ofhydrogen energy technologies in renewable energybased stand-alone powersystems. Renew Energy 2007;32(4):680–96.

[77] Koutroulis E et al. Methodology for optimal sizing of stand-alonephotovoltaic/wind-generator systems using genetic algorithms. SolarEnergy 2006;80(9):1072–88.

[78] Egido MA, Lorenzo E. The sizing of stand alone PV-systems: a review and aproposed new method. Solar Energy Mater Solar Cells 1992;26(1–2):51–69.

[79] Protogeropoulos C, Brinkworth BJ, Marshall R. Sizing and techno-economicaloptimization for hybrid solar PV–wind power systems with battery storage.Int J Energy Res 1997;21(6):465–79.

[80] Morgan TR. The performance and optimisation of autonomous renewableenergy systems. PhD Thesis, University of Wales, Division of MechanicalEngineering and Energy Studies, Cardiff; 1996.

[81] Baring-Gould EI, Manwell JF, Van Dijk V. HYBRID 2. NREL; 2002.[82] Notton G, Muselli M, Poggi P, Louche A. Autonomous photovoltaic systems:

influences of some parameters on the sizing: simulation time-step, input andoutput power profile. Renew Energy 1996;7(4):353–69.

[83] Celik A. A simplified model for estimating the monthly performance ofautonomous wind energy systems with battery storage. Renew Energy2003;28(4):561–72.

[84] Muselli M, Notton G, Louche A. Design of hybrid photo-voltaic powergenerator, with optimisation of energy management. Solar Energy1999;65(3):143–57.

[85] Kaye RJ. New approach to optimal sizing of components in stand-alonephotovoltaic power systems. In: Proceedings of the 24th IEEE photovoltaicspecialists conference, Part 1 (of 2), Waikoloa, HI, USA; 1994. p. 1192–5.

[86] Markvart T. Sizing of hybrid PV–wind energy systems. Solar Energy1996;59(4):277–81.

[87] Bucciarelli Jr LL. Estimating loss-of-power probabilities of standalonephotovoltaic solar energy systems. Solar Energy 1984;32(2):205–9.

[88] Bagul AD, Salameh ZM, Borowy B. Sizing of a stand-alone hybrid windphotovoltaic system using a three-event probability density approximation.Solar Energy 1996;56(4):323–35.

[89] Bucciarelli LLJ. The effect of day-to-day correlation in solar radiation on theprobability of loss-of-power in a stand-alone photovoltaic energy system.Solar Energy 1986;36(1):11–4.

[90] Gordon JM. Optimal sizing of stand-alone photovoltaic solar power systems.Solar Cells 1987;20(4):295–313.

[91] Tina G, Gagliano S, Raiti S. Hybrid solar/wind power system probabilisticmodeling for long-term performance assessment. Solar Energy2006;80(5):578–88.

[92] Karaki SH, Chedid RB, Ramadan R. Probabilistic performance assessment ofautonomous solar–wind energy conversion systems. IEEE Trans EnergyConvers 1999;14:766–72.

[93] Kellogg WD et al. Generation unit sizing and cost analysis for stand-alonewind, photovoltaic and hybrid wind/PV systems. IEEE Trans Energy Convers1998;13(1):70–5.

[94] Mellit A, Kalogirou SA, Hontoria L, Shaari S. Artificial intelligence techniquesfor sizing photovoltaic systems: a review. Renew Sustain Energy Rev2009;13(2):406–19.

[95] Ramirez-Rosado IJ, Bernal-Agustin JL. Genetic algorithms applied to thedesign of large power distribution systems. IEEE Trans Power Syst1998;13(2):696–703.

[96] Li F. A comparison of genetic algorithms with conventional techniques on aspectrum of power economic dispatch problems. Expert Syst Appl1998;15:133–42.

[97] Soteris A, Kalogirou. Optimization of solar systems using artificial neural-networks and genetic algorithms. Appl Energy 2004;77(4):383–405.

[98] Reddy KN, Agarwal V. Utility interactive hybrid distributed generationscheme with compensation feature. IEEE Trans Energy Convers2007;22(3):666–73.

[99] Park SJ, Kang BB, Yoon JP, Cha 1S, Lim JY. A study on the stand-alone operatingor photovoltaic wind power hybrid generation system. In: 35th annual IEEEpower electronics specialists conference; 2004. p. 2095–9.

[100] Valenciaga F, Puleston PF. Supervisor control for a stand-alone hybridgeneration system using wind and photovoltaic energy. IEEE Trans EnergyConvers 2005;20(2):398–440.

[101] Onar OC, Uzunoglu M, Alam MS. Dynamic modeling, design and simulation ofa wind/fuel cell/ultra-capacitor-based hybrid power generation system. JPower Sources 2006;161(1):707–22.

[102] Beltran B et al. Sliding mode power control of variable-speed wind. In:Electric machines and drives conference, vol. 2; 2007. p. 943–8.

[103] El-Shater TF, Eskander M, El-Hagry M. Hybrid PV/fuel cell system design andsimulation. In: 36th intersociety energy conversion engineering conference;2001. p. 112–21.

[104] Chedid R, Rahman S. Unit sizing and control of hybrid wind–solar powersystems. IEEE Trans Energy Convers 1997;12(1):79–85.

[105] Senjyu T, Hayashi D, Urasaki N, Funabashi T. Optimum configuration forrenewable generating systems in residence using genetic algorithm. IEEETrans Energy Convers 2006;21(1):459–67.

[106] Chedid RB, Karaki SH, El-Chamali C. Adaptive fuzzy control for wind–dieselweak power system. IEEE Trans Energy Convers 2000;15(1):71–8.

[107] Collette Y, Siarry P. Multiobjective optimization: principles and casestudies. Berlin: Springer; 2004.

[108] Muselli M, Notton G, Poggi P, Louche A. PV-hybrid power systems sizingincorporating battery storage: an analysis via simulation calculations. RenewEnergy 2000;20(1):1–7.

[109] Pelet X, Favrat D, Leyland G. Multiobjective optimisation of integrated energysystems for remote communities considering economics and CO2 emissions.Int J Therm Sci 2005;44(12):1180–9.

[110] Dufo-López, Bernal-Agustín. Multi-objective design of PV–wind–diesel–hydrogen–battery systems. Renew Energy 2008;33(12):2559–72.

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